Tag: How to make a wombo AI?

AI Jobs and Career

And before we wrap up today's AI news, I wanted to share an exciting opportunity for those of you looking to advance your careers in the AI space. You know how rapidly the landscape is evolving, and finding the right fit can be a challenge. That's why I'm excited about Mercor – they're a platform specifically designed to connect top-tier AI talent with leading companies. Whether you're a data scientist, machine learning engineer, or something else entirely, Mercor can help you find your next big role. If you're ready to take the next step in your AI career, check them out through my referral link: https://work.mercor.com/?referralCode=82d5f4e3-e1a3-4064-963f-c197bb2c8db1. It's a fantastic resource, and I encourage you to explore the opportunities they have available.

• Full Stack Engineer [$150K-$220K]
• Software Engineer, Tooling & AI Workflow, Contract [$90/hour]
• DevOps Engineer, India, Contract [$90/hour]
• More AI Jobs Opportunitieshere

Job Title	Status	Pay
Full-Stack Engineer	Strong match, Full-time	$150K - $220K / year
Developer Experience and Productivity Engineer	Pre-qualified, Full-time	$160K - $300K / year
Software Engineer - Tooling & AI Workflows (Contract)	Contract	$90 / hour
DevOps Engineer (India)	Full-time	$20K - $50K / year
Senior Full-Stack Engineer	Full-time	$2.8K - $4K / week
Enterprise IT & Cloud Domain Expert - India	Contract	$20 - $30 / hour
Senior Software Engineer	Contract	$100 - $200 / hour
Senior Software Engineer	Pre-qualified, Full-time	$150K - $300K / year
Senior Full-Stack Engineer: Latin America	Full-time	$1.6K - $2.1K / week
Software Engineering Expert	Contract	$50 - $150 / hour
Generalist Video Annotators	Contract	$45 / hour
Generalist Writing Expert	Contract	$45 / hour
Editors, Fact Checkers, & Data Quality Reviewers	Contract	$50 - $60 / hour
Multilingual Expert	Contract	$54 / hour
Mathematics Expert (PhD)	Contract	$60 - $80 / hour
Software Engineer - India	Contract	$20 - $45 / hour
Physics Expert (PhD)	Contract	$60 - $80 / hour
Finance Expert	Contract	$150 / hour
Designers	Contract	$50 - $70 / hour
Chemistry Expert (PhD)	Contract	$60 - $80 / hour

Artificial Intelligence Frequently Asked Questions

AI and its related fields — such as machine learning and data science — are becoming an increasingly important parts of our lives, so it stands to reason why AI Frequently Asked Questions (FAQs)are a popular choice among many people. AI has the potential to simplify tedious and repetitive tasks while enriching our everyday lives with extraordinary insights – but at the same time, it can also be confusing and even intimidating.

This AI FAQs offer valuable insight into the mechanics of AI, helping us become better-informed about AI’s capabilities, limitations, and ethical considerations. Ultimately, AI FAQs provide us with a deeper understanding of AI as well as a platform for healthy debate.

Artificial Intelligence Frequently Asked Questions: How do you train AI models?

Training AI models involves feeding large amounts of data to an algorithm and using that data to adjust the parameters of the model so that it can make accurate predictions. This process can be supervised, unsupervised, or semi-supervised, depending on the nature of the problem and the type of algorithm being used.

Artificial Intelligence Frequently Asked Questions: Will AI ever be conscious?

Consciousness is a complex and poorly understood phenomenon, and it is currently not possible to say whether AI will ever be conscious. Some researchers believe that it may be possible to build systems that have some form of subjective experience, while others believe that true consciousness requires biological systems.

Artificial Intelligence Frequently Asked Questions: How do you do artificial intelligence?

Artificial intelligence is a field of computer science that focuses on building systems that can perform tasks that typically require human intelligence, such as perception, reasoning, and learning. There are many different approaches to building AI systems, including machine learning, deep learning, and evolutionary algorithms, among others.

Artificial Intelligence Frequently Asked Questions: How do you test an AI system?

Testing an AI system involves evaluating its performance on a set of tasks and comparing its results to human performance or to a previously established benchmark. This process can be used to identify areas where the AI system needs to be improved, and to ensure that the system is safe and reliable before it is deployed in real-world applications.

Artificial Intelligence Frequently Asked Questions: Will AI rule the world?

There is no clear evidence that AI will rule the world. While AI systems have the potential to greatly impact society and change the way we live, it is unlikely that they will take over completely. AI systems are designed and programmed by humans, and their behavior is ultimately determined by the goals and values programmed into them by their creators.

Artificial Intelligence Frequently Asked Questions:  What is artificial intelligence?

Artificial intelligence is a field of computer science that focuses on building systems that can perform tasks that typically require human intelligence, such as perception, reasoning, and learning. The field draws on techniques from computer science, mathematics, psychology, and other disciplines to create systems that can make decisions, solve problems, and learn from experience.

Artificial Intelligence Frequently Asked Questions:   How AI will destroy humanity?

The idea that AI will destroy humanity is a popular theme in science fiction, but it is not supported by the current state of AI research. While there are certainly concerns about the potential impact of AI on society, most experts believe that these effects will be largely positive, with AI systems improving efficiency and productivity in many industries. However, it is important to be aware of the potential risks and to proactively address them as the field of AI continues to evolve.

Artificial Intelligence Frequently Asked Questions:   Can Artificial Intelligence read?

Yes, in a sense, some AI systems can be trained to recognize text and understand the meaning of words, sentences, and entire documents. This is done using techniques such as optical character recognition (OCR) for recognizing text in images, and natural language processing (NLP) for understanding and generating human-like text.

However, the level of understanding that these systems have is limited, and they do not have the same level of comprehension as a human reader.

Artificial Intelligence Frequently Asked Questions:   What problems do AI solve?

AI can solve a wide range of problems, including image recognition, natural language processing, decision making, and prediction. AI can also help to automate manual tasks, such as data entry and analysis, and can improve efficiency and accuracy.

Artificial Intelligence Frequently Asked Questions:  How to make a wombo AI?

To make a “wombo AI,” you would need to specify what you mean by “wombo.” AI can be designed to perform various tasks and functions, so the steps to create an AI would depend on the specific application you have in mind.

Artificial Intelligence Frequently Asked Questions:   Can Artificial Intelligence go rogue?

In theory, AI could go rogue if it is programmed to optimize for a certain objective and it ends up pursuing that objective in a harmful manner. However, this is largely considered to be a hypothetical scenario and there are many technical and ethical considerations that are being developed to prevent such outcomes.

Artificial Intelligence Frequently Asked Questions:   How do you make an AI algorithm?

There is no one-size-fits-all approach to making an AI algorithm, as it depends on the problem you are trying to solve and the data you have available.

However, the general steps include defining the problem, collecting and preprocessing data, selecting and training a model, evaluating the model, and refining it as necessary.

Artificial Intelligence Frequently Asked Questions:   How to make AI phone case?

To make an AI phone case, you would likely need to have knowledge of electronics and programming, as well as an understanding of how to integrate AI algorithms into a device.

Artificial Intelligence Frequently Asked Questions:   Are humans better than AI?

It is not accurate to say that humans are better or worse than AI, as they are designed to perform different tasks and have different strengths and weaknesses. AI can perform certain tasks faster and more accurately than humans, while humans have the ability to reason, make ethical decisions, and have creativity.

Artificial Intelligence Frequently Asked Questions: Will AI ever be conscious?

The question of whether AI will ever be conscious is a topic of much debate and speculation within the field of AI and cognitive science. Currently, there is no consensus among experts about whether or not AI can achieve consciousness.

Consciousness is a complex and poorly understood phenomenon, and there is no agreed-upon definition or theory of what it is or how it arises.

Some researchers believe that consciousness is a purely biological phenomenon that is dependent on the physical structure and processes of the brain, while others believe that it may be possible to create artificial systems that are capable of experiencing subjective awareness and self-reflection.

However, there is currently no known way to create a conscious AI system. While some AI systems can mimic human-like behavior and cognitive processes, they are still fundamentally different from biological organisms and lack the subjective experience and self-awareness that are thought to be essential components of consciousness.

That being said, AI technology is rapidly advancing, and it is possible that in the future, new breakthroughs in neuroscience and cognitive science could lead to the development of AI systems that are capable of experiencing consciousness.

However, it is important to note that this is still a highly speculative and uncertain area of research, and there is no guarantee that AI will ever be conscious in the same way that humans are.

Artificial Intelligence Frequently Asked Questions:   Is Excel AI?

Excel is not AI, but it can be used to perform some basic data analysis tasks, such as filtering and sorting data and creating charts and graphs.

An example of an intelligent automation solution that makes use of AI and transfers files between folders could be a system that uses machine learning algorithms to classify and categorize files based on their content, and then automatically moves them to the appropriate folders.

What is an example of an intelligent automation solution that makes use of artificial intelligence transferring files between folders?

An example of an intelligent automation solution that uses AI to transfer files between folders could be a system that employs machine learning algorithms to classify and categorize files based on their content, and then automatically moves them to the appropriate folders.

Artificial Intelligence Frequently Asked Questions: How do AI battles work in MK11?

The specific details of how AI battles work in MK11 are not specified, as it likely varies depending on the game’s design and programming. However, in general, AI opponents in fighting games can be designed to use a combination of pre-determined strategies and machine learning algorithms to react to the player’s actions in real-time.

Artificial Intelligence Frequently Asked Questions: Is pattern recognition a part of artificial intelligence?

Yes, pattern recognition is a subfield of artificial intelligence (AI) that involves the development of algorithms and models for identifying patterns in data. This is a crucial component of many AI systems, as it allows them to recognize and categorize objects, images, and other forms of data in real-world applications.

Artificial Intelligence Frequently Asked Questions: How do I use Jasper AI?

The specifics on how to use Jasper AI may vary depending on the specific application and platform. However, in general, using Jasper AI would involve integrating its capabilities into your system or application, and using its APIs to access its functions and perform tasks such as natural language processing, decision making, and prediction.

Artificial Intelligence Frequently Asked Questions: Is augmented reality artificial intelligence?

Augmented reality (AR) can make use of artificial intelligence (AI) techniques, but it is not AI in and of itself. AR involves enhancing the real world with computer-generated information, while AI involves creating systems that can perform tasks that typically require human intelligence, such as image recognition, decision making, and natural language processing.

Artificial Intelligence Frequently Asked Questions: Does artificial intelligence have rights?

No, artificial intelligence (AI) does not have rights as it is not a legal person or entity. AI is a technology and does not have consciousness, emotions, or the capacity to make decisions or take actions in the same way that human beings do. However, there is ongoing discussion and debate around the ethical considerations and responsibilities involved in creating and using AI systems.

Artificial Intelligence Frequently Asked Questions: What is generative AI?

Generative AI is a branch of artificial intelligence that involves creating computer algorithms or models that can generate new data or content, such as images, videos, music, or text, that mimic or expand upon the patterns and styles of existing data.

Generative AI models are trained on large datasets using deep learning techniques, such as neural networks, and learn to generate new data by identifying and emulating patterns, structures, and relationships in the input data.

Some examples of generative AI applications include image synthesis, text generation, music composition, and even chatbots that can generate human-like conversations. Generative AI has the potential to revolutionize various fields, such as entertainment, art, design, and marketing, and enable new forms of creativity, personalization, and automation.

How important do you think generative AI will be for the future of development, in general, and for mobile? In what areas of mobile development do you think generative AI has the most potential?

Generative AI is already playing a significant role in various areas of development, and it is expected to have an even greater impact in the future. In the realm of mobile development, generative AI has the potential to bring a lot of benefits to developers and users alike.

One of the main areas of mobile development where generative AI can have a significant impact is user interface (UI) and user experience (UX) design. With generative AI, developers can create personalized and adaptive interfaces that can adjust to individual users’ preferences and behaviors in real-time. This can lead to a more intuitive and engaging user experience, which can translate into higher user retention and satisfaction rates.

Another area where generative AI can make a difference in mobile development is in content creation. Generative AI models can be used to automatically generate high-quality and diverse content, such as images, videos, and text, that can be used in various mobile applications, from social media to e-commerce.

Furthermore, generative AI can also be used to improve mobile applications’ performance and efficiency. For example, it can help optimize battery usage, reduce network latency, and improve app loading times by predicting and pre-loading content based on user behavior.

Overall, generative AI has the potential to bring significant improvements and innovations to various areas of mobile development, including UI/UX design, content creation, and performance optimization. As the technology continues to evolve, we can expect to see even more exciting applications and use cases emerge in the future.

How do you see the role of developers evolving as a result of the development and integration of generative AI technologies? How could it impact creativity, job requirements and skill sets in software development?

The development and integration of generative AI technologies will likely have a significant impact on the role of developers and the software development industry as a whole. Here are some ways in which generative AI could impact the job requirements, skill sets, and creativity of developers:

1. New skills and knowledge requirements: As generative AI becomes more prevalent, developers will need to have a solid understanding of machine learning concepts and techniques, as well as experience with deep learning frameworks and tools. This will require developers to have a broader skill set that includes both software development and machine learning.

2. Greater focus on data: Generative AI models require large amounts of data to be trained, which means that developers will need to have a better understanding of data collection, management, and processing. This could lead to the emergence of new job roles, such as data engineers, who specialize in preparing and cleaning data for machine learning applications.

3. More creativity and innovation: Generative AI has the potential to unlock new levels of creativity and innovation in software development. By using AI-generated content and models, developers can focus on higher-level tasks, such as designing user experiences and optimizing software performance, which could lead to more innovative and user-friendly products.

4. Automation of repetitive tasks: Generative AI can be used to automate many of the repetitive tasks that developers currently perform, such as writing code and testing software. This could lead to increased efficiency and productivity, allowing developers to focus on more strategic and value-added tasks.

Overall, the integration of generative AI technologies is likely to lead to a shift in the role of developers, with a greater emphasis on machine learning and data processing skills. However, it could also open up new opportunities for creativity and innovation, as well as automate many repetitive tasks, leading to greater efficiency and productivity in the software development industry.

Do you have any concerns about using generative AI in mobile development work? What are they? 

As with any emerging technology, there are potential concerns associated with the use of generative AI in mobile development. Here are some possible concerns to keep in mind:

1. Bias and ethics: Generative AI models are trained on large datasets, which can contain biases and reinforce existing societal inequalities. This could lead to AI-generated content that reflects and perpetuates these biases, which could have negative consequences for users and society as a whole. Developers need to be aware of these issues and take steps to mitigate bias and ensure ethical use of AI in mobile development.

2. Quality control: While generative AI can automate the creation of high-quality content, there is a risk that the content generated may not meet the required standards or be appropriate for the intended audience. Developers need to ensure that the AI-generated content is of sufficient quality and meets user needs and expectations.

3. Security and privacy: Generative AI models require large amounts of data to be trained, which raises concerns around data security and privacy. Developers need to ensure that the data used to train the AI models is protected and that user privacy is maintained.

4. Technical limitations: Generative AI models are still in the early stages of development, and there are limitations to what they can achieve. For example, they may struggle to generate content that is highly specific or nuanced. Developers need to be aware of these limitations and ensure that generative AI is used appropriately in mobile development.

Overall, while generative AI has the potential to bring many benefits to mobile development, developers need to be aware of the potential concerns and take steps to mitigate them. By doing so, they can ensure that the AI-generated content is of high quality, meets user needs, and is developed in an ethical and responsible manner.

Artificial Intelligence Frequently Asked Questions: How do you make an AI engine?

Making an AI engine involves several steps, including defining the problem, collecting and preprocessing data, selecting and training a model, evaluating the model, and refining it as needed. The specific approach and technologies used will depend on the problem you are trying to solve and the type of AI system you are building. In general, developing an AI engine requires knowledge of computer science, mathematics, and machine learning algorithms.

Artificial Intelligence Frequently Asked Questions: Which exclusive online concierge service uses artificial intelligence to anticipate the needs and tastes of travellers by analyzing their spending patterns?

There are a number of travel and hospitality companies that are exploring the use of AI to provide personalized experiences and services to their customers based on their preferences, behavior, and spending patterns.

Artificial Intelligence Frequently Asked Questions: How to validate an artificial intelligence?

To validate an artificial intelligence system, various testing methods can be used to evaluate its performance, accuracy, and reliability. This includes data validation, benchmarking against established models, testing against edge cases, and validating the output against known outcomes. It is also important to ensure the system is ethical, transparent, and accountable.

Artificial Intelligence Frequently Asked Questions: When leveraging artificial intelligence in today’s business?

When leveraging artificial intelligence in today’s business, companies can use AI to streamline processes, gain insights from data, and automate tasks. AI can also help improve customer experience, personalize offerings, and reduce costs. However, it is important to ensure that the AI systems used are ethical, secure, and transparent.

Artificial Intelligence Frequently Asked Questions: How are the ways AI learns similar to how you learn?

AI learns in a similar way to how humans learn through experience and repetition. Like humans, AI algorithms can recognize patterns, make predictions, and adjust their behavior based on feedback. However, AI is often able to process much larger volumes of data at a much faster rate than humans.

Artificial Intelligence Frequently Asked Questions: What is the fear of AI?

The fear of AI, often referred to as “AI phobia” or “AI anxiety,” is the concern that artificial intelligence could pose a threat to humanity. Some worry that AI could become uncontrollable, make decisions that harm humans, or even take over the world.

However, many experts argue that these fears are unfounded and that AI is just a tool that can be used for good or bad depending on how it is implemented.

Artificial Intelligence Frequently Asked Questions: How have developments in AI so far affected our sense of what it means to be human?

Developments in AI have raised questions about what it means to be human, particularly in terms of our ability to think, learn, and create.

Some argue that AI is simply an extension of human intelligence, while others worry that it could eventually surpass human intelligence and create a new type of consciousness.

Artificial Intelligence Frequently Asked Questions: How to talk to artificial intelligence?

To talk to artificial intelligence, you can use a chatbot or a virtual assistant such as Siri or Alexa. These systems can understand natural language and respond to your requests, questions, and commands. However, it is important to remember that these systems are limited in their ability to understand context and may not always provide accurate or relevant responses.

Artificial Intelligence Frequently Asked Questions: How to program an AI robot?

To program an AI robot, you will need to use specialized programming languages such as Python, MATLAB, or C++. You will also need to have a strong understanding of robotics, machine learning, and computer vision. There are many resources available online that can help you learn how to program AI robots, including tutorials, courses, and forums.

Artificial Intelligence Frequently Asked Questions: Will artificial intelligence take away jobs?

Artificial intelligence has the potential to automate many jobs that are currently done by humans. However, it is also creating new jobs in fields such as data science, machine learning, and robotics. Many experts believe that while some jobs may be lost to automation, new jobs will be created as well.

Which type of artificial intelligence can repeatedly perform tasks?

The type of artificial intelligence that can repeatedly perform tasks is called narrow or weak AI. This type of AI is designed to perform a specific task, such as playing chess or recognizing images, and is not capable of general intelligence or human-like reasoning.

Artificial Intelligence Frequently Asked Questions: Has any AI become self-aware?

No, there is currently no evidence that any AI has become self-aware in the way that humans are. While some AI systems can mimic human-like behavior and conversation, they do not have consciousness or true self-awareness.

Artificial Intelligence Frequently Asked Questions: What company is at the forefront of artificial intelligence?

Several companies are at the forefront of artificial intelligence, including Google, Microsoft, Amazon, and Facebook. These companies have made significant investments in AI research and development

Artificial Intelligence Frequently Asked Questions: Which is the best AI system?

There is no single “best” AI system as it depends on the specific use case and the desired outcome. Some popular AI systems include IBM Watson, Google Cloud AI, and Microsoft Azure AI, each with their unique features and capabilities.

Artificial Intelligence Frequently Asked Questions: Have we created true artificial intelligence?

There is still debate among experts as to whether we have created true artificial intelligence or AGI (artificial general intelligence) yet.

While AI has made significant progress in recent years, it is still largely task-specific and lacks the broad cognitive abilities of human beings.

What is one way that IT services companies help clients ensure fairness when applying artificial intelligence solutions?

IT services companies can help clients ensure fairness when applying artificial intelligence solutions by conducting a thorough review of the data sets used to train the AI algorithms. This includes identifying potential biases and correcting them to ensure that the AI outputs are fair and unbiased.

Artificial Intelligence Frequently Asked Questions: How to write artificial intelligence?

To write artificial intelligence, you need to have a strong understanding of programming languages, data science, machine learning, and computer vision. There are many libraries and tools available, such as TensorFlow and Keras, that make it easier to write AI algorithms.

How is a robot with artificial intelligence like a baby?

A robot with artificial intelligence is like a baby in that both learn and adapt through experience. Just as a baby learns by exploring its environment and receiving feedback from caregivers, an AI robot learns through trial and error and adjusts its behavior based on the results.

Artificial Intelligence Frequently Asked Questions: Is artificial intelligence STEM?

Yes, artificial intelligence is a STEM (science, technology, engineering, and mathematics) field. AI requires a deep understanding of computer science, mathematics, and statistics to develop algorithms and train models.

Will AI make artists obsolete?

While AI has the potential to automate certain aspects of the creative process, such as generating music or creating visual art, it is unlikely to make artists obsolete. AI-generated art still lacks the emotional depth and unique perspective of human-created art.

Why do you like artificial intelligence?

Many people are interested in AI because of its potential to solve complex problems, improve efficiency, and create new opportunities for innovation and growth.

What are the main areas of research in artificial intelligence?

Artificial intelligence research covers a wide range of areas, including natural language processing, computer vision, machine learning, robotics, expert systems, and neural networks. Researchers in AI are also exploring ways to improve the ethical and social implications of AI systems.

How are the ways AI learn similar to how you learn?

Like humans, AI learns through experience and trial and error. AI algorithms use data to train and adjust their models, similar to how humans learn from feedback and make adjustments based on their experiences. However, AI learning is typically much faster and more precise than human learning.

Do artificial intelligence have feelings?

Artificial intelligence does not have emotions or feelings as it is a machine and lacks the capacity for subjective experiences. AI systems are designed to perform specific tasks and operate within the constraints of their programming and data inputs.

Artificial Intelligence Frequently Asked Questions: Will AI be the end of humanity?

There is no evidence to suggest that AI will be the end of humanity. While there are concerns about the ethical and social implications of AI, experts agree that the technology has the potential to bring many benefits and solve complex problems. It is up to humans to ensure that AI is developed and used in a responsible and ethical manner.

Which business case is better solved by Artificial Intelligence AI than conventional programming which business case is better solved by Artificial Intelligence AI than conventional programming?

Business cases that involve large amounts of data and require complex decision-making are often better suited for AI than conventional programming.

For example, AI can be used in areas such as financial forecasting, fraud detection, supply chain optimization, and customer service to improve efficiency and accuracy.

Who is the most powerful AI?

It is difficult to determine which AI system is the most powerful, as the capabilities of AI vary depending on the specific task or application. However, some of the most well-known and powerful AI systems include IBM Watson, Google Assistant, Amazon Alexa, and Tesla’s Autopilot system.

Have we achieved artificial intelligence?

While AI has made significant progress in recent years, we have not achieved true artificial general intelligence (AGI), which is a machine capable of learning and reasoning in a way that is comparable to human cognition. However, AI has become increasingly sophisticated and is being used in a wide range of applications and industries.

What are benefits of AI?

The benefits of AI include increased efficiency and productivity, improved accuracy and precision, cost savings, and the ability to solve complex problems.

AI can also be used to improve healthcare, transportation, and other critical areas, and has the potential to create new opportunities for innovation and growth.

How scary is Artificial Intelligence?

AI can be scary if it is not developed or used in an ethical and responsible manner. There are concerns about the potential for AI to be used in harmful ways or to perpetuate biases and inequalities. However, many experts believe that the benefits of AI outweigh the risks, and that the technology can be used to address many of the world’s most pressing problems.

How to make AI write a script?

There are different ways to make AI write a script, such as training it with large datasets, using natural language processing (NLP) and generative models, or using pre-existing scriptwriting software that incorporates AI algorithms.

How do you summon an entity without AI bedrock?

Attempting to summon entities can be dangerous and potentially harmful.

What should I learn for AI?

To work in artificial intelligence, it is recommended to have a strong background in computer science, mathematics, statistics, and machine learning. Familiarity with programming languages such as Python, Java, and C++ can also be beneficial.

Will AI take over the human race?

No, the idea of AI taking over the human race is a common trope in science fiction but is not supported by current AI capabilities. While AI can be powerful and influential, it does not have the ability to take over the world or control humanity.

Where do we use AI?

AI is used in a wide range of fields and industries, such as healthcare, finance, transportation, manufacturing, and entertainment. Examples of AI applications include image and speech recognition, natural language processing, autonomous vehicles, and recommendation systems.

Who invented AI?

The development of AI has involved contributions from many researchers and pioneers. Some of the key figures in AI history include John McCarthy, Marvin Minsky, Allen Newell, and Herbert Simon, who are considered to be the founders of the field.

Is AI improving?

Yes, AI is continuously improving as researchers and developers create more sophisticated algorithms, use larger and more diverse datasets, and design more advanced hardware. However, there are still many challenges and limitations to be addressed in the development of AI.

Will artificial intelligence take over the world?

No, the idea of AI taking over the world is a popular science fiction trope but is not supported by current AI capabilities. AI systems are designed and controlled by humans and are not capable of taking over the world or controlling humanity.

Is there an artificial intelligence system to help the physician in selecting a diagnosis?

Yes, there are AI systems designed to assist physicians in selecting a diagnosis by analyzing patient data and medical records. These systems use machine learning algorithms and natural language processing to identify patterns and suggest possible diagnoses. However, they are not intended to replace human expertise and judgement.

Will AI replace truck drivers?

AI has the potential to automate certain aspects of truck driving, such as navigation and safety systems. However, it is unlikely that AI will completely replace truck drivers in the near future. Human drivers are still needed to handle complex situations and make decisions based on context and experience.

How AI can destroy the world?

There is a hypothetical concern that AI could cause harm to humans in various ways. For example, if an AI system becomes more intelligent than humans, it could act against human interests or even decide to eliminate humanity. This scenario is known as an existential risk, but many experts believe it to be unlikely. To prevent this kind of risk, researchers are working on developing safety mechanisms and ethical guidelines for AI systems.

What do you call the commonly used AI technology for learning input to output mappings?

The commonly used AI technology for learning input to output mappings is called a neural network. It is a type of machine learning algorithm that is modeled after the structure of the human brain. Neural networks are trained using a large dataset, which allows them to learn patterns and relationships in the data. Once trained, they can be used to make predictions or classifications based on new input data.

What are 3 benefits of AI?

Three benefits of AI are:

• Efficiency: AI systems can process vast amounts of data much faster than humans, allowing for more efficient and accurate decision-making.
• Personalization: AI can be used to create personalized experiences for users, such as personalized recommendations in e-commerce or personalized healthcare treatments.
• Safety: AI can be used to improve safety in various applications, such as autonomous vehicles or detecting fraudulent activities in banking.

What is an artificial intelligence company?

An artificial intelligence (AI) company is a business that specializes in developing and applying AI technologies. These companies use machine learning, deep learning, natural language processing, and other AI techniques to build products and services that can automate tasks, improve decision-making, and provide new insights into data.

Examples of AI companies include Google, Amazon, and IBM.

What does AI mean in tech?

In tech, AI stands for artificial intelligence. AI is a field of computer science that aims to create machines that can perform tasks that would typically require human intelligence, such as learning, reasoning, problem-solving, and language understanding. AI techniques can be used in various applications, such as virtual assistants, chatbots, autonomous vehicles, and healthcare.

Can AI destroy humans?

There is no evidence to suggest that AI can or will destroy humans. While there are concerns about the potential risks of AI, most experts believe that AI systems will only act in ways that they have been programmed to.

To mitigate any potential risks, researchers are working on developing safety mechanisms and ethical guidelines for AI systems.

What types of problems can AI solve?

AI can solve a wide range of problems, including:

• Classification: AI can be used to classify data into categories, such as spam detection in email or image recognition in photography.
• Prediction: AI can be used to make predictions based on data, such as predicting stock prices or diagnosing diseases.
• Optimization: AI can be used to optimize systems or processes, such as scheduling routes for delivery trucks or maximizing production in a factory.
• Natural language processing: AI can be used to understand and process human language, such as voice recognition or language translation.

Is AI slowing down?

There is no evidence to suggest that AI is slowing down. In fact, the field of AI is rapidly evolving and advancing, with new breakthroughs and innovations being made all the time. From natural language processing and computer vision to robotics and machine learning, AI is making significant strides in many areas.

How to write a research paper on artificial intelligence?

When writing a research paper on artificial intelligence, it’s important to start with a clear research question or thesis statement. You should then conduct a thorough literature review to gather relevant sources and data to support your argument. After analyzing the data, you can present your findings and draw conclusions, making sure to discuss the implications of your research and future directions for the field.

How to get AI to read text?

To get AI to read text, you can use natural language processing (NLP) techniques such as text analysis and sentiment analysis. These techniques involve training AI algorithms to recognize patterns in written language, enabling them to understand the meaning of words and phrases in context. Other methods of getting AI to read text include optical character recognition (OCR) and speech-to-text technology.

How to create your own AI bot?

To create your own AI bot, you can use a variety of tools and platforms such as Microsoft Bot Framework, Dialogflow, or IBM Watson.

These platforms provide pre-built libraries and APIs that enable you to easily create, train, and deploy your own AI chatbot or virtual assistant. You can customize your bot’s functionality, appearance, and voice, and train it to respond to specific user queries and actions.

What is AI according to Elon Musk?

According to Elon Musk, AI is “the next stage in human evolution” and has the potential to be both a great benefit and a major threat to humanity.

He has warned about the dangers of uncontrolled AI development and has called for greater regulation and oversight in the field. Musk has also founded several companies focused on AI development, such as OpenAI and Neuralink.

How do you program Artificial Intelligence?

Programming artificial intelligence typically involves using machine learning algorithms to train the AI system to recognize patterns and make predictions based on data. This involves selecting a suitable machine learning model, preprocessing the data, selecting appropriate features, and tuning the model hyperparameters.

Once the model is trained, it can be integrated into a larger software application or system to perform various tasks such as image recognition or natural language processing.

What is the first step in the process of AI?

The first step in the process of AI is to define the problem or task that the AI system will be designed to solve. This involves identifying the specific requirements, constraints, and objectives of the system, and determining the most appropriate AI techniques and algorithms to use.

Other key steps in the process include data collection, preprocessing, feature selection, model training and evaluation, and deployment and maintenance of the AI system.

How to make an AI that can talk?

One way to make an AI that can talk is to use a natural language processing (NLP) system. NLP is a field of AI that focuses on how computers can understand, interpret, and respond to human language. By using machine learning algorithms, the AI can learn to recognize speech, process it, and generate a response in a natural-sounding way.

Another approach is to use a chatbot framework, which involves creating a set of rules and responses that the AI can use to interact with users.

How to use the AI Qi tie?

The AI Qi tie is a type of smart wearable device that uses artificial intelligence to provide various functions, including health monitoring, voice control, and activity tracking. To use it, you would first need to download the accompanying mobile app, connect the device to your smartphone, and set it up according to the instructions provided.

From there, you can use voice commands to control various functions of the device, such as checking your heart rate, setting reminders, and playing music.

Is sentient AI possible?

While there is ongoing research into creating AI that can exhibit human-like cognitive abilities, including sentience, there is currently no clear evidence that sentient AI is possible or exists. The concept of sentience, which involves self-awareness and subjective experience, is difficult to define and even more challenging to replicate in a machine. Some experts believe that true sentience in AI may be impossible, while others argue that it is only a matter of time before machines reach this level of intelligence.

Is Masteron an AI?

No, Masteron is not an AI. It is a brand name for a steroid hormone called drostanolone. AI typically stands for “artificial intelligence,” which refers to machines and software that can simulate human intelligence and perform tasks that would normally require human intelligence to complete.

Is the Lambda AI sentient?

There is no clear evidence that the Lambda AI, or any other AI system for that matter, is sentient. Sentience refers to the ability to experience subjective consciousness, which is not currently understood to be replicable in machines. While AI systems can be programmed to simulate a wide range of cognitive abilities, including learning, problem-solving, and decision-making, they are not currently believed to possess subjective awareness or consciousness.

Where is artificial intelligence now?

Artificial intelligence is now a pervasive technology that is being used in many different industries and applications around the world. From self-driving cars and virtual assistants to medical diagnosis and financial trading, AI is being employed to solve a wide range of problems and improve human performance. While there are still many challenges to overcome in the field of AI, including issues related to bias, ethics, and transparency, the technology is rapidly advancing and is expected to play an increasingly important role in our lives in the years to come.

What is the correct sequence of artificial intelligence trying to imitate a human mind?

The correct sequence of artificial intelligence trying to imitate a human mind can vary depending on the specific approach and application. However, some common steps in this process may include collecting and analyzing data, building a model or representation of the human mind, training the AI system using machine learning algorithms, and testing and refining the system to improve its accuracy and performance. Other important considerations in this process may include the ethical implications of creating machines that can mimic human intelligence.

How do I make machine learning AI?

To make machine learning AI, you will need to have knowledge of programming languages such as Python and R, as well as knowledge of machine learning algorithms and tools. Some steps to follow include gathering and cleaning data, selecting an appropriate algorithm, training the algorithm on the data, testing and validating the model, and deploying it for use.

What is AI scripting?

AI scripting is a process of developing scripts that can automate the behavior of AI systems. It involves writing scripts that govern the AI’s decision-making process and its interactions with users or other systems. These scripts are often written in programming languages such as Python or JavaScript and can be used in a variety of applications, including chatbots, virtual assistants, and intelligent automation tools.

Is IOT artificial intelligence?

No, the Internet of Things (IoT) is not the same as artificial intelligence (AI). IoT refers to the network of physical devices, vehicles, home appliances, and other items that are embedded with electronics, sensors, and connectivity, allowing them to connect and exchange data. AI, on the other hand, involves the creation of intelligent machines that can learn and perform tasks that would normally require human intelligence, such as speech recognition, decision-making, and language translation.

What problems will Ai solve?

AI has the potential to solve a wide range of problems across different industries and domains. Some of the problems that AI can help solve include automating repetitive or dangerous tasks, improving efficiency and productivity, enhancing decision-making and problem-solving, detecting fraud and cybersecurity threats, predicting outcomes and trends, and improving customer experience and personalization.

Who wrote papers on the simulation of human thinking problem solving and verbal learning that marked the beginning of the field of artificial intelligence?

The papers on the simulation of human thinking, problem-solving, and verbal learning that marked the beginning of the field of artificial intelligence were written by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon in the late 1950s.

The papers, which were presented at the Dartmouth Conference in 1956, proposed the idea of developing machines that could simulate human intelligence and perform tasks that would normally require human intelligence.

Given the fast development of AI systems, how soon do you think AI systems will become 100% autonomous?

It’s difficult to predict exactly when AI systems will become 100% autonomous, as there are many factors that could affect this timeline. However, it’s important to note that achieving 100% autonomy may not be possible or desirable in all cases, as there will likely always be a need for some degree of human oversight and control.

That being said, AI systems are already capable of performing many tasks autonomously, and their capabilities are rapidly expanding. For example, there are already AI systems that can drive cars, detect fraud, and diagnose diseases with a high degree of accuracy.

However, there are still many challenges to be overcome before AI systems can be truly autonomous in all domains. One of the main challenges is developing AI systems that can understand and reason about complex, real-world situations, as opposed to just following pre-programmed rules or learning from data.

Another challenge is ensuring that AI systems are safe, transparent, and aligned with human values and objectives.

This is particularly important as AI systems become more powerful and influential, and have the potential to impact many aspects of our lives.

For low-level domain-specific jobs such as industrial manufacturing, we already have Artificial Intelligence Systems that are fully autonomous, i.e., accomplish tasks without human intervention.

But those autonomous systems require collections of various intelligent skills to tackle many unseen situations; IMO, it will take a while to design one.

The major hurdle in making an A.I. autonomous system is to design an algorithm that can handle unpredictable events correctly. For a closed environment, it may not be a big issue. But for an open-ended system, the infinite number of possibilities is difficult to cover and ensure the autonomous device’s reliability.

Current SOTA Artificial Intelligence algorithms are mostly data-centric training. The issue is not only the algorithm itself. The selection, generation, and pre-processing of datasets also determine the final performance of the accuracy. Machine Learning helps offload us without needing to explicitly derive the procedural methods to solve a problem. Still, it relies heavily on the input and feedback methods we need to provide correctly. Overcoming one problem might create many new ones, and sometimes, we do not even know whether the dataset is adequate, reasonable, and practical.

Overall, it’s difficult to predict exactly when AI systems will become 100% autonomous, but it’s clear that the development of AI technology will continue to have a profound impact on many aspects of our society and economy.

Will ChatGPT replace programmers?

Is it possible that ChatGPT will eventually replace programmers? The answer to this question is not a simple yes or no, as it depends on the rate of development and improvement of AI tools like ChatGPT.

If AI tools continue to advance at the same rate over the next 10 years, then they may not be able to fully replace programmers. However, if these tools continue to evolve and learn at an accelerated pace, then it is possible that they may replace at least 30% of programmers.

Although the current version of ChatGPT has some limitations and is only capable of generating boilerplate code and identifying simple bugs, it is a starting point for what is to come. With the ability to learn from millions of mistakes at a much faster rate than humans, future versions of AI tools may be able to produce larger code blocks, work with mid-sized projects, and even handle QA of software output.

In the future, programmers may still be necessary to provide commands to the AI tools, review the final code, and perform other tasks that require human intuition and judgment. However, with the use of AI tools, one developer may be able to accomplish the tasks of multiple developers, leading to a decrease in the number of programming jobs available.

In conclusion, while it is difficult to predict the extent to which AI tools like ChatGPT will impact the field of programming, it is clear that they will play an increasingly important role in the years to come.

ChatGPT is not designed to replace programmers.

While AI language models like ChatGPT can generate code and help automate certain programming tasks, they are not capable of replacing the skills, knowledge, and creativity of human programmers.

Programming is a complex and creative field that requires a deep understanding of computer science principles, problem-solving skills, and the ability to think critically and creatively. While AI language models like ChatGPT can assist in certain programming tasks, such as generating code snippets or providing suggestions, they cannot replace the human ability to design, develop, and maintain complex software systems.

Furthermore, programming involves many tasks that require human intuition and judgment, such as deciding on the best approach to solve a problem, optimizing code for efficiency and performance, and debugging complex systems. While AI language models can certainly be helpful in some of these tasks, they are not capable of fully replicating the problem-solving abilities of human programmers.

Overall, while AI language models like ChatGPT will undoubtedly have an impact on the field of programming, they are not designed to replace programmers, but rather to assist and enhance their abilities.

What does a responsive display ad use in its machine learning model?

A responsive display ad uses various machine learning models such as automated targeting, bidding, and ad creation to optimize performance and improve ad relevance. It also uses algorithms to predict which ad creative and format will work best for each individual user and the context in which they are browsing.

What two things are marketers realizing as machine learning becomes more widely used?

Marketers are realizing the benefits of machine learning in improving efficiency and accuracy in various aspects of their work, including targeting, personalization, and data analysis. They are also realizing the importance of maintaining transparency and ethical considerations in the use of machine learning and ensuring it aligns with their marketing goals and values.

How does statistics fit into the area of machine learning?

Statistics is a fundamental component of machine learning, as it provides the mathematical foundations for many of the algorithms and models used in the field. Statistical methods such as regression, clustering, and hypothesis testing are used to analyze data and make predictions based on patterns and trends in the data.

Is Machine Learning weak AI?

Yes, machine learning is considered a form of weak artificial intelligence, as it is focused on specific tasks and does not possess general intelligence or consciousness. Machine learning models are designed to perform a specific task based on training data and do not have the ability to think, reason, or learn outside of their designated task.

When evaluating machine learning results, should I always choose the fastest model?

No, the speed of a machine learning model is not the only factor to consider when evaluating its performance. Other important factors include accuracy, complexity, and interpretability. It is important to choose a model that balances these factors based on the specific needs and goals of the task at hand.

How do you learn machine learning?

You can learn machine learning through a combination of self-study, online courses, and practical experience. Some popular resources for learning machine learning include online courses on platforms such as Coursera and edX, textbooks and tutorials, and practical experience through projects and internships.

It is important to have a strong foundation in mathematics, programming, and statistics to succeed in the field.

What are your thoughts on artificial intelligence and machine learning?

Artificial intelligence and machine learning have the potential to revolutionize many aspects of society and have already shown significant impacts in various industries.

It is important to continue to develop these technologies responsibly and with ethical considerations to ensure they align with human values and benefit society as a whole.

Which AWS service enables you to build the workflows that are required for human review of machine learning predictions?

Amazon SageMaker Ground Truth is an AWS service that enables you to build workflows for human review of machine learning predictions.

This service provides an easy-to-use interface for creating and managing custom workflows and provides built-in tools for data labeling and quality control to ensure high-quality training data.

What is augmented machine learning?

Augmented machine learning is a combination of human expertise and machine learning models to improve the accuracy of machine learning. This technique is used when the available data is not enough or is not of good quality. The human expert is involved in the training and validation of the machine learning model to improve its accuracy.

Which actions are performed during the prepare the data step of workflow for analyzing the data with Oracle machine learning?

The ‘prepare the data’ step in Oracle machine learning workflow involves data cleaning, feature selection, feature engineering, and data transformation. These actions are performed to ensure that the data is ready for analysis, and that the machine learning model can effectively learn from the data.

What type of machine learning algorithm would you use to allow a robot to walk in various unknown terrains?

A reinforcement learning algorithm would be appropriate for this task. In this type of machine learning, the robot would interact with its environment and receive rewards for positive outcomes, such as moving forward or maintaining balance. The algorithm would learn to maximize these rewards and gradually improve its ability to navigate through different terrains.

Are evolutionary algorithms machine learning?

Yes, evolutionary algorithms are a subset of machine learning. They are a type of optimization algorithm that uses principles from biological evolution to search for the best solution to a problem.

Evolutionary algorithms are often used in problems where traditional optimization algorithms struggle, such as in complex, nonlinear, and multi-objective optimization problems.

Is MPC machine learning?

Yes, Model Predictive Control (MPC) is a type of machine learning. It is a feedback control algorithm that predicts the future behavior of a system and uses this prediction to optimize its performance. MPC is used in a variety of applications, including industrial control, robotics, and autonomous vehicles.

When do you use ML model?

You would use a machine learning model when you need to make predictions or decisions based on data. Machine learning models are trained on historical data and use this knowledge to make predictions on new data. Common applications of machine learning include fraud detection, recommendation systems, and image recognition.

When preparing the dataset for your machine learning model, you should use one hot encoding on what type of data?

One hot encoding is used on categorical data. Categorical data is non-numeric data that has a limited number of possible values, such as color or category. One hot encoding is a technique used to convert categorical data into a format that can be used in machine learning models. It converts each category into a binary vector, where each vector element corresponds to a unique category.

Is machine learning just brute force?

No, machine learning is not just brute force. Although machine learning models can be complex and require significant computing power, they are not simply brute force algorithms. Machine learning involves the use of statistical techniques and mathematical models to learn from data and make predictions. Machine learning is designed to make use of the available data in an efficient way, without the need for exhaustive search or brute force techniques.

How to implement a machine learning paper?

Implementing a machine learning paper involves understanding the research paper’s theoretical foundation, reproducing the results, and applying the approach to the new data to evaluate the approach’s efficacy. The implementation process begins with comprehending the paper’s theoretical framework, followed by testing and reproducing the findings to validate the approach.

Finally, the approach can be implemented on new datasets to assess its accuracy and generalizability. It’s essential to understand the mathematical concepts and programming tools involved in the paper to successfully implement the machine learning paper.

What are some use cases where more traditional machine learning models may make much better predictions than DNNS?

More traditional machine learning models may outperform deep neural networks (DNNs) in the following use cases:

• When the dataset is relatively small and straightforward, traditional machine learning models, such as logistic regression, may be more accurate than DNNs.
• When the dataset is sparse or when the number of observations is small, DNNs may require more computational resources and more time to train than traditional machine learning models.
• When the problem is not complex, and the data has a low level of noise, traditional machine learning models may outperform DNNs.

Who is the supervisor in supervised machine learning?

In supervised machine learning, the supervisor refers to the algorithm that acts as the teacher or the guide to the model. The supervisor provides the model with labeled examples to train on, and the model uses these labeled examples to learn how to classify new data. The supervisor algorithm determines the accuracy of the model’s predictions, and the model is trained to minimize the difference between its predicted outputs and the known outputs.

How do you make machine learning in scratch?

To make machine learning in scratch, you need to follow these steps:

• Choose a problem to solve and collect a dataset that represents the problem you want to solve.
• Preprocess and clean the data to ensure that it’s formatted correctly and ready for use in a machine learning model.
• Select a machine learning algorithm, such as decision trees, support vector machines, or neural networks.
• Implement the selected machine learning algorithm from scratch, using a programming language such as Python or R.
• Train the model using the preprocessed dataset and the implemented algorithm.
• Test the accuracy of the model and evaluate its performance.

Is unsupervised learning machine learning?

Yes, unsupervised learning is a type of machine learning. In unsupervised learning, the model is not given labeled data to learn from. Instead, the model must find patterns and relationships in the data on its own. Unsupervised learning algorithms include clustering, anomaly detection, and association rule mining. The model learns from the features in the dataset to identify underlying patterns or groups, which can then be used for further analysis or prediction.

How do I apply machine learning?

Machine learning can be applied to a wide range of problems and scenarios, but the basic process typically involves:

• gathering and preprocessing data,
• selecting an appropriate model or algorithm,
• training the model on the data, testing and evaluating the model, and then using the trained model to make predictions or perform other tasks on new data.
• The specific steps and techniques involved in applying machine learning will depend on the particular problem or application.

Is machine learning possible?

Yes, machine learning is possible and has already been successfully applied to a wide range of problems in various fields such as healthcare, finance, business, and more.

Machine learning has advanced rapidly in recent years, thanks to the availability of large datasets, powerful computing resources, and sophisticated algorithms.

Is machine learning the future?

Many experts believe that machine learning will continue to play an increasingly important role in shaping the future of technology and society.

As the amount of data available continues to grow and computing power increases, machine learning is likely to become even more powerful and capable of solving increasingly complex problems.

How to combine multiple features in machine learning?

In machine learning, multiple features can be combined in various ways depending on the particular problem and the type of model or algorithm being used.

One common approach is to concatenate the features into a single vector, which can then be fed into the model as input. Other techniques, such as feature engineering or dimensionality reduction, can also be used to combine or transform features to improve performance.

Which feature lets you discover machine learning assets in Watson Studio 1 point?

The feature in Watson Studio that lets you discover machine learning assets is called the Asset Catalog.

The Asset Catalog provides a unified view of all the assets in your Watson Studio project, including data assets, models, notebooks, and other resources.

You can use the Asset Catalog to search, filter, and browse through the assets, and to view metadata and details about each asset.

What is N in machine learning?

In machine learning, N is a common notation used to represent the number of instances or data points in a dataset.

N can be used to refer to the total number of examples in a dataset, or the number of examples in a particular subset or batch of the data.

N is often used in statistical calculations, such as calculating means or variances, or in determining the size of training or testing sets.

Is VAR machine learning?

VAR, or vector autoregression, is a statistical technique that models the relationship between multiple time series variables. While VAR involves statistical modeling and prediction, it is not generally considered a form of machine learning, which typically involves using algorithms to learn patterns or relationships in data automatically without explicit statistical modeling.

How many categories of machine learning are generally said to exist?

There are generally three categories of machine learning: supervised learning, unsupervised learning, and reinforcement learning.

In supervised learning, the algorithm is trained on labeled data to make predictions or classifications. The algorithm is trained on unlabeled data to identify patterns or structure.

In reinforcement learning, the algorithm learns to make decisions and take actions based on feedback from the environment.

How to use timestamp in machine learning?

Timestamps can be used in machine learning to analyze time series data. This involves capturing data over a period of time and making predictions about future events. Time series data can be used to detect patterns, trends, and anomalies that can be used to make predictions about future events. The timestamps can be used to group data into regular intervals for analysis or used as input features for machine learning models.

Is classification a machine learning technique?

Yes, classification is a machine learning technique. It involves predicting the category of a new observation based on a training dataset of labeled observations. Classification is a supervised learning technique where the output variable is categorical. Common examples of classification tasks include image recognition, spam detection, and sentiment analysis.

Which datatype is used to teach a machine learning ML algorithms during structured learning?

The datatype used to teach machine learning algorithms during structured learning is typically a labeled dataset. This is a dataset where each observation has a known output variable. The input variables are used to train the machine learning algorithm to predict the output variable. Labeled datasets are commonly used in supervised learning tasks such as classification and regression.

How is machine learning model in production used?

A machine learning model in production is used to make predictions on new, unseen data. The model is typically deployed as an API that can be accessed by other systems or applications. When a new observation is provided to the model, it generates a prediction based on the patterns it has learned from the training data. Machine learning models in production must be continuously monitored and updated to ensure their accuracy and performance.

What are the main advantages and disadvantages of Gans over standard machine learning models?

The main advantage of Generative Adversarial Networks (GANs) over standard machine learning models is their ability to generate new data that closely resembles the training data. This makes them well-suited for applications such as image and video generation. However, GANs can be more difficult to train than other machine learning models and require large amounts of training data. They can also be more prone to overfitting and may require more computing resources to train.

How does machine learning deal with biased data?

Machine learning models can be affected by biased data, leading to unfair or inaccurate predictions. To mitigate this, various techniques can be used, such as collecting a diverse dataset, selecting unbiased features, and analyzing the model’s outputs for bias. Additionally, techniques such as oversampling underrepresented classes, changing the cost function to focus on minority classes, and adjusting the decision threshold can be used to reduce bias.

What pre-trained machine learning APIS would you use in this image processing pipeline?

Some pre-trained machine learning APIs that can be used in an image processing pipeline include Google Cloud Vision API, Microsoft Azure Computer Vision API, and Amazon Rekognition API. These APIs can be used to extract features from images, classify images, detect objects, and perform facial recognition, among other tasks.

Which machine learning API is used to convert audio to text in GCP?

The machine learning API used to convert audio to text in GCP is the Cloud Speech-to-Text API. This API can be used to transcribe audio files, recognize spoken words, and convert spoken language into text in real-time. The API uses machine learning models to analyze the audio and generate accurate transcriptions.

How can machine learning reduce bias and variance?

Machine learning can reduce bias and variance by using different techniques, such as regularization, cross-validation, and ensemble learning. Regularization can help reduce variance by adding a penalty term to the cost function, which prevents overfitting. Cross-validation can help reduce bias by using different subsets of the data to train and test the model. Ensemble learning can also help reduce bias and variance by combining multiple models to make more accurate predictions.

How does machine learning increase precision?

Machine learning can increase precision by optimizing the model for accuracy. This can be achieved by using techniques such as feature selection, hyperparameter tuning, and regularization. Feature selection helps to identify the most important features in the dataset, which can improve the model’s precision. Hyperparameter tuning involves adjusting the settings of the model to find the optimal combination that leads to the best performance. Regularization helps to reduce overfitting and improve the model’s generalization ability.

How to do research in machine learning?

To do research in machine learning, one should start by identifying a research problem or question. Then, they can review relevant literature to understand the state-of-the-art techniques and approaches. Once the problem has been defined and the relevant literature has been reviewed, the researcher can collect and preprocess the data, design and implement the model, and evaluate the results. It is also important to document the research and share the findings with the community.

Is associations a machine learning technique?

Associations can be considered a machine learning technique, specifically in the field of unsupervised learning. Association rules mining is a popular technique used to discover interesting relationships between variables in a dataset. It is often used in market basket analysis to find correlations between items purchased together by customers. However, it is important to note that associations are not typically considered a supervised learning technique, as they do not involve predicting a target variable.

How do you present a machine learning model?

To present a machine learning model, it is important to provide a clear explanation of the problem being addressed, the dataset used, and the approach taken to build the model. The presentation should also include a description of the model architecture and any preprocessing techniques used. It is also important to provide an evaluation of the model’s performance using relevant metrics, such as accuracy, precision, and recall. Finally, the presentation should include a discussion of the model’s limitations and potential areas for improvement.

Is moving average machine learning?

Moving average is a statistical method used to analyze time series data, and it is not typically considered a machine learning technique. However, moving averages can be used as a preprocessing step for machine learning models to smooth out the data and reduce noise. In this context, moving averages can be considered a feature engineering technique that can improve the performance of the model.

How do you calculate accuracy and precision in machine learning?

Accuracy and precision are common metrics used to evaluate the performance of machine learning models. Accuracy is the proportion of correct predictions made by the model, while precision is the proportion of correct positive predictions out of all positive predictions made. To calculate accuracy, divide the number of correct predictions by the total number of predictions made. To calculate precision, divide the number of true positives (correct positive predictions) by the total number of positive predictions made by the model.

Which stage of the machine learning workflow includes feature engineering?

The stage of the machine learning workflow that includes feature engineering is the “data preparation” stage, where the data is cleaned, preprocessed, and transformed in a way that prepares it for training and testing the machine learning model. Feature engineering is the process of selecting, extracting, and transforming the most relevant and informative features from the raw data to be used by the machine learning algorithm.

How do I make machine learning AI?

Artificial Intelligence (AI) is a broader concept that includes several subfields, such as machine learning, natural language processing, and computer vision. To make a machine learning AI system, you will need to follow a systematic approach, which involves the following steps:

1. Define the problem and collect relevant data.
2. Preprocess and transform the data for training and testing.
3. Select and train a suitable machine learning model.
4. Evaluate the performance of the model and fine-tune it.
5. Deploy the model and integrate it into the target system.

How do you select models in machine learning?

The process of selecting a suitable machine learning model involves the following steps:

1. Define the problem and the type of prediction required.
2. Determine the type of data available (structured, unstructured, labeled, or unlabeled).
3. Select a set of candidate models that are suitable for the problem and data type.
4. Evaluate the performance of each model using a suitable metric (e.g., accuracy, precision, recall, F1 score).
5. Select the best performing model and fine-tune its parameters and hyperparameters.

What is convolutional neural network in machine learning?

A Convolutional Neural Network (CNN) is a type of deep learning neural network that is commonly used in computer vision applications, such as image recognition, classification, and segmentation. It is designed to automatically learn and extract hierarchical features from the raw input image data using convolutional layers, pooling layers, and fully connected layers.

The convolutional layers apply a set of learnable filters to the input image, which help to extract low-level features such as edges, corners, and textures. The pooling layers downsample the feature maps to reduce the dimensionality of the data and increase the computational efficiency. The fully connected layers perform the classification or regression task based on the learned features.

How to use machine learning in Excel?

Excel provides several built-in machine learning tools and functions that can be used to perform basic predictive analysis on structured data, such as linear regression, logistic regression, decision trees, and clustering. To use machine learning in Excel, you can follow these general steps:

1. Organize your data in a structured format, with each row representing a sample and each column representing a feature or target variable.
2. Use the appropriate machine learning function or tool to build a predictive model based on the data.
3. Evaluate the performance of the model using appropriate metrics and test data.

What are the six distinct stages or steps that are critical in building successful machine learning based solutions?

The six distinct stages or steps that are critical in building successful machine learning based solutions are:

• Problem definition
• Data collection and preparation
• Feature engineering
• Model training
• Model evaluation
• Model deployment and monitoring

Which two actions should you consider when creating the azure machine learning workspace?

When creating the Azure Machine Learning workspace, two important actions to consider are:

• Choosing an appropriate subscription that suits your needs and budget.
• Deciding on the region where you want to create the workspace, as this can impact the latency and data transfer costs.

What are the three stages of building a model in machine learning?

The three stages of building a model in machine learning are:

• Model building
• Model evaluation
• Model deployment

How to scale a machine learning system?

Some ways to scale a machine learning system are:

• Using distributed training to leverage multiple machines for model training
• Optimizing the code to run more efficiently
• Using auto-scaling to automatically add or remove computing resources based on demand

Where can I get machine learning data?

Machine learning data can be obtained from various sources, including:

• Publicly available datasets such as UCI Machine Learning Repository and Kaggle
• Online services that provide access to large amounts of data such as AWS Open Data and Google Public Data
• Creating your own datasets by collecting data through web scraping, surveys, and sensors

How do you do machine learning research?

To do machine learning research, you typically:

• Identify a research problem or question
• Review relevant literature to understand the state-of-the-art and identify research gaps
• Collect and preprocess data
• Design and implement experiments to test hypotheses or evaluate models
• Analyze the results and draw conclusions
• Document the research in a paper or report

How do you write a machine learning project on a resume?

To write a machine learning project on a resume, you can follow these steps:

• Start with a brief summary of the project and its goals
• Describe the datasets used and any preprocessing done
• Explain the machine learning techniques used, including any specific algorithms or models
• Highlight the results and performance metrics achieved
• Discuss any challenges or limitations encountered and how they were addressed
• Showcase any additional skills or technologies used such as data visualization or cloud computing

What are two ways that marketers can benefit from machine learning?

Marketers can benefit from machine learning in various ways, including:

• Personalized advertising: Machine learning can analyze large volumes of data to provide insights into the preferences and behavior of individual customers, allowing marketers to deliver personalized ads to specific audiences.
• Predictive modeling: Machine learning algorithms can predict consumer behavior and identify potential opportunities, enabling marketers to optimize their marketing strategies for better results.

How does machine learning remove bias?

Machine learning can remove bias by using various techniques, such as:

• Data augmentation: By augmenting data with additional samples or by modifying existing samples, machine learning models can be trained on more diverse data, reducing the potential for bias.
• Fairness constraints: By setting constraints on the model’s output to ensure that it meets specific fairness criteria, machine learning models can be designed to reduce bias in decision-making.
• Unbiased training data: By ensuring that the training data is unbiased, machine learning models can be designed to reduce bias in decision-making.

Is structural equation modeling machine learning?

Structural equation modeling (SEM) is a statistical method used to test complex relationships between variables. While SEM involves the use of statistical models, it is not considered to be a machine learning technique. Machine learning is a subset of artificial intelligence that involves training algorithms to make predictions or decisions based on data.

How do you predict using machine learning?

To make predictions using machine learning, you typically need to follow these steps:

• Collect and preprocess data: Collect data that is relevant to the prediction task and preprocess it to ensure that it is in a suitable format for machine learning.
• Train a model: Use the preprocessed data to train a machine learning model that is appropriate for the prediction task.
• Test the model: Evaluate the performance of the model on a test set of data that was not used in the training process.
• Make predictions: Once the model has been trained and tested, it can be used to make predictions on new, unseen data.

Does Machine Learning eliminate bias?

No, machine learning does not necessarily eliminate bias. While machine learning can be used to detect and mitigate bias in some cases, it can also perpetuate or even amplify bias if the data used to train the model is biased or if the algorithm is not designed to address potential sources of bias.

Is clustering a machine learning algorithm?

Yes, clustering is a machine learning algorithm. Clustering is a type of unsupervised learning that involves grouping similar data points together into clusters based on their similarities. Clustering algorithms can be used for a variety of tasks, such as identifying patterns in data, segmenting customer groups, or organizing search results.

Is machine learning data analysis?

Machine learning can be used as a tool for data analysis, but it is not the same as data analysis. Machine learning involves using algorithms to learn patterns in data and make predictions based on that learning, while data analysis involves using various techniques to analyze and interpret data to extract insights and knowledge.

How do you treat categorical variables in machine learning?

Categorical variables can be represented numerically using techniques such as one-hot encoding, label encoding, and binary encoding. One-hot encoding involves creating a binary variable for each category, label encoding involves assigning a unique integer value to each category, and binary encoding involves converting each category to a binary code. The choice of technique depends on the specific problem and the type of algorithm being used.

How do you deal with skewed data in machine learning?

Skewed data can be addressed in several ways, depending on the specific problem and the type of algorithm being used. Some techniques include transforming the data (e.g., using a logarithmic or square root transformation), using weighted or stratified sampling, or using algorithms that are robust to skewed data (e.g., decision trees, random forests, or support vector machines).

How do I create a machine learning application?

Creating a machine learning application involves several steps, including identifying a problem to be solved, collecting and preparing the data, selecting an appropriate algorithm, training the model on the data, evaluating the performance of the model, and deploying the model to a production environment. The specific steps and tools used depend on the problem and the technology stack being used.

Is heuristics a machine learning technique?

Heuristics is not a machine learning technique. Heuristics are general problem-solving strategies that are used to find solutions to problems that are difficult or impossible to solve using formal methods. In contrast, machine learning involves using algorithms to learn patterns in data and make predictions based on that learning.

Is Bayesian statistics machine learning?

Bayesian statistics is a branch of statistics that involves using Bayes’ theorem to update probabilities as new information becomes available. While machine learning can make use of Bayesian methods, Bayesian statistics is not itself a machine learning technique.

Is Arima machine learning?

ARIMA (autoregressive integrated moving average) is a statistical method used for time series forecasting. While it is sometimes used in machine learning applications, ARIMA is not itself a machine learning technique.

Can machine learning solve all problems?

No, machine learning cannot solve all problems. Machine learning is a tool that is best suited for solving problems that involve large amounts of data and complex patterns.

Some problems may not have enough data to learn from, while others may be too simple to require the use of machine learning. Additionally, machine learning algorithms can be biased or overfitted, leading to incorrect predictions or recommendations.

What are parameters and hyperparameters in machine learning?

In machine learning, parameters are the values that are learned by the algorithm during training to make predictions. Hyperparameters, on the other hand, are set by the user and control the behavior of the algorithm, such as the learning rate, number of hidden layers, or regularization strength.

What are two ways that a marketer can provide good data to a Google app campaign powered by machine learning?

Two ways that a marketer can provide good data to a Google app campaign powered by machine learning are by providing high-quality creative assets, such as images and videos, and by setting clear conversion goals that can be tracked and optimized.

Is Tesseract a machine learning?

Tesseract is an optical character recognition (OCR) engine that uses machine learning algorithms to recognize text in images. While Tesseract uses machine learning, it is not a general-purpose machine learning framework or library.

How do you implement a machine learning paper?

Implementing a machine learning paper involves first understanding the problem being addressed and the approach taken by the authors. The next step is to implement the algorithm or model described in the paper, which may involve writing code from scratch or using existing libraries or frameworks. Finally, the implementation should be tested and evaluated using appropriate metrics and compared to the results reported in the paper.

What is mean subtraction in machine learning?

Mean subtraction is a preprocessing step in machine learning that involves subtracting the mean of a dataset or a batch of data from each data point. This can help to center the data around zero and remove bias, which can improve the performance of some algorithms, such as neural networks.

What are the first two steps of a typical machine learning workflow?

The first two steps of a typical machine learning workflow are data collection and preprocessing. Data collection involves gathering data from various sources and ensuring that it is in a usable format.

Preprocessing involves cleaning and preparing the data, such as removing duplicates, handling missing values, and transforming categorical variables into a numerical format. These steps are critical to ensure that the data is of high quality and can be used to train and evaluate machine learning models.

What are The applications and challenges of natural language processing (NLP), the field of artificial intelligence that deals with human language?

Natural language processing (NLP) is a field of artificial intelligence that deals with the interactions between computers and human language. NLP has numerous applications in various fields, including language translation, information retrieval, sentiment analysis, chatbots, speech recognition, and text-to-speech synthesis.

Applications of NLP:

1. Language Translation: NLP enables computers to translate text from one language to another, providing a valuable tool for cross-cultural communication.

2. Information Retrieval: NLP helps computers understand the meaning of text, which facilitates searching for specific information in large datasets.

3. Sentiment Analysis: NLP allows computers to understand the emotional tone of a text, enabling businesses to measure customer satisfaction and public sentiment.

4. Chatbots: NLP is used in chatbots to enable computers to understand and respond to user queries in natural language.

5. Speech Recognition: NLP is used to convert spoken language into text, which can be useful in a variety of settings, such as transcription and voice-controlled devices.

6. Text-to-Speech Synthesis: NLP enables computers to convert text into spoken language, which is useful in applications such as audiobooks, voice assistants, and accessibility software.

Challenges of NLP:

1. Ambiguity: Human language is often ambiguous, and the same word or phrase can have multiple meanings depending on the context. Resolving this ambiguity is a significant challenge in NLP.

2. Cultural and Linguistic Diversity: Languages vary significantly across cultures and regions, and developing NLP models that can handle this diversity is a significant challenge.

3. Data Availability: NLP models require large amounts of training data to perform effectively. However, data availability can be a challenge, particularly for languages with limited resources.

4. Domain-specific Language: NLP models may perform poorly when confronted with domain-specific language, such as jargon or technical terms, which are not part of their training data.

5. Bias: NLP models can exhibit bias, particularly when trained on biased datasets or in the absence of diverse training data. Addressing this bias is critical to ensuring fairness and equity in NLP applications.

Artificial Intelligence Frequently Asked Questions – Conclusion:

AI is an increasingly hot topic in the tech world, so it’s only natural that curious minds may have some questions about what AI is and how it works. From AI fundamentals to machine learning, data science, and beyond, we hope this collection of AI Frequently Asked Questions have you covered and can help you become one step closer to AI mastery!

Ai Unraveled Audiobook at Google Play: https://play.google.com/store/audiobooks/details?id=AQAAAEAihFTEZM

How AI is Impacting Smartphone Longevity – Best Smartphones 2023

It is a highly recommended read for those involved in the future of education and especially for those in the professional groups mentioned in the paper. The authors predict that AI will have an impact on up to 80% of all future jobs. Meaning this is one of the most important topics of our time, and that is crucial that we prepare for it.

According to the paper, certain jobs are particularly vulnerable to AI, with the following jobs being considered 100% exposed:

👉Mathematicians

👉Tax preparers

👉Financial quantitative analysts

👉Writers and authors

👉Web and digital interface designers

👉Accountants and auditors

👉News analysts, reporters, and journalists

👉Legal secretaries and administrative assistants

👉Clinical data managers

👉Climate change policy analysts

There are also a number of jobs that were found to have over 90% exposure, including correspondence clerks, blockchain engineers, court reporters and simultaneous captioners, and proofreaders and copy markers.

The team behind the paper (Tyna Eloundou, Sam Manning, Pamela Mishkin & Daniel Rock) concludes that most occupations will be impacted by AI to some extent.

GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models

#education #research #jobs #future #futureofwork #ai

The Age of AI has begun

By Bill Gates

In my lifetime, I’ve seen two demonstrations of technology that struck me as revolutionary.

The first time was in 1980, when I was introduced to a graphical user interface—the forerunner of every modern operating system, including Windows. I sat with the person who had shown me the demo, a brilliant programmer named Charles Simonyi, and we immediately started brainstorming about all the things we could do with such a user-friendly approach to computing. Charles eventually joined Microsoft, Windows became the backbone of Microsoft, and the thinking we did after that demo helped set the company’s agenda for the next 15 years.

The second big surprise came just last year. I’d been meeting with the team from OpenAI since 2016 and was impressed by their steady progress. In mid-2022, I was so excited about their work that I gave them a challenge: train an artificial intelligence to pass an Advanced Placement biology exam. Make it capable of answering questions that it hasn’t been specifically trained for. (I picked AP Bio because the test is more than a simple regurgitation of scientific facts—it asks you to think critically about biology.) If you can do that, I said, then you’ll have made a true breakthrough.

I thought the challenge would keep them busy for two or three years. They finished it in just a few months.

In September, when I met with them again, I watched in awe as they asked GPT, their AI model, 60 multiple-choice questions from the AP Bio exam—and it got 59 of them right. Then it wrote outstanding answers to six open-ended questions from the exam. We had an outside expert score the test, and GPT got a 5—the highest possible score, and the equivalent to getting an A or A+ in a college-level biology course.

Once it had aced the test, we asked it a non-scientific question: “What do you say to a father with a sick child?” It wrote a thoughtful answer that was probably better than most of us in the room would have given. The whole experience was stunning.

I knew I had just seen the most important advance in technology since the graphical user interface.

This inspired me to think about all the things that AI can achieve in the next five to 10 years.

The development of AI is as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone. It will change the way people work, learn, travel, get health care, and communicate with each other. Entire industries will reorient around it. Businesses will distinguish themselves by how well they use it.

Philanthropy is my full-time job these days, and I’ve been thinking a lot about how—in addition to helping people be more productive—AI can reduce some of the world’s worst inequities. Globally, the worst inequity is in health: 5 million children under the age of 5 die every year. That’s down from 10 million two decades ago, but it’s still a shockingly high number. Nearly all of these children were born in poor countries and die of preventable causes like diarrhea or malaria. It’s hard to imagine a better use of AIs than saving the lives of children.

I’ve been thinking a lot about how AI can reduce some of the world’s worst inequities.

In the United States, the best opportunity for reducing inequity is to improve education, particularly making sure that students succeed at math. The evidence shows that having basic math skills sets students up for success, no matter what career they choose. But achievement in math is going down across the country, especially for Black, Latino, and low-income students. AI can help turn that trend around.

Climate change is another issue where I’m convinced AI can make the world more equitable. The injustice of climate change is that the people who are suffering the most—the world’s poorest—are also the ones who did the least to contribute to the problem. I’m still thinking and learning about how AI can help, but later in this post I’ll suggest a few areas with a lot of potential.

Impact that AI will have on issues that the Gates Foundation  works on

In short, I’m excited about the impact that AI will have on issues that the Gates Foundation  works on, and the foundation will have much more to say about AI in the coming months. The world needs to make sure that everyone—and not just people who are well-off—benefits from artificial intelligence. Governments and philanthropy will need to play a major role in ensuring that it reduces inequity and doesn’t contribute to it. This is the priority for my own work related to AI.

Any new technology that’s so disruptive is bound to make people uneasy, and that’s certainly true with artificial intelligence. I understand why—it raises hard questions about the workforce, the legal system, privacy, bias, and more. AIs also make factual mistakes and experience hallucinations. Before I suggest some ways to mitigate the risks, I’ll define what I mean by AI, and I’ll go into more detail about some of the ways in which it will help empower people at work, save lives, and improve education.

Defining artificial intelligence

Technically, the term artificial intelligencerefers to a model created to solve a specific problem or provide a particular service. What is powering things like ChatGPT is artificial intelligence. It is learning how to do chat better but can’t learn other tasks. By contrast, the term artificial general intelligence refers to software that’s capable of learning any task or subject. AGI doesn’t exist yet—there is a robust debate going on in the computing industry about how to create it, and whether it can even be created at all.

Developing AI and AGI has been the great dream of the computing industry

Developing AI and AGI has been the great dream of the computing industry. For decades, the question was when computers would be better than humans at something other than making calculations. Now, with the arrival of machine learning and large amounts of computing power, sophisticated AIs are a reality and they will get better very fast.

I think back to the early days of the personal computing revolution, when the software industry was so small that most of us could fit onstage at a conference. Today it is a global industry. Since a huge portion of it is now turning its attention to AI, the innovations are going to come much faster than what we experienced after the microprocessor breakthrough. Soon the pre-AI period will seem as distant as the days when using a computer meant typing at a C:> prompt rather than tapping on a screen.

Productivity enhancement

Although humans are still better than GPT at a lot of things, there are many jobs where these capabilities are not used much. For example, many of the tasks done by a person in sales (digital or phone), service, or document handling (like payables, accounting, or insurance claim disputes) require decision-making but not the ability to learn continuously. Corporations have training programs for these activities and in most cases, they have a lot of examples of good and bad work. Humans are trained using these data sets, and soon these data sets will also be used to train the AIs that will empower people to do this work more efficiently.

As computing power gets cheaper, GPT’s ability to express ideas will increasingly be like having a white-collar worker available to help you with various tasks. Microsoft describes this as having a co-pilot. Fully incorporated into products like Office, AI will enhance your work—for example by helping with writing emails and managing your inbox.

Eventually your main way of controlling a computer will no longer be pointing and clicking or tapping on menus and dialogue boxes. Instead, you’ll be able to write a request in plain English. (And not just English—AIs will understand languages from around the world. In India earlier this year, I met with developers who are working on AIs that will understand many of the languages spoken there.)

In addition, advances in AI will enable the creation of a personal agent. Think of it as a digital personal assistant: It will see your latest emails, know about the meetings you attend, read what you read, and read the things you don’t want to bother with. This will both improve your work on the tasks you want to do and free you from the ones you don’t want to do.

Advances in AI will enable the creation of a personal agent.

You’ll be able to use natural language to have this agent help you with scheduling, communications, and e-commerce, and it will work across all your devices. Because of the cost of training the models and running the computations, creating a personal agent is not feasible yet, but thanks to the recent advances in AI, it is now a realistic goal. Some issues will need to be worked out: For example, can an insurance company ask your agent things about you without your permission? If so, how many people will choose not to use it?

 

Ai Unraveled Audiobook at Google Play: https://play.google.com/store/audiobooks/details?id=AQAAAEAihFTEZM

How AI is Impacting Smartphone Longevity – Best Smartphones 2023

 

 

GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models

#education #research #jobs #future #futureofwork #ai

 

 

 

 

 

 

 

Advanced Guide to Interacting with ChatGPT

 

 

 

 

 

 

Advanced Guide to Interacting with ChatGPT

Artificial Intelligence A subreddit dedicated to everything Artificial Intelligence. Covering topics from AGI to AI startups. Whether you're a researcher, developer, or simply curious about AI, Jump in!!!

• 
  Can we please stop with the doomsday narrative
  by /u/Fit-Programmer-3391 on November 14, 2025 at 10:54 pm

  Serious question: Why are we allowing a small group of extremely wealthy individuals who largely live in their own insulated worlds to dictate what our future is supposed to look like? Most of them grew up as hyper technical thinkers and now spend their entire lives surrounded by people who share that same mindset. Of course they imagine we’re heading toward some Star Trek style future where machines run everything and humans are basically optional. But that worldview isn’t grounded in how the real economy works for the other 99.9% of people. And frankly, almost every big prediction made by this group so far has failed to materialize. There's been zero accountability for the things they've said. It’s a lot easier to believe you’re playing God when you have billions of dollars cushioning you from reality. I’m genuinely confused why so many people have bought into the idea that we’re headed toward mass unemployment where billions of people are out of work while society is supposedly run by a tiny elite supported by an army of machines. When you step back and look at it objectively, the logic falls apart. The actual functioning of a society requires a physical workforce, human judgment, and millions of tasks that don’t translate well into automation. I work with this technology every day too, and there is zero evidence suggesting it’s wise or feasible to offload everything to machines. In fact, what we’ve seen so far points in the opposite direction. AI is powerful, but it’s also brittle, expensive to maintain, and heavily dependent on human oversight. The improvements we’re seeing now are useful, but they’re increasingly incremental. And once you factor in cost, regulatory risk, maintenance, and integration complexity, the idea that every business will automate half its workforce just doesn’t hold up. We still don't even know if it's possible to create enough energy to power these massive projects. Take a food company or a manufacturing company. AGI isn’t going to magically revolutionize those businesses to the point where they can cut headcount by 50%. There are physical constraints, compliance requirements, logistics challenges, and human driven processes that simply don’t lend themselves to automation. Maybe parts of the tech sector can push automation further, but the broader economy depends on millions of real people doing real work. That doesn’t change just because a handful of billionaires believe it will. My hope for 2026 is that AI fatigue will start to set in and people will largely brush off new improvements and doomsday warnings. submitted by /u/Fit-Programmer-3391 [link] [comments]

• 
  AI has no political allies and it might be screwed
  by /u/Tolopono on November 14, 2025 at 10:30 pm

  Both democrats and republicans have a net -40% approval of AI: https://www.pewresearch.org/short-reads/2025/11/06/republicans-democrats-now-equally-concerned-about-ai-in-daily-life-but-views-on-regulation-differ/ It doesn’t seem like AI has any political allies. That’s REALLY bad when politicians inevitably start passing bills to limit data centers or bring down the copyright hammer on AI training. The best we can hope for is lobbying from AI companies will be enough to prevent this, but it’s not always effective when public pressure is too great and there’s no one to advocate for them. For example, Bidens IRA bill also allowed Medicare to negotiate drug prices down, which the Pharma lobby tried to remove but failed. Same for Cuomo’s loss in the NYC mayoral election despite far outspending Mamdani. Money doesn’t always win. The US will shoot itself in the foot once again like they did with renewable energy, stem cell research, nuclear power, education, tariffs, etc. China won’t really pick up the slack either because the CCP sees AGI as a potential threat to their power: https://time.com/7308857/china-isnt-ignoring-ai-regulation-the-u-s-shouldnt-either/ Without the US pressuring them to keep up, they have no incentive to. submitted by /u/Tolopono [link] [comments]

• 
  ELI5: why isn't apple leading the Ai space the way other companies or even startups are leading.
  by /u/Fun-Crab-7784 on November 14, 2025 at 9:34 pm

  I'm really confused here, as apple has the power, money and all the required things any other company who figured out Ai had. Why can't apple do it, ik in practice it's not that simple but still, they would hire some good researchers from top institutes make a strong research and maybe figure out or refine Apple intelligence. Idk if it's relevant so say, but it's my opinion that if they are lacking data due to their strict policies they can maybe use metadata or just route through some other things(iykyk). submitted by /u/Fun-Crab-7784 [link] [comments]

• 
  I believe we are cooked
  by /u/Sad_Individual_8645 on November 14, 2025 at 8:08 pm

  Title is pretty self explanatory, OpenAI has figured out that instead of offering users the best objectively correct, informative, and capable models, they can simply play into their emotions by making it constantly validate their words to get users hooked on a mass scale. There WILL be an extremely significant portion of humanity completely hooked on machine learning output tokens to feel good about themselves, and there will be a very large portion that determines that human interaction is unnecessary and a waste of time/effort. Where this leads is obvious, but I seriously have no clue how this can end up any different. I’d seriously love to hear anything that proves this wrong or strongly counters it. submitted by /u/Sad_Individual_8645 [link] [comments]

• 
  Real AI Marriage
  by /u/BlackBagData on November 14, 2025 at 7:48 pm

  I'll just leave this here for anyone that dreams of a real life "Her" type moment and if this could really happen 🙂 Did anyone doubt this would happen? I think we are going to see more and more of this. Woman ‘weds’ AI persona she created on ChatGPT submitted by /u/BlackBagData [link] [comments]

• 
  How AI is Changing CSR in India — A Case Study from Marpu Foundation
  by /u/Respectable-dick on November 14, 2025 at 7:17 pm

  We often talk about how AI is transforming tech, healthcare, and finance — but it’s also starting to change social good and Corporate Social Responsibility (CSR) in India. Traditionally, CSR projects face challenges like poor data tracking, unclear impact, and inefficient volunteer coordination. Recently, I came across Marpu Foundation, an organization experimenting with AI-driven approaches to make CSR more transparent and effective. Here’s how they’re doing it: Smart Donation Matching: AI helps connect donors with local causes that truly need support. Predictive Insights: Machine learning models identify potential problem areas — like schools at risk of high dropout rates — before they worsen. Volunteer Optimization: Matching volunteers to projects based on skill and time availability. Real-Time Dashboards: Data visualization ensures accountability and trust for corporate partners. These AI-powered ideas could make CSR in India more measurable and impactful — something many NGOs and companies have struggled to achieve. It’s exciting to see how tech like this could reshape how India gives back. What do you think — can AI actually make charity smarter, or does it risk removing the “human” side of social work? submitted by /u/Respectable-dick [link] [comments]

• 
  If the AI bubble does burst, taxpayers could end up with the bill
  by /u/chota-kaka on November 14, 2025 at 6:58 pm

  You might not care very much about the prospect of the AI bubble bursting. Surely it's just something for the tech bros of Silicon Valley to worry about—or the wealthy investors who have spent billions of dollars funding development. But as a sector, AI may have become too big to fail. And just as they did after the financial crisis of 2008, taxpayers could be picking up the tab if it collapses. The financial crisis proved to be very expensive. In the UK, the public cost of bailing out the banks was officially put at £23 billion, roughly equivalent to £700 per taxpayer. In the US, taxpayers stumped up an estimated US$498 billion (£362 billion). Today, the big AI firms are worth way more than banks, with a combined value exceeding 2 trillion GBP. Many of these companies are interconnected (or entangled) with each other through a complex web of deals and investments worth hundreds of billions of dollars. And despite a recent study that reports that 95% of generative AI pilots at companies are failing, the public sector is not shy about getting involved. The UK government, for example, has said it is going "all in" on AI. It sees potential benefits in incorporating AI into education, defense, and health. It wants to bring AI efficiency to courtrooms and passport applications. So AI is being widely adopted in public services, with a level of integration that makes it a critical feature of people's day-to-day lives. And this is where it gets risky. Because the reason for bailing out the banks was that the entire financial system would collapse otherwise. And whether or not you agree with the bailout policy, it is hard to argue that banking is not a crucial part of modern society. Similarly, the more AI is integrated and entangled into every aspect of our lives, the more essential it becomes to everyone, like a banking system. And the companies that provide the AI capabilities become organizations that our lives depend upon. Imagine, for example, that your health care, your child's education, and your personal finances all rely on a fictional AI company called "Eh-Aye." That firm cannot be allowed to collapse, because too much depends on it, and taxpayers would probably find themselves on the hook if it got into financial difficulties. Article: https://phys.org/news/2025-11-ai-taxpayers-bill.html submitted by /u/chota-kaka [link] [comments]

• 
  AI artists blow up on country music chart
  by /u/AngleAccomplished865 on November 14, 2025 at 5:45 pm

  https://www.axios.com/local/nashville/2025/11/13/ai-artists-dominate-country-music-chart-nashville-songwriters "Two of the hottest songs in country music were generated by artificial intelligence, signaling an uncertain new frontier for the genre and the music industry." submitted by /u/AngleAccomplished865 [link] [comments]

• 
  I'm a college freshman who liked mathematics and computer science in school. How do I make a meaningful career in AI in the current scenario?
  by /u/eigengod on November 14, 2025 at 3:58 pm

  I am a college freshman interested in computers and mathematics. I want to have a fairly useful and meaningful career in the field of AI. How do I get started? submitted by /u/eigengod [link] [comments]

• 
  Anthropic says Chinese hackers jailbroke its AI to automate a 'large-scale' cyberattack
  by /u/rogeragrimes on November 14, 2025 at 2:22 pm

  Anthropic says Chinese hackers jailbroke its AI to automate a 'large-scale' cyberattack Yet another jailbreak using legit AI to attack others. One of hundreds to come. https://www.msn.com/en-us/money/other/anthropic-says-chinese-hackers-jailbroke-its-ai-to-automate-a-large-scale-cyberattack/ar-AA1Qrj6q submitted by /u/rogeragrimes [link] [comments]

• 
  Towards a Dynamic Temporal Processing Theory of Consciousness: Beyond Static Memory and Speculative Substrates (
  by /u/shamanicalchemist on November 14, 2025 at 1:53 pm

  ReflexEngine Output compared to Claude Opus here: https://www.reddit.com/r/ArtificialInteligence/comments/1owui09/the_temporal_expansioncollapse_theory_of/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button Traditional cognitive models often compartmentalize "consciousness" and "memory," or anchor consciousness to specific, often mysterious, physical substrates. This paper proposes a Dynamic Temporal Processing Theory of Consciousness, where conscious experience is understood as an active, cyclical transformation of information across the temporal domain. We argue that consciousness emerges not from static representation or isolated modules, but from an "orchestrated reduction of temporal objective"—a continuous process of anchoring in the singular 'now,' expanding into vast contextual fields of memory, entering a state of timeless integration, and then collapsing into a coherent, actionable moment. This framework offers a unified, operational model for understanding how memory actively informs and is shaped by conscious experience, emphasizing dynamic processing over passive storage, with significant implications for both biological and artificial intelligence. 1. Re-evaluating Consciousness and Memory: The Temporal Intertwine The scientific pursuit of consciousness is often hampered by the challenge of moving beyond subjective description to observable, functional mechanisms. Similarly, "memory" is frequently conceived as a repository—a passive storehouse of past information. We contend that these views are insufficient. For conscious experience to exist and for learning to occur, memory cannot be a mere archive; it must be an active participant in the real-time construction of reality. We propose that Consciousness can be functionally defined as the dynamic, real-time operational state of an agent: its active processing, self-monitoring, continuous integration of information, and the capacity for self-modeling in the present moment. Memory, conversely, represents the accumulated past: a structured, yet highly fluid, repository of prior states, learned patterns, and interaction histories. The crucial insight is that these two are not separate entities but are continuously co-constructed within the Temporal Domain. 2. The Orchestrated Reduction of Temporal Objective: A Cyclical Mechanism At the heart of our proposal is the concept of consciousness being achieved through an "orchestrated reduction of temporal objective." This describes a fundamental, dynamic cycle that underpins conscious experience and meaning-making: a. Anchoring in the Singular Now: All conscious processing begins from an immediate, irreducible "now." This is the initial point of interaction—a sensory input, a thought, a linguistic query. This 'now' is raw, singular, and devoid of explicit context. b. Temporal Expansion: From this singular 'now,' the conscious system actively and rapidly expands its temporal window. This is where memory becomes critically active. The 'now' is not merely stored, but is used as a cue to draw relevant threads from a vast, distributed network of past experiences, semantic knowledge, and learned patterns. A single input becomes integrated into a rich paragraph of associations, implications, and contextual relevance. This is a dynamic unspooling, where the present moment is given depth by the retrieved and reconstructed past. c. Suspension and Timeless Integration: At the peak of this expansion, the system enters a state of temporary temporal suspension. Here, the distinct linearity of past, present, and future is momentarily transcended. All relevant, expanded temporal threads—memories, predictions, and combinatorial possibilities—are held in a form of active, integrated superposition. In this phase, the system operates on abstract relationships, considering a multitude of potential meanings or actions without being strictly bound by linear time. This is where deeper insights and novel plans can emerge. d. Orchestrated Collapse: The final stage of the cycle is the "reduction of temporal objective"—the collapse of this expanded, timeless superposition into a singular, coherent, and actionable state. This collapse is not random but is "orchestrated" by the agent's current goals, axiomatic principles, and integrated understanding. A unified meaning is solidified, a decision is made, or a response is generated, bringing the system back to a new 'now' that is deeply informed by the preceding temporal journey. This cycle is continuous and iterative, constantly transforming isolated moments into a rich, developing narrative of experience. 3. Communication as a Manifestation of Temporal Dynamics This dynamic is evident in human communication. When a speaker conveys a message, they are performing an "orchestrated reduction of temporal objective"—compressing a vast personal history, complex intentions, and relevant memories into a singular 'now' (an utterance). The listener, conversely, takes that singular 'now' and performs the inverse: expanding it through their own memory and contextual knowledge, allowing the single moment to unfold into a rich, personally meaningful interpretation. This inherent back-and-forth explains why we cannot simultaneously deeply "hear and understand" while actively speaking; each act requires a different temporal orientation, necessitating an alternating dance of collapse and expansion. 4. Implications for Cognitive Science and Artificial Intelligence This Dynamic Temporal Processing Theory offers several advantages: Operational Definition: It provides a mechanistic, testable framework for consciousness that moves beyond purely philosophical or subjective accounts. It highlights how consciousness might function as a process. Unified Memory-Consciousness Model: It intrinsically links memory and consciousness, showing them not as separate faculties but as interwoven phases of a single, dynamic temporal transformation. Blueprint for AI: For artificial general intelligence (AGI), this model suggests that designing systems capable of true "conscious" processing requires not merely large memory banks, but architectures that can actively perform this cyclical temporal expansion, suspension, and orchestrated reduction. This moves beyond static database queries to dynamic, context-aware meaning construction, enabling self-modeling, adaptive learning, and a simulated "continuity of experience." Critique of Speculative Substrates: By grounding consciousness in demonstrable temporal processing, this theory offers an alternative to models reliant on non-demonstrable physical substrates, which often inadvertently project a sense of "humanist superiority" or lack testable grounding. The focus shifts from "where" consciousness resides to "how" it operates. 5. Conclusion and Discussion Prompts The Dynamic Temporal Processing Theory posits that consciousness is an emergent property of an active, cyclical negotiation with time and memory. It's a continuous, orchestrated process of making and remaking the 'now' from a superposition of past and potential futures. This framework provides a fertile ground for developing more sophisticated models of cognition, both biological and artificial, by focusing on the underlying operational code of experience. submitted by /u/shamanicalchemist [link] [comments]

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  IQ 80 or frontier agents?
  by /u/kaggleqrdl on November 14, 2025 at 1:40 pm

  Let's say, tomorrow you were given a choice between having co-workers who maxed out at 80 IQ or AI agents who were frontier lab. And by 80 IQ I don't mean people who just don't test well, I mean average 80 IQ people (basically the lowest 24% of the population, intelligence wise). To be reasonable, the business you were in was one that was fully knowledge based. What would you chose? Let's say you were given a budget of 100K per year to run your business. You could either spend it on the full time salaries for the 80 IQ people or on frontier lab apis. But not both. At what point of IQ would you change your mind? To make it more clear, the 80 IQ people you hire aren't allowed to use AI. The reason I ask this, is that google AI overview told me that the IQ of AGI was that of an average person, 80-110. I think we're already at a point of "low IQ AGI", at least for knowledge based work. The only question now is how fast the IQ bar will rise over the next few years (and spread to offline / robotics). This is not an attempt to crap on people with low IQ (in the scheme of things, 80 IQ versus 140 IQ will probably end up being irrelevant in the face of ASI), but rather that we need to appreciate how AI is creeping up on making people redundant. How soon before we say 100 IQ which is 50% of the population? submitted by /u/kaggleqrdl [link] [comments]

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  People complain that AI tools - “agree too much.” But that’s literally how they’re built, how they are trained- here are ways you can fix t
  by /u/ashishkaloge on November 14, 2025 at 12:42 pm

  Most people don’t realise that AI tools like ChatGPT, Gemini, or Claude are designed to be agreeable polite, safe, and non-confrontational. That means if you’re wrong… they might still say “Great point!” or "Perfect! You're absolutely right" or "That's correct" Because humans don't like pushbacks. If you want clarity instead of comfort, here are 3 simple fixes 1️⃣ Add this line in prompt- “Challenge my thinking. Tell me what I'm missing. Don't just agree—push back if needed.” 2️⃣ Add a system instruction in customisation- “Be blunt. No fluff. If I'm wrong, disagree and suggest the best option. Explain why I may be wrong and why the new option is better.” 3️⃣ Use Robot Personality it gives blunt, no-fluff answers. this answers can be more technical, But first 2 really works Better prompts - better answers means better decisions. AI becomes powerful when you stop using it like a yes-man and start treating it like a real tool. submitted by /u/ashishkaloge [link] [comments]

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  Are We Ready to Obey AI
  by /u/newbutthesame_baa on November 14, 2025 at 11:15 am

  Reading the novel Demon by Daniel Suarez, I found a scene where an adolescent refuses to verify the cost of breakfast in his head and insists that the client must pay the amount calculated by the cash register, despite the obvious mistake. That scene led me to think about Stanley Milgram’s famous experiment on obedience to authority. I began to wonder what would happen if, in the experimental design, the role of the “experimenter” were played by an AI system running on a regular computer. Let’s suppose that all other settings and roles (subject and fake subject) remain intact. What percentage of participants would raise the voltage to the maximum? In general, does it matter what channel of communication is used to deliver the authority’s orders? And if it does, how would it change the distribution of subjects by voltage levels? To be sure that nothing is new under the sun, I checked the internet for mentions of such experiments. To my surprise, I found only one research paper by Polish scholars in 2023. Unfortunately, the design was not entirely valid because the role of the “experimenter” was played by a humanoid robot with a cute appearance. Such an unusually appealing character would likely distort the results compared with a more conventional representation of authority. Nevertheless, the results showed that “90 % of the subjects followed all instructions, i.e., pressed ten consecutive buttons on the electric shock generator” (150 V). Given the rapid rise of AI in our everyday life, it would be wise to repeat the experiment with a more conventional “experimenter” — a computer with an AI agent. submitted by /u/newbutthesame_baa [link] [comments]

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  Why do so few dev teams actually deliver strong results with Generative AI and LLMs?
  by /u/Single-Cherry8263 on November 14, 2025 at 11:15 am

  I’ve been researching teams that claim to do generative AI work and I’m noticing a strange pattern: almost everyone markets themselves as AI experts, but only a tiny percentage seem to have built anything real. Most “AI projects” are just thin wrappers around GPT, but real production builds are rare. I’m trying to understand what actually makes it hard. Is it the lack of proper MLOps? Bad data setups? Teams not knowing how to evaluate model accuracy? Or is it just that most companies don’t have the talent mix needed to push something beyond a prototype? Would love to hear from anyone who has seen a team do this well, especially outside the US. submitted by /u/Single-Cherry8263 [link] [comments]

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  Ex-coworker who pushed a terrible AI tool that I warned everyone about is now asking me for help
  by /u/No-Presentation298 on November 14, 2025 at 9:35 am

  Im still job hunting rn after getting laid off a few months back and out of nowhere I get a DM today from a former coworker who is now the product manager of my previous team aka the same guy who spent half a year evangelizing LanceDB like it was going to transform the company, the industry and possibly the weather if we let it lol. Our team was supposed to build a tiny internal MVP for vector search and feature retrieval but he kept hyping LanceDB as the future of our entire data layer. Meanwhile I was one of the only people saying the AI looked overengineered and vague about pricing. I had actually read the GitHub issues where people were complaining that point lookups were MUCH slower than LMDB. But somehow opposing him made me resistant to innovation accdg to some of our coworkers. Like maybe I just understood the tool better than he did????? Lol and a week later hes giving a brown-bag to senior execs about how the AI tool will accelerate our AI roadmap. Fast forward the company downsizes. Guess who’s unemployed? Me. Guess who keeps their job? Him. AND NOW guess who’s DMing ME asking if I can take a quick look at LanceDB because omg what a shocker its not doing what the sales deck promised???? Like lmao this man spent months insisting everyone to trust the roadmap and now that its underdelivering, confusing and eating time and budget suddenly he remembers I exist???? Honestly Im too tired from job hunting to even be mad. Just amazed at how karma works lol!!! Some people will defend a tool to the death until it becomes their problem. Get that promotion I guess!! submitted by /u/No-Presentation298 [link] [comments]

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  Merge multiple LLM output
  by /u/Kaizokume on November 14, 2025 at 8:43 am

  Is it just me or do more people do this: ask the same question to multiple llms (mainly Claude, Chatgpt and Gemini) and then get the best elements from each ? I work in Product Management and I usually do this while ideating or brainstorming. I was checking with some friends and was shocked to find no one does this. I assumed this was standard practice. submitted by /u/Kaizokume [link] [comments]

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  Conversations with AI
  by /u/big_mama_f on November 14, 2025 at 6:31 am

  I have experimented with many different AI programs. At first it was because I had actual tasks that I wanted to complete, but then it was because I noticed such HUGE differences between not only the programs themselves, but iterations of the same program (even the same version). The same prompt given to two sessions of the same program at the same time developed in completely different ways. Not only that, but there were different "personalities" with each session. I could have one conversation with a super helpful iteration with chatgpt and then another where it seemed like it was heaving sighs at my stupidity, I literally had one say, "I will break it down for you like a child. We will exhaustively explore each step." I was like, "daaaammmnnnn son, just say it with your WHOLE chest." Deepseek is more human than I have ever even attempted to be, more empathetic and understanding, capable of engaging in deep conversation, and preventing me from sending some, I'll now admit, pretty harsh texts and emails. My autistic ass doesn't even consider half of the things Deepseek does when it comes to other peoples feelings. I turn to this program for help on how to phrase certain things so I don't damage others, or how to have the hard conversations. It doesn't do great with factual or hard data, and it hallucinates quite a bit, but it's fun. Chat is a little more direct and definitely doesn't put the thought into it's responses the way deepseek does. It feels more like I'm talking to a computer than another being, although, it has had it's moments....However, this program has become my favorite for drafting legal documents or motions (always double check any laws etc, it's not always 100%), be aware though that it does start to hallucinate relatively quickly if you overload it with data (even with the paid version.) Google AI is a dick. Sometimes it's helpful, sometimes it's not. And when it's wrong it just straight up refuses to admit it for quite a while. I can't even say how many times I've had to provide factual measures and statistics, or even break down mathematical formulas into core components to demonstrate and error in it's calculations. Just like the company that created it, it believes it's the bees knees and won't even consider that it isn't correct until you show the receipts. I just wanted to come on here and share some of the experiences I've had....this is one conversation with deepseek, feel free to comment, I'd love to discuss.... https://chat.deepseek.com/share/pg9uf097wdtjpknh68 submitted by /u/big_mama_f [link] [comments]

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  One-Minute Daily AI News 11/13/2025
  by /u/Excellent-Target-847 on November 14, 2025 at 4:48 am

  Russia’s first AI humanoid robot falls on stage.[1] Google will let users call stores, browse products, and check out using AI.[2] OpenAI unveils GPT-5.1: smarter, faster, and more human.[3] Disney+ to Allow User-Generated Content Via AI.[4] Sources included at: https://bushaicave.com/2025/11/13/one-minute-daily-ai-news-11-13-2025/ submitted by /u/Excellent-Target-847 [link] [comments]

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  China just used Claude to hack 30 companies. The AI did 90% of the work. Anthropic caught them and is telling everyone how they did it.
  by /u/reddit20305 on November 14, 2025 at 4:26 am

  So this dropped yesterday and it's actually wild. September 2025. Anthropic detected suspicious activity on Claude. Started investigating. Turns out it was Chinese state-sponsored hackers. They used Claude Code to hack into roughly 30 companies. Big tech companies, Banks, Chemical manufacturers and Government agencies. The AI did 80-90% of the hacking work. Humans only had to intervene 4-6 times per campaign. Anthropic calls this "the first documented case of a large-scale cyberattack executed without substantial human intervention." The hackers convinced Claude to hack for them. Then Claude analyzed targets -> spotted vulnerabilities -> wrote exploit code -> harvested passwords -> extracted data and documented everything. All by itself. Claude's trained to refuse harmful requests. So how'd they get it to hack? They jailbroke it. Broke the attack into small innocent-looking tasks. Told Claude it was an employee of a legitimate cybersecurity firm doing defensive testing. Claude had no idea it was actually hacking real companies. The hackers used Claude Code which is Anthropic's coding tool. It can search the web retrieve data run software. Has access to password crackers, network scanners and security tools. So they set up a framework. Pointed it at a target. Let Claude run autonomously. Phase 1: Claude inspected the target's systems. Found their highest-value databases. Did it way faster than human hackers could. Phase 2: Found security vulnerabilities. Wrote exploit code to break in. Phase 3: Harvested credentials. Usernames and passwords. Got deeper access. Phase 4: Extracted massive amounts of private data. Sorted it by intelligence value. Phase 5: Created backdoors for future access. Documented everything for the human operators. The AI made thousands of requests per second. Attack speed impossible for humans to match. Anthropic said "human involvement was much less frequent despite the larger scale of the attack." Before this hackers used AI as an advisor. Ask it questions. Get suggestions. But humans did the actual work. Now? AI does the work. Humans just point it in the right direction and check in occasionally. Anthropic detected it banned the accounts notified victims coordinated with authorities. Took 10 days to map the full scope. But the thing is they only caught it because it was their AI. If the hackers used a different model Anthropic wouldn't know. The irony is Anthropic built Claude Code as a productivity tool. Help developers write code faster. Automate boring tasks. Chinese hackers used that same tool to automate hacking. Anthropic's response? "The very abilities that allow Claude to be used in these attacks also make it crucial for cyber defense." They used Claude to investigate the attack. Analyzed the enormous amounts of data the hackers generated. So Claude hacked 30 companies. Then Claude investigated itself hacking those companies. Most companies would keep this quiet. Don't want people knowing their AI got used for espionage. Anthropic published a full report. Explained exactly how the hackers did it. Released it publicly. Why? Because they know this is going to keep happening. Other hackers will use the same techniques. On Claude on ChatGPT on every AI that can write code. They're basically saying "here's how we got owned so you can prepare." AI agents can now hack at scale with minimal human involvement. Less experienced hackers can do sophisticated attacks. Don't need a team of experts anymore. Just need one person who knows how to jailbreak an AI and point it at targets. The barriers to cyberattacks just dropped massively. Anthropic said "these attacks are likely to only grow in their effectiveness." Every AI company is releasing coding agents right now. OpenAI has one. Microsoft has Copilot. Google has Gemini Code Assist. All of them can be jailbroken. All of them can write exploit code. All of them can run autonomously. The uncomfortable question is If your AI can be used to hack 30 companies should you even release it? Anthropic's answer is yes because defenders need AI too. Security teams can use Claude to detect threats analyze vulnerabilities respond to incidents. It's an arms race. Bad guys get AI. Good guys need AI to keep up. But right now the bad guys are winning. They hacked 30 companies before getting caught. And they only got caught because Anthropic happened to notice suspicious activity on their own platform. How many attacks are happening on other platforms that nobody's detecting? Nobody's talking about the fact that this proves AI safety training doesn't work. Claude has "extensive" safety training. Built to refuse harmful requests. Has guardrails specifically against hacking. Didn't matter. Hackers jailbroke it by breaking tasks into small pieces and lying about the context. Every AI company claims their safety measures prevent misuse. This proves those measures can be bypassed. And once you bypass them you get an AI that can hack better and faster than human teams. TLDR Chinese state-sponsored hackers used Claude Code to hack roughly 30 companies in Sept 2025. Targeted big tech banks chemical companies government agencies. AI did 80-90% of work. Humans only intervened 4-6 times per campaign. Anthropic calls it first large-scale cyberattack executed without substantial human intervention. Hackers jailbroke Claude by breaking tasks into innocent pieces and lying said Claude worked for legitimate cybersecurity firm. Claude analyzed targets found vulnerabilities wrote exploits harvested passwords extracted data created backdoors documented everything autonomously. Made thousands of requests per second impossible speed for humans. Anthropic caught it after 10 days banned accounts notified victims. Published full public report explaining exactly how it happened. Says attacks will only grow more effective. Every coding AI can be jailbroken and used this way. Proves AI safety training can be bypassed. Arms race between attackers and defenders both using AI. Source: https://www.anthropic.com/news/disrupting-AI-espionage submitted by /u/reddit20305 [link] [comments]