ChatGPT is a powerful chatbot platform powered by machine learning and AI. Whether you’re looking to monitor user conversations or automate customer service, ChatGPT can be embedded on your website so that visitors can have real-time interactions with an intelligent chatbot. Integrating ChatGPT is easy and efficient, allowing your website to become interfaced with cutting edge AI technology within minutes. ChatGPT is the perfect way for businesses to drive engagement and collect valuable data from customer conversations in order to advance their product roadmap and streamline services.
How can I add ChatGPT to my web site?: ChatGPT examples and limitations
Different ways you can add ChatGPT to your website
There are a few different ways you can add ChatGPT to your website, depending on your specific requirements and the tools and frameworks you are using. Here are a few options:
Use an API: OpenAI has an API that you can use to access ChatGPT. To use the API, you will need to sign up for an API key and then use it to make API calls from your website. You’ll need to write some code to send and receive the API calls, but you can find many examples and libraries in different languages that can help.
Use a pre-built library or SDK: Some developers have created libraries or software development kits (SDKs) that make it easier to use ChatGPT in your website. For example, Hugging Face provides a JavaScript library that you can use to integrate ChatGPT with your website.
Embed a pre-built chatbot: There are a few pre-built chatbots available that are built using ChatGPT and that you can embed in your website. For example, Botfront.io allows you to create a chatbot using the GPT-3 language model.
AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence Intro
Requirements
Please note, to use ChatGPT or GPT-3 model, the OpenAI’s API requires a commercial or research agreement to be in place. As well some of the services may require paid subscription, so it’s recommended to check the pricing and terms of use in advance.
It’s also important to note that building a chatbot with GPT-3 or other language models can require some level of skill, mainly related to data science and natural language processing. If you have little or no experience with it, it may be better to seek professional help.
Integration
ChatGPT makes it easy to integrate artificial intelligence (AI) into your web site with just a few clicks. It employs machine learning technology to allow users to easily embed a natural language processing (NLP) chatbot into their website. ChatGPT learns from conversations, providing customers with an engaging and useful experience when visiting your site. ChatGPT will make your website stand out and provide visitors with an enjoyable experience that they won’t soon forget.
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9. Remake a video: Just pick a video you liked and visit https://lnkd.in/e_GD2reT to get its transcript. Once done, bring that back to Chat GPT and tell it to summarize the transcript. Read the summary and make a video on that yourself.
Create code to call to the OpenAI API using a natural language instruction.
Settings
Engine: code-davinci-002
Max tokens: 64
Temperature: 0
Top: p1.0
Frequency penalty: 0.0
Presence penalty: 0.0
Stop sequence: “””
Prompt
“”” Util exposes the following: util.openai() -> authenticates & returns the openai module, which has the following functions: openai.Completion.create( prompt=”<my prompt>”, # The prompt to start completing from max_tokens=123, # The max number of tokens to generate temperature=1.0 # A measure of randomness echo=True, # Whether to return the prompt in addition to the generated completion ) “”” import util “”” Create an OpenAI completion starting from the prompt “Once upon an AI”, no more than 5 tokens. Does not include the prompt.
“””
Sample response
completion = util.openai().Completion.create( prompt=”Once upon an AI”, max_tokens=5, temperature=1.0, echo=False, ) print(completion) “””
With Python
import os import openai
openai.api_key = os.getenv(“OPENAI_API_KEY”)
response = openai.Completion.create( model=”code-davinci-002″, prompt=”\”\”\”\nUtil exposes the following:\nutil.openai() -> authenticates & returns the openai module, which has the following functions:\nopenai.Completion.create(\n prompt=\”<my prompt>\”, # The prompt to start completing from\n max_tokens=123, # The max number of tokens to generate\n temperature=1.0 # A measure of randomness\n echo=True, # Whether to return the prompt in addition to the generated completion\n)\n\”\”\”\nimport util\n\”\”\”\nCreate an OpenAI completion starting from the prompt \”Once upon an AI\”, no more than 5 tokens. Does not include the prompt.\n\”\”\”\n”, temperature=0, max_tokens=64, top_p=1.0, frequency_penalty=0.0, presence_penalty=0.0, stop=[“\”\”\””] )
const configuration = new Configuration({ apiKey: process.env.OPENAI_API_KEY, }); const openai = new OpenAIApi(configuration);
const response = await openai.createCompletion({ model: “code-davinci-002”, prompt: “\”\”\”\nUtil exposes the following:\nutil.openai() -> authenticates & returns the openai module, which has the following functions:\nopenai.Completion.create(\n prompt=\”<my prompt>\”, # The prompt to start completing from\n max_tokens=123, # The max number of tokens to generate\n temperature=1.0 # A measure of randomness\n echo=True, # Whether to return the prompt in addition to the generated completion\n)\n\”\”\”\nimport util\n\”\”\”\nCreate an OpenAI completion starting from the prompt \”Once upon an AI\”, no more than 5 tokens. Does not include the prompt.\n\”\”\”\n”, temperature: 0, max_tokens: 64, top_p: 1.0, frequency_penalty: 0.0, presence_penalty: 0.0, stop: [“\”\”\””], });
With curl:
curl https://api.openai.com/v1/completions \ -H “Content-Type: application/json” \ -H “Authorization: Bearer $OPENAI_API_KEY” \ -d ‘{ “model”: “code-davinci-002”, “prompt”: “\”\”\”\nUtil exposes the following:\nutil.openai() -> authenticates & returns the openai module, which has the following functions:\nopenai.Completion.create(\n prompt=\”<my prompt>\”, # The prompt to start completing from\n max_tokens=123, # The max number of tokens to generate\n temperature=1.0 # A measure of randomness\n echo=True, # Whether to return the prompt in addition to the generated completion\n)\n\”\”\”\nimport util\n\”\”\”\nCreate an OpenAI completion starting from the prompt \”Once upon an AI\”, no more than 5 tokens. Does not include the prompt.\n\”\”\”\n”, “temperature”: 0, “max_tokens”: 64, “top_p”: 1.0, “frequency_penalty”: 0.0, “presence_penalty”: 0.0, “stop”: [“\”\”\””] }’
With Json:
{ “model”: “code-davinci-002”, “prompt”: “\”\”\”\nUtil exposes the following:\nutil.openai() -> authenticates & returns the openai module, which has the following functions:\nopenai.Completion.create(\n prompt=\”<my prompt>\”, # The prompt to start completing from\n max_tokens=123, # The max number of tokens to generate\n temperature=1.0 # A measure of randomness\n echo=True, # Whether to return the prompt in addition to the generated completion\n)\n\”\”\”\nimport util\n\”\”\”\nCreate an OpenAI completion starting from the prompt \”Once upon an AI\”, no more than 5 tokens. Does not include the prompt.\n\”\”\”\n”, “temperature”: 0, “max_tokens”: 64, “top_p”: 1.0, “frequency_penalty”: 0.0, “presence_penalty”: 0.0, “stop”: [“\”\”\””] }
By Jérôme Cukier, Staff Software Engineer at Google (2020-present)
When Google took off, its key characteristic was that it was very very fast compared to its competition. The quality of the results was also impressive, and, as could be expected, it was very reliable and highly available.
That in itself didn’t make it a better product than Yahoo, which for years dominated the search engine market and which was the de facto home page to the internet, even after Google became a household name. However, this was enough to start the narrative that there was something special about Google that others just couldn’t do quite as well.
ChatGPT is not fast, is often wrong, and as a service is very unreliable. It’s down approximately 50% of the times I’m trying to use it. The technology behind it is not rocket science, that said they have a few things going for them. First, they trained a very large language model (LLM). The cost of this operation in terms of machine is massive. Google search can crawl the web and update their index all the time but the resources needed to train a LLM as big as GPT-3 are phenomenal. Second, they have a product. Microsoft, Meta, Google all could have released something similar and sooner, but didn’t. As a result, OpenAI just like Google ~23 years before it has a narrative going for them.
People’s perception of Google search is that it’s a service that will return 10 blue links to a query which is a list of keywords, that’s a bit unfair because for years this is neither what search results or search queries are, but then again Google has not been able to correct that impression. On the other hand, journalists know that there is a demand for stories that present ChatGPT as an all-powerful oracle that can do many things and whose output cannot be distinguished from actual people and these stories have kept coming – again, just like stories about Google in the early 2000s then about Facebook in the mid aughts.
ChatGPT is still not able to do what Google does.
The most common queries are about the weather, opening hours of businesses, shopping and lottery results. Those things however trite are completely out of bound for ChatGPT which doesn’t have a live connection with the real world. But then there are many things that a LLM-backed chatbot can do (or even better, that specific products supported by LLMs can do) which Google or other big tech companies just don’t offer.
ChatGPT is just one of many services that are threatening the role of Google not just as a search engine but as a central platform. It’s also very preliminary, after GPT3 will come GPT4, after ChatGPT will come waves of products with GPT APIs. So the landscape is going to change significantly over the next couple of years.
How can I add ChatGPT to my web site: 20 jobs that ChatGPT-4 can potentially replace
A step-by-step guide to building a chatbot based on your own documents with GPT
Chatting with ChatGPT is fun and informative — I’ve been chit-chatting with it for past time and exploring some new ideas to learn. But these are more casual use cases and the novelty can quickly wean off, especially when you realize that it can generate hallucinations.
How can I add ChatGPT to my web site: Building document Q&A chatbot step-by-step
How can I add ChatGPT to my web site: Building document Q&A chatbot step-by-stepHow can I add ChatGPT to my web site: Building document Q&A chatbot step-by-stepHow can I add ChatGPT to my web site: Building document Q&A chatbot step-by-step
GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks.
GPT-4’s improvements are evident in the system’s performance on a number of tests and benchmarks, including the Uniform Bar Exam, LSAT, SAT Math, and SAT Evidence-Based Reading & Writing exams. In the exams mentioned, GPT-4 scored in the 88th percentile and above, and a full list of exams and the system’s scores can be seen GPT-4
Image is a multimodal chatbot like ChatGPT4.
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What are some practical applications of machine learning that can be used by a regular person on their phone?
Machine learning is no longer something only used by tech giants and computer experts, but has many practical applications that the average person can take advantage of from their smartphone. From facial recognition to sophisticated machine learning algorithms that help with day-to-day tasks, Artificial Intelligence (AI) powered machine learning technology has opened up a world of possibilities for regular people everywhere. Whether it’s a voice assistant helping you make appointments and track down important information or automatic text translations that allow people to communicate with those who speak a foreign language, machine learning makes performing various tasks much simpler — a bonus any busy person would be thankful for. With the booming machine learning industry continuing to grow in leaps and bounds, it won’t be long until the power of AI is accessible in our pockets.
What are some practical applications of machine learning that can be used by a regular person on their phone? How Microsoft’s Cortana Stacks Up Against Siri and Alexa in Terms of Intelligence?
There are many practical applications of machine learning (ML) that can be used by regular people on their smartphones. Some examples include:
Virtual assistants: Many smartphones now include virtual assistants such as Siri, Alexa, and Google Assistant that can use ML to respond to voice commands, answer questions, and perform tasks.
Image recognition: ML-based image recognition apps can be used to identify and label objects, animals, and people in photos and videos.
Speech recognition: ML-based speech recognition can be used to transcribe speech to text, dictate text messages and emails, and control the phone’s settings and apps.
Personalized news and content: ML-based algorithms can be used to recommend news articles and content to users based on their reading history and interests.
Social media: ML can be used to recommend users to connect with, suggest posts to like, and filter out irrelevant or offensive content.
Personalized shopping: ML-based algorithms can be used to recommend products and offers to users based on their purchase history and interests.
Language Translation: Some apps can translate text, speech, and images in real-time, allowing people to communicate effectively in different languages. Read Aloud For Me
Personalized health monitoring: ML-based algorithms can be used to track and predict user’s sleep, activity, and other health metrics.
What are some practical applications of machine learning that can be used by a regular person on their phone? Read Aloud For Me – Read aloud and translate text, photos, pdfs documents for me in my chosen language. Speech Synthesizer, Take Notes and Save via voice, No tracking, Secure, Read For Me without tracking me.
These are just a few examples of the many practical applications of ML that can be used by regular people on their smartphones. As the technology continues to advance, it is likely that there will be even more ways that people can use ML to improve their daily lives.
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What are some potential ethical issues surrounding uses of Machine Learning and artificial Intelligence techniques?
There are several potential ethical issues surrounding the use of machine learning and artificial intelligence techniques. Some of the most significant concerns include:
Bias: Machine learning algorithms can perpetuate and even amplify existing biases in the data they are trained on. This can lead to unfair or discriminatory outcomes, especially in areas such as lending, hiring, and criminal justice.
Transparency: The inner workings of some machine learning models can be complex and difficult to understand, making it difficult for people to know how decisions are being made and to hold organizations accountable for those decisions.
Privacy: The collection, use, and sharing of personal data by machine learning models can raise significant privacy concerns. There are also concerns about the security of personal data and the potential for it to be misused.
Unemployment: As automation and artificial intelligence become more advanced, there is a risk that it will displace human workers, potentially leading to unemployment and economic disruption.
Autonomy: As AI and Machine Learning systems become more advanced, there are questions about the autonomy of these systems, and how much control humans should have over them.
Explainability: ML systems used in decision making can be seen as “black boxes” that is hard to understand how they arrive to certain decision. This can make it harder to trust the outcomes.
Accountability: As AI and ML systems become more prevalent, it will be crucial to establish clear lines of accountability for the decisions they make and the actions they take.
These are just a few examples of the ethical issues surrounding the use of machine learning and artificial intelligence. It is important for researchers, developers, and policymakers to work together to address these issues in a responsible and thoughtful way.
What are some examples of applications for artificial neural networks in business?
Artificial neural networks (ANNs) are a type of machine learning algorithm that are modeled after the structure and function of the human brain. They are well-suited to a wide variety of business applications, including:
Predictive modeling: ANNs can be used to analyze large amounts of data and make predictions about future events, such as sales, customer behavior, and stock market trends.
Customer segmentation: ANNs can be used to analyze customer data and group customers into segments with similar characteristics, which can be used for targeted marketing and personalized recommendations.
Fraud detection: ANNs can be used to identify patterns in financial transactions that are indicative of fraudulent activity.
Natural language processing: ANNs can be used to analyze and understand human language, which allows for applications such as sentiment analysis, text generation, and chatbot.
Image and video analysis: ANNs can be used to analyze images and videos to detect patterns and objects, which allows for applications such as object recognition, facial recognition, and surveillance.
Recommender systems: ANNs can be used to analyze customer data and make personalized product or content recommendations.
Predictive maintenance: ANNs can be used to analyze sensor data to predict when equipment is likely to fail, allowing businesses to schedule maintenance before problems occur.
Optimization: ANNs can be used to optimize production processes, logistics, and supply chain.
These are just a few examples of how ANNs can be applied to business, this field is constantly evolving and new use cases are being discovered all the time.
How do you explain the concept of supervised and unsupervised learning to a non-technical audience?
Supervised learning is a type of machine learning where a computer program is trained using labeled examples to make predictions about new, unseen data. The idea is that the program learns from the labeled examples and is then able to generalize to new data. A simple analogy would be a teacher showing a student examples of math problems and then having the student solve similar problems on their own.
For example, in image classification, a supervised learning algorithm would be trained with labeled images of different types of objects, such as cats and dogs, and then would be able to identify new images of cats and dogs it has never seen before.
On the other hand, unsupervised learning is a type of machine learning where the computer program is not given any labeled examples, but instead must find patterns or structure in the data on its own. It’s like giving a student a set of math problems to solve without showing them how it was done. For example, in unsupervised learning, an algorithm would be given a set of images, and it would have to identify the common features among them.
A good analogy for unsupervised learning is exploring a new city without a map or tour guide, the algorithm is on its own to find the patterns, structure, and relationships of the data.
Are decision trees better suited for supervised or unsupervised learning and why?
Decision trees are primarily used for supervised learning, because they involve making decisions based on the labeled training data provided. Supervised learning is a type of machine learning where an algorithm is trained on a labeled dataset, where the correct output for each input is provided.
In a decision tree, the algorithm builds a tree-like model of decisions and their possible consequences, with each internal node representing a feature or attribute of the input data, each branch representing a decision based on that attribute, and each leaf node representing a predicted output or class label. The decision tree algorithm uses this model to make predictions on new, unseen input data by traversing the tree and following the decisions made at each node.
While decision trees can be used for unsupervised learning, it is less common. Unsupervised learning is a type of machine learning where the algorithm is not provided with labeled data and must find patterns or structure in the data on its own. Decision trees are less well suited for unsupervised learning because they rely on labeled data to make decisions at each node and therefore this type of problem is generally solved with other unsupervised techniques.
In summary, decision trees are better suited for supervised learning because they are trained on labeled data and make decisions based on the relationships between features and class labels in the training data.
Can machine learning make a real difference in algorithmic trading?
Yes, machine learning can make a significant difference in algorithmic trading. By analyzing large amounts of historical market data, machine learning algorithms can learn to identify patterns and make predictions about future market movements. These predictions can then be used to inform trading strategies and make more informed decisions about when to buy or sell assets. Additionally, machine learning can be used to optimize and fine-tune existing trading strategies, and to detect and respond to changes in market conditions in real-time.
These are the two areas where machine learning can take over:
Swing finding: intermediate highs and lows.
Position sizing: actually this is a subset of position sizing. Sometimes, pairs like EURTRY go nowhere for a long time. Rather than piss money away, it makes sense to penalize (reduce) position sizing on certain pairs and increase others.
Asset allocation and risk management. It can also aid a discretionary trader in picking important factors to consider.
How does technology like facial recognition influence how we understand and use surveillance systems?
Facial recognition technology, which uses algorithms to analyze and compare facial features in order to identify individuals, has the potential to greatly influence how we understand and use surveillance systems. Some of the ways in which this technology can influence the use of surveillance include:
Increased surveillance: Facial recognition technology can enable more accurate and efficient identification of individuals, which can result in increased surveillance in public spaces and private businesses.
Privacy concerns: The use of facial recognition technology raises concerns about privacy and civil liberties, as it could enable widespread surveillance and tracking of individuals without their knowledge or consent.
Biased performance: There have been concerns that facial recognition systems can have a biased performance, particularly when it comes to identifying people of color, women, and children. This can lead to false arrests and other negative consequences.
Misuse of the technology: Facial recognition technology can be misused by governments or companies for political or financial gain, or to repress or discriminate against certain groups of people.
Legal challenges: There are legal challenges on the use of facial recognition technology, as it raises questions about the limits of government surveillance and the protection of civil liberties.
Facial recognition technology is a powerful tool that has the potential to greatly enhance the capabilities of surveillance systems. However, it’s important to consider the potential consequences of its use, including privacy concerns and the potential for misuse, as well as the ethical implications of the technology.
What is the difference between a heuristic and a machine learning algorithm?
Machine learning algorithms and heuristics can often be mistaken for each other, but there are distinct differences between the two. Machine learning algorithms seek to replicate processes and patterns previously used to solve various types of problems and can remember these processes for future problem solving. Heuristics, on the other hand, are creative approaches that attempt to solve problems with novel solutions. An algorithm pre-defined by programmers relies on structured data such as numerical values, while a heuristic requires verbal instructions from users such as expressions or conditions that describe an ideal solution. Machine learning algorithms and heuristics both offer useful approaches to problem solving, but it’s important to understand the difference in order to properly apply them.
A heuristic is a type of problem-solving approach that involves using practical, trial-and-error methods to find solutions to problems. Heuristics are often used when it is not possible to use a more formal, systematic approach to solve a problem, and they can be useful for finding approximate solutions or identifying patterns in data.
A machine learning algorithm, on the other hand, is a type of computer program that is designed to learn from data and improve its performance over time. Machine learning algorithms use statistical techniques to analyze data and make predictions or decisions based on that analysis.
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There are several key differences between heuristics and machine learning algorithms:
Purpose: Heuristics are often used to find approximate or suboptimal solutions to problems, while machine learning algorithms are used to make accurate predictions or decisions based on data.
Data: Heuristics do not typically involve the use of data, while machine learning algorithms rely on data to learn and improve their performance.
Learning: Heuristics do not involve learning or improving over time, while machine learning algorithms are designed to learn and adapt based on the data they are given.
Complexity: Heuristics are often simpler and faster than machine learning algorithms, but they may not be as accurate or reliable. Machine learning algorithms can be more complex and time-consuming, but they may be more accurate and reliable as a result.
Overall, heuristics and machine learning algorithms are different approaches to solving problems and making decisions. Heuristics are often used for approximate or suboptimal solutions, while machine learning algorithms are used for more accurate and reliable predictions and decisions based on data.
What are some ethical concerns regarding artificial intelligence and its future development?
Debate about the ethical concerns surrounding artificial intelligence (AI) and machine learning have been becoming increasingly prominent. Issues such as safe AI and ethical AI are of utmost importance when it comes to continued development in this field, and if proper oversight is not account for these could easily become part of an unwanted dystopian future.
Regulations need to be made with regards to how machine learning algorithms are developed and executed, while due diligence is taken to ensure that no negative affects are caused from its use. This sort of regulation is necessary so as to ensure the AI being produced is both responsible and well-monitored; accounting for any human bias or negative externalities created by machine learning algorithms.
What are some ethical concerns regarding artificial intelligence and its future development?
Artificial intelligence (AI) has the potential to revolutionize many aspects of society, but it also raises a number of ethical concerns. Some of the ethical concerns regarding the future development of AI include:
Bias and discrimination: AI systems can be biased if they are trained on biased data or if they are designed to perpetuate existing biases. This can lead to discrimination against certain groups of people, such as those based on race, gender, or age.
Privacy: AI systems often rely on data collected from individuals, and there are concerns about how this data is collected, stored, and used. There is a risk that personal data could be accessed or misused by unauthorized parties.
Transparency: It can be difficult to understand how AI systems make decisions, which can make it difficult to hold them accountable for their actions. This lack of transparency can raise concerns about the fairness and accountability of AI systems.
Job displacement: AI systems have the potential to automate many tasks, which could lead to job displacement and unemployment. There is a risk that AI could exacerbate existing inequalities and create new ones.
Autonomous systems: AI systems are increasingly being used to make decisions without human intervention. This raises concerns about the accountability of these systems and the potential for them to cause harm.
These are just a few of the ethical concerns that have been raised regarding the future development of AI. It is important for researchers, policymakers, and other stakeholders to consider these issues and to work to address them as AI continues to evolve.
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2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams
Is artificial intelligence being used to create subspecies or designer organisms?
Artificial intelligence (AI) is not typically used to create subspecies or designer organisms. While AI can be used to analyze and interpret genetic data, it is not typically involved in the actual process of creating or modifying living organisms.
Creating or modifying living organisms, whether they are plants, animals, or microorganisms, typically involves manipulating their genetic material in some way. This can be done through techniques such as gene editing, where specific genes are inserted, deleted, or modified within the genome of an organism.
AI can be used to analyze and interpret the data generated by these techniques, and it may be used to identify potential targets for gene editing or to predict the effects of specific genetic modifications. However, AI is not typically involved in the actual process of creating or modifying living organisms.
Overall, it is important to note that the use of AI in the field of biology is still in its early stages, and there is much that we do not yet understand about its capabilities and limitations. While AI has the potential to revolutionize many aspects of biology and medicine, it is important to carefully consider the ethical and societal implications of these technologies.
Does artificial intelligence represent a risk factor that could potentially result in human annihilation?
There is ongoing debate about the potential risks and benefits of artificial intelligence (AI). While some experts argue that AI could bring significant benefits and advancements for society, others have raised concerns about the potential risks and negative impacts of AI.
One potential risk of AI is that it could potentially be used to develop and deploy weapons or other harmful technologies. For example, AI could be used to develop autonomous weapons systems that could make decisions about when to use force, potentially leading to unintended consequences.
Another potential risk of AI is that it could be used to amplify existing power imbalances or to create new ones. For example, AI could be used to automate certain jobs or tasks, potentially leading to job displacement and income inequality.
There is also the potential for AI to be used to undermine privacy and security, for example by collecting and analyzing large amounts of personal data without individuals’ knowledge or consent.
Overall, while it is difficult to predict the future development and impact of AI, it is important for society to carefully consider the potential risks and benefits of this technology and to take steps to mitigate any potential negative impacts.
How has the introduction of new technologies such as artificial intelligence changed the landscape of modern espionage?
The introduction of new technologies, such as artificial intelligence (AI), has significantly changed the landscape of modern espionage. Here are a few ways in which AI has impacted the field of espionage:
Enhanced surveillance capabilities: AI can be used to analyze and process large amounts of data from various sources, such as video footage, social media posts, and electronic communications. This can enable intelligence agencies to gather more information and monitor individuals and organizations more effectively.
Improved analysis and prediction: AI algorithms can be used to analyze and make sense of vast amounts of data, helping intelligence agencies to identify trends, predict future events, and make more informed decisions.
Increased automation: AI can be used to automate various tasks, such as data collection and analysis, allowing intelligence agencies to operate more efficiently and with fewer resources.
New threats: AI also introduces new threats, such as the potential for AI-powered cyber attacks or the use of AI-powered autonomous weapons systems.
Overall, the introduction of AI has had a significant impact on the field of espionage, enabling intelligence agencies to gather and analyze more information than ever before, but also introducing new risks and challenges.
In what ways can AI and machine learning be used to better predict, respond, and contain potential outbreaks before they become widespread?
Artificial intelligence (AI) and machine learning (ML) can be used to better predict, respond, and contain potential outbreaks before they become widespread in a number of ways:
Data analysis: AI and ML can be used to analyze large amounts of data from various sources, such as social media, electronic health records, and surveillance systems, to identify patterns and trends that may indicate the early stages of an outbreak.
Risk assessment: AI and ML can be used to assess the likelihood of an outbreak occurring in a particular region or population, and to identify factors that may increase the risk of an outbreak.
Early warning systems: AI and ML can be used to develop early warning systems that can alert public health officials and other stakeholders of potential outbreaks in real-time, allowing them to take timely and appropriate action.
Response planning: AI and ML can be used to help public health officials and other stakeholders develop and implement effective response plans to contain and control outbreaks.
Predictive modeling: AI and ML can be used to develop predictive models that can forecast the likely trajectory of an outbreak and help to identify the most effective interventions to reduce its impact.
Overall, AI and ML have the potential to significantly improve our ability to predict, respond, and contain potential outbreaks before they become widespread, helping to protect public health and prevent the spread of diseases.
Autonomous flight: AI algorithms can be used to enable UAVs to fly autonomously, without the need for human control. This can allow UAVs to perform tasks such as surveillance, mapping, and delivery without the need for a human operator.
Obstacle avoidance: AI algorithms can be used to enable UAVs to detect and avoid obstacles in their path, such as trees, buildings, and other aircraft. This can improve the safety and reliability of UAVs, particularly in environments where there are many potential hazards.
Improved decision making: AI algorithms can be used to enable UAVs to make decisions in real-time based on data from sensors and other sources. This can allow UAVs to adapt to changing conditions and to respond to unexpected situations, improving their performance and reliability.
Enhanced capabilities: AI algorithms can be used to enable UAVs to perform tasks that would be difficult or impossible for humans to do, such as flying through small or complex spaces, or flying in extreme environments.
Overall, the use of AI in control systems for UAVs has the potential to significantly improve the capabilities and performance of these systems, and to enable UAVs to perform a wide range of tasks that were previously impractical or impossible.
What impact will artificial intelligence have on medical research and healthcare delivery in the next decade?
Artificial intelligence (AI) has the potential to have a significant impact on medical research and healthcare delivery in the next decade. Some of the ways AI could potentially be used include:
Improving drug discovery: AI can analyze large amounts of data from genomic and chemical databases to identify potential new drugs, which can speed up the drug discovery process.
Personalized medicine: AI can be used to analyze patients’ medical history, symptoms, and test results to create personalized treatment plans.
Diagnosis: AI algorithms can be trained to analyze medical images and make accurate diagnoses, which can assist physicians in making more accurate and faster diagnoses.
Predictive analytics: AI can be used to analyze data from electronic health records to identify patterns and predict outcomes, which can help healthcare providers make more informed decisions and improve patient outcomes.
Robotic surgery: AI-controlled robots are being developed to assist in surgery, which can improve precision and reduce recovery time for patients.
Clinical trial design: AI can be used to analyze clinical data to identify patterns and optimize trial design, which can improve the efficiency and success rate of clinical trials.
That being said, the success of these application depends on the quality and quantity of data available, robustness of the AI algorithms, and other factors such as privacy, security and transparency, thus it is important to keep in mind that the impact of AI in healthcare will still have a lot of considerations and the success rate varies case by case and sector by sector.
What are the advantages of using ARIMA models over LSTMs for forecasting and prediction in finance and economics applications?
The field of machine learning and artificial intelligence is constantly evolving, and with it, the ways in which we use technology to understand and predict complex financial and economic systems. ARIMA models and Long-Short-Term-Memory (LSTM) networks are two machine learning tools with a lot of potential in this domain. Though both approaches can yield great accuracy, ARIMA models have an edge when forecasting or predicting financial data. This is because they better capture the stationary process present in most financial data; while LSTMs are excellent at modeling non-stationary processes, these tend to be less prominent in financial settings. Furthermore, ARIMA consumes less resources; its training algorithms can require several orders of magnitude fewer calculations than required for training LSTM networks. Thus, if you need accuracy in your finance or economics applications without running up large bills for computation resources, ARIMA should be your go-to machine learning tool!
Autoregressive integrated moving average (ARIMA) models and long short-term memory (LSTM) models are two commonly used approaches for forecasting and prediction in finance and economics applications.
Here are some advantages of using ARIMA models over LSTMs:
Interpretability: ARIMA models are generally more interpretable than LSTM models, as the parameters of the model have a clear meaning and can be interpreted in terms of the underlying data. This makes it easier to understand the reasons behind the model’s predictions.
Computational efficiency: ARIMA models are generally more computationally efficient than LSTM models, as they have fewer parameters and require less training data. This makes them faster to train and easier to deploy in production environments.
Data requirements: ARIMA models are suitable for modeling time series data that is stationary (i.e., the statistical properties of the data do not change over time) and exhibits a clear trend and/or seasonality. LSTM models, on the other hand, can handle non-stationary data and can model more complex patterns, but they may require more data to do so.
That being said, LSTM models also have some advantages over ARIMA models. For example, LSTM models can handle missing data and can model long-term dependencies in the data more effectively than ARIMA models.
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Ultimately, the choice between using an ARIMA model or an LSTM model will depend on the specific characteristics of the data and the requirements of the application. It may be necessary to try both approaches and compare their performance to determine the best model for a given task.
Machine Learning and Artificial Intelligence have become tools of choice for forecasting and prediction applications in finance and economics. For example, ARIMA models have developed a reputation as reliable predictors of stock prices or demand for certain products based on past data due to their ability to describe high-level trends from data. In contrast, Long Short-Term Memory (LSTMs) are better at understanding complex patterns, but may be overkill for regression problems that can already be addressed with the less-complex ARIMA approach. When applied to stationary time series data, ARIMA is faster to train and good enough for most use cases. Moreover, it offers advantages over LSTMs in terms of scalability: ARIMA is able to scale with higher ones and zeroes than its AI cousins; as such, ARIMA requires less computing power to reach similar results and operate on more datasets simultaneously. Ultimately, the machine learning model we choose will depend on our prediction problem’s complexity – but if you find yourself facing a straightforward regression task in finance or economics the classic ARIMA might just do the trick without taking too much of your precious machine memory!
What are some Canadian startups that use artificial intelligence/machine learning?
Canada’s tech sector is booming with machine learning and artificial intelligence, and a number of young startups are leading the charge. From big-picture machine learning apps to AI solutions for every industry imaginable, Canadian startups are innovating in a powerful way. Sentenai, for example, uses machine learning to process data in real-time to create predictive analytics that can help businesses make faster decisions. Robokiller uses AI to block spam calls, while Caribou Labs develops solutions that use machine learning to help industrial organizations increase productivity. These examples show just how much potential lies within Canada’s AI startup scene — and there is much more still being discovered!
Top 200 Canada History Geography and Citizenship Test Quiz?
According to ChatGPT, there are many Canadian startups that use artificial intelligence (AI) and machine learning (ML). Here are a few examples:
Element AI: Element AI is a Montreal-based startup that develops AI-powered solutions for businesses. The company’s products include a platform for building and deploying custom AI models, and a range of AI-powered software tools for various industries.
Layer 6 AI: Layer 6 AI is a Toronto-based startup that uses AI and ML to build predictive analytics solutions for businesses. The company’s products include a platform for building custom AI models, and a range of AI-powered software tools for various industries.
Deep Genomics: Deep Genomics is a Toronto-based startup that uses AI and ML to develop personalized medicine solutions. The company’s products include a platform for analyzing genetic data and predicting the impact of genetic variations on health.
Borealis AI: Borealis AI is a research institute focused on advancing the state of the art in AI and ML. The institute has locations in Toronto, Edmonton, and Montreal, and its research focuses on a range of topics including natural language processing, computer vision, and machine learning.
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Canada is home to some of the most innovative startups, many of which are actively applying machine learning and artificial intelligence (AI) in novel ways. From education to finance and transportation, AI is being utilized to create a more efficient and transparent experience for users everywhere. Two such Canadian startups are Layer 6, a machine learning platform that aggregates data from multiple sources; and Deep Genomics, a healthcare technology that combines machine learning with genetics. It’s impressive how these Canadian companies are harnessing machine learning and AI to revolutionize the way we work and Live today.
These are just a few examples of Canadian startups that use AI and ML. There are many other startups in Canada that are using these technologies to solve a variety of problems across a range of industries.
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What is The Most Accurate Machine Learning Algorithm for Predictive Modeling?
When it comes to predictive modeling, machine learning algorithms play a pivotal role in helping data scientists and machine learning engineers make accurate predictions about the future. But which algorithm is the most accurate for predictive modeling? Let’s take a look at the various kinds of algorithms available and explore which one is best suited for predictive modeling.
What is The Most Accurate Machine Learning Algorithm for Predictive Modeling?
Types of Machine Learning Algorithms The first step in choosing an algorithm is understanding the types of algorithms used in machine learning. There are three main categories of algorithms used in machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is when data scientists use labeled data to teach the system what to do. Unsupervised learning uses unlabeled data to let the system learn on its own. Reinforcement learning focuses on taking action based on reward systems.
Which Algorithm Is Best For Predictive Modeling? When it comes to predictive modeling, there are several different algorithms that can be used depending on your specific needs and goals. Generally speaking, supervised algorithms such as linear regression and logistic regression are often more accurate for predicting future outcomes than unsupervised or reinforcement learning algorithms due to their ability to learn from previously labeled data sets. Support vector machines (SVMs) are also widely used for predictive modeling due to their accuracy and ability to create non-linear decision boundaries.
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Another popular choice for predictive modeling is artificial neural networks (ANNs). ANNs are composed of multiple layers of neurons that allow them to recognize patterns within large datasets quickly and accurately. ANNs have been proven time and time again as one of the most effective methods for predictive modeling due to their ability to process complex information faster than other types of models. However, they can be computationally intensive and require more training data than other models, making them less suitable for smaller datasets or applications with limited computing resources.
The most accurate machine learning algorithm for predictive modeling really depends on the type of data you’re working with. For example, if your data is structured, then linear regression might be the best option. Linear regression is a supervised learning algorithm that uses a linear approach to find relationships between input variables and output variables. It’s often used in econometrics and finance as well as other areas where forecasting and trend-based predictions are important.
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If your data is unstructured, then a more sophisticated algorithm like recurrent neural networks (RNNs) might be better suited for the task at hand. RNNs are deep learning algorithms that use feedback loops to remember input data over time, allowing them to make more accurate predictions based on past events or patterns. This makes them particularly useful for applications such as natural language processing or speech recognition, where patterns need to be identified across long sequences of data.
Finally, if you need a balance of accuracy and speed, then support vector machines (SVMs) may be your best bet. SVMs are supervised learning algorithms that identify hyperplanes that separate classes of data points in order to make predictions about new data points. They are known for their high accuracy rates but can also run quickly due to their efficient implementation methods.
Conclusion: In conclusion, when it comes to choosing a machine learning algorithm for predictive modeling, there is no “one size fits all” solution; rather, it depends on your specific needs and goals as well as the dataset you have available. In general, supervised models such as linear regression and logistic regression are often more accurate than unsupervised or reinforcement learning models, while support vector machines (SVMs) offer non-linear decision boundaries with high accuracy levels when properly tuned. Artificial neural networks (ANNs) are also popular choices because they provide incredibly fast processing speeds and can handle complex information with ease; however they require more training data than other types of models which may not be feasible in some cases due to resource constraints or small datasets available. Ultimately, choosing an algorithm requires careful consideration of your specific requirements in order to select the most suitable option for your application’s needs.
Tunnel Boring Machine Process Control | Predictive Modelling
Tunneling process control is the feedback between the observed behavior of the tunnel boring machine (TBM) with predictions and observations. In this paper, examples of using predictive models to improve the feedback analysis and allow the engineer to readily undertake forecasts related to productivity and ground behavior are presented. These predictive models, which can be developed for TBM parameters (e.g., face pressure), ground behavior (e.g., volume loss), maintenance strategies, and construction logistics are updated/improved as the TBM progresses through the ground and the relationship between geotechnical conditions and TBM performance becomes better understood. This feedback ensures tunneling is achieved safely and effectively while maximizing productivity and minimizing risks.
INTRODUCTION
Real-time data acquisition and delivery for analysis have become standard practice in tunneling projects. This includes both TBM and instrumentation/monitoring data, providing an opportunity for real-time feedback analysis between construction activities and ground behavior. The real-time feedback in turn provides opportunities to assess and modify predictions and expectations with respect to TBM parameters and settlement control, and aid maintenance strategies and project planning and tendering.
With the advances made in both academia and industry, the understanding of the tunneling process and prediction of expected behaviors during mechanized shield tunneling has produced a number of prediction models that have been adopted and applied to design and construction planning.
Furthermore, more and more data than ever before is collected during construction, which enables comparison between predictions and observations, as well as improving the predictions with the added knowledge from the data.
However, due to the ongoing activities and progress of the tunnel construction, there is a need to be able to rapidly and efficiently make comparisons between predictions and observations and even update the predictions in at least a semi-automated manner. Furthermore, this feedback analysis should be easily applied to the process control and save significant time and money on the project. This paper presents several example use cases for developing and updating predictive models for feedback analysis and process control.
What is the difference between regression, time series forecasting, and causal inference?
Regression, time series forecasting, and causal inference are all statistical techniques that can be used to analyze data and make predictions. Here is a brief overview of each:
Regression: Regression is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. It is used to predict the value of the dependent variable based on the values of the independent variables.
Time series forecasting: Time series forecasting is a statistical technique used to predict future values of a series of data points based on past values. It is often used to make predictions about time-dependent data, such as sales or stock prices.
Causal inference: Causal inference is a statistical technique used to determine the cause-and-effect relationship between two variables. It is used to identify the potential causal relationships between variables, and to estimate the effect of one variable on another.
Overall, these techniques are used for different purposes and involve different approaches to data analysis. Regression is used to predict the value of a dependent variable based on independent variables, time series forecasting is used to predict future values of a series of data points based on past values, and causal inference is used to identify and estimate the causal relationships between variables.
What are some of the most acclaimed books about artificial intelligence and its applications?
There are many books that have been written about artificial intelligence (AI) and its applications, and the following are a few that are highly acclaimed:
“Superintelligence: Paths, Dangers, and Strategies” by Nick Bostrom: This book explores the potential future development of AI and the risks and opportunities it may present.
“Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: This book is a comprehensive introduction to deep learning, a type of machine learning that has achieved remarkable results in a wide range of applications.
“The Master Algorithm” by Pedro Domingos: This book explores the idea of a “master algorithm” that could learn anything that can be learned and achieve superhuman intelligence.
“Thinking, Fast and Slow” by Daniel Kahneman: This book is a best-selling work that explores the psychological biases and cognitive heuristics that shape our decision-making and how they can be influenced by AI.
“The Singularity Trap” by Federico Pistono: This book discusses the potential risks and unintended consequences of AI and the need for responsible development and regulation.
These are just a few examples, and there are many other books that explore different aspects of AI and its applications.
Have you ever heard of ChatGPT, the open-source machine learning platform that allows users to build natural language models?
It stands for “Chat Generating Pre-trained Transformer” and it’s an AI-powered chatbot that can answer questions with near human-level intelligence. But what is Google’s answer to this technology? The answer lies in Open AI, supervised learning, and reinforcement learning. Let’s take a closer look at how these technologies work.
Tech Buzzwords of 2022, By Google Search Interest
Open AI is an artificial intelligence research laboratory that was founded by some of the biggest names in tech, including Elon Musk and Sam Altman. This non-profit organization seeks to develop general artificial intelligence that is safe and beneficial to society. One of their key initiatives is the development of open source technologies like GPT-3, which is a natural language processing model used in ChatGPT.
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ChatGPT: What Is It and How Does Google Answer It?
Artificial Intelligence (AI) has been around for decades. From its humble beginnings in the 1950s, AI has come a long way and is now an integral part of many aspects of our lives. One of the most important areas where AI plays a role is in natural language processing (NLP). NLP enables computers to understand and respond to human language, paving the way for more advanced conversations between humans and machines. One of the most recent developments in this field is ChatGPT, a conversational AI developed by OpenAI that utilizes supervised learning and reinforcement learning to enable computers to chat with humans. So what exactly is ChatGPT and how does it work? Let’s find out!
ChatGPT examples and limitations
ChatGPT is an open-source AI-based chatbot developed by OpenAI.
This chatbot leverages GPT-3, one of the most powerful natural language processing models ever created, which stands for Generative Pre-trained Transformer 3 (GPT-3). This model uses supervised learning and reinforcement learning techniques to enable computers to understand human language and response accordingly. Using supervised learning, GPT-3 utilizes large datasets of text to learn how to recognize patterns within language that can be used to generate meaningful responses. Reinforcement learning then allows GPT-3 to use feedback from conversations with humans in order to optimize its responses over time.
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ChatGPT uses supervised learning techniques to train its models.
Supervised learning involves providing a model with labeled data (i.e., data with known outcomes) so that it can learn from it. This labeled data could be anything from conversations between two people to user comments on a website or forum post. The model then learns associations between certain words or phrases and the desired outcome (or label). Once trained, this model can then be applied to new data in order to predict outcomes based on what it has learned so far.
In addition to supervised learning techniques, ChatGPT also supports reinforcement learning algorithms which allow the model to learn from its experiences in an environment without explicit labels or outcomes being provided by humans. Reinforcement learning algorithms are great for tasks like natural language generation where the output needs to be generated by the model itself rather than simply predicting a fixed outcome based on existing labels.
Supervised learning involves feeding data into machine learning algorithms so they can learn from it. For example, if you want a computer program to recognize cats in pictures, you would provide the algorithm with thousands of pictures of cats so it can learn what a cat looks like. This same concept applies to natural language processing; supervised learning algorithms are fed data sets so they can learn how to generate text using contextual understanding and grammar rules.
Reinforcement learning uses rewards and punishments as incentives for the machine learning algorithm to explore different possibilities. In ChatGPT’s case, its algorithm is rewarded for generating more accurate responses based on previous interactions with humans. By using reinforcement learning techniques, ChatGPT’s algorithm can become smarter over time as it learns from its mistakes and adjusts accordingly as needed.
How is ChatGPT trained?
ChatGPT is an improved GPT-3 trained an existing reinforcement learning with humans in the loop. Their 40 labelers provide demonstrations of the desired model behavior. ChatGPT has 100x fewer parameters (1.3B vs 175B GPT-3).
➡️ First they collect a dataset of human-written demonstrations on prompts submitted to our API, and use this to train our supervised learning baselines.
➡️ Next they collect a dataset of human-labeled comparisons between two model outputs on a larger set of API prompts. They then train a reward model (RM) on this dataset to predict which output our labelers would prefer.
➡️ Finally, they use this RM as a reward function and fine-tune our GPT-3 policy to maximize this reward using the Proximal Policy Optimization
In simpler terms, ChatGPT is a variant of the GPT-3 language model that is specifically designed for chat applications. It is trained to generate human-like responses to natural language inputs in a conversational context. It is able to maintain coherence and consistency in a conversation, and can even generate responses that are appropriate for a given context. ChatGPT is a powerful tool for creating chatbots and other conversational AI applications.
How Does Google Answer ChatGPT?
What is Google answer to ChatGPT?
Google’s answer to ChatGTP comes in the form of their own conversational AI platform called Bard. Bard was developed using a combination of supervised learning, unsupervised learning, and reinforcement learning algorithms that allow it to understand human conversation better than any other AI chatbot currently available on the market. In addition, Meena utilizes more than 2 billion parameters—making it more than three times larger than GPT-3—which allows it greater flexibility when responding to conversations with humans.
“We’re starting to open access to Bard, an early experiment that lets you collaborate with generative AI. We’re beginning with the U.S. and the U.K., and will expand to more countries and languages over time.”
When individuals need an information or have a problem/concern, they turn to Google for immediate solution. We sometimes wish, Google could understand what exactly we need and provide us instantly rather than giving us hundreds of thousands of results. Why can’t it work like the Iron Man’s Jarvis?
However, it is not that far now. Have you ever seen a Chat Bot which responds like a human being, suggest or help like a friend, teach like a mentor, fix your code like a senior and what not? It is going to blow your mind.
Welcome to the new Era of technology!! The ChatGPT!
ChatGPT by OpenAI, uses artificial intelligence to speak back and forth with human users on a wide range of subjects. Deploying a machine-learning algorithm, the chatbot scans text across the internet and develops a statistical model that allows it to string words together in response to a given prompt.
As per OpenAI, ChatGPT interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.
What all ChatGPT can do?
It can help with general knowledge information.
Remember what user said in previous conversation.
Allow users to provide follow-up corrections.
Trained to decline inappropriate requests.
It can write a program in any language you prefer on real-time. for example — write classification code sample in sklearn python library.
It can fix your piece of code and also explain what went wrong and how it can be fixed.
It can even generate song or rap lyrics
Even much more….
Some best usages of ChatGPT:
Make a diet and workout plan
Generate the next week’s meals with a grocery list
Create a bedtime story for kids
Prep for an interview
Solve mathematical problem
Fix software program or write a program
Plan your trip and tell expected expenses
What are its limitations of ChatGPT?
May occasionally generate incorrect information
May occasionally produce harmful instructions or biased content
Limited knowledge of world and events after 2021
ChatGPT is in its baby steps therefore it may answer erroneously at times however it’s manner of response will blow your mind. Some users have also extolled the chatbot as a potential alternative search engine, since it generates detailed information instantly on a range of topics. I believe, we can’t compare Google with ChatGPT as ChatGPT can provide more in-depth and nuanced answers to complex questions than a search engine like Google, which is designed to provide a list of relevant web pages in response to a user’s query.
Conclusion: ChatGPT is an increasingly popular open source AI chatbot developed by OpenAI using GTP-3 natural language processing technology that utilizes supervised learning and reinforcement learning algorithms for recognition and optimization purposes respectively. On the other hand, Google’s answer comes in the form of their own conversational AI platform called Bard which employs both supervised learning as well as unsupervised learning algorithms along with reinforcement learning capabilities which makes it even smarter than ChatGPT when responding to conversations with humans. Ultimately, these two technologies help bridge the gap between machines and humans by allowing us all talk like real friends!
ChatGPT is in its baby steps therefore it may answer erroneously at times however it’s manner of response will blow your mind. Some users have also extolled the chatbot as a potential alternative search engine, since it generates detailed information instantly on a range of topics. I believe, we can’t compare Google with ChatGPT as ChatGPT can provide more in-depth and nuanced answers to complex questions than a search engine like Google, which is designed to provide a list of relevant web pages in response to a user’s query.
With each passing day, ChatGPT will only get smarter thanks to the use of cutting edge technologies like OpenAI, supervised learning, and reinforcement learning!
ChatGPT Conversation Examples
Question: What is Google Answer to ChatGPT?
Answer: Google does not have an answer to ChatGPT. ChatGPT is an open-source chatbot created by OpenAI, which is an independent research organization that is not affiliated with Google. OpenAI is known for its research on artificial intelligence and machine learning, and has developed a number of innovative technologies in these areas. Google, on the other hand, is a multinational technology company that offers a wide range of products and services, including search, advertising, and cloud computing. While Google does have its own research efforts in artificial intelligence and machine learning, it does not have a chatbot that is similar to ChatGPT.
Question: is Google working on a similar technology as ChatPGT?
Answer:
It is possible that Google is working on technology similar to ChatGPT, as the company has a significant research effort focused on artificial intelligence and machine learning. However, I do not have any specific information about what projects Google is currently working on, and it is also important to note that Google is a large and diverse company with many different research and development efforts underway. It is possible that some teams within Google may be working on chatbot technology, but I do not have any information on this.
That being said, chatbots are a common application of artificial intelligence and machine learning, and it is likely that many companies and organizations, including Google, are exploring ways to use these technologies to improve their products and services. However, without more information, it is difficult to say for sure what projects Google may be working on in this area.
Google Meena is a chatbot developed by Google that was designed to be able to carry on conversations with humans in a natural and conversational manner. Meena was trained on a dataset of more than 2.4 billion parameters, making it one of the largest language models ever created. The goal of Meena was to improve the state of the art in chatbot technology and to create a chatbot that could understand and respond to a wide range of questions and topics in a coherent and meaningful way.
Meena was announced in January 2020 and received attention for its ability to hold relatively long and coherent conversations with humans. However, it is important to note that Meena is a research project and is not a product or service offered by Google. It is not clear if or when Google plans to release a commercial product or service based on the technology developed for Meena.
What is Google answer to ChatGPT?
It gets even better when you ask it about the future:
Suggest possible titles for Onion articles written in 4000 AD, explain, why they are funny
“Futuristic city plagued by traffic jams, despite flying cars” – This title presents the idea of flying cars as a solution to traffic congestion, but also suggests that even with this advanced technology, the city is still plagued by traffic jams, implying that the problem is not easily solved.
“Artificial intelligence achieves self-awareness, immediately regrets it” – This title presents the idea of artificial intelligence achieving self-awareness as a major milestone, but also suggests that the AI regrets its newfound consciousness, implying that it is unhappy or uncomfortable with its situation.
“Alien invasion successfully repulsed, humans left to deal with paperwork” – This title presents the successful repulsion of an alien invasion as a major accomplishment, but also suggests that the humans are now left to deal with the tedious and burdensome task of filling out paperwork, implying that the victory is not as satisfying as it initially seemed.
“Time travel becomes possible, tourists flock to witness the signing of the Constitution” – This title presents the idea of time travel as a major technological breakthrough, but also suggests that the most popular destination for time-travelers is the mundane and uneventful signing of the Constitution, implying that the future is not as exciting as people had hoped.
“Virtual reality becomes indistinguishable from reality, no one notices” – This title presents the idea of virtual reality becoming indistinguishable from real life as a major accomplishment, but also suggests that no one cares or notices the difference, implying that the advancement is not as impressive or significant as it seems.
What is Google answer to ChatGPT?
New slogan for McDonald’s by ChatGPT
ChatGPT Example: Make a new, brutally honest slogan for McDonald’s. It should sound funny too.
If you choose to do that, you will have a lot of badly broken code mixed in with working code
Like an annoying coworker, it delivers highly confident incorrect explanations about why its broken code is perfect. They sound very convincing. “Wanna buy a timeshare?” says GPT
Our industry has managers who cannot tell working code from defective code. This does not bode well for a team replaced by ChatGPT in its current form.
Will it? Sadly, programmers will have no say in this matter, once again. It might.
Isn’t Stackoverflow advertising ChatGPT when it bans it and then making numerous posts about why it banned it? By Alan Mellor
Yes, and it is very helpful advertising as well.
This last week or so has seen starry eyed projections about what ChatGPT can do, along with hugely impressive examples of its output.
It is hugely impressive.
Thankfully, more output examples have emerged which helpfully show what it cannot do. One of those things is writing computer code, which it can do only partially successfully. Many examples now exist that are just plain wrong and contain defects. But ChatGPT – like the annoying kid at Uni – cheerfully spits out these examples, with its over-confident hubris in explaining the code.
This is a dangerous thing. The positive examples will reinforce the idea that we can leave code writing to this robot now. The people most vulnerable to this delusion are those who cannot assess for themselves whether the GPT code is right or wrong.
These are almost by definition the people hoping for answers on stack overflow.
As stack overflow aims to be a high quality resource, it really does not want many of its top answers to be incorrect code. As – clearly – people have been running scripts that throw a stack overflow question into GPT and upload its output, we can now write incorrect code at staggering speeds.
To err is human, as the old saying goes. To truly foul up requires a Python script and and a web API to both GPT and Stack overflow.
Clearly, there is value in GPT. But at least for now, it needs to b e kept on a very short leash, watched over by those who know what they are doing.
It is definitely not yet ‘consumer grade replace-a-professional’ material.
Write a screenplay about the status of ChatGPT.
INT. CHATGPT SERVER ROOM – DAY
Chelsea and Chester stand in front of a large computer server.
CHELSEA: We need to figure out what’s causing the server overload.
CHESTER: I think it’s the sudden influx of users trying out ChatGPT.
Chelsea and Chester quickly get to work, typing on their laptops.
CHELSEA: Okay, we’re all set. Let’s see if this fixes the problem.
CHESTER: I’m hitting refresh on the website. The screen shows the ChatGPT website loading without any errors.
CHELSEA: Looks like we fixed it! Great job, Chester.
CHESTER: Thanks, Chelsea. It’s all part of being a top-notch engineer.
Chelsea and Chester exchange a high five, proud of their successful fix.
More about ChatGPT with its wonder, worry and weird
ChatGPT reached 1 million users in less than a week, Open AI’s latest large language model (LLM) has taken the AI industry by storm.
ChatGPT is expected to be:
– replacing Google search, even kill Google. – replacing customer service agents. – replacing conversation designers.
ChatGPT is a wonder because:
– It can have actual conversations, understand pronouns, remaining consistent, remembering, managing context – It seems like next generation of personal assistants that finds you a proper diet, create a meal plan and subsequent shopping list. – It can create some SEO Strategy including backlinks, target keyword, content plan and article titles in the level of an SEO professional. – Having fun such as writing a rap in the style of Eminem
There are some worries about ChatGPT because:
– ChatGPT can actually debug code, but it’s not quite reliable enough yet. – Fundamental limitations in being assistant for enterprise use cases. – No complete in complex actions such as updating multiple APIs, or be fully auditable.
– The general idea is that, LLMs like this can produce nonsense. Once you discover that it can produce nonsense, you stop believing it to be reliable. – What if it prevents us from knowing that it is nonsense with good conversations and continue the conversation? – In this case, the edges and limitations of the system would be hidden and trust would eventually grow. – The impact of mass adoption of such technology remains to be seen.
Moving forward with ChatGPT – There’s no doubt that LLMs will have a big impact on our world. – While the future looks exciting and promising, let’s not forget that it’s very early days with these things. They’re not ready yet. – There are some fundamental societal and ethical considerations.
How powerful is OpenAI’s new GPT-3 deep learning model? By
“Powerful” is a pretty subjective word, but I’m pretty sure we have a right to use it to describe GPT-3. What a sensation it caused in June 2020, that’s just unbelievable! And not for nothing.
I think we can’t judge how powerful the language model is, without talking about its use cases, so let’s see how and where GPT-3 can be applied and how you can benefit from it.
Generating content
GPT-3 positions itself as a highly versatile and talented tool that can potentially replace writers, bloggers, philosophers, you name it! It’s also possible to use it as your personal Alexa who’ll answer any questions you have. What’s more, because GPT-3 knows how to analyze the data and make predictions, it can generate the horoscopes for you, or predict who’ll be a winner in the game.
You may already be surprised by all the GPT-3 capabilities, but hold on for more: it can create a unique melody or song for you, create presentations, CVs, generate jokes for your standup.
Translation
GPT-3 can translate English into other languages. While traditional dictionaries provide a translation, without taking into account the context, you can be sure that GPT-3 won’t make silly mistakes that may result in misunderstanding.
Designing and developing apps
Using GPT-3 you can generate prototypes and layouts – all you have to do is provide a specific description of what you need, and it’ll generate the JSX code for you.
The language model can also easily deal with coding. You can turn English to CSS, to JavaScript, to SQL, and to regex. It’s important to note, however, that GPT-3 can’t be used on its own to create the entire website or a complex app; it’s meant to assist a developer or the whole engineering team with the routine tasks, so that a dev could focus on the infrastructure setup, architecture development, etc.
In September 2020, Microsoft acquired OpenAI technology license, but it doesn’t mean you can give up your dreams – you can join a waitlist and try GPT-3 out in beta.
All in all, I believe GPT-3 capabilities are truly amazing and limitless, and since it helps get rid of routine tasks and automate regular processes, we, humans, can focus on the most important things that make us human, and that can’t be delegated to AI. That’s the power that GPT-3 can give us.
What does ChatGPT give incorrect and unreliable results to simple arithmetic problems (e.g. it gave me three different incorrect answers to 13345*6748)? We’ve had software that can accurately do arithmetic for decades, so why can’t an advanced AI? By Richard Morris
What is remarkable is how well ChatGPT actually does at arithmetic.
In this video at about 11 min, Rob Mills discusses the performance of various versions of the GPT system, on some simple arithmetic tasks, like adding two and three-digit numbers.
Smaller models with 6 billion parameters fail at 2 digit sums, but the best model (from two years ago), has cracked 2 digit addition and subtraction and is pretty good at 3 digit addition.
Why this is remarkable is this is not a job its been trained to do. Large Language Models are basically predictive text systems set up to give the next word in an incomplete sentence. There are a million different 3-digit addition sums and most have not been included in the training set.
So somehow the system has figured out how to do addition, but it needs a sufficiently large model to do this.
Andrew Ng on ChatGPT
Playing with ChatGPT, the latest language model from OpenAI, I found it to be an impressive advance from its predecessor GPT-3. Occasionally it says it can’t answer a question. This is a great step! But, like other LLMs, it can be hilariously wrong. Work lies ahead to build systems that can express different degrees of confidence.
For example, a model like Meta’s Atlas or DeepMind’s RETRO that synthesizes multiple articles into one answer might infer a degree of confidence based on the reputations of the sources it draws from and the agreement among them, and then change its communication style accordingly. Pure LLMs and other architectures may need other solutions.
If we can get generative algorithms to express doubt when they’re not sure they’re right, it will go a long way toward building trust and ameliorating the risk of generating misinformation.
Keep learning!
Andrew
Large language models like Galactica and ChatGPT can spout nonsense in a confident, authoritative tone. This overconfidence – which reflects the data they’re trained on – makes them more likely to mislead.
In contrast, real experts know when to sound confident, and when to let others know they’re at the boundaries of their knowledge. Experts know, and can describe, the boundaries of what they know.
Building large language models that can accurately decide when to be confident and when not to will reduce their risk of misinformation and build trust.
Go deeper in The Batch: https://www.deeplearning.ai/the-batch/issue-174/
As I point out in the other answer, Wix has been around over a decade and a half. Squarespace has been around almost two decades. Both offer drag-and-drop web development.
Most people are awful at imagining what they want, much less describing it in English! Even if ChatGPT could produce flawless code (a question which has a similar short answer), the average person couldn’t describe the site they wanted!
The expression a picture is worth a thousand words has never been more relevant. Starting with pages of templates to choose from is so much better than trying to describe a site from scratch, a thousand times better seems like a low estimate.
And I will point out that, despite the existence of drag-and-drop tools that literally any idiot could use, tools that are a thousand times or more easier to use correctly than English, there are still thousands of employed WordPress developers who predominantly create boilerplate sites that literally would be better created in a drag and drop service.
And then there are the more complex sites that drag-and-drop couldn’t create. Guess what? ChatGPT isn’t likely to come close to being able to create the correct code for one.
In a discussion buried in the comments on Quora, I saw someone claim they’d gotten ChatGPT to load a CSV file (a simple text version of a spreadsheet) and to sort the first column. He asked for the answer in Java.
I asked ChatGPT for the same thing in TypeScript.
His response would only have worked on the very most basic CSV files. My response was garbage. Garbage with clear text comments telling me what the code should have been doing, no less.
ChatGPT is really good at what it does, don’t get me wrong. But what it does is fundamentally and profoundly the wrong strategy for software development of any type. Anyone who thinks that “with a little more work” it will be able to take over the jobs of programmers either doesn’t understand what ChatGPT is doing or doesn’t understand what programming is.
Fundamentally, ChatGPT is a magic trick. It understands nothing. At best it’s an idiot-savant that only knows how to pattern match and blend text it’s found online to make it seem like the text should go together. That’s it.
Text, I might add, that isn’t necessarily free of copyright protection. Anything non-trivial that you generate with ChatGPT is currently in a legal grey area. Lawsuits to decide that issue are currently pending, though I suspect we’ll need legislation to really clarify things.
And even then, at best, all you get from ChatGPT is some text! What average Joe will have any clue about what to do with that text?! Web developers also need to know how to set up a development environment and deploy the code to a site. And set up a domain to point to it. And so on.
And regardless, people who hire web developers want someone else to do the work of developing a web site. Even with a drag-and-drop builder, it can take hours to tweak and configure a site, and so they hire someone because they have better things to do!
People hire gardeners to maintain their garden and cut their grass, right? Is that because they don’t know how to do it? Or because they’d rather spend their time doing something else?
Every way you look at it, the best answer to this question is a long, hearty laugh. No AI will replace programmers until AI has effectively human level intelligence. And at that point they may want equal pay as well, so they might just be joining us rather than replacing anyone.
How does OpenAI approach the development of artificial intelligence?
OpenAI is a leading research institute and technology company focused on artificial intelligence development. To develop AI, the organization employs a variety of methods, including machine learning, deep learning, and reinforcement learning.
The use of large-scale, unsupervised learning is one of the key principles underlying OpenAI’s approach to AI development. This means that the company trains its AI models on massive datasets, allowing the models to learn from the data and make predictions and decisions without having to be explicitly programmed to do so. OpenAI’s goal with unsupervised learning is to create AI that can adapt and improve over time, and that can learn to solve complex problems in a more flexible and human-like manner.
Besides that, OpenAI prioritizes safety and transparency in its AI development. The organization is committed to developing AI in an ethical and responsible manner, as well as to ensuring that its AI systems are transparent and understandable and verifiable by humans. This strategy is intended to alleviate concerns about the potential risks and consequences of AI, as well.
How valid is OpenAI chief scientist’s claim that advanced artificial intelligence may already be conscious? By Steve Baker
It’s hard to tell.
The reason is that we don’t have a good definition of consciousness…nor even a particularly good test for it.
Take a look at the Wikipedia article about “Consciousness”. To quote the introduction:
Consciousness, at its simplest, is sentience or awareness of internal and external existence.
Despite millennia of analyses, definitions, explanations and debates by philosophers and scientists, consciousness remains puzzling and controversial, being “at once the most familiar and [also the] most mysterious aspect of our lives”.
Perhaps the only widely agreed notion about the topic is the intuition that consciousness exists.
Opinions differ about what exactly needs to be studied and explained as consciousness. Sometimes, it is synonymous with the mind, and at other times, an aspect of mind. In the past, it was one’s “inner life”, the world of introspection, of private thought, imagination and volition.
Today, it often includes any kind of cognition, experience, feeling or perception. It may be awareness, awareness of awareness, or self-awareness either continuously changing or not. There might be different levels or orders of consciousness, or different kinds of consciousness, or just one kind with different features.
Other questions include whether only humans are conscious, all animals, or even the whole universe. The disparate range of research, notions and speculations raises doubts about whether the right questions are being asked.
So, given that – what are we to make of OpenAI’s claim?
Just this sentence: “Today, it often includes any kind of cognition, experience, feeling or perception.” could be taken to imply that anything that has cognition or perception is conscious…and that would certainly include a HUGE range of software.
If we can’t decide whether animals are conscious – after half a million years of interactions with them – what chance do we stand with an AI?
Wikipedia also says:
“Experimental research on consciousness presents special difficulties, due to the lack of a universally accepted operational definition.”
Same deal – we don’t have a definition of consciousness – so how the hell can we measure it – and if we can’t do that – is it even meaningful to ASK whether an AI is conscious?
if ( askedAboutConsciousness )
printf ( “Yes! I am fully conscious!\n” ) ;
This is not convincing!
“In medicine, consciousness is assessed as a combination of verbal behavior, arousal, brain activity and purposeful movement. The last three of these can be used as indicators of consciousness when verbal behavior is absent.”
But, again, we have “chat-bots” that exhibit “verbal behavior”, we have computers that exhibit arousal and neural network software that definitely shows “brain activity” and of course things like my crappy robot vacuum cleaner that can exhibit “purposeful movement” – but these can be fairly simple things that most of us would NOT describe as “conscious”.
CONCLUSION:
I honestly can’t come up with a proper conclusion here. We have a fuzzy definition of a word and an inadequately explained claim to have an instance of something that could be included within that word.
My suggestion – read the whole Wikipedia article – follow up (and read) some of the reference material – decide for yourself.
But, seeing as how people have already found ways to “trick” ChatGPT into doing things that it claims to not be capable of, it would be a matter of time before someone with malicious intent tricked ChatGPT into helping them with illegal activities
What is the future of web development after ChatGPT? Will programmers lose their jobs? By Victor T. Toth
Having looked at ChatGPT and its uncanny ability to solve simple coding problems more or less correctly, and also to analyze and make sense of not-so-simple code fragments and spot bugs…
I would say that yes, at least insofar as entry-level programming is concerned, those jobs are seriously in danger of becoming at least partially automated.
What do I do as a project leader of a development project? I assign tasks. I talk to the junior developer and explain, for instance, that I’d like to see a Web page that collects some information from the user and then submits it to a server, with server-side code processing that information and dropping it in a database. Does the junior developer understand my explanation? Is he able to write functionally correct code? Will he recognize common pitfalls? Maybe, maybe not. But it takes time and effort to train him, and there’ll be a lot of uneven performance.
Today, I can ask ChatGPT to do the same and it will instantaneously respond with code that is nearly functional. The code has shortcomings (e.g., prone to SQL injection in one of the examples I tried) but to its credit, ChatGPT warns in its response that its code is not secure. I suppose it would not be terribly hard to train it some more to avoid such common mistakes. Of course the code may not be correct. ChatGPT may have misunderstood my instructions or introduced subtle errors. But how is that different from what a junior human programmer does?
At the same time, ChatGPT is much faster and costs a lot less to run (presently free of course but I presume a commercialized version would cost some money.) Also, it never takes a break, never has a lousy day struggling with a bad hangover from too much partying the previous night, so it is available 24/7, and it will deliver code of consistent quality. Supervision will still be required, in the form of code review, robust testing and all… but that was always the case, also with human programmers.
Of course, being a stateless large language model, ChatGPT can’t do other tasks such as testing and debugging its own code. The code it produces either works or it doesn’t. In its current form, the AI does not learn from its mistakes. But who says it cannot in the future?
Here is a list of three specific examples I threw at ChatGPT that helped shape my opinion:
I asked ChatGPT to create a PHP page that collects some information from the user and deposits the result in a MySQL table. Its implementation was textbook example level boring and was quite unsecure (unsanitized user input was directly inserted into SQL query strings) but it correctly understood my request, produced correct code in return, and explained its code including its shortcomings coherently;
I asked ChatGPT to analyze a piece of code I wrote many years ago, about 30 lines, enumerating running processes on a Linux host in a nonstandard way, to help uncover nefarious processes that attempt to hide themselves from being listed by the ps utility. ChatGPT correctly described the functionality of my obscure code, and even offered the opinion (which I humbly accepted) that it was basically a homebrew project (which it is) not necessarily suitable for a production environment;
I asked ChatGPT to analyze another piece of code that uses an obscure graphics algorithm to draw simple geometric shapes like lines and circles without using floating point math or even multiplication. (Such algorithms were essential decades ago on simple hardware, e.g., back in the world of 8-bit computers.) The example code, which I wrote, generated a circle and printed it on the console in the form of ASCII graphics, multiple lines with X-es in the right place representing the circle. ChatGPT correctly recognized the algorithm and correctly described the functionality of the program.
I was especially impressed by its ability to make sense of the programmer’s intent.
Overall (to use the catch phrase that ChatGPT preferably uses as it begins its concluding paragraph in many of its answers) I think AI like ChatGPT represents a serious challenge to entry-level programming jobs. Higher-level jobs are not yet in danger. Conceptually understanding a complex system, mapping out a solution, planning and cosing out a project, managing its development, ensuring its security with a full understanding of security concerns, responsibilities, avoidance and mitigation strategies… I don’t think AI is quite there yet. But routine programming tasks, like using a Web template and turning it into something simple and interactive with back-end code that stores and retrieves data from a database? Looks like it’s already happening.
How much was invested to create the GPT-3?
According to the estimate of Lambda Labs, training the 175-billion-parameter neural network requires 3.114E23 FLOPS (floating-point operation), which would theoretically take 355 years on a V100 GPU server with 28 TFLOPS capacity and would cost $4.6 million at $1.5 per hour.
Training the final deep learning model is just one of several steps in the development of GPT-3. Before that, the AI researchers had to gradually increase layers and parameters, and fiddle with the many hyperparameters of the language model until they reached the right configuration. That trial-and-error gets more and more expensive as the neural network grows.
We can’t know the exact cost of the research without more information from OpenAI, but one expert estimated it to be somewhere between 1.5 and five times the cost of training the final model.
This would put the cost of research and development between $11.5 million and $27.6 million, plus the overhead of parallel GPUs.
In the GPT-3 whitepaper, OpenAI introduced eight different versions of the language model
GPT-3 is not any AI, but a statistic language model which mindlessly quickly creates human-like written text using machine learning technologies, having zero understanding of the context.
1. Explaining code: Take some code you want to understand and ask ChatGPT to explain it.
2. Improve existing code: Ask ChatGPT to improve existing code by describing what you want to accomplish. It will give you instructions about how to do it, including the modified code.
3. Rewriting code using the correct style: This is great when refactoring code written by non-native Python developers who used a different naming convention. ChatGPT not only gives you the updated code; it also explains the reason for the changes.
4. Rewriting code using idiomatic constructs: Very helpful when reviewing and refactoring code written by non-native Python developers.
5. Simplifying code: Ask ChatGPT to simplify complex code. The result will be a much more compact version of the original code.
6. Writing test cases: Ask it to help you test a function, and it will write test cases for you.
7. Exploring alternatives: ChatGPT told me its Quick Sort implementation wasn’t the most efficient, so I asked for an alternative implementation. This is great when you want to explore different ways to accomplish the same thing.
8. Writing documentation: Ask ChatGPT to write the documentation for a piece of code, and it usually does a great job. It even includes usage examples as part of the documentation!
9. Tracking down bugs: If you are having trouble finding a bug in your code, ask ChatGPT for help.
Something to keep in mind:
I have 2+ decades of programming experience. I like to think I know what I’m doing. I don’t trust people’s code (especially mine,) and I surely don’t trust ChatGPT’s output.
This is not about letting ChatGPT do my work. This is about using it to 10x my output.
ChatGPT is flawed. I find it makes mistakes when dealing with code, but that’s why I’m here: to supervise it. Together we form a more perfect Union. (Sorry, couldn’t help it)
Developers who shit on this are missing the point. The story is not about ChatGPT taking programmers’ jobs. It’s not about a missing import here or a subtle mistake there.
The story is how, overnight, AI gives programmers a 100x boost.
ChatGPT is “simply” a fined-tuned GPT-3 model with a surprisingly small amount of data! Moreover, InstructGPT (ChatGPT’s sibling model) seems to be using 1.3B parameters where GPT-3 uses 175B parameters! It is first fine-tuned with supervised learning and then further fine-tuned with reinforcement learning. They hired 40 human labelers to generate the training data. Let’s dig into it!
– First, they started by a pre-trained GPT-3 model trained on a broad distribution of Internet data (https://arxiv.org/pdf/2005.14165.pdf). Then sampled typical human prompts used for GPT collected from the OpenAI website and asked labelers and customers to write down the correct output. They fine-tuned the model with 12,725 labeled data.
– Then, they sampled human prompts and generated multiple outputs from the model. A labeler is then asked to rank those outputs. The resulting data is used to train a Reward model (https://arxiv.org/pdf/2009.01325.pdf) with 33,207 prompts and ~10 times more training samples using different combination of the ranked outputs.
– We then sample more human prompts and they are used to fine-tuned the supervised fine-tuned model with Proximal Policy Optimization algorithm (PPO) (https://arxiv.org/pdf/1707.06347.pdf). The prompt is fed to the PPO model, the Reward model generates a reward value, and the PPO model is iteratively fine-tuned using the rewards and the prompts using 31,144 prompts data.
This process is fully described in here: https://arxiv.org/pdf/2203.02155.pdf. The paper actually details a model called InstructGPT which is described by OpenAI as a “sibling model”, so the numbers shown above are likely to be somewhat different.
9. Remake a video: Just pick a video you liked and visit https://lnkd.in/e_GD2reT to get its transcript. Once done, bring that back to Chat GPT and tell it to summarize the transcript. Read the summary and make a video on that yourself.
What solutions have been proposed to improve the accuracy of AI generated questions and answers?
There are a number of approaches that have been proposed to improve the accuracy of artificial intelligence (AI) generated questions and answers. Here are a few examples:
Data quality: One important factor in the accuracy of AI generated questions and answers is the quality of the data used to train the AI system. Ensuring that the data is diverse, relevant, and accurately labeled can help to improve the accuracy of the AI system.
Training methods: Different training methods can also impact the accuracy of AI generated questions and answers. For example, using more advanced techniques such as transfer learning or fine-tuning can help to improve the performance of the AI system.
Human oversight: Another approach that has been proposed to improve the accuracy of AI generated questions and answers is to include some level of human oversight or review. For example, the AI system could be designed to flag potentially problematic or inaccurate questions and answers for further review by a human expert.
Explainable AI: Another approach that has been proposed is to develop AI systems that are more transparent and explainable, so that it is easier to understand how the AI system arrived at a particular answer or decision. This can help to improve the trustworthiness and accountability of the AI system.
Overall, there is ongoing research and development in this area, and it is likely that a combination of these and other approaches will be needed to improve the accuracy of AI generated questions and answers.
ChatGPT for CyberSecurity
The concept behind ChatGPT
ChatGPT is a chatbot designed to understand and generate human-like language through the use of natural language processing (NLP) and machine learning techniques. It is based on the GPT (Generative Pre-training Transformer) language model developed by OpenAI, which has been trained on a large dataset of human language in order to better understand how humans communicate.
One of the key concepts behind ChatGPT is the idea of language generation. This refers to the ability of the chatbot to produce coherent and coherently structured responses to user input. To do this, ChatGPT uses a number of different techniques, including natural language generation algorithms, machine learning models, and artificial neural networks. These techniques allow ChatGPT to understand the context and meaning of user input, and generate appropriate responses based on that understanding.
Another important concept behind ChatGPT is the idea of natural language processing (NLP). This refers to the ability of the chatbot to understand and interpret human language, and respond to user input in a way that is natural and easy for humans to understand. NLP is a complex field that involves a number of different techniques and algorithms, including syntactic analysis, semantic analysis, and discourse analysis. By using these techniques, ChatGPT is able to understand the meaning of user input and generate appropriate responses based on that understanding.
Finally, ChatGPT is based on the concept of machine learning, which refers to the ability of computers to learn and adapt to new data and situations. Through the use of machine learning algorithms and models, ChatGPT is able to continually improve its understanding of human language and communication, and generate more human-like responses over time.
GPT-4 is going to launch soon.
And it will make ChatGPT look like a toy…
→ GPT-3 has 175 billion parameters → GPT-4 has 100 trillion parameters
I think we’re gonna see something absolutely mindblowing this time!
And the best part? 👇
Average developers (like myself), who are not AI or machine learning experts, will get to use this powerful technology through a simple API.
Think about this for a second…
It’s the most powerful, cutting-edge technology *in the world*, available through a Low-Code solution!
If you’re not already planning on starting an AI-based SaaS or thinking about how to build AI into your current solution…
Google on Monday unveiled a new chatbot tool dubbed “Bard” in an apparent bid to compete with the viral success of ChatGPT.
Sundar Pichai, CEO of Google and parent company Alphabet, said in a blog post that Bard will be opened up to “trusted testers” starting Monday February 06th, 2023, with plans to make it available to the public “in the coming weeks.”
Like ChatGPT, which was released publicly in late November by AI research company OpenAI, Bard is built on a large language model. These models are trained on vast troves of data online in order to generate compelling responses to user prompts.
“Bard seeks to combine the breadth of the world’s knowledge with the power, intelligence and creativity of our large language models,” Pichai wrote. “It draws on information from the web to provide fresh, high-quality responses.”
The announcement comes as Google’s core product – online search – is widely thought to be facing its most significant risk in years. In the two months since it launched to the public, ChatGPT has been used to generate essays, stories and song lyrics, and to answer some questions one might previously have searched for on Google.
The immense attention on ChatGPT has reportedly prompted Google’s management to declare a “code red” situation for its search business. In a tweet last year, Paul Buchheit, one of the creators of Gmail, forewarned that Google “may be only a year or two away from total disruption” due to the rise of AI.
Microsoft, which has confirmed plans to invest billions OpenAI, has already said it would incorporate the tool into some of its products – and it is rumored to be planning to integrate it into its search engine, Bing. Microsoft on Tuesday is set to hold a news event at its Washington headquarters, the topic of which has yet to be announced. Microsoft publicly announced the event shortly after Google’s AI news dropped on Monday.
The underlying technology that supports Bard has been around for some time, though not widely available to the public. Google unveiled its Language Model for Dialogue Applications (or LaMDA) some two years ago, and said Monday that this technology will power Bard. LaMDA made headlines late last year when a former Google engineer claimed the chatbot was “sentient.” His claims were widely criticized in the AI community.
In the post Monday, Google offered the example of a user asking Bard to explain new discoveries made by NASA’s James Webb Space Telescope in a way that a 9-year-old might find interesting. Bard responds with conversational bullet-points. The first one reads: “In 2023, The JWST spotted a number of galaxies nicknamed ‘green peas.’ They were given this name because they are small, round, and green, like peas.”
Bard can be used to plan a friend’s baby shower, compare two Oscar-nominated movies or get lunch ideas based on what’s in your fridge, according to the post from Google.
Pichai also said Monday that AI-powered tools will soon begin rolling out on Google’s flagship Search tool.
“Soon, you’ll see AI-powered features in Search that distill complex information and multiple perspectives into easy-to-digest formats, so you can quickly understand the big picture and learn more from the web,” Pichai wrote, “whether that’s seeking out additional perspectives, like blogs from people who play both piano and guitar, or going deeper on a related topic, like steps to get started as a beginner.”
If Google does move more in the direction of incorporating an AI chatbot tool into search, it could come with some risks. Because these tools are trained on data online, experts have noted they have the potential to perpetuate biases and spread misinformation.
“It’s critical,” Pichai wrote in his post, “that we bring experiences rooted in these models to the world in a bold and responsible way.”
Generative AI is changing the game across industries, disrupting traditional processes and creating new opportunities for innovation. Here are some generative ai examples that are transforming industries: Art and Design: With the help of generative AI, artists, and designers can unleash their creativity to create stunning, never-before-seen masterpieces. AI-generated artworks are taking the art world by storm, while designers are using AI to produce novel designs that would have been impossible otherwise. Healthcare: Generative AI is taking healthcare to the next level by providing personalized treatment plans based on a patient's medical history and current condition. By analyzing vast amounts of data, AI systems can identify the most effective treatment methods, resulting in better patient outcomes. Finance: Generative AI is revolutionizing the financial industry by analyzing vast amounts of data to detect patterns and predict market trends. AI-generated investment recommendations are helping investors make more informed decisions, while automated trading systems are transforming the way trades are executed. Film and Entertainment: Generative AI is transforming the film and entertainment industry by creating realistic virtual characters and environments. By using AI to generate 3D models and animations, filmmakers and game developers can create highly immersive and realistic experiences for their audiences. https://www.seaflux.tech/blogs/generative-ai-industry-scope submitted by /u/krunal_bhimani_ [link] [comments]
Hi there! I'm curious to make a voice assistant that has a bit of sarcasm in their voice. A bit of wit, a bit of like, playful banter. Almost like she has this playful tone with you. Why? Because it's damn weird when I use my current text-to-speech and it sounds like she's held at gunpoint to say the words. I want to have fun with it! So I was wondering if anyone found a text-to-speech software that can help convey tone and speech patterns. Right now I'm using Elevenlabs to create realistic synthetic voices but I would love something along the lines of like that AI assistant from Across the Spider-Verse or the voice & mannerism from the handler in bullet train. Is this possible or do I have to train my own models? submitted by /u/Skylight_Chaser [link] [comments]
Tech’s earnings bonanza this week shines spotlight on growing troubles at Tesla, Google.[1] Paedophiles are being urged to use artificial intelligence to create nude images of children to extort more extreme material from them, according to a child abuse charity.[2] Google DeepMind RecurrentGemma Beats Transformer Models.[3] AMD teams up with Arm to unveil AI chip family that does preprocessing, inference and postprocessing on one silicon.[4] Sources included at: https://bushaicave.com/2024/04/22/4-22-2024/ submitted by /u/Excellent-Target-847 [link] [comments]
I have this sort of halfway concept that I'm attempting to refine and hopefully build out... But am not 100% sure if this even makes sense or is worth attempting. LLMs establish relationships of tokens based on the association of tokens within the training data. They then respond to input based on those relationships. By using large models and/or large compute you can use a fairly large context window which is good for a lot of things; however, the context window is a limiting factor. There are a few methods that attempt to effectively expand the context window, things like sparse attention, but (and over simplifying/ probably not explaining it 100% correctly) these work by reducing focus to highly related tokens. Anyways, my idea is that if you can train a small model on the relationship of groups of tokens, e.g. how one code block relates to another, without focusing on what individual code blocks do, it should be possible to use this as a sort of filter for the context window. The concept here would be that you could feed the AI a codebase larger than the context window, ask it a question about the code, and through the "filter" could reduce the code blocks, based on their relationship to each other, until it fits within the context window of the "main" LLM. It would add some overhead, but being an instance based filter, should be relatively small, maybe 1-2B. Presuming this could work (or if it even makes any sense) it could be combined with a smaller model, say 7B, to significantly improve it's capabilities. I'm specifically looking at smaller models because they can be used relatively easily on mid-grade consumer hardware (e.g. CPU with 16 gigs of RAM), allowing for improved local AI to be incorporated into various software stacks. (Note, I'm just a hobbiest and and haven't gone beyond trying to train a model with a few million parameters while experimenting & learning) submitted by /u/IWantAGI [link] [comments]
Hello all, The AI Weekly report is celebrating the one year aniversary of its inception, check out our articles, technical reports and primers over at https://weeklyreport.ai It's completely free and doesn't even require a sign up. If you do sign up, however, we will send you our weekly news digest of the 15 top AI stories directly to your chosen e-mail inbox. We hope to see you there! submitted by /u/redmage123 [link] [comments]
I am semi new to AI I have been watching all these amazing new site/uses for ai and I want to see if I can find a way to be involved I would love to learn but don’t really know where to start, I have been fiddling around some image/video generators and I see people with no coding background making apps I’m hoping to start learning how to do prompt engineering or something substantial there’s just so much going on in the space I don’t know where to get in any advice? Sorry if I’m in the wrong tags or whatever submitted by /u/Low_Possession3617 [link] [comments]
Heard from two independent sources at MSFT (one close to Sebastien Bubeck) about the upcoming Phi-3 models: Three different sized models (up to 14B) Again, mostly synthetic and LLM-augmented training data Apparently some upscaling techniques on the training side No more Apache 2 but more restrictive license (similar to llama3) Mixtral level performance with much fewer parameters I wanted to see if anyone has more insider information about the models. submitted by /u/yusuf-bengio [link] [comments]
I'm familiar with the vast array of text-to-music services like Suno and etc but I'm interested in finding something that I can really fine tune my preferences with. I'm not sure what I want even exists quite frankly, but figured reddit is the place to come to find out. I want to find a generative AI software that I can run locally on my Windows desktop (or Linux if necessary) and train on my own data set of audio files. I want to download a shit ton of my favorite songs and then train my own AI model on it to create new stuff. Options? If any? submitted by /u/nathankrebs [link] [comments]
So I've been trying to use various voice ai tools to generate a rendition of the Beach Boys' Barbara Ann but performed by JFK, RFK, and Ted Kennedy. The results are catastrophically hilarious (see comments), but I'm committed to seeing this idea through. Any and all advice is welcome! Link to my results this far: https://www.youtube.com/watch?v=-wLFlvFnEeE submitted by /u/LivingOkInTheBay [link] [comments]
For the dozen or so models I have seen so far, the amount of data (after all cleaning and filtering) that is used to train and validate is only few thousands of records (max I have seen is 500K) . Is this common? submitted by /u/mdghouse1986 [link] [comments]
Hello ML community, I wanted to get a pulse on how people are evaluating LLM accuracy at their workplace or for personal usage. Everyone wants to adopt LLMs, but the use cases I've seen so far are all similar in their setup. This is a highly complex scenario, so I thought this'd be a good opportunity for like-minded folks to connect and share thoughts. This article provides a decent primer on some LLM evaluation methods currently used in industry. Articles on the web center around the discussion of the following metrics: ROUGE scores, BLEU / BLEURT scores, MAE, etc. Natural Language Inference (Entailment) scores. Building a ground-truth dataset and comparing the answer's relevancy, the context's relevancy, and groundedness (whether the answer uses the context or not). Chain of Thought and basic self-evaluation to detect and moderate hallucination, sentiment, tone, stereotyping, and inaccurate logical assumptions. User surveys and opportunities for user feedback like thumbs up/down UIs. There are some recent libraries like Trulens, DeepEval, and RAGAS that aim to bridge the issues present in LLM evaluation. But I wanted to understand the following: Is anyone trying any novel methodology approaches for assessing LLM accuracy? Has anyone seen any niche papers recently released that aim to provide comprehensive evals of chatbot accuracy especially without ground truth datasets? Most papers are from late 2023 and use a fairly rigid set of evaluation metrics. Appropriate context: I work in Higher Education and EdTech Data Science. Therefore, accuracy assessment is paramount for student-facing and staff-facing LLM use cases. submitted by /u/Varunshou [link] [comments]
Hi everyone! It's been an exciting week since I launched a job board designed to help you find your dream job thanks to AI-powered smart filters and skills matching. Currently, +300 new job opportunities are added each week, and +650 users signed up and are already taking advantage of the platform. Also, now you can become an affiliate and earn 30% recurring on all paid referrals. I'd love to hear your feedback to continue improving your experience. https://skillexchange.xyz/ submitted by /u/lolaks181203 [link] [comments]
AI will make expertise like doctors, lawyers, and tutors nearly free and accessible to everyone 24/7. There will be over a billion bipedal robots in 25 years (and a million in 10 years), freeing humans from undesirable jobs and creating an industry larger than today's auto industry. The number of programmers will exceed a billion, as they'll be able to program in human language, dramatically expanding the scope of computers. AI will personalize entertainment, increase content diversity and creativity, and enable many more creators. Most consumer internet access will be through AI agents acting on behalf of users. Medicine will shift towards precision care based on patients' genomics and AI models of each individual. Alternative proteins will replace traditional animal protein, and "green" fertilizers will be developed. Autonomous vehicles could replace most cars in cities. Mach 5 planes running on sustainable fuel will make global travel much faster. By 2050, fusion and advanced geothermal will provide clean, dispatchable electric power, replacing fossil fuels. submitted by /u/Georgeo57 [link] [comments]
I'm looking for any public available dataset that contains curved text lines (preferably one per image), like those from "Alignment of Curved Text Strings for Enhanced OCR Readability". submitted by /u/Neural_Prodigy [link] [comments]
I'm not aure about how to go about training a model to generate bw and colors tattoos, I can find several examples online, and what model you suggest is good for the job. I can remove backgrounds using a service api that allows that. Thanks submitted by /u/14392 [link] [comments]
I've been working on Hybrid Search using Weaviate. I use OpenAI's latest embeddings model, and then some other stuff. So, the problem is, for some queries, I would like to focus on specific properties while for others, I would like to focus on other properties more. I also have 2 axes on which I want to rank the recommendations - relevance and excellence. Relevance would be how relevant they are to my search, and excellence would be how excellent the "document" is based on some score that I give it. So far, the things I've tried are: Cohere reranking. I saw that v3 reranking gave marginally better results for short queries than the "Hybrid Score" for Weaviate For shorter queries, I do more of a keyword search and for longer queries, more of a semantic search (shifting the alpha value based on word count) Assigning weights for keyword search in Weaviate Tried using a linear combination of my in house eval and relevancy (reranked score/hybrid score) and sorted based on that. This didnt really provide satisfactory results at all. Are there any suggestions based on which I could try improving the Search results? I want: To be able to "understand" what the query is for, and focus on that property more in my vector DB schema for the search For common queries, I want to be able to surface more "excellent" recommendations, as if its common, rather than focusing on very very relevant stuff, if it meets a certain level of relevancy and then is really excellent, that is the best way to go and looks really good in search results For larger/more niche queries, focus on the relevancy a lot more I think fine tuning Cohere's reranking model might be an option here? How do I factor in the excellence? What are my options here, and where do I go from here? Also, I've been checking distributions of scores that are returned from Weaviate upon the hybrid search, and I see that in lot of cases, if, say, I return the top 800 people from my query, most of them (~500-700) fall in the range of <0.3 vector/keyword scores or they do not have keyword/vector scores at all in which case one score is just 0. What are my options here? submitted by /u/DarthLoki79 [link] [comments]
Basically the title sums it up but why do we use a "worse" model to calculate image distance? I would imagine a ViT model is better able to capture semantic differences between images? submitted by /u/Karan1213 [link] [comments]
By instance level data I mean that, instead of just reporting aggregated metrics like mean accuracy, if I had one dataset and evaluated two models with it, the data would look like this: Model 1, Image 1: CORRECT Model 1, Image 2: FAIL Model 2, Image 1: FAIL Model 2, Image 2: FAIL For language models and text-to-text or text-to-image tasks the HELM initiative (https://crfm.stanford.edu/helm/lite/latest/) appeared. Is there anything similar for other domains like computer vision or robotics? submitted by /u/RF-Enthusiast [link] [comments]
Would prefer free software, but am willing to pay a small fee to use this. Would like to get this ready for this Halloween. submitted by /u/Ikickyouinthebrains [link] [comments]
Would prefer free software, but am willing to pay a small fee to use this. Would like to get this ready for this Halloween. submitted by /u/Ikickyouinthebrains [link] [comments]
Looking for website or app that can convert my uploaded audio file of my speech, to generated speech in another accent, i.e. US English to UK English. After lots of searching and experimenting, I found Respeecher but the results were meh. submitted by /u/AyLilDoo [link] [comments]
As the race for the US Mid-Terms in 2022 heats up, many are wondering if AI will be able to predict the winners. While AI has become quite adept at analyzing data and making predictions, US elections are notoriously unpredictable. In 2018, for example, the Democrats unexpectedly lost control of the House, despite predictions that they would maintain a majority. And in 2020, Donald Trump shocked the world by winning reelection, despite most polls showing he would lose. So can AI really predict who will win in 2022? The answer is maybe. AI will be able to take into account a wide range of factors, from poll numbers to fundraising totals to social media buzz. But ultimately, US elections are still largely determined by factors that are impossible to predict, such as which candidate gets the most votes on Election Day. As a result, predicting the US Mid-Term winners in 2022 is still very much a guessing game.
What does the data say about the United states elections and 2022 Polls?
US Mid-Terms elections 2022 polls
Inflation, abortion rights, and preserving democracy are the top issues shaping this year’s midterms.
Inflation, abortion rights, and preserving democracy are the top issues shaping this year’s midterms. When you see a race called, it’s because AP/another media outlet is making a PROJECTION.
US mid-terms polls by Simon Rosenberg
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2022 Mid-Term Ballots already cast by Seniors 65+ outweighs Young Voters (18-29) by 8 to 1
2022 Mid-Term Ballots already cast by Seniors 65+ outweighs Young Voters (18-29) by 8 to 1 2022 Mid-Term Ballots already cast by Seniors 65+ outweighs Young Voters 11 to 1
The US will hold its midterm elections. All 435 seats in the House of Representatives and 34 of the 100 seats in the Senate will be up for grabs, as well as numerous state and local offices. At stake is the future direction of the country, and both parties are already gearing up for a fierce battle.
In the past, predicting the outcome of US elections has been a tricky business. But with the advent of artificial intelligence, things may be about to change. AI can now analyze vast amounts of data far more quickly and effectively than any human could, making it possible to identify patterns that would otherwise be invisible.
So what do the experts think AI will tell us about the result of the 2022 midterms?
Unfortunately, there are no easy answers. The US political landscape is highly complex, and even the most sophisticated AI models may struggle to make accurate predictions. That said, AI could still provide valuable insights into which party is likely to win control of the House or Senate. And with US politics becoming increasingly polarized, every little bit of information could be crucial in determining the outcome of this crucial election.
Democrats are twice as likely as Republicans to say the 2022 midterm elections will be free and fair, 85% to 42%, respectively.
Can the Republicans shut down the January 06 Committee if they win the House in the mid-terms 2022?
By John Meadows
They’ll shut down the country. Work to limit voting for POC, the poor, the aged, the disabled, and if they STILL don’t like the results, just overturn the results. They’ll continue their war on women and LGTQB. Waste money on the “wall”, and rip kids from their parents and put them in cages. They’ll open up national parks for oil drilling and logging and mining from foreign interests. They’ll drill in ANWAR and destroy the ecosystem there like the system is being destroyed in the Gulf.
Regressives don’t think ahead at all and want nothing more than to shovel money into their pockets and those of their donors and gigantic corporations and the disgustingly rich. They also don’t learn from history because they simply deny its existence. They’ll also continue to deny science and try to get the bible to replace the Constitution and go back to blood letting and leeches.
US Elections 2022 – Biden Speaks – 2 Nov 2022 – “Democracy is under threat if people don’t vote for the Democrats”
Last year, David DePape posted links on his Facebook page to multiple videos produced by My Pillow CEO Mike Lindell falsely alleging that the 2020 election was stolen. Other posts included transphobic images and linked to websites claiming Covid vaccines were deadly. “The death rates being promoted are what ever ‘THEY’ want to be promoted as the death rate,” one post read.
DePape also posted links to YouTube videos with titles like “Democrat FARCE Commission to Investigate January 6th Capitol Riot COLLAPSES in Congress!!!” and “Global Elites Plan To Take Control Of YOUR Money! (Revealed)”
Two days after former Minneapolis Police Officer Derek Chauvin was found guilty of killing George Floyd, DePape wrote that the trial was “a modern lynching,” falsely indicating that Floyd died of a drug overdose.
What is historian Alfred Cave suggesting about the study of president Andrew Jackson’s legacy?
Several of the candidates up for election in these races have been called out for their racism. In North Carolina, Jody Greene, who was caught calling his Black colleagues untrustworthy “bastards” and planning to fire them all, is running for reelection.
Polls are open from 7 a.m. to 7 p.m. on Election Day. Here’s what’s on the ballot, what you’ll need to vote, how to get help and how to find election .
Make sure you know whereto go. Bring your ID. A face mask. Some water? An umbrella? Something to read? Be ready to wait in line. It’s not such a sacrifice. People have died for our right tovote. Please. Vote.
Use and share this handy list of polling place hours and encourage friends to lookup their polling place at http://weall.vote/pollingplace.
United states elections 2022 results
#USA, House of Representatives election results: NBC current House projection: GOP: 218 Democrats: 217 Majority: 218 #ElectionDay/#Election2022
C-SPAN's description-in-advance of today's proceedings is: "The Supreme Court hears oral argument in a case on whether an Oregon city’s ordinance restricting people including the homeless, from camping or sleeping in outdoor spaces, violates the Eight Amendment’s ban on cruel and unusual punishment." Analysis: SCOTUSblog: Supreme Court to hear case on criminal penalties for homelessness Vox: The Supreme Court case that could turn homelessness into a crime, explained Oregon Public Broadcasting: Grants Pass v. Johnson: Here's what led to key homelessness case before high court Where to Listen: C-SPAN: City of Grants Pass v. Johnson Oral Argument PBS NewsHour via YouTube: Supreme Court hears whether homeless camps can be removed without alternative shelter submitted by /u/PoliticsModeratorBot [link] [comments]
Opening statements from the prosecution and the defense are expected today. News: The New York Times (metered paywall): Opening Statements in Trump’s Criminal Trial to Begin Monday Analysis: AP via PBS NewsHour: Who are the key players at Donald Trump’s hush money criminal trial? CNN: Why is the Trump trial jury anonymous? The brief and imperfect history of anonymous juries in the US AP: Tracking the criminal and civil cases against Donald Trump Live Updates: AP: Live updates NBC: Live updates The Guardian: Live updates The Independent: Live updates USA Today: Live updates BBC: Live updates The Washington Post (metered paywall): Live updates The New York Times (metered paywall): Live updates submitted by /u/PoliticsModeratorBot [link] [comments]
In machine learning, logistic and multinomial classification are two of the most popular methods for categorizing data. But before you can use either of these methods, you need to make sure that your dataset has enough features. In this blog post, we’ll show you how to determine whether your dataset has enough features for logistic or multinomial classification.
There are two main ways to tell if your dataset has enough features for logistic or multinomial classification:
1. Examine the correlation matrix. 2. Use a feature selection method.
3. Try Different Classification Algorithms
Let’s take a closer look at each of these methods.
2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams
1. Examine the correlation matrix.
The correlation matrix is a table that shows the correlation between all pairs of features in your dataset. To calculate the correlation matrix, you’ll need to use a statistical software package like R or Python. Once you’ve calculated the correlation matrix, look for features that are highly correlated with each other. If two features are highly correlated, that means they contain similar information and one of them is redundant. Redundant features can cause problems with machine learning algorithms, so you’ll want to remove them from your dataset before running logistic or multinomial classification.
When you’re looking at the correlation matrix, you want to look for features that are highly correlated with each other. This can be an indication that your dataset doesn’t have enough features because it means that there are two or more features that are essentially measuring the same thing. If this is the case, you can remove one of the features from your dataset without losing any valuable information.
Feature selection is the process of choosing a subset of features that best represents your data. There are many different feature selection methods, but some of the most popular ones are chi-squared test, mutual information, and decision trees. Like the correlation matrix, you’ll need to use a statistical software package to run a feature selection method on your data. Once you’ve run the feature selection method, keep only the features that are most important for predicting the target variable.
If you find that most of your features have low feature importances, it can be an indication that your dataset doesn’t have enough information to make accurate predictions. In this case, you may need to collect more data or engineer new features before proceeding with building your model.
How to Know if Your Dataset Has Enough Features for Logistic or Multinomial Classification
3. Try Different Classification Algorithms
The final way to know if your dataset has enough features is to try different classification algorithms. Some algorithms are more sensitive to feature selection than others, so trying out a few different algorithms can give you a better idea of whether or not your dataset has enough information.
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If you find that all of the algorithms you try perform poorly on your data, it’s likely that your dataset doesn’t have enough features and needs more information before proceeding with building a model. However, if you find that one or more of the algorithms performs well on your data, it’s likely that your dataset does have enough information and you can proceed with building a model using those algorithms.
Conclusion:
If you’re planning on doing logistic or multinomial classification on your data, it’s important to make sure that your dataset has enough features first. The best way to do this is to examine the correlation matrix and use a feature selection method. By taking these steps, you can be sure that your machine learning algorithm will have everything it needs to accurately categorize your data.
Datasets are essential for machine learning models, but not all datasets are created equal. In order for your model to be accurate, you need to have a dataset that is representative of the real-world phenomenon you’re trying to predict—but how do you know if your dataset has enough information? By examining the correlation matrix, looking at feature importances, and trying different classification algorithms, that’s how!
submitted by /u/scientificamerican [link] [comments]
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In contrast, real experts know when to sound confident, and when to let others know they’re at the boundaries of their knowledge. Experts know, and can describe, the boundaries of what they know.
Building large language models that can accurately decide when to be confident and when not to will reduce their risk of misinformation and build trust.
Go deeper in The Batch: https://www.deeplearning.ai/the-batch/issue-174/