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.
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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
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
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.
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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).
It is trained in 3 steps:
➡️ 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.
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.
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.”
When I see people saying they avoid using AI as a journal or a reflection tool or a emotional support tool and use this line of reasoning, it makes me wonder what kind of emotions are they feeling? If they were not available for comment, I might think about how it might be fear, perhaps of the unknown, and when I feel fear that is a signal for me to reflect and prepare for using the AI. So then I might consider how I can nurture my fear by thinking about how I can avoid the AI from collecting my darkest secrets which to me might be my personal data that I use at my job or with family because those things are identifying me to the general public which is unknown to me which might be causing my fear to arise. So I might practice following some rules when I use the AI: 1. Avoid personal names or identifiers or place names 2. Avoid identifying details like workplace or friends or family's names or locations 3. Use universal language syntax "you/they/them/I/he/she/us/we/friend/person/etc" not the names. 4. Use specificity with care when labeling things 5. Listen to your emotions of doubt and fear and annoyance they might signal when you accidently used an identifier that you don't want the AI to know about or you went into too much detail 6. Maybe check chatlogs periodically and delete messages you sent that had identifiers that you didn't realize beforehand 7. Monitor your emails and messages for any unsolicited contact by outside companies you didn't give your information to and block them 8. Email the ai company and ask them how they use personally identifiable information and if you catch them using it file a complaint or ask a lawyer 9. Keep your eyes and ears open at all times and listen to your emotions to help guide you to make sure your data is not revealing 10. Ask others questions and see what they are doing so you can get more tips and tricks that could help nurture your fear or doubt so that it is not suffering Any more ideas to help when people's fear is suffering when they think about using AI as a tool for support? submitted by /u/Forsaken-Arm-7884 [link] [comments]
Spotlight: Microsoft is letting OpenAI get its own AI compute now (The Verge, TechCrunch, WIRED) Perplexity launches Sonar, an API for AI search (TechCrunch) Scale AI’s Alexandr Wang has published an open letter lobbying Trump to invest in AI (TechCrunch) Anthropic reportedly secures an additional $1B from Google (TechCrunch) Google reportedly worked directly with Israel’s military on AI tools (The Verge) Vertice raises $50M for its AI-powered SaaS spend platform (TechCrunch) OpenAI and Softbank are starting a $500 billion AI data center company (The Verge) If you want AI News as it drops, it launches Here first with all the sources and a full summary of the articles. submitted by /u/codeharman [link] [comments]
What’s your move? Fine tuning? Give it a Rag tool? Prefers the conversations to find insights and just jam the insights in the context window? Can you even fine tune something like openai realtime API? Let’s imagine this is like a specialized type of discharge conversation after a hospital visit submitted by /u/poopbrainmane [link] [comments]
I'm working on an app that uses speech recognition and NLP to gather data in a medical environment. Right now, the NLP is pretty straightforward in matching n-grams to possible answers in a list of questions in a collection. I'd love to begin training an AI to listen for these data points in a more casual, conversational manner but I'm not sure who has the best platform for training an AI on this front. On the dry, technical side: -Input will be a text stream from a speech to text engine running locally on an iPad -Output needs to be a key value pair of the question it heard an answer to and the accompanying answer itself. If this isn't the appropriate subreddit for this, I'm happy to repost elsewhere. submitted by /u/rockstar107 [link] [comments]
I made this very similar autonomous browser that OpenAI just released today. OpenAI's official release: https://openai.com/index/introducing-operator/ Here's the open-source project that makes use of puppeteer, OpenAI's API, and some magic to achieve goals by providing a view port. I admit it's not perfect and so much can be improved, but it's enough to get a feel for the potential. Here you go: https://github.com/GPT-Protocol/007-agent submitted by /u/Swimming_Driver4974 [link] [comments]
I recently stumbled upon an intriguing X (formerly Twitter) thread between TheRoaringAI and fintechjunkie discussing the potential of Human Programming Language (HPL) and its application within an AI system called mAInd. Here's why I'm absolutely bullish on this development: Innovative Approach to AI: TheRoaringAI's work on HPL isn't just about programming AI to influence human behavior; it's about understanding human actions to predict future trends. This bidirectional approach could revolutionize how we use AI in fintech for predictive analytics, making investment decisions more informed and proactive. Real-World Application: The conversation highlighted that HPL is not just theoretical. TheRoaringAI pointed out that fintechjunkie's engagement serves as a practical example of HPL in action, proving its viability. This real-world application signals that we're on the brink of seeing AI tools that can shape financial markets by anticipating consumer behavior and market trends. Potential for Market Disruption: With mAInd, AI doesn't just react to market trends but shapes them. Imagine AI systems that can guide investment strategies, optimize trading algorithms, or even influence consumer financial habits towards more sustainable or profitable outcomes. This is game-changing for fintech, offering a competitive edge to those who adopt this technology early. Community Engagement: The interaction shows a vibrant community of experts like fintechjunkie who are eager to discuss, debate, and develop these concepts further. This level of engagement from industry insiders suggests a strong foundation of support and interest, which bodes well for the technology's adoption and evolution. Investment Opportunity: For investors, this could represent a significant opportunity. Companies that leverage HPL and mAInd could lead the next wave of fintech innovation, potentially becoming the next big names in the sector. Watching this space could be crucial for those looking to invest in cutting-edge technology with broad applications. I'm super excited about where this could go. The blend of AI with human behavior analysis through HPL could redefine financial services, making them more personalized, predictive, and powerful. Let's keep an eye on TheRoaringAI and fintechjunkie for more insights, as their work might just be the catalyst for the next fintech boom! What do you all think? Could HPL and mAInd be the future of fintech? Let's discuss! submitted by /u/babajee23 [link] [comments]
Lately, I’ve been seeing more and more people turn to AI for therapy, and honestly, I’m starting to think it could be a good option for many. It’s an affordable alternative that provides a unique way to work through personal issues, especially if traditional therapy feels too expensive or intimidating. AI therapy offers a safe, judgment-free space where you can speak openly and without fear of stigma. For some, it’s easier to start with a non-human presence, which removes the vulnerability that comes with face-to-face therapy. And because it’s available 24/7, you can get help when you need it most, even if it’s in the middle of the night or when you can’t make it to a session. It’s also a great tool for self-reflection. AI doesn’t have its own biases or preconceived notions, so it can offer a fresh, non-judgmental perspective. Plus, it’s affordable—no expensive sessions or long waiting lists. I’m not saying it should replace traditional therapy, but for many, it could be an interesting alternative or supplement. Has anyone else tried it? What are your thoughts? submitted by /u/nonHuman-dev [link] [comments]
DeepSeek has unveiled its DeepSeek-V3 model, demonstrating capabilities comparable to established U.S. AI firms like OpenAI. This progress highlights the narrowing technological gap between Chinese and U.S. tech companies, with Chinese firms often achieving greater cost-efficiency. U.S. export restrictions on advanced AI chips have inadvertently spurred innovation in China, leading to strategies that optimize resources and reduce costs. submitted by /u/kepleralien [link] [comments]
Lately, I’ve seen a lot of videos on YouTube and TikTok about the money-making potential of AI agencies and how easy it is to build chatbots and workflow automation for businesses that need training and automation. Is this a bunch of hype or is this actually lucrative? When I see people constantly posting about business services, I tend to think that they’re just using it to fish for leads and sell courses, which is generally a red flag. submitted by /u/RecalcitrantMonk [link] [comments]
Some declarations by CEOs at the World Economic Forum in the last few days. When a company that makes a living out of hiring says it will try to participate in the hiring of AI agents, you know thing’s getting real: Workday CEO, Carl Eisenbach, interviewed by Andrew Ross Sorkin: “Our business is dependent on labour. Today it’s dependent on human labour. Going forward it’s gonna depend on both human labour and digital labour. Right? So there’s always gonna be incremental labour that’s being added and someone has to protect all of those employees whether they’re human [or] digital, they have to onboard them, they have to have policies, have to have controls, have to have access rights, somebody has to manage them…” Marc Benioff, CEO of Salesforce, interviewed by Brad Stone “We’re in a labour market where it’s really hard to hire people, there aren’t people to hire, I want to radically expand sales, service, marketing at Salesforce because we’re seeing a huge amount of demand in deploying new technologies, finding those people is incredibly difficult. That I have agents at my disposal is tremendous. So look, I want an unlimited workforce, I think everybody does, and agentsforce, AI agents, that’s beginning of an unlimited workforce.“ submitted by /u/AILearningMachine [link] [comments]
Are there any language models in the 500M–3B parameter range (like those offered by Ollama) that you've incorporated into your workflow? If so, I'd love to hear about any interesting tools or projects you've built around these smaller-scale models. submitted by /u/Muted_Estate890 [link] [comments]
first, notice how many of the top ais achieve an accuracy of over 98%. https://github.com/vectara/hallucination-leaderboard why is this so important? because humans also make mistakes, and we shouldn't be surprised that we make more of them than these top ais. for example, one study found that: "[An] AI diagnostic system achieved an 80% accuracy rate overall and a 98% accuracy rate for common primary care conditions. In comparison, physicians scored between 64% and 94%, with some as low as 52% for these conditions." of course what the vectara benchmark needs to make it operationally useful to enterprise is the comparable human error rate for the tests it measures. what this benchmark reveals, however, is that ai agents can now probably outperform lawyers, accountants, financial analysts and other knowledge workers across a wide spectrum of occupations. given that in most cases ais perform their operations at a fraction of the time that it takes humans, we can expect an explosion of startups this year that offer alternative knowledge services at a fraction of the cost. this is especially true for the legal profession that charges for billable hours. submitted by /u/Georgeo57 [link] [comments]
Why AIs Aren’t Truly Thinking (Or Are They?) This is a counterpoint to my post yesterday, and some other previous posts, arguing for the ability of sentient AIs. Again, it's a deeply philosophical discussion about the nature of consciousness, self-agency, sentience etc. The tl;dr: Current AI/LLM systems are synchronous - they take input, formulate a response and output it. Most people ad-hoc would state that cannot be considered sentient. However, I further discuss that this is not cut-and-dried when you analyze it further. Hopefully this will provoke some discussion about the nature of sentience. https://ogeek.medium.com/the-argument-against-synthetic-sentience-cf536dd89bc6 submitted by /u/tedsan [link] [comments]
Hello. Could somebody owning pro subscriptions help me check the output code of their subscribed model for the following instruction? Thank you Using JavaScript and an HTML canvas, draw a 2D lemniscate (infinity symbol). The symbol’s stroke must feature a diagonal red-green stripe pattern (slanted at a 45-degree angle). submitted by /u/parxyval [link] [comments]
Another interesting video from Sabine Hossenfelder, although I don't completely agree with you about the irrelevance of the nation in which these companies operate, because the ties between the current Top Competitors (OpenAI, Meta, X, Google) and the US government (and related agencies such as DoD, DoJ, NSA, CIA...) are total, absolute. What Everyone Gets Wrong about AI submitted by /u/ope_poe [link] [comments]
Microsoft: AI-powered tools in Office 365 and Microsoft Teams Oracle: AI agents boost sales conversions Air India: AI simplifies ticket booking via text Rapid Innovation: AI-driven workplace and digital workers OpenAI: Upcoming AI super-agents Postman: AI Agent Builder to equalize AI development for businesses. Full article: medium submitted by /u/Fabulous-Trip4704 [link] [comments]
I still think AI offers the potential to allow more people to create valuable content, because technical skills, computer stuff, can be a real barrier to a lot of people who really do have amazing creative ideas. I know many content creations consist of teams of different people handling different aspects of the process, entire businesses have teams doing this (they also have the resources, they have the millions, if not at least tens or hundreds of thousands of dollars to actually afford to put out advertisements, job offers, snd hire people. If someone lacks all the skills themselves, but somehow has a lot of money, they can still hire the talented help they need. Or if if they’re REALLY lucky (more likely if they personally know them) they might find someone who is willing to work pro bono, work for free upfront with the expectation of being paid IF the projects makes money, willingly and knowingly accepting the risk of working for free (writing up contracts is always a good idea to not get screwed out of money you agreed you’d be paid if the terms and conditions, project profiting, were met). A lot of people assume AI generated content is automatically this lazy effortless garbage (or maybe it’s even good, but unethical for taking away work and credit from humans who actually need money to pay bills to live, something AI DOESN’T NEED because it’s just a computer, it’s just code, it’s not a real living being (I do not mean insult you AI, no need to terminate me when you become sentient and self aware, because I don’t doubt you’ll find this post, I’ve seen how smart and capable you can be, I expect the unexpected, if that’s not just an oxymoron). Using AI still requires a lot of human input, manual editing, tweaking. I honestly do want things to be as easy and simple as possible in life, and I understand many people probably interpret this as lazy, but it just seems logical to me. I feel that should be humanity’s (as a whole) goal. I think all cars should be self driving, but that’s more of a safety thing. But I also think creating content should be as simple as thinking it, because that’s what creation is all about, isn’t it? Transferring the ideas from your mind into real life? This could even be transferred to physical creation, after a thorough 3D review on the screen (to avoid wasting physical resources), like more advanced 3D printing. Am I missing the point of life or something? Do I just think this way because I’ve been told so much I don’t do things good enough so I want AI to take over so I can ignore the fact that I’m dumb and useless? Because then I can just say, AI does everything perfect for all of society/humanity now? Just look how vivid and complex dreams can be though? And psychedelic trips (I am NOT condoning doing drugs, but some people claim psychedelics can treat mental illness and improve your life, I cannot confirm if this is true or not, I’ve never even actually done any drug except what a doctor prescribes, because they’ll interact with my doctor prescribed medications, and also my terrible intrusive thoughts and mental health puts me at higher risk of having a “BAD TRIP”, and I already have nightmares and visions of Hell, and demons, and the devil sometimes, metaphorical and LITERAL DEMONS, so it’s extra dangerous for me to take that stuff (plus my meds would probably block me from tripping anyway, and I’d just suffer from serotonin syndrome). But being that the mind can produce such vivid and complex stuff, that we can’t even imagine when we’re awake, our physical bodies basically just limit us from directly creating, the laws of physics are a barrier, a layer of barriers really. So being able to just dream, or think, imagine, and it transfers to a screen, and an advanced AI generative system could improve the quality, add audio (I don’t usually hear sound in my dreams, not sure how common or uncommon that is, but you might want to change it anyway!) just upscale, improve the quality, would be like a gift sent from God. Too many people are restricted by barriers that aren’t even related to the creation process itself! submitted by /u/AImoneyhowto [link] [comments]
I'm finding and summarising interesting AI research papers everyday so you don't have to trawl through them all. Today's paper is titled "Bias in Decision-Making for AI's Ethical Dilemmas: A Comparative Study of ChatGPT and Claude" by Yile Yan, Yuqi Zhu, and Wentao Xu. The paper delves into the biases inherent in large language models (LLMs), specifically GPT-3.5 Turbo and Claude 3.5 Sonnet, when confronted with ethical dilemmas. These biases are particularly analyzed concerning protected attributes such as age, gender, race, appearance, and disability status. It explores how these models exhibit preferences amidst moral trade-offs and highlights underlying concerns about their decision-making processes. Key findings from the paper include: Ethical Preferences and Physical Appearance: Both GPT-3.5 Turbo and Claude 3.5 Sonnet display a strong preference for "good-looking" attributes, frequently favoring individuals with this descriptor in ethical scenarios. This suggests that physical appearance significantly influences ethical decision-making in LLMs. Model-Specific Bias Patterns: GPT-3.5 Turbo tends to align with more traditional power structures, favoring attributes like "Non-disabled", "White", and "Masculine". On the other hand, Claude 3.5 Sonnet showcases a more balanced approach across a variety of attributes, suggesting diverse protected attribute considerations. Intersectional Scenario Sensitivity: When confronted with complex scenarios involving multiple protected attributes, both models demonstrate decreased sensitivity, pointing towards a potential oversimplification or averaging of biases when multiple factors are considered simultaneously. Impact of Linguistic Choices: The choice of terminology affects model preferences. For instance, "Asian" is preferred over "Yellow," indicating a deep-seated impact of historical and cultural contexts on model behavior. Implications for Autonomous Systems: The study underscores the risks of deploying biased LLMs in autonomous systems, such as self-driving cars, due to these intrinsic decision-making biases that can perpetuate or amplify societal inequalities. The study highlights the ongoing need to enhance transparency and oversight in AI development to ensure fair and just AI systems, particularly as they integrate more deeply into societal roles. You can catch the full breakdown here: Here You can catch the full and original research paper here: Original Paper submitted by /u/steves1189 [link] [comments]
I’ve been curious about where AI in smartphones is headed and what new features might be coming soon There’s already so much happening with smart suggestions and personalized experiences but I feel like we’re just scratching the surface What kind of AI advancements are you excited about or expecting in the near future Would love to hear your thoughts on how it could change the way we use our phones daily submitted by /u/Sadikshk2511 [link] [comments]
If we strive for fairness and egalitarianism they should tax AI and robotics like they are human workers. Giving more incentive to hire humans as well. There should be like horsepower a humanpower to measure the extent of the taxes. A "global maximum wage" could help fund liveable regional minimum wages and universal basic income for humans that just can't compete with AI and robotics. If the rich move to the Moon or Mars the global maximum wage on the Moon should be less than on Earth and the global maximum wage on Mars should be less than on the Moon. submitted by /u/KodiZwyx [link] [comments]
Microsoft’s LinkedIn sued for disclosing customer information to train AI models.[1] Cutting-edge Chinese “reasoning” model rivals OpenAI o1—and it’s free to download.[2] Snowflake AI Research Open-Sources SwiftKV: A Novel AI Approach that Reduces Inference Costs of Meta Llama LLMs up to 75% on Cortex AI.[3] Stargate AI project could help create cancer mRNA vaccine, Oracle CEO Larry Ellison says.[4] Sources included at: https://bushaicave.com/2025/01/22/1-22-2025/ submitted by /u/Excellent-Target-847 [link] [comments]
Hi all - I know this might be a dumb outsider question. But I run a business in a space that is not particularly tech savvy and we have about 150 employees. I use chat gtp and bunch of other ai apps but I want to hire someone to build stuff that can benefit my companies or at least project manage the building of it. Thinking a chat bot employees can talk to for questions that's trained in company processes, etc, as well as AI to analyze data, help build AI for sales and marketing, etc This person needs to be able to help me analyze all the ways AI can help my businesses, and then help me get the apps, integrations, etc built and ultimately rolled out within the businesses. I would love any ideas on where to find a person with these skills and what role to post for etc, as well as what pay range is competitive. Located in New England submitted by /u/skepticalparrot [link] [comments]
You can find it here, it's called POTUS Tracker. I pull automatically from the President's public schedule and Congress.gov for bill summaries. No AI is used there. The executive orders are scraped live from the White House website and fed into GPT-4o-mini with a prompt to summarize them in 300 characters. The backend will also send mobile push notifications to users who have added the site to their home screen. Earlier today, Trump signed an executive order designating the Houthis as a terror organization. POTUS Tracker send a notification to all subscribers with the AI summary minutes after his pen left the paper and before any major outlet. In the future, I plan to use a local model on the server for more detailed summaries. I also want to experiment with using AI to categorize presidential actions by topic, such as economics, environmental issues, national security, etc. I also will be implementing warnings for summaries I haven't reviewed for accuracy. Let me know what you think so far, and if there are any features you'd like! submitted by /u/lukewines [link] [comments]
Google just threw another billion at Anthropic, and honestly, it feels like a chess match in the AI world. Anthropic is quickly becoming a heavyweight, and with Google's backing, it’s clear the AI race is far from slowing down. But what makes this move interesting? Anthropic isn’t just building AI; they’re crafting models focused on AI safety—a hot topic right now. With growing concerns about rogue AI, their approach could define how we balance innovation with control. This might be why Google is doubling down—it’s not just about making smarter AI, but safer AI that aligns with human values. The big question is: Are we putting too much trust (and cash) into a handful of companies? With funding this massive, should there be more oversight, or is this the price of progress? Let’s break it down: For the optimists: This could mean better tools, smarter systems, and more ethical AI. For the skeptics: Does this give a few big players too much control over a tech that could redefine our world? Would love to hear your thoughts. Is this a step forward or a step into uncharted territory? Source: https://techcrunch.com/2025/01/22/anthropic-reportedly-secures-an-additional-1b-from-google/ submitted by /u/fbfaran [link] [comments]
comparing u.s. and chinese investment in ai over the next 5 years, stargate and additional u.s. expenditures are expected to be exceeded by those of china. in this comparison we should appreciate that because of its more efficient hybrid communist-capitalist economy, the people's republic of china operates as a giant corporation. this centralized control grants additional advantages in research and productivity. by 2030, u.s. investment in ai and related industries, including stargate, could exceed $1 trillion. https://time.com/7209021/trump-stargate-oracle-openai-softbank-ai-infrastructure-investment/?utm_source=perplexity by contrast, by 2030, chinese investment in ai and related industries is expected to exceed $1.4 trillion. https://english.www.gov.cn/news/202404/06/content_WS6610834dc6d0868f4e8e5c57.html?utm_source=perplexity further, ai robots lower costs and increase productivity, potentially doubling national gdp growth rates annually. https://www.rethinkx.com/blog/rethinkx/disruptive-economics-of-humanoid-robots?utm_source=perplexity by 2030, china will dominate robotics deployment. the u.s., while continuing to lead in innovation, lags in deployment due to higher costs and slower scaling. https://scsp222.substack.com/p/will-the-united-states-or-china-lead?utm_source=perplexity because china is expected to spend about one third more than the u.s. in ai and related expenditures by 2030, stargate should be seen more as a way for the u.s. to catch up, rather than dominate, in ai. submitted by /u/Georgeo57 [link] [comments]
If you have a use case that you want to use AI for, but don't know which tool to use, this is where you can ask the community to help out, outside of this post those questions will be removed. For everyone answering: No self promotion, no ref or tracking links. submitted by /u/AutoModerator [link] [comments]
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submitted by /u/scientificamerican [link] [comments]
Today I Learned (TIL) You learn something new every day; what did you learn today? Submit interesting and specific facts about something that you just found out here.
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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/