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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 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.
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).
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.”
Hi everyone, I’m not sure if this is the right sub for a post of this kind so please redirect me if necessary. I am a mathematics student in college and I admit to using AI a lot this semester on my homework in my classes (both my math classes and non-STEM electives). Now that the semester is closing, I’m reflecting on this and realized that my learning was very much stunted this semester due to my AI habits. For my math classes, I used AI on every assignment, mostly for speed, but a lot of the time I wouldn’t even think about the problem and I’d just put it through ChatGPT and as long as the answer used our course content, I’d go with it. I know that this is considered misuse of AI, but from everything I’ve seen this semester, 90% of students who use AI also use it in this way. Students who claim to use AI only for creating study materials, checking work, etc, are for the most part lying. I don’t have the discipline to use AI in a healthier way for my assignments. As a result, I learned way less content throughout the semester and had to cram for exams, and got lower exam scores than previous semesters. I decided that I’m going to ban myself from using AI next semester, but I’m wondering if this will also hurt me. All of my peers will be misusing AI for their assignments, so I will probably get lower scores than them on assignments despite putting in more work. My classes are all curved, so this will directly affect me. Also, the nature of education is shifting because of AI, and I’m sure we’ll see AI more accepted in academia in the future, and I’m wondering if I will be at a disadvantage when we reach that point if I avoid it all together as it’s developing. For the record, I plan on going to grad school and pursuing a career in academia, so this “issue” for me won’t leave once I graduate. Does anyone have any thoughts? I also want to open the discussion for general thoughts on AI use in college. submitted by /u/PerplexedKale [link] [comments]
Hey everyone! So, my idea was quite simple - 1. Get the Amazon product URL. 2. Query Amazon for similar products. 3. Let the AI choose the best offer or alternative. At first, it seemed like the simplest idea ever. However, AI still struggles with basic concepts that I find straightforward. For instance, if I search for “iPhone,” it will find a case and happily say, “I just saved you 99%!”. I’m trying to avoid using taxonomy, but I couldn’t get good results without explicitly telling the AI to ignore items like cases, screen protectors, and so on. Unfortunately, it couldn’t comprehend this on its own. I believe I’ve figured out most of the issues, but I’m still working on it. Please let me know if you find this useful. submitted by /u/Talhelfg [link] [comments]
“Thanks to decades of data creation and graphics innovation, we advanced incredibly quickly for a few years. But we’ve used up these accelerants and there’s none left to fuel another big leap. Our gains going forward will be slow, incremental, and hard-fought.” “Reviewing the history of machine learning, we can both understand how the field advanced so quickly and why LLMs have hit a wall.” Original Link: https://www.dbreunig.com/2024/12/05/why-llms-are-hitting-a-wall.html submitted by /u/contextbot [link] [comments]
Representing a single image in current LVLMs can require hundreds or even thousands of tokens. This results in significant computational costs, which grow quadratically as input image resolution increases, thereby severely impacting the efficiency of both training and inference. To address this challenge, researchers conducted an empirical study revealing that all visual tokens are necessary for LVLMs in the shallow layers, and token redundancy progressively increases in the deeper layers of the model. To this end, they propose PyramidDrop, a visual redundancy reduction strategy for LVLMs to boost their efficiency in both training and inference with neglectable performance loss. Original Article: https://medium.com/aiguys/are-tiny-transformers-the-future-of-scaling-e6802621ec57 The below image explains pretty well about the redundancy. We can clearly see that by the time we reach the 16th layer, we see very few activations. https://preview.redd.it/gglh6kxij65e1.png?width=828&format=png&auto=webp&s=48d88664493f297d277e13cdab5a07522b1a33f6 Imagine this scenario: https://preview.redd.it/ukjnnn4kj65e1.png?width=356&format=png&auto=webp&s=f5b1be98a470f71214a8617ca31efd8c1adac8f1 You have a small fleet of birds flying in the sky. When we pass this image to our vision models. Most of the tokens will look like this: [Sky, Sky, Sky, Skye,……Bird, Sky…Sky… Sky] In short, the [Sky] token will be repeated so many times. I should have conveyed the [Sky] token once and that should have been enough, but that’s not the case with most current Vision Language Models. And to solve this problem researchers introduce PyramidDrop. https://preview.redd.it/d05bce3lj65e1.png?width=622&format=png&auto=webp&s=e47bcbfb77a5b494c7e1280767bb9a0e7abd2971 Information for answering the instructions. With the layer increases, the redundancy of image tokens increases rapidly. At layer 16, even preserving only 10% of image tokens will not cause an obvious performance decline. Notably, at layer 24, the model performance is nearly irrelevant to the image tokens, indicating that the model has already captured the necessary image information and the image tokens are redundant for the model now. Previous research on image token compression typically drops image tokens before passing them to the language model or uses a fixed compression ratio across all language model layers. However, redundancy is not consistent across different layers. Redundancy of image tokens is relatively minimal in the shallow layers and becomes progressively larger in deeper layers. Thus, uniformly compressing image tokens across layers may lead to the loss of valuable information in the shallow layers while retaining unnecessary redundancy in the deeper layers. LVLM (Large Vision Langauge Models) pays attention to most of the image tokens at shallow layers and the attention to different tokens shows a uniform pattern. On the contrary, in the middle of the LVLMs, the attention shows a sparse pattern and mainly focuses on the question-related image's local parts. PyramidDrop, which fully leverages layer-wise redundancy to compress image tokens. To maximize training efficiency while preserving the essential information of the image tokens, PyramidDrop divides the forward pass of the LLM into multiple stages. In the shallow layers, we retain a higher proportion of image tokens to preserve the entire vision information. At the end of each stage, it partially drops the image tokens, until nearly all the image tokens are eliminated in the deeper layers. This approach allows us to optimize training efficiency while maintaining critical information. https://preview.redd.it/ty1eylomj65e1.png?width=828&format=png&auto=webp&s=572932ba20b204a32deb7e7448cf616fff22f07e Not only does this technique make the Infernce faster for LVLMs, but in some cases, it even increases the performance. But then the question is how can a smaller model with the same architecture perform better? We know from other experiments that giving too much context to LLMs, actually leads to a decrease in the performance. This seems to confuse the model about what is actually important in a given token sequence. But I have my own hypothesis on this, based on the research I read on Mechanistic interpretability. The idea here is that if the model has too many parameters, it will go more toward memorization, but if I reduce the number of parameters, the model is forced to learn the abstractions instead of relying on memorization. As we see in the Grokking. The model starts with Memorization, and by the time it reaches the generalization, almost all the parameters go close to zero, except the ones that strengthen the generalized solution of that problem. submitted by /u/Difficult-Race-1188 [link] [comments]
Leveraging decentralized technologies and AI can revolutionize automation across various industries. Business Applications of AI Agent Networks It can offer significant opportunities for businesses. For example, a company could develop a network of specialized AI agents tailored to specific departments. These agents might analyze market trends, optimize marketing strategies, identify sales leads, and deliver customer support—all with minimal human intervention. Such automation could fundamentally transform operations, allowing AI agents to handle tasks typically requiring human oversight. This shift has the potential to increase efficiency, reduce costs, and free employees to focus on strategic initiatives. Towards Fully Autonomous Swarms The ultimate goal is to enable fully autonomous multi-agent systems, or "swarms." These systems possess the following key characteristics: Self-Directing: Once initiated, the swarm autonomously pursues its mission without supervision. It can adapt its actions based on heuristic principles or specific mission parameters. Self-Correcting: The swarm detects and addresses errors—whether technical, strategic, or epistemic—without external input. Self-Improving: Over time, the swarm enhances its capabilities, learning from its environment and experiences. Multi-Agent Systems and Decentralization Multi-agent systems (MAS) are composed of interacting intelligent agents that solve problems beyond the capacity of individual agents or monolithic systems. Recent advancements, such as large language models (LLMs), have enabled sophisticated interactions among these agents, opening new research avenues. Integrating MAS with blockchain introduces decentralized AI systems, which offer unprecedented benefits: Data security: Blockchain ensures data integrity through tamper-proof storage. Trust and transparency: Immutable records on blockchain foster confidence in AI decisions. Distributed intelligence: Decentralized networks enable collaboration among autonomous agents, enhancing efficiency. Challenges in Centralized AI and the Need for Decentralization Centralized AI systems face several issues, such as vulnerability to data tampering, lack of data provenance, and potential bias in decision-making. Blockchain technology addresses these concerns by enabling decentralized, trusted, and secure data storage and transactions. Smart contracts further allow programmable governance for data sharing and decision-making among agents. Advantages of Decentralized AI Systems: Enhanced Data Security: Blockchain's cryptographic architecture ensures sensitive data remains secure. Improved Trust: Transparent decision-making processes recorded on the blockchain increase public confidence in AI. Efficient Collaboration: Decentralized systems eliminate reliance on central authorities, fostering collective decision-making. Optimized Resource Use: Blockchain-based decentralized systems ensure scalable, efficient storage and data management. Synergies Between Blockchain and AI: The convergence of blockchain and AI unlocks transformative potential across industries. Key benefits include: Transparency: Blockchain's immutable ledger provides an auditable trail of AI decisions, addressing concerns about the "black box" nature of AI systems. Data Security: AI leverages blockchain’s decentralized architecture to enhance security and detect threats. Scalability: AI optimizes blockchain performance by improving consensus mechanisms and transaction validation. Data Monetization: Decentralized marketplaces powered by blockchain enable secure data sharing, with individuals maintaining control over their data. Applications Across Industries Healthcare: Systems can use blockchain for decentralized medical records, while AI processes this data for predictive analytics and personalized care. Supply Chain: Projects can integrate blockchain for traceability and AI for demand forecasting and fraud detection. Finance: Platforms can crowdsource AI models using blockchain, democratizing investment decision-making. Education: AI-powered learning systems leverage blockchain for secure data management and personalized education plans. IoT Security: Blockchain-secured IoT devices, combined with AI for threat detection, ensure robust security and uptime. Energy Management: Blockchain-enabled peer-to-peer energy trading, optimized by AI algorithms, promotes efficiency and cost savings. Opportunities and Challenges Neural Networks and Blockchain: By ensuring data integrity and fostering decentralized collaboration, blockchain enhances neural network applications in sectors like healthcare. However, the computational complexity of blockchain remains a challenge for real-time operations. Machine Learning: Blockchain promotes secure environments for decentralized model training. Yet, scalability and privacy concerns must be addressed. Natural Language Processing (NLP): Blockchain can validate information sources for NLP applications like chatbots. However, challenges include synchronizing dynamic language models with blockchain's immutable structure. Integrating AI with blockchain has the potential to reshape industries, offering systems that are more transparent, secure, and efficient. While technical and regulatory challenges remain, ongoing advancements in both fields promise streamlined solutions that fully realize the transformative power of decentralized AI. submitted by /u/CuriousActive2322 [link] [comments]
On Thursday, Italian startup iGenius and Nvidia (NASDAQ: NVDA) announced plans to deploy one of the world’s largest installations of Nvidia’s latest servers by mid-next year in a data center located in southern Italy. The data center will house around 80 of Nvidia’s cutting-edge GB200 NVL72 servers, each equipped with 72 “Blackwell” chips, the company’s most powerful technology. iGenius, valued at over $1 billion, has raised €650 million this year and is securing additional funding for the AI computing system, named “Colosseum.” While the startup did not disclose the project's cost, CEO Uljan Sharka revealed the system is intended to advance iGenius’ open-source AI models tailored for industries like banking and healthcare, which prioritize strict data security. For Colosseum, iGenius is utilizing Nvidia’s suite of software tools, including Nvidia NIM, an app-store-like platform for AI models. These models, some potentially reaching 1 trillion parameters in complexity, can be seamlessly deployed across businesses using Nvidia chips. “With a click of a button, they can now pull it from the Nvidia catalog and implement it into their application,” Sharka explained. Colosseum will rank among the largest deployments of Nvidia’s flagship servers globally. Charlie Boyle, vice president and general manager of DGX systems at Nvidia, emphasized the uniqueness of the project, highlighting the collaboration between multiple Nvidia hardware and software teams with iGenius. “They’re really building something unique here,” Boyle told Reuters. Source: Abbo News submitted by /u/SmythOSInfo [link] [comments]
Hello folks, I've been working on an agentic solution where you can have an autonomous agent taking live calls. We're using a pipeline of Speech to Text, LLM for generating responses and then Text to Speech. In this pipeline, Speech to text is causing some issues because it's difficult to determine when exactly a sentence is over since the user can take pauses. Moreover, when multiple inputs go into LLM, multiple responses are generated and they queue up for Text to speech. How would you solve this problem? How would you also handle cases where the user interrupts the agent? submitted by /u/Leo2000Immortal [link] [comments]
We all saw in class the trade off between bias and variance, that we don't want our train loss to keep going down and our test loss go up. But in practice I feel like doing hyperparameter tuning for classic ML models with GridSearchCV / BayesSearchCV is not enough. Even though I do cross validation, the search.best_model obtained at the end is almost always overfitting. How can you actually perform a search that will give you a robust generalized model with higher chances ? submitted by /u/desslyie [link] [comments]
Hello r/ArtificalIntelligence , I was wondering if any of you amazing people will know a tool like the one below that doesn't use open ai, ChatGPT because I do not have a API funding, I would like something I could host or the ai API be free, if it is a easy code edit I would be willing to do it but thank your for the help and sorry if I sound dumb. https://github.com/RayVentura/ShortGPT submitted by /u/Jaxondevs [link] [comments]
Video here. Their website here. It's still under development, but apparently it can reply to emails, order pizza, and more. I'm not related to them in any way. submitted by /u/sarrcom [link] [comments]
Head to head of meme-interpretability with the same image and text prompt! Anecdotal but interesting responses. Also clear winner! submitted by /u/No_Cartoonist8629 [link] [comments]
In my academic field (social sciences) I deal with the problem of bias in SA models. My previous work showed that deep learning SA systems inherit bias (e.g. nonrepresentative of the population political bias) from annotators: https://arxiv.org/abs/2407.13891 Now I devised a solution that used a technique I call semantic blinding to provide only the bare necessary information for the model to predict emotions in text, leaving no signal for the model to overfit and produce bias from: https://arxiv.org/abs/2411.12493 Interested to hear your thoughts before I publish the SProp Gnn. Do you think it could be useful beyond the academia? submitted by /u/Hub_Pli [link] [comments]
OpenAI Is Working With Anduril to Supply the US Military With AI.[1] Meta unveils a new, more efficient Llama model.[2] Murdered Insurance CEO Had Deployed an AI to Automatically Deny Benefits for Sick People.[3] NYPD Ridiculed for Saying AI Will Find CEO Killer as They Fail to Name Suspect.[4] Sources included at: https://bushaicave.com/2024/12/06/12-6-2024/ submitted by /u/Excellent-Target-847 [link] [comments]
When would the phase 2 decision come out? I know the date is December 9th, but would there be chances for the result to come out earlier than the announced date? or did it open the result at exact time in previous years? (i.e., 2024, 2023, 2022 ....) Kinda make me sick to keep waiting. submitted by /u/No-Style-7975 [link] [comments]
So, AI is progressing faster at a rate that has never been seen before. Im going to enter the workforce soon, as I am 16, and so I was wondering what careers to get into and not to get into because I know AI will probably change everything soon. My main interests are Computer Science and Political Science, but I don’t know how much AI is going to change those type of jobs. submitted by /u/misobean56 [link] [comments]
Meta released Llama3.3 yesterday which is a 70B model outperforming Llama3.1 405B on various metrics. For usage, groq is providing a free API key for Llama3.3. Check out how to use it : https://youtu.be/ZQoPOuSbmZs?si=7gBuE-qCGa19Jbw1 submitted by /u/mehul_gupta1997 [link] [comments]
Few months ago, I migrated from TF 2.0 to Jax. I found that jax is significantly faster than Tf. I noticed in the official documentation that it relies on XLA default that uses JIT compilation which makes execution faster. I also noticed that TF graphs also have option to enable JIT compilation with XLA. But still jax dominates TF with XLA. I just want to know why. submitted by /u/Odd-Detective289 [link] [comments]
Zosimos of Panopolis is the author of some of if not THE oldest texts recorded on alchemy. What if we were to merge the practise of ancient alchemy with modern day artificial intelligence? We’ll look no further because in Techno alchemy we do just that! https://youtu.be/jGF4HWELfRw?si=7pNCrqgibt_-9YX4 submitted by /u/ShelterCorrect [link] [comments]
I help with devrel for https://github.com/katanemo/archgw - an intelligent gateway for agents. Offers smart intent routing, fast function-calling, prompt guardrails and observability so that you can focus on the stuff that matters the most submitted by /u/AdditionalWeb107 [link] [comments]
I was laid off and decided to use this time wisely to switch careers. Willing to do the hard work and I know it won’t be overnight but need a starting point to enter to ecosystem. ETA: i.e. like conferences to attend? Where does everyone building community? submitted by /u/toyheartz [link] [comments]
Multimodal AI is changing the game by combining text, images, and even video into a single, cohesive system. It’s being talked about as a major leap in AI capabilities. What industries do you think will benefit the most from this tech? And are there any challenges you see in integrating these models into everyday use? Would love to hear everyone's thoughts! submitted by /u/Frosty_Programmer672 [link] [comments]
I've been doing some thinking and deep diving into AI research and came across a fascinating concept called 'relational intelligence.' Here's what it's all about: Most people think of AI as just number-crunching machines, but I'm fascinated by the potential for a more nuanced form of intelligence. Relational intelligence is more than AI mimicking human consciousness - it's about crafting systems that adapt to and genuinely understand context. Imagine AI that doesn't just respond mechanically, but actually synthesizes information dynamically. Think of it as the difference between a simple calculator and a conversation partner who actually gets the nuances of what you're saying. In fields like healthcare, education, and customer service, this could be revolutionary - systems that genuinely understand the complexity of human needs. We're not trying to create human-like consciousness, but something entirely new: an intelligence that complements human thinking while being uniquely its own thing. I'm really curious to hear your thoughts: 1. How do you see relational intelligence potentially transforming different industries? 2. Can AI develop a meaningful form of intelligence without human emotions? 3. What challenges might we face in developing this approach? Disclaimer: Just exploring ideas here, not claiming we've solved AI consciousness or anything. submitted by /u/That-Pension4540 [link] [comments]
Hello everyone, I am looking for methods that can automatically categorize and select layers from for transfer learning. If you know any such methods or research please let me know or share. Thanks submitted by /u/reshail_raza [link] [comments]
It's nice to find people who share my views on A.I. This doesn't mean (as some might try and tell me) that I covet an 'echo chamber' - it means I find it comforting to know that there are others who have the ability to use A.I. and A.I. language models (in my case) as a tool to help them achieve their goals. submitted by /u/Libertyforzombies [link] [comments]
I have used gpt’s for personal use, code samples, image generation and understand the general use cases. However, could someone help me understand how companies use AI. Meaning, I see several roles that require AI (tech and non-tech) and I want to understand deeper if they are requiring the use of gpt’s or something else. How does a company sandbox the AI’s information to just knowledge that the company doesn’t mind sharing. Taking as an example a supply chain co, or healthcare co, what would be the use case for AI? Are we building LLM’s from the ground up? Edit: for a little more specificity, I see the following in a lot of jd’s and would like to generically understand what this means ‘Knowledge of AI, machine learning, natural language processing, and computer vision technologies and applications’ submitted by /u/trainermade [link] [comments]
And by learn, I mean deep understanding of the concepts and be able to make stuff with AI. I got no idea where to start, what concepts should I learn and what concepts should I reserve for later. I'm already familiar with some data related stuff, I've done some data analysis and some machine learning but I feel like my understanding is just surface lvl. How do you recommend one should go about learning "AI", and what are some books/resources that you recommend? submitted by /u/yoho7202 [link] [comments]
I'm designing an interactive AI coding workshop and am currently building out the section focused on rapid prototyping for computer science and info tech educators and leaders. I'm seeking insights and perspectives on free, accessible tools that enable beginner-friendly AI-assisted coding exploration for them to undergo a hands on exploration of learning so they can consider teaching and learning implications. Specific context for feedback: Target audience: Emerging developers/students Goal: Learn AI-assisted web application development Constraints: Zero-cost tools Duration: 30-minute interactive challenge Key questions for community input: Beyond Replit(agent has a cost) and bolt.new, what free platforms support AI-assisted coding for beginners/teachers/students? What web application concepts would effectively demonstrate coding/CS principles? What AI code generation tools provide the most educational value for novices? I'm particularly interested in platforms that: Offer free tiers Have integrated AI coding assistance Support web/frontend development Provide learning-oriented environments Would love insights from developers, educators, and tech community members on crafting an engaging, accessible learning experience! The goal is to design a session that demystifies AI-assisted development while keeping barriers to entry low. Here is my sample prompt I am testing out on several tools to give you a sense of what I am thinking: A web-based interactive learning platform designed to teach 5th-grade students about fundamental programming concepts (sequences, events, loops, and conditionals) aligned with CSTA standard 1B-AP-10. Core Features: - Interactive code playground with simple, age-appropriate examples - Visual representations of program flow and execution - Step-by-step demonstrations of each programming concept - Guided exercises with immediate feedback - Teacher notes and explanatory comments for classroom use - Sample programs demonstrating real-world applications UI/Style: - Playful, education-focused interface with cartoon-style programming elements - Color-coded blocks and visual cues to distinguish different programming concepts - Kid-friendly animations that illustrate code execution and program flow - Interactive elements that respond to student interactions with encouraging feedback submitted by /u/coffeechug [link] [comments]
Imagine a customer support chatbot for an e-commerce platform that retrieves relevant product details from its knowledge base and performs web searches for additional information. Furthermore, it remembers past conversations to deliver a seamless and personalized experience for returning users. Here is how it works: - Store your own data in the knowledge base—in our case, a Website URL. - Convert the data into embeddings and save it in the Qdrant Vector Database. - Use phidata Agentic Workflow to combine Tools, LLM, Memory, and the Knowledge Base. Code Implementation Video: https://www.youtube.com/watch?v=CDC3GOuJyZ0 submitted by /u/External_Ad_11 [link] [comments]
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What do you think of the list? What would you add? LeBron James scores 40,000 career points Mondo Duplantis smashes Olympic pole vault records Spain’s historic Euro 2024 victory, featuring - - Lamine Yamal’s stunning debut Rafael Nadal bids farewell to tennis with an emotional retirement Novak Djokovic finally captures Olympic gold in Paris Caitlin Clark and Angel Reese redefine women’s basketball and its impact Record-breaking Super Bowl LVIII captivates millions The AFC Asian Cup and AFCON showcase football’s global influence Simone Biles makes a triumphant Olympic comeback with record-breaking performances Steph Curry delivers an unforgettable Olympic final performance submitted by /u/bakenzo [link] [comments]
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/