What is Google answer to ChatGPT?
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.
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.
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 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.
ChatGPT uses supervised learning techniques to train its models.
Supervised learning involves providing a model with labeled data (i.e., data with known outcomes) so that it can learn from it. This labeled data could be anything from conversations between two people to user comments on a website or forum post. The model then learns associations between certain words or phrases and the desired outcome (or label). Once trained, this model can then be applied to new data in order to predict outcomes based on what it has learned so far.
In addition to supervised learning techniques, ChatGPT also supports reinforcement learning algorithms which allow the model to learn from its experiences in an environment without explicit labels or outcomes being provided by humans. Reinforcement learning algorithms are great for tasks like natural language generation where the output needs to be generated by the model itself rather than simply predicting a fixed outcome based on existing labels.
Supervised learning involves feeding data into machine learning algorithms so they can learn from it. For example, if you want a computer program to recognize cats in pictures, you would provide the algorithm with thousands of pictures of cats so it can learn what a cat looks like. This same concept applies to natural language processing; supervised learning algorithms are fed data sets so they can learn how to generate text using contextual understanding and grammar rules.
Reinforcement learning uses rewards and punishments as incentives for the machine learning algorithm to explore different possibilities. In ChatGPT’s case, its algorithm is rewarded for generating more accurate responses based on previous interactions with humans. By using reinforcement learning techniques, ChatGPT’s algorithm can become smarter over time as it learns from its mistakes and adjusts accordingly as needed.
How is ChatGPT trained?
ChatGPT is an improved GPT-3 trained an existing reinforcement learning with humans in the loop. Their 40 labelers provide demonstrations of the desired model behavior. ChatGPT has 100x fewer parameters (1.3B vs 175B GPT-3).
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.
➡️ Finally, they use this RM as a reward function and fine-tune our GPT-3 policy to maximize this reward using the Proximal Policy Optimization
In simpler terms, ChatGPT is a variant of the GPT-3 language model that is specifically designed for chat applications. It is trained to generate human-like responses to natural language inputs in a conversational context. It is able to maintain coherence and consistency in a conversation, and can even generate responses that are appropriate for a given context. ChatGPT is a powerful tool for creating chatbots and other conversational AI applications.
How Does Google Answer ChatGPT?
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.
Try ChatGPT here
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?
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.
Question: What about Google Meena?
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.
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.
New slogan for McDonald’s by ChatGPT
Should… should we be concerned?
I asked ChatGPT to write a poem about rust
ChatGPT chooses Democrats over Republicans
- It is not intended to do that
- 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.
Should it? No.
Can it? No.
Will it? Sadly, programmers will have no say in this matter, once again. It might.
Yes, and it is very helpful advertising as well.
This last week or so has seen starry eyed projections about what ChatGPT can do, along with hugely impressive examples of its output.
It is hugely impressive.
Thankfully, more output examples have emerged which helpfully show what it cannot do. One of those things is writing computer code, which it can do only partially successfully. Many examples now exist that are just plain wrong and contain defects. But ChatGPT – like the annoying kid at Uni – cheerfully spits out these examples, with its over-confident hubris in explaining the code.
This is a dangerous thing. The positive examples will reinforce the idea that we can leave code writing to this robot now. The people most vulnerable to this delusion are those who cannot assess for themselves whether the GPT code is right or wrong.
These are almost by definition the people hoping for answers on stack overflow.
As stack overflow aims to be a high quality resource, it really does not want many of its top answers to be incorrect code. As – clearly – people have been running scripts that throw a stack overflow question into GPT and upload its output, we can now write incorrect code at staggering speeds.
To err is human, as the old saying goes. To truly foul up requires a Python script and and a web API to both GPT and Stack overflow.
Clearly, there is value in GPT. But at least for now, it needs to b e kept on a very short leash, watched over by those who know what they are doing.
It is definitely not yet ‘consumer grade replace-a-professional’ material.
Write a screenplay about the status of ChatGPT.
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.
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.
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.
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/
ChatGPT to save time with insurance denials
Tech Buzzwords of 2022, By Google Search Interest
I just answered a similar question.
Short answer is, “Hahahahahahaha no.”
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.
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”.
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.
Well, I asked it directly.
Here’s what it answered:
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
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- Substitute for google search
While ChatGPT is lacking info beyond 2021 and is occasionally incorrect and bias, many users leverage its ability to:
- Answer specific questions
- simplify complicated topics
All with an added bonus – no ads
2- Study Partner
Type “learn”, then paste a a link to your online textbook (or individual chapters).
Ask Chatbot to provide questions based on your textbook.
Now you have a virtual study buddy.
3- Train YOUR OWN Chatbot
I bet you didn’t know it is possible to :
- Integrate ChatGPT into your website
- Train it with customized information
A virtual customer service bot that can hold a conversation and answer questions (meaningfully).
When it comes to turbulent personal questions, Chatbot may spit out a disclaimer, but it will also give you straightforward and actionable advice.
ChatGPT is opening the development of:
to virtually everyone.
It’s a lengthy and technical process, but all you need is a killer idea and the right prompts.
Bonus: It also de-bugs your existing code for you.
6- Outline your content marketing strategy
7- Craft all your marketing materials
8- Creative Writing
A list for those who write code:
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.
Ignore this at your own peril.
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.
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#machinelearning #datascience #ChatGPT
2. ChatGPT Writer : It use ChatGPT to generate emails or replies based on your prompt!
3. WebChatGPT: WebChatGPT (https://chrome.google.com/webstore/detail/webchatgpt/lpfemeioodjbpieminkklglpmhlngfcn) gives you relevant results from the web!
4. YouTube Summary with ChatGPT: It generate text summaries of any YouTube video!
5. TweetGPT: It uses ChatGPT to write your tweets, reply, comment, etc.
6. Search GPT: It display the ChatGPT response alongside Google Search results
7. ChatGPT or all search engines: You can now view ChatGPT responses on Google and Bing!
8. Save all your Prompts?: The `ChatGPT History` extension has you covered!
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.
10. Search what people are Prompting with FlowGPT
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…
👉 Start now!
Google unveils its ChatGPT rival
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.”
- [D] If you had one year and basic knowledge of ML? How would you design it to get maximum benefit out of it?by /u/Cool_Bhidu (Machine Learning) on December 7, 2023 at 9:38 pm
Hello, Let's assume a scenario where you know Python, basic probability, basic linear algebra, a few concepts like supervised and unsupervised, etc., basic statistics, and basic software experience. What could be realistic things that I can learn within a year - given I will devote complete one year to learn ML and nothing else. and there is a organization which keeps me accountable. submitted by /u/Cool_Bhidu [link] [comments]
- [D] Thoughts on Mamba?by /u/ExaminationNo8522 (Machine Learning) on December 7, 2023 at 9:29 pm
I ran the NanoGPT of Karparthy replacing Self-Attention with Mamba on his TinyShakespeare Dataset and within 5 minutes it started spitting out the following: https://preview.redd.it/4r96tp6lxx4c1.png?width=836&format=png&auto=webp&s=10f2f61cd4cea96f4f903cb2070835fc5d1df951 https://preview.redd.it/32ler5vnxx4c1.png?width=622&format=png&auto=webp&s=dd00e53f43dd0afa058758a987901ee6789d2258 https://preview.redd.it/sc96i4xoxx4c1.png?width=678&format=png&auto=webp&s=94d2ed279054363d3ed2b6beed65be89468582b0 So much faster than self-attention, and so much smoother, running at 6 epochs per second. I'm honestly gobsmacked. submitted by /u/ExaminationNo8522 [link] [comments]
- [P] flex-prompt: a flexible prompt rendering engine that ensures you'll never exceed your LLM's context length againby /u/queerviolet (Machine Learning) on December 7, 2023 at 8:49 pm
When working with LLMs, I frequently experience token agony. Error: This model's maximum context length is 4097 but you are trying to push in all of War and Peace, you imbecile Perhaps you've experienced it too! The issue is particularly pronounced with retrieval augmented pipelines, since you have potentially quite a large set of documents which you could perhaps include in the prompt if only you knew how big it could be. I got tired of hacking around this headache, so I wrote flex-prompt to address it. I wish I didn't have to. Perhaps someone can point me to a better solution! But I couldn't find one, so alas, here it is. flex-prompt provides a basic layout and component model to help you describe how you want the pieces of your prompt to grow and shrink and a token-aware renderer which renders your prompt to fit your model's window. Github, Intro to flex prompt colab Quick examples You can just render(Flex(...)), and flex prompt will fit the prompt into the context window, and tell you how many tokens are left over for the response: from flex_prompt import render, Flex, Expect rendered = render( Flex([ "Given the text, answer the question.", "--Text--", WAR_AND_PEACE, "--End Text--", "Question: What's the title of this text?", "Answer:", Expect() ], join='\n'), model='text-davinci-002') # rendered.output is the string to send to the model # rendered.max_response_tokens is how many tokens you can # request in response without exceeding the model's context window print(rendered.output, rendered.max_response_tokens) More typically, you'll want to define a prompt which takes parameters. To do this, you can create a class (probably a dataclass) which derives Flexed: from flex_prompt import Flexed, Expect from dataclasses import dataclass @dataclass class Ask(Flexed): text: str question: str answer: str | Expect = Expect() instruct: str = "Given a text, answer the question." flex_join = '\n' # yielded items will be joined by newlines def content(self, _ctx): if self.instruct: yield 'Given the text, answer the question.' yield '' yield '-- Begin Text --' # note: we're using `Flex` here just to attach a flex_weight # to the text, telling the renderer we'd like more space for the # text than anything else. yield Flex([self.text], flex_weight=2) yield '-- End Text --' yield 'Question: ', self.question yield 'Answer: ', self.answer The renderer works much as you might expect. You can `yield` anything which you can pass to the top-level render function, including other components, creating a whole tree. Note that the component above can be used to render both the actual prompt and examples. Examples simply have an answer. This is useful for experimenting with different ways of structuring a prompt while ensuring that all the examples we present to the LLM are in the same format. LangChain and Haystack Integrations Flex prompt doesn't really care how you execute your prompt. For convenience, render(model=) does accept both LangChain and Haystack models: ask_tolstoy = Ask(text=WAR_AND_PEACE, question="Who wrote this?") # Using LangChain from langchain.llms import OpenAI lc_llm = OpenAI() rendering = render(ask_tolstoy, model=lc_llm) print(lc_llm(rendering.output, max_tokens=rendering.max_response_tokens)) # Using Haystack from haystack.nodes import PromptModel hs_llm = PromptModel(model_name_or_path='text-davinci-002', api_key=os.environ['OPENAI_API_KEY']) rendering = render(ask_tolstoy, model=hs_llm) print(hs_llm.invoke(rendering.output, max_tokens=rendering.max_response_tokens)) Is it worth it? As models grow larger and larger context windows, I've asked myself whether this is worth it. Won't context sizes eventually big enough to put in everything we might want without worry? One response: "everything I might want" is a very, very big set, plausibly bigger than any window size we're going to see soon. Another: being able to do this kind of token accounting is useful even if we don't completely fill context windows. For example, we might be able to augment our prompt with examples, documents, and tips. How much space should we allocate to each? The answer might well be model-dependent. How do we figure it out? Flex prompt's output, a Rendering object, actually holds the entire component tree. You can look through the object to see how many tokens were allocated to each child. This is currently very manual, but it does provide the bedrock infrastructure to e.g. run tests to discover the optimal balance of augmented data for a given prompt and model. Additionally, the right admixture (and for that matter, the right phrasing) may well be model-dependent. Flex prompt currently provides only very limited model-specific rendering (you can look at ctx.target, but it doesn't tell you much), but there's no reason that can't be significantly improved. At the extreme limit is prompt erasure, where we fine-tune a model to require no or minimal instructions/examples for a given set of prompts. Flex prompt can enable transitions like this with no changes to the pipelines themselves: you'd still use the same prompt components, they'd just render differently if the target is a fine-tuned model vs. a generic one. Status & Future Work Flex prompt is very much in early development. I would love to hear if and how people find it useful, and would love input and contributions! Some things I'd like to tackle in the future: Rendering message lists. Flex prompt currently only renders strings, though it's set up to be able to render any type of output. Message histories basically grow without bound, so supporting this seems like a no-brainer. Pagination. If your rendering overflows (as above, where we're trying to stuff the entirety of war and peace into a prompt), flex prompt will clip the offending pieces to fit. But there's currently no way to get "the next page". But the Rendering actually retains enough information to do this! It would be great to be able to call render(...).pages() to get the sequence of prompts as we "scroll" whatever has overflowed. This is medium-hanging fruit—a little tricky because we do have to descend the tree of renderings to find the exact one(s) which overflowed and then update only those. Token accounting. As mentioned above, you can currently grovel around in Rendering and look at the pieces of the prompt. This would be more useful if it were a little easier, e.g. if you could use rendering[Examples] to find all the parts rendered by the Examples component, or rendering['advice'] to find all the parts which are tagged (somehow) as "advice". The use case here is prompt optimization: discovering the optimal number or percentage of tokens to allot to each thing we might want to drop into the prompt. More integrations. Currently, flex prompt only supports OpenAI models. You can register your own target finders, but it would be great to have more support out of the box. This is mostly a matter of digging around and finding the tokenizers and window sizes for common models, and then writing the appropriate target finders. Contributions very welcome! Model tuning. As mentioned above, the rendering context could provide a mechanism for fetching model-specific parameters. The basic idea is that ctx[param] will evaluate param against the context, and then we can define some parameter types which load their model-specific values from gestures vaguely somewhere. Thanks for reading! Flex prompt Github Intro to flex prompt colab My website. shameless plug: I have a lot of engineering experience and a bit of machine learning experience and I am currently looking for a job submitted by /u/queerviolet [link] [comments]
- The Future of the Professionsby /u/amonle (Artificial Intelligence Gateway) on December 7, 2023 at 8:31 pm
In the future, how will we solve problems for which 'professionals' are currently our best answer? submitted by /u/amonle [link] [comments]
- [D] Is there a tool that indicates which parts of the input prompt impact the LLM's output the most?by /u/ToughOpening (Machine Learning) on December 7, 2023 at 8:29 pm
Hi, Is there a tool that indicates which parts of the input prompt impact the LLM's output the most? I do not care which LLM the tool is for if it exists. I guess it could be backtracked via the weights of each node in the neural network, but you guys are smarter than me so I'll listen to y'all. My use case is I have a prompt that slightly changes variation to variation. The output of the model is "Yes" or "No", so I want to see which parts of the prompt I change impact its response Best, A Reddit User submitted by /u/ToughOpening [link] [comments]
- I asked ChatGPT to illustrate the Biblical 10 plagues of Egypt. Half of them are bugs and half of them are rain, there are 12 of them, and all the people are frogs.by /u/Kalocacola (ChatGPT) on December 7, 2023 at 8:18 pm
submitted by /u/Kalocacola [link] [comments]
- Public Datasets With At Least 2 Rows Per Subject? [D]by /u/ZeApelido (Machine Learning) on December 7, 2023 at 8:11 pm
Are you aware of any public datasets that have at least 2 trials / samples / rows per subject? Could be in any domain. Preferably with > 100 subjects, and the tests not sampled years apart (but not dealbreakers). For instance, a large cohort of patients who have had ECG scans collected on 2 separate occasions. I am slowly working my way through the PhysioNet databases: https://physionet.org/about/database/ Most of course only have one scan per subject. submitted by /u/ZeApelido [link] [comments]
- Does any site have API access for an unrestricted model atm? (as a service)by /u/cobalt1137 (Artificial Intelligence Gateway) on December 7, 2023 at 8:05 pm
Really trying to find an unrestricted model in order to help me build part of my project on top of. One element needs to be unrestricted. Anyone away or something like this? Can't seem to find submitted by /u/cobalt1137 [link] [comments]
- When you close your fridge doorby /u/GreenLionRPG (ChatGPT) on December 7, 2023 at 8:01 pm
I asked for different scenarios showing what my food does when I close the door to my fridge. submitted by /u/GreenLionRPG [link] [comments]
- AI-Driven Data & Analytics Platform, MedMatrix, Implemented by Los Angeles Dermatology Institute: MedBright AI (MBAI.c MBAIF)by /u/massettawm (Artificial Intelligence Gateway) on December 7, 2023 at 7:53 pm
Today, MedBright AI (MBAI.c MBAIF) announced that Dermatology Institute & Skin Scare Center, a leader in dermatologic services in Southern California, has chosen to implement MedMatrix to improve its clinic operations. The Center specializes in cosmetic, medical and surgical dermatology in Los Angeles and is led by board-certified dermatologist Paul Yamauchi, MD, PhD, who stated regarding the news: “There is clearly a need for AI to help operations and the efficiency of clinics,” and “I’m excited to see the improvements we can make as a result of using the MedMatrix platform.” MedMatrix The industry-leading AI-powered data analytics platform, MedMatrix, harnesses the power of artificial intelligence to evaluate and analyze key aspects of healthcare facilities, including patient needs and resource matching, revenue cycle management, patient demographics, facility geography, and competition. MedMatrix is set to drastically improve healthcare operations by empowering doctors and operators to enhance clinic revenue by 10%, reduce physician burnout and improve patient satisfaction. Through its advanced AI and data analytics capabilities, MedMatrix works alongside healthcare professionals to optimize each of these critical areas, resulting in more efficient clinic operations and improved patient outcomes. Previous News This news follows the recent election of two US healthcare experts to its Board of Directors, Dr. Jaime Gerber, who will serve as Chairman of the Board, and Dr. Konita Wilks. Specializing in preventative cardiology and complex cardiology cases that involve coronary artery disease, peripheral artery disease and risk factor modification, Dr. Gerber is a Professor of Medicine at Yale Medical School and serves as the co-director and director of a range of programs. With over 25 years of experience as a dynamic, visionary leader in healthcare and the military, Dr. Wilks is the CEO of Olive View-UCLA Medical Center, a 355-bed general acute care hospital that employs nearly 5500 persons and has a $900-million dollar annual operating budget. With the complexity and rise of data in healthcare expected to increase the use of artificial intelligence in the field, MBAI is strongly positioned to capitalize will result in a significant and positive impact in the field. Investor presentation: https://assets-global.website-files.com/654400b037e09f716a28c32b/655f911132d0b133ffc9ca1c_Medbright%20Q4%202023%20Investor%20Presentation.pdf Full News Release: https://www.medbright.ai/blog-posts/medbright-ai-announces-beta-customer-for-medmatrix-the-ai-driven-data-and-analytics-platform Posted on Behalf of MedBright AI Investments Inc. submitted by /u/massettawm [link] [comments]
- [D] Llms/Generative AI citing sourcesby /u/edixtor93 (Machine Learning) on December 7, 2023 at 7:48 pm
This might be something the big boys in AI are considering, or maybe already doing. If not, would it be such a crazy endeavor? Technically speaking... With artists claiming copyright infringement, with Meta using Facebook and Instagram images (which I assume we all blindly agreed to in tos), and in general more and more backlash against AI using sources without permission or whatever. Wouldn't a system where during training it somehow keeps track of sources such that when the model is finally trained and ready to use, it could generate whatever it generates along with a probably long list of citations of the sources it used to generate the work of the prompt? Ain't saying this would be an easy thing to do, hell I know very little of how it all works, but wouldn't a system like this put a lot of people at ease? Knowing that at the very least their work is being credited? Opening the floor for thoughts and opinions. submitted by /u/edixtor93 [link] [comments]
- [D] How do LLMs Combine with Traditional ML approaches?by /u/yoquierodata (Machine Learning) on December 7, 2023 at 7:38 pm
I’ve got experience in “traditional” ML like building classification and regression models with GB Trees and the like, so I’m curious how, if at all LLMs can be combined with other ML modeling approaches. If my use case entails structured data as well as something like chat history, is there a need to “combine” the modeling approaches? Thanks for any resources or input you might have. submitted by /u/yoquierodata [link] [comments]
- One of my favoritesby /u/ExtremelyVW (ChatGPT) on December 7, 2023 at 7:38 pm
submitted by /u/ExtremelyVW [link] [comments]
- AI File Organizer trained on personal Structureby /u/Abujajuba (Artificial Intelligence Gateway) on December 7, 2023 at 7:37 pm
I already saw someone‘s post which asked for an AI tool to automatically organize unsorted folders full of random files. I get that this is pretty much to ask and difficult to do. How would things change though, if I already have a nice folder structure? Is there a tool out there which can be trained to my personal folders? I‘m tired of sorting scanned (ocr) pdfs into the correct folder and have already tried to automate most of reoccurring documents (e.g monthly internet bill). As soon as a new kind of document comes in though (e.g. new bill of just changed electricity provider), I would like this one to also automatically be sorted to „ElectrivityCompanyB“->„Bills“. Is such a tool realistic? submitted by /u/Abujajuba [link] [comments]
- [D] Considering Switching to DS/ML from SWE.by /u/EquivalentAbies6095 (Machine Learning) on December 7, 2023 at 7:26 pm
How is the day to day for Data Science/ML? I am thinking about changing careers as I am really not enjoying certain aspects of Software Engineering. I did an 4 month ML internship, but I don't feel like I got the full grasp of the day to day. I know this can vary from company to company and team to team. For some background, I have ~4 years of experience as a SWE, BS in CS from top 50 school, and MS in CS from top 10 school. submitted by /u/EquivalentAbies6095 [link] [comments]
- [D] Is ICPRAI a Reputable ML Conference? Seeking Input!by /u/SufficientAd3564 (Machine Learning) on December 7, 2023 at 7:17 pm
Hey ML folks! Found ICPRAI on aideadlin.es but don't know much about it. Any insights on the conference quality, review process, or personal experiences? Considering submitting a paper and looking for advice. Your thoughts are much appreciated! submitted by /u/SufficientAd3564 [link] [comments]
- Chatgpt as boxed software instead of a subscription webapp.by /u/Ghudda (ChatGPT) on December 7, 2023 at 7:16 pm
submitted by /u/Ghudda [link] [comments]
- [P] Machinery failure detection with training on the edge using Arduino-compatible boardsby /u/prokyber (Machine Learning) on December 7, 2023 at 7:05 pm
Bender Bending Rodríguez Some time ago I posted about the C++ library I made for training decision trees on the edge (using any Arduino-compatible board). People were asking reasonable questions, like is it actually good for something. So, recently I decided to make a little proof of concept project by training it to detect fan 'failure' based on it's vibration patterns. The code is in the examples folder. The code is practically the same as the code for 'physical activity recognition' except for the fact that I increased the sampling frequency a little bit. Here is a video of the whole process. (Sorry for my bad English) First part of the video is learning two states('ok' and 'failure') then goes the classification. I guess the coolest thing is that how fast and easy it is. I wonder If it is possible to make unsupervised anomaly detection on the edge using KNN... submitted by /u/prokyber [link] [comments]
- OpenAI is very resistant to carefully and meticulously crafted jailbreaks attempting to get it to generate copyrighted images of Mickey Mouse.by /u/1jl (ChatGPT) on December 7, 2023 at 6:17 pm
submitted by /u/1jl [link] [comments]
- Anyone else that likes to combine random stuff? What are your best?by /u/AIThinks (ChatGPT) on December 7, 2023 at 5:57 pm
Added the video so you can see the prompts used submitted by /u/AIThinks [link] [comments]
- Two-minute Daily AI Update (Date: 12/07/2023): News from Google, Meta, Apple, AMD, McDonald’s, and moreby /u/RohitAkki (Artificial Intelligence Gateway) on December 7, 2023 at 5:20 pm
Continuing with the exercise of sharing an easily digestible and smaller version of the main updates of the day in the world of AI. Google launches Gemini, its most capable AI model yet that can beat GPT-4 - Gemini can generalize and seamlessly understand, operate across and combine different types of information, including text, code, audio, image, and video. Gemini Ultra’s performance exceeds current SoTA results on 30 of the 32 widely-used benchmarks for LLM research and development. With a score of 90.0%, it is also the first model to outperform human experts on MMLU. Gemini 1.0 is now rolling out across a range of Google products and platforms, including Bard. Meta launches standalone AI image generator and other new AI experiences - Meta is rolling out Imagine with Meta, which allows users to create images by describing them in natural language. It also shared updates to some of its core AI experiences and new capabilities you can discover across its family of apps. Check out the newsletter for the details! Apple quietly releases a framework called MLX to build foundation models - MLX is an array framework for machine learning on Apple silicon, by Apple ML research. It also released deep learning model library MLX Data. Both are accessible through open-source repositories like GitHub and PyPI. Google unveils AlphaCode 2, powered by Gemini - It is an improved version of the code-generating AlphaCode introduced by Google’s DeepMind lab roughly a year ago. In a subset of programming competitions hosted on Codeforces, a platform for programming contests, AlphaCode 2– coding in languages Python, Java, C++, and Go– performed better than an estimated 85% of competitors. Google announces the Cloud TPU v5p, its most powerful AI accelerator yet - With Gemini’s launch, Google also launched an updated version of its Cloud TPU v5e, which launched into general availability earlier this year. Google observed 2X speedups for LLM training workloads using TPU v5p vs. v4. AMD’s Instinct MI300 AI chips to challenge Nvidia; backed by Microsoft, Dell, And HPE - The chips (also backed by Lenovo and Oracle) represent AMD’s biggest challenge yet to Nvidia’s AI computing dominance. It claims that the MI300X GPUs come with better memory and AI inference capabilities than Nvidia’s H100. McDonald’s will use Google AI to make sure your fries are fresh, or something? - McDonald’s is partnering with Google to deploy generative AI beginning in 2024 and will be able to use GenAI on massive amounts of data to optimize operations. At least one outcome will be– according to the company– “hotter, fresher food” for customers. While that’s unclear, we can expect more AI-driven automation at the drive-throughs. Gmail gets a powerful AI update to fight spam with the 'RETVec' feature - It helps make text classifiers more efficient and robust and works conveniently across all languages and characters. Google has made it open-source, allowing developers to use its capabilities to invent resilient and efficient text classifiers for server-side and on-device applications. More detailed breakdown of these news and innovations in the daily newsletter. submitted by /u/RohitAkki [link] [comments]
- Crypto schemer AGI builtby /u/DaveRamalho (Artificial Intelligence Gateway) on December 7, 2023 at 5:10 pm
I created a Crypto schemer AGI using a specific AGI FrameWork using GPT. Let me know your thoughts after talking to it. https://chat.openai.com/g/g-KfHEfZGfb-cryptoschemer You can ask it about its layers of thoughts , build a personalized path if you are confused/lost and much more. submitted by /u/DaveRamalho [link] [comments]
- Text to Music AI?by /u/MarkPles (Artificial Intelligence Gateway) on December 7, 2023 at 4:56 pm
So I've been thinking. I'm someone who can memorize a song after a few times of listening to it. But when studying for school I can't memorize anything. So I'm wondering if there's an AI that can turn text into songs submitted by /u/MarkPles [link] [comments]
- Review: Google's New Gemini Pro Through Bard Is... Horrible - Seem Like a Google Search Extension - Are The Ultra Test Results Equivalent to Teaching to the STEM Test? Where is Gemini Ultra?by /u/Xtianus21 (Artificial Intelligence Gateway) on December 7, 2023 at 4:49 pm
submitted by /u/Xtianus21 [link] [comments]
- [D] Undergrad contemplating a Machine Learning PhD, but worried about what that truly entails.by /u/FM-2070 (Machine Learning) on December 7, 2023 at 4:46 pm
I'm a 3rd year at a T5 school, and I've been doing research since high school. I've got a few publications under my belt and my professor (at a different T3 institution) is expecting my current work will get me a first authorship to Nature Medicine (or a similar top journal). It's all been applied ML/DL work, especially in reducing the cost of healthcare. I've been keeping up a high GPA and I've been trying to plan out the next year to set myself up as well as I can for a PhD. In theory, what I really want to do is figure out next-generation learning algorithms, especially those motivated by principles from the brain. I've found this incredibly exciting, and freshman year I'd get up at 5, spend most of the day learning about this stuff and implementing my ideas, skip all my classes, and barely manage As. This was to little avail (nothing was really that novel, I just kept reinventing old techniques), so by my second year I went all-in on applied work so I could rack up publications. Consequently, at this point, I'm honestly a little out of touch with my favorite area of work. My obsession with that specific domain of research is what makes me think that what I want is a PhD. Personally, truth be told, I generally dislike the process of applied research. Most of it is, of course, just handing data and applying existing ideas in ML (maybe with some cool strokes of insight here and there). While I think it's really important and meaningful, I definitely don't enjoy it in the same way. I don't feel that thrill of ideation, and I'm not sure if I'd rather just try solving problems like that in industry. So my interest in research is already somewhat specific. My end goal is to continue to do research, either at big tech companies where I can work on new innovations at the forefront of AI or simply in universities. The least painful viable path to that is what I want, and a PhD seems like what I'd need to get there. There are a couple things I'm worried about though: As soon as things start shifting too far from my core interests, while I suck it up and still work hard, I don't feel the same sheer enjoyment doing research. The results are cool and mean something, but the process is less fun. I feel more like I'm doing ML engineering than research. Might as well just do industry or a master's instead? A PhD is 6-7 years. I know this sounds a bit silly (like, duh - that's what a PhD is! it takes time, and everything is a gamble), but that honestly scares me. The fear of future regret, my worries that I'll perish from not publishing enough (or being forced to spend all my time doing research I don't truly enjoy), or just general fear of me not being cut out to take advantage of opportunities a PhD confers and ultimately just ending up in the same place/worse off than if I'd just entered industry straight away. I have mixed feelings about SDE/MLE/DS roles in big tech. I've had internships in legacy big tech companies and honestly the fact that work was kinda chill, big impacts took less effort, work-life balance was not nonexistent and the problems were somewhat interesting made it an overall enjoyable experience. But when I'm thinking about my life long-term, I want to produce something that actually means something to me. Helping make (however minuscule) minor contributions to fundamental ideas is at the intersection of "fun" and "meaningful" for me, which is why it's my top pick. Second to that is applied work in healthcare, third applied work in space exploration. Not sure if a PhD is the way to go for those two. Anyway, this is sort of an infodump, and I was generally just hoping to get some advice from people with more experience and practical wisdom than I do. Thank you for taking the time to read this. submitted by /u/FM-2070 [link] [comments]
- So, take that.by /u/AnanasInHawaii (ChatGPT) on December 7, 2023 at 4:36 pm
submitted by /u/AnanasInHawaii [link] [comments]
- Do diffusion models depend on user input to learn?by /u/-TracerBullet (Artificial Intelligence Gateway) on December 7, 2023 at 4:36 pm
Right off the bat, I know very little about this stuff. But I have seen arguments on FB wherein opponents of AI art say that even if you use it just for personal "fun", you are contributing to some vast scraping of data that could potentially rob artist's of their livelihood. Proponents of AI then chime in that that's not how diffusion models work. Neither side explains their position in much detail. Does it train independent of users or does it require more use to develop? submitted by /u/-TracerBullet [link] [comments]
- How to Use Blackbox AI: Revolutionize Your Coding with Blackbox AI!by /u/Chisom1998_ (Artificial Intelligence Gateway) on December 7, 2023 at 3:39 pm
In this video, we explore the mind-blowing capabilities of Blackbox AI, a revolutionary AI tool that outshines the rest, especially for tech-savvy individuals, web developers, and coding enthusiasts. 🌟 Seeking simplicity and precise answers? Blackbox AI has you covered! Say goodbye to generic responses and hello to dedicated coding solutions. Discover how to integrate Blackbox AI seamlessly into your Chrome browser and VS Code editor, allowing you to extract text from videos and images effortlessly. No more external searches – get instant solutions within your coding environment. 👉 Ready to embark on this coding adventure? Simply search 'Blackbox AI' in your Chrome browser, click on https://www.blackbox.ai/, and follow the steps to sign up. Post any coding question, and Blackbox AI will deliver detailed and helpful answers. 🤖 Ask questions like 'How to connect TV in Node.js' or 'Step-by-step process of connecting MongoDB database to Node.js.' Blackbox AI will provide the insights you need! 📚 Testing another question: 'Importance of automation testing.' Watch as Blackbox AI effortlessly explains the software testing methodology, utilizing specialized tools to validate software functionalities. https://youtu.be/0RzWr9REbaE submitted by /u/Chisom1998_ [link] [comments]
- New custom GPTs for 30-40% improvement in all tasks: Neuro Nexus & Aligned GPTby /u/No-Transition3372 (Artificial Intelligence Gateway) on December 7, 2023 at 3:39 pm
submitted by /u/No-Transition3372 [link] [comments]
- [D] I keep seeing ambiguous use of terminology in academic and SoTA papersby /u/reverendCappuccino (Machine Learning) on December 7, 2023 at 3:29 pm
I think the community should do better and maybe even converge explicitly on some terminology, even though in still developing fields it is normal to have concepts that are discovered to be equal or different on the run. I keep seeing "residual connection" as "skip connection", but I think the latter is the identity map and the former is the residual function. I keep seeing kernels and filters used interchangeably but it would be nice to have a way to express the "depthwise" slices of the 2D convolution filters (whose weights are in a 3D tensor). I keep seeing embedding dimension for that of a Query/Key matrix, but then PyTorch MultiHeadAttention divides the projection instead of repeating it (iykyk I'm not talking about the efficiency turnaround per se). This are surely or mostly minor issues, but slow down code implementations and mathematical understanding whenever notation is unclear and code isn't available, which unfortunately happens quite often. What bothers you, what do you find helpful? submitted by /u/reverendCappuccino [link] [comments]