What is Google answer to ChatGPT?

What is Google answer to ChatGPT?

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.

What is Google answer to ChatGPT?
Tech Buzzwords of 2022, By Google Search Interest

Open AI is an artificial intelligence research laboratory that was founded by some of the biggest names in tech, including Elon Musk and Sam Altman. This non-profit organization seeks to develop general artificial intelligence that is safe and beneficial to society. One of their key initiatives is the development of open source technologies like GPT-3, which is a natural language processing model used in ChatGPT.

2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams
2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams

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!

What is Google answer to ChatGPT?
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.

AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence Intro
AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence
Intro

ChatGPT uses supervised learning techniques to train its models.

Supervised learning involves providing a model with labeled data (i.e., data with known outcomes) so that it can learn from it. This labeled data could be anything from conversations between two people to user comments on a website or forum post. The model then learns associations between certain words or phrases and the desired outcome (or label). Once trained, this model can then be applied to new data in order to predict outcomes based on what it has learned so far.

In addition to supervised learning techniques, ChatGPT also supports reinforcement learning algorithms which allow the model to learn from its experiences in an environment without explicit labels or outcomes being provided by humans. Reinforcement learning algorithms are great for tasks like natural language generation where the output needs to be generated by the model itself rather than simply predicting a fixed outcome based on existing labels.

Supervised Learning

Supervised learning involves feeding data into machine learning algorithms so they can learn from it. For example, if you want a computer program to recognize cats in pictures, you would provide the algorithm with thousands of pictures of cats so it can learn what a cat looks like. This same concept applies to natural language processing; supervised learning algorithms are fed data sets so they can learn how to generate text using contextual understanding and grammar rules.

Reinforcement Learning

Reinforcement learning uses rewards and punishments as incentives for the machine learning algorithm to explore different possibilities. In ChatGPT’s case, its algorithm is rewarded for generating more accurate responses based on previous interactions with humans. By using reinforcement learning techniques, ChatGPT’s algorithm can become smarter over time as it learns from its mistakes and adjusts accordingly as needed.


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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

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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?
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.”

Google Bard
Google Bard to rival ChatGPT

Is ChatGPT the End of Google?

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.


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Gemini, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, Promp Engineering)

What all ChatGPT can do?

  1. It can help with general knowledge information.
  2. Remember what user said in previous conversation.
  3. Allow users to provide follow-up corrections.
  4. Trained to decline inappropriate requests.
  5. It can write a program in any language you prefer on real-time. for example — write classification code sample in sklearn python library.
  6. It can fix your piece of code and also explain what went wrong and how it can be fixed.
  7. It can even generate song or rap lyrics
  8. Even much more….

Some best usages of ChatGPT:

  1. Make a diet and workout plan
  2. Generate the next week’s meals with a grocery list
  3. Create a bedtime story for kids
  4. Prep for an interview
  5. Solve mathematical problem
  6. Fix software program or write a program
  7. Plan your trip and tell expected expenses

What are its limitations of ChatGPT?

  1. May occasionally generate incorrect information
  2. May occasionally produce harmful instructions or biased content
  3. 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

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.

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.

r/ChatGPT - this is insane
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.

r/ChatGPT - This alternate ending to Breaking Bad is wild
What is Google answer to ChatGPT?

r/ChatGPT - This alternate ending to Breaking Bad is wild

New slogan for McDonald’s by ChatGPT

ChatGPT Example
ChatGPT Example: Make a new, brutally honest slogan for McDonald’s. It should sound funny too.

Should… should we be concerned?

r/ChatGPT - Should… should we be concerned?
What is Google answer to ChatGPT?

r/ChatGPT - Should… should we be concerned?

I asked ChatGPT to write a poem about rust

r/rustjerk - I asked ChatGPT to write a poem about rust

ChatGPT chooses Democrats over Republicans

r/ChatGPT - 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.

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:

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– 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.

“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.

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  • 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 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.

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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/

What is Google's answer to ChatGPT
What is Google’s answer to ChatGPT

List of ChatGPT's examples, capabilities and limitations

ChatGPT to save time with insurance denials

Tech Buzzwords of 2022, By Google Search Interest

Tech Buzzwords of 2022, By Google Search Interest
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.

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”.

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.

Well, I asked it directly.

Here’s what it answered:

Should we be scared of ChatGPT?
Should we be scared of ChatGPT?

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.

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.

The GPT-3 economy

Here are 8 ways ChatGPT can save you thousand of hours in 2023

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.

Boom.

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

The result:

A virtual customer service bot that can hold a conversation and answer questions (meaningfully).

4- Counsellor

When it comes to turbulent personal questions, Chatbot may spit out a disclaimer, but it will also give you straightforward and actionable advice.

5- Coding

ChatGPT is opening the development of:

  • Apps
  • Games
  • Websites

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.

Follow me for more Machine Learning content!

#machinelearning #datascience #ChatGPT

People have already started building awesome apps on top of #ChatGPT: 10 use cases 
1. Connect your ChatGPT with your Whatsapp.
Link: https://github.com/danielgross/whatsapp-gpt

2. ChatGPT Writer : It use ChatGPT to generate emails or replies based on your prompt!
Link: https://chrome.google.com/webstore/detail/chatgpt-writer-email-writ/pdnenlnelpdomajfejgapbdpmjkfpjkp/related

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!
Link: https://chrome.google.com/webstore/detail/youtube-summary-with-chat/nmmicjeknamkfloonkhhcjmomieiodli/related

5. TweetGPT: It uses ChatGPT to write your tweets, reply, comment, etc.
Link: https://github.com/yaroslav-n/tweetGPT

6. Search GPT: It display the ChatGPT response alongside Google Search results
Link: https://github.com/wong2/chat-gpt-google-extension

7. ChatGPT or all search engines: You can now view ChatGPT responses on Google and Bing!
Link: https://chrome.google.com/webstore/detail/chatgpt-for-search-engine/feeonheemodpkdckaljcjogdncpiiban?ref=producthunt

8. Save all your Prompts?: The `ChatGPT History` extension has you covered!
Link: https://chrome.google.com/webstore/detail/chatgpt-prompt-genius/jjdnakkfjnnbbckhifcfchagnpofjffo

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
Link: https://flowgpt.com

#searchengines #people #building #google #comment #video #writer

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:

  1. 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.
  2. 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.
  3. 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.
  4. 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!

Cause this is gonna be one of the biggest opportunities of this century 🚀#technology #opportunities #ai #machinelearning #planning

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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.”

Read more at https://www.cnn.com/2023/02/06/tech/google-bard-chatgpt-rival

ChatGPT-4

chatGPT4

  • [Discussion] How AI exactly have their own consciousness?
    by /u/Illustrious_Board_75 (Machine Learning) on February 13, 2025 at 10:12 pm

    Hey, I’m sure this question has been asked before, but I’m an IT support guy who recently got interested in AI. I understand the basics of neural networks—how they take inputs, apply weights, sum them up, add bias, and pass through an activation function. At the end of the day, it’s just optimizing a huge mathematical function. But when scaled up, AI models seem to develop their “own way of thinking” or even “sense of self.” How does that emerge purely from mathematical optimization? Is it just an illusion of complexity, or is there something deeper going on? Would love to hear thoughts from those more experienced in AI! submitted by /u/Illustrious_Board_75 [link] [comments]

  • [R] Mutation-Guided LLM-based Test Generation at Meta
    by /u/AhmedMostafa16 (Machine Learning) on February 13, 2025 at 9:26 pm

    submitted by /u/AhmedMostafa16 [link] [comments]

  • [D] How did you find your specialty?
    by /u/violincasev2 (Machine Learning) on February 13, 2025 at 8:39 pm

    For context, I’m an undergrad looking forward to applying to PhD programs next year. I’m certain I want to study ML, but that’s a very broad topic. I’ve dipped my toes all around, doing research/projects in NLP, interpretability, diffusion, recommendation systems, manifold/geometric methods, and will be doing work in music and maybe in RL. How did you all find your domains, and how important is it to know precisely what I want going into grad school? submitted by /u/violincasev2 [link] [comments]

  • [D] Upscaling model
    by /u/jiraiya1729 (Machine Learning) on February 13, 2025 at 6:56 pm

    I need a model which upscales the current image resolution with more emphasis on inference time ( in milli secs ) Do you guys know any model? submitted by /u/jiraiya1729 [link] [comments]

  • [R] AlignRec Outperforms SOTA Models in Multimodal Recommendations
    by /u/skeltzyboiii (Machine Learning) on February 13, 2025 at 5:13 pm

    AlignRec, introduced in AlignRec: Aligning and Training in Multimodal Recommendations (CIKM '24), tackles misalignment in multimodal recommendation systems. Traditional methods struggle to integrate diverse content types—text, images, and categorical IDs—due to semantic gaps. AlignRec addresses this by optimizing three alignment tasks: inter-content (ICA), content-category (CCA), and user-item (UIA). ICA unifies semantic representations with an attention-based encoder, CCA enhances feature alignment using contrastive learning, and UIA refines user-item representations via cosine similarity loss. A key innovation is AlignRec’s two-stage training: pre-training aligns visual and textual data, while fine-tuning incorporates user behavior for optimized recommendations. Tested on Amazon datasets, it outperforms nine SOTA models, excelling in long-tail recommendations. By bridging multimodal semantic gaps, AlignRec improves both accuracy and robustness, advancing multimodal AI-driven recommendations. For a deeper dive into the framework and results, see the full paper write-up here: https://www.shaped.ai/blog/multimodal-alignment-for-recommendations submitted by /u/skeltzyboiii [link] [comments]

  • [D] How you do ML research from scratch?
    by /u/AntelopeWilling2928 (Machine Learning) on February 13, 2025 at 4:40 pm

    Someone who has published their works at top ML conferences (NIPS, ICML, ICLR) or domain oriented conferences (CVPR, ICCV, ACL, EMNLP, KDD, SIGIR). 1. How do you get from 0 to your first paper? 2. How much is your skill (Pytorch, or domain knowledge)? 3. What is the whole process that you follow to become good at implementing your ideas? 4. How do you come up with an idea and solution? submitted by /u/AntelopeWilling2928 [link] [comments]

  • [R] SWE-agent is the new open-source SOTA on SWE-bench Lite
    by /u/ofirpress (Machine Learning) on February 13, 2025 at 3:35 pm

    SWE-agent is an open source software engineering agent that works with any kind of model. Our 1.0 release adds tons of new features: massively parallel runs; cloud-based deployment; extensive configurability with tool bundles; new command line interface & utilities. Completely open-source (MIT), extensive configuration, easy to hack. Since it uses LiteLLM for LM interfacing, you can use it with a local LM: we've used it with Qwen and other community members have used it with Llama. https://github.com/swe-agent/swe-agent SWE-agent is now powered by our new SWE-ReX package (also MIT licensed), a lightweight, general purpose sandboxed code execution engine that supports local Docker, AWS, Modal deployments https://github.com/SWE-agent/swe-rex. You can use it to easily build your own agent with code execution from scratch without the hassle of figuring out how to communicate with running docker containers! SWE-agent is developed by us at Princeton University & Stanford. We'll be here if you have any questions. submitted by /u/ofirpress [link] [comments]

  • [D] Could reasoning LLMs help use identify relevant works a lot better today?
    by /u/MadEyeXZ (Machine Learning) on February 13, 2025 at 3:05 pm

    I know there are lots of helpful services that help you digest the latest papers in arXiv, like arxiv-sanity, paper digest, arXivist, IArxiv, etc. Most of them uses ML (TF-IDF) to rank papers according to your interest, but even with their help, I am still flooded with papers. Most of the tools are built pre-LLM (especially pre-reasoning model), do you guys think reasoning LLMs could help us identify relevant works from arXiv daily publication a lot better? Or have you heard of any existing approaches? submitted by /u/MadEyeXZ [link] [comments]

  • [R] Text-to-SQL in Enterprises: Comparing approaches and what worked for us
    by /u/SirComprehensive7453 (Machine Learning) on February 13, 2025 at 1:44 pm

    Hi everyone! Text-to-SQL is a popular GenAI use case, and we recently worked on it with some enterprises. Sharing our learnings here! These enterprises had already tried different approaches—prompting the best LLMs like O1, using RAG with general-purpose LLMs like GPT-4o, and even agent-based methods using AutoGen and Crew. But they hit a ceiling at 85% accuracy, faced response times of over 20 seconds (mainly due to errors from misnamed columns), and dealt with complex engineering that made scaling hard. We found that fine-tuning open-weight LLMs on business-specific query-SQL pairs gave 95% accuracy, reduced response times to under 7 seconds (by eliminating failure recovery), and simplified engineering. These customized LLMs retained domain memory, leading to much better performance. We put together a comparison of all tried approaches on medium. Let me know your thoughts and if you see better ways to approach this. https://preview.redd.it/kqfabsdkuwie1.png?width=1920&format=png&auto=webp&s=88251e0cfa246f2bf1f779e708ab03a96a3c0255 submitted by /u/SirComprehensive7453 [link] [comments]

  • [D] License issue with self-collected dataset using online image
    by /u/RepresentativeAd985 (Machine Learning) on February 13, 2025 at 1:34 pm

    So I am working on a dataset by collecting and annotating online images. Unfortunately not all of the images are under CC license. Is it appropriate to only include links for these images in my published dataset? (Like is it considered fair use or would it causes any trouble?) Is there any popular public image datasets including images not under CC license that I should refer to? I’m very not familiar with these copyright related things so apologies in advance if I made any mistakes in the description of the question. submitted by /u/RepresentativeAd985 [link] [comments]

  • [R] Automated Capability Discovery: Using Foundation Models to Self-Explore and Evaluate AI Abilities
    by /u/Successful-Western27 (Machine Learning) on February 13, 2025 at 9:43 am

    This paper introduces a framework called Automated Capability Discovery (ACD) that uses one foundation model to systematically explore and evaluate the capabilities of another model. The core idea is to treat capability discovery as an experimental science, where one model acts as a scientist generating hypotheses and designing tests. Key technical points: - Framework consists of four main components: task generation, execution, evaluation, and analysis - Uses prompting strategies to make the evaluator model generate diverse, meaningful tests - Implements a feedback loop where test results inform future task generation - Evaluation includes both binary success/failure and detailed analysis - Tested on GPT-4, Claude, and Llama models as both evaluators and subjects Results: - Discovered thousands of previously undocumented capabilities - 89% agreement between AI evaluator and human verification on capability assessments - Generated tests covered broad capability categories from basic (arithmetic) to complex (creative writing) - Successfully identified known model limitations - Showed strong correlation between automated and manual evaluation methods I think this approach could transform how we understand and evaluate AI systems. Instead of relying solely on predefined benchmarks or manual testing, we could have continuous, automated exploration of model capabilities. This would be especially valuable for rapid testing of new models and identifying unexpected abilities or limitations. I think the main challenge will be ensuring the evaluator model isn't limited by the same blindspots as the subject model. There's also the question of how well this generalizes beyond language models to other AI architectures. TLDR: New framework uses AI models to automatically discover and evaluate the capabilities of other AI models, showing strong agreement with human evaluations and finding thousands of previously unknown abilities. Full summary is here. Paper here. submitted by /u/Successful-Western27 [link] [comments]

  • [D] Need suggestions for image classification problem in 2025
    by /u/Eternal1314 (Machine Learning) on February 13, 2025 at 5:55 am

    Back in late 2022 I have trained a image classification model (medical images, high res) using EfficientNet_V2 with around 20k of data. Now I want to retrain the model since I have access to a larger amount of data (~300k). I want to ask for few suggestions. I have tried using ViT before, but its performance is relatively bad. I have read some comments back in the days that ViT has some issues on handling high res image. But now I noticed that Nvidia is using Transformer on DLSS. I assume that high res is no longer the problem of ViT. Which ViT model on image classification is recommended to try? I have been always using pre-trained weight as starting point and do the finetuning, because I was told to do so by many articles/online information I have read and it does perform better. Is it still recommend to use pre-trained weight in 2025? Especially most image model are train on low res data (224-512) and my dataset are high res. Is CNN outdated in 2025? I think the competition of CNN and Transformer on image-related problem are unclear at 2023. But started from mid-2024 I saw lots of people saying Transformer has won. submitted by /u/Eternal1314 [link] [comments]

  • [D] Inquiry on PGR metric used in Weak to Strong Generalization
    by /u/nyesslord (Machine Learning) on February 13, 2025 at 5:49 am

    Not sure if this is the appropriate subreddit or not but I have a question on Weak-to-Strong Generalization paper. The experiments in the paper measure PGR which compares the relative performance of a stronger model after it's trained against the labels of weaker model vs just the performance of a weaker model for a variety of tasks. Would it not be more appropriate to measure the performance gains from just the base strong model instead? Is this because the paper is trying to draw analogies to humans aligning stronger AI systems and so cares more about performance gains relative to humans? submitted by /u/nyesslord [link] [comments]

  • [D] Creating a causal DAG for irregular time-series data
    by /u/Sea_Farmer5942 (Machine Learning) on February 13, 2025 at 1:47 am

    Hey guys, So I've made a previous post recently about causal inference with irregular time-series data. I like the idea of using a dynamic Bayesian network to do so, hence I've reworded the question to this. I am unsure how to tackle time-series data where there is an irregular sampling resolution. Specifically, in a sport scenario where there are 2 teams and the data is event-by-event data, where these events, such as passing the ball, occur sequentially from the start to the end of the match. Ultimately, I would like to explore causal effects of interventions in this data. Someone recommended the use of an SSM. To my understanding, when it is discretised, it could be represented as a DAG? Then I have a structure to represent these causal relationships. Other workflows could be: - this library: https://github.com/jakobrunge/tigramite - using ARIMA to detrend the time-series data then use some sort of Bayesian inference to capture causal effects - using a SSM to create a causal structure and Bayesian inference to capture causal effects - making use of the CausalImpact library - also GSP then using graph signals as input to causal models like BART Although I suggested 2 libraries, I like the idea of setting out a proper causal workflow rather than letting a library do everything. This is just so I can understand causal inference better. I initially came across this interesting paper: https://arxiv.org/pdf/2312.09604 which doesn't seem to work with irregular sampling resolutions. There is also bucketing the time-series data, which would result in a loss of information. Cause-effects wouldn't happen straight away in this data, so bucketing it in half-a-second or second could work. I'm quite new to causal inference, so any critique or suggestions would be welcome! Many thanks! submitted by /u/Sea_Farmer5942 [link] [comments]

  • [R] Is there any good books/tutorials on combining CV and NetCDF files together
    by /u/Necessary-Arm-6055 (Machine Learning) on February 13, 2025 at 1:27 am

    Hi, I have to do machine learning model. In the process of combining data I am doing fine with the CSV file, but NetCdf. I am just lost and don’t know where to start to learn about combining them together . Any advice at all would help submitted by /u/Necessary-Arm-6055 [link] [comments]

  • [R] "o3 achieves a gold medal at the 2024 IOI and obtains a Codeforces rating on par with elite human competitors"
    by /u/we_are_mammals (Machine Learning) on February 12, 2025 at 10:55 pm

    Competitive Programming with Large Reasoning Models OpenAI We show that reinforcement learning applied to large language models (LLMs) significantly boosts performance on complex coding and reasoning tasks. Additionally, we compare two general-purpose reasoning models - OpenAI o1 and an early checkpoint of o3 - with a domain-specific system, o1-ioi, which uses hand-engineered inference strategies designed for competing in the 2024 International Olympiad in Informatics (IOI). We competed live at IOI 2024 with o1-ioi and, using hand-crafted test-time strategies, placed in the 49th percentile. Under relaxed competition constraints, o1-ioi achieved a gold medal. However, when evaluating later models such as o3, we find that o3 achieves gold without hand-crafted domain-specific strategies or relaxed constraints. Our findings show that although specialized pipelines such as o1-ioi yield solid improvements, the scaled-up, general-purpose o3 model surpasses those results without relying on hand-crafted inference heuristics. Notably, o3 achieves a gold medal at the 2024 IOI and obtains a Codeforces rating on par with elite human competitors. Overall, these results indicate that scaling general-purpose reinforcement learning, rather than relying on domain-specific techniques, offers a robust path toward state-of-the-art AI in reasoning domains, such as competitive programming. https://arxiv.org/abs/2502.06807 submitted by /u/we_are_mammals [link] [comments]

  • [D] Fine-tuning to replace complicated activations with simpler ones
    by /u/bjourne-ml (Machine Learning) on February 12, 2025 at 6:47 pm

    Consider the following problem. I want to run a pre-trained network for inference on accelerator hardware that doesn't support certain activation layers. Are there established techniques for fine-tuning the weights so that they will work with other activation functions? Suppose the network is EfficientNet which uses SeLU. Can I somehow fine-tune the weights to fit for ReLU or GeLU activations instead? I don't want to retrain from scratch. submitted by /u/bjourne-ml [link] [comments]

  • [P] Optimize leave-one-out cross-validation for lasso regression
    by /u/rnburn (Machine Learning) on February 12, 2025 at 5:14 pm

    Given an n×p feature matrix, X, a target vector, y, and λ ≥ 0, lasso regression) estimates the parameters, β, of a linear model by solving the optimization problem https://preview.redd.it/myapv7panqie1.png?width=1200&format=png&auto=webp&s=f35a7e4ba36cf125878884820c708b374790af45 Lasso regression is a popular method for estimating linear models as it performs both regularization and variable selection. But a natural question for users is, how do we choose λ? Often this is done by estimating prediction error with k-fold cross-validation and applying an optimization algorithm to find a value of λ that approximately minimizes the cross-validation proxy for prediction error. Many software packages choose smaller values of k as that can be more computationally tractable. (For example, sklearn’s LassoCV model defaults to 5-fold cross-validation). But small k can bias the estimation of prediction error, particularly in high-dimensional settings. More recently leave-one-out cross-validation, with k = n, has emerged as a better alternative with lower bias, [1]. Computed naively, leave-one-out cross-validation is expensive since it would require fitting lasso regression n times for each value of λ. Making use of the matrix inversion lemma, though, it is possible to compute an approximate form of leave-one-out cross-validation efficiently for GLMs [2, 3]. Going a step further, and making some adjustments to the LARS algorithm, it is actually possible to efficiently compute and optimize leave-one-out cross-validation exactly for the case of lasso regression. Before getting into details, here is a quick demo using the diabetes data set distributed with sklearn and the software package bbai: from sklearn.datasets import load_diabetes from bbai.glm import Lasso X, y = load_diabetes(return_X_y=True) model = Lasso().fit(X, y) In a few fractions of a second, this bit of code will fit a lasso regression model with λ set to exactly minimize the leave-one-out cross-validation error. As an artifact of the leave-one-out LARs algorithm (LoLARS), bbai also produces a piecewise quadratic function that computes LOOCV for any value of λ: Leave-one-out cross-validation error as a function of the lasso hyperparameter λ. We can see that LOOCV error is minimized at λ=22.18. Dots represent validation checks using a brute-force approach. Validating is easy since we can check the function against brute force computations, and the dots along the curve show such checks. You can view a notebook with the full example here and see additional validation in the test suite. Sketch of LoLARS algorithm The Karush-Kuh-Tucker (KKT) optimality conditions tell us that if β is a solution to lasso regression, then it satisfies the conditions https://preview.redd.it/rg9adsvkpqie1.png?width=1200&format=png&auto=webp&s=d21ba419f113419ec0cf9e33cf74e402270c4b20 It follows that a solution to lasso regression can be described as a piecewise linear function of λ where on each segment the active (i.e. non-zero) regressors are given by https://preview.redd.it/vc62hhhopqie1.png?width=1200&format=png&auto=webp&s=e8c3eeefe3ae3dab4c66318ce2f3752b263b6f0a where X_A denotes the active part of the design matrix X. LARS solves lasso regression by computing the piecewise linear segments of the β(λ) function. It starts at λ = ∞ where all regressors are zero and works its way backwards. Consider, for example, the data set https://preview.redd.it/66i1tsktpqie1.png?width=1200&format=png&auto=webp&s=fc7112b69cf063feaa07f9e67f7aaa4a8bffc0df Letting red, green, and blue denote the three regressors, LARS solves for the solution path Solution path produced by the LARS algorithm. The graph represents the regressors, β, as a function of λ. Vertical lines delineate the piecewise linear segments of the solution path and are numbered in the order visited by LARS. Dropping values, LARS produces the activation path Ordered active sets of regressors for the LARS algorithm. Now, let’s consider solving LARS for each leave-one-out subset. Each LARS solution produces a piecewise linear path β−i(λ). Thus, leave-one-out cross-validation error https://preview.redd.it/47k3cdt6qqie1.png?width=1200&format=png&auto=webp&s=85e81c78e96fd7789b26e441bbd31adaeb01894c will be a piecewise quadratic function of λ. Running LARS independently for the subsets would be expensive. The key to an efficient implementation is making use of the matrix inversion lemma: https://preview.redd.it/h0u3snzcqqie1.png?width=1200&format=png&auto=webp&s=6608c7a6c47dc33cd58e726c21c102db80b104de where https://preview.redd.it/vl8rra7fqqie1.png?width=1200&format=png&auto=webp&s=54d3d880cdaf8dccc5c8d3a36645bfa03667e7e7 When the activation paths of leave-one-out subsets overlap, applying the matrix inversion lemma significantly reduces the overhead of solving each LARS solution path and the cost of leave-one-out LARS is largely determined by the extent to which the leave-one-out activation paths diverge. References [1]: Kamiar Rahnama Rad, Wenda Zhou, Arian Maleki. Error bounds in estimating the out-of-sample prediction error using leave- one-out cross validation in high-dimensions. https://arxiv.org/abs/2003.01770 [2]: Kamiar Rahnama Rad, Arian Maleki. A scalable estimate of the extra-sample prediction error via approximate leave-one-out. https://arxiv.org/abs/1801.10243 [3]: Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab Mirrokni. Approximate Leave-One-Out for Fast Parameter Tuning in High Dimen- sions. https://arxiv.org/abs/1807.02694 submitted by /u/rnburn [link] [comments]

  • [R] TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models
    by /u/hardmaru (Machine Learning) on February 12, 2025 at 4:51 pm

    submitted by /u/hardmaru [link] [comments]

  • [R] New Paper: Can frontier models self-explore and discover their own capabilities in an open-ended way?
    by /u/MolassesWeak2646 (Machine Learning) on February 12, 2025 at 4:34 pm

    Title: Automated Capability Discovery via Model Self-Exploration Authors: Cong Lu, Shengran Hu, Jeff Clune. Paper: https://arxiv.org/abs/2502.07577 Abstract: Foundation models have become general-purpose assistants, exhibiting diverse capabilities across numerous domains through training on web-scale data. It remains challenging to precisely characterize even a fraction of the full spectrum of capabilities and potential risks in any new model. Existing evaluation approaches often require significant human effort, and it is taking increasing effort to design ever harder challenges for more capable models. We introduce Automated Capability Discovery (ACD), a framework that designates one foundation model as a scientist to systematically propose open-ended tasks probing the abilities of a subject model (potentially itself). By combining frontier models with ideas from the field of open-endedness, ACD automatically and systematically uncovers both surprising capabilities and failures in the subject model. We demonstrate ACD across a range of foundation models (including the GPT, Claude, and Llama series), showing that it automatically reveals thousands of capabilities that would be challenging for any single team to uncover. We further validate our method's automated scoring with extensive human surveys, observing high agreement between model-generated and human evaluations. By leveraging foundation models' ability to both create tasks and self-evaluate, ACD is a significant step toward scalable, automated evaluation of novel AI systems. https://preview.redd.it/1zamtbjzjqie1.png?width=1204&format=png&auto=webp&s=95c177136d8c77abd0b8fb4fda3d8d7f01b7a04f submitted by /u/MolassesWeak2646 [link] [comments]

  • Structured data parsing [D]
    by /u/ashblue21 (Machine Learning) on February 12, 2025 at 2:37 pm

    I am trying to build a pipeline that parses pretty complex table structures including multiline column headers and quite possibly inline images/text etc. My current approach is to use LLM's to clean the table structure and write pandas code to query the table, I first extract the row at which data starts and then merge the columns into single line and get the LLM to rename them and provide a description. Post that I ask it to write me pandas code based on the query and then use the output to generate a response, currently I am also on the way to get the first two steps done using heuristics/fine tuned SETbert and quite possibly other ML models, post which I would call the LLM to write python code and generate a response, this works ok for many tables but starts to fall apart for more complicated pipelines. Would anyone be aware of other approaches to get better results, specifically what models did you use/fine tune to get this to work? Thanks submitted by /u/ashblue21 [link] [comments]

  • [D] Causal inference in irregular time series data?
    by /u/Sea_Farmer5942 (Machine Learning) on February 12, 2025 at 12:17 pm

    Hey guys, A lot of methods I have read assume a fixed sampling resolution, which makes sense. There is also pre-processing the data by bucketing it, however is there any material you guys have read which handles a non-fixed sampling resolution, given that causal effects do occur over multiple events. What would the causal structure look like? Here is a paper I was reading, but I believe one of the conditions is regular sampling intervals: https://arxiv.org/pdf/2312.09604 Many thanks submitted by /u/Sea_Farmer5942 [link] [comments]

  • [R] LLMs as Few-Shot Data Annotators for Multilingual Text Detoxification
    by /u/Successful-Western27 (Machine Learning) on February 12, 2025 at 10:17 am

    This paper introduces a method for using LLMs as few-shot learners to generate high-quality parallel datasets for text detoxification. The key innovation is using modern LLMs to create paired toxic/non-toxic text examples that maintain semantic meaning while reducing toxicity. Main technical points: - Uses few-shot prompting with carefully curated example pairs - Implements multi-stage filtering to ensure quality - Validates semantic preservation using automated metrics - Achieves better toxicity reduction while maintaining meaning compared to existing methods - Creates larger, higher-quality parallel datasets than previous approaches Results: - Outperforms existing detoxification models on standard benchmarks - Shows strong cross-domain generalization - Demonstrates effectiveness with just 3-5 examples - Maintains semantic similarity scores >0.85 - Reduces toxicity scores by >60% on test sets I think this could be particularly valuable for content moderation systems that need to preserve meaning while removing harmful content. The ability to generate high-quality parallel data could help train better downstream detoxification models. I think the few-shot approach is especially promising because it reduces the need for large annotated datasets, which are expensive and time-consuming to create manually. TLDR: Modern LLMs can generate high-quality parallel toxic/non-toxic text pairs using few-shot learning, enabling better training data for detoxification systems while maintaining semantic meaning. Full summary is here. Paper here. submitted by /u/Successful-Western27 [link] [comments]

  • [R] LLMs Can Teach Themselves to Better Predict the Future
    by /u/jsonathan (Machine Learning) on February 12, 2025 at 6:31 am

    submitted by /u/jsonathan [link] [comments]

  • [D] What happened to SSMs and linear attentions?
    by /u/ApartmentEither4838 (Machine Learning) on February 11, 2025 at 9:27 pm

    Someone who is upto date with this area of research can summarize what is current state of SSMs and softmax attention alternatives? Are they used in cusomer focused models yet or are still in research? Does their promise only appears to be in benchmarks on a paper? or are the hardware accelerators have etched the attention so that it is fully juiced up and using SSMs or linear attention alternatives only provide marginal gains which does appeal with the level of complexity in them? submitted by /u/ApartmentEither4838 [link] [comments]

  • [D] Simple Questions Thread
    by /u/AutoModerator (Machine Learning) on February 9, 2025 at 4:00 pm

    Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]

  • [D] Monthly Who's Hiring and Who wants to be Hired?
    by /u/AutoModerator (Machine Learning) on January 31, 2025 at 3:30 am

    For Job Postings please use this template Hiring: [Location], Salary:[], [Remote | Relocation], [Full Time | Contract | Part Time] and [Brief overview, what you're looking for] For Those looking for jobs please use this template Want to be Hired: [Location], Salary Expectation:[], [Remote | Relocation], [Full Time | Contract | Part Time] Resume: [Link to resume] and [Brief overview, what you're looking for] ​ Please remember that this community is geared towards those with experience. submitted by /u/AutoModerator [link] [comments]

References:

1- https://vikaskulhari.medium.com/chatgpt-end-of-google-f6a958f38ac2

2- https://en.wikipedia.org/wiki/Meena 

3- https://en.wikipedia.org/wiki/ChatGPT

4- https://ai.googleblog.com/2020/01/towards-conversational-agent-that-can.html

5- https://www.reddit.com/r/ChatGPT/

6- https://djamgaai.web.app

7- https://www.linkedin.com/feed/update/urn:li:activity:7008020246934482945?utm_source=share&utm_medium=member_desktop

8- https://enoumen.com/2023/02/11/artificial-intelligence-frequently-asked-questions/

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