# Googles New AI Research Is Incredible! (The Sky Is the limit....)

## Метаданные

- **Канал:** TheAIGRID
- **YouTube:** https://www.youtube.com/watch?v=FlFe8sMiKOE
- **Дата:** 22.09.2024
- **Длительность:** 13:44
- **Просмотры:** 33,426

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Links From Todays Video:
https://x.com/denny_zhou/status/1835761801453306089

00:00 - Introduction and Danny Zhou's statement
00:38 - Explanation of chain of thought prompting
01:41 - Importance and limitations of Transformers
03:01 - Breakdown of the groundbreaking claim
04:16 - Intermediate reasoning tokens explained
05:44 - Constant depth sufficiency discussion
06:55 - How this changes AI understanding
07:42 - Viral post and AGI implications
08:59 - Detailed analysis of AGI claims
10:39 - Significance of the research findings
11:44 - Transformers' versatility and future implications
12:26 - Closing thoughts and call to action
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## Содержание

### [0:00](https://www.youtube.com/watch?v=FlFe8sMiKOE) Introduction and Danny Zhou's statement

so today was a rather fascinating day as we got Denny Joe the person who is the founder and lead of the reasoning team at Google deepmind with a profound statement that fundamentally changes our understanding of Transformers he stated that we have mathematically proven that Transformers can solve any problem provided they are allowed to generate as many intermediate reasoning tokens as needed remarkably constant depth is sufficient so this is where we take a look at this paper they are referring to called Chain of Thought empowers Transformers to solve inherently serial problems so of course most of you know what Chain of Thought prompting is but for those of you who don't here's a quick example let's imagine you're asking a friend to help you plan a birthday party with standard prompting

### [0:38](https://www.youtube.com/watch?v=FlFe8sMiKOE&t=38s) Explanation of chain of thought prompting

you might ask what kind of party should I throw for my 30th birthday and your friend might simply reply you should have a beach barbecue now while that's helpful you don't know why they suggested that or how they came to that conclusion now with Chain of Thought prompting you'd ask what kind of party should I throw for my 30th birthday but this time you add can you walk me through your thinking and here's where your friend might respond okay let's think about this one it's your 30th so it should be something special but not too wild two you love the outdoors and it's summertime three you have a lot of friends so you need space for everyone four you enjoy casual relaxed environments five cooking is one of your hobbies and now putting all that together I think a Beach barbecue would be perfect you can enjoy the outdoors have plenty of space for friends keep it casual and show off your grilling skills now this is Chain of Thought in action instead of just getting an answer you see the reasoning process it's like the difference between a teacher just marking your math homework right or wrong versus showing you how to solve the problem step by step now this approach helps in complex problem solving because it allows us to see and check each step of the ai's reasoning just like we can follow our friends thought process in planning the party

### [1:41](https://www.youtube.com/watch?v=FlFe8sMiKOE&t=101s) Importance and limitations of Transformers

it's especially useful when dealing with tricky questions where the reasoning is as important as the final answer so why is this stuff even important it's because Transformers actually have inherent limitations with their design so for Transformers are like the brain of modern AI language models think of them as incredibly smart text process that can understand and generate humanlike text now Transformers have a unique superpower and this is their ability to handle many pieces of information at the same time it's like having a team of expert researchers who can all read different parts of a book simultaneously and then instantly share their understanding with each other however Transformers struggle with tasks that need to be done step by step one after the other for example Transformers might struggle with a math problem that requires multiple steps to solve or a logic puzzle where each clue Builds on the previous one there Des design to look at everything at once which is great for many tasks but not ideal for problems that need a chain of reasoning this limitation is why the Chain of Thought prompting is so important it's like giving our Factory a blueprint that shows the step-by-step process for building a house by encouraging the AI to show its work we're helping it overcome its natural limitation with sequential tasks now let's actually break down this statement and why it's truly groundbreaking regarding the paper Transformers solving any problem when the authors state that Transformers can solve any problem they are making a bold Claim about the potential universality

### [3:01](https://www.youtube.com/watch?v=FlFe8sMiKOE&t=181s) Breakdown of the groundbreaking claim

of Transformers as computational model Transformers have already proven their prowess in natural language processing and a variety of other tasks however their ability to solve any problem has always been viewed within certain limitations based on their design as parallel processing units now to say that a Transformer can solve any problem implies that it has the potential to emulate the functionality of a general purpose computer or more technically a touring machine provided it is configured correctly this is a sweeping claim because it suggests that Transformers are not just specialized tools for textt generation or language understanding but they could in theory be applied to any domain of computation ranging from mathematical problem solving to complex decision-making tasks however and here's where you should pay attention there's a critical qualification in this claim the Transformer needs to be allowed to generate as many intermediate reasoning tokens as needed this caveat actually highlights the importance of intermediate reasoning in the problem solving process the implication here is that while Transformers can solve any problem the solution may not be direct or immediate instead it requires a gradual step-by-step approach where the model builds up its understanding and solution iteratively using a sequence of intermediate steps now let's talk about where he says provided they are allowed to generate as many intermediate reasoning tokens as needed the concept

### [4:16](https://www.youtube.com/watch?v=FlFe8sMiKOE&t=256s) Intermediate reasoning tokens explained

of intermediate reasoning tokens is Central to this discussion and is what underpins the Chain of Thought mechanism to understand this better let's draw an analogy to human reasoning when humans solve a complex problem such as a ma mathematical proof or a logical puzzle they do not jump directly to the answer instead they go through a series of intermediate steps each step is reasoned out based on the results or insights obtained from the previous steps these intermediate steps which might involve breaking down the problem into smaller sub problems or formulating partial Solutions are crucial for arriving at The Final Answer similarly for Transformers to solve complex problems they need to be capable of generating these intermediate reasoning steps which in the context of AI are referred to as tokens each token represents a part of the model's thought process or reasoning chain that builds upon the previous tokens this chain is not predefined or fixed it can be dynamically constructed based on the problem at hand and the specific reasoning path the model takes the Chain of Thought technique is essentially about guiding the model to think step by step rather than attempting to LEAP directly to the solution instead of providing just an answer the model outputs a series of intermediate tokens that outline its reasoning process this approach not only provides a more interpretable way to understand how the model reaches a decision but it also dramatically expands the types of problems the Transformer can handle it's like giving the model the ability to explain its work as it goes along which is crucial for solving more intricate and challenging problems that cannot be addressed through simple parallel computation now let's talk about the

### [5:44](https://www.youtube.com/watch?v=FlFe8sMiKOE&t=344s) Constant depth sufficiency discussion

last part which is the most surprising constant depth is sufficient in the realm of neural networks depth refers to the number of layers in the model the depth of a network is often equated with its capacity to learn complex representations of data more layers generally mean more complex features can be learned therefore deeper models have been the go-to solution for tackling harder problems because they have more computational power to process and Abstract data in multiple steps however deeper models also come with increased computational cost more memory usage and longer training times they are more complex to design optimize and maintain which poses practical challenges in deploying them in real world scenarios hence the idea that a Transformer with a constant fixed and relatively small number of layers meaning it doesn't get deeper as the problems get more complex if it could still solve any problem is quite radical so in summary what the authors are saying is that you don't need to stack more and more layers onto a Transformer to increase its problem solving capability instead if you enable the model to generate as many intermediate steps tokens as necessary it can achieve the same computational outcomes this is because these intermediate steps effectively unroll the complexity over time rather than

### [6:55](https://www.youtube.com/watch?v=FlFe8sMiKOE&t=415s) How this changes AI understanding

embedding it in a deep stack of layers the constant depth allows the model to compute one step of the reasoning process at a time but by generating a sequence of such steps it can build up to solve very complex problems so how does this actually change our understanding of AI this concept challenges the conventional wisdom that deeper models are inherently better for more complex tasks instead it suggests that we can build highly capable models that remain shallow but Leverage The Power of generating intermediate reasoning steps to perform sequential computation the mathematics behind this claim as explained in the paper draws on the concept of circuit comp complexity the authors use circuit complexity to explain why K is so powerful they show that without Co Transformers can only solve problems that belong to simpler classes like ac0 which are solvable in parallel without needing much sequential

### [7:42](https://www.youtube.com/watch?v=FlFe8sMiKOE&t=462s) Viral post and AGI implications

computation however when K is introduced the class of problems that Transformers can solve expands dramatically with coot a Transformer can simulate the behavior of circuits that solve problems in the P poly class this is a vast Improvement because it encompasses a wide range of problems that require both parallel and serial computation now I do believe that this post went quite viral as on a first glance the statement that Transformers can solve any problem under certain conditions does sound ambitious and might lead some to think about the potential for artificial general intelligence however it is important to clarify what the authors are actually saying in the context of this paper and whether this really implies AGI or not when the authors state that Transformers can solve any problem they are specifically referring to the theoretical capabilities of Transformers to simulate certain types of computations when equipped with the Chain of Thought mechanism and allowed to generate as many intermediate steps as needed this means that in theory Transformers can perform any computation that a circuit of arbitrary size can perform given enough time and steps this is a significant finding that demonstrates the flexibility and power of Transformers enhanced by K so does this mean that Transformers with Chain of Thought are AGI I mean not really but it's still pretty insane so let's actually take a look at things in more

### [8:59](https://www.youtube.com/watch?v=FlFe8sMiKOE&t=539s) Detailed analysis of AGI claims

detail DET the claim made by the authors is primarily about the theoretical expressiveness of Transformers in theory they show that Transformers with a Chain of Thought can compute anything that can be represented by a Boolean Circuit of polinomial size this theoretical result is akin to saying that a Transformer could simulate any computational process given enough steps however this is not the same as saying a Transformer can operate with the broad intelligence adaptability and understanding that we associate with AGI the author's claim can be likened to saying that these Transformers have a form of computational universality in computer science a touring machine is a universal model of computation that can simulate any other machine given the correct instructions however being able to perform any computation does not mean it has understanding awareness or the ability to autonomously pursue goals like a human even if Transformers can emulate a touring machine's functionality in principle this does not mean they possess general intelligence sadly AGI requires the integration of many cognitive abilities learning perception reasoning planning and more that work seamlessly across a variety of tasks the fact that Transformers with Coe can solve complex Problems by generating intermediate reasoning steps does actually show that these models are more powerful than previously thought however their ability to solve problems still heavily depends on how well they are guided or prompted without proper prompting and structure these models can fail spectacularly now current Transformers even with coot do not possess the meta cognitive abilities to autonomously break down new problems understand novel contexts without large scale data or dynamically adjust their strategies in the way an AGI would need

### [10:39](https://www.youtube.com/watch?v=FlFe8sMiKOE&t=639s) Significance of the research findings

to so overall whilst this paper is an AGI it certainly is a large step towards it and it's incredible because it mathematically proves that Transformers when using Chain of Thought can solve any type of problem that can be represented by a certain kind of logic circuit known as POI in computer science and think of it this way imagine Transformers are like problems solving machines before we knew they could solve some tricky problems when guided step by step but we didn't know just how powerful they could become this paper shows that with enough steps in their reasoning process like a detailed checklist they can handle extremely complex problems not just easy ones it's like discovering that your calculator can secretly perform Advanced calculus as long as you let it show all the steps another major finding is that Transformers don't need a lot of layers to solve complex problems if they use Chain of Thought traditionally we thought that deeper models with more layers were always better at handling difficult tasks because they could process more information this paper shows that even Transformers with a small fixed number of layers constant depth can solve very hard problems as long as they are allowed to think step

### [11:44](https://www.youtube.com/watch?v=FlFe8sMiKOE&t=704s) Transformers' versatility and future implications

by step this is like saying you don't need a huge complex computer to solve a big problem you just need a small clever computer that can carefully follow a long list of instructions so before this research Transformers were seen mainly as good at doing things in parallel solving many parts of a problem at once like sorting a deck of cards quickly by having multiple people each sort apart they weren't considered good at solving problems that need careful step-by-step thinking this paper changes that view it shows that with Chain of Thought Transformers can handle both types of problems the quick parallel ones and the more complex sequential ones that require reasoning through several steps this makes Transformers much more versatile than we thought they can be guided to think out loud and work through problems that need deeper logical thinking like a human would now

### [12:26](https://www.youtube.com/watch?v=FlFe8sMiKOE&t=746s) Closing thoughts and call to action

some of you might be wondering how this compared to open I's Recent research with their new model open ai1 you've probably heard that open ai1 ranks incredibly high on tough challenges like competitive programming code forces and also ranks among the top 500 in a qualifier for the USA math Olympiad and even outperforms humans with phds in science problems now these are impressive Feats but how does all this tie into what we're talking about with Chain of Thought and Transformers well both open ai's new research and the recent paper on Chain of Thought are built around the same core idea teaching AI to think step by step rather than just spitting out an answer the recent paper provides the theory behind why this works if you guide AI through a series of reasoning steps it can solve much harder problems open ai1 shows this idea in action its success in programming Math and Science isn't because it's just bigger or has more data it's because it has been trained to think more like humans do breaking down problems into steps so this is why this is such a shift both the paper Google deepmind and open eyes research suggest a shift in how we should build AI in the future instead of just focusing on making models bigger the key is to make them better thinkers this means more focus on how we train them to reason and solve problems step by step which can lead to more efficient powerful and versatile AI

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*Источник: https://ekstraktznaniy.ru/video/14061*