Google Does it Again
Source: tai_blog

Google Deepmind has done it again. And this time  its a double win.   They have presented AlphaProof and AlphaGeometry 2  models that have achieved silver medalist-level performance by solving challenging International Mathematical Olympiad problems  competing with the best humanity has to offer.   This is a highly interesting piece of research  as it gives insight into the cutting-edge of what the AI industry represents in terms of mathematical and reasoning progress.   At the same time  it serves as a clear indication of what Google is working on in the future: cracking the code to create what would certainly take us close to a real super AI: a depth generalizer.   But what do I mean by that?   Learning about AI is useless unless it helps you make better decisions. This is what my newsletter intends to achieve  a newsletter written for AI analysts  strategists  investors  and Leaders that looks to answer the most pressing questions in AI:   Are we in a bubble?; Is AI actually Intelligent?; or why does Taiwan matter so much?   In a nutshell  having the overall technological  geopolitical  and economic view of the industry in weekly  easy-to-follow reports.   U+1F3DDU+1F3DD Subscribe today below:   TheTechOasisThe newsletter to stay ahead of the curve in AIthetechoasis.beehiiv.com   Depth vs BreadthAt some time in your AI journey  you may have wondered: what made ChatGPT different from everything that came before?   The field of AI is older than most of us and can be traced back to the Dartmouth Summer Research Project on Artificial Intelligence in 1956.   That said  Alan Turing first introduced the notion of AI in its historically significant Computing Machinery & Intelligence in 1950. One way or the other  its safe to say that AI has become prominent in our lives over the last two years.   A man way ahead of his time.   To put into perspective how advanced to his time Alan Turing was  he conceived machine thinking as The Imitation Game. Well  74 years later  most AI systems  including ChatGPT  are literally the embodiment of this idea.   And only a handful of AI systems  casually the ones we are looking at today  are not based on pure human imitation  making our story today even more relevant.   Before the advent of Large Language Models (LLMs) like ChatGPT  all AI was deeply narrow. In other words  we trained models to perform one task as well as possible  but these models could not work well in more than one task.   This idea  called depth  governed the industry for decades  not because that was everything we wanted  but because generalization  when a model can perform various tasks  was just a pipe dream.   LLMs like ChatGPT sort of solved that problem. However  this has come at a sacrifice.   The ChatGPT FallacyWhile this weak generalization we have achieved with ChatGPT (they still completely fail in tasks where their memorization capabilities cant help them) has been extremely economically fruitful to those building these models (especially Big Tech  which has added around $7 trillion combined since November 2022 excluding Tesla)  its also a setback in other regards.   For reference  $7 trillion is more than the combined value of the stock markets of the UK  Germany  and Spain  and you would still have a spare trillion dollars.   Despite what markets will indicate based on valuations  LLMs are good at many things but great at none.   In other words  we have sacrificed task-specific greatness in lieu of models that  while they can write a Shakespearean poem and talk to you about nuclear fission  both responses will be surprisingly average.   This is what I call The ChatGPT Fallacy  or the greatest mistake one can make when using ChatGPT.   When testing the model's prowess  most people will often ask it what they dont know themselves instead of asking it something they have the criteria to know if it's good. When tested in the latter form  the real limitations of ChatGPT become apparent quickly.   In more technical terms  our best models are currently losing depth (per-task prowess) for better generalization (doing many tasks but being meh at them).   While this turn is understandable from a business perspective (markets pay handsomely)  it has had tragic consequences  and we might be going backward.   From AlphaGo to ChatGPT and BackBefore ChatGPT came into our lives  the most impressive AI the world had ever seen was the AlphaGo model family  deep neural networks (just like ChatGPT in that sense) that achieved superhuman capabilities in the game of Go  a Chinese board game (with AlphaZero being the state-of-the-art not only in Go but also in Shogi and Chess).   It even defeated Lee Sedol  the champion at the time  in a historical event that even led to documentaries on the feat.   But how do Alpha (Go and Zero) work?   They are based on Monte Carlo Tree Search  but on steroids. The idea is that for each new movement  the models explore thousands or even millions of possible outcomes for each movement they could make  estimating the expected maximum cumulative reward to decide on the one that its probabilities suggest is the best.   In a way  you can think of these models as machines that look ahead of their current move to choose the best one. Interestingly  this is the precise capability researchers are trying to instill in LLMs to improve their reasoning  using methods similar to MCTS  like Tree-of-Thought  depicted below  where the LLM explores different solution paths before deciding on one.   For more detail on the convergence of both worlds on the path to conquering true intelligence  read my deep dive Is AI Really Intelligent?   Although the AlphaX models could only play Go (and a handful of other board games)  they were better than any human in history at them. But as money has flown into LLMs  the quest for superhuman AIs like these has mostly stalled in recent years.   Obviously  merging both worlds is the ultimate goal  and here is where our fellows at Google come in: Can we create an LLM with star capabilities at many tasks?   On the Measure of RewardsDespite what many may believe  thanks to OpenAIs fame  Google Deepmind is head and shoulders above the rest when it comes to achieving depth.   This is because they are great at Reinforcement Learning  a field where robots are incentivized to perform actions in an environment to achieve a goal by punishing or rewarding them depending on the outcome of each action.   As we need to measure the rewards continuously  this method requires an auxiliary model  a reward model  to perform this evaluation. In a way  RL is like playing a game  but a game in which the model learns based on this reward feedback.   As you may have guessed  the quality of the outcome depends heavily on choosing the correct reward and punishment mechanisms  which is a really hard thing to define  especially in robotics.   In particular  NVIDIA has been proposing AIs that build reward functions for some time  achieving impressive results we covered in my newsletter a while back.   In a particular set of cases  like AlphaGo  this method can create superhuman AIs that  by performing self-improvement  sometimes known as self-play  or using its outputs as feedback  can transcend human limitations and become much better than us at certain tasks.   A good example is this video we shared in this newsletter months ago  a robot that  in just six hours of training  becomes superhuman at the Labyrinth game.   Well  now Deepmind is experimenting with this idea in mathematics theorem proving  and the results are humbling for humans.   In The Conquest of Maths ReasoningGoogle Deepmind has presented two models:   AlphaProof  a model that excels at proving mathematical statements using Lean  a programming language that aims to work as a proof assistant.AlphaGeometry 2.0  a new version of a model I covered in detail in my Medium blog that excels at geometry theorem proving.And  in both cases  LLMs play a crucial role.   AlphaProofTo design AlphaProof  they created a self-improvement loop of theorem proving with AlphaZero  the model we discussed earlier  by using an LLM to draft the mathematical statements in a formal way that AlphaZero can then try to prove.   Then  for those theorems that AlphaZero successfully proves  they use them to reinforce that behavior  aka they use them as a signal to improve the model.   The reason for this is that adequate data for such type of training is almost non-existent.   Thus  using Geminis capabilities to rewrite data (depicted as the Formalizer network above)  they created 100 million formal problems on which they trained AlphaZero.   So  in a way  AlphaProof does the same thing that AlphaZero does when playing Go  but instead of exploring the next best movement  its capable of exploring the next best proof step in its path to finding the proof to a given theorem.   AlphaGeometry 2As for the latter  AlphaGeometry is an even more interesting model. In a nutshell  its a neurosymbolic AI model  a combination of an LLM (Gemini) and symbolic engines.   But what do I mean by that?   A neurosymbolic system is an AI system that combines a neural network  in this case  an LLM  with hard-coded systems of human-written code that can perform accurate calculations or actions if we constrain the problem enough.   For instance  a symbolic engine might be a mathematics software written by humans that takes in a set of constraints and calculates the output (like being provided the length of two sides of a right triangle and using the Pythagoras theorem to compute the third side).   And what is the role of the LLM here? They search.   But search what?   Symbolic engines are lists of conditions written by humans in the form of if x happens  do y. They are the epitome of what we call Symbolic AI  which  in reality  is just machines imitating intelligent human actions in highly constrained environments.   But heres the thing: when facing an open geometry theorem problem  there are potentially infinite ways to approach the solution.   Symbolic engines cant search; they are limited by the number of scenarios that the humans who coded that engine thought of. In other words  when faced with open problems  they dont work.   So what does Gemini (Googles LLM) do?   When faced with an open problem like proving a geometry theorem  it suggests what researchers call auxiliary constructions (depicted in red and blue below  with the black lines being the original problem)  cues added to the problem that constrain the space of possible solutions.   For instance  in the image below  Gemini proposes computing point E (the right angle in triangle AEB)  which is then used to compute other triangles that narrow the problem and facilitate the solution.   In laymans terms  AlphaGeometry 2 works as follows:   Gemini suggests possible solution paths to the symbolic engines performing the computation. Simply put  we are narrowing down an open problemThen  the symbolic engines can compute and test whether its sufficient to prove the theorem.In a nutshell  this method breaks intelligence into two parts: idea generation  and idea verification  which is a very common way of defining intelligence in the AI industry these days. First  a system proposes possible ways of solving a problem  and the second system computes and verifies whether that path is correct.   However  Google Deepmind takes a slightly different approach from most (at least in this research).   While most AI enthusiasts think of solving intelligence as an end-to-end neural network (think OpenAI)  where idea generation and verification are both done by neural networks (mainly LLMs)  here Google suggests that the verification part is done by human-written code  which is what neurosymbolic AI is.   While we cant know which of the two methods will prevail  the Olympic silver medal tells you all you need to know about how powerful this method is.   And  once again  Google is openly telling the world: When it comes to deep AI  everyone else is in our rearview mirror.   And now what?In a way  these two models exemplify what  in a nutshell  is the next frontier of AI: combining LLM-powered search with RL-trained models that excel in depth (aka  excel at specific tasks).   Unlocking this paradigm at scale could create the first deep generalizer that  akin to humans  can not only perform several tasks but  upon facing a complex problem  can search the space of possible solutions until it finds the best one.   To me  this sounds a lot like what most people think of AGI when trying to define it.   Markets-wise  its unclear how Google will monetize this; these models seem more like research projects. However  the very smart use of neurosymbolic systems  which not only learn faster but are cheaper to run  suggests that Google could release AlphaGeometry 2 to academia to enhance the works of mathematicians worldwide.   That said  I feel research like this should have a major effect on markets  but it doesnt  as investors usually look at numbers and what a handful of biased tech tycoons say in deeply orchestrated interviews.   However  when considering these investors sole job is to predict the future and make a return  seeing a company like Google present research like this should be extremely optimistic and even a major signal that Google might soon release a commercially available AI mathematician copilot.   For business inquiries on AI strategy or analysis  reach out at nacho@thewhitebox.ai   If you have enjoyed this article  I share similar thoughts in a more comprehensive and simplified manner for free on my LinkedIn.   If preferable  you can connect with me through X.