Microsoft's STOP: New AI That Taught Itself to Outcode Humans (Virtually AGI)

So Microsoft has developed a new system called Self Taught Optimizer, or STOP for short, that can generate high quality code for various tasks and domains and improve itself over time by learning from its own mistakes. And this brings us a step closer to AGI. If you're like me, you probably have a lot of questions about this technology. How does it work? What can it do? Why is it important? Well, don't worry, because I'm going to answer all of these questions and more in this video. But before getting into the details, here's why code optimization is such a big deal in the first place. It's all about enhancing a code's performance by using fewer resources like CPU time, memory or network bandwidth. By doing this, software works better, especially when tasks need to be done quickly and affordably. Yet improving code isn't simple. Writing the best code for various situations needs a lot of knowledge, and depending on the computer and the programming language, the way to optimize might change. So this work often takes a lot of time and needs people to fine tune it. Imagine if this could be automated. Imagine a system that can create the best code for any job without human help. Even better, what if it could learn and correct its own mistakes? This is what Microsoft has done with stop. STOP is a novel system that combines two powerful ideas, Tree of Thoughts and Program Aided Language Models. TOTI is a framework that uses large language models to generate intermediate steps for solving problems in natural language. PAL is a method that uses LLMs to generate programs as intermediate steps for solving problems in natural language, but offloads the execution of these programs to a programmatic runtime such as a Python interpreter. Now, Stop combines these two ideas by using LLMs to generate both natural language steps and program steps for solving problems in natural language. However, unlike TOT or pal, STOP does not stop at generating intermediate steps. It also evaluates these steps using various metrics such as correctness, efficiency, readability, and simplicity. Based on these evaluations, STOP selects the best steps for each problem and generates the final solution as a piece of code. But here's the coolest part. STOP does not just generate code once and forget about it. It also keeps track of its own code generation process and learns from it over time. It actually uses a recursive self improvement mechanism that allows it to identify its own weaknesses and strengths and adjust its parameters accordingly. For example, if STOP generates incorrect or inefficient code for some problem, it will try to find out why it made such mistakes and how it can avoid them in the future. Similarly, if it generates correct and efficient code for some problem, it will try to find out what it did right and how it can replicate such success in other problems. Now let's dig a bit deeper into what fuels its ability to get better over time. Recursive Self Improvement this idea isn't brand new. It's been around in the big world of artificial intelligence. Yet when it comes to making code, that's where STOP really shines. Recursive self improvement is all about learning from what you've done before to make smarter moves next time around. It's like having a built in habit of checking your work and finding ways to do better. In this case, it means taking a good hard look at the code it creates, spotting what could be better, and then tweaking the process to up its game all on its own. By doing this, the system not only boosts the caliber of the code, but also inches closer to standing on its own two feet, which is crucial on the road towards artificial general intelligence. This cycle of self check and self tweak that STOP has going on is a sneak peek into a future where AI systems keep growing and adjusting all by themselves, which is a big deal as we aim for smarter and more independent systems. This way the system can continuously optimize its own code generation process by learning from its own experience, which makes it a self taught system that can achieve high quality code generation without requiring any external feedback or supervision. Now, as cool as the idea of self improving code generation is, it's essential to have checks and balances to prevent any runaway scenarios or unintended consequences. The system is designed with safety at its core. It operates within a controlled environment. To ensure that its self improvement doesn't go off the rails, it has built in protocols to monitor the code it generates, ensuring that it aligns with the intended goals and adheres to the necessary standards. This level of safety assurance is vital, especially when venturing into the territory of self improving technologies where the potential for missteps is real. By having a robust safety framework, STOP not only advances code generation, but does so with a level of responsibility that underscores the importance of controlled self improvement in our journey towards artificial general intelligence. So what is STOP capable of with its impressive skills? According to a research paper from Microsoft, this system can produce top notch code for a variety of areas. These areas include mathematical reasoning, symbolic reasoning, algorithmic reasoning, natural language processing, computer vision, data analysis, web development, game development, and so on. The study also reveals that STOP is superior to other leading systems for creating code like Touti, Pal, Codex, GPT3 and more. This superiority is seen in terms of how accurate, efficient, clear, straightforward, universal, sturdy and expandable its codes are. In the realm of mathematical reasoning, the approach that STOP takes is clear, efficient and straightforward, utilizing the least number of math operations and grouping symbols possible. Plus, it gives a step by step explanation, making it easy for anyone to grasp. So it doesn't just give answers that are right and swift. Its solutions are also clear and uncomplicated. Each solution comes with a breakdown, making them very user friendly. On top of that, its solutions are versatile and resilient, meaning they can manage various inputs and unexpected situations without any hitches. STOP has many abilities, including its self taught optimization skill. If you'd like to see more examples of what STOP can do, check out the paper linked in the video Description Honestly, I think this technology is mind blowing. I mean, think about it. This is a system that can generate optimal code for any task and domain without requiring any human guidance or supervision. Moreover, it has the ability to learn from its coding process, identify its own mistakes and inefficiencies, and then correct them. It doesn't just generate code, but also optimizes it without needing external feedback. And that's why I believe STOP is one of the most groundbreaking technologies in artificial intelligence recently. It has the potential to transform the way we write and use software applications and to open up new possibilities and opportunities for innovation and creativity. But what do you think? Do you agree with me or do you have some doubts or questions about it? Let me know in the comments below. I would love to hear your thoughts and opinions on this topic. And that's it for this video. I hope you enjoyed it and learned something new. If you did, please give it a thumbs up and subscribe to my channel for more videos like this. 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