00:00So Microsoft has developed a new system called Self-Taught Optimizer, or STOP for short,
00:06that can generate high-quality code for various tasks and domains and improve itself over time
00:12by learning from its own mistakes. And this brings us a step closer to AGI. If you're like me,
00:18you probably have a lot of questions about this technology. How does it work? What can it do?
00:23Why is it important? Well, don't worry, because I'm going to answer all of these questions and
00:27more in this video. But before getting into the details, here's why code optimization is such a
00:32big deal in the first place. It's all about enhancing a code's performance by using fewer
00:37resources like CPU time, memory, or network bandwidth. By doing this, software works better,
00:44especially when tasks need to be done quickly and affordably. Yet improving code isn't simple.
00:50Writing the best code for various situations needs a lot of knowledge, and depending on the computer
00:56and the programming language, the way to optimize might change. So this work often takes a lot of
01:02time and needs people to fine-tune it. Imagine if this could be automated. Imagine a system that can
01:08create the best code for any job without human help. Even better, what if it could learn and correct
01:14its own mistakes? This is what Microsoft has done with STOP. STOP is a novel system that combines two
01:20powerful ideas, Tree of Thoughts, TOT, and Program-Aided Language Models, PAL. TOTI is a framework that uses
01:28large language models to generate intermediate steps for solving problems in natural language.
01:33PAL is a method that uses LLMs to generate programs as intermediate steps for solving problems in natural
01:39language, but offloads the execution of these programs to a programmatic runtime, such as a Python
01:45interpreter. Now, STOP combines these two ideas by using LLMs to generate both natural language steps
01:52and program steps for solving problems in natural language. However, unlike TOT or PAL, STOP does not
01:59stop at generating intermediate steps. It also evaluates these steps using various metrics such as
02:05correctness, efficiency, readability, and simplicity. Based on these evaluations, STOP selects the best steps for
02:13each problem and generates the final solution as a piece of code. But here's the coolest part.
02:19STOP does not just generate code once and forget about it. It also keeps track of its own code
02:25generation process and learns from it over time. It actually uses a recursive self-improvement mechanism
02:31that allows it to identify its own weaknesses and strengths and adjust its parameters accordingly.
02:36For example, if STOP generates incorrect or inefficient code for some problem,
02:41it will try to find out why it made such mistakes and how it can avoid them in the future. Similarly,
02:47if it generates correct and efficient code for some problem, it will try to find out what it did right
02:53and how it can replicate such success in other problems. Now let's dig a bit deeper into what fuels
02:59its ability to get better over time. Recursive self-improvement. This idea isn't brand new. It's been around in the big
03:05world of artificial intelligence. Yet when it comes to making code, that's where STOP really shines.
03:11Recursive self-improvement is all about learning from what you've done before to make smarter moves next
03:17time around. It's like having a built-in habit of checking your work and finding ways to do better.
03:22In this case, it means taking a good hard look at the code it creates, spotting what could be better,
03:28and then tweaking the process to up its game, all on its own. By doing this, the system not only
03:34boosts the caliber of the code, but also inches closer to standing on its own two feet, which is
03:40crucial on the road towards artificial general intelligence. This cycle of self-check and self-tweak
03:45that STOP has going on is a sneak peek into a future where AI systems keep growing and adjusting
03:52all by themselves, which is a big deal as we aim for smarter and more independent systems. This way,
03:58the system can continuously optimize its own code generation process by learning from its own
04:04experience, which makes it a self-taught system that can achieve high-quality code generation without
04:09requiring any external feedback or supervision. Now, as cool as the idea of self-improving code generation
04:15is, it's essential to have checks and balances to prevent any runaway scenarios or unintended
04:21consequences. The system is designed with safety at its core. It operates within a controlled
04:26environment to ensure that its self-improvement doesn't go off the rails. It has built-in protocols
04:32to monitor the code it generates, ensuring that it aligns with the intended goals and adheres to the
04:38necessary standards. This level of safety assurance is vital, especially when venturing into the territory
04:45of self-improving technologies where the potential for missteps is real. By having a robust
04:51safety framework, STOP not only advances code generation, but does so with a level of
04:56responsibility that underscores the importance of controlled self-improvement in our journey towards
05:01artificial general intelligence. So, what is STOP capable of with its impressive skills?
05:07According to a research paper from Microsoft, this system can produce top-notch code for a variety of
05:12areas. These areas include mathematical reasoning, symbolic reasoning, algorithmic reasoning, natural
05:19language processing, computer vision, data analysis, web development, game development, and so on.
05:24The study also reveals that STOP is superior to other leading systems for creating code,
05:29like TOTI, PAL, Codex, GPT-3, and more. This superiority is seen in terms of how accurate, efficient,
05:37clear, straightforward, universal, sturdy, and expandable its codes are. In the realm of mathematical
05:43reasoning, the approach that STOP takes is clear, efficient, and straightforward, utilizing the least
05:49number of math operations and grouping symbols possible. Plus, it gives a step-by-step explanation,
05:56making it easy for anyone to grasp. So, it doesn't just give answers that are right and swift,
06:01its solutions are also clear and uncomplicated. Each solution comes with a breakdown, making them very
06:07user-friendly. On top of that, its solutions are versatile and resilient, meaning they can manage
06:13various inputs and unexpected situations without any hitches. STOP has many abilities, including its
06:19self-taught optimization skill. If you'd like to see more examples of what STOP can do, check out the
06:24paper linked in the video description. Honestly, I think this technology is mind-blowing. I mean,
06:30think about it. This is a system that can generate optimal code for any task and domain,
06:35without requiring any human guidance or supervision. Moreover, it has the ability to learn from its
06:41coding process, identify its own mistakes and inefficiencies, and then correct them. It doesn't
06:46just generate code, but also optimizes it without needing external feedback. And that's why I believe
06:53STOP is one of the most groundbreaking technologies in artificial intelligence recently. It has the
06:58potential to transform the way we write and use software applications, and to open up new possibilities
07:05and opportunities for innovation and creativity. But what do you think? Do you agree with me? Or do you
07:11have some doubts or questions about it? Let me know in the comments below. I would love to hear your
07:16thoughts and opinions on this topic. And that's it for this video. I hope you enjoyed it and learned
07:21something new. If you did, please give it a thumbs up and subscribe to my channel for more videos like this.
07:27And don't forget to hit the bell icon to get notified whenever I upload a new video. Thank you so much
07:33for watching, and I'll see you in the next one.
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