Microsoft’s STOP: The New AI That Taught Itself to Outcode Humans (Virtually AGI) 🤖💻 – AI Revolution - video Dailymotion

Ai Revolution

Microsoft has unveiled STOP, an astonishing AI system that has virtually taught itself to outcode human programmers. 🚀 With capabilities approaching artificial general intelligence (AGI), STOP can understand complex coding tasks, debug, and generate efficient code faster than ever before.  This breakthrough challenges traditional AI limits, showcasing self-learning algorithms that improve without direct human supervision. STOP’s potential to transform software development, automation, and innovation is immense, raising new questions about the future role of human coders.  Is this the dawn of a new era where AI and humans collaborate like never before? Dive into the revolution.  #MicrosoftSTOP #AGI #ArtificialIntelligence #AIRevolution #SelfLearningAI #CodeGeneration #ProgrammingAI #MachineLearning #TechInnovation #FutureOfCoding #Automation #DeepLearning #AIAdvancements #SmartTech #SoftwareDevelopment #AIInTech #NextGenAI #AIResearch #VirtualAGI #CodingRevolution

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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.

Microsoft’s STOP: The New AI That Taught Itself to Outcode Humans (Virtually AGI) 🤖💻 – AI Revolution

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