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DeepMind's AlphaCode Explained: Everything You Need to Know

#artificialintelligence

Programming has been for a long time a high-status, high-demand skill. Companies and businesses across industries depend at a very foundational level on the ability of human developers: People who write and understand the language of computers. Recently, with the advent of large language models, AI companies have begun to explore the possibilities of systems that can learn to code. OpenAI's Codex -- embedded into GitHub Copilot -- was the first notable example. Codex can read simple natural language commands and instructions and write code that matches the intention of the user. Yet, writing small programs and solving easy tasks is "far from the full complexity of real-world programming." AI models like Codex lack the problem-solving skills that most programmers rely on in their day-to-day jobs. That's the gap DeepMind wanted to fill with AlphaCode, an AI system that has been trained to "understand" natural language, design algorithms to solve problems, and then implement them into code. AlphaCode displays a unique skillset of natural language understanding and problem-solving ability, combined with the statistical power characteristic of large language models. The system was tested against human programmers on the popular competitive programming platform Codeforces. AlphaCode averaged a ranking of 54.3% across 10 contests, which makes it the first AI to reach the level of human programmers in competitive programming contests. I've studied the AlphaCode paper to understand what AlphaCode is and isn't, what these impressive results mean, what are the implications, and what the future holds for AI and human developers. I've also researched what AI experts and competitive programmers are saying about AlphaCode, so you have different independent perspectives to form your own. This article is a thorough review divided into 6 sections (and their respective subsections). I will include comments throughout the article to explore some questions, ideas, and results in more depth.


Deepmind: Is "Gato" a precursor for general artificial intelligence?

#artificialintelligence

Deepmind's Gato solves many tasks, but none of them really well. Does the new AI system nevertheless lead the way for general artificial intelligence? Hot on the heels of OpenAI's DALL-E 2, Google's PaLM, LaMDA 2, and Deepmind's Chinchilla and Flamingo, the London-based AI company is showing off another large AI model that outperforms existing systems. Yet Deepmind's Gato is different: The model can't text better, describe images better, play Atari better, control robotic arms better, or orient itself in 3D spaces better than other AI systems. But Gato can do a bit of everything. Deepmind trained the Transformer-based multi-talent with images, text, proprioception, joint moments, keystrokes, and other discrete and continuous observations and actions.


DeepMind researcher claims new AI could lead to AGI, says 'game is over'

#artificialintelligence

According to Doctor Nando de Freitas, a lead researcher at Google's DeepMind, humanity is apparently on the verge of solving artificial general intelligence (AGI) within our lifetimes. In response to an opinion piece penned by yours truly, the scientist posted a thread on Twitter that began with what's perhaps the boldest statement we've seen from anyone at DeepMind concerning its current progress toward AGI: It's about making these models bigger, safer, compute efficient, faster at sampling, smarter memory, more modalities, INNOVATIVE DATA, on/offline, … 1/N https://t.co/UJxSLZGc71 It's about making these models bigger, safer, compute efficient, faster at sampling, smarter memory, more modalities, INNOVATIVE DATA, on/offline, … 1/N Solving these scaling challenges is what will deliver AGI. Research focused on these problems, eg S4 for greater memory, is needed. Rich Sutton is right too, but the AI lesson ain't bitter but rather sweet.


Is DeepMind's new reinforcement learning system a step toward general AI?

#artificialintelligence

All the sessions from Transform 2021 are available on-demand now. This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. One of the key challenges of deep reinforcement learning models -- the kind of AI systems that have mastered Go, StarCraft 2, and other games -- is their inability to generalize their capabilities beyond their training domain. This limit makes it very hard to apply these systems to real-world settings, where situations are much more complicated and unpredictable than the environments where AI models are trained. But scientists at AI research lab DeepMind claim to have taken the "first steps to train an agent capable of playing many different games without needing human interaction data," according to a blog post about their new "open-ended learning" initiative. Their new project includes a 3D environment with realistic dynamics and deep reinforcement learning agents that can learn to solve a wide range of challenges.


Is DeepMind's new reinforcement learning system a step toward general AI?

#artificialintelligence

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. One of the key challenges of deep reinforcement learning models--the kind of AI systems that have mastered Go, StarCraft 2, and other games--is their inability to generalize their capabilities beyond their training domain. This limit makes it very hard to apply these systems to real-world settings, where situations are much more complicated and unpredictable than the environments where AI models are trained. But scientists at AI research lab DeepMind claim to have taken the "first steps to train an agent capable of playing many different games without needing human interaction data," according to a blog post about their new "open-ended learning" initiative. Their new project includes a 3D environment with realistic dynamics and deep reinforcement learning agents that can learn to solve a wide range of challenges. The new system, according to DeepMind's AI researchers, is an "important step toward creating more general agents with the flexibility to adapt rapidly within constantly changing environments."