✳️ Edge#166: DeepMind's AlphaCode Can Generate Code at the Level of Programming Competitions
On Thursdays, we dive deep into one of the freshest research papers or technology frameworks that is worth your attention. Our goal is to keep you up to date with new developments in AI to complement the concepts we debate in other editions of our newsletter. Happy Valentine! LAST day to subscribe with 50% OFF. We 🫀 and 🧠 you 💥 What’s New in AI: DeepMind’s AlphaCode Can Generate Code at the Level of Programming CompetitionsA few months ago, OpenAI shocked the AI world by unveiling Codex, a model that could generate programming code based on natural language instructions. Many experts thought Codex was an outlier example in the particularly challenging and elusive field of code generation. Well, last week, DeepMind followed OpenAI’s footsteps and announced AlphaCode, a transformer model that can generate code at the level of programming competitions. Competitive programming is one of the toughest challenges of ML-based code generation. Problems in programming competitions do not only take a strong level of language interpretation and common sense reasoning but also take hours to implement. It is not as simple as generating a code-snippet. Obviously, the current generation of ML systems is far from exhibiting common sense reasoning, but massively large neural networks have excelled at different code generation aspects by simple filtering and sampling. In that sense, DeepMind’s decided to leverage large transformer models as the foundation for AlphaCode →let’s deep dive into AlphaCode (last day to subscribe for only $24/YEAR) |
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🕸 Edge#165: AutoRegressive Networks
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In this issue: we discuss AutoRegressive Networks; we explore DeepMind's PixelRNN and PixelCNN, two of the most important autoregressive models for image generation; we overview MMGeneration, a new
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