🤩💥 'What's New in AI' Recap 2
Recap collections help you navigate specific topics and fill the gaps if you missed something
What’s New in AI – our deep dives into the latest updates in AI research and technology. Here we discuss in-depth the recent research papers or technology frameworks that are worth your attention. Our goal is to keep you up to date with new developments in AI in a way that complements the concepts we are debating in other editions of our newsletter.
💥 What’s New in AI: Facebook Research Open Sourced HiPlot and Polygames to Advance Deep Learning Experimentation
Large consumer technology companies such as Google, Uber, Amazon and Facebook are exposed to some of the most advanced deep learning use cases we can imagine. It’s not a surprise that these technology giants have been at the center of innovation in deep learning technologies. Fortunately, these companies have been actively open-sourcing many of the technologies they have incubated in-house as part of their deep learning solutions. Facebook has been one of the most active contributors to open-source deep learning projects, starting with its hallmark framework: PyTorch.
The Facebook AI Research (FAIR) team has been regularly releasing new frameworks and tools that advance the lifecycle of PyTorch applications. Earlier this year, the FAIR team unveiled two unique initiatives that are focused on advancing deep learning research with a specific focus on the PyTorch ecosystem: ->become Premium to read our deep dive into HiPlot and Polygames
💥 What’s New in AI: Google Research’s Meena is a Language Model That Can Chat About Anything
Natural Language Understanding (NLU) has been one of the most active areas of research of the last few years and has produced some of the most widely adopted AI systems to date. However, despite all the progress, most conversational systems remain highly constrained to a specific domain, which contrasts with our ability as humans to naturally converse about different topics. In NLU theory, those specialized conversational agents are known as closed-domain chatbots. The alternative is an emerging area of research known as open-domain chatbots that focuses on building conversational agents that chat about virtually anything a user wants. If effective, open-domain chatbots might be a key piece in the journey to humanize computer interactions ->read the full article about Meena, a new deep learning model that can power chatbots able to engage in conversations about any domain
💥 What’s New in AI: Facebook ReBeL is an Open-Source Bot that can Master Poker and Other Imperfect Information Games
The idea behind ReBeL is SO SIMPLE as it is clever. If AlphaZero showed success with reinforcement learning + search strategies in perfect-information games, then why not transform imperfect-information games to perfect-information equivalents? I know, I know, it sounds too good to be true but ->let’s look at an example
💥 What’s New in AI: How DeepMind’s MuZero Mastered Go, Chess, Shogi and Atari Without Knowing the Rules
MuZero represents a major evolution in the use of reinforcement learning algorithms for long-term planning. Models like AlphaGo broke ground in the use of reinforcement learning and tree-search algorithms to master games like Go. That model was extended by AlphaGo-Zero, which was able to learn Go without knowing the rules, followed by AlphaZero which mastered not only Go but other perfect-information environments like Chess and Shogi. MuZero extends AlphaZero with robust search capabilities to also master complex environments like Atari, which deviates from classical planning methods ->subscribe to read further
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