They have major impact on your reputation, cause players to abandon, and take a big bite from your bottom line. Join Dean Takahashi and others to learn how to defend your game against cheaters and pirates.
Facebook today proposed NetHack as a grand challenge for AI research, for which the company is launching a competition at the NeurIPS 2021 AI conference in Sydney, Australia. It’s Facebook’s assertion that NetHack, an ’80s video game with simple visuals that’s considered among the hardest in the world, can enable data scientists to benchmark state-of-the-art AI methods in a complex environment without the need to run experiments on a powerful computer.
Games have served as AI benchmarks for AI for decades, but things really kicked into gear in 2013 — the year Google’s DeepMind demonstrated a system that could play Pong, Breakout, Space Invaders, Seaquest, Beamrider, Enduro, and Q*bert at superhuman levels. The advancements aren’t merely improving game design, according to experts like DeepMind cofounder Demis Hassabis. Rather, they’re informing the development of systems that might one day diagnose illnesses, predict complicated protein structures, and segment CT scans.
In particular, reinforcement learning — a type of AI that can learn strategies to orchestrate large systems like manufacturing plants, traffic control systems, financial portfolios, and robots — is transitioning from research labs to highly impactful, real-world applications. For example, self-driving car companies like Wayve and Waymo are using reinforcement learning to develop the control systems for their cars. And via Microsoft’s Bonsai, Siemens is employing reinforcement learning to calibrate its CNC machines.