Last Week in AI #153: OpenAI's InstructGPT is less-toxic than GPT-3, Meta to build fastest AI supercomputer, Deepfake regulations across the world, and more!
OpenAI's InstructGPT uses RL to improve responses, Meta to build the fastest AI supercomputer with 16k GPUs, new Deepfake regulations in the EU, UK, US, and China
OpenAI’s GPT-3 has marked a major turning point in AI research since its announcement in 2020, with its impressive performance on a multitude of tasks it was not trained for being a huge revelation. While significant, this advance did come with a caveat; GPT-3 and similar models trained on massive amounts of uncurated data from the internet can exhibit misinformation, sexism, ageism, racism, and conspiracies. OpenAI’s new paper Training language models to follow instructions with human feedback presents an approach for avoiding these issues, by taking a model such as GPT-3 and further training it with humans providing feedback for both desirable and undesirable outputs for any given task. The resulting model, InstructGPT, is more truthful and less prone to creating toxic outputs while having essentially comparable performance to GPT-3 on standard benchmarks. InstructGPT models are now deployed as the default in OpenAI’s API.
Our take: This is very cool! While not especially novel in terms of its technical approach, the demonstration of this technique working on a model as large as GPT-3 is quite significant and likely to influence similar future work. A lot of work has demonstrated the potential negative ramifications of using models such as GPT-3, so it’s also significant that OpenAI itself undertook this research and has even replaced GPT-3 with InstructGPT in its commercial offerings.
On Monday, January 24, Meta introduced the AI Research SuperCluster (RSC). The result of nearly two years of work, the supercomputer has 6,080 Nvidia GPUs. Now ranked fifth, Meta claims RSC will become the fastest supercomputer in the world once it is fully built—at that stage, it will house around 16,000 GPUs. According to Meta, the supercomputer will primarily be used for research and products will not come from it for years. Its primary purpose is to use Meta’s vast troves of data to develop more capable AI systems that can learn from a variety of modalities and understand situations contextually.
Our take: Recent work from Meta like data2vec hints at the possible directions their research might go using the RSC. With this level of computing power, we can expect to see a lot of interesting research from Meta in the coming years. At the same time, this development does follow the trend of imbalance between the amount of compute (and data) available to researchers in industry vs elsewhere, such as academia.
Deepfake Regulations in EU, UK, and US and China
Last week, the European Parliament ratified new regulations on Deepfakes that were “surprisingly limited” - it just requires large online platforms (e.g. Facebook, YouTube) to label known deepfake videos as such. It does not ban any type of deepfakes or require platforms to actively identify them. UK’s new draft bill seems also similarly lax, and critics complain of “unclear and impractical” consequences for harmful deepfake applications. The US has more stringent regulations, but they happen at the state level, not the federal. Texas banned political deepfakes in 2019, without mentioning anything about deepfake pornography. Virginia and New York, banned unlawful dissemination or sale of deepfakes, while California and Maryland banned pornographic deepfakes.
China’s recent deepfake regulation proposal is much more detailed, and it covers all forms of AI-assisted generative content: text, images, audio, faces, voice, video, and even virtual scenes. Interestingly, China seems mostly focused on audio deepfakes in the role of spreading fake news. The regulation requires deepfake providers to register their applications with the government, clear labels of deepfakes and portals for user complaints, and platforms that host deepfakes to bear liability for algorithm and content management. See more details on this Twitter thread. The proposal is a draft and not yet made into law.
Our take: The comparison among these countries’ approaches to deepfake regulation reflects their different political systems and attitude toward regulating emerging technologies. The western nations are more on the “wait and see” side, avoiding overregulation and probably waiting for a “DeepfakeGate” to summon the necessary political will for more comprehensive legislation. China is a lot more proactive and maybe responding to what it sees as a threat to the government’s control of online information.
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