Key Takeaways from AI Conference SF, Day 1: Domain Specific Architectures, Emerging China, AI Risks

DSAs provide a great opportunity for innovation as hardware and software are designed from the scratch focused on a very specific goal.Emerging China – evolving from copying ideas to true innovationFor decades China lagged far behind the west in the field of technology innovation..However, in the field of AI China has made formidable progress in the last few years including several unicorn startups and initiatives from major technology leaders (such as Alibaba and Baidu)..Overall, as we look back it is clear that the fads were more political than technical.Fundamental limits on deep learning:Time and again, experts have publicly announced that there are fundamental limits on deep learning such as inability to do long-term planning or train using sparse sampling, and these limits have been shattered within a few years..The deep learning algorithm has been making strong advances across disciplines – speech recognition, machine translation, object recognition, etc.; much stronger progress compared to the domain-specific solutions..To understand the scale comparison here is an analogy – if your phone battery lasted 1 day in 2012 and had similar growth rate, it would last 800 years in 2018, and would last 100 million years in 2023.He concluded his keynote asking for proactively thinking about safety and policy for AGI to address scenarios such as: machines pursuing goals mis-specified by their operator, malicious humans subverting deployed systems, or out-of-control economy that grows without resulting in improvement to human lives.Ben Lorica, Chief Data Scientist, O’Reilly Media and Roger Chen, CEO, Computable Labs delivered an insightful talk on Unlocking Innovation in AI, focused on Compute and Data aspects..Sharing data about usage on Safari, O’Reilly Online Learning platform, for 2017 and 2018, they highlighted the strong increase in learning for Deep Learning, Neural Networks, and Artificial Intelligence – particularly, Keras, PyTorch, and Reinforcement Learning.A survey of about 8,000 IT leaders across 84 countries (CIO Survey by Harvey Nash and KPMG) revealed a growing interest in AI and automation..Location proved out to be an important metric, as the interest in RPA (Robotic Process Automation) is particularly more in China compared to rest of world.In 2018, we are seeing an average of 90+ ML papers per day on arxiv..Compared to a few years ago, there is a strong trend of democratization in ML/AI/DL research, particularly in open-source ML libraries.The hardware sector is no more limited to traditional players such as Intel and AMD..We are seeing hardware participation from leading tech companies.We are witnessing a steady increase in startups focused on hardware for edge devices across China, US, and rest of the world.Despite great advances in hardware, the major bottleneck for AI (particularly for deep learning) continues to be labelled data.. More details

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