2019年AI报告.pdf

State of AI ReportJune 28, 2019#AIreportstateof.aiIan HogarthNathan BenaichAbout the authorsNathan is the founder of Air Street Capital, a VC partnership of industry specialists investing in intelligent systems. He founded the Research and Applied AI Summit and the RAAIS Foundation to advance progress in AI, and writes the AI newsletter nathan.ai. Nathan is also a Venture Partner at Point Nine Capital. He studied biology at Williams College and earned a PhD from Cambridge in cancer research. Nathan Benaich Ian Hogarth Ian is an angel investor in 50+ startups with a focus on applied machine learning. He is a Visiting Professor at UCL working with Professor Mariana Mazzucato. Ian was co-founder and CEO of Songkick, the global concert service used by 17m music fans each month. He studied engineering at Cambridge. His Masters project was a computer vision system to classify breast cancer biopsy images. stateof.ai 2019Introduction | Research | Talent | Industry | Politics | China | Predictions | Conclusion #AIreportArticial intelligence (AI) is a multidisciplinary eld of science whose goal is to create intelligent machines.We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence. In this report, we set out to capture a snapshot of the exponential progress in AI with a focus on developments in the past 12 months. Consider this report as a compilation of the most interesting things weve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future. This edition builds on the inaugural State of AI Report 2018, which can be found here: stateof.ai/2018We consider the following key dimensions in our report:- Research: Technology breakthroughs and their capabilities.- Talent: Supply, demand and concentration of talent working in the eld. - Industry: Large platforms, nancings and areas of application for AI-driven innovation today and tomorrow. - China: With two distinct internets, we review AI in China as its own category.- Politics: Public opinion of AI, economic implications and the emerging geopolitics of AI.Collaboratively produced in East London, UK by Ian Hogarth (soundboy) and Nathan Benaich (nathanbenaich).stateof.ai 2019Introduction | Research | Talent | Industry | Politics | China | Predictions | Conclusion #AIreportThanks to the following people for suggesting interesting content and/or reviewing this years Report. Jack Clark, Kai Fu Lee, Jade Leung, Dave Palmer, Gabriel Dulac-Arnold, Roland Memisevic, Franois Chollet, Kenn Cukier, Sebastian Riedel, Blake Richards, Moritz Mueller-Freitag, Torsten Reil, Jan Erik Solem and Alex Loizou. Thank yousstateof.ai 2019Introduction | Research | Talent | Industry | Politics | China | Predictions | Conclusion #AIreportDenitionsArticial intelligence (AI): A broad discipline with the goal of creating intelligent machines, as opposed to the natural intelligence that is demonstrated by humans and animals. It has become a somewhat catch all term that nonetheless captures the long term ambition of the eld to build machines that emulate and then exceed the full range of human cognition.Machine learning (ML): A subset of AI that often uses statistical techniques to give machines the ability to “learn“ from data without being explicitly given the instructions for how to do so. This process is known as “training” a “model” using a learning “algorithm” that progressively improves model performance on a specic task.Reinforcement learning (RL): An area of ML that has received lots of attention from researchers over the past decade. It is concerned with software agents that learn goal-oriented behavior by trial and error in an environment that provides rewards or penalties in response to the agents actions (called a “policy”) towards achieving that goal.Deep learning (DL): An area of ML that attempts to mimic the activity in layers of neurons in the brain to learn how to recognise complex patterns in data. The “deep” in deep learning refers to the large number of layers of neurons in contemporary ML models that help to learn rich representations of data to achieve better performance gains. stateof.ai 2019Introduction | Research | Talent | Industry | Politics | China | Predictions | Conclusion #AIreportAlgorithm: An unambiguous specication of how to solve a particular problem.Model: Once a ML algorithm has been trained on data, the output of the process is known as the model. This can then be used to make predictions.Supervised learning: This is the most common kind of (commercial) ML algorithm today where the system is presented with labelled examples to explicitly learn from.Unsupervised learning: In contrast to supervised learning, the ML algorithm has to infer the inherent structure of the data that is not annotated with labels.Transfer learning: This is an area of research in ML that focuses on storing knowledge gained in one problem and applying it to a different or related problem, thereby reducing the need for additional training data and compute. Natural language processing (NLP): Enables machines to analyse, understand and manipulate textual data. Computer vision: Enabling machines to analyse, understand and manipulate images and video. Denitionsstateof.ai 2019Introduction | Research | Talent | Industry | Politics | China | Predictions | Conclusion #AIreportScorecard: Reviewing our predictions from 2018stateof.ai 2019Introduction | Research | Talent | Industry | Politics | China | Predictions | Conclusion #AIreportOur 2018 prediction Outcome? Whats the evidence?Breakthrough by a Chinese AI lab Chinese labs win ActivityNet (CVPR 2018); train ImageNet model in 4 mins.DeepMind RL Starcraft II breakthrough AlphaStar beats one of the worlds strongest StarCraft II players 5-0. A major research lab “goes dark” MIRI “non-disclosed by default” and OpenAI GPT-2.The era of deep learning continues Yes, but not entirely clear how to evaluate this. Drug discovered by ML produces positive clinical trial results M&A worth $5B of EU AI cos by China/US OECD country government blocks M&A of an ML co by USA/China Access to Taiwanese/South Korean semiconductor companies is an explicit part of the US-China trade warstateof.ai 2019Introduction | Research | Talent | Industry | Politics | China | Predictions | Conclusion #AIreportSection 1: Research and technical breakthroughsstateof.ai 2019Introduction | Research | Talent | Industry | Politics | China | Predictions | Conclusion #AIreportRewarding curiosity enables OpenAI to achieve superhuman performance at Montezumas Revenge.Reinforcement learning (RL) conquers new territory: Montezumas Revenge In 2015, DeepMinds DQN system successfully achieved superhuman performance on a large number of Atari 2600 games. A major hold out was Montezumas Revenge. In October 2018, OpenAI achieved superhuman performance at Montezumas with a technique called Random Network Distillation (RND), which incentivised the RL agent to explore unpredictable states. This simple but powerful modication can be particularly effective in environments where broader exploration is valuable. The graph on the right shows total game score achieved by different AI systems on Montezumas Revenge. stateof.ai 2019Introduction | Research | Talent | Industry | Politics | China | Predictions | Conclusion #AIreport