Artificial intelligence is a game-changer. It could boost global productivity from 0.8% to 1.4% a year. But with thorny issues like job automation and data privacy, does AI-spurred growth come at a cost?
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Category: robotics/AI
https://youtube.com/watch?v=c-Y7TQCaD38
Yuval’s works on the future of the digital world cause the globe to take notice and discuss. At OMR (Online Marketing Rockstars), Yuval Noah Harari primarily talked about the developments and consequences of artificial intelligence. After his keynote, German journalist and entrepreneur Kai Diekmann conducted an interview on-stage.
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This post was prompted by a colleague sharing with me this recent study: www.ncbi.nlm.nih.gov/pmc/articles/PMC6389801/
The authors found that out of 516 studies evaluating the performance of ML algorithms for the diagnostic analysis of medical images, only 31 had externally validated their algorithms.
This should concern us all.
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Extremely happy to be able to already share with you the two videos from our last salon🚀! We gathered not one but three individuals who have been pre-eminent luminaries in their fields for 30 years to discuss their alternative approaches to the current AI paradigm: Kim Eric Drexler, Robin Hanson, and Mark S. Miller.
Allison Duettmann (Foresight Institute) discusses alternative approaches to the current AI paradigm with three individuals who have been pre-eminent luminaries in their fields for 30 years: Eric Drexler, Robin Hanson, and Mark S. Miller.
Eric Drexler:
Drexler is widely known for his seminal studies of advanced nanosystems and scalable atomically precise manufacturing (APM), a prospective technology using arrays of nanoscale devices to guide chemically-reactive molecular encounters, thereby structuring matter from the bottom up. Drexler’s current research explores prospects for advanced AI technologies from the perspective of structured systems development, potential applications, and global implications. Key considerations in this work include advances in AI-enabled automation of AI research and development, and the potential role of thorough automation in accelerated development of comprehensive AI services.
Mark S. Miller:
Mark S. Miller is a pioneer of agoric (market-based secure distributed) computing and smart contracts, the main designer of the E and Dr. SES distributed persistent object-capability programming languages, inventor of Miller Columns, an architect of the Xanadu hypertext publishing system, a representative to the EcmaScript committee, a former Google research scientist and member of the WebAssembly (Wasm) group, and a senior fellow of the Foresight Institute. Eric and Mark co-authored the Agoric Papers, which have recently received substantial attention in the cryptocommerce community, 30 years after their initial release: https://agoric.com/assets/pdf/papers/markets-and-computation-agoric-open-systems.pdf
Robin Hanson:
Robin Hanson is associate professor of economics at George Mason University and research associate at the Future of Humanity Institute of Oxford University Press published his book The Age of Em: Work, Love and Life When Robots Rule the Earth in June 2016, and his book The Elephant in the Brain: Hidden Motives in Everyday Life, co-authored with Kevin Simler, in January, 2018. Professor Hanson has 900 media mentions, given 350 invited talks, and his blog OvercomingBias.com has had eight million visits. He has pioneered prediction markets since 1988 and suggests “futarchy”, a form of governance based on prediction markets. He was a principal architect of the first internal corporate markets, at Xanadu, of the first web markets, the Foresight Exchange, of DARPA’s Policy Analysis Market, and of IARPA’s combinatorial markets DAGGRE and SCICAST. He coined the phrase “The Great Filter” as part of an effort to understand why the universe looks so dead.
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By pieter spronck and jaap van den herik
While the audiovisual qualities of games have improved significantly over the last twenty years, game artificial intelligence (AI) has been largely neglected. Since the turn of the century game development companies have discovered that nowadays it is the quality of the game AI that sets apart good games from mediocre ones. The Institute of Knowledge and Agent Technology (IKAT) of the Universiteit Maastricht examines methods to enhance game AI with machine learning techniques. Several typical characteristics of games, such as their inherent randomness, require novel machine learning approaches to allow them to deal with game AI.
Most commercial computer games contain computer-controlled agents that oppose the human player. ‘Game AI’ encompasses the decision-making capabilities of these agents. For implementing game AI, especially for complex games, developers usually resort to rule-based techniques in the form of scripts. Scripts have the advantage that they are easy to understand and can be used to implement fairly complex behaviour.
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Recent attempts to move beyond narrow AI applications in industry have struggled to gain traction. ReThink Robotics, a leading startup founded by AI founding MIT researcher Dr. Rodney Brooks to create adaptive collaborative robots for industrial robotics, closed its doors in October 2018 and has since had its IP acquired by HAHN Group. In a retrospective published by The Robot Report, several contributing factors led to the shutdown. ReThink’s reliance on series elastic actuators compromised the precision and repeatability found in typical actuators in favor of safety, which likely led to efforts to compensate on hardware through software.
While the company utilized innovative machine control and machine vision technologies in iterating on their robots, the combination of mechanical motion of firmware at the heart of their products led to a narrow range of issues at varying quality. This made Baxter and Sawyer, ReThink’s flagship industrial robots, ill-suited for adaptive industrial use.
Other companies attempting to build adaptive robots, including Jibo, have met similar troubles. Touted as an interactive social robot with a personality, Jibo launched their eponymous robot in November 2017 with an emphasis on naturalistic human-computer interaction, but entered the market with more limited functionality than cheaper smart assistant speakers. The company has since closed down and transferred ownership of their IP to SQN Venture Partners in November 2018.
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The AI taught itself the skill through a technique called reinforcement learning — essentially, it picked up the rules of the game over thousands of matches in randomly generated environments.
A paper on their research was published today in Science.
“How you define teamwork is not something I want to tackle,” Max Jaderberg, a DeepMind researcher who worked on the project told The New York Times. “But one agent will sit in the opponent’s base camp, waiting for the flag to appear, and that is only possible if it is relying on its teammates.”
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