Federated Learning enables data scientists to create AI without compromising users confidentiality. This method is set to disrupt the centralized AI paradigm, in which better algorithm always comes at the cost of collecting more and more personal data. Thus, Federated Learning allows powerful network effects in industries where data cannot be transferred to third parties due to confidentiality reasons (health, bank, etc.).
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Thinking about Systems
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For those who are passionate about systems thinking.
Curated by
Steve Wilhite
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Dan Gailey, CEO of Synapse AI, explains why sharing AI breakthroughs will create a more interesting ecosystem and better AI models. And of course there's a blockchain component. 
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The old story of AI is about human brains working against silicon brains. The new story of IA will be about human brains working with silicon brains. As it turns out, most of the world is the opposite of a chess game: Non-zero-sum — both players can win.
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Singularity theorists have the burden of proof to explain why machine design can exhibit the kind of feedback loop that would be needed to cause an intelligence explosion.
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We’ve created an internet that senses, thinks, and acts. How can we protect ourselves if it goes wrong?
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Tim O'Reilly says the algorithms that shape our economy must be rewritten if we want to create a more human-centered future.
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Today we released our second annual research report on the state of artificial intelligence. Since last year’s report, we’ve seen early stage AI technologies continue to filter into many everyday…
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Artificial intelligence is making the limits of human knowledge painfully obvious
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The world's top AI researchers met to consider the threats posed by their research. The global economy could be the first casualty.
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This is a long (7,500 word) transcript of the talk. You can scan it to see the slides and accompanying exhibits if that is easier. Or even read it in more than one sitting…. Exponential View has a…
Steve Wilhite's insight:
Long read, but well worth the investment of time.
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One of the most intriguing public discussions to emerge over the past year is humanity’s wrestling match with the threat and promise of artificial intelligence. AI has long lurked in our collective
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Machine learning models trained on data from blockchain-based marketplaces have the potential to create the world’s most powerful artificial intelligences. They combine two potent primitives: private machine learning, which allows for training to be done on sensitive private data without revealing it, and blockchain-based incentives, which allow these systems to attract the best data and models to make them smarter. The result is open marketplaces where anyone can sell their data and keep their data private, while developers can use incentives to attract the best data for their algorithms to them.
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What should be in a code of standards for public sector use of algorithmic decision making?
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Thanks to advances in AI and machine learning, a slow but steady transformation is coming to education—under the hood. Already, AI algorithms are helping enhance education by collecting, analyzing, and correlating every interaction that takes place in physical and virtual classrooms, and helping teachers to address the specific pain points of each student. This might be the beginning of a revolution in one of the oldest and most valuable social skills that mankind has developed, and an imperative in a world where humans live and work alongside smart machines.
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Will robots dupe us into doing something dumb or dangerous because we trust them too much? That's one area of study for robotics expert Dr. Ayanna Howard. "I'm a hybrid—a classical engineer by training and a computer scientist by trade. So I examine problems through systems thinking. I came to robotics with the background of looking at robotics as a system of components, with the human as one of those components whose inputs need to be tweaked, with well-defined parameters. For example, in my work, "emotions" provide a valuable input into the robot, and vice versa. Within this systems thinking framework, human feedback influences robot behavior, and robot behavior influences human behavior, and so on."
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If you ever hear that implementing algorithmic decision-making tools to enable social services or other high stakes government decision-making will increase efficiency or reduce the cost to taxpayers…
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By creating user interfaces which let us work with the representations inside machine learning models, we can give people new tools for reasoning.
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Manolis Mavrikis enjoys a reasoned appeal to universities to meet the teaching challenges posed by AI
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The quest to evolve neural networks through evolutionary algorithms.
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Large-scale brainlike systems are possible with existing technology—if we’re willing to spend the money
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Mauricio Armellini and Tim Pike. This post highlights some of the possible economic implications of the so-called “Fourth Industrial Revolution” — whereby the use of new technologies and artificial intelligence (AI) threatens to transform entire industries and sectors. Some economists have argued that, like past technical change, this will not create large-scale unemployment, as labour…
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Google's #AI translation tool seems to have invented its own language.
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From Amazon and Facebook to Google and Microsoft, leaders of the world’s most influential technology firms are highlighting their enthusiasm for Artificial Intelligence (AI). But what is AI? Why is…
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