Daily Archives: June 7, 2017

In BriefNine experts weighed in on the future of artificialintelligence and machine learning recently for IEEE Spectrum. Theiranswers provide a glimpse into what's coming in the world of AI andwhat to expect from the Singularity. AI Visionaries

Artificial intelligence (AI) is progressing so fast that there are new developments in the field almost every week. The tendrils of AI grow further into human life and continue to rapidly intertwine with our reality, and this process will only accelerate. Some worry about the consequences of a future in which AIs have more capabilities than humans, while some relish this prospect. IEEE Spectrum has just published a special issue for June 2017, which reports on the views of nine visionaries, technologists, and futurists on whats coming in AI.

Each expert was asked, When will we have computers as capable as the brain? Ray Kurzweil thinks this will happen in 2029, while Jrgen Schmidhuber simply agrees that it will be soon, and Gary Marcus estimates that it will happen 20 to 50 years from now. Nick Bostrom predicts within a small number of decades. Rodney Brooks is a little more conservative, estimating 50 to 100 years, while both Robin Hanson and Martine Rothblatt think that it will happen within the 21st century.

Ruchir Puris answer to this question was perhaps the most interesting: A human brain is fundamentally different than being a champion chess, Jeopardy!, or Go player. It is something that entails essential traits like caring, empathy, sharing, ingenuity, and innovation. These human brain traits might prove to be elusive to machines for a long time. . .. Although AIs impact on society will accelerate further. . .it will be a while before we will be able to holistically answer [that] question.

So, How will brainlike computers change the world? Robin Hanson thinks that humans will get rich from robot labor, while Gary Marcus anticipates major advancements in science and medicine and Martine Rothblatt agrees with Kurzweil that we will essentially eventually become downloadable and therefore immortal. Ray Kurzweil sees AI as a massive brain extender, and therefore a problem solver, making every aspect of our lives better. Rodney Brooks thinks making realistic predictions about this isnt possible since its too far off, and instead posits that in 20 years, baby boomers including Kurzweil will be assisted by in-home computers, but wont be immortal. Jrgen Schmidhuber thinks that AIs will be fascinated by the possibilities of space as they become self-motivated and pursue their own goals.

Finally, Do you have any qualms about a future in which computers have human-level (or greater) intelligence? Carver Mead points out that people always fear new technologies, even though history shows that we have continually benefitted from them. Robin Hanson thinks anyone who doesnt have qualms about a change this momentous isnt paying attention, but Martine Rothblatt doesnt have qualms, because she thinks human needs will shape a Darwinian market for robots. Ray Kurzweil thinks we will avoid peril and gain optimally by merging with AI. Nick Bostrom is concerned by the problem of scalable control of AI, while Rodney Brooks says he has no qualms at all, and that qualming is not useful, even for Nick Bostrom. Gary Marcus doesnt see clear solutions to potential problems yet, but thinks that future technologies will provide them.

The experts had different ideas about many things, but there was no dispute about the most important point: the singularity is coming, and its closer than we think.

Excerpt from:

Experts Weigh in on AI and the Singularity - Futurism

Singularity Universitys Exponential Finance Summit begins today and runs through June 9 in New York, the finance industrys bustling capital. You can tune into the summit as it happens from anywhere with this livestream.

Singularity Hub is also covering the event as it brings together financial and technology leaders from across the industry. From exciting startups like Lemonade and HyperScience to established financial institutions such as BlackRock and Bank of America, well be learning about how emerging technologies are changing the workings of the finance industry and how financial services companies do business.

At the summit, experts will dive into:

Ric Edelman, founder of Edelman Financial Services, and Sharon Sputz, director of Columbia Universitys Data Science Institute, will discuss the future of financial advice and investing. Angela Strange, partner at Andreesen Horowitz, will break down exponential technology and insurance, and BlackRocks chief talent officer, Matthew Breitfelder, will take a look at the future of work.

Of course, as usual, well also keep an eye on talks and question-and-answer sessions with Ray Kurzweil, Singularity University cofounder and chancellor, and Peter Diamandis, Singularity University cofounder and chairman.

Be sure to join the conversation on the future of finance in real-time on Twitter with@SingularityHuband@xfinanceor using the hashtag#xfin.

Much of the latest technology driving fintech is still new, and its impact has yet to be fully fleshed outwhich should make for an interesting summit.

Image Credit: Pond5

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Tune Into the Future of Fintech at Exponential Finance This Week - Singularity Hub

Singularity University is dismayed by the Trump administrations choice to withdraw from the Paris Accord. Climate change is one of the greatest risks to humankind, and the decisions we make over the next few decades will impact life on earth for thousands of years.

At SU were proud to support the responsible development of exponential technologies, such as AI, robotics, nanotechnology, and digital biology, that may provide solutions to climate change. These exponential technologies should be nurtured in enabling policy environments, but independent of the decisions made by politicians, SU will move forward with our plans to address climate change.

Were proud to see an increase in breakthroughs that greatly improve our stewardship of the planet and global abundance such as in vitro meat production, carbon capture techniques, genetic engineering of climate resilient crops, advances in atmospheric water extraction, and countless others.

While this is a disappointing decision, there are more powerful forces at work. The global response to the federal governments decision has renewed our faith in the common goodness of humankind. Innovation will continue. We will move forward.

We at SU provide access to a deep and broad innovation ecosystem that includes forward thinking corporations (e.g., Deloitte, Google, Lowes), development organizations (e.g., Stockholm Resilience Center, Unicef, World Wide Fund for Nature), and governments around the world. We will continue to work across industries and disciplines to bring abundance to all.

We welcome you to join our bold march into the future.

Image Credit: Pond5

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Beyond Politics: Innovating for a Sustainable Future - Singularity Hub

Humankind lost another important battle with artificial intelligence (AI) last month when AlphaGo beat the worlds leading Go player Kie Je by three games to zero.

AlphaGo is an AI program developed by DeepMind, part of Googles parent company Alphabet. Last year it beat another leading player, Lee Se-dol, by four games to one, but since then AlphaGo has substantially improved.

Kie Je described AlphaGos skill as like a god of Go.

AlphaGo will now retire from playing Go, leaving behind a legacy of games played against itself. Theyve been described by one Go expert as like games from far in the future, which humans will study for years to improve their own play.

Go is an ancient game that essentially pits two playersone playing black pieces the other whitefor dominance on board usually marked with 19 horizontal and 19 vertical lines.

Go is a far more difficult game for computers to play than chess, because the number of possible moves in each position is much larger. This makes searching many moves aheadfeasible for computers in chessvery difficult in Go.

DeepMinds breakthrough was the development of general-purpose learning algorithms that can, in principle, be trained in more societal-relevant domains than Go.

DeepMind says the research team behind AlphaGo is looking to pursue other complex problems, such as finding new cures for diseases, dramatically reducing energy consumption or inventing revolutionary new materials. It adds:

"If AI systems prove they are able to unearth significant new knowledge and strategies in these domains too, the breakthroughs could be truly remarkable. We cant wait to see what comes next."

This does open up many opportunities for the future, but challenges still remain.

AlphaGo combines the two most powerful ideas about learning to emerge from the past few decades: deep learning and reinforcement learning. Remarkably, both were originally inspired by how biological brains learn from experience.

In the human brain, sensory information is processed in a series of layers. For instance, visual information is first transformed in the retina, then in the midbrain, and then through many different areas of the cerebral cortex.

This creates a hierarchy of representations where simple, local features are extracted first, and then more complex, global features are built from these.

The AI equivalent is called deep learning; deep because it involves many layers of processing in simple neuron-like computing units.

But to survive in the world, animals need to not only recognize sensory information, but also act on it. Generations of scientists and psychologists have studied how animals learn to take a series of actions that maximize their reward.

This has led to mathematical theories of reinforcement learning that can now be implemented in AI systems. The most powerful of these is temporal difference learning, which improves actions by maximizing expectation of future reward.

By combining deep learning and reinforcement learning in a series of artificial neural networks, AlphaGo first learned human expert-level play in Go from 30 million moves from human games.

But then it started playing against itself, using the outcome of each game to relentlessly refine its decisions about the best move in each board position. A value network learned to predict the likely outcome given any position, while a policy network learned the best action to take in each situation.

Although it couldnt sample every possible board position, AlphaGos neural networks extracted key ideas about strategies that work well in any position. It is these countless hours of self-play that led to AlphaGos improvement over the past year.

Unfortunately, as yet there is no known way to interrogate the network to directly read out what these key ideas are. Instead, we can only study its games and hope to learn from these.

This is one of the problems with using such neural network algorithms to help make decisions in, for instance, the legal system: they cant explain their reasoning.

We still understand relatively little about how biological brains actually learn, and neuroscience will continue to provide new inspiration for improvements in AI.

Humans can learn to become expert Go players based on far less experience than AlphaGo needed to reach that level, so there is clearly room for further developing the algorithms.

Also, much of AlphaGos power is based on a technique called back-propagation learning that helps it correct errors. But the relationship between this and learning in real brains is still unclear.

The game of Go provided a nicely constrained development platform for optimizing these learning algorithms. But many real-world problems are messier than this and have less opportunity for the equivalent of self-play (for instance self-driving cars).

So, are there problems to which the current algorithms can be fairly immediately applied?

One example may be optimization in controlled industrial settings. Here the goal is often to complete a complex series of tasks while satisfying multiple constraints and minimizing cost.

As long as the possibilities can be accurately simulated, these algorithms can explore and learn from a vastly larger space of outcomes than will ever be possible for humans. Thus DeepMinds bold claims seem likely to be realized, and as the company says, we cant wait to see what comes next.

This article was originally published on The Conversation. Read the original article.

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No More Playing Games: AlphaGo AI to Tackle Some Real World Challenges - Singularity Hub