Category Archives: Human Robots

Singularity University’s Exponential Finance Summit begins today and runs through June 9 in New York, the finance industry’s 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, we’ll 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:
The future of blockchain and digital currencies.
How artificial intelligence is being used in finance.
The further decentralization and digitization of banking.
What quantum computing can do for finance.
How major institutions are evolving strategies to take advantage of new fintech startups and technology.
Ric Edelman, founder of Edelman Financial Services, and Sharon Sputz, director of Columbia University’s 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 BlackRock’s chief talent officer, Matthew Breitfelder, will take a look at the future of work.
Of course, as usual, we’ll 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 @SingularityHub and @xfinance or using the hashtag #xfin.
Much of the latest technology driving fintech is still new, and its impact has yet to be fully fleshed out—which should make for an interesting summit.
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Singularity University is dismayed by the Trump administration’s 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 we’re 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.
The 2017 Global Solutions Program this summer will focus on challenges facing our climate and environment.
We’ve had 23 Global Impact Challenges aimed at climate change this year already.
We have flourishing companies in our startup ecosystem, such as Getaround, addressing congestion; Semtive, improving the way we generate energy; Modern Meadow, biofabricating leather; Impact Vision, addressing supply chain inefficiencies and food waste; BlueOak, collecting and converting the e-waste into a sustainable source of metals; and so many others.
Energy is a key topic discussed at all of our events, including our recent Exponential Manufacturing Summit in Boston a few weeks ago and Exponential Finance Summit this week in New York.
We’re 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 government’s 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.
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Humankind lost another important battle with artificial intelligence (AI) last month when AlphaGo beat the world’s leading Go player Kie Je by three games to zero.
AlphaGo is an AI program developed by DeepMind, part of Google’s 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 AlphaGo’s skill as “like a god of Go.”
AlphaGo will now retire from playing Go, leaving behind a legacy of games played against itself. They’ve 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.
Ready, set, Go
Go is an ancient game that essentially pits two players—one playing black pieces the other white—for dominance on board usually marked with 19 horizontal and 19 vertical lines.
A typical game of Go: simple to learn but a lifetime to master.Flickr/Alper Cugun, CC BYGo 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 ahead—feasible for computers in chess—very difficult in Go.
DeepMind’s 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 can’t wait to see what comes next."
This does open up many opportunities for the future, but challenges still remain.
Neuroscience meets AI
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.
The best moves
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 couldn’t sample every possible board position, AlphaGo’s neural networks extracted key ideas about strategies that work well in any position. It is these countless hours of self-play that led to AlphaGo’s 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 can’t 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 AlphaGo’s 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.
What’s next?
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 DeepMind’s bold claims seem likely to be realized, and as the company says, we can’t wait to see what comes next.
This article was originally published on The Conversation. Read the original article. Continue reading →