May 14, 2015 | ScienceBlog.com Today the National Science Foundation (NSF) announced two, five-year, center-scale awards totaling $8.75 million to advance the state-of-the-art in medical and cyber-physical systems (CPS). One …
Getty Images What was once just a figment of the imagination of some our most famous science fiction writers, artificial intelligence (AI) is taking root in our everyday lives. We’re still a few years away from having robots at our beck and call, but AI has already had a profound impact in more subtle ways.…
A few years ago, investors and startups were chasing “big data”. Now we’re seeing a similar explosion of companies calling themselves artificial intelligence, machine learning, or collectively “machine intelligence”. The Bloomberg Beta fund, which is focused on the future of work, has been investing in these approaches.
Computers are learning to think, read, and write. They’re also picking up human sensory function, with the ability to see and hear (arguably to touch, taste, and smell, though those have been of a lesser focus).
Machine intelligence technologies cut across a vast array of problem types (from classification and clustering to natural language processing and computer vision) and methods (from support vector machines to deep belief networks). All of these technologies are reflected on this landscape.
What this landscape doesn’t include, however important, is “big data” technologies. Some have used this term interchangeably with machine learning and artificial intelligence, but I want to focus on the intelligence methods rather than data, storage, and computation pieces of the puzzle for this landscape (though of course data technologies enable machine intelligence).
We’ve seen a few great articles recently outlining why machine intelligence is experiencing a resurgence, documenting the enabling factors of this resurgence. Kevin Kelly, for example chalks it up to cheap parallel computing, large datasets, and better algorithms.
Machine intelligence is enabling applications we already expect like automated assistants (Siri), adorable robots (Jibo), and identifying people in images (like the highly effective but unfortunately named DeepFace). However, it’s also doing the unexpected: protecting children from sex trafficking, reducing the chemical content in the lettuce we eat, helping us buy shoes online that fit our feet precisely, anddestroying 80's classic video games.
Big companies have a disproportionate advantage, especially those that build consumer products. The giants in search (Google, Baidu), social networks (Facebook, LinkedIn, Pinterest), content (Netflix, Yahoo!), mobile (Apple) and e-commerce (Amazon) are in an incredible position. They have massive datasets and constant consumer interactions that enable tight feedback loops for their algorithms (and these factors combine to create powerful network effects) — and they have the most to gain from the low hanging fruit that machine intelligence bears. Best-in-class personalization and recommendation algorithms have enabled these companies’ success (it’s both impressive and disconcerting that Facebook recommends you add the person you had a crush on in college and Netflix tees up that perfect guilty pleasure sitcom). Now they are all competing in a new battlefield: the move to mobile. Winning mobile will require lots of machine intelligence: state of the art natural language interfaces (like Apple’s Siri), visual search (like Amazon’s “FireFly”), and dynamic question answering technology that tells you the answer instead of providing a menu of links (all of the search companies are wrestling with this).Large enterprise companies (IBM and Microsoft) have also made incredible strides in the field, though they don’t have the same human-facing requirements so are focusing their attention more on knowledge representation tasks on large industry datasets, like IBM Watson’s application to assist doctors with diagnoses.
Via Dr. Stefan Gruenwald
Researchers have mimicked the way the human brain processes information with the development of an electronic long-term memory cell, which mirrors the brain’s ability to simultaneously process and store multiple strands of information. The development brings them closer to imitating key electronic aspects of the human brain — a vital step toward creating a bionic brain and unlocking treatments for Alzheimer’s and Parkinson’s diseases.
Science 2.0 Neuronal Network 3-D Models Reveal Organizational Principles Of Sensory Cortex Science 2.0 In their April publication in Cerebral Cortex, Max Planck Institute for Biological Cybernetics researchers Dr.
These days, many of us in consumer and enterprise tech companies are working on predictive systems that provide modest but valuable augmentation of human intelligence and business processes. I think this scale of ambition is a good fit for the current state of the art in machine learning and probabilistic inference. Think personal assistants like Siri or Google Now, predictive analytics in the enterprise for churn detection and ad campaign targeting, and personalized news apps like Prismatic.
But I think that the long term story is much more exciting, and much further from our experience with synthetic intelligence to date. I believe that we are on the path to building the equivalent of global-scale nervous systems. I’m thinking Gaia’s brain: distributed but unified intelligences that gather data from sensors all over the world, and that synthesize those data streams to perceive the overall state of the planet as naturally as we perceive with our own sensory systems. This isn’t just big data–this is big inference.
To make this idea of a global intelligence more concrete, consider the startup Premise. As a first step toward the kind of perceptual systems that I am talking about, Premise is using various signals from the public internet as a set of massively distributed sensory organs, and then leveraging this information to develop more informative economic indexes.
Now consider what other problems such systems could solve in the coming decades. We could gain a true understanding of the climate system on a granular but global level. We could track and coordinate every vehicle on the planet, to improve energy efficiency and optimize scheduling to all but eliminate traffic jams. Or moving from vehicles to parts and materials, we could create and manage truly robust supply chains that maintain efficiency and resilience in the face of unexpected events. The possibilities go on, and are truly awesome.
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