Chips and software that draw on the brain’s ability to do massive parallel processing could make for far smarter, more power-efficient technologies.
[Thank You MIT Technology Review | By Dave Talbot 10.10.13]
Brain chip: Qualcomm CTO Matt Grob says the new technology will soon be ready to ship.
The world’s largest smartphone chipmaker, Qualcomm, says it is ready to start helping partners manufacture a radically different kind of a chip—one that mimics the neural structures and processing methods found in the brain.
The approach is emerging as a way to enable machines to perform complex tasks while consuming far less power. IBM has been prototyping similar chips (see “IBM Scientists Show Blueprints for Brainlike Computing),” and the area is the focus of intense research around the world (see “Building a Brain on a Silicon Chip” and “Intel Reveals Neuromorphic Chip Design”).
Speaking in a sponsored talk at MIT Technology Review’s EmTech conference today, Qualcomm CTO Matt Grob said that by next year his company would take on partners to design and manufacture such chips for applications ranging from artificial vision sensors to robot controllers and even brain implants. The technology might also lead to smartphones that can sense and process information far more efficiently.
Today’s computer systems are built with separate units for storing information and processing it sequentially, a design known as the Von Neumann architecture. By contrast, brainlike architectures process information in a distributed, parallel way, modeled after how the neurons and synapses work in a brain.
Brain bot: Qualcomm has developed robots that use its neuro-inspired chips.
Qualcomm has already developed new software tools that simulate activity in the brain.
These networks, which model the way individual neurons convey information through precisely timed spikes, allow developers to write and compile biologically inspired programs. Qualcomm is using this approach to build a class of processors called neural processing units (NPUs). It envisions NPUs that are massively parallel, reprogrammable, and capable of cognitive tasks like classification and prediction. “What we’re talking about is scale, making it into a platform,” said Grob during his talk. “We want to make it easier for researchers to make a part of the brain.”
Recommendation engines offer a fantastic organizing principle for integrating the physical and digital elements of people’s lives.
[Thank You Harvard Business Review | by Michael Schrage 10.09.13]
To-do lists, in fact, will likely be the internet’s next multibillion dollar global innovation. The only serious question is whether the to-do breakthroughs come from Google, Amazon, Microsoft, Apple, LinkedIn or a Zuckerberg yet to emerge from the venture capital chrysalis. To-do lists are too big, too rich and too transcendent a business and technical opportunity to ignore. All the digital ingredients exist; what’s missing is the right intrapreneurial—or entrepreneurial—master kitchen to mix them.
Multibillion dollar markets in gray market to-do lists already exist; they’re called recommendation engines. Amazon and Netflix have effectively branded themselves as superior recommenders. Those Web 2.0 innovators succeed through blending algorithmic alchemy and data-driven decisions into user experiences inviting exploration, sampling and outright purchase. You’re under no obligation to do any of those things, of course. But the odds are you— rightly—take many of those recommendations seriously. The better their recommendations, the more you trust their judgment. They’ve little incentive to squander your time or their credibility.
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