MIT Technology Review provides an update on Numenta
Numenta (artificial intelligence company) has been working on their algorithms since 2005 and are finally preparing to release a version that is ready to be used in products. Numenta’s technology is aimed at variety of applications, such as judging whether a credit card transaction is fraudulent, anticipating what a Web user will click next, or predicting the likelihood that a particular hospital patient will suffer a relapse.
The Numenta algorithms can analyze and extrapolate from complex patterns because they borrow techniques from parts of the human brain that have evolved to interpret complex data streaming in from our senses and use it to predict what might be coming.
The system’s ability to make predictions about unfolding events is rooted in its unique capacity for processing temporal, or time-dependent, data. Conventional learning software cannot do that, because it can’t handle input consisting of many variables that change over time. Instead, engineers generally have to extract the handful of variables they think are useful and feed them into the algorithms.
That “pre-processing” isn’t necessary in models inspired by studies of biological brains, Arel says. Instead, the learning system can decide for itself what is important and what isn’t. This is an emerging field dubbed deep machine learning. “Most academic efforts are focused on processing images, though,” he says. “What’s unique about Numenta is that it’s able to handle temporal data, which opens up different kinds of applications.” Among the examples Hawkins envisions: businesses could better analyze human speech or patterns of electricity use in buildings.
But while this approach raises the prospect of systems that can learn about any kind of data rather than being specialized to just one task, Numenta still has to prove that its technology is widely applicable and cost-effective. It’s also unclear how the company will bring the technology to market, but it will probably be in the form of development tools rather than off-the-shelf products. “Now that the technology is really working,” Hawkins says, “next year will see us switch into product-development mode.”
A bank’s computer system has just 10 milliseconds to decide whether to authorize a transaction. Numenta’s technology makes separate sets of rules for different categories of transactions unnecessary. Instead, a raw feed of each person’s spending patterns is used to train a set of algorithms so they can learn that customer’s habits. At any moment the system has an internalized representation of past events that it uses to predict what kinds of transactions are likely to come next. If a new transaction doesn’t fit those expectations, it can be flagged as potential fraud. In this approach, the fraud detectors are always up to date
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