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January 20, 2015

Can Big Data Give Us Bionic Brains?

The rise of big data and analytics promises to transform many aspects of our lives, chief among them how we synthesize data and make decisions. Some theorize that big data will augment our own cognitive capabilities to the point where it’s like we have bionic brains.

According to Deloitte Analytics, the field of cognitive analytics is an extension of cognitive computing, and is made up of three main components: machine learning, natural language processing, and advanced analytics. These technologies, combined with massive amounts of unstructured data, promises to help enhance human decision-making in certain fields.

“The convergence of machine and human intelligence is disrupting traditional decision-making by equipping people with knowledge that was almost unimaginable just a few years ago,” Deloitte says in “Bionic brains,” a chapter of its recently released Analytics Trends 2015 report.

“With the rise of big data and machine-to-machine communications, analytical models and algorithms are increasingly being embedded into complex event processing [CEP] and other automated workflow environments,” the company says. “Automated decision-making is probably here to stay, enhanced by a host of cognitive analytics applications.”

Bolstering this trend is the fact that the human-to-machine connection is becoming more natural and familiar to people, the group says. As people become more inclined to trust and accept decisions made by cognitive analytic-powered machines, the decisions are getting faster and better.

The way Dr. Kirk Bourne sees it, the emerging field of cognitive analytics is the inevitable destination for the current wave of big data analytics. In a recently blog post for MapR Technologies, Dr. Bourne laid out the case for cognitive analytics.

John Kelly

John Kelly, IBM senior vice president and head of IBM research

“Cognitive analytics is the best paradigm for data-driven discovery and decision-making,” he writes. “Machine learning algorithms applied to big data will mine the data for historical trends, real-time behaviors, predicted outcomes, and optimal responses. The cognitive machine [powered by cognitive analytics algorithms] can be deployed to operate autonomously [without human intervention[ in appropriate settings.  Of course, not all applications should be left to a machine to make the decision, but it is not unreasonable to allow a machine to mine your massive data collections autonomously for new, surprising, unexpected, important, and influential discoveries.”

While cognitive analytics may be the next logical area to pursue breakthroughs following prescriptive analytics, we don’t have “bionic brains” just yet, and harnessing the massive data flows of the future will require new technologies. IBM Watson is perhaps the most visible vehicle for the type of cognitive analytics development that people like Dr. Bourne see coming down the road.

According to IBM’s Dr. John Kelly, who is IBM’s senior vice president and heads up IBM Research, better cognitive analytics will naturally flow from our challenge in just keeping up with data growth.

“We must augment our human-mental capacity to deal with this self-created wave of data,” Dr. Kelly said at the 2014 IBM THINK Forum in October. “There’s no limit to the volume of data that we’re going to create, until or if we ever run out of the ability to store it–and even at that point the data will be pouring in at us faster than we can deal with it.”

As we struggle to make sense of enormous data flows in the future, Kelly sees us turning to technologies like IBM’s Watson. Kelly outlined three broad areas that IBM should concentrate on if Watson will truly augment human cognition.

  1. Contextual awareness. “We have to give Watson eyes or the ability to see, because so many of our decisions depend on the context and the awareness of the environment that we’re in,” Kelly says.
  1. Advanced NLP. “The second thing that’s absolutely required if we’re going to make this a seamless augmentation of our cognitive capabilities is to create a system that has a complete natural language dialog with us as human beings,” Kelly says. “It’s no longer a question of smart Siri or a simple Q&A, but a system that can talk to you, understand you, and start to think about what is it you’re trying to say to me, not just the exact language you’re using.”
  1. New underlying computing infrastructure. “It took 35,000 watts of power in [Watson system that played Jeopardy! to beat those two 20-watt lightbulbs, if you will. Something is wrong. We need different underlying technology if we’re going to really create systems that operate something like the brain in terms of efficiency.”

For anybody who’s ever wondered what the limits of human knowledge are, the prospect of having what amounts to a bionic brain is utterly remarkable and completely fascinating. While there are prominent technologists who are starting to sound the alarm bells about the rise of artificial intelligence–Elon Musk being among them–the potential benefits that emerging fields like cognitive analytics can bring to humans demands that further investigation be conducted.

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