In the first week of October I participated in a Cognitive Systems Colloquium hosted by IBM at its Thomas J. Watson Research Center. IBM defines cognitive systems as “a category of technologies that uses natural language processing and machine learning to enable people and machines to interact more naturally to extend and magnify human expertise and cognition. These systems will learn and interact to provide expert assistance to scientists, engineers, lawyers, and other professionals in a fraction of the time it now takes.”
The need for such systems is a result of the explosive growth of data all around us. Not only are we now able to collect huge amounts of real-time data about people, places and things, but far greater amounts can be derived from the original data through feature extraction and contextual analysis. One of the key lessons from Watson, - IBM’s question-answering system which in 2011 won the Jeopardy! Challenge against the two best human Jeopardy! players, - was that the very process of analyzing data increases the amount of data by orders of magnitude.
This is challenging our ability to store and analyze all that data. The new generation of cognitive systems will require innovation breakthroughs at every layer of our IT systems, including technology components, system architectures, software platforms, programming environment and the interfaces between machines and humans.
In the opening presentation, IBM Research director John Kelly summarized both the promise and challenges of cognitive systems. Kelly just published Smart Machines: IBM's Watson and the Era of Cognitive Computing co-written with IBM writer and strategist Steve Hamm.
Data-driven cognitive systems are quite different from the programmable systems we’ve been using for over 60 years. Just about all computers in use today are based on the Von Neumann architectural principles laid out in 1945 by mathematician John von Neumann. Any problem that can be expressed as a set of instructions can be codified in software and executed in such stored-program machines. This architecture has worked very well for many different kinds of scientific, business, government and consumer applications but is limited in its ability to deal with large amounts and varieties of unstructured information in real time.
Our brains have evolved to do so quite well over millions of years. For example, Watson required 85,000 watts of power, compared to around 20 watts for the brains of the human players. But, while our brains are incredibly efficient, they can’t keep up on their own with the huge volumes of information now coming at us from all sides as well as with the increasing complexity of so many human endeavors, - including medical diagnoses, financial advice or business strategies.HT: vbounded who notes:
So, just like we invented industrial machines to helps us enhance our strength and speed, we now need to develop this new generation of machines to help us enahnce our cognitive capabilities. In fact, the architectures of such cognitive systems have more in common with the structure of the human brain than with those of classic Von Neumann machines....MORE
"people using data and models they don't understand creates lots of opportunities"