“In this recently released excerpt, Kelly and Hamm point out that cognitive systems represent the third era in the history of computing. In the first era, computers were essentially tabulating machines that counted things. The tabulating era began in the 19th century with the work of Charles Babbage, Herman Hollerith and other inventors. These early computer were used in a number of applications including national population censuses and the control of looms and other industrial machines. “
Digital technologies are now found all around us, from the billions of mobile devices carried by almost every person in the planet to the explosive growth of what McKinsey is now calling the Internet of All Things. These digital devices are generating gigantic amounts of information every second of every hour of every day, and we are now asking our computers to help us make sense of all this data. What is it telling us about the environment we live in? How can we use it to make better decisions? Can it help us understand our incredible complex economies and societies?
And, in fact, such data-driven, sense-making, insight-extracting, problem-solving cognitive computers seem to have more in common with the structure of the human brain than with the architecture of a classicVon Neumann machine. But, while inspired by the way our brains process and make sense of information, the objective of cognitive machines is not to think like a human, something we barely understand.
So, just like we invented industrial machines to helps us overcome our physical limitations, we now need to develop a new generation of machines to help us get around our cognitive limitations.
A cognitive system, on the other hand, can analyze many thousands of options at the same time, including the large number of infrequently occurring ones as well as ones that the expert has never seen before. It evaluates the probability of each option being the answer to the problem, and then comes up with the most likely options, that is, those with the highest probabilities. Moreover, the cognitive system has access to huge amounts of information of all kinds, both structured and unstructured, including not only books and documents, but also speech, pictures, videos and so on. These cognitive systems are truly beginning to augment our human cognitive capabilities much as earlier machines have augmented our physical ones.
“The goal isn’t to replicate human brains, though. This isn’t about replacing human thinking with machine thinking. Rather, in the era of cognitive systems, humans and machines will collaborate to produce better results – each bringing their own superior skills to the partnership. The machines will be more rational and analytic – and, of course, possess encyclopedic memories and tremendous computational abilities. People will provide judgment, intuition, empathy, a moral compass and human creativity.”
“Around that time, two forces coincided, each amplifying the disruptive capacity of the other. First, the deployment of the digital microprocessor and packet-switched networking marked the beginning of the rise of the digital infrastructure that would eventually span the globe, driven by exponential performance improvements in computing, storage, and bandwidth technologies. Digital technology unfolded on top of a second force that had been building for a few decades: a global movement in public policy towards economic liberalization which was systematically reducing barriers to the movement of goods, money, people, and ideas across the boundaries of nations and industries.”
Evidence of this pressure is starkly captured in the return on assets (ROA) for all public companies in the US since 1965. Over this period, there has been a sustained and dramatic erosion in performance: ROA has collapsed by 75 percent.
The widespread erosion of ROA confirms that our management practices and institutions are struggling to respond to the relentless pressure. Why haven’t they responded more effectively?
Almost 80 years ago, Ronald Coase won the Nobel Prize in Economics for an essay that suggested that we do this for the sake of scalable efficiency — it costs less to coordinate activity within a firm than across independent entities
Perhaps we need to move from a rationale of scalable efficiency to one of scalable learning — designing institutions and architectures of relationships across institutions that help all participants to learn faster as more participants join.
The importance of lifelong learning in a rapidly evolving information society
The need to decentralize organizations around employees — viewing them as assets capable of expanding growth rather than as fixed costs to be eliminated — and to move away from standardized and tightly-specified process flows
The need for institutions to focus on building capability around core strengths — one of the reasons he was an early proponent of outsourcing as a way to simplify operations and to focus management on what really matters
The importance of focusing on the dynamics of evolving economic and social processes rather than on static equilibrium models.
Posted from Diigo. The rest of my favorite links are here.