Posted by L.V. on January 8, 2016 at 10:54am Posted by L.V. on January 8, 2016 at 10:54am

So are Apple, IBM, and Amazon. In fact, every major technology company is investing in machine intelligence to improve their existing products or to develop entirely new ones. This transformative technology is poised to affect just about every industry out there. VB Profiles partnered with Shivon Zilis to better understand its impact and to present the Machine Intelligence 2.0 landscape. Above: This chart is part of VB Profiles Machine Intelligence Series.

Shivon Zilis will participate in a panel discussion at Strata Hadoop World New York 2016, "Where's the puck headed?," considering the big trends in big data and explaining what the field will look like down the road. A year ago today, I published my original attempt at mapping the machine intelligence ecosystem. So much has happened since. I spent the last 12 months geeking out on every company and nibble of information I can find, chatting with hundreds of academics, entrepreneurs, and investors about machine intelligence. This year, given the explosion of activity, my focus is on highlighting areas of innovation, rather than on trying to be comprehensive.

So are Apple, IBM, and Amazon. In fact, every major technology company is investing in machine intelligence to improve their existing products or to develop entirely new ones. This transformative technology is poised to affect just about every industry out there. VB Profiles partnered with Shivon Zilis to better understand its impact and to present the Machine Intelligence 2.0 landscape. Above: This chart is part of VB Profiles Machine Intelligence Series.

A year ago today, I published my original attempt at mapping the machine intelligence ecosystem. So much has happened since. I spent the last 12 months geeking out on every company and nibble of information I can find, chatting with hundreds of academics, entrepreneurs, and investors about machine intelligence. This year, given the explosion of activity, my focus is on highlighting areas of innovation, rather than on trying to be comprehensive. Despite the noisy hype, which sometimes distracts, machine intelligence is already being used in several valuable ways.

A year ago today, I published my original attempt at mapping the machine intelligence ecosystem. So much has happened since. I spent the last 12 months geeking out on every company and nibble of information I can find, chatting with hundreds of academics, entrepreneurs, and investors about machine intelligence. This year, given the explosion of activity, my focus is on highlighting areas of innovation, rather than on trying to be comprehensive. Despite the noisy hype, which sometimes distracts, machine intelligence is already being used in several valuable ways.

C. COOPER 21 3 Data representation--the key to conceptualisation: D. B. VIGOR 33 MECHANISED MATHEMATICS 45 4 An approach to analytic integration using ordered algebraic expressions: L. I. HODGSON 47 5 Some theorem-proving strategies based on the resolution principle: J. L DARLINGTON 57 MACHINE LEARNING AND HEURISTIC PROGRAMMING 73 6 Automatic description and recognition of board patterns in Go-Moku: A. M. MURRAY and E. W. Etcomc

The readers of this book will very probably be familiar with its predecessor, Machine Intelligence, I, published under the same imprint and edited by N. L. Collins and Donald Michie, and so they will be aware of the fascination and familiar with the wide extent of the field of enquiry being investigated here, difficult as it may be to define it precisely. We may start with: how to make a machine which can do what we could do (so that we will be free to do something else); how to make a machine which can do what we could do if we were more accurate and patient (so that we can eliminate human error); how to make a machine which can do what we could do, but do it more quickly (os that we can learn the answer to the problem before the problem changes). From this we advance to: how to make the machine (and ourselves) more efficient on the average by defining sub-goals, by using sub-optimal search procedures, by throwing away information totally, partially, or temporarily, for the sake of getting something done within an acceptable time. Next we have: how can we assist the machine to recognise limited successes; how can we use the machine's current experience to suggest more appropriate sub-goals; how in fact can we and the machine best act together? We are now confronted with the questions: how can we converse with the machine during the period of operation; what is a suitable language for such a conversation; what can the machine best do, what can we best do, and how should our respective roles be determined?