At year's end, IBM (IBM) selects a new innovation that has the potential to change the world.
This time it chose cognitive computers. This new generation of computers will be designed to mimic the human senses of touch, sight, hearing, taste and smell. Computers will learn and adapt.
IBM, which late Tuesday released Q4 results that beat expectations, predicts cognitive computers will emerge over the next five years. Some of the technology is here, but more needs to be achieved before all the pieces of the puzzle fit, Bernie Meyerson, IBM vice president of innovation, explains in an interview with IBD.
IBD: Why does IBM say cognitive systems are the new wave in computing?
Meyerson: If you look at traditional computing, it was driven mostly by the fact that you continuously scaled microprocessor chips down in size (to add more circuitry and boost performance of the machines they power). People are familiar with this described as Moore's Law (coined by Intel (INTC) co-founder Gordon Moore).
It is important to understand that we have traveled an immense distance, but there is a limit to Moore's Law. In three or four more generations, we are done. You won't be scaling transistor technology smaller any longer. Silicon will still be important. You will stack chips to make more dense blocks. But at the end of the day, one of the biggest buttons we press for constant advances will be gone.
IBD: And so cognitive computing will then come into play?
Meyerson: Cognitive computing is a completely different approach to drive performance in computers. These machines will perform better because they learn, they adapt, they sense — and by doing that you don't program it so much as you can teach the system to learn. That is incredibly efficient, compared to what you can do today, where you literally type in millions of lines of code to get the machine to do what you want. This is a machine that can observe and follow.
IBD: Meaning the machine becomes more humanlike?
Meyerson: What makes humans so efficient are your senses. You have touch, sight, hearing, taste and smell. This makes humans a far more efficient machine in comparison to a computer.
When IBM put its Watson computer against (two) Jeopardy world champions, keep in mind that Watson was powered by 80,000 watts. The individual people it went against were running at about 20 watts. So with one-4,000th of the power, they stayed in the game (though Watson won). This tells you there is something very different going on between those two systems.
Humans are unbelievably efficient in terms of how they interact with their environment compared to a computer. And that is why there is this interest in changing the architectural premise of how a computer runs.
IBD: Is Watson a cognitive computer?
Meyerson: Yes. Watson is the embodiment of cognitive computing. For example, it can be taught not only to recognize the right and probable answer to a medical diagnostic issue such as a cancer, but it can also learn from uncertain data, even if you have conflicting data. Watson, because it is probabilistic, might not know the exact answer, but if the odds favor or point to one answer it will assign a high probability to the correct answer.
With cognitive computing, it's about providing the computer a richer set of data to make decisions. The idea behind cognitive machines is that you don't program them, you teach them. You should, in theory, be able to have a physician sit down and say these 300 things I am about to show on these images are an aneurysm and I want you to find the others I may have missed. Watson looks at a vast amount of data and extracts the coincidences to understanding what you are looking at.
IBD: How does sight come into play with cognitive computing?
Meyerson: If you only stream an image in terms of bits of data, say an MRI image, that is incredibly inefficient because the computer has to crawl through all this data and try to reassemble an image. What if you take it up several levels so that the system understands what it is looking at? It can look at a brain scan while receiving input from a physician. The computer can learn to recognize an aneurysm based on the characteristics of many different aneurysms the physician has pointed out.
So you literally have trained the computer by saying "look at this image, this is what an aneurysm looks like." The computer can then find other images for you. It learns from the input. That is a radical departure from today, where a picture is treated as a collection of bits, not as a collection of things within an image.
IBD: And how does sound come into play?
Meyerson: You're talking about not just hearing the actual sound, not just hearing a word, but also understanding the meaning of that at the edge. It's like that gut feeling you have when listening to someone and the verbal attributes. This is the ability to detect the stress in the voice, for example. When a baby is upset, you may not know why. You don't know, because it's baby talk, why it's crying or complaining. But what if you could take all of those sounds and apply it through an interpretive capability, literally having an understanding from the tenor, pitch, vibrations, all those subtle elements, and connect that to observed physical issues and be able to truly understand why the child is upset?
IBD: What about taste and smell?
Meyerson: We already, at the simplistic level, have biomarker identification. In areas such as public security we can recognize toxins in the air. That is fairly routine. Imagine if you had many sensors all on a small chip, each sensitized to a different molecule. The sensors might correspond to a different taste that you think is either good or bad. Over the course of time the system could actually come up with a fingerprint of what you like and don't. It could tell you what the best tasting things are for you, based on those inputs.
IBD: And what is IBM doing to make all this possible?
Meyerson: We're inventing it. That is what we do and why we've been around for 100 years.