Israel called Zebra Medical Vision. They do radiology. And they do a lot of radiology. But here’s the fun part. In India today, they are offering reading any image for a dollar. Now that’s impactful. There aren’t enough radiologists in India. So when you get a scan in India—you may get a CT scan, an MRI, an ultrasound, an X-ray—you might wait for a week for the radiologist to read it and write a report. But with Zebra when you get the image, you have the analysis done almost immediately if you are connected. For a dollar. That’s exciting to me. And guess what, we will see better results than a radiologist and way faster. Higher quality, faster, and dirt cheap. The radiologist couldn’t do a phone call for a dollar. So what else do you need?
And you can talk to Ginger.io. They’re doing mental health that way. AliveCor is doing cardiology for basically 10 bucks a month. In traditional cardiology you might take one ECG a year but AliveCor’s average patient is doing 200 of them a year. They’re treating an ECG like a blood glucose reading for a diabetic. Why shouldn’t it be that way? If I think I might die from cardiac disease, or have a heart attack any day, I’m going to monitor it every damn day. Just like diabetics do. And then you can’t have a human read it or it will get too expensive, so a machine has to say, ‘Hey, you have atrial fibrillation or not today.’ Even if I wanted an appointment, and I can afford the appointment, I’d call Stanford and they’d give me an appointment in two weeks. First I’d see a cardiologist. He’d prescribe the ECG. I’d then take a week to get that, and then I’d consult with the cardiologist. It’d be two months. It can be done much better with newer tools. I can do it at home. [Editor’s note: Khosla Ventures is an investor in Zebra, Ginger.io, and AliveCor.]
And I want to build a cancer oncologist. There’s no reason an oncologist should be a human being. Look, the right kind of oncologist isn’t the research oncologist. They know the most. But the guys who know how to take care of a patient are the community oncologists in Fresno or Stockton. They cannot always read all these journals, but they care for patients. They actually know what somebody’s daughter or son is doing, and they have that connection. That’s what you need. They can be assisted with a virtual tumor board or an A.I. oncologist.
X: What about Watson Health? On the surface, isn’t Watson exactly what you’re talking about? Scouring data, finding correlations.
VK: Watson is not A.I., it’s statistics. Watson was designed to demonstrate the power of hardware. They had this supercomputer—Deep Blue I think—and they wrote Watson as basically a statistical NLP [natural language processing] package. Then they said, ‘OK, how do we prove it to the world? We can play Jeopardy.’ And they won Jeopardy. But you don’t need intelligence to win Jeopardy, you need statistical power. When machines beat humans at chess, that was computing power, raw computing power. But if you do, if you are a powerful enough computer, you can beat a human at chess.
Now you go from chess to Go, you do not have enough computing power to compute Go. Because chess only has 64 squares. Go has a lot more possibilities. They’re not computable. And then you say, can you actually make deductions? This is probably the best way to explain what statistics is, and what A.I. is. What you need in Go is “intuition” about patterns. It’s not really intuition, it’s whatever is called intuition because you can’t compute every possibility. So if you look at how DeepMind’s opponents describe what AlphaGo was doing, they said it had intuition. [Editor’s note: DeepMind is the British company acquired by Google that produced the AlphaGo neural net program that first beat a human Go champion last year.] How else did they describe it? Creative. In Go, if you are very good, you learn to control line four. It’s sort of like the role of the queen in chess. If you control line four, you get geopolitical influence over the whole board. That’s a well-known strategy. AlphaGo figured out how to do that with line five. No human had ever done that.
That’s the difference between A.I. and what used to be called machine learning, but really was statistical techniques. Watson could do A.I.. IBM has the scientific talent to do A.I., but they chose to package Watson and market it for what it wasn’t. I think they
