There are some standard machine learning approaches that use more interpretable systems that are based on rules that can be considered, because rules are very intuitive for people. They can work with rules: If the temperature is greater than this and the humidity is such-and-such, you're going to be uncomfortable.
There are limits. Some of those real-world problems are complex enough that a simple set of rules is not sufficient, and I think that's why this is such an important research topic with lots of people looking at it. It's trying to come up with explanations that are true but also useful. That trade-off is what we're looking at.
IDGNS: We alluded to the idea of legal responsibility earlier. Is there any work being done on making these explanations sufficiently reliable and linked to the reasoning of the AI system that they could be relied on in a legal process?
MC: That's a question that's probably beyond my knowledge because it involves systems in the legal framework, but my thinking there is that for decisions that matter, ultimately we need people to be responsible for the coming decades. Recommendations from a computer, along with explanations of those recommendations, are useful tools but they're only tools, and in the end, a human decision-maker needs to take responsibility.
IDGNS: Tell us about the approaches you're working on for helping computers work with people, and the domains they can be used in?
MC: We talked a little about health care. Let me give you a specific example. Using deep learning approaches, we at IBM have developed an image recognition system for skin cancer so, given a photograph of, say, a lesion on the skin, it will be able to classify or identify that lesion with very high accuracy, in fact often higher accuracy than human experts.
But it doesn't understand the full context of the patient, and so that's why this is just one piece of information that needs to be provided to the physician. They see the patient in person, they see their history, and they see the recommendation, say "This lesion has 85 percent chance of being cancer, it should be biopsied." That's the recommendation, but the doctor can say, "Oh, well in this case I know it's not a problem becauseâŚ." That's one example.
Another I'm quite interested in is what we call "aging in place." You equip the homes of an aging population with sensors, internet of things technologies, and then have AI-based systems monitoring those sensors looking for warning signs of anomalous behaviors that may indicate a problem, bringing that to the attention of the caregivers.
One of the biggest deficits in the world today is we don't know where to focus our attention. There is just so much information that if we had a system that could help us focus our attention on things that are important, that'd be a great way to build a collaborative system.
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