The original AI researchers were very interested in games because they were extremely complex. Huge numbers of possible positions and gains were available, yet they’re simple in a certain way. They’re simple in that the moves are well-defined, the goals are well-defined. So you don’t have to solve everything all at once.
With chess in particular, in the work on Deep Blue at IBM, what became apparent, what computers could do on our problem like that was bringing a massive amount of compute resource to do deeper searches, to investigate more options of moves in chess than was previously possible. So this was another crossover point, in the development of AI and cognitive computing.
The questions that IBM was able to answer with jeopardy were questions that weren’t simply looking up in the database, and finding the answer somewhere. Rather it required information retrieval over lots of different information resources. Then the combining of these together using machine learning that could arrive at answers that went beyond what was simply written somewhere. Now, our technology is so much better and so much more advanced that we’re really ready to move on and to tackle much more challenging problems that have this ill-defined or messy nature.
Every industry from oil and gas, to healthcare, to media and entertainment, to retail are just being swamped by a tsunami of unstructured data. That can be multimedia, can be images, it can be video, it can be text. It’s really the ability to understand that data that is becoming critical.
One of the most valuable applications of cognitive computing is in the health domain. Medical providers, physicians, nurses, assistants face enormous challenges, leveraging all of the available information that’s out there. The medical literature increases by about 700,000 articles every year. There’s already millions of journal articles out there, and today’s imaging technologies produce very rich amount of information. In fact, a particular scan might have 5,000 images in it. You combine the image analysis with natural language understanding, and text analysis, leveraging the medical literature, leveraging the patient’s medical history, the physician has got a lot more information and knowledge at their disposal to help them make the best diagnosis possible.
Clearly, there is intersection of what the computer can do and what people are able to do, that gives you something that’s better than each of them individually. What is going to be truly interesting is to see what is the best way for them to have really symbiotic type of interaction, taking advantage of each other’s strengths to collectively solve a problem?
You have to be able to interpret the questions and come up with the right answers no matter what the topic. So I think the ideal scenario for AI in the modern world is not to try and develop a system that completely autonomously handles every aspect of a problem, but have collaboration between machines doing what they do best and people doing what they do best. I can imagine that combination will do better than either one by themselves. We’re constantly here looking for what’s the next grand challenge problem we can take on? That’s not just around the corner or a year away, but it’s going to take a multi-year effort. When we get there, we’ll have something that’s valuable for the world.
What does AI have in store for us in the future? My crystal ball is a little cloudy, so I don’t know if I have a prediction that I would bet money on. What I do know is, what we have seen is the way AI has progressed, it starts fairly slowly, but then it gains steam exponentially. One good example is, DeepMind put the system together that won in the game of Go. It’s a 2,500-year-old game, which won against a human opponent. But what’s most amazing is while the first system was able to outdo 2,500 years of human history in the game, the second generation of that system was able to outplay the first generation of the system in less than one year. And it only took about 40 hours of training for it to be able to achieve that level of proficiency in that game. And it won 100 games out of 100. So what we do know is the pace of which this technology is accelerating is just breathtaking. And it’s not something we can predict very well. So if there was one prediction I were to make it’s, it’s going to get faster, it’s going to get better, it’s going to get cheaper, and it is going to happen very, very rapidly within a very short period of time.
It’s going to be an interesting world. It’s going to evolve very rapidly. Because these technologies not only can perform tasks that we’ve never seen automated before, but learn as they do them and continue to improve. So what we’ll see is we’ll deploy systems and every year they’ll get incrementally better at the tasks they’re trying to do. So we won’t have to drive our own cars anymore. Hopefully, we won’t have to put away our own dishes. There’ll be a lot of simple things in our lives that become automated and become eliminated from our daily task list. It’s a revolution that we’ve been through before. The first washing machines, the first dishwashers, etc. All these things that helped us, enriched our lives and simplified the way we live and increased our comfort. And I think we’ll see many more such evolutions as we watch the AI world unfold.
Particularly in healthcare, I think we’ll see faster recovery times, we’ll see better patient outcomes, we’ll see people spending less money and time in hospitals and in various care centers. AI has been game changing for healthcare. There’s so much information that we can take out of a particular system and apply it to another. We can build all sorts of models that can be hugely beneficial to long-term care. So that’s something that I’m very excited about and seeing that evolve. Looking into the crystal ball, I’m always apprehensive to make long-term predictions, but my hope is that we are able to indeed deploy these collaborative robotic systems, including self-driving cars; to make people’s live better.
The idea is to use AI and robotics technology to improve the quality of life for the whole spectrum of society. So I would be looking forward to a future in which AI plays a central role in freeing us from the dull, the mundane, the dirty, the dangerous jobs, and hopefully providing us with more time to spend with our families. Better health, potentially, better healthcare through data analysis. And so I think if envision the future that I hope for will come to pass in a number of years, we’ll be really leveraging these technologies to make our lives better and to free ourselves from dull, dirty, dangerous work.
In a world of dramatic digital transformation, companies are looking to AI to really help them shape the future of work. AI can predict and inform future outcomes. It enables people to do higher value work and businesses to imagine new models. It can automate decisions, processes, experiences but AI is not magic. The truth is there is no AI without IA or information architecture, but many organizations can’t start because 80% of their data is locked in silos and not business-ready.
(Author is a columnist and can be reached at: [email protected])
