Despite extraordinary progress, AI is still in its infancy. Learn about where machine learning fits and find out why still there is room for experimentation and radical ideas in data science.
(upbeat music) - Although we have made a lot of progress in AI, I think it's still very early days. I do not believe the solutions and the way we do AI today would be similar to what would happen in five years. Things will continue to change in this area very rapidly. We still have a lot of obstacles when we are actually deploying AI today. There is still a lot of new theory to be developed. So keep your eyes open. Do not get fixated on the solutions that exist today. Question all the assumptions. Keep learning. Keep experimenting, and be bold. Don't think that the field is saturated and the experts in the field today have solved most of the problems. There's still a lot to be done. - In the past few years machine learning has been one of the fastest growing areas in AI. Machine learning can help you identify patterns without even really knowing what you're looking for. In a sense, you have artificial intelligent machines learning what's in your data and letting your organization know what is has found. Something you'll have to do as a machine learning specialist is pick the best algorithm for your challenge. The key thing to remember is that you can think of each machine learning algorithm as a potential tool. You can experiment to find the best one, or you can work with several different tools as a way to improve your accuracy. - What does it mean to do data science responsibly? How do you do it with intent? How do you do things with purpose? To think about the entire set of implications that are out there, and there's no solid, easy answer here. There's just progress that has to be made across all these fronts of asking are we okay with this. How do we get external input? All those other pieces have to come together to actually ask are we comfortable with this? Would the people are impacted be comfortable this? And what are the unknown unknowns, the implications that are out there that we can't see coming? - [Aki] Although AI can be a powerful tool, one of its biggest drawbacks is that it cannot tell you why it made a certain decision or recommendation. It is essentially a black box. For decisions that have large financial implications, impact a very large number of people deeply, or have life or death consequences, adoption of AI has been slower. XAI may alleviate concerns over transparency, bias, and reliability, and speed up the adoption of AI in these areas. - Communication cuts to the very heart of who we are as human beings. It's the language that we share that helps us understand larger concepts, such as community, law, and justice. As human beings, we're always trying to do a better job communicating. So it's not much of a surprise that we want our machines to do the same thing. The main challenge is that we can't communicate with machines in the same way that they communicate with each other. Natural language processing gives machines the ability to better understand the larger world.