One of the most important insights that I’ve gotten in working with biologists and ecologists is that today it’s actually not really known on a scientific basis how well different conservation interventions will work. And it’s because we just don’t have a lot of data.
The question is what are we doing in the industry, or what is the machine learning research community doing, to combat instances of algorithmic bias? So I think there is a certain amount of good news, and it’s the good news that I wanted to focus on in my talk today.
Computers can tell stories but they’re always stories that humans have input into a computer, which are then just being regurgitated. But they don’t make stories up on their own. They don’t really understand the stories that we tell. They’re not kind of aware of the cultural importance of stories. They can’t watch the same movies or read the same books we do. And this seems like this huge missing gap between what computers can do and humans can do if you think about how important storytelling is to the human condition.
Machine learning systems that we have today have become so powerful and are being introduced into everything from self-driving cars, to predictive policing, to assisting judges, to producing your news feed on Facebook on what you ought to see. And they have a lot of societal impacts. But they’re very difficult to audit.
The smartphone is the ultimate example of a universal computer. Apps transform the phone into different devices. Unfortunately, the computational revolution has done little for the sustainability of our Earth. Yet, sustainability problems are unique in scale and complexity, often involving significant computational challenges.