Ingrid Vasiliu-Feltes, Chief Quality And Innovation Officer, Mednax
During the last decade, Data Science emerged and matured as a field, transforming several industries and promising to do the same for many more. However, this is not the first time that “AI” is at the forefront of attention, as we are now experiencing the 3rd (some would argue 4th) major AI wave. Each of the previous ones were followed by a respective winter. In this article, I will summarize what in my opinion is different this time, as well as certain trends that are either already happening or just down the road. The second part of the article will focus on practical aspects, such as how to identify potential applications for Data Science. Finally, the article will conclude with a forward look at the topic of dangers related to AI.
While the promise of Data Science and Machine Learning has created a lot of traction, we had before two other cases of AI summers: in the 1950s and 1960s, we had the “age of reasoning” and prototype AI, followed by the AI winter of the 1970s. Similarly, in the 1980s and early 1990s, we had a second boom in the “age of knowledge representation,” that produced some expert systems.
But again, a second AI winter came in the mid1990s, lasting till the mid-2000s. What is different this time? The first significant differenceis that now we have a critical mass of applications that already benefit from Machine Learning, at our current level of technical capability.
Digital era is described as a “shift from an industrialto an informationbased economy by using technological devices as a medium or communication
For many use cases, the benefits are not a future promise but are already here, and any further technical improvements will only increase them. The second difference is that the virtuous loop of more data is leading to better algorithms and improved applications. Thus more (and better) data has generated a multitude of trends that are reinforcing each other, which was not the case in the two previous AI summers. Will this be enough to avoid a third AI winter? It’s, of course, not possible to say, but even in the pessimistic scenario, we will see more incremental applications over what has already proven to work. This is perhaps the key takeaway: although certainly there is hype, this time is arguably different from the previous two AI summers, as we already have a range of use cases with strong practical impact and a few trends that synergize to further expand that range.
What are these reinforcing trends that I am referring to? Some of them are technical, from improved hardware and software libraries to train Machine Learning models at scale, to better fundamental algorithms that can take advantage of the more data now available. Perhaps the more interesting ones though are multidisciplinary: for example, the improvements in ML coupled with advances in design promise to change the way we interact with computers, including our mobile phones. The move towards channels like voice is already happening, while more futuristic means of human-computer interaction might become practical in the future. Moreover, trends like interpretable AI and fairness in Machine Learning promise to make our predictions easier to understand and explain, while also ensuring integrity in the decision process.
Given that Data Science is already making a difference across fields and many companies are expressing their commitment to the adoption of these methods, a question that often arises is what are the use cases where we can apply Data Science and where to start? The first thing I would check is if there is historical data in the domain