A sympathetic friend can be quite as dear as a brother — Homer
We live in an interesting world. Nobody knows what happens next. Even if they think they do, they don’t know how soon. Take the case of Artificial Intelligence (AI). AI has always progressed in spurts. There were two AI winters where research and investment in AI waned. In the past decade, we have seen a resurgence and increased interest in AI. Today, most of the applications of AI are confined to solving lower order problems compared to what AI is potentially capable of. However, the world is swinging between observation and paranoia as it concocts various scenarios about how AI will affect the world. This article offers three frameworks to look at a world where AI will be accessible by both the large Goliaths (Facebook, Google and the ilk) as well as by common men as it gets rapidly democratized.
There is a heated debate on many issues surrounding AI i.e. what constitutes AI?, how do we use AI?, how do we ensure data privacy without stifling innovation?, what are economic, moral and ethical implications of AI? Right now, it seems like many blind men are touching different parts of the elephant and defining the problem from their viewpoint. Harmonization of approaches will require a common mission. In human terms, that mission is an existential crisis. We are not there yet. Therefore, the debate rages on and holds the world in its sway. There a few different ways of looking at AI outlined below.
Reasoning By Analogy
If data is the new fuel, then platforms such as Facebook, Amazon, Google, Baidu, Tencent or Alibaba are the next OPEC. The difference between the OPEC (Oil Producing and Exporting Countries) which together control 82% of the worlds oil reserves and the big 9 AI companies (Baidu, Tencent, Alibaba, Facebook, Google, Apple, IBM, Amazon and Microsoft) is that the data that is residing within and feeding the algorithms of the big 9 companies will come not just from their home countries but globally.
This is because the best way to remove bias out of algorithms is to increase the sample size and create a very diverse sample size.In that sense…