Note: this blog entry has been written by Manuel Bruscas, co-founder of BcnAnalytics. The opinions expressed in this article are the author’s own
It is no doubt that Machine Learning (ML) has become one of the more prominent and successful fields within Artificial Intelligence (AI). ML is now considered one of the key battlefields for companies such as Amazon, Google, Apple or Facebook. Additionally, money flows from Venture Capital to almost every start-up using ML to disrupt any random industry. As Bill Gates said a few years ago “If you invent a breakthrough in artificial intelligence, so machines can learn (…) that is worth 10 Microsofts“.
Interestingly ML has been here for decades (ask Alan Turing!), but most of the time it seemed to be a slept giant facing a cold and long winter. Now it seems to be fully emerging and expectations are massive. In fact, and according to some experts, we are about to start a new era where machines will replace people in almost every single activity we currently consider “as human” (doctors, lawyers or teachers, your profession is also at risk!). As a result, ML is getting a lot of traction in the media and even an increasing number of philosophers and sociologists are discussing how AI and ML will change the role of human being as we know it. In that regards, I strongly recommend the book “Homo Deus: A Brief History of Tomorrow” by Yuval Noah Harari.
I certainly share the excitement about all the opportunities ML brings (and I do also share the concerns about ML impact in human beings, to be honest). However, I also see the risk of Machine Learning ends up becoming a new bubble. Like the “BIG Data hype” we had a few years ago. You might remember: companies were told they only needed to invest money in BIG DATA and then all their problems would go away. CEOs and top management believed in a brave new BIG DATA world where they only had to plug and play a new infrastructure. Reality has been very different and although BIG DATA has added tremendous value to some organisations, in many others it has only generated disappointment. In most of the cases big-data winners have been some smart vendors who sold the dream and have been squeezing the big-data orange very effectively. As a rule of thumb, I would say anyone using the expressions “Big Data”, “Machine Learning” and “almost real time” in the same sentence is probably a -sorry to say- “bullshiter”. (Note: if that person also adds some comments about an Attribution solution, you probably should watch your pocket!).
So, what is wrong with Machine Learning? Nothing, “per se”. But we must be realistic about what can be achieved with ML “in the near future” and what cannot be achieved (reality versus myths). Unfortunately, there is not a magic button (yet). From my perspective, setting the right expectations is always a good start: we need to be clear about the real potential ML has in our organisations in the next 12-18 months to ensure we allocate the right resources. If we are too bullish we will probably miss expectations and therefore generate frustration. If that happens Machine Learning can face (again) a cold winter.
Also, and given not all companies/sectors are equal, a ML strategic review is highly recommended before moving too quickly into execution: Which business areas could benefit more from ML? Do we really need ML in all the business areas? Which ML initiatives can bring value in the short term and which ones are a long-term investment? We should always bear in mind there are many great projects where you only need “old-school” Analytics / Data Scientists (yes, believe me, you can bring tones of insights without Machine Learning). Last, but not least, companies also need to ensure they appoint the right people to manage Machine Learning Initiatives. Successful organisations will have to create a middle layer between the technical teams and the pure business teams. I call these people “translators”.
In Bcnanalytics we truly believe Machine Learning is a crucial topic. That is why we are organising a series of events and talks to generate debates and discussions which will help the community to better shape this fascinating topic: understanding its potential but also raising its risks. Welcome to the Machine, and as Dave Bowman once said: “Hello, HAL. Do you read me, HAL?”