7.1 The Future of Machine Learning

Without machine learning, there can be no interpretable machine learning. Therefore we have to guess where machine learning is heading, before we talk about interpretability.

Machine learning (or “AI”) is associated a lot of promises and expectations. But let’s start with a less optimistic observation: While science develops a lot of fancy machine learning tools, in my experience it is quite difficult to integrate them into existing processes and products. Not because it’s not possible, but because it simply takes time for companies and institutions to catch up. In the gold rush of the current AI hype, companies open up “AI labs”, “Machine Learning Units” and hire “Data Scientists”, “Machine Learning Experts”, “AI engineers”, and so on, but the reality is rather frustrating (in my experience): Often companies do not even have data in the required form and the data scientist waits inactive for months. Sometimes companies have such high expectation due to the AI hype in the media that the data scientists could never fulfill them. And often nobody knows how to integrate this new kind of people into existing structures and many more problems. That leads to my first prediction:

Machine learning will grow up slowly but steadily.

Digitization is advancing, and the temptation of automation is constantly pulling. Even if the path of machine learning adoption is slow and stony, machine learning is constantly moving from science to business processes, products and real world applications.

I believe we need to better explain to non-experts what types of problems can be formulated as machine learning problems. I know many highly paid data scientists who perform Excel calculations or classical business intelligence with reporting and SQL queries instead of machine learning. But a few companies are already successfully applying machine learning, with the large Internet companies at the forefront. We need to find better ways to integrate machine learning into processes and products, train people and create machine learning tools that are easy to use. I believe that machine learning will become a lot easier to use: We can already see that machine learning becomes more accessible, for example through cloud services (“Machine Learning as a service” - just to spray a few buzz words). Once machine learning has matured - and this baby has already made its first steps - my next prediction is:

Machine learning will fuel (almost) everything.

Based on the principle “Whatever can be automated will be automated”, I conclude that, whenever possible, tasks will be reformulated as prediction problems and solved with machine learning. Machine learning is a form of automation or can at least be part of it. Many tasks currently performed by humans are being replaced by machine learning. Here are just a few examples:

  • Automation of the sorting / deciding on / filling out documents (e.g. in insurance companies, the legal sector or consulting firms)
  • Data-driven decisions such as credit applications
  • Drug discovery
  • Quality controls in assembly lines
  • Self-driving cars
  • Diagnosis of diseases
  • Translation. I am literally using this right now: A translation service (DeepL) powered by deep neural networks to improve my sentences by translating them from English into German and back into English.

The breakthrough for machine learning is not only achieved through better computers / more data / better software, but also:

Interpretability tools catalyze the adoption of machine learning.

Based on the premise that the goal of a machine learning model can never be perfectly specified, it follows that interpretable machine learning is necessary to close the gap between the misspecified and the actual goal. In many areas and sectors, interpretability will be the catalyst for the adoption of machine learning. Some anecdotal evidence: Many people I have talked to do not use machine learning because they can’t explain the model to others. I believe that interpretability will tackle this issue and make machine learning attractive to organisations and people that demand a degree of transparency. In addition to the misspecification of the problem, many industries require interpretability, whether for legal reasons, risk aversion or to gain insight into the underlying problem. Machine learning automates the modeling process and moves the human a bit further away from the data and the underlying problem: This increases the risk of problems with design of experiments, choice of training distribution, sampling procedure, data coding, feature engineering, etc. Interpretation tools make it easier to identify these problems