Currently, Machine Learning and Deep learning are being intensively used in research with promising results, particularly Neural Networks as they dramatically accelerate computationally intensive processes, e.g., drug discovery . Research benefits from a high degree of liberty that is hardly seen in other areas, thus being the ideal candidate for investing in powerful, though complex, Neural Networks architectures – only limited by budget allocation capacity. On the other end, there are all those industries that are subject to heavy regulations for which interpretability of Deep Learning models is a problem. Thus, following the example of drug discovery, the adoption of Neural Networks for the subsequent manufacture of these new drugs is a no go right now, according to Good Manufacturing Practices regulation in Pharma and Biotechnology.

Moving away from highly regulated industries, AI has already demonstrated its feasibility to generate potential value from the use of pattern recognition (people’s faces, sign language, etc.) to fully autonomous equipment (self-driven cars, home vacuum cleaners, etc.) thanks to Computer Vision using Convolutional Neural Networks; and from voice-led instructions to activate appliances, select and execute tasks, to text mining or simultaneous translation using Natural Language Processing technology. No doubt Deep Learning is the field of AI that delivers the closest Human-to-Machine and Machine-to-Machine interactions without simply mimicking Human behaviour as Robotics Process Automation does from a collection of pre-set rules.

For sure, we can all look astonished at the performance of a fully autonomous car, but AI still has a lot to do to make the self-driving experience fully secure, and again regulation has a lot to say. Then maybe simple yet irrelevant things like telling Siri that you feel happy, and it selects one of your most-loved songs to play becomes impressive as we are absolutely unconscious of the swift connection we have built over AI.

Still, to me, the most impressive task that AI can accomplish is yet to come and it will deal with Human health, and more precisely, the diagnosis and prognosis of patients’ life-threatening conditions. The challenge is paramount as there are no two equal persons in the world – yes, even identical twins have genetic differences . Additionally, any intervention, such as any initiated treatment, severely biases patients’ health data. Hence, a predictive model aiming at diagnosing, for instance, the risk of metastases in breast cancer patients, is subject to an excessive number of factors (variables aka features) that dramatically reduces any subset of available patients to an insufficient number of samples that could be decently used for model training. And here the word ‘decently’ has all connotations: legal, medical, social, cultural, ethical, and economical. How to deal with data privacy and data protection is just an example of issues that easily prompts our minds regarding patients and healthcare institutions, data controllers, and data processors. But these issues are all inherent to AI and they must be addressed in full alignment of all healthcare stakeholders, a common will, and a titanic effort.