NeurIPS 2020 has come to an end, but what we learned last week regarding AI and ML will be applied in 2021 and beyond.Wow! What a week it has been for our engineering crew. NeurIPS 2020 was seven days of amazing insights and deep, thought-provoking presentations. We met a lot of excited engineers looking for ways to change the world, and it has definitely been an invigorator for our whole team. It was also an excellent learning experience, and our team wanted to share a few of our key themes and learnings from the NeurIPS 2020 conference.
AI is reshaping the way we build software and conduct researchWe would like to start by highlighting an informative talk by Dr. Chris Bishop, the Laboratory Director at Microsoft Research in Cambridge and Professor of Computer Science at the University of Edinburgh, who dedicated his talk to the artificial intelligence (AI) revolution that is happening right now. AI has helped us accomplish great things and solve many challenging problems. However, Bishop argues that the real revolution exits in the way we are approaching software development and research. One example that he gave is that in the last 40 years, engineers and software developers were busy programming computers. This means that in order to accomplish a specific task using a computer program, developers had to explicitly define each part of the software by writing many lines of code that list out all of the actions that the computer needs to take in order to achieve its final goal. However, Bishop highlights that in the next 40 years, instead of programming computers, engineers and software developers will be busy training them.
This is the core notion of Machine Learning, the highly regarded AI methodology behind our successful product EnsoSleep.One of the main advantages of machine learning (ML) is that it provides the ability to solve many non-programmable problems by training the software on a large amount of data which allows it to learn the task in hand without being explicitly programmed with many lines of code. Consequently, it is not a surprise to us that before Machine Learning was used to solve challenging problems in healthcare, robust automation of Sleep Staging seemed impossible.
Machine Learning Enables Further Customization of AI AlgorithmsFurthermore, Bishop highlighted that Machine Learning allows a level of customization that wasn’t available before. With Machine Learning, each AI model can be fine-tuned and tailored to the user’s needs and preferences. Many AI solutions work to improve the user experience and increase efficiency. In the past, this level of customization would have been extremely expensive to implement and maintain. Bishop also highlighted the following quote by Dr. Felix Nensa, consultant at the Institute of Diagnostic and Interventional Radiology and Neuroradiology at University Essen, Germany:
“AI will not replace radiologists, but radiologists who do not use AI will be replaced by those who do,” said Dr. Felix Nensa at NeurIPS 2020.This is an extremely important quote that emphasizes the potential of AI to empower clinicians, allowing them to work more efficiently and dedicate more of their time to their patients and their treatment plans. This is one of our main goals at EnsoData, making our AI technologies accessible to clinicians worldwide and help them adopt our AI tools so they can efficiently use them in their practice. You can find Bishop’s full talk on the NeurIPS 2020 site.