Maksim gave a very interesting presentation on Machine Learning, from his perspective as a physicist.
Machine Learning, AI and NLP are some of the most exciting emerging technologies. They are becoming ubiquitous and will profoundly change the way our society functions. In this talk I hope I can provide a unique perspective, as someone who has entered the field coming from a more traditional Physics background.
Physics and Machine Learning have much in common. I will explain how the two fields relate and how a physical point of view can help elucidate many ML concepts. I will show how we can use Python code to generate illustrative visualizations of Machine Learning algorithms. Using these visual tools I will demonstrate SVMs, overfitting, clustering and dimensional reduction. I will explain how intution, common sense and careful statistics matter much when doing Machine Learning, and I’ll describe some tools used in production.
Maksim used Jupyter Notebooks for the demonstration parts of his talk. It’s a great way to show snippets of code as well as plotting charts – I’ve also been using it for a Python library that I’m working on.
The big take-away was that the audience should think of machine learning as very accessible – although there are hard problems left to research, there are a lot of materials available on the internet and much can be understood readily, especially from a visual perspective.
IAmWire.com published this excellent post on artificial intelligence.
The last 10 years have been about building a world that is mobile-first. In the next 10 years, we will shift to a world that is AI-first.” (Sundar Pichai, CEO of Google, October 2016)
Having witnessed the scope of AI-based projects referenced at 2016’s Intel Software Developer Conference, it’s hard to argue. The range of disciplines that have been revolutionised in the last couple of years by deep learning is deeply impressive. Step changes in speech recognition and image classification have been shown and there’s massive potential for development in other areas such as medical diagnosis. Here’s what Deep Mind Medical have to say:
We think that machine learning technology, a type of artificial intelligence, can bring huge benefits to medical research. By using this technology to analyse medical data, we want to find ways to improve how illnesses are diagnosed and treated. Our goal is to help clinicians to give faster, better treatment to their patients and all our research work is done in collaboration with doctors and professional healthcare researchers.
There could be very few jobs that remain untouched by artificial intelligence in the coming years, but hopefully there’ll still be plenty of programming jobs.
Interesting article on LinkedIn about intelligent machines collaborating and communicating together to make decisions.
For example, sensors on your roof could sense that it’s a sunny day. The roof sensor is connected to the thermostat, and based on the data the thermostat receives, it automatically adjusts the inside temperature on the sunny side of the house and lowers the automatic shades before you do.
Imagine having all of the airplanes flying over every country wirelessly communicating to each other—not to all of the pilots, but the planes themselves communicating to each other real time flying conditions such as temperature, humidity, wind direction, speed, and turbulence. Each plane can then better anticipate potential issues and help the pilots respond before there is a problem.