This is the seventh post of my column Deep Learning for Life Sciences where I give concrete examples of how Deep Learning can already now be applied in Computational Biology, Genetics and Bioinformatics. In the previous posts, I demonstrated how to use Deep Learning for Ancient DNA, Single Cell Biology, OMICs Data Integration, Clinical Diagnostics and Microscopy Imaging. Today we are going to dive into the exciting History of Human Evolution and learn that it is straightforward to borrow methodology from the Natural Language Processing (NLP) and apply it to Human Population Genetics in order to infer regions of Neanderthal introgression in modern human genomes. When ancestors of Modern Humans migrated out of Africa 50 000 - 70 000 years ago, they encountered Neanderthals and Denisovans, two groups of ancient hominins that populated Europe and Asia at that time. We know that Modern Humans interbred with both Neanderthals and Denisovans since there is evidence of the presence of their DNA in genomes of Modern Humans of non-African origin.

The co-founder of Deepmind, Google's flagship artificial intelligence company, has left his post to take up another position within the multinational technology company. Mustafa Suleyman announced on Twitter he would be joining Google's team looking at the opportunities and impacts of applied artificial intelligence. Suleyman was placed on leave from DeepMind in August. At the time the company did not say why he was placed on leave but claimed the decision was mutual, adding that he was expected to be back by the end of the year. In a tweet on 5 December, Suleyman said: "After a wonderful decade at DeepMind, I'm very excited to announce that I'll be joining the fantastic team at Google to work on opportunities and impacts of applied AI technologies. "Can't wait to get going!

The next time you clean up your LEGO collection, you're going to wish you had this machine at home to do the dirty work. Built pretty much entirely with LEGO pieces (plus a Raspberry Pi and some motors), this thing is able to sort virtually any LEGO piece that comes down its conveyer belts, thanks to artificial intelligence. It uses a neural network--or a set of algorithms that recognize patterns, similar to the human brain--to match the real-world LEGO pieces with 3D images of the pieces that the machine has been fed during training. The machine, which was built by YouTuber Daniel West, isn't the first of its kind--though it looks to be the most effective. There are plenty of other contraptions on YouTube showing crazy machines that others have built to sort LEGO bricks, like machines that sort LEGO axles, specifically, and others that spin plastic cups around to catch parts.

Online shoppers typically string together a few words to search for the product they want, but in a world with millions of products and shoppers, the task of matching those unspecific words to the right product is one of the biggest challenges in information retrieval. Using a divide-and-conquer approach that leverages the power of compressed sensing, computer scientists from Rice University and Amazon have shown they can slash the amount of time and computational resources it takes to train computers for product search and similar "extreme classification problems" like speech translation and answering general questions. The research will be presented this week at the 2019 Conference on Neural Information Processing Systems (NeurIPS 2019) in Vancouver. The results include tests performed in 2018 when lead researcher Anshumali Shrivastava and lead author Tharun Medini, both of Rice, were visiting Amazon Search in Palo Alto, California. In tests on an Amazon search dataset that included some 70 million queries and more than 49 million products, Shrivastava, Medini and colleagues showed their approach of using "merged-average classifiers via hashing," (MACH) required a fraction of the training resources of some state-of-the-art commercial systems.

I will start with a confession – there was a time when I didn't really understand deep learning. I would look at the research papers and articles on the topic and feel like it is a very complex topic. I tried understanding Neural networks and their various types, but it still looked difficult. Then one day, I decided to take one step at a time. I decided to start with basics and build on them. I decided that I will break down the steps applied in these techniques and do the steps (and calculations) manually, until I understand how they work. It was time taking and intense effort – but the results were phenomenal. Now, I can not only understand the spectrum of deep learning, I can visualize things and come up with better ways because my fundamentals are clear.

All the world's tech giants from Alibaba to Amazon are in a race to become the world's leaders in artificial intelligence (AI). These companies are AI trailblazers and embrace AI to provide next-level products and services. Here are 10 of the best examples of how these companies are using artificial intelligence in practice. Chinese company Alibaba is the world's largest e-commerce platform that sells more than Amazon and eBay combined. Artificial intelligence (AI) is integral in Alibaba's daily operations and is used to predict what customers might want to buy.

This free course is published by the Massachusetts Institute of Technology (MIT). This is a week-long, self-paced course that will introduce you to Deep Learning technology and many of its industrial applications, from translation algorithms to image and object recognition, game playing, and more.

In previous posts (here and here) I introduced Double Q learning and the Dueling Q architecture. These followed on from posts about deep Q learning, and showed how double Q and dueling Q learning is superior to vanilla deep Q learning. However, these posts only included examples of simplistic environments like the OpenAI Cartpole environment. These types of environments are good to learn on, but more complicated environments are both more interesting and fun. They also demonstrate better the complexities of implementing deep reinforcement learning in realistic cases. In this post, I'll use similar code to that shown in my Dueling Q TensorFlow 2 but in this case apply it to the Open AI Atari Space Invaders environment.

Memento broke many of the traditional paradigms in the film industry by structuring two parallel narratives, one chronologically going backwards and one going forward. The novel form narrative implemented in Memento forces the audience to constantly reevaluate their knowledge of the plot and they keep learning small details every few minutes of the film. It turns out that replaying a knowledge sequence backwards for small time intervals is an incredibly captivating method of learning. Intuitively, the Memento form of learning seems like perfect for AI agents. Last year, researchers from OpenAI leveraged that learning methodology to created AI agents that learned to play Montezuma's Revenge using a single demonstration.

Many believe SageMaker, the machine learning (ML) service from Amazon Web Services, has truly democratised the adoption of artificial intelligence (AI) and data science by making it available for developers, corporations, and laymen alike. What you may not know is that the idea behind this AWS offering took root in Chennai a few years ago, when a software engineer on an annual pilgrimage back home was nursing a bad bout of jet jag. Swami Sivasubramanian, VP, Machine Learning, AWS, is considered a pioneer in cloud computing. The 41-year-old joined Amazon in 2005 after completing a PhD in distributed computing, making him one of the early employees for an idea that is now a $36 billion ARR business. Over the years, he has built 40 AWS services along with his team.