Intel's Gadi Singer believes his most important challenge is his latest: using artificial intelligence (AI) to reshape scientific exploration. In a Q&A timed with the first Intel AI DevCon event, the Intel vice president and architecture general manager for its Artificial Intelligence Products Group discussed his role at the intersection of science -- computing's most demanding customer -- and AI, how scientists should approach AI and why it is the most dynamic and exciting opportunity he has faced. How is AI changing science? Scientific exploration is going through a transition that, in the last 100 years, might only be compared to what happened in the '50s and '60s, moving to data and large data systems. In the '60s, the amount of data being gathered was so large that the frontrunners were not those with the finest instruments, but rather those able to analyze the data that was gathered in any scientific area, whether it was climate, seismology, biology, pharmaceuticals, the exploration of new medicine, and so on.

Last week we talked about a very particular type of NNet called Convolutional Neural Network. We can definitely dive deeper into Conv Nets but the essence of the topology was broadly covered in the previous post. We will revisit the Conv Nets after we have covered all the topologies, as discussed in the previous post. The architecture for this week is Recurrent Neural Network or RNN. The key difference between a RNN and any Feed Forward Normal/Deep Network is the recurrence or cyclic nature of this architecture.

I've spent the last six months heads down building a new machine learning tool called Weights and Biases with my longtime cofounder Chris Van Pelt, my new cofounder and friend Shawn Lewis and brave early users at Open AI, Toyota Research, Uber and others. Now that it's public I wanted to talk a little bit about why I'm (still) so excited about building machine learning tools. I remember the magic I felt training my first machine learning algorithm. It was 2002 and I was taking Stanford's 221 class from Daphne Koller. I had procrastinated so I spent 72 hours straight in the computer lab building a reinforcement learning algorithm that played game after game of Othello against itself.

Arm is definitely targeting deep-neural-network (DNN) machine-learning (ML) applications with its proposed hardware designs, but its initial ML hardware descriptions were a bit vague (Figure 1). Though the final details aren't ready yet, ARM has exposed more of the architecture. The Arm ML processor is supported by the company's Neural Network (NN) software development kit that bridges the interface between ML software and the underlying hardware. This allows developers to target Arm's CPU, GPU, and ML processors. In theory, waiting for the ML hardware will allow a critical mass of software to be available when the real hardware finally arrives.

Google's AutoML is a new up-and-coming (alpha stage) cloud software suite of Machine Learning tools. It's based on Google's state-of-the-art research in image recognition called Neural Architecture Search (NAS). NAS is basically an algorithm that, given your specific dataset, searches for the most optimal neural network to perform a certain task on that dataset. AutoML is then a suite of machine learning tools that will allow one to easily train high-performance deep networks, without requiring the user to have any knowledge of deep learning or AI; all you need is labelled data! Google will use NAS to then find the best network for your specific dataset and task.

Describing deep learning as a black box is not meant to denigrate the practice. After, all, we're thrilled that, when we build a convolutional neural network with hundreds of input variables and more than a thousand hidden layers (as the biggest CNNs are), it just works. We don't exactly know how it works, but we're grateful that it does work. If we had we been required to explicitly code a program to do the same thing as the CNN does, it likely would be a complete disaster. We simply could not build the decision-making systems we're building today without the benefit of self-learning machines.

With the availability of high performance CPUs and GPUs, it is pretty much possible to solve every regression, classification, clustering and other related problems using machine learning and deep learning models. However, there are still various factors that cause performance bottlenecks while developing such models. Large number of features in the dataset is one of the factors that affect both the training time as well as accuracy of machine learning models. You have different options to deal with huge number of features in a dataset. In this article, we will see how principal component analysis can be implemented using Python's Scikit-Learn library.

Consumerization is the design, marketing, and selling of products and services targeting the individual end consumer. Apple CEO Tim Cook recently promoted a $100-per-year iPhone app called Derm Expert. Derm Expert allows doctors to diagnose skin problems using only their iPhone. Doctors take a photo of a patient's skin condition and then Derm Expert diagnoses the problem and prescribes treatment. Doctors can effectively treat patients without a high performance computer or an expensive technology environment.

Artificial intelligence is already set to affect countless areas of your life, from your job to your health care. New research reveals it could soon be used to analyze your heart. AI could soon be used to analyze your heart. A study published Wednesday found that advanced machine learning is faster, more accurate and more efficient than board-certified echocardiographers at classifying heart anatomy shown on an ultrasound scan. The study was conducted by researchers from the University of California, San Francisco, the University of California, Berkeley, and Beth Israel Deaconess Medical Center.

As concerns over privacy and data security on social networks grow, U of T Engineering researchers led by Professor Parham Aarabi and graduate student Avishek Bose have created an algorithm to dynamically disrupt facial recognition systems. "Personal privacy is a real issue as facial recognition becomes better and better," says Aarabi. "This is one way in which beneficial anti-facial-recognition systems can combat that ability." Their solution leverages a deep learning technique called adversarial training, which pits two artificial intelligence algorithms against each other. Aarabi and Bose designed a set of two neural networks: the first working to identify faces, and the second working to disrupt the facial recognition task of the first.