Artificial Intelligence (AI), machine learning, and deep learning are taking the healthcare industry by storm. They are not pie in the sky technologies any longer; they are practical tools that can help companies optimize their service provision, improve the standard of care, generate more revenue, and decrease risk. Nearly all major companies in the healthcare space have already begun to use the technology in practice; here I present some of the important highlights of the implementation, and what they mean for other companies in healthcare. The best metaphor I found describing the importance of AI is presented by Bertalan Meskó in one of his articles. It seems that the question is not "if" but "when" AI will revolutionize the healthcare. It took some time for the medical community to accept the stethoscope.

Chinese internet search giant Baidu has developed an AI system that can clone an individual's voice! An year in the making, the text to speech system, called Deep Voice, can generate synthetic human voices using deep neural networks. According to the information shared by Baidu Research, they claim that it takes their trained model just three seconds to replicate and output a person's voice. Baidu's research team used voice cloning techniques to develop the AI system which they expect will have noteworthy applications in personalizing human-machine interface. Both Speaker Adaptation and Speaker Encoding (requiring minimal audio) provide quality performance and can be integrated in the Deep Voice model along with speaker embeddings without having to compromise the quality of the source audio. You can check out some audio samples provided by Baidu's Research team which consist of original and synthesized voices.

I started this article with the hopes of confronting a few misconceptions about Deep Learning (DL), a field of Machine Learning that is simultaneously labelled a silver bullet and research hype. The truth lies somewhere in the middle, and I hope I can un-muddy the waters -- at least a little bit. Importantly, I hope to clarify some processes to attack DL problems and also discuss why it performs so well in some areas such as Natural Language Processing (NLP), image recognition, and machine-translation while failing at others. Media often portrays Deep Learning as a magical recipe to the end of the world or the solution to all life's problems. In reality, it is anything but. Moreover, while DL has its fair share of strange behaviour and unexplained results, it is ultimately meritocratically driven.

I initially dismissed David Weinberger's report of alien knowledge as tabloid sensationalism. But as the recommendations for his essay accumulated, it gave me pause. Weinberger's post rewards a close reading. My intent here is to present a more incremental, less revolutionary perspective on AI and machine learning. I believe the historical antecedents paint a far more earthly, but perhaps no less sensational, picture. I also believe Weinberger is accurately expressing the concerns (and possibly even hopes) of many within the community, expert and layperson alike.

Frameworks are only an intermediary step to the wider adoption of machine learning in applications. What's needed are more visual products and those are still a couple of years away. The current machine learning (ML) focus on frameworks is a middle step in the needed evolution of the productization of ML and its inclusion through the application environment. In order to truly succeed, the ML vendors need to think more like a business user and less like a programmer. One way to start is to learn the lesson the business intelligence (BI) sector provides.

It is efficient for organizations to connect business knowledge with data-guided solutions. Data science enables businesses to derive data-driven decisions. Data, and more importantly analytics, are changing the way we see our machines, our processes and our operations. The fusion of data science with other technologies would enhance the decision-making approach, making it more eloquent and accurate. Let's study in brief, how this fusion helps different industry verticals.

In today's post I'm going to share with you the 7 best deep learning books (in no particular order) I have come across and would personally recommend you read. Some of these deep learning books are heavily theoretical, focusing on the mathematics and associated assumptions behind neural networks and deep learning. Other deep learning books are entirely practical and teach through code rather than theory. And even other deep learning books straddle the line, giving you a healthy dose of theory while enabling you to "get your hands dirty" and learn by implementing (these tend to be my favorite deep learning books). For each deep learning book I'll discuss the core concepts covered, the target audience, and if the book is appropriate for you.

Deep reinforcement learning has rapidly become one of the hottest research areas in the deep learning ecosystem. The fascination with reinforcement learning is related to the fact that, from all the deep learning modalities, is the one that resemble the most how humans learn. In the last few years, no company in the world has done more to advance the stage of deep reinforcement learning than Alphabet's subsidiary DeepMind. Since the launch of its famous AlphaGo agent, DeepMind has been at the forefront of reinforcement learning research. A few days ago, they published a new research that attempts to tackle one of the most challenging aspects of reinforcement learning solutions: multi-tasking. Since we are infants, multi-tasking becomes an intrinsic element of our cognition.

Deep Learning never ceases to amaze me. It has had a profound impact on several domains, beating benchmarks left and right. Image classification using convolutional neural networks (CNNs) is fairly easy today, especially with the advent of powerful front-end wrappers such as Keras with a TensorFlow back-end. But what if you want to identify more than one object in an image? This problem is called "object localization and detection."

I've been promising since I started this blog to present some of the key design decisions and architectural choices we have made. Time constraints have limited that but this weekend I have finally put together an overview of what we're doing and how we approach the problem. Just for clarity, this architecture is the full solution when we go into production. The infrastructure we are using for our live trading diary is identical except it doesn't link through to the hedging engine. With the relatively small amount of capital we are trading with this level of integration wasn't required – but it will be essential as we move onto a full production footing.