Artificial intelligence will soon change how we conduct our daily lives. Are companies prepared to capture value from the oncoming wave of innovation? Yes, they have a fine MRI machine and powerful software to generate the images. But that's where the machines bog down. The radiologist has to find and read the patient's file, examine the images, and make a determination. What if artificial intelligence (AI) could jump-start that process by enabling real-time and more accurate diagnoses or guidance, beyond what human eyes can see?

Do you use artificial intelligence (AI)? It might sound like a high-brow discussion for coders and data scientists, but AI is everywhere. If you use an Amazon Echo, you use AI. If you use Facebook or Netflix, AI is used on you. AI is a catch-all term for several different technologies – including machine learning, neural networks, voice recognition and natural language processing – but they all have one thing in common (or should do); they allow machines to learn how to respond to your needs.

Artificial intelligence is a moving target. Here's how to take better aim. Artificial intelligence (AI) seems to be everywhere. We experience it at home and on our phones. Before we know it--if entrepreneurs and business innovators are to be believed--AI will be in just about every product and service we buy and use. In addition, its application to business problem solving is growing in leaps and bounds.

With our 2018 Machine Learning predictions, we're taking another shot at Machine Learning clairvoyance with some brand new calls while also upping the ante to serious "double dog dare you" territory by reiterating some of our previous calls. We'd like to stress that the predictions made here are shared through a lense of "Machine Learning in the enterprise". As such, we're less concerned with predicting the twists and turns in the heady world of Machine Learning research and more concerned with the experience of the typical enterprise when looking to leverage the technology to reach its quarterly, annual or longer-term strategic business goals. With that out of the way, let's start by setting the tone based on some recent market research findings on the state of the industry as it stands now. It's a well-known fact that the tech giants have put their dollars where their mouths are when it comes to acquiring Machine Learning/AI talent.

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Note: Readers can access the code for this tutorial on GitHub. Long short-term memory (LSTM) networks have been around for 20 years (Hochreiter and Schmidhuber, 1997), but have seen a tremendous growth in popularity and success over the last few years. LSTM networks are a specialized type of recurrent neural network (RNN)--a neural network architecture used for modeling sequential data and often applied to natural language processing (NLP) tasks. The advantage of LSTMs over traditional RNNs is that they retain information for long periods of time, allowing for important information learned early in the sequence to have a larger impact on model decisions made at the end of the sequence. In this tutorial, we will introduce the LSTM network architecture and build our own LSTM network to classify stock market sentiment from messages on StockTwits.

Hyperscalers have billions of users who get access to their services for free, but the funny thing is that these users act like they are paying for it and expect for these services to be always available, no excuses. Organizations and consumers also rely on Facebook, Google, Microsoft, Amazon, Alibaba, Baidu, and Tencent for services that they pay for, too, and they reasonably expect that their data will always be immediately accessible and secure, the services always available, their search returns always popping up milliseconds after their queries are entered, and the recommendations that come to them personalized for them. These hyperscalers have built networks of massive datacenters, spanning the globe, to ensure the data and services are close to their customers and that latency doesn't become a problem. Given all this, disaster recovery becomes a critical part of the business. Hyperscale companies need to make sure business can continue as usual even if a datacenter goes down.

With the omnipresence of the term artificial intelligence (AI) and the increased popularity of deep learning, a lot of security practitioners are being lured into believing that these approaches are the magic silver bullet we have been waiting for to solve all of our security challenges. But deep learning -- or any other machine learning (ML) approach -- is just a tool. And it's not a tool we should use on its own. We need to incorporate expert knowledge for the algorithms to reveal actual security insights. Before continuing this post, I will stop using the term artificial intelligence and revert back to using the term machine learning.

This year, RE-WORK will be continuing the Global Healthcare Series, focusing on the AI and deep learning tools and techniques set to revolutionise healthcare applications, medicine & diagnostics.

Almost two years after the acquisition by Intel, the deep learning chip architecture from startup Nervana Systems will finally be moving from its codenamed "Lake Crest" status to an actual product. In that time, Nvidia, which owns the deep learning training market by a long shot, has had time to firm up its commitment to this expanding (if not overhyped in terms of overall industry dollar figures) market with new deep learning-tuned GPUs and appliances on the horizon as well as software tweaks to make training at scale more robust. In other words, even with solid technology at a reasonable price point, for Intel to bring Nervana to the fore of the training market–and push its other products for inference at scale along with that current, it will take a herculean effort–one that Intel seems willing to invest in given its aggressive roadmap for the Nervana-based lineup. The difference now is that at least we have some insight into how (and by how much) this architecture differs from GPUs–and where it might carve out a performance advantage and more certainly, a power efficiency one. The Nervana Intel chip will be very similar to the first generation of chips Nervana was set to bring to market pre-acquisition but with the added benefit of more expertise and technology from Intel feeding developments that put the deep learning chip on a yearly cadence schedule, according to Nervana's first non-founder employee four years ago and now head of AI hardware within Intel, Carey Kloss.