Palantir CEO Alex Karp Says Going Public Is'A Possibility' 'Tis the season for the public relations exercise known as "here's what we think (or hope) will happen in the tech sector next year," flooding my inbox with predictions for 2017. No one knows what will happen tomorrow, let alone over the next 12 months, but the exercise yields interesting insights into what's hot (and what's not) in technology today. Artificial intelligence (and machine/deep learning) is the hottest trend, eclipsing, but building on, the accumulated hype for the previous "new big thing," big data. The new catalyst for the data explosion is the Internet of Things, bringing with it new cybersecurity vulnerabilities. The rapid fluctuations in the relative temperature of these trends also create new dislocations and opportunities in the tech job market.

Machine learning is playing an increasingly important role in many areas of our businesses and our lives and is being employed in a range of computing tasks where programming explicit algorithms is infeasible. At Amazon, machine learning has been key to many of our business processes, from recommendations to fraud detection, from inventory levels to book classification to abusive review detection. And there are many more application areas where we use machine learning extensively: search, autonomous drones, robotics in fulfillment centers, text and speech recognitions, etc. Among machine learning algorithms, a class of algorithms called deep learning hascome to represent those algorithms that can absorb huge volumes of data and learn elegant and useful patterns within that data: faces inside photos, the meaning of a text, or the intent of a spoken word. A set of programming models has emerged to help developers define and train AI models with deep learning; along with open source frameworks that put deep learning in the hands of mere mortals.

Is there a next phase for cloud computing? During the past few years, cloud computing has become a mainstream element of modern software solutions just as common as websites or databases. The cloud computing market is a race vastly dominated by four companies: Amazon, Microsoft, Google and IBM with a few other platforms with traction in specific regional markets such as AliCloud in China. In such a consolidated market, it's hard to imagine a technology being disruptive enough to alter the existing dynamics. Artificial intelligence (AI) is the type of technology with the potential to not only improve the existing cloud platform incumbents but also power a new generation of cloud computing technologies. The thesis of a new generation of cloud computing platforms might seem ludicrous at first but it also presents a very intriguing argument.

MedyMatch Technology, a company based in Tel Aviv, Israel, leverages artificial intelligence, deep learning, and computer vision technologies to offer patient-specific clinical decision support. Their application helps radiologists and emergency room physicians to detect signs of intracranial hemorrhages, which are difficult to diagnose by standard analysis of imaging data alone. The Medgadget team recently had an opportunity to speak with Gene Saragnese, Chairman and Chief Executive Officer of MedyMatch, to discuss their technology and its significance in depth. Prior to joining MedyMatch in January of 2016, Gene was the Chief Executive Officer of Philips Imaging and a member of Philips Healthcare's Executive Team. A graduate of Rutgers College of Engineering in New Jersey, he has also previously served as GE Healthcare's Chief Technology Officer and has held management roles with GE, RCA, Martin Marietta, and Lockheed Martin.

Unfortunately, the new training platform created by OpenAI, a San Francisco-based nonprofit, is only available to AI -- so if you're human, you're out of luck. The new'college' is, in actuality, a training platform called Universe, whereby AI can interact with games, web browsers, protein folding software, and "transfer learning," which allows them to take what they've learned in one application and apply it to another. The AI engages via Virtual Network Computing, or VNC, which involves them sending simulated mouse and keyboard strokes. The Universe digital suite's home is in the OpenAI artificial intelligence learning center in San Francisco, where developers will begin "measuring and training AI agents." OpenAI is the non-profit brainchild of entrepreneurs Elon Musk and Peter Thiel, who have made no secret of their ambitions to greatly accelerate the research and development of transhumanist technologies.

Understand convolutions (and why they are so much easier to grasp than they seem). Study Convolutional Neural Networks (what they are used for, why we use them, etc.). Review the building blocks of Convolutional Neural Networks, including: Discover common network architecture patterns you can use to design architectures of your own with minimal frustration and headaches. Utilize out-of-the-box CNNs for classification that are pre-trained and ready to be applied to your own images/image datasets (VGG16, VGG19, ResNet50, etc.).

For a deep dive into the current state of AI and where we might be headed in coming years, check out our free ebook "What is Artificial Intelligence," by Mike Loukides and Ben Lorica. A robot takes a big step forward, then falls. The next time, it takes a smaller step and is able to hold its balance. The robot tries variations like this many times; eventually, it learns the right size of steps to take and walks steadily. What we see here is called reinforcement learning. It directly connects a robot's action with an outcome, without the robot having to learn a complex relationship between its action and results. The robot learns how to walk based on reward (staying on balance) and punishment (falling).

Artificial Intelligence Just Broke Steve Jobs' Wall of Secrecy WIRED -- www.wired.com And as it happens, AI is more important to the future of tech giants like Apple than any other. Consumer trends come to light due to digital emmersion. The Ericsson ConsumerLab research gives you an insight in 10 hot consumer trends for 2017. We hear and read in the popular media about Artificial Intelligence (AI) all the time. We have movies about them.

In this post, we shall cover a few of the top, open-source artificial intelligence (AI) tools for the Linux ecosystem. Currently, AI is one of the ever advancing fields in science and technology, with a major focus geared towards building software and hardware to solve every day life challenges in areas such as health care, education, security, manufacturing, banking and so much more. Below is a list of a number of platforms designed and developed for supporting AI, that you can utilize on Linux and possibly many other operating systems. Remember this list is not arranged in any specific order of interest. Deeplearning4j is a commercial grade, open-source, plug and play, distributed deep-learning library for Java and Scala programming languages.

I still remember when I trained my first recurrent network for Image Captioning. Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of images that were on the edge of making sense. Sometimes the ratio of how simple your model is to the quality of the results you get out of it blows past your expectations, and this was one of those times. What made this result so shocking at the time was that the common wisdom was that RNNs were supposed to be difficult to train (with more experience I've in fact reached the opposite conclusion). Fast forward about a year: I'm training RNNs all the time and I've witnessed their power and robustness many times, and yet their magical outputs still find ways of amusing me. This post is about sharing some of that magic with you.