Guide dogs are great for helping people who are blind or visually impaired navigate the world. But try getting a dog to read aloud a sign or tell you how much money is in your wallet. Seeing AI, an app developed by Microsoft AI & Research, has the answers. It essentially narrates the world for blind and low-vision users, allowing them to use their smartphones to identify everything from an object or a color to a dollar bill or a document. Since the app's launch last year, it's been downloaded 150,000 times and used in 5 million tasks, some of which were completed on behalf of one of the world's most famous blind people.

Francois Chollet, in his book "Deep Learning with Python," outlines early on an overview for developing neural networks with Keras. Generalizing from a simple MNIST example earlier in the book, Chollet simplifies the network building process, as relates directly to Keras, to 4 main steps. This is not a machine learning workflow, nor is it a complete framework for approaching a problem to solve with deep learning. These 4 steps pertain solely to the portion of your overall neural network machine learning workflow where Keras comes into play. While Chollet then spends the rest of his book sufficiently filling in the necessary details to utilize it, let's take a preliminary look at the workflow by way of an example.

Deep Learning (DL) has become more than just a buzzword in the Artificial Intelligence (AI) community – it is reshaping global business through the prolific use of autonomous, self-teaching systems, which can build models by directly studying images, text, audio, or video data. Such systems can use that data for future pattern recognition. According to many technical professionals, businesses can reap the full benefits of AI only when the appropriate levels of competency is developed in advanced data technologies such as Machine Learning (ML) and Deep Learning for extracting reliable business insights. This unilateral opinion among professionals implies that the skill gaps have to be identified and training must be in place to make the best use of available technologies and tools. The Gartner article Artificial Intelligence and the Enterprise indicates an urgent need to develop teams of highly skilled Data Science expert "who can manage the complexity of data, analytical methods and machine learning associated with AI, and help apply it with workers, customers and constituents."

There are several competing processor efforts targeting deep learning training and inference but even for these specialized devices, the old performance ghosts found in other areas haunt machine learning as well. Some believe that the way around the specter of Moore's Law as well as Dennard scaling and data movement limitations is to start thinking outside of standard chip design and look to the human brain for inspiration. This idea has been tested out with various brain-inspired computing devices, including IBM's TrueNorth chips among others over the years. However, deep learning presents new challenges architecturally and in terms of software development for such devices. With GPUs dominating in neural network training in particular (with other devices vying to tackle training and inference on the same device), the question is where brain-inspired approaches might fit in the market and what advantages they might have over GPUs, even in theory (or software, for that matter).

Even though I just skimmed it so far, I think this is a great paper! I think the structure of this framework seems very clean and reasonable. However, I wonder what benefits there are in using this over probabilistic programs. As far as I can tell programs have far greater expressive power, but I can see that it's easier for a computer to reason about a graph than a program, and therefore optimize it further.

Here is the June 2018 issue of Computer Vision News, the magazine of the algorithm community published by RSIP Vision: 46 pages worth reading about Computer Vision, Image Processing, Deep Learning and Artificial Intelligence.

Download Anaconda for Windows installer, Choose Python 3.6 version, 64-bit installer (Libraries needed for Deep Learning will be done during the workshop by participants to get the understanding of how to install them)

This Neural Network tutorial will help you understand what is a neural network, how a neural network works, what can the neural network do, types of neural network and a usecase implementation on how to classify between photos of dogs and cats. Deep Learning uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Most deep learning methods involve artificial neural networks, modeling how our brains work. Neural networks are built on Machine Learning algorithms to create an advanced computation model that works much like the human brain.

As debates around AI's value continue, the risk of an AI winter is real. We need to level set what is real and what is imagined so that the next press release you see describing some amazing breakthrough is properly contextualized. Unquestionably, the latest spike of interest in AI technology using machine learning and the neuron-inspired deep learning is behind incredible advancements in many software categories. Achievements such as language translation, image and scene recognition and conversational UIs that were once the stuff of sci-fi dreams are now a reality. Even as software using AI-labeled techniques continues to yield tremendous improvements in most software categories, both academics and skeptical observers have observed that such algorithms fall far short of what can be reasonably considered intelligent.

June 04, 2018, Taipei: Tata Elxsi, a global design and technology services company, is showcasing solutions and services for digital technologies including IoT, AI and Extended Reality at Computex Taipei between June 5-9, 2018. "Tata Elxsi is working with leading operators, OEMs and ODMs across industries such as automotive, communications, broadcast, consumer electronics and healthcare for digital transformation", said Nitin Pai, Senior Vice President - Marketing, Tata Elxsi. "We look forward to collaborating with Taiwanese companies to help them create smarter, connected products and services for the global market." Edge IoT platform: Tata Elxsi's powerful software platform allows easy integration, management and control of diverse devices and sensors for applications such as smart home and connected healthcare. Edge AI solution: Tata Elxsi's Deep Neural Network (DNN) edge compiler allows AI algorithms to be embedded on low power, low compute and unconnected devices, transforming them into smart edge devices.