A generative adversarial network (GAN) is composed of two separate networks - the generator and the discriminator. It poses the unsupervised learning problem as a game between the two. In this post we will see why GANs have so much potential, and frame GANs as a boxing match between two opponents. Deep learning is famously biologically inspired and many of the major concepts in deep learning are intuitive and grounded in reality. The fundamental truth of deep learning is that it's hierarchical -- the layers in a network and the representations they learn build on each other.

Deep Neural Networks, especially Convolutional Neural Networks (CNN), allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-arts in visual object recognition, object detection, text recognition and many other domains such as drug discovery and genomics. In addition, many solid papers have been published in this topic, and some high quality open source CNN software packages have been made available. There are also well-written CNN tutorials or CNN software manuals. However, it might lack a recent and comprehensive summary about the details of how to implement an excellent deep convolutional neural networks from scratch.

Faster chips, more affordable storage, and open libraries are giving neural network new momentum, and companies are now in the process of figuring out how to optimize it across a variety of markets. The roots of neural networking stretch back to the late 1940s with Claude Shannon's Information Theory, but until several years ago this technology made relatively slow progress. The rush toward autonomous vehicles -- which relies on neural networking to collect data from many sensors -- changed all of that. Work is underway by established companies, startups, and universities around the globe, and funding is pouring into neural networking, as well as related markets such as embedded vision, machine learning, and artificial intelligence. "Mass market economics, increased processing power and improving computational vision techniques equals opportunities for new mass markets to be created," said Tim Ramsdale, general manager of the Imaging and Vision Group at ARM. "But all of this has to be done in real time. Having lights turn on as soon as you appear at the door is critical. That means a minimum of 30 frames per second, and preferably 60 frames per second. To do that you have to have processing at the edge, and processing at the edge means low power."

An in-depth look at one of the most interesting stealth startups to tackle self-driving cars yet. Context: Drive.ai is an autonomous driving startup that was born in Stanford University's Artificial Intelligence Research Lab and aims to build a "brain" that can power self-driving on any car rather than building the actual vehicle itself. I will analyze Drive.ai on 4 different aspects of the company: Team, Market, Competition, Product, in order to deduce insights into the autonomous startup landscape and where Drive.ai The domain expertise that each of the individuals I researched definitely makes Drive.ai For example, Carol Reiley, the President/Co-Founder of Drive.ai

Today we present the Mapillary Vistas Dataset--the world's largest and most diverse publicly available, pixel-accurately and instance-specifically annotated street-level imagery dataset for empowering autonomous mobility and transport at the global scale. Since we started our expedition to collaboratively visualize the world with street-level images, we have collected more than 130 million images from places all around the globe. While this number keeps growing at a frantic pace, we are putting serious efforts into researching, implementing, publishing, and releasing smarter computer vision models that can help us understand the semantics within this data. As stated in an earlier blog post, we keep advancing supervised deep learning models as our primary workhorses to extract information that is valuable to our community and improve our products. However, such models are inherently hungry for data--and in particular for a lot of precisely annotated data.

INCEPTION IS A VIRTUAL ACCELERATOR FOR MORE THAN 2,000 AI STARTUPS. GENETESIS: AI-Powered Biomagnetic Chest Pain Triage 2. LUNIT: Software for Medical Data Analytics and Interpretation 3. INSILICO MEDICINE: AI for Drug Discovery, Biomarker Dev't 4. SIGTUPLE: Smart Screening Powered by Data-driven Intelligence 5. BAY LABS: AI Technologies for Cardiovascular Imaging and Care THE NOMINEES FOR THE "BEST SOCIAL INNOVATION" AI STARTUPS ARE … 7. Genetesis is building solutions that allow physicians to detect and localize sources of abnormality in the heart. The Genetesis CardioFlux platform allows clinicians to visualize the heart's inherent electrical activity in dynamic 3D maps. TECHNOLOGY LEARN MORE Genetesis 8. Lunit develops advanced software for medical data analytics and interpretation via cutting-edge deep learning technology. They aim to help physicians make accurate and consistent clinical decisions through our data-driven imaging biomarker technology.

Twitter's (TWTR) investment in computers that function like a human brain is paying off, in more ways than one. Aside from helping to draw people back to the social media website, the company's growing status in artificial intelligence (AI) is also attracting attention from high profile investors. In an interview with CNBC's Squawk Alley, billionaire businessman Mark Cuban confirmed that he has recently been buying Twitter stock, as well as shares in other big names that work in AI, deep learning and machine learning, such as Amazon (AMZN) and Netflix (NFLX). In the interview, which sent the company's embattled shares up 4 per cent, Cuban claimed that firms specializing in AI are likely to experience better productivity and higher revenues off a smaller workforce, which should subsequently boost earnings. Cuban's encouraging comments came after the social media giant credited better than expected quarterly earnings to machine learning techniques.

According to Gartner, the number of open positions for deep learning experts grew from almost zero in 2014 to 41,000 today. Much of this growth is being driven by high tech giants, such as Facebook, Apple, Netflix, Microsoft, Google, and Baidu. These big players and others have invested heavily in deep learning projects. Besides hiring experts, they have funded deep learning projects and experiments and acquired deep learning related companies. And these investments are only the beginning.

In April 2017, DeepMind CEO Demis Hassabis drew on his eclectic experiences as an AI researcher, neuroscientist and videogames designer to discuss what is happening at the cutting edge of AI research, including the recent historic AlphaGo Go, and its future potential impact on fields such as science and healthcare, and how developing AI can help human better understand the human mind and explore new knowledge. Demis Hassabis (born 27 July 1976) was born to a Greek Cypriot father and a Chinese mother and grew up in North London. A child prodigy in chess, Hassabis reached master standard at the age of 13 with an Elo rating of 2300 (at the time the second highest rated player in the world Under-14 after Judit Polgár who had a rating of 2335) and captained many of the England junior chess teams. Now he is a pioneer in artificial intelligence, a neuroscientist, computer game designer, entrepreneur, and world-class games player.

Diagnosing disease is one of the more labor-intensive aspects of the healthcare system. It also happens to be one that is particularly well-suited to being performed by machine learning algorithms. While work in this area is in its early stages, the technology is evolving rapidly and appears poised to transform diagnostic medicine. Thanks largely to the huge volumes of data collected from patients, medical diagnostics is an ideal domain for machine learning. Much of the diagnostic data is image-based, such as X-rays, MRI scans, and ultrasound imagery, but can also include things like genomic profiles, epidemiological data, blood tests, biopsy results, and even medical research papers. As a result, there is a wealth of data available for training neural networks and for other machine learning techniques.