EyeSense, an iPad app developed in Egypt, has the ability to "learn" objects in its environment, having been trained by its users. A visually impaired person can point their device in the direction of where they think something might be–say, a coffee cup–and a voice will say that the app recognizes that object. "The key strength of the app is that it also recognizes basic facial expressions, like winks or smiles. This enhances human interaction," says Joanna Marczak, a spokesperson for its developer, ID Labs. To train the app, you place things in front of the device's camera at several angles, telling it about the items.

Artificial Intelligence Market Global Artificial Intelligence Market: Snapshot Globally, there is a wave of artificial intelligence across various industries, especially consumer electronics and healthcare. The wave is likely to continue in the years to come with the expanding base of applications of the technology. The global market for artificial intelligence is expected to witness phenomenal growth over the coming years as organizations worldwide have started capitalizing on the benefits of such disruptive technologies for effective positioning of their offerings and customer reach. According to a study by Transparency Market Research (TMR), the global market for artificial intelligence is estimated to post an impressive 36.1% CAGR between 2016 and 2024, rising to a valuation of US$3,061.35 The upward growth of the market is, however, hampered by the low upfront investments.

Earlier this month, a resident in a rural province of South Korea found a small, sky-blue unmanned plane perched nose-up against a tree in a remote forest. The drone belonged to the North Korean military and apparently had flown south for five hours, capturing images of a sensitive new U.S. anti-missile system before crashing. It lacked missiles or other weapons like the larger, more advanced models used by U.S. forces in the Middle East. But South Korean officials and security experts consider such flights unlawful incursions. As world powers focus on North Korea's emerging nuclear capabilities, its drone program is raising concerns about espionage and aggravating already tense relations with its southern neighbor.

Welcome to the first post of my series Deep Learning for Rookies by me, a rookie. I'm writing as a reinforcement learning strategy to process and digest the knowledge better. But if you are a deep learning rookie, then this is for you as well because we can learn together as rookies! Deep learning is probably one of the hottest tech topics right now. Large corporations and young startups alike are all gold-rushing this fancy field. If you think big data is important, then you should care about deep learning. The Economist says that data is the new oil in the 21st Century. If data is the crude oil, databases and data warehouses are the drilling rigs that digs and pumps the data on the internet, then think of deep learning as the oil refinery that finally turns crude oil into all the useful and insightful final products.

It's widely accepted that artificial intelligence (AI) will have a huge impact on our lives in the coming decades -- but what's its value to the global economy? According to a new report, global GDP will be 14% higher in 2030 as a result of AI -- the equivalent of $15.7 trillion, more than the current output of China and India combined. The report, Sizing the Prize, was launched by PwC in a session at the World Economic Forum's Annual Meeting of the New Champions 2017 in Dalian, China. Improvements to labour productivity will account for over half of all economic gains from AI between now and 2030, while increased consumer demand resulting from product enhancements will account for the rest. Regional gains will be most strongly felt in China, which will receive a 26% boost to GDP in 2030, followed by North America (14.5%).

I know this isn't Instagram, but fuck it. I'm writing an article about what I had for lunch. If you haven't tuned out already, don't worry. Earlier today, I attended a press event for IBM's Watson at London's iconic Wimbledon tennis grounds. Sandwiching the presentations and panels (pun 100-percent intended) was a'cognitive breakfast' and a'cognitive lunch.' Say what?

This paper considers the use of Machine Learning (ML) in medicine by focusing on the main problem that this computational approach has been aimed at solving or at least minimizing: uncertainty. To this aim, we point out how uncertainty is so ingrained in medicine that it biases also the representation of clinical phenomena, that is the very input of ML models, thus undermining the clinical significance of their output. Recognizing this can motivate both medical doctors, in taking more responsibility in the development and use of these decision aids, and the researchers, in pursuing different ways to assess the value of these systems. In so doing, both designers and users could take this intrinsic characteristic of medicine more seriously and consider alternative approaches that do not "sweep uncertainty under the rug" within an objectivist fiction, which everyone can come up by believing as true.

We propose a simple and efficient approach to learning sparse models. Our approach consists of (1) projecting the data into a lower dimensional space, (2) learning a dense model in the lower dimensional space, and then (3) recovering the sparse model in the original space via compressive sensing. We apply this approach to Non-negative Matrix Factorization (NMF), tensor decomposition and linear classification---showing that it obtains $10\times$ compression with negligible loss in accuracy on real data, and obtains up to $5\times$ speedups. Our main theoretical contribution is to show the following result for NMF: if the original factors are sparse, then their projections are the sparsest solutions to the projected NMF problem. This explains why our method works for NMF and shows an interesting new property of random projections: they can preserve the solutions of non-convex optimization problems such as NMF.

Machine learning, which used to be something only computer scientists in server rooms discussed, has become a hot topic, along with big data and artificial intelligence (AI). Machines develop algorithms that allow them to make predictions, such as the shows you might want to watch on Showmax or Netflix, to use a simple example. Machines will also update their models as new data is received, without human intervention. Advances in machine learning have caused many to fear that machines will replace human jobs. However, I'd like to argue that it can offer enormous benefits to business, and that now is the time to invest in it.

WASHINGTON – The Trump administration has authorized the sale of unarmed surveillance drones to India, the manufacturer said Friday, as the two nations' leaders prepare for their first face-to-face meeting. India initiated its request to buy 22 Guardian MQ-9B unmanned aircraft for maritime surveillance last year. The deal is estimated to be worth about $2 billion. The offer is still subject to congressional approval. The green light from the administration marks a further deepening in defense ties before India's Prime Minister Narendra Modi meets with President Donald Trump at the White House on Monday. Modi's two-day visit to Washington, which starts Sunday, takes place amid uncertainty over the relationship because of differences on trade and other issues.