In less than 12 hours, three different people offered to pay me if I'd spend an hour talking to a stranger on the phone. All three said they'd enjoyed reading an article I'd written about Google building a new computer chip for artificial intelligence, and all three urged me to discuss the story with one of their clients. Each described this client as the manager of a major hedge fund, but wouldn't say who it was. The requests came from what are called expert networks--research firms that connect investors with people who can help them understand particular markets and provide a competitive edge (sometimes, it seems, through insider information). These expert networks wanted me to explain how Google's AI processor would affect the chip market.

The number one goal in United Nations' Sustainable Development Goals for 2030 is: eliminate poverty. Today, around 1 billion people, that's roughly one seventh of the world's population, live in extreme poverty by earning less than 1.90$ per day. Though studies reveal that global poverty is reducing, we are still a long way from our goal. To eradicate poverty, we first need the poverty distribution across the globe. The following diagram gives a rough estimate.

At the time of writing, AI ethics is a hot topic, given that several global players (namely Google, Amazon, Facebook, IBM and Microsoft) have founded the Partnership on Artificial Intelligence to Benefit People and Society. Their aims are to advance public awareness and define standards that researchers can use as guidelines. You have to wonder at the future benefits of this organization when the chosen name sounds like a company in Southeast Asia. Apple is conspicuously absent, perhaps unwilling to participate in any venture where the supply chain is not under its full control or a profit margin is not defined. However, there are other efforts underway.

The Chinese are coming and they're hungry for games companies. They need new content to feed their 560 million avid gamers, who contribute to the biggest gaming market in the world - worth an estimated $24.4bn (£19.8bn) in 2016, according to Newzoo. Chinese firms have already spent more than $111bn on foreign acquisitions this year, according to Dealogic, with some of the biggest deals involving gaming companies. Internet giant Tencent - which owns the WeChat and QQ Games platforms - bought Finnish Clash of Clans mobile games maker Supercell for $8.6bn earlier this year. Tencent already owns League of Legends maker Riot Games, and has minority stakes in Epic Games and Activision Blizzard, the World of Warcraft maker.

PUNE, India, Nov. 3, 2016 /PRNewswire-iReach/ -- The need to enhance the decision-making capability of machines is driving the Machine Learning as a service (MLaaS) market. MLaaS with the help of pattern recognition, advanced analytical methodologies, and APIs is able to make better decisions. With the use of machine learning algorithm, decision-making abilities improve over time without being explicitly programmed. However, the lack of skilled consultants to deploy machine learning services and government and compliance issues are limiting the growth of MLaaS solutions in the market. Healthcare industry among all the verticals is expected to gain the maximum traction during the forecast period.

This post was written by Ofir Press. Ofir is a graduate student at Tel-Aviv University's Deep Learning Lab. His main focus is on using deep learning for natural language processing. "Due to the recent achievements of artificial neural networks across many different tasks (such as face recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it. If you already have a basic understanding of linear algebra, calculus, probability and programming: I recommend starting with Stanford's CS231n. The course notes are comprehensive and well-written. The slides for each lesson are also available, and even though the accompanying videos were removed from the official site, re-uploads are quite easy to find online. If you don't have the relevant math background: There is an incredible amount of free material online that can be used to learn the required math knowledge. Gilbert Strang's course on linear algebra is a great introduction to the field. For the other subjects, edX has courses from MIT on both calculus and probability. If you are interested in learning more about machine learning: Andrew Ng's Coursera class is a popular choice as a first class in machine learning. There are other great options available such as Yaser Abu-Mostafa's machine learning course which focuses much more on theory than the Coursera class but it is still relevant for beginners. Knowledge in machine learning isn't really a prerequisite to learning deep learning, but it does help. In addition, learning classical machine learning and not only deep learning is important because it provides a theoretical background and because deep learning isn't always the correct solution. Geoffrey Hinton's Coursera class "Neural Networks for Machine Learn... covers a lot of different topics, and so does Hugo Larochelle's "Neural Networks Class".

The Note 7 is still a mystery to us. We still don't know what caused those explosions but we have some possible explanations. We won't add to the pain the South Korea tech giant is experiencing so let's just focus on the upcoming Galaxy S8. The company is believed to be launching only one premium flagship starting next year so if that's the case, the Galaxy S8 should be really special. We've heard a lot about it although nothing has been confirmed yet.

Finding a wine that pairs perfectly with your unique palate can be difficult, but new technology lets anyone personalize and customize vino with a touch of button. Called Vinfusion, this system lets users craft a glass of wine using terms like'fiery' or'sweet' by pressing options in an accompanied linked to a tabletop tap. The machine houses four red wines that are blended to perfection by a unique flavor algorithm and is then dispensed into a glass. Vinfusion lets users craft their wine using terms like'fiery' or'sweet' by pressing options in an accompanied linked to a tabletop tap. Users choose between light and full-bodied, soft and fiery, and sweetness using an accompanied app on a tablet.

We consider the problem of maximizing a monotone submodular function under noise. There has been a great deal of work on optimization of submodular functions under various constraints, resulting in algorithms that provide desirable approximation guarantees. In many applications, however, we do not have access to the submodular function we aim to optimize, but rather to some erroneous or noisy version of it. This raises the question of whether provable guarantees are obtainable in presence of error and noise. We provide initial answers, by focusing on the question of maximizing a monotone submodular function under a cardinality constraint when given access to a noisy oracle of the function. We show that: - For a cardinality constraint $k \geq 2$, there is an approximation algorithm whose approximation ratio is arbitrarily close to $1-1/e$; - For $k=1$ there is an algorithm whose approximation ratio is arbitrarily close to $1/2$. No randomized algorithm can obtain an approximation ratio better than $1/2+o(1)$; -If the noise is adversarial, no non-trivial approximation guarantee can be obtained.

Economists have become increasingly interested in studying the nature of production functions in social policy applications, with the goal of improving productivity. Traditionally models have assumed workers are homogenous inputs. However, in practice, substantial variability in productivity means the marginal productivity of labor depends substantially on which new workers are hired--which requires not an estimate of a causal effect, but rather a prediction. Annoyed at being automatically tagged with Facebook's facial-recognition system? Wearing a pair of tie-dye-looking glasses could help.