Pretty much every tech startup that boasts its use of artificial intelligence is actually focused on an ultra-specific problem. Visual effects company Digital Domain has an AI algorithm that automates and enhances video editing; healthcare startup Babylon developed an AI chatbot to answer the constant barrage of patient questions. They do what they're supposed to. But they'll never lead to something more, something bigger. No matter how great these AI systems sound, no startup will ever stumble upon artificial general intelligence (AGI), which is essentially a complete, human-like AI system that has become truly intelligent.

More than a dozen human rights groups have sent a letter to Google urging the company not to offer censored internet search in China, amid reports it is planning to again begin offering the service in the giant Asian market. The joint letter dated Tuesday calls on CEO Sundar Pichai to explain what Google is doing to safeguard users from the Chinese government's censorship and surveillance. It describes the censored search engine app, codenamed "Dragonfly", as representing "an alarming capitulation by Google on human rights. "The Chinese government extensively violates the rights to freedom of expression and privacy; by accommodating the Chinese authorities' repression of dissent, Google would be actively participating in those violations for millions of internet users in China," said the letter That follows a letter earlier this month signed by more than a thousand Google employees protesting the company's secretive plan to build a search engine that would comply with Chinese ...

In December, Google announced it was opening an artificial intelligence lab in Beijing and in June, Google invested $550 million in JD.com, a Chinese e-commerce platform that is second only to Alibaba in the country. The companies said they would collaborate on retail solutions around the world without mentioning China, where Google services including Gmail and YouTube are blocked.

Drones have been a part of the modern lifestyle for a while now and are commonly used for aerial photography for sports and other events professionally. With the rise of lightweight and hence portable drones that come with cameras in the market, these gadgets have gained popularity for personal use as well. While anyone can buy and work with drones, using them in cities and certain areas may need discretion. As our government prepares to allow remotely flying vehicles from December, 2018; check out these brands that you can purchase drones from- Image source: www.dji.com DJI Mavic Air is loved for amazing portability, with its fold-able body that you can carry for your trekking and hiking trip to the mountains.

Chinese AI startups raised $5 billion in venture capital funding during 2017 -- a half-billion more than their U.S. counterparts -- indicating investor confidence in the technologies, business strategies, and market potential of Chinese startups, according to research by market advisory firm ABI Research. In global terms, investment values for AI startups increased 150 percent -- from $4 billion to $10.7 billion -- between 2016 and 2017. U.S. startups raised $4.4 billion from 155 investments, while Chinese startups netted $4.9 billion from 19 investments. "The bullish sentiment shared among Chinese investors is a clear sign that China is going all-in in artificial intelligence," said Lian Jye Su, a principal analyst at ABI. "The government of China is setting clear policy guidelines for the future development of AI, and startups are responding with cutting edge AI technologies across many industries." ABI found that AI startups in Europe have diversified interests across different industries and verticals, mainly for use cases such as cybersecurity, digital ID, public safety, healthcare, and IoT.

Artificial intelligence (AI) startups in China raised nearly $5 billion in venture capital (VC) funding in 2017, overtaking their US counterparts in yet another part of the AI race, according to a Monday report from ABI Research. Overall investments in AI startups increased 150% globally year-over-year in 2017, growing from $4 billion in 2016 to $10.7 billion last year, the report found. AI startups in the US raised $4.4 billion from 155 investments. Meanwhile, Chinese startups raised $4.9 billion from only 19 investments, indicating a focus on mature AI applications with strong commercial viability and successful use cases, the report noted. SEE: IT leader's guide to the future of artificial intelligence (Tech Pro Research) The Chinese government has also made AI a priority, laying out a major policy plan calling for the nation to become the world's primary AI innovation center by 2030, attracting more investment in AI startups, the report noted.

Artificial intelligence is presented as the opposite of natural intelligence, which is demonstrated by animals. By that definition, AI would appear to be free from the social neuroses and discriminations that can plague humans. But machine learning originates from human makers, meaning those shortcomings can be passed along via algorithms and data input. AI is increasingly customer-facing: It includes asking Siri details about an upcoming trip or turning on Netflix and seeing recommendations based on viewing habits. AI touches numerous points along the customer journey, meaning its limitations can have organization-wide consequences.

President Trump thinks Google's search engine is "rigged." By featuring more mainstream news outlets and relatively fewer conservative sites in the results he sees, Trump tweeted Tuesday, Google is "suppressing" right-wing views on its platform. Trump escalated his attacks Tuesday afternoon in remarks from the Oval Office, warning that "Google and Twitter and Facebook, they are treading on very, very troubled territory and they have to be careful." It's easy to see how Trump arrived at this conclusion, because in many ways his experience mirrors that of millions of Americans who've awoken to the dominance of Google -- and Facebook, and Twitter -- in their everyday lives without being quite certain how it wound up there. We rely constantly on Google to find out what to buy, which restaurants to eat at and how to get from one place to another.

With recent progress in algorithms and the availability of massive amounts of computation power, application of machine learning techniques is becoming a hot topic in the oil and gas industry. One of the most promising aspects to apply machine learning to the upstream field is the rock facies classification in reservoir characterization, which is crucial in determining the net pay thickness of reservoirs, thus a definitive factor in drilling decision making process. For complex machine learning tasks like facies classification, feature engineering is often critical. This paper shows the inclusion of physics-motivated feature interaction in feature augmentation can further improve the capability of machine learning in rock facies classification. We demonstrate this approach with the SEG 2016 machine learning contest dataset and the top winning algorithms. The improvement is roboust and can be $\sim5\%$ better than current existing best F-1 score, where F-1 is an evaluation metric used to quantify average prediction accuracy.

Solving statistical learning problems often involves nonconvex optimization. Despite the empirical success of nonconvex statistical optimization methods, their global dynamics, especially convergence to the desirable local minima, remain less well understood in theory. In this paper, we propose a new analytic paradigm based on diffusion processes to characterize the global dynamics of nonconvex statistical optimization. As a concrete example, we study stochastic gradient descent (SGD) for the tensor decomposition formulation of independent component analysis. In particular, we cast different phases of SGD into diffusion processes, i.e., solutions to stochastic differential equations. Initialized from an unstable equilibrium, the global dynamics of SGD transit over three consecutive phases: (i) an unstable Ornstein-Uhlenbeck process slowly departing from the initialization, (ii) the solution to an ordinary differential equation, which quickly evolves towards the desirable local minimum, and (iii) a stable Ornstein-Uhlenbeck process oscillating around the desirable local minimum. Our proof techniques are based upon Stroock and Varadhan's weak convergence of Markov chains to diffusion processes, which are of independent interest.