The classic example of "crowd wisdom" dates back to 1906, when Sir Francis Galton observed a contest in which attendees were asked to guess the weight of an ox at a country fair in England. In what many consider to be the first experiment on crowd wisdom, the average of the 800 guesses was within one pound of being correct. Consider that these kinds of experiments can now be done digitally – across cultures and time zones and fairly instantaneously. The classic experiment was reenacted recently with a digital crowd when a photo of a cow was posted online and viewers were invited to guess her weight. More than 17,000 votes were cast and the average guess was within 5 percent of being accurate.

On March 5, at the opening meeting of the National People's Congress, China's top legislature's annual session, Premier Li Keqiang announced that China will accelerate research and development (R&D) in new and emerging industries, such as artificial intelligence (AI). It is the first time that China's highest national meeting has included AI in the Government Work Report. The report's singling out of AI indicates Beijing's prioritization of the industry in its economic agenda, and therefore its determination to support its growth. In recent years, China's leadership has been increasingly thinking about how to ensure their competitive edge in the AI industry. The acceleration of China's policy efforts to advance AI development began in 2014, when President Xi Jinping called for innovation and breakthroughs in science and technology, including AI, at the opening ceremony of the 17th Congress of the Chinese Academy of Sciences.

When you ask Siri for directions, peruse Netflix's recommendations or get a fraud alert from your bank, these interactions are led by computer systems using large amounts of data to predict your needs. The market is only going to grow. By 2020, the research firm IDC predicts that AI will help drive worldwide revenues to over $47 billion, up from $8 billion in 2016. Still, Coursera co-founder ANDREW NG, adjunct professor of computer science, says fears that AI will replace humans are misplaced: "Despite all the hype and excitement about AI, it's still extremely limited today relative to what human intelligence is." Ng, who is chief scientist at Baidu Research, spoke to the Graduate School of Business community as part of a series presented by the Stanford MSx Program, which offers experienced leaders a one-year, full-time learning experience.

Tata Consultancy Services (BSE: 532540, NSE: TCS), a leading global IT services, consulting and business solutions organization, today unveiled its Global Trend Study titled, "Getting Smarter by the Day: How AI is Elevating the Performance of Global Companies." Focused on the current and future impact of Artificial Intelligence (AI), the study polled 835 executives across 13 global industry sectors in four regions of the world, finding that 84% of companies see the use of AI as "essential" to competitiveness, with a further 50% seeing the technology as "transformative." Widespread AI adoption expected across job functions Exploring the views and actions of decision makers from global companies with average revenues of $20 billion, the study revealed AI is spreading across almost all areas of a company. The biggest adopters of AI today are, not surprisingly, IT departments, with two-thirds (67%) of survey respondents using AI to detect security intrusions, user issues and deliver automation. However, by 2020, almost a third (32%) of companies believe AI's greatest impact will be in sales, marketing or customer service, while one in five (20%) see AI's impact being largest in non-customer facing corporate functions, including finance, strategic planning, corporate development, and HR.

JUDY WOODRUFF: For decades, researchers have worked to create a better and more direct connection between a human brain and a computer to improve the lives of people who are paralyzed or have severe limb weakness from diseases like ALS. Those advances have been notable, but now the work is yielding groundbreaking results. CAT WISE: Dennis Degray is a 64-year-old quadriplegic who is writing a sentence on the computer screen in front of him using only his brain. A former volunteer firefighter, Degray had a bad fall 10 years ago which severed his spinal cord. As part of an early stage clinical research study led by Stanford University, Degray and two other volunteer participants with ALS had small sensors implanted in their brains in an area called the motor cortex, which controls movement.

Certain kinds of autonomous vehicles may not be safe, especially in an emergency situation, according to a new study published by the Lords Science and Technology Committee on Wednesday. With driverless technology, drivers may become over-reliant and complacent. However, with the development in the automotive technology over time, accidents by human error may be significantly reduced -- but they just might increase before they do. The committee also reported people may use driverless cars for shorter distances, as well, causing laziness and may prevent them from "getting exercise by walking." The UK Economic Opportunity split vehicles into levels from 0 to 5. Zero was fully controlled by an individual, and five was completely automated. According to peers on the committee, there was a "very dangerous" problem with vehicles reaching the middle of the scale, BBC News reported.

In many data exploration tasks it is meaningful to identify groups of attribute interactions that are specific to a variable of interest. For instance, in a dataset where the attributes are medical markers and the variable of interest (class variable) is binary indicating presence/absence of disease, we would like to know which medical markers interact with respect to the binary class label. These interactions are useful in several practical applications, for example, to gain insight into the structure of the data, in feature selection, and in data anonymisation. We present a novel method, based on statistical significance testing, that can be used to verify if the data set has been created by a given factorised class-conditional joint distribution, where the distribution is parametrised by a partition of its attributes. Furthermore, we provide a method, named astrid, for automatically finding a partition of attributes describing the distribution that has generated the data. State-of-the-art classifiers are utilised to capture the interactions present in the data by systematically breaking attribute interactions and observing the effect of this breaking on classifier performance. We empirically demonstrate the utility of the proposed method with examples using real and synthetic data.

We propose a flexible convex relaxation for the phase retrieval problem that operates in the natural domain of the signal. Therefore, we avoid the prohibitive computational cost associated with "lifting" and semidefinite programming (SDP) in methods such as PhaseLift and compete with recently developed non-convex techniques for phase retrieval. We relax the quadratic equations for phaseless measurements to inequality constraints each of which representing a symmetric "slab". Through a simple convex program, our proposed estimator finds an extreme point of the intersection of these slabs that is best aligned with a given anchor vector. We characterize geometric conditions that certify success of the proposed estimator. Furthermore, using classic results in statistical learning theory, we show that for random measurements the geometric certificates hold with high probability at an optimal sample complexity. Phase transition of our estimator is evaluated through simulations. Our numerical experiments also suggest that the proposed method can solve phase retrieval problems with coded diffraction measurements as well.

This work addresses various open questions in the theory of active learning for nonparametric classification. Our contributions are both statistical and algorithmic: -We establish new minimax-rates for active learning under common \textit{noise conditions}. These rates display interesting transitions -- due to the interaction between noise \textit{smoothness and margin} -- not present in the passive setting. Some such transitions were previously conjectured, but remained unconfirmed. -We present a generic algorithmic strategy for adaptivity to unknown noise smoothness and margin; our strategy achieves optimal rates in many general situations; furthermore, unlike in previous work, we avoid the need for \textit{adaptive confidence sets}, resulting in strictly milder distributional requirements.

Estimation of the number of endmembers existing in a scene constitutes a critical task in the hyperspectral unmixing process. The accuracy of this estimate plays a crucial role in subsequent unsupervised unmixing steps i.e., the derivation of the spectral signatures of the endmembers (endmembers' extraction) and the estimation of the abundance fractions of the pixels. A common practice amply followed in literature is to treat endmembers' number estimation and unmixing, independently as two separate tasks, providing the outcome of the former as input to the latter. In this paper, we go beyond this computationally demanding strategy. More precisely, we set forth a multiple constrained optimization framework, which encapsulates endmembers' number estimation and unsupervised unmixing in a single task. This is attained by suitably formulating the problem via a low-rank and sparse nonnegative matrix factorization rationale, where low-rankness is promoted with the use of a sophisticated $\ell_2/\ell_1$ norm penalty term. An alternating proximal algorithm is then proposed for minimizing the emerging cost function. The results obtained by simulated and real data experiments verify the effectiveness of the proposed approach.