Artificial intelligence will have a dramatic impact on business by 2020, according to study released this week by IT services, consulting and business solutions provider Tata Consultancy Services (TCS). The firm's study, "Getting Smarter by the Day: How AI is Elevating the Performance of Global Companies," shows that 84 percent of the 835 executives TCS surveyed from North America, Europe, Asia-Pacific and Latin America said their companies see the use of AI as "essential" to competitiveness. Artificial intelligence in the real world: What can it actually do? What are the limits of AI? And how do you go from managing data points to injecting AI in the enterprise?

DJI Technology Inc, the largest civilian drone maker, is claiming in a new study that "59 lives have been saved by civilian drones in 18 different incidents, with one life a week being saved by drones on average." The study was published on March 14, 2017, and is based on reports in the news. The majority of the rescues have occurred in USA and China, although instances have occurred in Canada and Turkey as well. One third were saved by civilians using their hobbyist drones and not by emergency personnel, showing their far-reaching abilities. The Hindu of Dec 7 reported that in Chennai, India during heavy flash floods the city police "were able to locate as many as 200 people with the help of drones and rescued all of them."

Who are the Women in STEM who changed the world through science, technology, engineering and mathematics? We asked this question of women at Ayogo, adding "who are the women in STEM you wish you knew?" Here's our list of 13 amazingly cool women in STEM who we wish we knew. Born in 1918, Katherine Johnson, graduated from university at 18. She was awarded the Presidential Medal of Freedom in 2015 for a lifetime of work as a pioneering physicist, mathematician and space scientist. She and her colleagues, Dorothy Vaughan and Mary Jackson did the calculations that guided NASA's 1962 Friendship 7 Mission.

Can you name what technologies fall within the realm of artificial intelligence (AI)? According to our research report, "Outlook on Artificial Intelligence in the Enterprise", 88% of respondents claiming they do not use AI technologies went on to cite using specific solutions that rely on AI techniques. At Narrative Science, we view the AI ecosystem from a data-driven perspective. We think this perspective is important because it ultimately determines which technology is a best-fit for a business problem even if the end-results are similar. In the IIA's "Introduction to the Intelligent Systems Ecosystem", Chief Scientist Kris Hammond, states it well when he says: One of the more difficult issues associated with these emerging intelligent systems is that while each is built on a very different technological foundation, they tend to be bundled together and are often viewed as indistinguishable.

PISCATAWAY, N.J.--(BUSINESS WIRE)--IEEE, the world's largest technical professional organization dedicated to advancing technology for humanity, today announced the "OurAIVision" Campaign in conjunction with the recently introduced paper, Ethically Aligned Design: A Vision for Prioritizing Human Wellbeing with Artificial Intelligence and Autonomous Systems (AI/AS). Launched by The IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems, Ethically Aligned Design is a first version of a global document encouraging technologists to prioritize ethical considerations in the creation of autonomous and intelligent technologies. The first leg of the "OurAIVision" Campaign Tour was held in Mumbai, India, where representatives from The IEEE Global Initiative received direct feedback about Ethically Aligned Design from AI experts, engineers, and technologists. The tour's next stop in Tokyo, Japan in March will collect further input to Version 2 of Ethically Aligned Design. Interested parties can support the global campaign activities using Twitter hashtag #OurAIVision or learn more by visiting the Campaign website.

Are you considering including chatbots in your marketing and customer support? Wondering how consumers are responding to chatbots? In this article, you'll discover new research that shares consumer reactions to chatbots and the issues businesses should address before integrating chatbots. According to a recent study by technology company Retale, 58% of the 500 Millennials surveyed have used chatbots. Of the 42% of respondents who had not used them, 53% were interested in using them, 26% had no interest in using them, and 20% were neutral.

Just days later, we got our first hint Buffett himself was buying Apple (NASDAQ:AAPL)! Followed shortly after by that bombshell CNBC interview (here's the transcript), where he revealed an $18 billion plus Apple investment*, the vast majority bought by him in January and recent months (and the rest by Todd and Ted in early-2016). What better confirmation of my assertion that value investors - even the greatest of them all, at 86 years of age - would be wise to pose such a question to themselves? In fact, I wonder if it's the largest stock investment ever made by a single investor (again, in terms of cost)? Sure, Todd, Ted, and Charlie did provide some inspiration and feedback here, but we can be sure Buffett never buys anything until he makes his own mind up! Thoughts?] Was I confident I'd see Buffett talking up an Apple position just weeks later? And maybe I chose it specifically as the largest, most obvious and controversial value stock out there. Not to mention, the head fake I pulled: While I did summarize its attractive fundamentals and valuation, my post clearly wasn't intended to be a detailed thesis. But hey, it was still the right question at the right time, so I'll take the kudos!

It's called the prisoner's dilemma and it goes like this: You and a fellow gang member are in jail. One of you committed a heinous crime, but the prosecutor doesn't know who. Instead, you are both booked on a lesser charge and serving a one-year sentence in solitary confinement. But here's the deal: The prosecutor has given you both an opportunity to rat. If you betray your fellow gang member but are not ratted on, you walk free while your buddy serves a three-year term.

This paper presents a novel approach for localising a GPS (Global Positioning System)-denied Unmanned Aerial Vehicle (UAV) with the aid of a GPS-equipped UAV in three-dimensional space. The GPS-equipped UAV makes discrete-time broadcasts of its global coordinates. The GPS-denied UAV simultaneously receives the broadcast and takes direction of arrival (DOA) measurements towards the origin of the broadcast in its local coordinate frame (obtained via an inertial navigation system (INS)). The aim is to determine the difference between the local and global frames, described by a rotation and a translation. In the noiseless case, global coordinates were recovered exactly by solving a system of linear equations. When DOA measurements are contaminated with noise, rank relaxed semidefinite programming (SDP) and the Orthogonal Procrustes algorithm are employed. Simulations are provided and factors affecting accuracy, such as noise levels and number of measurements, are explored.

Singular values of a data in a matrix form provide insights on the structure of the data, the effective dimensionality, and the choice of hyper-parameters on higher-level data analysis tools. However, in many practical applications such as collaborative filtering and network analysis, we only get a partial observation. Under such scenarios, we consider the fundamental problem of recovering spectral properties of the underlying matrix from a sampling of its entries. We are particularly interested in directly recovering the spectrum, which is the set of singular values, and also in sample-efficient approaches for recovering a spectral sum function, which is an aggregate sum of the same function applied to each of the singular values. We propose first estimating the Schatten $k$-norms of a matrix, and then applying Chebyshev approximation to the spectral sum function or applying moment matching in Wasserstein distance to recover the singular values. The main technical challenge is in accurately estimating the Schatten norms from a sampling of a matrix. We introduce a novel unbiased estimator based on counting small structures in a graph and provide guarantees that match its empirical performance. Our theoretical analysis shows that Schatten norms can be recovered accurately from strictly smaller number of samples compared to what is needed to recover the underlying low-rank matrix. Numerical experiments suggest that we significantly improve upon a competing approach of using matrix completion methods.