Collaborating Authors


Is Russia's Largest Tech Company Too Big to Fail?


It was February 11, his birthday, and the 58-year-old billionaire CEO and cofounder of Yandex, the Russian tech behemoth, was in the sort of open, engaging mood that could be called privetliviy, after the casual Russian word privet for hello. He was speaking from his car in Tel Aviv, bragging about his father--an oil geologist in his eighties who had "discovered" oil in Israel, Volozh said--as we chatted about my upcoming trip to Tel Aviv to interview him for this story. For more than 20 years, Yandex has been known as "Russia's Google": It began as a search engine in 1997 and still has a 60 percent share of the Russian search market. But for the past decade, this tag has understated the company's inescapable ubiquity in Russians' daily life. Yandex Music is the country's leader in paid music streaming, and Yandex Taxi is the top ride-hailing app.

Counterfactual Memorization in Neural Language Models Artificial Intelligence

Modern neural language models widely used in tasks across NLP risk memorizing sensitive information from their training data. As models continue to scale up in parameters, training data, and compute, understanding memorization in language models is both important from a learning-theoretical point of view, and is practically crucial in real world applications. An open question in previous studies of memorization in language models is how to filter out "common" memorization. In fact, most memorization criteria strongly correlate with the number of occurrences in the training set, capturing "common" memorization such as familiar phrases, public knowledge or templated texts. In this paper, we provide a principled perspective inspired by a taxonomy of human memory in Psychology. From this perspective, we formulate a notion of counterfactual memorization, which characterizes how a model's predictions change if a particular document is omitted during training. We identify and study counterfactually-memorized training examples in standard text datasets. We further estimate the influence of each training example on the validation set and on generated texts, and show that this can provide direct evidence of the source of memorization at test time.

ZoomInfo to Acquire Conversation Intelligence Leader to Enable Insight-Driven Targeting, Coaching, and Decision-Making for Go-to-Market Teams


VANCOUVER, Wash.--(BUSINESS WIRE)--ZoomInfo (NASDAQ: ZI), a global leader in modern go-to-market software, data, and intelligence, today announced it has agreed to acquire, More than 20,000 global revenue teams trust ZoomInfo to power their go-to-market motions and drive efficient results. The planned acquisition of Chorus will add a new category of actionable insights to ZoomInfo's world-class intelligence layer, unlocking workflows and driving engagement informed by conversations. The acquisition expands ZoomInfo's total addressable market to $70 billion, and is expected to be accretive to growth immediately, generate positive adjusted operating income within 12 months, and be accretive to cash flow in the second half of FY 2022. Chorus uses machine learning and artificial intelligence to capture and analyze prospect and customer calls, meetings, and emails.

Artificial Intelligence (AI) in Construction Market SWOT Analysis by Size, Status and Forecast to 2021-2027 - The Manomet Current


Latest published market study on Global Artificial Intelligence (AI) in Construction Market provides an overview of the current market dynamics in the Artificial Intelligence (AI) in Construction space, as well as what our survey respondents--all outsourcing decision-makers--predict the market will look like in 2027. The study breaks market by revenue and volume (wherever applicable) and price history to estimates size and trend analysis and identifying gaps and opportunities. Some of the players that are in coverage of the study are Renoworks Software, SmarTVid.Io, Jaroop,, Get ready to identify the pros and cons of regulatory framework, local reforms and its impact on the Industry. Market Factor Analysis: In this economic slowdown, impact on various industries is huge.

OR-Gym: A Reinforcement Learning Library for Operations Research Problem Artificial Intelligence

Reinforcement learning (RL) has been widely applied to game-playing and surpassed the best human-level performance in many domains, yet there are few use-cases in industrial or commercial settings. We introduce OR-Gym, an open-source library for developing reinforcement learning algorithms to address operations research problems. In this paper, we apply reinforcement learning to the knapsack, multi-dimensional bin packing, multi-echelon supply chain, and multi-period asset allocation model problems, as well as benchmark the RL solutions against MILP and heuristic models. These problems are used in logistics, finance, engineering, and are common in many business operation settings. We develop environments based on prototypical models in the literature and implement various optimization and heuristic models in order to benchmark the RL results. By re-framing a series of classic optimization problems as RL tasks, we seek to provide a new tool for the operations research community, while also opening those in the RL community to many of the problems and challenges in the OR field.

Intel Completes Tender Offer for Mobileye Intel Newsroom


SANTA CLARA, Calif., and JERUSALEM, Aug. 8, 2017 -- Intel Corporation (NASDAQ: INTC) and Mobileye N.V. (NYSE: MBLY) today announced the completion of Intel's tender offer for outstanding ordinary shares of Mobileye, a global leader in the development of computer vision and machine learning, data analysis, localization and mapping for advanced driver assistance systems and autonomous driving. The acquisition is expected to accelerate innovation for the automotive industry and positions Intel as a leading technology provider in the fast-growing market for highly and fully autonomous vehicles. The combination of Intel and Mobileye will allow Mobileye's leading computer vision expertise (the "eyes") to complement Intel's high-performance computing and connectivity expertise (the "brains") to create automated driving solutions from cloud to car. Intel estimates the vehicle systems, data and services market opportunity to be up to $70 billion by 2030. "With Mobileye, Intel emerges as a leader in creating the technology foundation that the automotive industry needs for an autonomous future," said Intel CEO Brian Krzanich.

Google for help if you want a hand on artificial intelligence, machine learning


SAN FRANCISCO: Machine learning and AI-based startups can Google for help as the search giant launches its Google Developers Launchpad Studio Accelerator Programme for startups to build and scale their products across the globe. The accelerator programme is targeting startups in all global markets, including India, as well as homegrown players in the US. The length of the programme is still being worked out. "In the past four years (of Google Launchpad Accelerator), we have learned a lot while supporting early and late-stage founders," said Roy Glasberg, the global lead at Google Developers Launchpad. "While working with startups on innovative solutions, such as applying artificial intelligence to solve transportation problems in Israel, improving tele-medicine in Brazil and optimising online retail in India, we have learned that these firms require specialised services," Glasberg said. - #AI News: Market research disruptor Remesh announces $2.25 million seed round


Newswire) Remesh, a software company that is reinventing market research through artificial intelligence (AI), today announced the closing of its $2.25 million seed investment round that brings its total funding to $3.85 million. The round is led by LionBird Ventures, a venture capital firm investing in early stage digital health and business services companies with offices in Tel Aviv and Chicago. The round also includes Reimagine Holdings Group, a holding company focused on growing consumer insights and marketing services companies, as well as individual investors, representing a mix of new and returning investors. "We believe that Remesh has shown real potential to change the way brands, consultants and agencies listen to feedback from their audiences," said Ed Michael, Managing Partner at LionBird Ventures. "Remesh has recognized a way to solve for a number of inefficiencies in market research using artificial intelligence.

Concurrent Auctions Across The Supply Chain

Journal of Artificial Intelligence Research

With the recent technological feasibility of electronic commerce over the Internet, much attention has been given to the design of electronic markets for various types of electronically-tradable goods. Such markets, however, will normally need to function in some relationship with markets for other related goods, usually those downstream or upstream in the supply chain. Thus, for example, an electronic market for rubber tires for trucks will likely need to be strongly influenced by the rubber market as well as by the truck market. In this paper we design protocols for exchange of information between a sequence of markets along a single supply chain. These protocols allow each of these markets to function separately, while the information exchanged ensures efficient global behavior across the supply chain. Each market that forms a link in the supply chain operates as a double auction, where the bids on one side of the double auction come from bidders in the corresponding segment of the industry, and the bids on the other side are synthetically generated by the protocol to express the combined information from all other links in the chain. The double auctions in each of the markets can be of several types, and we study several variants of incentive compatible double auctions, comparing them in terms of their efficiency and of the market revenue.