Coral is Google’s quiet initiative to enable AI without the cloud (The Verge) Google Coral, a stealth initiative, promises to make computers faster and more secure with on-device AI. Although the project actually has its roots in Google’s “AIY” range of do-it-yourself machine learning kits, the long-term focus is on enterprise customers in industries like the automotive world and health care.
Ancestry turned to AI to bring down cloud costs (CIO Dive) Ancestry spent two years migrating a database of over 20 million members away from data centers and into Amazon Web Services. After the move, the company turned attention to optimizing its presence in the cloud with Opsani, an AIOps company that relies on machine learning to manage cloud workloads.
Smart Data based Ensemble for Imbalanced Big Data Classification (arXiv) Big Data scenarios pose a new challenge to traditional data mining algorithms since they are not prepared to work with such amount of data. Smart Data refers to data of enough quality to improve the outcome from a data mining algorithm. Existing data mining algorithms inability to handle Big Datasets prevents the transition from Big to Smart Data. Experiments carried out in 21 Big Datasets have proved that the authors’ ensemble classifier outperforms classic machine learning models with an added data balancing method, such as Random Forests.
Identifying Table Structure in Documents using Conditional Generative Adversarial Networks (arXiv) Hierarchically-related data is rendered as tables, and extracting information from tables in such documents presents a significant challenge. The authors propose a top-down approach, first using a conditional generative adversarial network to map a table image into a standardized skeleton table form denoting approximate row and column borders without table content, then deriving latent table structure using xy-cut projection and Genetic Algorithm optimization.
Modeling and solving the multimodal car- and ride-sharing problem (arXiv) The authors introduce the multimodal car-and ride-sharing problem (MMCRP), in which a pool of cars is used to cover a set of ride requests, while uncovered requests are assigned to other modes of transport (MOT). The problem can be formulated as a vehicle scheduling problem. In order to solve the problem, an auxiliary graph is constructed in which each trip starting and ending in a depot, and covering possible ride-shares, is modeled as an edge in a time-space graph. They propose a two-layer decomposition algorithm based on column generation, where the master problem ensures that each request can only be covered at most once, and the pricing problem generates new promising routes by solving a kind of shortest path problem in a time-space network.
Beyond Near- and Long-Term: Towards a Clearer Account of Research Priorities in AI Ethics and Society (arXiv) One way of carving up the broad “AI ethics and society” research space that has emerged in recent years is to distinguish between “near-term” and “long-term” research. While such ways of breaking down the research space can be useful, we put forward several concerns about the near/long-term distinction gaining too much prominence in how research questions and priorities are framed. We highlight some ambiguities and inconsistencies in how the distinction is used and argue that while there are differing priorities within this broad research community, these differences are not well-captured by the near/long-term distinction.
Should Artificial Intelligence Governance be Centralised? Design Lessons from History (arXiv) The authors draw on the history of other international regimes to identify advantages and disadvantages in centralizing AI governance. Some considerations, such as efficiency and political power, speak in favor of centralization. Conversely, the risk of creating a slow and brittle institution speaks against it, as does the difficulty in securing participation while creating stringent rules. Other considerations depend on the specific design of a centralized institution. Centralization entails trade-offs and the details matter.
The Penetration of Internet of Things in Robotics: Towards a Web of Robotic Things (arXiv) Some of the benefits of IoT in robotics have been discussed in related work. This paper moves one step further, studying the actual current use of IoT in robotics, through various real-world examples encountered through bibliographic research. The paper also examines the potential of WoT, together with robotic systems, investigating which concepts, characteristics, architectures, hardware, software and communication methods of IoT are used in existing robotic systems, which sensors and actions are incorporated in IoT-based robots, as well as in which application areas. Finally, the current application of WoT in robotics is examined and discussed.
AI and ML in Society
The Problem with Hiring Algorithms (Machine Learning Times) Brian Gallagher of NYU’s Ethical Systems summarizes the status of facial recognition and other types of analytics used to assess potential employees in interviews as well as whether or not they function as intended.
Rethinking Business Strategy in the Age of AI (Harvard Business) For the first time in 100 years, new technologies such as artificial intelligence are causing firms to rethink their competitive strategy and organizational structure, say Marco Iansiti and Karim R. Lakhani, authors of the new book Competing in the Age of AI.
Although the CCPA enforcement deadline has passed, we found that possibly over 70% of orgs might not have started with their CCPA compliance programs. We detail the current state of CCPA compliance and 3 steps organizations lagging need to take in order to strategize their path to compliance.
Financial services businesses can use DLP to eliminate the risk of data exfiltration and boost their overall security strategy. Learn what the different types of PII are, what’s really at stake when this data is at risk, and how laws only do some of the work needed to keep data safe.
Maynard Webb, a Nightfall investor, tech veteran, and industry thought leader recently joined us for a discussion. We talk about how his early career shaped his values and perspective, what motivated him to write his New York Times bestseller, Rebooting Work, as well as how the coronavirus will reboot the tech industry.
It's impossible to understand cloud security without first understanding the shared responsibility model. First touted by AWS, the shared responsibility model is now a staple of many services and the best way of understanding on which parties specific security obigations lie.