I have been talking a lot this year about democratizing digital. It is about empowering everyone to have a digital experience and enabling everyone to participate in the digital economy. This trend is large-scale, with broad business and social impact. This was clear earlier this month at the Microsoft Government Leaders Summit in Washington, D.C. There, I met with federal agency and department leaders to discuss how cloud computing and artificial intelligence (AI) are delivering new levels of innovation and impacting entire markets and industries. The U.S. Department of Agriculture (USDA) offers one of the best examples of the power of data and AI to transform agricultural productivity through the FarmBeats initiative.

Some of the biggest businesses in Australia will trial a series of eight principles around artificial intelligence, developed as part of the Morrison Government's AI Ethics Framework. NAB, Commonwealth Bank, Telstra, Microsoft and Flamingo AI have signed up to test the principles to ensure they deliver practical benefits and translate into real world solutions. Minister for Industry, Science and Technology Karen Andrews said AI is a powerful technology that can create jobs, boost the economy and improve our quality of life and is an important part of the Government's economic plan. "The Morrison Government is determined to create an environment where AI helps the economy and everyday Australians to thrive. The eight AI ethics principles are just one part of this vision," Minister Andrews said.

Autonomous vehicles will fail to reach their full potential until ubiquitous and extremely reliable high-speed communications networks with very low latency are available. These networks are key to facilitating the realtime, instantaneous communications among vehicles and supporting infrastructure that must exist before vehicles can continuously and autonomously traverse city streets void of vigilant human oversight. The deployment of 5G (fifth generation) cellular technologies will represent a giant leap forward toward the use of autonomous vehicles for swift, efficient, and safe travel. However, the successful deployment of 5G (and subsequent) technologies depends upon the support of, and coordination among, federal, state, and local governments. Without this support and coordination, only the most lucrative markets will likely benefit from new technologies, and autonomous vehicles will remain technologically insular due to the scale of telecommunications investment required for the mass autonomous vehicle market.

The U.S. needs to tackle the challenges of adapting artificial intelligence systems for modern warfare, much like the "titans of industry" transformed Detroit into an "arsenal of democracy" during World War II, Defense Secretary Mark Esper said yesterday. "Mastering artificial intelligence will require similar vision, ambition and commitment," Esper said at a conference hosted by the National Security Commission on Artificial Intelligence. "We need the full force of American intellect and ingenuity working in harmony across the public and private sectors." Artificial Intelligence, sometimes called "machine learning," refers to advanced computer algorithms that can use data to "learn" and therefore make choices without human input. Last week, a Pentagon advisory board released proposed guidelines for the ethical deployment of AI-enabled weapons on the battlefield.

Effectively capturing graph node sequences in the form of vector embeddings is critical to many applications. We achieve this by (i) first learning vector embeddings of single graph nodes and (ii) then composing them to compactly represent node sequences. Specifically, we propose SENSE-S (Semantically Enhanced Node Sequence Embedding - for Single nodes), a skip-gram based novel embedding mechanism, for single graph nodes that co-learns graph structure as well as their textual descriptions. We demonstrate that SENSE-S vectors increase the accuracy of multi-label classification tasks by up to 50% and link-prediction tasks by up to 78% under a variety of scenarios using real datasets. Based on SENSE-S, we next propose generic SENSE to compute composite vectors that represent a sequence of nodes, where preserving the node order is important. We prove that this approach is efficient in embedding node sequences, and our experiments on real data confirm its high accuracy in node order decoding.

Machine learning has achieved great success in many applications, including electroencephalogram (EEG) based brain-computer interfaces (BCIs). Unfortunately, many machine learning models are vulnerable to adversarial examples, which are crafted by adding deliberately designed perturbations to the original inputs. Many adversarial attack approaches for classification problems have been proposed, but few have considered target adversarial attacks for regression problems. This paper proposes two such approaches. More specifically, we consider white-box target attacks for regression problems, where we know all information about the regression model to be attacked, and want to design small perturbations to change the regression output by a pre-determined amount. Experiments on two BCI regression problems verified that both approaches are effective. Moreover, adversarial examples generated from both approaches are also transferable, which means that we can use adversarial examples generated from one known regression model to attack an unknown regression model, i.e., to perform black-box attacks. To our knowledge, this is the first study on adversarial attacks for EEG-based BCI regression problems, which calls for more attention on the security of BCI systems.

Finite State Controllers (FSCs) are an effective way to represent sequential plans compactly. By imposing appropriate conditions on transitions, FSCs can also represent generalized plans that solve a range of planning problems from a given domain. In this paper we introduce the concept of hierarchical FSCs for planning by allowing controllers to call other controllers. We show that hierarchical FSCs can represent generalized plans more compactly than individual FSCs. Moreover, our call mechanism makes it possible to generate hierarchical FSCs in a modular fashion, or even to apply recursion. We also introduce a compilation that enables a classical planner to generate hierarchical FSCs that solve challenging generalized planning problems. The compilation takes as input a set of planning problems from a given domain and outputs a single classical planning problem, whose solution corresponds to a hierarchical FSC. 1 Introduction Finite state controllers (FSCs) are a compact and effective representation commonly used in AI; prominent examples include robotics [ Brooks, 1989 ] and video-games [ Buckland, 2004] . In planning, FSCs offer two main benefits: 1) solution compactness [ B ackstr om et al., 2014 ]; and 2) the ability to represent generalized plans that solve a range of similar planning problems. This generalization capacity allows FSCs to represent solutions to arbitrarily large problems, as well as problems with partial observability and non-deterministic actions [ Bonet et al., 2010; Hu and Levesque, 2011; Srivastava et al., 2011; Hu and De Giacomo, 2013 ] .

If anybody read the word Artificial Intelligence (AI), he starts thinking that he will come to a shopping mall on his self-driven car and there a robot opens his car door. All services are automated without human presence, like greeter, house staff, and security guard. Every job is done by a robot. This perception is very much aligned for AI in near future, but are we adopting AI properly in Pakistan? Are we following ethical codes to use AI for improvement of human conditions?

MINNEAPOLIS--(BUSINESS WIRE)-- U.S. Bank (USBK) has hired technology leader Dr. Tanushree Luke to lead Artificial Intelligence (AI) efforts at the company. In this role, she will drive the continued development of the AI practice within the U.S. Bank Innovation group and AI strategies across the enterprise. This press release features multimedia. U.S. Bank has hired technology leader Dr. Tanushree Luke to lead Artificial Intelligence (AI) efforts at the company. Dr. Luke's career has spanned multiple industries and sectors.

A prominent rights group is urging the Federal Trade Commission to take on the recruiting-technology company HireVue, arguing the firm has turned to unfair and deceptive trade practices in its use of face-scanning technology to assess job candidates' "employability." The Electronic Privacy Information Center, known as EPIC, on Wednesday filed an official complaint calling on the FTC to investigate HireVue's business practices, saying the company's use of unproven artificial-intelligence systems that scan people's faces and voices constituted a wide-scale threat to American workers. HireVue's "AI-driven assessments," which more than 100 employers have used on more than a million job candidates, use video interviews to analyze hundreds of thousands of data points related to a person's speaking voice, word selection and facial movements. The system then creates a computer-generated estimate of the candidates' skills and behaviors, including their "willingness to learn" and "personal stability." Candidates aren't told their scores, but employers can use those reports to decide whom to hire or disregard. The the Utah-based company was the subject of a Washington Post report last month, in which AI researchers criticized its technology as "profoundly disturbing" and "opaque."