Sen. Rob Portman recently introduced a bill with bipartisan backing to develop a national cloud computer for AI research, which would allow those with barriers to entry or facing hurdles to more efficiently progress their work.The bill, if passed, would establish a task force to develop a roadmap for development of AI technology, which figures to have wide ranging impacts on fields such as health care, transport and communications, among others, over the next few decades.Artificial intelligence is the simulation of human intelligence processes, learning, in machines. According to a release from Portman's office, the legislation would convene a group of technical experts across academia, government, and industry to develop a detailed roadmap for how the United States can build, deploy, govern, and sustain a national research cloud.A companion bill has been introduced in the U.S. House of Representatives."We With China focused on toppling the United States' leadership in AI, we need to redouble our efforts with a sustained commitment to the best and brightest by developing a national research cloud to ensure our technical researchers get the tools they need to succeed," said Portman."This …

A bipartisan cadre of tech-focused legislators in the House and Senate have introduced legislation that would direct the federal government to develop a national cloud computing infrastructure for artificial intelligence research. Introduced by Sens. Rob Portman, R-Ohio, and Martin Heinrich, D-N.M., Thursday, the National Cloud Computing Task Force Act would convene a mix of technical experts across academic, industry and government. The group would develop a nuanced roadmap for how the nation should build, deploy, govern and sustain a national research cloud for AI. "With China focused on toppling the United States' leadership in AI, we need to redouble our efforts with a sustained commitment to the best and brightest by developing a national research cloud to ensure our technical researchers get the tools they need to succeed," Portman said in a statement. "By democratizing access to computing power we ensure that any American with computer science talent can pursue their good ideas."

Musk was responding to a massive feature story published in the MIT Technology Review about OpenAI, the AI research lab founded in part by Elon Musk, alongside others. The lab operates with the mission of developing safe and ethical AI that'll be good for the world. But MIT Tech's reporting tells of how Open AI went from being a transparent organization to a relatively opaque one (hence Musk's preceding Tweet about OpenAI needing to "be more open"). Musk's ability to self-aggrandize or self-flagellate is usually surprising in equal measure, but never shocking: Industries often argue for their own regulation as a way to keep government regulators off their backs. Though credit where it's due: Musk has been, as in the case of when he argued in favor of regulating autonomous weapons, more substantially -- and more effectively -- vocal than most when it comes to regulating AI. Whether or not this will have any substantial effects on other companies (statements from CEOs, regulatory commission efforts, etc) let alone Tesla or OpenAI will be nothing if not a compelling plot to watch.

The next time you load up the latest "Call of Duty" video game, you will likely notice a new message from its developers: Black Lives Matter. Infinity Ward, the development studio that makes "Call of Duty," added a message on screen that appears right before the game starts condemning racism and social injustice. "Our community is hurting," reads a portion the message. "The systemic inequalities our community experiences are once again center stage. Call of Duty and Infinity Ward stand for equality and inclusion. We stand against the racism and injustice our Black community endures. Until change happens and Black Lives Matter, we will never truly be the community we strive to be." "Call of Duty," published by Activision, is the latest example of companies and brands using their platforms to speak out on social issues.

None of these people exist. These images were generated using deepfake technology. Last month during ESPN's hit documentary series The Last Dance, State Farm debuted a TV commercial that has become one of the most widely discussed ads in recent memory. It appeared to show footage from 1998 of an ESPN analyst making shockingly accurate predictions about the year 2020. As it turned out, the clip was not genuine: it was generated using cutting-edge AI.

National security and antitrust are rarely part of the same conversation. The realities of today's AI ecosystem should challenge that dynamic. American AI innovation is concentrated in the private sector--particularly within its largest, most dominant firms. As these firms face antitrust scrutiny, policymakers and lawmakers alike need to consider the AI ecosystem that they will have a hand in creating. They will need to contemplate its competitiveness, its innovativeness, its responsiveness to defense and national-security needs, and its accessibility to government.

This paper presents LDP-Fed, a novel federated learning system with a formal privacy guarantee using local differential privacy (LDP). Existing LDP protocols are developed primarily to ensure data privacy in the collection of single numerical or categorical values, such as click count in Web access logs. However, in federated learning model parameter updates are collected iteratively from each participant and consist of high dimensional, continuous values with high precision (10s of digits after the decimal point), making existing LDP protocols inapplicable. To address this challenge in LDP-Fed, we design and develop two novel approaches. First, LDP-Fed's LDP Module provides a formal differential privacy guarantee for the repeated collection of model training parameters in the federated training of large-scale neural networks over multiple individual participants' private datasets. Second, LDP-Fed implements a suite of selection and filtering techniques for perturbing and sharing select parameter updates with the parameter server. We validate our system deployed with a condensed LDP protocol in training deep neural networks on public data. We compare this version of LDP-Fed, coined CLDP-Fed, with other state-of-the-art approaches with respect to model accuracy, privacy preservation, and system capabilities.

Today the United States crumbles under the weight of two pandemics: coronavirus and police brutality. Both wreak physical and psychological violence. And both are animated by technology that we design, repurpose, and deploy--whether it's contact tracing, facial recognition, or social media. We often call on technology to help solve problems. But when society defines, frames, and represents people of color as "the problem," those solutions often do more harm than good.

Zoom will strengthen security protections on its phone calls – but only if people pay, according to the company's chief executive Eric Yuan. Those accounts and organisations that pay for the premium service will have their encryption increased, so that calls cannot be intercepted, he announced. But people using the free version will not benefit from those protections, so that their calls can be watched by law enforcement. The company has attracted greater business during the coronavirus pandemic, with multitudes of people working from home and continuing their lives via video call, but has seen people entering video calls they should not have been in – a practise called "zoombombing" – which has led to people being subject to harassment or made to view footage of child abuse. "Free users for sure we don't want to give that because we also want to work together with FBI, with local law enforcement in case some people use Zoom for a bad purpose," Yuan said as part of the company's financial results for the first quarter of 2020.

This has led to the development of a plethora of domain-dependent and context-specific methods for dealing with the interpretation of machine learning (ML) models and the formation of explanations for humans. Unfortunately, this trend is far from being over, with an abundance of knowledge in the field which is scattered and needs organisation. The goal of this article is to systematically review research works in the field of XAI and to try to define some boundaries in the field. From several hundreds of research articles focused on the concept of explainability, about 350 have been considered for review by using the following search methodology. In a first phase, Google Scholar was queried to find papers related to "explainable artificial intelligence", "explainable machine learning" and "interpretable machine learning". Subsequently, the bibliographic section of these articles was thoroughly examined to retrieve further relevant scientific studies. The first noticeable thing, as shown in figure 2 (a), is the distribution of the publication dates of selected research articles: sporadic in the 70s and 80s, receiving preliminary attention in the 90s, showing raising interest in 2000 and becoming a recognised body of knowledge after 2010. The first research concerned the development of an explanation-based system and its integration in a computer program designed to help doctors make diagnoses [3]. Some of the more recent papers focus on work devoted to the clustering of methods for explainability, motivating the need for organising the XAI literature [4, 5, 6].