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.

While the philosophical literature has extensively studied how decisions relate to arguments, reasons and justifications, decision theory almost entirely ignores the latter notions and rather focuses on preference and belief. In this article, we argue that decision theory can largely benefit from explicitly taking into account the stance that decision-makers take towards arguments and counter-arguments. To that end, we elaborate a formal framework aiming to integrate the role of arguments and argumentation in decision theory and decision aid. We start from a decision situation, where an individual requests decision support. In this context, we formally define, as a commendable basis for decision-aid, this individual's deliberated judgment, popularized by Rawls. We explain how models of deliberated judgment can be validated empirically. We then identify conditions upon which the existence of a valid model can be taken for granted, and analyze how these conditions can be relaxed. We then explore the significance of our proposed framework for decision aiding practice. We argue that our concept of deliberated judgment owes its normative credentials both to its normative foundations (the idea of rationality based on arguments) and to its reference to empirical reality (the stance that real, empirical individuals hold towards arguments and counter-arguments, on due reflection). We then highlight that our framework opens promising avenues for future research involving both philosophical and decision theoretic approaches, as well as empirical implementations.

The market is expected to reach $15.44 billion by 2024 at a CAGR of 8.30% with improvements in early disease diagnosis, finds Frost & Sullivan October 28, 2019 – With both large pharmaceutical companies and mid-sized biotechs adopting emerging technologies such as artificial intelligence and machine learning, there has been a reorientation of drug discovery and development, validation, testing, and clinical deployment. Scientific advancements such as cell and gene therapies and understanding of the microbiome, plus improvements in early disease diagnostics, are expected to drive the $9.56 billion neurodegenerative disorder (ND) therapeutics market for Alzheimer's and Parkinson's Diseases (AD/PD) toward $15.44 billion in 2024 at a compound annual growth rate (CAGR) of 8.30%. "With the alarming attrition rate of clinical pipeline for Alzheimer's and Parkinson's, and elusive success of therapies in their ability to modify disease, the future of the therapy hinges on the course of action companies take today," said Khushbu Jain, Transformational Health Industry Analyst. "As the understanding of science behind disease deepens and offers new pathways for drug development, pharma companies will have to seek additional avenues for revenue and unconventional partnerships to offer immediate solutions to patients. The most lucrative partners remain digital platform providers that can help manage the disease better, help expedite drug discovery and, ultimately, deliver on outcome-based care."

LAS VEGAS (Reuters) - As vehicles get smarter, your car will be keeping eyes on you. This week at CES, the international consumer electronics show in Las Vegas, a host of startup companies will demonstrate to global automakers how the sensor technology that watches and analyzes drivers, passengers and objects in cars will mean enhanced safety in the short-term, and revenue opportunities in the future. Whether by generating alerts about drowsiness, unfastened seat belts or wallets left in the backseat, the emerging technology aims not only to cut back on distracted driving and other undesirable behavior, but eventually help automakers and ride-hailing companies make money from data generated inside the vehicle. In-car sensor technology is deemed critical to the full deployment of self-driving cars, which analysts say is still likely years away in the United States. Right now, self-driving cars are still mainly at the testing stage.

Artificial intelligence has already started to shape our lives in ubiquitous and occasionally invisible ways. In its new documentary, In The Age of AI, FRONTLINE examines the promise and peril this technology. AI systems are being deployed by hiring managers, courts, law enforcement, and hospitals -- sometimes without the knowledge of the people being screened. And while these systems were initially lauded for being more objective than humans, it's fast becoming clear that the algorithms harbor bias, too. It's an issue Joy Buolamwini, a graduate researcher at the Massachusetts Institute of Technology, knows about firsthand. She founded the Algorithmic Justice League to draw attention to the issue, and earlier this year she testified at a congressional hearing on the impact of facial recognition technology on civil rights. "One of the major issues with algorithmic bias is you may not know it's happening," Buolamwini told FRONTLINE.

On the first episode of the final season of HBO comedy series Silicon Valley, tech startup engineer Bertram Gilfoyle lets an AI version of himself take over his instant messaging duties. "Do you need the real me for this conversation?" he asks his colleague. It may sound extreme, but the existence of spellcheckers predates the personal computer by a decade. Since 1992, grammar checking has also come as standard in word processors. For the better part of a generation, we've been OK with robots watching and correcting our language, occasional run-ins with Clippy aside.

Artificial intelligence should respect human rights, diversity and privacy -- while being a far cry from Terminator-style robots -- according to new federal ethics guidelines. Technology Minister Karen Andrews will today release an eight-point guidance she wants companies to adopt in a bid to prevent people from being exploited. The guidelines stipulate all AI should benefit individuals, society and the environment. It should prevent discrimination, respect privacy and only operate in accordance with their intended purpose. The guidelines also recommend human oversight of AI always be enabled and there should be timely processes to allow people to challenge the use or output of information.

The B-52 will be armed with long-range, nuclear cruise missiles… the B-2 will elude the most modern air defenses and the B-1B bomber will fire hypersonic weapons -- if the Air Force's plan for the next several decades comes to fruition. Air Force weapons developers are immersed in an intricate plan to bring the service's bomber fleet into future decades -- by adding weapons, avionics and networking technologies to current aircraft and moving quickly to bring new B-21 bombers to the force. The current thinking is centered upon methods of compensating for what service leaders identify as a "bomber deficit," and therefore finding ways to maximize the performance of the aircraft it has in the inventory. "There are only 156 allied bombers and they all belong to us. We are working on the growth of a requirement for long-range strike," Gen. Timothy Ray, Commander of Global Strike Command, told reporters at the Air Force Association Air, Space and Cyber Conference in September.

A Happy Meal and AI might not be the most obvious pairing but 2019 has seen McDonald's hit the headlines for its fast-paced investments in tech. In March, it acquired online personalisation startup Dynamic Yield for more than £232.8m This sparked a series of acquisitions, paving the way for what seems like the beginning of a journey for the iconic fast food chain to become the Amazon of food. A month later, it bought a 9.9% stake in mobile software company Plexure in a deal valued at about £3.8m, according to reports. The New Zealand-based company already helps with McDonald's mobile app but this surge of investment will see the chain have unprecedented access to Plexure's new tech for its app.

In the consumer realm, technologies based on artificial intelligence (AI) are slowly changing the way we manage everyday tasks. Take the driving app Waze, for example. Waze uses crowdsourced data, social networking conversations, and cognitive learning to help shave time off daily commutes by providing the most efficient route based on current conditions and individual driving preferences. Or consider products like Nest. Gone are the days of paying to heat or cool your house while no one's home.