The NYPD will part ways with "Digidog," the robotic police dog that became the subject of a City Council subpoena after images of it went viral. The department told The Post on Wednesday that it ended a contract with Boston Dynamics to lease the four-legged robo-cop. "The contract has been terminated and the dog will be returned," a spokesperson said. The sudden termination comes after a clip of the machine patrolling a Manhattan housing project went viral, sparking backlash and drawing comparisons to the dystopic TV series "Black Mirror." Mayor Bill de Blasio then urged the NYPD to "rethink" its use of the robot.

Life Whisperer, the fertility arm of AI healthcare company Presagen, has made a significant breakthrough in using artificial intelligence to non-invasively help embryologists rank and select genetically healthy embryos in IVF. Currently, PGT-A genetic testing requires a portion of a healthy embryo to be removed and sent away for testing. The procedure is invasive, costly, and potentially risky. Presagen CEO, Dr Michelle Perugini said: "Some future parents are just not comfortable with the thought of having to biopsy their embryo, which may ultimately become their baby. PGT-A testing is conducted because evidence suggests genetically healthy embryos can increase pregnancy success and reduce miscarriage for IVF patients who are desperate for children."

AI algorithms on our computers and smartphones have quickly become a pervasive part of everyday life, with relatively little attention to their scope, integrity, and how they shape our attitudes and behaviours. Spanish researchers have now shown experimentally that people's voting and dating preferences can be manipulated depending on the type of persuasion used. "Every day, new headlines appear in which Artificial Intelligence (AI) has overtaken human capacity in new and different domains," write Ujue Agudo and Helena Matute, from the Universidad de Deusto, in the journal PLOS ONE. "This results in recommendation and persuasion algorithms being widely used nowadays, offering people advice on what to read, what to buy, where to eat, or whom to date," they add. "[P]eople often assume that these AI judgements are objective, efficient and reliable; a phenomenon known as machine bias."

A novel device designed to help stroke patients recover wrist and hand function has been approved by the US Food and Drug Administration (FDA). Called IpsiHand, the system is the first brain-computer interface (BCI) device to ever receive FDA market approval. The IpsiHand device consists of two separate parts – a wireless exoskeleton that is positioned over the wrist, and a small headpiece that records brain activity using non-invasive electroencephalography (EEG) electrodes. The system is based on a discovery made by Eric Leuthardt and colleagues at the Washington University School of Medicine over a decade ago. It is well known that each side of the brain controls movement on the opposite side of the body, so if a stroke damages motor function on the right side of the brain movement on a person's left side will be affected.

Stakeholders who have had to get to grips with the GDPR will find many of the concepts in the Regulation familiar. From the risk-based approach, to the requirements around transparency and information provision as well as record-keeping, territorial scope and enforcement, cybersecurity and data governance, there are recognisable requirements. The Regulation defines an AI system as "software that is developed with one or more of the techniques and approaches listed in Annex I and can, for a given set of human-defined objectives, generate outputs such as content, predictions, recommendations or decisions influencing the environments they interact with". The Regulation takes a risk-based approach to AI systems. Some types of AI as set out in Title II, are considered to carry unacceptable risk and are prohibited.

The U.S. Federal Trade Commission just fired a shot across the bow of the artificial intelligence industry. On April 19, 2021, a staff attorney at the agency, which serves as the nation's leading consumer protection authority, wrote a blog post about biased AI algorithms that included a blunt warning: "Keep in mind that if you don't hold yourself accountable, the FTC may do it for you." The post, titled "Aiming for truth, fairness, and equity in your company's use of AI," was notable for its tough and specific rhetoric about discriminatory AI. The author observed that the commission's authority to prohibit unfair and deceptive practices "would include the sale or use of – for example – racially biased algorithms" and that industry exaggerations regarding the capability of AI to make fair or unbiased hiring decisions could result in "deception, discrimination – and an FTC law enforcement action." Bias seems to pervade the AI industry.

No HAL 9000s or Ultrons on this continent, thank you very much. The European Union wants to avoid the worst of what artificial intelligence can do -- think creepy facial recognition tech and many, many Black Mirror episodes -- while still trying to boost its potential for the economy in general. According to a draft of its upcoming rules, obtained by POLITICO, the European Commission would ban certain uses of "high-risk" artificial intelligence systems altogether, and limit others from entering the bloc if they don't meet its standards. Companies that don't comply could be fined up to €20 million or 4 percent of their turnover. The Commission will unveil its final regulation on April 21.

Limited to speeds of up to 37mph on motorways, Automated Lane Keeping Systems in vehicles have been offered a route to their legal introduction on UK roads. The Department for Transport claimed that the technology could improve road safety by reducing human error, which contributes to over 85 per cent of accidents. "The driver will be able to hand control over to the vehicle, which will constantly monitor speed and keep a safe distance from other cars," it said. Self-driving technology in cars, buses, and delivery vehicles "could spark the beginning of the end of urban congestion, with traffic lights and vehicles speaking to each other to keep traffic flowing, reducing emissions and improving air quality in our towns and cities," DfT said. The technology could create around 38,000 new jobs in a UK industry that could be worth £42bn by 2035, the department added. Yet Whitehall's enthusiasm for the technology flies in the face of evidence that it is impractical, unsafe, and undesirable.

The use of deep neural networks (DNNs) in safety-critical applications like mobile health and autonomous driving is challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability to problems with malicious inputs. Cyber-physical systems employing DNNs are therefore likely to suffer from safety concerns. In recent years, a zoo of state-of-the-art techniques aiming to address these safety concerns has emerged. This work provides a structured and broad overview of them. We first identify categories of insufficiencies to then describe research activities aiming at their detection, quantification, or mitigation. Our paper addresses both machine learning experts and safety engineers: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods. The latter ones might gain insights into the specifics of modern ML methods. We moreover hope that our contribution fuels discussions on desiderata for ML systems and strategies on how to propel existing approaches accordingly.

The increasing polarization of online political discourse calls for computational tools that are able to automatically detect and monitor ideological divides in social media. Here, we introduce a minimally supervised method that directly leverages the network structure of online discussion forums, specifically Reddit, to detect polarized concepts. We model polarization along the dimensions of agenda setting and framing, drawing upon insights from moral psychology. The architecture we propose combines graph neural networks with structured sparsity and results in representations for concepts and subreddits that capture phenomena such as ideological radicalization and subreddit hijacking. We also create a new dataset of political discourse covering 12 years and more than 600 online groups with different ideologies.