If business wants to use AI at scale, adhering to the technical guidelines for responsible AI development isn't enough. It must obtain society's explicit approval to deploy the technology. Six years ago, in March 2016, Microsoft Corporation launched an experimental AI-based chatbot, TayTweets, whose Twitter handle was @TayandYou. Tay, an acronym for "thinking about you," mimicked a 19-year-old American girl online, so the digital giant could showcase the speed at which AI can learn when it interacts with human beings. Living up to its description as "AI with zero chill," Tay started off replying cheekily to Twitter users and turning photographs into memes. Some topics were off limits, though; Microsoft had trained Tay not to comment on societal issues such as Black Lives Matter. Soon enough, a group of Twitter users targeted Tay with a barrage of tweets about controversial issues such as the Holocaust and Gamergate. They goaded the chatbot into replying with racist and sexually charged responses, exploiting its repeat-after-me capability. Realizing that Tay was reacting like IBM's Watson, which started using profanity after perusing the online Urban Dictionary, Microsoft was quick to delete the first inflammatory tweets. Less than 16 hours and more than 100,000 tweets later, the digital giant shut down Tay.

MOSCOW (UrduPoint News / Sputnik - 27th May, 2022) The United States and the United Kingdom have signed an agreement to strengthen cooperation on promoting responsible development and use of artificial intelligence in defense, the US Defense Department said. The deal was concluded as part of the sixth iteration of the US-UK Defense Dialogue held in London on Thursday under the chair of US Deputy Defense Secretary Kathleen Hicks and UK Permanent Under Secretary for the Ministry of Defense David Williams. "The leaders signed an Artificial Intelligence Cooperation Statement of Intent, setting shared strategic objectives for the U.S. Department of Defense and the UK Ministry of Defence related to AI (artificial intelligence) readiness," a statement read. The agreement will boost the sides' collaboration in "maintaining and increasing interoperability and promoting values-based global leadership for the responsible development, procurement, and use of AI in defense," according to the statement.

Essentials | 29.05.2022 | weekly digest highlighting the top articles about: - Differences between Artificial Intelligence, Machine Learning, and Deep Learning. - Machine Learning at the Edge. - Artificial Intelligence Developments Financial Institutions Should Expect in 2022. - Public/Private Partnerships and Cybersecurity. - Mastercard Launches Cybersecurity “Experience Centre”. - How AI and 5G Will Usher in a New Tech Era.

To represent a 3D scene from a 2D image, a light field network encodes the 360-degree light field of the 3D scene into a neural network that directly maps each camera ray to the color observed by that ray. The new machine-learning system can generate a 3D scene from an image about 15,000 times faster than other methods. Humans are pretty good at looking at a single two-dimensional image and understanding the full three-dimensional scene that it captures. Artificial intelligence agents are not. Yet a machine that needs to interact with objects in the world -- like a robot designed to harvest crops or assist with surgery -- must be able to infer properties about a 3D scene from observations of the 2D images it's trained on.

I sat in a dark room, eyes closed, with a device strapped to my head that looked like a futuristic bike helmet. For 10 minutes, while I concentrated on not accidentally opening my eyes, the prongs sticking out of this gadget and onto my scalp measured a health marker I never thought to assess: my cognitive health. When I booked my brain wave recording (also known as electroencephalography, or EEG), I expected to pull up to an office park with medical clinic vibes, but instead my GPS led me to an ocean-view storefront decorated like a cross between a surf shop and a luxury spa, with a sign in the window promising "Mental Wellness, Reimagined." Located in Cardiff-by-the-Sea, a wealthy coastal town north of San Diego, Wave Neuroscience promises to help your brain perform better with a noninvasive treatment that uses magnets on the brain. We're talking mental clarity, improved focus and concentration, and even a shift in mood.

University of Minnesota Twin Cities researchers have developed a more accurate, less invasive technology that allows amputees to move a robotic arm using their brain signals instead of their muscles. Many current commercial prosthetic limbs use a cable and harness system that is controlled by the shoulders or chest, and more advanced limbs use sensors to pick up on subtle muscle movements in a patient's existing limb above the device. But, both options can be cumbersome, unintuitive, and take months of practice for amputees to learn how to move them. Researchers in the University's Department of Biomedical Engineering, with the help of industry collaborators, have created a small, implantable device that attaches to the peripheral nerve in a person's arm. When combined with an artificial intelligence computer and a robotic arm, the device can read and interpret brain signals, allowing upper limb amputees to control the arm using only their thoughts. The researchers' most recent paper is published in the Journal of Neural Engineering, a peer-reviewed scientific journal for the interdisciplinary field of neural engineering.

Fully autonomous vehicles are already operating on city streets, but a study published last year in AI and Ethics reported that 74% of survey respondents said they do not trust AVs nor believe AVs can perform better than a normal driver. However, a number of companies are developing technologies to enhance safety for and around autonomous vehicles in urban areas. According to Statista, 58 million autonomous vehicles are projected to be sold globally in 2030. Cities need to be ready for them. "The nation's city leaders see that AV technology is here so it cannot be ignored or left in a regulatory limbo while it operates on our streets," said Houston city council member and Vice Mayor Pro Tem Martha Castex-Tatum in testimony to the U.S. House of Representatives Committee on Transportation and Infrastructure earlier this year.

Lillian Pierson is a CEO & data leader that supports data professionals to evolve into world-class leaders & entrepreneurs. To date, she's helped educate over 1.3 million data professionals on AI and data science. Lillian has authored 6 data books with Wiley & Sons Publishers as well as 8 data courses with LinkedIn Learning. She's supported a wide variety of organizations across the globe, from the United Nations and National Geographic, to Ericsson and Saudi Aramco, and everything in between. She is a licensed Professional Engineer, in good standing.

When it was first commercialized, the steam engine cost much more than other power sources available – until it didn't. The engine, developed to pump water from flooded mines, allowed for deeper and less costly digging of coal. Then came faster transportation with cheaper shipping of more products and – as accessibility increased with efficiency – more people. The full promise of a breakthrough isn't in what it does initially. It's in what it enables, eventually.

Artificial Intelligence (AI) systems have been involved in numerous scandals in recent years. For instance, take the COMPAS recidivism algorithm. The algorithm evaluated the likelihood that defendants will commit another crime in the future. It was widely used in the US criminal justice system to inform decisions about who can be set free at all stages of the process. In 2016, ProPublica exposed that COMPAS's predictions were biased: its mistakes favored white over black defendants. Black defendants were twice as likely to be labeled as high risk to reoffend but not actually reoffend.