Some of the recommendations address getting all EU member states on board and having central oversight through the European Commission to collaborate and ensure consistency. The recommendations primarily deal with identifying funding for AI system development and encouraging regulations to minimize risks to humans and society. AI can enhance economies when used correctly, according to the report. It recommends creating a friendly environment for AI developers and investors. The report stresses that action should begin immediately to harness the opportunities AI can offer the EU and to ensure member states are competitive in the global space.

The U.S. Department of Commerce's National Institute of Standards and Technology (NIST) released a draft of a plan for the federal creation of AI standards. The plan aims to meet the economic and national security needs of the United States and help the country maintain global dominance. "U.S. engagement in establishing AI standards is critical; AI standards developed without the appropriate level and type of involvement of U.S. interests may exclude or disadvantage U.S.-based companies in the marketplace, as well as government agencies," the draft report (PDF) reads. "Moreover, due to the foundational nature of standards, the lack of U.S. stakeholder engagement in the development of AI standards can negatively impact the innovativeness and competitiveness of U.S. interests in the long term." The document, titled "U.S. Leadership in AI: Plan for Federal Engagement in Developing Technical Standards and Related Tools, " was put together in response to a Trump executive order mandating the creation of a federal AI engagement plan within 180 days.

A woman's job application is rejected because of a recruiting algorithm that favors men's résumés. A girl dies by suicide after graphic images of self-harm are pushed up on her feed by social media algorithms. A black teen steals something and gets rated high-risk for committing future crime by an algorithm used in courtroom sentencing, while a white man steals something of similar value and gets rated low-risk. In recent years, advances in computer science have yielded algorithms so powerful that their creators have presented them as tools that can help us make decisions more efficiently and impartially. But the idea that algorithms are unbiased is a fantasy; in fact, they still end up reflecting human biases.

Employed across industries, AI applications unlock smartphones using facial recognition, make driving decisions in autonomous vehicles, recommend entertainment options based on user preferences, assist the process of pharmaceutical development, judge the creditworthiness of potential homebuyers, and screen applicants for job interviews. AI automates, quickens, and improves data processing by finding patterns in the data, adapting to new data, and learning from experience. In theory, AI is objective--but in reality, AI systems are informed by human intelligence, which is of course far from perfect. Humans typically select the data used to train machine learning algorithms and create parameters for the machines to "learn" from new data over time. Even without discriminatory intent, the training data may reflect unconscious or historic bias. For example, if the training data shows that people of a certain gender or race have fulfilled certain criteria in the past, the algorithm may "learn" to select those individuals at the exclusion of others.

Agents who will embrace AI-driven technology will get a competitive edge in the market. AI will be beneficial for the consumers as can ease the process of finding, viewing, and purchasing a home. FREMONT, CA: Artificial Intelligence and Machine Learning are infiltrating every industry, including real estate. AI and ML are not only enhancing the procedure of searching for suitable homes but also predicting the property values in the market. Here are three predictions for how AI can revolutionize the future of the real estate.

The 2019 World Artificial Intelligence Conference in Shanghai kicks off today. Alibaba co-founder and executive chairman Jack Ma and Tesla CEO Elon Musk are expected to hold a free-wheeling debate about artificial intelligence.

Meet Project Overlord: The Marines' Plan for Robot Ships to Move Their Soldiers and Supplies Earlier this year Navy leaders requested $400 million from Congress for two LUSVs in the 2020 proposed defense budget, with eight more to be purchased over the next five years. WASHINGTON – U.S. Marine Corps leaders plan to capitalize on a U.S. Navy plan to develop a Large Unmanned Surface Vessel (LUSV) for long-range resupply missions, and troop transport for Marine Corps warfighters. Smith made his comments today at the Association for Unmanned Vehicle Systems International (AUVSI) Defense, Protection, and Security conference in Washington. The future Navy LUSV could rendezvous with Navy amphibious assault ships offshore to move Marines and supplies quickly where needed, at perhaps lower costs and less risk to human ship crews than is possible today, Smith told AUVSI attendees in a keynote address. Unmanned systems "are less expensive than people," Smith pointed out in his address.

The Defense Department's artificial intelligence center is expecting a transformative year in 2020 with more than double its budget from 2019 and new projects that further ingrain AI into military operations. DoD's Joint Artificial Intelligence Center (JAIC) will embark on a new project next year called AI for maneuver and fires. "The project will focus on individual lines of effort or product lines oriented on warfighting operations like operations/intelligence fusion, joint all-domain command and control, accelerated sensor to shooter timelines, autonomous and swarming systems, target development and operations center workflows," Lt. Gen. Jack Shanahan, JAIC director, said at the Pentagon Friday. That all sounds like a lot of buzzwords, but a real-life example can be found right in your home. Often times you'll open up Netflix and it will say a show is a 95% match for you based on your past watching habits.

Google's deep-learning research, involving a large dataset of retinal images used in diagnosis of diabetic retinopathy (DR), a diabetes-linked pathology that causes irreversible blindness, shows the transformative power of artificial intelligence (AI) in healthcare. In 2016, the tech giant announced its deep-learning algorithm that had been trained using a dataset of 128,000 images--each of which had been reviewed by 3-7 expert ophthalmologists from a panel of 54--to accurately interpret underlying symptoms (microaneurysms, haemorrhages, hard exudates, etc) from fundus images (a specific type of imaging of the eye) and detect referable DR. Given the pathology affects 18% of the 70 million diabetics in India--and with 415 million diabetics worldwide, is now the fastest growing cause of blindness--Google's algorithm vastly improves the prospects of DR being screened by doctors faster, and in greater numbers than is possible in an unassisted scenario. For countries strained for resources and healthcare infrastructure, this is truly a manna since diagnosis in the early stages can prevent/delay onset of blindness. The algorithm's performance was tested with 12,000 images, with the majority opinion of panels of expert ophthalmologists drawn from Google's pool of 54 on each of these images set as the reference standard.

Artificial intelligence has been used to analyse high-resolution digital X-ray images of the world famous Ghent Altarpiece, as part of an investigative project led by UCL. The finding is expected to improve our understanding of art masterpieces and provide new opportunities for art investigation, conservation and presentation. Researchers from the National Gallery, Duke University and UCL worked with technical images acquired from the brothers Van Eyck's Ghent Altarpiece, a large and complex 15th-century altarpiece in St Bavo's Cathedral, Belgium. The paper, 'Artificial Intelligence for Art Investigation: Meeting the Challenge of Separating X-ray Images of the Ghent Altarpiece', demonstrates how academics used a newly developed algorithm to study mixed X-ray images containing features from the front and back of the painting's double-sided panels, which scientists have deconstructed into two clear images. These images are part of a comprehensive set of high resolution pictures acquired using different imaging techniques as part of the altarpiece's on-going conservation by the Royal Institute for Cultural Heritage (KIK-IRPA), providing a wealth of data to interrogate and interpret.