How might you characterize the conversational style of a digital assistant like Siri? No matter your impression, it stands to reason that striking the wrong tone could dissuade users from engaging with it in the future. Perhaps that's why in a paper ("Mirroring to Build Trust in Digital Assistants") accepted to the Interspeech 2019 conference in Graz, Austria, researchers at Apple investigated a conversational assistant that considered users' preferred tones and mannerisms in its responses. They found that people's opinions of the assistant's likability and trustworthiness improved when it mirrored their degree of chattiness, and that the features necessary to perform the mirroring could be extracted from those people's speech patterns. "Long-term reliance on digital assistants requires a sense of trust in the assistant and its abilities. Therefore, strategies for building and maintaining this trust are required, especially as digital assistants become more advanced and operate in more aspects of people's lives," wrote the paper's coauthors.

The US Food and Drug Administration (FDA) has cleared an artificial intelligence (AI) algorithm from GE Healthcare that analyzes chest x-rays for pneumothorax and helps flag suspected cases for radiologists to prioritize reading, the company announced today. The algorithm, part of a set of other quality-assurance algorithms named the Critical Care Suite, was developed to run on a GE Healthcare mobile x-ray device. The software is not yet for sale, and an outside expert expressed concern about its false positive rate. The idea for the application came from bedside clinician experience of waiting for radiologists to read chest x-rays, said Rachael Callcut, MD, MSPH, a surgeon and director of data science for the Center for Digital Health Innovation at the University of California, San Francisco. UCSF proposed developing the feature as part of a development partnership with GE Healthcare.

After AI Sky Replacement and AI Structure there is more artificial intelligence to be featured in the upcoming Skylum Luminar 4 (learn more): AI-powered portrait and skin enhancements. Portrait Enhancer is a collection of tools that help improve the photo of any person in a natural, yet pleasing way. These tools are brand new to Luminar 4 making it now possible to highlight and improve primary features of a person's face. Thanks to the AI technology, faces and skin are automatically detected throughout a photo. AI Skin Enhancer allows photographers to automatically remove various skin imperfections like acne, freckles and moles, in addition to smoothing the skin.

Businesses using innovative artificial intelligence (AI) solutions should turn their attention to Brussels as the next EU Commission promises to come forward with legislation on AI within 100 days of taking office on 1 November. European politicians have expressed concerns about some applications of AI and are convinced that immediate legislation is the answer. However, attempting to regulate a technology like AI, with its variety of applications, is a complex process and may produce unintended negative consequences. With a new parliament in place and a new Commission about to start its term, the EU is going through the process of setting its priorities for the next five years. Technology and digital policy remain key areas of interest for EU policy-makers, following on from the General Data Protection Regulation (GDPR) and Digital Single Market (DSM) Strategy.

Outside Germany, one of BMW's key research organizations for business and manufacturing technology is the IT Innovation and Research Center. With bases both in Silicon Valley, as well as Greenville, South Carolina, the IT center carries out research for systems and tools across the enterprise, including financial services, sales and marketing, engineering, quality, HR, production and logistics. It is part of the carmaker's central BMW Group IT department led from Munich, which coordinates the company's enterprise and manufacturing IT backbone. Similar to other laboratory locations across BMW, the IT center operates to a large extent in research mode. It has a strong connection, for example, to Clemson University, with whom it shares a campus at the International Center for Automotive Research, working closely with engineering and software professors and students.

Artificial intelligence is infiltrating every industry, allowing vehicles to navigate without drivers, assisting doctors with medical diagnoses, and mimicking the way humans speak. But for all the authentic and exciting ways it's transforming the tasks computers can perform, there's a lot of hype, too. As Jeremy Achin, CEO of newly minted unicorn DataRobot, puts it: "Everyone knows you have to have machine learning in your story or you're not sexy." The inherently broad term gets bandied about so often that it can start to feel meaningless and can be trotted out by companies to gussy up even simple data analysis. To help cut through the noise, Forbes and data partner Meritech Capital put together a list of private, U.S.-based companies that are wielding some subset of artificial intelligence in a meaningful way and demonstrating real business potential from doing so. One makes robots that can whir around shoppers to help workers restock shelves. Another scans recruiting pitches for unconscious bias. A third analyzes massive data sets to make street-by-street weather predictions. To be included on the list, companies needed techniques like machine learning (where systems learn from data to improve upon tasks), natural language processing (which enables programs to "understand" written or spoken language), or computer vision (which relates to how machines "see") to be a core part of their business model and future success. Find all the details on our methodology here. The honorees span categories like human resources, security, insurance, and finance, with healthcare, transportation, and infrastructure startups best represented on the list.

TGS, a leading provider of multi-client geoscience and engineering data for Exploration & Production companies, and Quantico Energy Solutions (Quantico), an artificial intelligence (AI) company focused on subsurface solutions for Exploration & Production companies, today announced a technology collaboration to leverage their respective offerings in seismic data, AI-based well logs, and AI-based seismic inversion. The joint solution addresses the critical challenges in earth modeling workflows; specifically, insufficient seismic and log data, lengthy time until results, and difficulties mapping advanced geomechanical and petrophysical attributes. TGS will leverage its industry leading data library of seismic and well log data in the key regions of oil and gas activity across the world. In addition, the collaboration will feature TGS's Analytics Ready LAS (ARLAS) solution. Adding to the largest commercial digital log library in the world, ARLAS utilizes machine learning algorithms to predict missing curve responses in today's digital well log data.

Artificial intelligence technology used by police forces in the UK to predict future crimes replicates - and in some cases amplifies - human prejudices, according to a new report. While "predictive policing" tools have been used in the UK since at least 2004, advances in machine learning and AI have enabled the development of more sophisticated systems. These are now used for a wide range of functions including facial recognition and video analysis, mobile phone data extraction, social media intelligence analysis, predictive crime mapping and individual risk assessment. However, the report by the Royal United Services Institute (RUSI) warns that human biases are being built into these machine learning algorithms, resulting in people being unfairly discriminated against due to their race, sexuality and age. One police officer who was interviewed for the report commented that: "Young black men are more likely to be stop and searched than young white men, and that's purely down to human bias. "That human bias is then introduced into the datasets, and bias is then generated in the outcomes of the application of those datasets." In addition to these inherent biases, the report points out that individuals from disadvantaged sociodemographic backgrounds are likely to engage with public services more frequently. As a result, police often have access to more data relating to these individuals, which "may in turn lead to them being calculated as posing a greater risk". Matters could worsen over time, another officer said, when software is used to predict future crime hotspots. "We pile loads of resources into a certain area and it becomes a self-fulfilling prophecy, purely because there's more policing going into that area, not necessarily because of discrimination on the part of officers," the officer said. The report also warns that police forces could become over-reliant on the AI to predict future crimes, and discount other relevant information. "Officers often disagree with the algorithm.

It often takes years and hundreds of millions of dollars to discover a novel drug candidate. It requires the identification of promising molecules that can grab on to the right protein, synthesizing a compound, and then testing it. The process is so complicated that it has defied most computational methods to shorten it. But a paper published Monday in Nature Biotechnology describes a new method using artificial intelligence that, within 46 days, generated compounds capable of hitting a specific disease target. Unlock this article by subscribing to STAT Plus and enjoy your first 30 days free!