Recognizing a hotel from an image of a hotel room is important for human trafficking investigations. Images directly link victims to places and can help verify where victims have been trafficked, and where their traffickers might move them or others in the future. Recognizing the hotel from images is challenging because of low image quality, uncommon camera perspectives, large occlusions (often the victim), and the similarity of objects (e.g., furniture, art, bedding) across different hotel rooms. To support efforts towards this hotel recognition task, we have curated a dataset of over 1 million annotated hotel room images from 50,000 hotels. These images include professionally captured photographs from travel websites and crowd-sourced images from a mobile application, which are more similar to the types of images analyzed in real-world investigations. We present a baseline approach based on a standard network architecture and a collection of data-augmentation approaches tuned to this problem domain.

Amazon's controversial facial recognition software, Rekognition, is facing renewed criticism. A new study from the MIT Media Lab found that Rekognition may have gender and racial biases. In particular, the software performed worse when identifying gender for females and darker-skinned females. Amazon's controversial facial recognition software, Rekognition, is facing renewed criticism. When the software was presented with a number of female faces, it incorrectly labeled 19 percent of them as male.

YouTube said Friday it is retooling its recommendation algorithm that suggests new videos to users in order to prevent promoting conspiracies and false information, reflecting a growing willingness to quell misinformation on the world's largest video platform after several public missteps. In a blog post that YouTube plans to publish Friday, the company said that it was taking a "closer look" at how it can reduce the spread of content that "comes close to -- but doesn't quite cross the line" of violating its rules. YouTube has been criticized for directing users to conspiracies and false content when they begin watching legitimate news. The change to the company's recommendation algorithms is the result of a six-month-long technical effort. It will be small at first -- YouTube said it would apply to less than 1 percent of the content of the site -- and affects only English-language videos, meaning that much unwanted content will still slip through the cracks.

Facial-recognition software developed by Amazon and marketed to local and federal law enforcement as a powerful crime-fighting tool struggles to pass basic tests of accuracy, such as correctly identifying a person's gender, new research released Thursday says. Researchers with M.I.T. Media Lab also said Amazon's Rekognition system performed more accurately when assessing lighter-skinned faces, raising concerns about how biased results could tarnish the artificial-intelligence technology's use by police and in public venues, including airports and schools. Amazon's system performed flawlessly in predicting the gender of lighter-skinned men, the researchers said, but misidentified the gender of darker-skinned women in roughly 30 percent of their tests. Rival facial-recognition systems from Microsoft and other companies performed better but were also error-prone, they said. The problem, AI researchers and engineers say, is that the vast sets of images the systems have been trained on skew heavily toward white men.

When looking for answers to legal questions, people increasingly start their searches online. But what they find isn't always very useful--prompting the law schools at Stanford University and Suffolk University to team up to harness artificial intelligence (AI) to help people identify their specific legal issues. Historically, machines have struggled to understand context in human speech. For example, if someone says, "I'm getting kicked out of my house," most people understand that the person is not being physically kicked but is rather being removed from his or her home--or, to use the legal term, evicted. But machines typically can't understand "kicked out of my house" as "evicted" without being trained through a large number of similar questions.

A Florida state appellate court ruled last week that Willie Allen Lynch, who was convicted in 2016 for selling crack cocaine, had no right to view photos of other suspects identified by the facial recognition search that led to his arrest. In 2015, undercover agents working with the Jacksonville Sheriff's Office photographed a man selling $50 of cocaine. Detectives were unable to identify him, so they decided to turn to the Face Analysis Comparison Examination System, known as FACES, which draws from a database consisting of more than 33 million driver's license and law enforcement photos. The software, which is designed to return multiple potential matches for a given image, named Lynch and four other suspects. Upon further investigation, detectives arrested Lynch for the crime.

Ethics and accountability will be among the most significant challenges for artificial intelligence (AI) in 2019, according to a survey of researchers at Georgia Tech's College of Computing. In response to an email query about AI developments that can be expected in 2019, most of the researchers – whether talking about machine learning (ML), robotics, data visualizations, natural language processing, or other facets of AI – touched on the growing importance of recognizing the needs of people in AI systems. "In 2019, I hope we will see AI researchers and practitioners start to frame the debate about proper and improper uses of artificial intelligence and machine learning in terms of human rights," said Associate Professor Mark Riedl. "More and more, interpretability and fairness are being recognized as critical issues to address to ensure AI appropriately interacts with society," said Ph.D. student Fred Hohman. Questions about the rights of end users of AI-enabled services and products are becoming a priority, but Riedl said more is needed.

The 2018 program brought 24 aspiring data scientists from across the world to Chicago and 15 to Lisbon. They were current (or recent) graduate and undergraduate students from quantitative and computational fields – from computer science and machine learning, to statistics, math, physical sciences and engineering, to social sciences, public health and public policy. From May 28th to August 17th, they worked in teams of 3-4 on data science projects in partnership with nonprofits and government agencies, to tackle data-intensive high impact problems in education, public health, public safety, criminal justice, environmental issues, city operations, and social services, learning from full-time experienced mentors and project managers.

Microsoft is planning to implement self-designed ethical principles for its facial recognition technology by the end of March, as it urges governments to push ahead with matching regulation in the field. The company in December called for new legislation to govern artificial intelligence software for recognising faces, advocating for human review and oversight of the technology in some critical cases, as a way to mitigate the risks of biased outcomes, intrusions into privacy and democratic freedoms. "We do need to lead by example and we're working to do that," Microsoft President and chief legal officer Brad Smith said in an interview, adding that some other companies are also putting similar principles into place. Smith said the company plans by the end of March to "operationalise" its principles, which involves drafting policies, building governance systems and engineering tools and testing to make sure it's in line with its goals. It also involves setting controls for the company's global sales and consulting teams to prevent selling the technology in cases where it risks being used for an unwanted purpose.

About The Job Research happens across Google everyday, in many different teams. Our research has already impacted user-facing services across Google including Search, Maps and Google Now, and is central to the success of Google Cloud and our planet-scale computing, storage, and networking infrastructure. We do research differently here. Research Interns aren't cloistered in the lab, but instead they work closely with Software Engineers to discover, invent, and build at the largest scale. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.