Algorithms increasingly govern our social world, transforming data into scores or rankings that decide who gets credit, jobs, dates, policing, and much more. The field of "algorithmic accountability" has arisen to highlight the problems with such methods of classifying people, and it has great promise: Cutting-edge work in critical algorithm studies applies social theory to current events; law and policy experts seem to publish new articles daily on how artificial intelligence shapes our lives, and a growing community of researchers has developed a field known as "Fairness, Accuracy, and Transparency in Machine Learning." The social scientists, attorneys, and computer scientists promoting algorithmic accountability aspire to advance knowledge and promote justice. But what should such "accountability" more specifically consist of? At a two-day, interdisciplinary roundtable on AI ethics I recently attended, such questions featured prominently, and humanists, policy experts, and lawyers engaged in a free-wheeling discussion about topics ranging from robot arms races to computationally planned economies.

AI is one of the biggest buzzwords in tech (and in general) these days, and there's no question AI gets a lot of hype, both for better and for worse. But the latest round of machine learning--which trains algorithms on heaps of data so they can analyze images, find patterns in huge data sets, or simply answer our queries ("Alexa, what's the weather today?")--is finding an increasing number of useful applications too. Beneath the hype, machine learning's problem-solving potential is likely just getting started. At the SU Ventures Demo Faire at Singularity University's Global Summit, representatives from four startups presented their company's missions and business models. Monique Giggy, vice president of SU Ventures, moderated the session and introduced the speakers. She explained that SU Ventures seeks out and helps future-focused entrepreneurs translate big ideas into tangible, worldwide impact using exponential technologies.

We've all, at some stage, received these unwanted emails advertising products and services we most certainly have no interest in. Early attempts at blocking spam in the early 2000s produced a limited result. By creating antispam signatures – effectively manually created rules – the spam tide was only minimally stemmed with 50% of billions of spam mails being blocked while others still slipped through the net. The solution was found in machine learning, which analyses immense amounts of data and works out particular patterns. What resulted was the overall enhancement of defence and a catch of around 95% of spam.

A new facial recognition technology caught a man trying to enter the US using a passport belonging to someone else, US officials say. Officials with the US Customs and Border Protection (CBP) and the Office of Field Operations (OFO) intercepted a 26-year-old man, the agencies referred to as an "imposter", who reportedly attempted to use a French passport belonging to someone else, at Washington's Dulles International Airport. The man was travelling to the US from Brazil. "The officer utilised CBP's new facial comparison biometric technology which confirmed the man was not a match to the passport he presented," the CBP press release read. It added: "A search revealed the man's authentic Republic of Congo identification card concealed in his shoe."

For those looking to take advantage of emerging technologies coming down the pipeline, figuring out what artificial intelligence means for your business might be a good place to start. In its latest look at technologies that could drive competitive advantage, Gartner predicts about 80% of emerging tech will incorporate AI within the next two years. CIO Journal's Sara Castellanos has more. AI is becoming easier for enterprises to access via the cloud and integrate with other technologies such as edge computing, Gartner says. The use of virtual assistants and deep neural networks, which roughly try to mimic the operations of the human brain, are predicted to become mainstream within two to five years.

Pilot program uses cameras to match passengers to government pictures on file; Claudia Cowan reports from San Jose on the new software. A man trying to illegally enter the United States this week was tripped up by state-of-the-art facial recognition technology, marking the first instance of the system being used at an airport to help federal officials flag a fraud, U.S. Customs and Border Protection said Thursday. A 26-year-old man traveling from Brazil entered Washington Dulles International Airport and presented agents with a French passport on Wednesday, authorities said. But agents deployed the new technology to determine the man wasn't who he claimed to be. The man became nervous when agents referred him for a secondary search, officials said.

The facial recognition technology the US is testing for airports has caught its first imposter merely three days after Washington Dulles International started using it. According to US Customs and Border Protection (CBP), a 26-year-old man from Sao Paulo, Brazil successfully fooled people with a French passport until he presented it to a Dulles officer who used the new facial comparison biometric technology. The system determined that his face wasn't a match with the person in the passport, and he was sent for a comprehensive check, which revealed the Republic of Congo ID hidden inside his shoe. While privacy advocates are concerned that the technology could be used to track people and could put law-abiding citizens and visitors it can't recognize in trouble, CBP believes it will significantly bolster airport security and also speed up processing time for travelers. According to The Star, it already reduced international travelers' wait times by four minutes at the Mineta San Jose International Airport. Dulles first tested the use of facial recognition as a security measure back in 2015, but it has only just implemented the fledgling technology on August 20th.

As society has evolved, technology has as well, and there is a growing awareness that already-established police techniques -- if used exclusively -- are somewhat out-of-date and oftentimes quite expensive for what they offer. When departments sink valuable resources into maintaining old systems instead of investing into newer, more efficient, and cost-effective technologies -- especially in an era of budget cuts where law enforcement agencies are forced to make difficult decisions as to where to cut funding -- these agencies are missing out on a valuable source of information. One only needs to look at history to witness the evolution of criminal investigations. Fingerprinting, DNA analysis, and computer information systems such as CODIS (Combined DNA Index System) and NDIS (National DNA Index System) have improved investigatory efforts considerably; however, as technology continues to evolve -- and criminals are openly taking advantage of this new technology -- law enforcement agencies may be missing out on a valuable opportunity if they don't embrace more openly the tremendous benefits such new technology brings. The United States spends more than $100 billion annually on law enforcement and incarceration, and this figure does not even consider other economic impacts of crime in terms of victims' costs, property devaluation, and higher outlays for companies to ensure their security.

Lung cancer continues to be a major healthcare challenge with high morbidity and mortality rates among both men and women worldwide. The majority of lung cancer cases are of non-small cell lung cancer type. With the advent of targeted cancer therapy, it is imperative not only to properly diagnose but also sub-classify non-small cell lung cancer. In our study, we evaluated the utility of using Apple Create ML module to detect and sub-classify non-small cell carcinomas based on histopathological images. After module optimization, the program detected 100% of non-small cell lung cancer images and successfully subclassified the majority of the images. Trained modules, such as ours, can be utilized in diagnostic smartphone-based applications, augmenting diagnostic services in understaffed areas of the world.

We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. The training data consists of a texture-mapped 3D object model or images of the object in known 6D poses. The benchmark comprises of: i) eight datasets in a unified format that cover different practical scenarios, including two new datasets focusing on varying lighting conditions, ii) an evaluation methodology with a pose-error function that deals with pose ambiguities, iii) a comprehensive evaluation of 15 diverse recent methods that captures the status quo of the field, and iv) an online evaluation system that is open for continuous submission of new results. The evaluation shows that methods based on point-pair features currently perform best, outperforming template matching methods, learning-based methods and methods based on 3D local features. The project website is available at bop.felk.cvut.cz.