Either take a new photo from within the app or import a pre-existing one (don't bother with anything aside from vertical shots) and pick from one of about 20 filters, then export to your social network of choice. Same goes for transforming into a The Scream-like brushstroke patterns. The development team tells TechCrunch that the goal is to add two or more new filters each day, and expects to have 40 within a month. The results are pretty impressive, and unlike Paper Camera on Android, your phone isn't doing any of the heavy lifting here. The processing is done via Prisma's remote servers, and the outfit claims that no photos are stored or viewed from its side of things.

As we're on the cusp of using machine learning for rendering basically all kinds of consequential decisions about human beings in domains such as education, employment, advertising, health care and policing, it is important to understand why machine learning is not, by default, fair or just in any meaningful way. This runs counter to the widespread misbelief that algorithmic decisions tend to be fair, because, y'know, math is about equations and not skin color. Examples of this misbelief are common and evident in a recent piece on data-driven crime fighting that appeared in the Financial Times, which Cathy O'Neil brought to my attention. Ironically, Gilian Tett is well known for reporting on the failure of such things as "multi-variable equations" in the wake of the financial crisis, but she is perplexingly quick to accept that multi-variable equations are neutral and therefore fair, because the "computer experts" (whatever that means) at the police station asserted them to be so. My goal is not to belabor this one example.

Machine-learning algorithms are ubiquitous these days. Technology giants like Netflix Inc., Amazon.com Inc. and Google Inc. use them to suggest items customers might like based on their past browsing. Scientists use them to identify gene mutations associated with treatment resistance or amenable to targeted drug therapy. And doctors use them for image classification, early disease detection and better treatment outcomes. These algorithms can improve quality of life and can even help save lives.

Google has released a paper on AI safety in collaboration with researchers from Stanford University, University of California, Berkeley and OpenAI.

Researchers have developed a series of AI-based systems that can interpret pathology images and identify the presence and absence of metastatic cancer. The AI systems could lead to new and improved diagnostic methods and treatment. A group of researchers from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School in Boston have teamed up to develop new diagnostic methods based on artificial intelligence (AI). Humayun Irshad, PhD research fellow at Harvard Medical School and one of the lead authors on the research, says that their group is using all kinds of different computational methods to improve diagnostic techniques. "We are developing robust and efficient computational methods to improve diagnostic and prognostic assessment of pathological samples," Irshad says.

As we all know, Google is constantly looking to provide more relevant results for its users -- hence, the regular algorithm updates that frequently frustrate webmasters and anyone else's SEO efforts. The Pagerank algorithm that founders Sergey Brin and Larry Page introduced in the early days of Google was a step in the right direction, but it certainly wasn't the ultimate solution for improving the quality of search results. In fact, the search giant recently unveiled a new AI (yes, that stands for artificial intelligence) called RankBrain to help the engine better understand the queries users type into the search field. The real intention of this AI wasn't to change visitors' search engine results pages (SERPs) -- rather, it was to predict them. As a machine-learning system, RankBrain actually teaches itself how to do something instead of needing a human to program it.

Machine learning started out as the idea of giving a machine human intelligence. The discipline was originally intertwined with artificial intelligence (AI), as scientists wove together the fields of computer science, mathematics, statistics, probability, expert systems and neural networks. The original benchmark for machine learning and artificial intelligence was the Turing Test, created by British mathematician and computer scientist Alan Turing. "A computer was said to be able to'thin' if a human interrogator could not tell it apart, through conversation, from a human being". Since then, machine learning has been reorganized as a separate field from AI, with the aim of finding solutions to solvable problems using methods based in statistics and probability theory.

The FBI has access to nearly 412 million photos in its facial recognition system--perhaps including the one on your driver's license. But according to a new government watchdog report, the bureau doesn't know how error-prone the system is, or whether it enhances or hinders investigations. Since 2011, the bureau has quietly been using this system to compare new images, such as those taken from surveillance cameras, against a large set of photos to look for a match. That set of existing images is not limited to the FBI's own database, which includes some 30 million photos. The bureau also has access to face recognition systems used by law enforcement agencies in 16 different states, and it can tap into databases from the Department of State and the Department of Defense.

The magic of data from multiple sources, known as crowdsourcing, extends to apps, artificial intelligence and large-scale studies, among other advancements. While sourcing input data and sourcing processing grunt are two completely different endeavors, the kernel of an idea at the core is the same; teamwork. In the halls of MIT, long a bastion of scientific and technological breakthroughs and the minds that create them, the spirit of teamwork is alive and well in more ways than one. A team of researchers have decided to create an eye tracking system for smartphones, but rather than calling participants to the lab to let the robot scope out their eyeballs in action, they decided to allow just about anybody to jump in through the use of a special iOS app. This, and a push on Amazon's Mechanical Turk, has resulted in oodles of data from over 1,500 people, as opposed to the normal 50 or so that such a study normally draws in.

Accenture's technology R&D head urges China to scale up smart machine trials at home and abroad, Chen Yingqun and Zhang Xia report. China should step up its efforts to adopt artificial intelligence in its industries to boost the country's economic transformation, according to French technology expert Marc Carrel-Billiard. The development of artificial intelligence is a hot topic in China, he said, especially since the central government unveiled the Made in China 2025 strategy, which largely aims to upgrade the manufacturing industry with high-technology over the next decade. AI refers to machines or systems that can understand, learn and act independently, allowing them to take on cognitive functions otherwise performed by a human, such as problem-solving. Carrel-Billiard said such technology is important due to the shift toward greater connectivity, either through cloud computing or smart networks.