The aim of their collaboration is to achieve machine-based translation between the languages of the European Union so that comprehensible texts are achieved for as many language combinations as possible. Two of the EU-funded research projects are being led by the Saarbrücken computer linguist Josef van Genabith. Anyone who wants to learn Finnish has to be prepared to deal with a complex grammar that includes fifteen different cases. The grammatical cases are marked in part by appending syllables to nouns resulting in a dizzying array of word forms and expressive possibilities. "Teaching a computer to understand all these grammatical nuances and to translate them correctly into another language is exceptionally difficult," says Josef van Genabith, Professor of Translation-Oriented Language Technologies at Saarland University and a Scientific Director at the German Research Center for Artificial Intelligence (DFKI). His team is therefore following a different path.

The White House wound up a nationwide series of workshops on artificial intelligence today on a cautionary note: Yes, AI promises to ease many of humanity's ills, but humanity needs to make sure that flesh-and-blood policymakers are firmly in charge. Latanya Sweeney, director of the Data Privacy Lab at Harvard's Institute of Quantitative Social Science, said AI programs should be made to reflect the norms agreed upon by human society. "I want the people we elect controlling those norms, not the technology itself. Those norms should include supporting social equity and diversity, said Alicia Glen, New York City's deputy mayor for housing and urban development. "At its best, artificial intelligence can be a tool to promote equity, and it obviously can create huge economic opportunity for a lot of people," she said. "But it can also have discriminatory effects, whether they're intended or unintended.

Google is adding to its already substantial set of machine learning technologies with the acquisition of a French company called Moodstocks, a visual recognition machine learning technology company. Object recognition is one of the more difficult problems for machine learning, and it's a problem that Google has been working on for a while. In a blog post announcing the deal, Google noted that many of its services including Google Translate and Smart Reply Inbox already rely on machine learning technologies. The addition of Moodstock will help with visual recognition. Vincent Simonet, head of the R&D Center of Google France wrote in the blog post that Google has made great strides in terms of visual recognition technology -- for instance, if you search the word "party" or "beach" you'll get a good image match.

A few years ago, the general public thought artificial intelligence (AI) was but a futuristic technology exclusive to science fiction. That is until DeepMind was created in 2010, an artificial intelligence (AI) company that was later bought by Google in 2014, and is now making big strides in the industry. DeepMind currently boasts fully functioning artificial agents capable of doing human tasks like learning how to play video games as well as performing similar cognitive functions like accessing key pieces of information from a short-term memory. It sounds surreal, like something out of an Isaac Asimov novel. These artificial agents, or programs, are using what's called reinforcement learning (RL).

At the International Symposium on Biomedical Imaging in Prague this past April, a Harvard-based artificial intelligence system won the Camelyon16 challenge, a competition comprised of participants introducing their individual AI system and its ability to facilitate automated lymph node metastasis diagnosis. Referred to as PathAl, the computing system identifies cancerous cells through deep learning--an algorithmic technique that accumulates copious amounts of unstructured data and organizes it into clusters before analyzing it for patterns. Deep learning is predominately used in speech recognition systems like Apple's Siri and Microsoft's Cortana. According to one of the challenge's organizers, Jeroen van der Laak of Radboud University Medical Center in Netherlands, the technology featured in the competition went "way beyond" his expectations, as the AI's accuracy proved strikingly close to that of human beings. In addition, van der Laak said AI technology has the propensity to intrinsically redefine the way histopathological images are handled in the medical community.

This is a relatively small (read: exclusive) workshop (less than 30 attendees), fostering close interaction and collaboration between attendees. The workshop is based on a challenge-response format where a series of (shorter than typical) presentations outline important challenges (rather than results), which are then collaboratively addressed by several small teams which subsequently present their results to the whole group. Presentations are 25-minutes long, followed by 45-minute teamwork. Results of teamwork is subsequently presented to the whole group in a 45-minute session. The workshop concludes on the third day (optional) with a half-day trip (9:00 - 14:00) to the Icelandic countryside (depending on sufficient sign-up).

The AI actually takes the form of a Facebook Messenger bot that asks drinkers a series of questions about the beer. Questions take the form of 1 to 10 ratings, yes or no queries and multiple choice responses. Those answers about taste and preferences are then used to find trends that could be used to improve the final product over time. IntelligentX sees the use of AI as a way to put the drinker in the same room with the brewer making the beer, a physical meeting that's nearly impossible to set up in real life. What's more, the system can collect and analyze data much master than a human can with pencil and paper.

NETHERLANDS -Machine learning is a type of artificial intelligence that allows computer programs to learn things when exposed to new data, without being reprogrammed. Now, researchers have matched machine learning methods with a special technique of magnetic resonance imaging (MRI) that measures blood perfusion (absorption rate of this tissue) throughout the brain to detect early forms of dementia. MRI can help diagnose Alzheimer's disease. However, early diagnosis is difficult. Scientists have long known that Alzheimer's disease is a gradual process and that the brain undergoes functional changes before the structural changes associated with the disease visually displayed on the test results.

Amazon's robotic Picking Challenge this past weekend demonstrated the advancement in deep learning robots and showed how they may come to rule fulfillment warehouses in the future. "The machine studied 3D scans of the stockroom items to help it decide how to manipulate items with its gripper and suction cup," Engadget explained. "That adaptive AI made a big difference, to put it mildly. The arm got a near-flawless score in the stowing half of the event, and was over three times faster at picking objects than last year's champion (100 per hour versus 30)." "The robot needs to be able to handle variety and operate in an unstructured environment," Carlos Hernández Corbato from TU Delft Robotics Institute told TechRepublic.com. "We are really happy that we have been able to develop this successful system."

Alzheimer's is a devastating chronic neurodegenerative disease that currently affects about 5.4 million people in the U.S. alone. Alzheimer's patients suffer progressive mental deterioration, which eventually impairs even basic bodily functions like walking and swallowing. While Alzheimer's can increasingly be managed, one of the big challenges of the disease is early diagnosis. MRI machines can be used to confirm advanced cases, but by the time the disease has reached this stage, brain tissue is gone and there is no way to restore it. Could machine-learning tools be used to help detect and identify Alzheimer's disease before it is currently possible to do so?