The International Conference on Machine Learning (ICML) 2018 will be held July 10–15 in Stockholm, Sweden. Yesterday, from more than 600 accepted papers, the prestigious conference announced its Best Paper Awards. Two papers shared top honours. Researchers Anish Athalye of MIT and Nicholas Carlini and David Wagner of UC Berkeley's Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples; and Delayed Impact of Fair Machine Learning, from a UC Berkeley research group led by Lydia T. Liu and Sarah Dean. The Best Paper Runner Up Awards go to Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices, from Professor Zengfeng Huang of Fudan University; The Mechanics of n-Player Differentiable Games from DeepMind and University of Oxford's David Balduzzi and Sebastien Racaiere, James Martens, Jakob Foerster, Karl Tuyls and Thore Graepel; and Fairness Without Demographics in Repeated Loss Minimization, from a Stanford research group including Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang.

A couple of months ago I left Pivotal to join idealo.de Besides the usual tasks like building out the data science team, setting up the infrastructure and many more administrative stuff, I had to define the ML powered product roadmap. And associated with this was also the definition of a Minimum Viable Product (MVP) for machine learning products. The question I often face though, here at idealo and actually also back at my time at Pivotal, is what actually a good MVP means? In this article, I will shed some lights on the different dimensions of a good MVP for machine learning products drawing in the experiences that I've gained so far.

Traffic hell is alive and well in Los Angeles. In 2017, Angelenos were stuck on the road for 102 hours each (more than four full days), costing the city $19.2 billion, according to INRIX's annual global traffic scorecard. Traffic is almost as bad--and costly--in Moscow, Sao Paulo, and London. But this is the 21st century! Can't AI fix these problems by optimizing traffic flow?

Three, it turns out, is better than one. At least that's how it worked for a trio of former rivals who teamed up to claim the just-announced top prize in this year's Data Science Bowl. The fourth annual event focused on one of healthcare's most pressing problems -- the soaring cost and time needed to discover new drugs. A record-setting 18,000 participants battled over 90 days to deliver a deep learning algorithm to accelerate a crucial step in the drug-discovery pipeline: identifying the nucleus of each cell. This year's Data Science Bowl was "driven by a very real need to develop new treatments faster and more accurately," said Anne Carpenter, director of the imaging platform at the Broad Institute of MIT and Harvard, the nonprofit partner for the contest.

At the 2018 Automatica Fair in Munich, the International Federation for Robotics (IFR) presented a preview of the annual sales statistics for the robotics sector at the CEO roundtable. The final statistics will be published by IFR in cooperation with VDMA in October. The preliminary numbers are interesting in many ways. First of all the sales of robots increased by 29% year-over-year during 2017. The total number of shipped robots went up to 381 thousand units, of which 2/3 are sold in Asia.

American technology firm Nvidia along with researchers from Aalto University and the Massachusetts Institute of Technology have published a paper outlining a new artificial intelligence (AI) system that can almost flawlessly retouch grainy or pixelated pictures. The software uses deep-learning to automatically remove noise and artifacts. What makes this system so unique is that it can teach itself to fix corrupted photos simply by looking at them. Previous deep learning work in image retouching was centered on training neural networks to restore images by comparing noisy and clear images. This new method, however, only requires two corrupted images to proceed with removing noise. "It is possible to learn to restore signals without ever observing clean ones, at performance sometimes exceeding training using clean exemplars," reads the paper.

American technology firm Nvidia along with researchers from Aalto University and the Massachusetts Institute of Technology have published a paper outlining a new artificial intelligence (AI) system that can almost flawlessly retouch grainy or pixelated pictures. The software uses deep-learning to automatically remove noise and artifacts. What makes this system so unique is that it can teach itself to fix corrupted photos simply by looking at them. Previous deep learning work in image retouching was centered on training neural networks to restore images by comparing noisy and clear images. This new method, however, only requires two corrupted images to proceed with removing noise. "It is possible to learn to restore signals without ever observing clean ones, at performance sometimes exceeding training using clean exemplars," reads the paper.

The government has published its long-awaited Brexit White Paper. The document is 104 pages long and follows last week's Chequers agreement which set out the sort of relationship the UK wants with the EU after Brexit. The White Paper is split into four chapters: economic partnership, security, cooperation and institutional arrangements. So here are the key excerpts from the chapter on "economic partnership" and what they mean. This is a line that emerged in the Chequers statement last Friday, and it is one of the most important in this White Paper. It is the UK government's answer to the concerns expressed by businesses that rely on "just-in-time" manufacturing supply chains (such as car manufacturers), and to the need to avoid the reimposition of a hard border in Ireland.

Unleashing a legal and ethical debate worldwide, AI (artificial intelligence) is progressing with leaps and bounds as it portends to change human society forever. For example, if a driverless car meets with an accident involving fatalities, it is the algorithm operator who faces "product liability" rules. In the case of AI used in conventional war, machines killing humans is an ethically chilling concept. Carrying major implications, it is feared that the proto-AI technologies of today are going to evolve into true AI super-intelligence very rapidly without giving enough time for research into the pros and cons. As apprehensions of a "hyper-war scenario" build up, the main challenge remains: how to place the human factor in AI and prevent a drastic downgrade in military security as combat involving the technology changes the dimensions of warfare.

Apple is about to launch three new iPhones. And that is just the beginning, according to new rumours. The company will update just about every single one of its products around the September event where it usually launches new products, a new report has claimed. Previous reports have suggested that Apple will launch not just one new iPhone at the event, but three. And all of them will be packed with the new features that first arrived with the iPhone X.