Le Point's cover story this week is on artificial intelligence (AI). Stanford University started a study - The One Hundred Year Study on Artificial Intelligence - in 2014. It is supposed to go on for a century and will try to anticipate how AI will affect every aspect of our lives including politics, jobs, healthcare and privacy. The elaborate article also gives an outline of the evolution of AI and the organisations involved in research and its use in self-driven cars. The piece also makes an interesting comparison between costs per hour of a human and a robot from 1990 till now.

Governments are already using data and analytics in a number of ways to help them become better informed and provide superior services for their citizens. For both central and local governments, an increasing number of back end processing and citizen engagement opportunities are emerging for smart use of artificial intelligence and its many subfields. The biggest area for potential quick wins will be the vast processing that occurs in various administration tasks. This includes improving awareness of patterns in data, to create new theses and models. Bringing together data from different areas and using algorithms that learn, can create new insights.

Will progress in Artificial Intelligence provide humanity with a boost of unprecedented strength to realize a better future, or could it present a threat to the very basis of human civilization? The future of artificial intelligence is up for debate, and the Origins Project is bringing together a distinguished panel of experts, intellectuals and public figures to discuss who's in control. Eric Horvitz, Jaan Tallinn, Kathleen Fisher and Subbarao Kambhampati join Origins Project director Lawrence Krauss. Recorded Saturday, February 25th, 2017 Eric Horvitz is managing director of Microsoft Research's main Redmond Lab, an American computer scientist, and technical fellow at Microsoft. Horvitz received his PhD and MD degrees at Stanford University, and has continued his research and work in areas that span theoretical and practical challenges of machine learning and inference, human-computer interaction, artificial intelligence, and more.

In her SXSW session, titled Dark Days: AI and the Rise of Fascism, Crawford, who studies the social impact of machine learning and large-scale data systems, explained ways that automated systems and their encoded biases can be misused, particularly when they fall into the wrong hands. "Just as we are seeing a step function increase in the spread of AI, something else is happening: the rise of ultra-nationalism, rightwing authoritarianism and fascism," she said. All of these movements have shared characteristics, including the desire to centralize power, track populations, demonize outsiders and claim authority and neutrality without being accountable. Machine intelligence can be a powerful part of the power playbook, she said. One of the key problems with artificial intelligence is that it is often invisibly coded with human biases.

We pick the cell size to be bounded by (r)/( (n)), so that each grid cell will contain at most one sample, and thus the grid can be implemented as a simple n-dimensional array of integers: the default 1 indicates no sample, a non-negative integer gives the index of the sample located in a cell. Step 1. Select the initial sample, x0, randomly chosen uniformly from the domain.

There was a point in time when brands were the aspiration, but in our new world, brands have become entirely subservient to people. The explosion in data availability and advances in AI are changing the relationship brands have with their customers. In the past, deregulation and access to marketing tools gave way for the proliferation of commoditized services. A hyper-competitive world, where choice is abundant, was born. Surviving meant optimizing quality for a lower cost.

The rise of AI and machine learning is impacting every sector, not just in terms of the marketing tools that exist to reach consumers with the right messages at the right time, but in the very products that brands can offer to their audience. From automated chatbots to intelligent recommendation engines, AI is enabling brands to personalise the products in new and exciting ways. One of the latest firms to take advantage of this technology is P&G skincare brand Olay, which has recently expanded its Olay Skin Advisor service to customers worldwide. The AI-powered platform is designed to help women better understand their skin, and find the products best-suited to their personal skincare needs. Mobile Marketing Magazine spoke to Dr. Frauke Neuser, principal scientist at Olay, about what led the brand to embracing AI. "What people don't realise is we have a lot of expertise and a lot of data in the area of imaging, both image capture and image analysis, and that's one of the core elements of the Olay Skin Advisor," said Dr. Neuser.

The drones fell out of the sky over China Lake, California, like a colony of bats fleeing a cave in the night. Over 100 of them dropped from the bellies of three Boeing F/A-18 Super Hornet fighter jets, their sharp angles cutting across the clear blue sky. As they encircled their target, the mechanical whir of their flight sounded like screaming. This was the world's largest micro-drone swarm test. Conducted in October 2016 by the Department of Defense's Strategic Capabilities Office and the Navy's Air Systems Command, the test was the latest step in what could be termed a swarm-drone arms race.

We design, conduct and present the results of a highly personalized baseline emotion recognition experiment, which aims to set reliable ground-truth estimates for the subject's emotional state for real-life prediction under similar conditions using a small number of physiological sensors. We also propose an adaptive stimuli-selection mechanism that would use the user's feedback as guide for future stimuli selection in the controlled-setup experiment and generate optimal ground-truth personalized sessions systematically. Initial results are very promising (85% accuracy) and variable importance analysis shows that only a few features, which are easy-to-implement in portable devices, would suffice to predict the subject's emotional state.

The massive amount of available data potentially used to discover patters in machine learning is a challenge for kernel based algorithms with respect to runtime and storage capacities. Local approaches might help to relieve these issues. From a statistical point of view local approaches allow additionally to deal with different structures in the data in different ways. This paper analyses properties of localized kernel based, non-parametric statistical machine learning methods, in particular of support vector machines (SVMs) and methods close to them. We will show there that locally learnt kernel methods are universal consistent. Furthermore, we give an upper bound for the maxbias in order to show statistical robustness of the proposed method.