Major smartphone makers are following Apple and Google into the smartphone trend of 2017: artificial intelligence. LG's G6 could feature Google Assistant, Samsung said its Galaxy S8 will feature its own AI assistant, and Huawei's Mate 9 is the first phone with Amazon's Alexa. The Chinese conglomerate has been especially vocal about AI in their smartphones--unusual for top smartphone makers, who are normally secretive about their latest devices. Huawei said they will have an AI-focused "Superphone" in 2020--just a year before their goal to be the world's top smartphone maker. The Chinese tech giant has been sitting at No. 3 since last year, behind Samsung and Apple.

Darktrace co-founder Poppy Gustafsson recently predicted, at TechCrunch Disrupt London, that malicious actors will increasingly use artificial intelligence to create more sophisticated spearphishing attacks. Criminals are just as capable of using artificial intelligence as those trying to thwart them, according to security vendor ESET's 2017 trends report, with "next-gen" security marketers throwing around the buzzwords "machine learning," "behavioral analysis" and more. That's making it more difficult for potential customers to sift through all the hype. It predicts the rise of "jackware" or Internet-of-Things ransomware, such as locking the software in cars until a ransom is paid. UK-based security vendor Darktrace takes the view that determined hackers will get into your network, so a perimeter-based strategy won't work.

Pentesting tools like Metasploit, Burp, ExploitPack, BeEF, etc. are used by security practitioners to identify possible vulnerability points and to assess compliance with security policies. Pentesting tools come with a library of known exploits that have to be configured or customized for your particular environment. This configuration typically takes the form of a DSL or a set of fairly complex UIs to configure individual attacks. There are two major shortcomings with this approach (1) scanning doesn't yield perfect knowledge (2) scanning generates significant network traffic and can run for a very long time on a large network (Sarraute). It is perhaps due to these shortcomings (and maybe 0day exploits) that "most testing tools, provide no guarantee of soundness. Indeed, in the last few years, several reports have shown that state-of-the-art web application scanners fail to detect a significant number of vulnerabilities in test applications" (Doupé).

It wasn't long ago that we hosted a webinar for our clients that focused on emerging messaging channels for customer service. The shiny new object in the room by far was SMS chatbots. Using SMS as a support channel was cool, but the real goal of the pilot was to implement chatbots and see how many customer interactions could be automated. All of the wind went out of the sails when we realized that thousands of interactions would be required to train the bot and our client's volume was somewhere in the low hundreds. The pilot was abandoned shortly thereafter and the search for other technology that increase customer service efficiency continued.

Connected devices, Smart City, home automation, e-health, Big Data ... In recent years, the concepts of communicating objects have multiplied. In reality, they are all one facet of the same upheaval - the Internet of Things. Cars can be driven without a driver, TVs are going online, and heating systems are activated automatically to the arrival of the residents. The Internet is making many processes in daily life easier. The Internet of Things, or IoT, which enable devices to communicate with people or machines, is actually working in many places in our daily life already.

Many driver assistance systems such as Adaptive Cruise Control require the identification of the closest vehicle that is in the host vehicle's path. This entails an assignment of detected vehicles to the host vehicle path or neighboring paths. After reviewing approaches to the estimation of the host vehicle path and lane assignment techniques we introduce two methods that are motivated by the rationale to filter measured data as late in the processing stages as possible in order to avoid delays and other artifacts of intermediate filters. These filters generate discrete posterior probability distributions from which a path or "lane" index is extracted by a median estimator. The relative performance of those methods is illustrated by a ROC using experimental data and labeled ground truth data.

We have seen a fast growing interest in current activities around AI startups and research in the last couple of months. Headlines like "2016 was the year AI came of age", "AI was everywhere in 2016", and "The Great A.I. Awakening" were all over the media in the ending weeks of 2016 and we are curious about what 2017 will bring. I found particularly interesting that the current applications, future potential, and possible risks even attracted interest beyond the tech community through TV shows like Westworld, coverage on traditional media and even Obama's farewell address. Sadly, for many of us tech enthusiasts here in Europe, we sometimes feel like there is way less movement on this side of the Atlantic than in the Silicon Valley. However, with major acquisitions like DeepMind, Magic Pony Technology, Movidius, Vision Factory, and Dark Blue Labs, Europe has shown that it is actually leading the way in AI and machine learning.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. You may recognize some of the robots featured in this 10-minute overview of DLR's Robotics and Mechatronics Center: Rollin' Justin, Toro, LBR iiwa. But the one that caught our attention (starting at 5:40) is the super sleek ROboMObil vehicle.

This is the first of a multi-part series explaining the fundamentals of deep learning by long-time tech journalist Michael Copeland. Artificial intelligence is the future. Artificial intelligence is science fiction. Artificial intelligence is already part of our everyday lives. All those statements are true, it just depends on what flavor of AI you are referring to.

A team of researchers has developed a system for driverless cars that lets pedestrians signal the car to either stop or continue driving with a wave of the hand. The system uses an LED display and sensors to detect a pedestrian nearby. The system could have an impact on the safety of driverless cars in cities where many pedestrians share the road with drivers. Once the car is aware of a pedestrian's presence, the pedestrian can raise their hand as a stop sign, which makes the figure on the LED screen turn green, stopping the car. The system, called Blink, was created by researchers at the Royal College of Art and Imperial College London.