Deep neural networks have progressed immensely in recent years. Far from being moonshots, they are now at a point where they are better than humans at several tasks. They are capable of understanding text without previously encountering the words, of translating different languages, they can even cut transcription errors by half. And now, it seems they can recognize and replicate art (and a number of art styles). A team from the University of Freiburg in Germany have used deep neural networks to copy the artistic style of famous painters and paste them into video clips from movies and TV shows.

The weekend is upon us once again (woohoo!), and after another week packed with updates, announcements, rumors and insights, its arrival is certainly welcome. And as ever, 7 Days is here too, to walk you through what's been happening and bring you up to speed. We begin our odyssey this week in the UK, where the European Commission has blocked Three's proposed 10.25 billion acquisition of O2 UK, which would have created the country's largest mobile network operator. The EC determined that the deal would harm innovation, limit competition, and increase prices across the market. Cloud CRM platform Salesforce suffered an outage earlier this week affecting a relatively small proportion of its customers.

Computers will have developed "common sense" within a decade and we could be counting them among our friends not long afterwards, one of the world's leading AI scientists has predicted. Professor Geoff Hinton, who was hired by Google two years ago to help develop intelligent operating systems, said that the company is on the brink of developing algorithms with the capacity for logic, natural conversation and even flirtation. The researcher told the Guardian said that Google is working on a new type of algorithm designed to encode thoughts as sequences of numbers – something he described as "thought vectors". Although the work is at an early stage, he said there is a plausible path from the current software to a more sophisticated version that would have something approaching human-like capacity for reasoning and logic. "Basically, they'll have common sense."

MLconf in Seattle is a week away and we are getting a glimpse. Ethics in machine learning is the hottest conversation right now. Hear how a quantum molecular dynamic model made Uber service more reliable, get practical advice on next revolution in text search, and learn about multi-classification evaluation and ensemble learning. Franziska Bell, Data Science Manager at Uber, will talk about how a quantum molecular dynamic model for enzymes made Uber more reliable as a service. And of course, you don't need to be a quantum mechanics to be a data scientist.

For the image manipulation I used the Python Imaging Library (PIL).

This origami robot is designed to be swallowed in an ice capsule (left) before unfolding inside the stomach. Scientists have created a tiny, ingestible robot made from pig intestine that might be able to retrieve swallowed objects without surgery. The robot is built from a magnet attached to folds of dried meat typically used in sausage casings. It needs no tether or power source, instead being guided by magnets outside the body. Though swallowed in an ice capsule, the robot unfolds inside the stomach as its casing melts.

In the fourth issue of this monthly digest series you can find out how Qualcomm is bringing deep learning and AI to smart devices, why Daimler sent self-driving trucks all across Europe, how to imitate Rembrandt's best work with the help of deep learning, and much more. From the Smithsonian comes news--and a must-see fascinating video--about a painting created using data from more than 168,000 fragments of Rembrandt's work, trained to paint in Rembrandt's signature style. Over the course of 18 months, a group of engineers, Rembrandt experts and data scientists analyzed 346 of Rembrandt's works, then trained a deep learning engine to "paint" in the master's signature style. In order to stay true to Rembrandt's art, the team decided to flex the engine's muscles on a portrait. They analyzed the demographics of the people Rembrandt painted over his lifetime and determined that it should paint a Caucasian male between 30 and 40 years of age, complete with black clothes, a white collar and hat, and facial hair.

This "robotic mermaid" could be more than just a clever way to retrieve sunken treasure (and disappoint amorous sailors). It hints at how humans and robots may someday work together in all sorts of difficult environments. The submersible humanoid robot, called OceanOne, was developed at Stanford University. It recently retrieved priceless artifacts from King Louis XIV's La Lune, a 350-year-old galleon wrecked off Toulon in southern France in 1664. OceanOne has two arms, a head, and a tail-like appendage fitted with motorized propellers.

An unclassified 2016 Department of Defense (DoD) document, the Human Systems Roadmap Review, reveals that the US military plans to create artificially intelligent (AI) autonomous weapon systems, which will use predictive social media analytics to make decisions on lethal force with minimal human involvement. Despite official insistence that humans will retain a "meaningful" degree of control over autonomous weapon systems, this and other Pentagon documents dated from 2015 to 2016 confirm that US military planners are already developing technologies designed to enable swarms of "self-aware" interconnected robots to design and execute kill operations against robot-selected targets. More alarmingly, the documents show that the DoD believes that within just fifteen years, it will be feasible for mission planning, target selection and the deployment of lethal force to be delegated entirely to autonomous weapon systems in air, land and sea. The Pentagon expects AI threat assessments for these autonomous operations to be derived from massive data sets including blogs, websites, and multimedia posts on social media platforms like Twitter, Facebook and Instagram. The raft of Pentagon documentation flatly contradicts Deputy Defense Secretary Robert Work's denial that the DoD is planning to develop killer robots.

IS BIG DATA TAKING US CLOSER TO THE DEEPER QUESTIONS IN ARTIFICIAL INTELLIGENCE? What I'm worried about and what I'm thinking about these days is if we're really making progress in AI. I'm also interested in the same kind of question in neuroscience, which is that we feel like we're making progress, but are we? There's huge progress in AI, or at least huge interest in AI--a bigger interest than there's ever been in my lifetime. I've been interested in AI since I was a little kid trying to program computers to play chess, and do natural language databases, and things like that, though not very well. I've watched the field and there have been ups and downs. There were a couple of AI winters where people stopped paying attention to AI altogether. People who were doing AI stopped saying that they were in the field of AI. They say, "Yes, I do artificial intelligence," where two years ago they would have said, "I do statistics." Even though there's a lot of hype about AI and a lot of money being invested in AI, I feel like the field is headed in the wrong direction. There's been a local maximum where there's a lot of low-hanging fruit right now in a particular direction, which is mainly deep learning and big data. People are very excited about the big data and what it's giving them right now, but I'm not sure it's taking us closer to the deeper questions in artificial intelligence, like how we understand language or how we reason about the world. The big data paradigm is great in certain scenarios. One of the most impressive advances is in speech recognition. You can now dictate into your phone and it will transcribe most of what you say right most of the time. That doesn't mean it understands what you're saying. Each new update of Siri adds a new feature. First, you could ask about movie times, then sports, and so forth. The natural language understanding is coming along slowly. You wouldn't be able to dictate this conversation into Siri and expect it to come out with anything whatsoever.