Artificial intelligence (AI) takes the power of computing systems to a different level. It is amazing to even think that a computing system can emulate human beings. There are many fantastic examples of AI in various areas of our lives. That said, computing systems are still considered limited in their capabilities because they cannot think creatively like human beings. While AI can process and analyze complex data, it still does not have much prowess in areas that involve abstract, nonlinear and creative thinking.

The ability to argue, to express our reasoning to others, is one of the defining features of what it is to be human. Processes of argumentation run our governments, structure scientific endeavour and frame religious belief. So should we worry that new advances in artificial intelligence are taking steps towards equipping computers with these skills? As technology reshapes our lives, we are all getting used to new ways of working and new ways of interacting. Millennials have known nothing else.

Up to half of all jobs in Britain are vulnerable to a revolution in robotic technology and artificial intelligence that threatens to replace human workers with machines, … response to the economic challenge, with the establishment of a regulator to oversee the "ethical use of robotics and artificial intelligence".

The government must intervene to stop automation driving up wage inequality, a think tank has warned. The Institute for Public Policy Research said robots would not necessarily be bad for the economy. However, it warned lower-skilled jobs were much more likely to be phased out in the coming decades, and only higher-skilled workers would be able to command better wages. The government said it was committed to making automation work for everyone. According to the Institute for Public Policy Research (IPPR) - a centre-left think tank - automation could raise UK productivity growth by between 0.8 to 1.4% annually, and boost GDP by 10% by 2030.

You might have watched a movie like The Terminator or I, Robot and considered that the artificial intelligence potential it portrays is a far cry from our current technologies (there's no real fear of bots powered by Samsung Bixby overtaking the planet, that's for sure). After investigating a recently published Google research paper (via Quartz), it looks like we might be closer to this reality than you might think. The paper, titled "Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions," highlights a new Google text-to-speech system called Tacotron 2, which is capable of a near-human level of AI voice reproduction. To achieve this, Tacotron 2 uses a pair of neural networks: one to create a visual representation of specific audio frequencies and a second (called "WaveNet") to recreate this visual data as sound. Google launched a website alongside the paper to show-off what this tech could lead to in practice; there, Google provides examples of how Tacotron 2 handles phrase semantics (like distinguishing between the noun and verb of "present"), intonation and difficult words that might trip some of us humans up like "otolaryngology."

Over 13 months in 2016–17 the U.S. Federal Communications Commission conducted an "incentive auction" to repurpose radio spectrum from broadcast television to wireless internet. In the end, the auction yielded $19.8 bn, $10.05 bn of which was paid to 175 broadcasters for voluntarily relinquishing their licenses across 14 Ultra High Frequency (UHF) channels. Stations that continued broadcasting were assigned potentially new channels to fit as densely as possible into the channels that remained. The government netted more than $7 bn (used to pay down the national debt) after covering costs (including retuning). A crucial element of the auction design was the construction of a solver, dubbed SAT-based Feasibility Checker (SATFC), that determined whether sets of stations could be "repacked" in this way; it needed to run every time a station was given a price quote.

Writing high-performance code on modern machines requires not just locally optimizing inner loops, but globally reorganizing computations to exploit parallelism and locality--doing things such as tiling and blocking whole pipelines to fit in cache. This is especially true for image processing pipelines, where individual stages do much too little work to amortize the cost of loading and storing results to and from off-chip memory. As a result, the performance difference between a naive implementation of a pipeline and one globally optimized for parallelism and locality is often an order of magnitude. However, using existing programming tools, writing high-performance image processing code requires sacrificing simplicity, portability, and modularity. We argue that this is because traditional programming models conflate the computations defining the algorithm with decisions about intermediate storage and the order of computation, which we call the schedule. We propose a new programming language for image processing pipelines, called Halide, that separates the algorithm from its schedule. Programmers can change the schedule to express many possible organizations of a single algorithm. The Halide compiler then synthesizes a globally combined loop nest for an entire algorithm, given a schedule. Halide models a space of schedules which is expressive enough to describe organizations that match or outperform state-of-the-art hand-written implementations of many computational photography and computer vision algorithms. Its model is simple enough to do so often in only a few lines of code, and small changes generate efficient implementations for x86, ARM, Graphics Processors (GPUs), and specialized image processors, all from a single algorithm. Halide has been public and open source for over four years, during which it has been used by hundreds of programmers to deploy code to tens of thousands of servers and hundreds of millions of phones, processing billions of images every day. Computational photography and computer vision algorithms require highly efficient implementations to be used in practice, from power-constrained mobile devices to data centers processing billions of images. This is not a simple matter of programming in a low-level language such as C: even in C, the performance difference between naïve and highly optimized image processing code for the same algorithm is often an order of magnitude. Unfortunately, optimization usually comes at a large cost in programmer time and code complexity, as computation must be globally reorganized to efficiently exploit the memory system (locality, e.g., in caches) and many execution units (parallelism, e.g., across threads and vector lanes). Image processing pipelines are both wide and deep: they consist of many data-parallel stages that benefit hugely from parallel execution across pixels, but stages are often memory bandwidth limited--they do little work per load and store.

NSFW Warning: This story may contain links to and descriptions or images of explicit sexual acts. If you want to understand the myriad issues concerning sex robots that humanity needs to grapple with, you have two options. You can either spend several years studying for a PhD in either of those fields, or you can sit down in front of your TV. Many of the preoccupations that were on display at the third International Congress on Love and Sex with Robots are ones that have already been explored in pop culture. From Futurama to Westworld, going back to Weird Science and The Stepford Wives, the questions that academics are currently pondering have already been played out, fictionally at least, on TV.

The field of study that focuses on the interactions between human language and computers is called Natural Language Processing, or NLP for short. It sits at the intersection of computer science, artificial intelligence, and computational linguistics (Wikipedia). "Nat ur al Lan guage Pro cessing is a field that cov ers com puter un der stand ing and ma nip u la tion of hu man lan guage, and it's ripe with pos sib il it ies for news gath er ing," Anthony Pesce said in Natural Language Processing in the kitchen. "You usu ally hear about it in the con text of ana lyz ing large pools of legis la tion or other doc u ment sets, at tempt ing to dis cov er pat terns or root out cor rup tion." NLP is a way for computers to analyze, understand, and derive meaning from human language in a smart and useful way.

Looking back at the year's most-read WIRED business stories, one theme clearly emerges: people are very concerned with the future of work. Will the robot revolution will eradicate positions? What are the right skills for future-proofing ourselves? Other stories captured our readers attention too, of course. People seem to be equally concerned with how social media is retraining human brains and upending social norms.