Contrary to popular belief, the steadfast march toward automation is affecting all sorts of fields -- not just blue-collar industries like manufacturing and transportation. Already, artificially intelligent systems (AI) are reviewing contracts and mining documents in discovery, determining which job candidates get callbacks, and selecting the inventory retailers choose to highlight for particular customers. Now, at least one publication is using it to help supply generate "thought starters" that might later become published articles. According to a report in Digiday this morning, Forbes' product team recently began internally testing an AI tool that supplies story threads. It builds on the publisher's semi-automated topics recommendation feature in Bertie, its content management system (CMS), that produces writing prompts based on reporters' previous work.

A.I. is this year's buzzword of choice across the Tech industry, and speculation about what this field can achieve is already running rife. Let's separate fact from fiction and make some sense of all the hype. As we start the new year, the Tech propaganda machine is already ramping up its next generation of buzzwords, promising paradigm shifts and silver bullets that will make whole industries obsolete, enable huge efficiency gains, and make the world a better place. Blockchain, which used to top keyword search trends and social media posts, suffered a significant decline in interest, partly due to the fact that its initial hype was residual from the Bitcoin bubble. It seems that this year's buzzword of choice is going to be Artificial Intelligence.

A Chinese rover is making its tracks on the soft surface of the'dark' side of the moon after touching down on our nearest celestial neighbour. The Yutu-2 - or Jade Rabbit 2 - rover drove off its lander's ramp and onto the exterior of the moon's far side at 10:22pm Beijing time (2:22 pm GMT) on Thursday, about 12 hours after the Chinese spacecraft carrying it came to rest. A photo was later posted online by China's space agency revealing the rover several yards away from the spacecraft. The tracks it makes on the surface of the moon will be forever immotalised and will never be lost as there is no wind on the moon due to its lack of an atmosphere. By 5pm Beijing time (9am GMT) the three fifteen-foot long antennaes on Chang'e-4 had also been fully unfurled to enable the low-frequency radio spectrometre to begin work. The rover which is currently meandering around the moon on six independently controlled wheels, has also established a robust connection with its relay satellite, Queqiao. Yutu-2 has already completed environmental perception, route planning, walking to where it is pictured currently and starting its scientific operations. Chinese state media also reports that the cameras on the machine have been turned on and are working normally. The other equipment will be turned on one by one, according to the Chinese space agency CNSA. Jade Rabbit 2 weighs 308lbs (139kg) and has six individually powered wheels so it can continue to operate even if one wheel fails.

Earlier this year, Amir Avni used neural networks to troll the subreddit/r/Colorization -- a community where people colorize historical black and white images manually using Photoshop. They were astonished with Amir's deep learning bot. What could take up to a month of manual labour could now be done in just a few seconds. I was fascinated by Amir's neural network, so I reproduced it and documented the process. First off, let's look at some of the results/failures from my experiments (scroll to the bottom for the final result). Today, colorization is usually done by hand in Photoshop. In short, a picture can take up to one month to colorize. A face alone needs up to 20 layers of pink, green and blue shades to get it just right. This article is for beginners. Yet, if you're new to deep learning terminology, you can read my previous two posts here and here, and watch Andrej Karpathy's lecture for more background. I'll show you how to build your own colorization neural net in three steps. We'll build a bare-bones 40-line neural network as an "alpha" colorization bot.

Chances are, you're exposed to artificial intelligence every day. And artificial intelligence has been the cause of many of the technological breakthroughs in the past several years - from robots to Tesla (TSLA) . But while there are certainly naysayers to the technological development, AI seems set to become the future of predictive tech. But, what actually is artificial intelligence, and how does it work? Better still, how is AI being used in 2019?

More than 200 firms contributed to our request for ideas on what the global tech trends will be in 2019. This year it's all about data - a small, rather dull word for something that is profoundly changing the world we live in. New technologies, from voice-controlled speakers to "internet of things" (IoT) sensors, connected cars to fitness wearables, are vastly increasing the amount of digital data we produce. And artificial intelligence (AI), machine learning and cloud computing are transforming the way we store, analyse and apply it. "In 2019 smart sensors will start to be found everywhere, automating data collection to satisfy the voracious appetite of AI," says Tim Harper, a former European Space Centre engineer and now founder of G2O Water Technologies.

Drones have gained popularity in a wide range of field ranging from aerial photography, aerial mapping, and investigation of electric power lines. Every drone that we know today is carrying out some kind of control algorithm at the low level in order to manoeuvre itself around. For the quadrotor to either control itself autonomously or to develop a high-level user interface for us to control it, we need to understand the basic mathematics behind how it functions. This paper aims to explain the mathematical modelling of the dynamics of a 3 Dimensional quadrotor. As it may seem like a trivial task, it plays a vital role in how we control the drone. Also, additional effort has been taken to explain the transformations of the drone's frame of reference to the inertial frame of reference.

As sound event classification moves towards larger datasets, issues of label noise become inevitable. Web sites can supply large volumes of user-contributed audio and metadata, but inferring labels from this metadata introduces errors due to unreliable inputs, and limitations in the mapping. There is, however, little research into the impact of these errors. To foster the investigation of label noise in sound event classification we present FSDnoisy18k, a dataset containing 42.5 hours of audio across 20 sound classes, including a small amount of manually-labeled data and a larger quantity of real-world noisy data. We characterize the label noise empirically, and provide a CNN baseline system. Experiments suggest that training with large amounts of noisy data can outperform training with smaller amounts of carefully-labeled data. We also show that noise-robust loss functions can be effective in improving performance in presence of corrupted labels.

Almost six months ago (May 28th 2018) I posted the "AI winter is well on its way" post that went viral. The post amassed nearly a quarter million views and got picked up in Bloomberg, Forbes, Politico, Venturebeat, BBC, Datascience Podcast and numerous other smaller media outlets and blogs [1, 2, 3, 4, ...], triggered violent debate on Hacker news and Reddit. I could not have anticipated this post to be so successful and hence I realized I touched on a very sensitive subject. One can agree with my claims or not, but the sheer popularity of the post almost itself serves as a proof that something is going on behind the scenes and people are actually curious and doubtful if there is anything solid behind the AI hype. Since the post made a prediction, that the AI hype is cracking (particularly in the space of autonomous vehicles) and as a result we will have another "AI winter" episode, I decided to periodically go over those claims, see what has changed and bring some new evidence.

Nearly two decades after its release, "Minority Report" still seems to be as prescient as the film's eerie crime-fighting "precogs," offering a clarifying vision of the future that continues to manifest in the real world. Though it debuted way back in 2002, the film highlighted technologies like driverless cars, hyper-targeted advertising and robotic insects -- all of which exist in 2019. Now, it appears Steven Spielberg's cinematic premonition may have included another technology that is potentially one step closer to reality: gesture-controlled sensing technology. Translated to English: technology that would allow us to control televisions, smartphones and computers without touching them, not unlike Tom Cruise's character, John Anderton, manipulating floating digital images like a conductor directing an orchestra (though he uses gloves instead of a baton). For years, Google's Advanced Technology and Projects (ATAP) lab has been seeking to create motion sensors that might be used in similar technology, an effort the company dubbed Project Soli.