The companies have integrated the automation technology into Watchwith's data-driven advanced advertising products. "What used to potentially require thousands of man-hours is now an automated process within the Watchwith platform," Watchwith says in a statement. By embedding artificial intelligence into the video advertising inventory creation process, Watchwith MAF gives TV networks and premium video publishers the power to create, manage and sell contextually relevant native video advertising at scale. "And the result is the highly scalable, native digital video advertising solution the TV industry needs to compete with Facebook, YouTube, Snapchat and other native digital video distribution platforms."

Watchwith has acquired the Arris Media Analysis Framework (MAF), a cloud-based machine learning and automated metadata generation platform. MAF was developed in Arris research labs, and the technology analyzes, tags and describes video at a frame level, which eliminates manual tagging. The companies have integrated the automation technology into Watchwith's data-driven advanced advertising products. "What used to potentially require thousands of man-hours is now an automated process within the Watchwith platform," Watchwith says in a statement. The combined solution is able to automatically determine the optimal timing and location within a TV episode to deliver in-program advertising and tune-in messages.

Chemists and mathematicians from the Skolkovo Institute of Science and Technology (Skoltech) and Moscow State Universite (MSU) have suggested checking the composition of medical plants by means of machine learning technologies, the Skoltech press service said. They have come up with automatizing computer assisted data analysis based on high-performance liquid chromatography and mass spectrometry. "Machine learning is when a computer can be taught to analyze the chemical composition of herbal medicine based on the previously known data on chemical analysis," Skoltech said. According to the researchers, the market of herbal remedies has been rapidly developing in the recent years, as it provides an alternative to synthetic drugs. But there are still no existing effective methods of plant material quality control.

After demonstrating discontinuous jumps in image recognition performance and defeating Korean grandmaster Lee Se-dol at Go, a game long resistant to computer mastery, deep learning has kicked up a swirling cloud of hype. On the one hand, serious folks are studying how to prevent a recursively self-improving super intelligence from seizing Earth's reins from humanity. On the other, IBM's "cognitive" marketing claims are rightly being called out as hyperbolic. I think much of the excitement derives from the tremendous strides deep learning has recently made in processing less-structured input, like images and voice, that relate to the way we perceive the world. From a business perspective the challenge is to soberly assess the usefulness of new machine learning techniques for application in the near future.

In addition, the built-in artificial intelligence will be able to predict situations, producing ready-made proposals for the border protection. "The system is fully based on domestic policy decisions that ensure protection of information resources against data loss, hackers and other unauthorized interventions," the press service quoted the deputy director of OPK Sergei Skokov as saying. The developers also noted that the new system is intended not only to collect different types of information, but also contains elements of artificial intelligence which will allow for analysis and forecasting of the situation and work out proposals for the protection of borders, by calculating steps and routes that offenders may take, as well as the necessary measures to prevent malicious acts, including the assessment of possible risks. The state borders need protection due to ever rising threats. Since the beginning of this year in the Rostov region, more than 60 "wanted" persons were found and arrested.

A version of this essay was originally published at Tech.pinions, a website dedicated to informed opinions, insight and perspective on the tech industry. At the recent Code Conference, Jeff Bezos made a rather provocative statement when he said that when we talk about technology, we are on "the edge of a golden era." When it comes to artificial intelligence, Bezos said, "It's probably hard to overstate how big of an impact it's going to have on society over the next 20 years." He also said that Amazon has 1,000 people working on its Alexa platform, which powers the company's popular voice-controlled Echo device. Of course, Bezos is hardly alone with this line of thinking about artificial intelligence and its impact.

Stephen Hawking made headlines at the end of 2014 when, in a BBC interview, he said that we should be very wary of developing "full artificial intelligence" as it "could spell the end of the human race." His doomsday musings were hardly original. SpaceX's Elon Musk had said the same thing earlier that year, warning that AI is "potentially more dangerous than nukes." The worrisome idea of computers possessing greater than human intelligence, coupled with a sudden independent consciousness, was first termed "The Singularity" back in 1993, in a paper by the computer scientist Vernor Vinge. And while his initial predictions about vast computer improvements merely mirrored the foresight of others – like the expected frequent doubling in computer power envisioned by Intel co-founder Gordon Moore in 1965 – Vinge believed it would lead to "change comparable to the rise of human life on Earth." As we all know, computers already control and facilitate much of our daily life from banking to robotic automobile assembly, and no one wants to return to the old days of manual drudgery for menial tasks like repetitive spot welding.

I'm working on a project in the e-commerce field involving product entity matching (given 2 product listings from 2 different marketplaces, such as Amazon and eBay, determine if they are the same product) I'm a real newbie in ML but I am deeply excited by it, and I was thinking of several ways we could potentially utilize ML to improve our matching algorithm. This one is maybe far-fetched, but perhaps we can use Deep Learning to do practically all the work by itself? I'm really clueless about how I would train for these purposes, would I need to manually create a gigantic dataset (especially for no. I'd love to hear feedback from you guys if I'm just talking nonesense or is there any potential to what I'm thinking?

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