Finland is a forerunner in autonomous and remotely controlled solutions that are renewing the industrial sector, logistics and the way people move. The use of autonomous systems in various fields of society requires robust investment in competence, innovations and technology. VTT will strengthen the development of autonomous systems by founding a subsidiary VTT SenseWay Oy, focusing on such systems. The new company will seek access to the global markets from Turku, where some of the world's leading expertise in autonomous shipping systems can already be found. Autonomous systems are also making a strong entry into other transport and logistics sectors and mobile work machines, and VTT will respond to this demand with its solutions.

While many chatbots didn't live up to the hype, industries like fintech, healthcare, and retail are quietly adopting the technology to free up busy professionals' time and offer guided, personalized experiences to consumers. In 2016, chatbots were all the rage. That year, Facebook made the Messenger bot platform the centerpiece of its F8 developer conference. Microsoft's Satya Nadella referred to chat as the "third run-time" -- an indispensable piece of operating a platform, second only to the operating system and the web browser. Mentions of chatbots in earnings calls and press releases skyrocketed, and for many, it seemed that chatbots might be the next big disruptive technology. Thousands of companies commissioned their own chatbots in anticipation. In the end, though, the expected paradigm shift didn't happen. There are many reasons why chat didn't take off in 2016. For one, consumers found that many of the tasks the first chatbots were built to perform -- like relaying the news or finding a recipe -- took more time when a bot was involved. Another problem was that bots regularly needed human assistance to understand commands. Even Facebook's much-hyped personal assistant, M, closed down shortly after it was revealed that human handlers were responsible for some 70% of the bot's responses. But while many chatbots didn't meet users' high expectations, they haven't entirely fallen short. Today, the bots are still being used across industries like fintech, healthcare, sales and CRM, retail, and even law -- and they're having important, though quiet, effects. The important chatbots of 2019 aren't all-knowing virtual butlers; they're highly targeted applications of conversational technology. While they may seem less flashy, these bots are advancing their technology and making a demonstrable impact on their industries.

In Hong Kong, there are over two and a half thousand startups, employing nearly 10,000 people. Companies like Zegal are tapping into this deep talent pool and quickly changing the game for businesses that might have balked at complex legal documentation previously. "Historically, legal has been an intimidating topic area for individual companies, especially small and medium enterprises and law firms," says Tai Hung-Chou, CTO of Zegal. "We provide one place where clients and lawyers can work to get contracts drafted and executed, all online. In the future, with advanced natural language processing and artificial intelligence tools, we will [be able to] allow access to a vast array of legal precedents that can be injected during the drafting process."

Mozilla, the nonprofit behind the free and open-source Firefox web browser, is partnering with Montreal-based artificial intelligence startup Element AI to push for ethical use of AI. To that effect, the two companies are exploring the idea of data trusts, a proposed data collection approach that aims to provide individuals with greater control over their personal information. The aim, the companies said, is to offer an alternative model to the current broken consent-based system of data collection such as the EU GDPR regulations. It's easy to see why. As artificial intelligence and machine learning (ML) continues to infiltrate different aspects of our day-to-day lives, the technology is now doing more than ever -- for both good and bad.

Machines are getting better at writing. They can finish our sentences. They can reply to our emails. They can write news reports and even novels. But just because they can doesn't mean they should.

Technology is transforming our food chain, with some of the most important innovation occurring in the rise of precision farming. Together, artificial intelligence (AI) and automation are revamping the agriculture industry, helping farmers operate efficiently and in new ways. Today's farm is powered by data, along with a variety of devices and technologies, including sensors, GPS satellites, drones, and robots. This combination of automation and farming may mean to less friction and fewer obstacles for farmers, both for crop-based decisions and interacting with the USDA for permits. These exciting advancements, however, are not possible without dependable and scalable data centers.

Technology is transforming our food chain, with some of the most important innovation occurring in the rise of precision farming. Together, artificial intelligence (AI) and automation are revamping the agriculture industry, helping farmers operate efficiently and in new ways. Today's farm is powered by data, along with a variety of devices and technologies, including sensors, GPS satellites, drones, and robots. This combination of automation and farming may mean to less friction and fewer obstacles for farmers, both for crop-based decisions and interacting with the USDA for permits. These exciting advancements, however, are not possible without dependable and scalable data centers.

Can a machine-learning algorithm detect the intimations of mortality among a group of cancer patients, telling doctors and patients who should be having conversations about the end? A particular machine-learning computer model can do so, based on the factors found in electronic health records (EHRs), according to a new paper by University of Pennsylvania School of Medicine researchers in the journal JAMA Network Open. "In this cohort study, machine learning algorithms based on structured electronic health record data accurately identified patients with cancer at risk of short-term mortality," they wrote. Of the patients flagged as being "high priority" by the machine, 51% died within a 180-day window, according to the authors. Less than 4% of the patients deemed "lower priority" died in the same time frame, according to the findings.

Corporations would rather have an employee base full of robots and a select few humans to monitor the robots because it saves them money in labor cost. Borrowing without a maximum limitation means it is easy, and often more affordable, for corporations to invest in robots or automation than their labor force. It is cheaper to take a loan from a bank to finance the purchase of Artificial Intelligence software or robotic investing than it is to re-train workers or engage in improving work skills. So the bottom line is that corporations would rather have an employee base full of robots and a select few humans to monitor the robots because it saves them money in labor cost in robotic investing. You can thank the floating currency system inaugurated by Nixon for this change in the work culture of American corporations because it made technological innovation less expensive to finance which makes it harder for people to earn enough for a decent quality of life.

With data taken from "the front page of the Internet", this guide will introduce the top 10 Reddit datasets for machine learning. Connect with researchers and engineers working on related problems in machine learning, deep learning, natural language... Researchers from Russian corporation Neurobotics and the Moscow Institute of Physics and Technology have found a way to visualize a person's brain activity as actual images mimicking what they observe in real time. This will enable new post-stroke... Amazon is hiring more developers for Alexa than Google for everything. I explain the theory of "margins" that serves as a theoretical explanation for why AdaBoost has been observed not to overfit in practice.