Sure, you know your mobile phone is essential, but exactly how much can you rely on it? A few days ago in Japan, Google threw down a gauntlet: how far can you get in a foreign country, where you can't be sure of finding an English speaker, where the words, even the alphabet, are unfamiliar, and where the address system is notoriously tricky? So there I was, in Tokyo, charged with solving a series of puzzles using a smartphone and nothing more. First, I had to get myself from bustling Tokyo (where English speakers are plentiful and, because they are Japanese, endlessly helpful) to the distant city of Kanazawa. I had a JR train pass, which is the best way to get around Japan for a foreigner and which offers fantastic value, though you must buy it before you arrive in the country.

To companies grappling with complex data projects powered by artificial intelligence, a system that Nvidia calls an "AI supercomputer in a box" is a welcome development. Early customers of Nvidia's DGX-1, which combines machine-learning software with eight of the chip maker's highest-end graphics processing units (GPUs), say the system lets them train their analytical models faster, enables greater experimentation, and could facilitate breakthroughs in science, health care, and financial services. Data scientists have been leveraging GPUs to accelerate deep learning--an AI technique that mimics the way human brains process data--since 2012, but many say that current computing systems limit their work. Faster computers such as the DGX-1 promise to make deep-learning algorithms more powerful and let data scientists run deep-learning models that previously weren't possible. It costs $129,000, more than systems that companies could assemble themselves from individual components.

It's difficult to predict how artificial intelligence technology will change over the next 10 to 20 years, but there are plenty of gains to be made. By 2018, robots will supervise more than 3 million human workers; by 2020, smart machines will be a top investment priority for more than 30 percent of CIOs. Everything from journalism to customer service is already being replaced by AI that's increasingly able to replicate the experience and ability of humans. What was once seen as the future of technology is already here, and the only question left is how it will be implemented in the mass market. Over time, the insights gleaned from the industries currently taking advantage of AI -- and improving the technology along the way -- will make it ever more robust and useful within a growing range of applications.

AI Business did an interview with Rulex's Andrea Ridi about the areas of challenge in implementing AI, and how he believes the professional services industry will change by adopting the technology. Rulex provides revolutionary AI software that enables business and process experts to embed automated real time predictive intelligence in applications, infrastructure, and IoT edge apps. Rulex's proprietary machine learning algorithms automatically learn and extract predictive if-then logical rules from raw data with no need for speculative data exploration or iterative scientific experimentation. Unlike the math-based predictive models produced by conventional machine learning algorithms, Rulex's logic-based models are compact and efficient, and can be easily used for making predictions on highly distributed systems and low cost, low power IoT devices. With the Rulex platform, business analysts and solution developers can easily create a new class of advanced applications for automated decision making, self-managing networks, and real-time native prediction on IoT edge devices.

Amazon's Prime Air makes first drone delivery Google launches Waymo, its self-driving car company - Duration: 1:01. Trump's conflicts of interest are unprecedented - Duration: 2:40. Rex Tillerson's complicated relationship with climate change - Duration: 0:55. Trump says this fighter jet is too expensive - Duration: 1:14. Starbucks' next CEO: We'll never have robots - Duration: 2:27.

To companies grappling with complex data projects powered by artificial intelligence, a system that Nvidia calls an "AI supercomputer in a box" is a welcome development. Early customers of Nvidia's DGX-1, which combines machine-learning software with eight of the chip maker's highest-end graphics processing units (GPUs), say the system lets them train their analytical models faster, enables greater experimentation, and could facilitate breakthroughs in science, health care, and financial services. Data scientists have been leveraging GPUs to accelerate deep learning--an AI technique that mimics the way human brains process data--since 2012, but many say that current computing systems limit their work. Faster computers such as the DGX-1 promise to make deep-learning algorithms more powerful and let data scientists run deep-learning models that previously weren't possible. It costs $129,000, more than systems that companies could assemble themselves from individual components.

Amazon has started its drone delivery trial in Cambridge, UK, the company announced Wednesday. In a tweet, Amazon CEO Jeff Bezos revealed Amazon Prime Air, which promises to deliver packages with small drones in 30 minutes or less after placing an order. The company did not say when the service will be available. "We will deploy when and where we have the regulatory support needed to safely realize our vision," said Amazon. "We're excited about this technology and one day using it to deliver packages to customers around the world in 30 minutes or less."

On Dec. 7, a bag of popcorn, along with an Amazon Fire TV stick, left a Cambridge warehouse in the U.K. and 13 minutes later, both were accepted by an Amazon customer, one of two who had agreed to be part of the test program.

Late one Friday night in early November, Jun Rekimoto, a distinguished professor of human-computer interaction at the University of Tokyo, was online preparing for a lecture when he began to notice some peculiar posts rolling in on social media. Apparently Google Translate, the company's popular machine-translation service, had suddenly and almost immeasurably improved. Rekimoto visited Translate himself and began to experiment with it. He had to go to sleep, but Translate refused to relax its grip on his imagination. Rekimoto wrote up his initial findings in a blog post. First, he compared a few sentences from two published versions of "The Great Gatsby," Takashi Nozaki's 1957 translation and Haruki Murakami's more recent iteration, with what this new Google Translate was able to produce. Murakami's translation is written "in very polished Japanese," Rekimoto explained to me later via email, but the prose is distinctively "Murakami-style."

It emphasizes flexibility through the elegant use of object-oriented design patterns.