There's been a turbulence in the Star Wars franchise recently – and that's a good thing. First, there was the arrival of "The Mandalorian" on the Disney streaming service, which became operational earlier this week. All this action comes ahead of "Star Wars: The Rise of Skywalker" (in theaters Dec. 20), the ninth and last film in that saga. But these two recent entries focus on the past. "The Mandalorian" takes place after about five years after Luke Skywalker, Princess Leia, Han Solo and the rebels overthrew the Empire in 1983's "Star Wars: Return of the Jedi."

Not too long ago, automated flying machines whizzing around the skies were concepts gracing the pages of science fiction novels. Today, drones are used and seen frequently. From capturing incredible aerial shots for documentaries, to inspecting large structures to spot potential weaknesses, drones have become invaluable tools. As the Wright brothers' first successful airplane evolved into the sophisticated aircraft we see today, the very first drones too, have evolved and improved – and are continuing to do so, thanks to artificial intelligence. AI is paving the way for a new generation of self-flying drones, which can carry out tasks without requiring a human operator.

Making the case for AI, or any nascent technology for that matter, can be a struggle for companies today. While large enterprises know they need to be fast, agile and innovation-obsessed to survive disruption, their age-old policies, antiquated systems, disconnected data and entrenched corporate habits can be serious blockers to adoption. With a century plus-long tradition of engineering excellence, we at Siemens knew we had a challenge to transform ourselves in order to continue to lead in the AI era. Adopting a mindset of risk-taking and innovation from the inside-out had to be a key part of that transformation journey. Thanks to our recent efforts with the IBM Data Science and AI Elite team, together with the IBM Garage at the Watson IoT Center in Munich, we recently made a critical breakthrough on our journey to AI. Working with partners such as IBM and others, we developed a proof of concept to showcase how we could harness AI and blockchain to drastically reduce our employees carbon output--not through mandates, but through incentivizing more eco-friendly behavior.

MIT's Mini Cheetah robot is capable of running, walking, jumping and turning. How many times can I say it--over and over again, until it becomes like mental nails against a mental chalkboard? We all know the quote from Elon Musk that says something to the effect that "artificial intelligence (AI) is far more dangerous than nukes." The company has been making robots since 1992, and its astute credo is: "Changing your idea of what robots can do." One of those ideas is to have a robot do parkour, jumping over logs and leaping steps without breaking pace.

Technological change is the only constant in today's business world, disrupting everything from large organizations to small start-ups. Disruption affects everyone, but will you be the disruptor or the disrupted? You must pay close attention to the Hard Trends shaping the future of your industry, your business, and the outside world to identify opportunities used to innovate and grow rapidly, additionally using those Hard Trends to solve any problems your organization and customers might have before they occur. The shared definition and understanding of the words we use is an issue in business. While several companies are on course to use artificial intelligence (AI), machine learning (ML), and deep learning (DL), others hardly understand the fundamental differences between these powerful technologies.

X-37 was cofounded by Atomwise Inc. and a team of experienced pharmaceutical developers from Velocity Pharmaceutical Development. In addition to the above development programs, the team at X-37 will identify additional high-value drug targets, generate novel drug leads against these targets using Atomwise's world-class AI platform for structure-based drug design, and develop each of these drug programs to a medically-relevant inflection point, where it can be acquired by or partnered with a major pharmaceutical company to be brought to market. X-37 makes use of an LLC structure permitting each drug development program to be housed in a separate virtual company under the parent LLC. This structure is tax efficient and flexible, in that it allows X-37 to divest individual drug development programs, while maintaining the parent company and team. X-37 began operation in 2018 and has already generated promising novel hit molecules against several targets of high interest to the pharmaceutical industry.

Sign in to report inappropriate content. IntelliSpace Discovery is an integrated AI solution that enables the entire process of generating new AI applications, providing data integration, training, and deployment in the research setting. Learn more: https://www.usa.philips.com/healthcar.... *IntelliSpace Discovery is for research use only and cannot be used for patient diagnosis or treatment selection.

At least, that was the message LivePerson CEO Robert LoCascio delivered to CNBC's Jim Cramer on Friday. "When we think about 2020, I really think it's the start of everyone having [AI]," LoCascio said on "Mad Money." "AI is now becoming something that's not just out there. It's something that we use to drive everyday life." LivePerson, based in New York City, provides the mobile and online messaging technology that companies use to interact with customers.

We introduce a reinforcement learning framework for retail robo-advising. The robo-advisor does not know the investor's risk preference, but learns it over time by observing her portfolio choices in different market environments. We develop an exploration-exploitation algorithm which trades off costly solicitations of portfolio choices by the investor with autonomous trading decisions based on stale estimates of investor's risk aversion. We show that the algorithm's value function converges to the optimal value function of an omniscient robo-advisor over a number of periods that is polynomial in the state and action space. By correcting for the investor's mistakes, the robo-advisor may outperform a stand-alone investor, regardless of the investor's opportunity cost for making portfolio decisions.

As a mental disorder progresses, it may affect brain structu re, but brain function expressed in brain dynamics is affected much earlier. Captu ring the moment when brain dynamics express the disorder is crucial for early dia gnosis. The traditional approach to this problem via training classifiers either pro ceeds from handcrafted features or requires large datasets to combat the m n problem when a high dimensional fMRI volume only has a single label that carries le arning signal. Large datasets may not be available for a study of each disorder, or rare disorder types or subpopulations may not warrant for them. In this paper, w e demonstrate a self-supervised pre-training method that enables us to pre -train directly on fMRI dynamics of healthy control subjects and transfer the learn ing to much smaller datasets of schizophrenia. Not only we enable classificatio n of disorder directly based on fMRI dynamics in small data but also significantly sp eed up the learning when possible. This is encouraging evidence of informat ive transfer learning across datasets and diagnostic categories.