The European Robotics League, funded by the European Commission to advance research, development and innovation in robotics and artificial intelligence, is the umbrella for three robotics competitions: ERL Consumer, ERL Emergency and ERL Professional service robots. All three leagues meet every two years in the ERL Smart Cities Robotics Challenge, showcasing how real robots can make our lives better in urban environments. The Challenge The SciRoc challenge will be held in the smart shopping mall of the Centre:MK. The challenge focuses on smart shopping and is divided into a series of episodes, each consisting of a task to be performed addressing specific research challenges. In order to accomplish their tasks, robots will have to cooperate with the simulated digital infrastructure of a smart shopping mall.

Rather than waiting days to identify and prepare answers, Machine Learning can immediately adapt with all the information available.

"AI, machine learning–these have been a part of our lives in some form or the another for quite some time," says Chahal, "but we still hear a great deal …

In the 2003 reboot of Battlestar Galactica we learned that "this has all happened before and will all happen again." And now, that prophecy is coming true, again. On Tuesday, news broke that NBC's new streaming service, Peacock, will debut a new version of Battlestar Galactica written and produced by Sam Esmail, famous for his work on Mr. Robot. But, because Battlestar has already been rebooted, and kind of recently, the newly announced series is either a losing sci-fi gamble or the best reboot idea in years. Back in 2003, the late Richard Hatch actively tried to sabotage the Sci-Fi Channel's "reimagining" of Battlestar Galactica.

The boom in popularity for podcasting has given a new voice to the world of spoken word content that had been largely left for dead with the decline of broadcast radio. Now riding the wave of that growth, a startup called Descript that's building tools to make the art of creating podcasts -- or any other content that involves working with audio -- a little easier with audio transcription and editing tools, has a trio of news announcements: funding, an acquisition, and the launch of a new tool that brings some of the magic of natural language processing and AI to the medium by letting people create audio of their own voices based on text that they type. Descript, the latest startup from Groupon founder Andrew Mason, created as a spinoff of his audio-guide business Detour (which got acquired by Bose last year), is today announcing $15 million in funding, a Series A for expanding the business (including hiring more people) that's coming from Andreessen Horowitz (it also funded the startup's seed round in 2017) and Redpoint. Along with that, the company has acquired a small Canadian startup, Lyrebird -- which had, like Descript, also built audio editing tools. Together, the two are rolling out a new feature for Descript called Overdub: people will now be able to create "templates" of their voices that they can in turn use to create audio based on words that they type, part of a bigger production suite that will also let users edit multiple voices on multiple tracks.

In the age of Artificial Intelligence (AI) and Key Performance Indicators (KPI's) only creativity will open doors to new opportunities and long-term success. And when others think that this soon will be under the all-powerful paw of technologies, some believe that creativity will help humans thrive and monetize on their talents, knowledge and unusual approaches to problem-solving. Buckle up, because this is my first article of the Creativity series. And it seems there is quite a journey ahead! Always surrounded by scientists I got used to hearing about miracles being born within the walls of laboratories.

Advances in AI and computer graphics over the last several years are now being harnessed to create, modify, and disseminate modified or fabricated images, audio, and video content, often referred to broadly as synthetic media. These new content generation and modification capabilities have significant, global implications for the legitimacy of information online, the quality of public discourse, the safeguarding of human rights and civil liberties, and the health of democratic institutions--especially given that some of these techniques may be used maliciously as a source of misinformation, manipulation, harassment, and persuasion. The ability to create synthetic or manipulated content that is difficult to discern from real events frames the urgent need for developing new capabilities for detecting such content, and for authenticating trusted media and news sources. AI techniques are being developed to detect and defend against synthetic and modified content. However, further investment and collaboration will be required for the advancement and application of these techniques, and for strengthening capacity in organizations and communities affected by these developments.

Last month, HBO launched a handy website for figuring out which of its original shows to watch. But instead of using computer algorithms to sort through its vast catalog, the company hired people to make their case for each series by making video testimonials. The site, called "Recommended by Humans," is more marketing stunt than product strategy--HBO has no plans to offer an app-based version for mobile or TV devices, and it declined to say how humans might factor into its forthcoming HBO Max service. But its very existence was a statement about our current moment in tech. We've realized that recommendation algorithms aren't as infallible as tech companies once made them out to be, and that handing the curation job to people still has value.

I am looking for the state-of-the-art entity extraction/relation extraction algorithms that are practical to implement and use for commercial information extraction. Mr. Wilken works at Foobar, Inc (transitively he is then the CEO of Foobar, Inc.). In my experience I've used CRF used hand crafted features for entity tagging followed by a classifier to determine relations between entities use hand crafted features. This is a pretty old school approach and does not leverage any of the advances in word embeddings (Glove, BERT, etc.). I know there are also methods for doing joint entity relation extraction.