Check Out: WorkDone Inc. – A Tokenized, AI Digital Workforce Platform Developer WE JUST MIGHT ADD THEM TO OUR PAI, IF IT'S COOL WITH YOU… Whether its autonomous vehicles, or digital assistants such as Apple's Siri, or Amazon's Alexa, the race to Artificial Intelligence dominance is on; sparking what some are calling The Next Industrial Revolution. Given that some of the most adopted technologies over the past few decades are the ones that simplify our lives, it's not difficult to imagine a future where artificial intelligence (A.I. or AI) transforms the way we work. In many cases, several tech experts believe artificial intelligence will ultimately replace humans with software and machines. That's why, when LA-based tech startup, WorkDone Inc., came across the vetting desk at Digital Arts Media Network ("DigitalAMN"), we were intrigued. WorkDone is developing an AI platform that caters to small and large organizations looking to deploy a digital workforce composed of software bots.

I recently covered Voice Summit the largest voice tech conference that brings the conversational design ecosystem together in one place. The conference was hosted at New Jersey Institute of Technology. I was amazed about the impact voice technology will have on our lives. Voice search will account 50 % of all search results by 2020. Voice technology is already in use in many home devices such as Amazon Alexa and Google home.

In this work, we study recommendation systems modelled as contextual multi-armed bandit (MAB) problems. We propose a graph-based recommendation system that learns and exploits the geometry of the user space to create meaningful clusters in the user domain. This reduces the dimensionality of the recommendation problem while preserving the accuracy of MAB. We then study the effect of graph sparsity and clusters size on the MAB performance and provide exhaustive simulation results both in synthetic and in real-case datasets. Simulation results show improvements with respect to state-of-the-art MAB algorithms.

The two main tasks in the Recommender Systems domain are the ranking and rating prediction tasks. The rating prediction task aims at predicting to what extent a user would like any given item, which would enable to recommend the items with the highest predicted scores. The ranking task on the other hand directly aims at recommending the most valuable items for the user. Several previous approaches proposed learning user and item representations to optimize both tasks simultaneously in a multi-task framework. In this work we propose a novel multi-task framework that exploits the fact that a user does a two-phase decision process - first decides to interact with an item (ranking task) and only afterward to rate it (rating prediction task). We evaluated our framework on two benchmark datasets, on two different configurations and showed its superiority over state-of-the-art methods.

LOS ANGELES – Law enforcement officials are looking into whether a New York man arrested in California for killing a woman he met on a dating app may have killed others. Danueal Drayton was arrested in Los Angeles last week and charged with raping and strangling a woman. Two law enforcement officials told The Associated Press on Monday that Drayton talked about killing at least five others in Connecticut and New York. Investigators are trying to determine whether his claims are true. The officials said Drayton did not admit killing Samantha Stewart, a nurse found dead in her Queens apartment, though police believe he's responsible for her murder.

They can identify patterns in voice commands and react to them according to predefined algorithms. In HR these are employed when the initial contact with applicants is being made – often when it comes to answering standard questions about an advertised position. As chatbots they can support an ongoing interaction between recruiter and applicant during the recruiting process, in this case they quite simply increase the recruiters accessibility or the applicant. Second field of application: „Natural Language Processing" (NLP), this technology supports the scanning of letters of application to characterize the applicants range and use vocabulary and his "wording" in general. NLP can assist in writing job advertisements, by using a language which is exactly targeted towards the preferred group of applicants.

Critics of the notch on Apple's iPhone X will probably be peeved at Google's next smartphone. New images purportedly show the yet-to-be-released Pixel 3 XL in a white colorway, according to XDA-Developers. They reveal that the Pixel 3 XL is likely to include a large notch, as well as a sizable'chin,' or space at the bottom of the phone's display. Critics of the notch on Apple's iPhone X are likely to be peeved by Google's next smartphone. The photos provide more details after another leak in early June claimed to show the Pixel 3 XL in black. Like the photos shared earlier this year, these show off a Pixel phone with a larger form-factor, an orange power button on the right side of the device, single-lens rear camera, dual front-facing cameras and stereo speakers.

Assistants are a big part of the future of human-computer interaction. So how you address the your artificially intelligent friend in the cloud is important. Star Trek's "computer" is accurate, but cold and sterile. Of the current leading assistant technologies, three have aimed at friendly first-name approachability. Amazon has Alexa, which sounds fun, female, and friendly.

Imagine you were traveling on business and you just arrived into town and had an emergency. Your luggage was sent to the wrong city or you were late for a critical meeting and you needed directions to the convention center. The check-in counter has a dozen guests waiting and the concierge is busy. Well, you may be in luck, because a four-foot robot with an open tablet computer and no waiting line is standing in the corner, and just may be able to come to your assistance. Pepper is an intelligent assistant that costs a bit more than a smart speaker, and can engage you in ways that go beyond ordering pizza or playing your favorite Top 40 tunes on the radio.

Some call artificial intelligence the fourth industrial revolution -- and it fascinates us, raises many questions, excites us, even scares us a little. But there is no denying that AI makes our daily lives so much easier than it was a few years ago, and much more than we imagined. When we talk about AI, we immediately think about what we use every day, such as virtual assistants or chatbots (e.g., Siri or Cortana), smartphones that identify us through fingerprint or facial recognition, cars that are able to detect pedestrians and to park themselves (often better than humans do). We also think about computers that recognize and analyze documents automatically. AI is also widely present in the business environment.