Fiction is full of robots with feelings. Like that emotional kid David, played by Haley Joel Osment, in the movie A.I. Or WALL•E, who obviously had feelings for EVE-uh. Robby the Robot sounded pretty emotional whenever warning Will Robinson of danger. Not to mention all those emotional train-wreck, wackadoodle robots on Westworld. But in real life robots have no more feelings than a rock submerged in novocaine.

Apple's new iPhone 11 Pro Max is seen above. Q: When I interview someone for a position or meet someone for the first time, I like to see what they post on social media. You can tell a lot about a person this way! Is there a trick to finding a person's social accounts? A: Since the beginning, social media has been a double-edged sword: first and foremost, it's a great way to share significant life events and professional information with friends and colleagues.

There are some really good tools for tagging data and creating datasets for ML like doccano. Most of these are web GUIs though which I find hard and annoying to extend. Some time back, I made a system for tagging within Emacs which then got extended into a sort of framework. If you live inside Emacs and are willing to spend some time creating a fast tagging workflow, you can try tog which lets you create custom data taggers by writing a few rendering and parsing functions. It's all Emacs Lisp, so you can extend everything.

James Dean, who has been dead for 64 years, is set to star in an upcoming movie about the Vietnam War. According to the Hollywood Reporter, the movie, an adaptation of Gareth Crocker's novel, Finding Jack, will feature a computer-generated image (CGI) version of Dean. "We searched high and low for the perfect character to portray the role of Rogan, which has some extreme complex character arcs, and after months of research, we decided on James Dean," says Anton Ernst, one of the film's co-directors. Dean will be constructed through "full-body" CGI using archival footage from his films – he will be physically captured through the movements of an actor and voiced by another actor. The fact that the directors couldn't find an actor of this capability in an age where much so much untapped talent is available is questionable.

And while we don't always know the full impact of such technological advancement, with Artificial Intelligence (AI) it's becoming increasingly clear that AI will disrupt nearly every industry in one way or another. While many people fear a future in which robots have eliminated human employees from the workforce, AI technology is not nearly that sophisticated yet. Sure, an autonomous train is already helping a company automate its mining operations in Australia, Google is making music with AI and a robot named Flippy has a job flipping burgers in Southern California, but the technology has quite a ways to go before it'll free us from all mundane and repetitive tasks. While experts argue whether AI will ultimately create more jobs than it destroys, we know it is already being used to help humans do their jobs better and augment the number of tasks they can handle concurrently. The technology has been around longer than most people realize.

Machines don't understand much of anything, especially not things such as ironic speech, but machine learning may be able to assist humanity in some way by counting the instances of linguistic and semantic constructions that indicate satire or misleading news, according to a new study by tech startup AdVerifai, in partnership with George Washington University and Amazon's AWS. A lot of nuances of writing are lost on the internet -- things such as irony. That's why satirical material such as the writing of Andy Borowitz on the website of The New Yorker magazine has to be labeled as satire, to make sure we know. Scientists in recent years have become concerned: What about writing that isn't properly understood, such as satire mistaken for the truth, or, conversely, deliberate disinformation campaigns that are disguised as innocent satire? And so began a quest to divine some form of machine learning technology that could automatically identify satire as such and distinguish it from deliberate lies.

Umba's software uses machine learning to work out a person's credit score, based on alternative data, including the type of phone they use, the apps …

During the last months of my mother's life, as she ventured further from lucidity, she was visited by music. In collusion with her dementia, her hearing loss filled her consciousness with musical hallucinations. Sometimes welcome, more often not, her musical visitations were vivid, yet segmented and tattered. She would occasionally comment on the singers. On rare occasions she would identify the performer. Mitch Miller, who wrote oppressively cheerful arrangements of popular songs from the 1950s, seemed to command a prominent role in her hallucinations.

-- This paper proposes a framework which is able to generate a sequence of three-dimensional human dance poses for a given music. The proposed framework consists of three components: a music feature encoder, a pose generator, and a music genre classifier . We focus on integrating these components for generating a realistic 3D human dancing move from music, which can be applied to artificial agents and humanoid robots. The trained dance pose generator, which is a generative autoregressive model, is able to synthesize a dance sequence longer than 5,000 pose frames. Experimental results of generated dance sequences from various songs show how the proposed method generates humanlike dancing move to a given music. In addition, a generated 3D dance sequence is applied to a humanoid robot, showing that the proposed framework can make a robot to dance just by listening to music. Dance is one of the most important form of performing arts that having been emerged in all known cultures. As one of the specific subcategory of under theatrical dance, choreography associated with music is also one of the most popular forms that have usually been designed and physically performed by professional choreographers.