The Arghon team shoots the final two scenes of the day. After a long day of shooting for the artificial intelligence promo, delirium has started to set in. Subscribe, subscribe, subscribe to ArghonTV for more videos, vlogs, and Arghon demos coming your way! Subscribe to get the updates about all the cool stuff we're doing at Arghon!! http://bit.ly/1X0wQg3 Google Play: https://play.google.com/store/apps/de... Arghon is an artificial intelligence startup. Arghon's mission is to bring meaningful convenience to the world through personal artificial intelligence.

Being overweight carries with it a social stigma, as does being a man who embraces the feminine, but for those in the gay community who live at the intersection of such identities, life can be like the worst case of double jeopardy. To Jamal Lewis, however, who is also black and who identifies as "gender deviant," being fat and effeminate is a source of power and a subject worthy of exploration in a documentary titled "No Fats, No Femmes." "For me, I'm just interested in the spaces that people are afraid to occupy," said Lewis, who uses "he-she" as a gender pronoun. "I think there is something to be learned from what we are most afraid of, and so, if that's what I was taught to be afraid of, well [forget] that. I am the Fat Femme."

Elon Musk's AI research group has opened a "gym" for robots, to ensure that they can be properly tested. The new project is an attempt to bring together training for artificially intelligent machines, allowing them to be fairly compared with each other – and avoid any problem results. The gym has been launched as the first project from OpenAI, a research group that is funded by backers including Mr Musk as well as a range of other tech leaders. The project launched in December and aims to "advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return". Boston Dynamics describes itself as'building dynamic robots and software for human simulation'.

Question answering has recieved more focus as large search engines have basically mastered general information retrieval and are starting to cover more edge cases. Question answering happens to be one of those edge cases, because it could involve a lot of syntatic nuance that doesn't get captured by standard information retrieval models, like LDA or LSI. Hypothetically, deep learning models would be better suited to this type of task because of their ability to capture higher-order syntax. Two papers, "Applying deep learning to answer selection: a study and an open task" (Feng et. Personally, I am a lot lazier than them, and I don't understand CNNs very well, so I would like to use an existing framework to build one of their models to see if I could get similar results. Keras is a really popular one that has support for everything we might need to put the model together. The Github repository for this project can be found here. See the instructions here on how to install Keras.

Heller goes on, "For years, computing has been stuck in an'if/then' paradigm. 'Computers are good at A B, or A B, but they are bad at A is similar to B' says Olley. 'Until now, only humans could handle'similar to' situations, but with machine learning, we can train algorithms to perform highly complex functions from describing an image to making judgement calls.' Say you want to sort and categorize all of your digital photos. 'If every picture of a dog were identical, it would be easy for an application to recognize dog photos and tag them appropriately,' Olley says. 'But dogs are not identical to one another, so the machine needs to see a series of photos labelled'dog' until it learns to recognize dogs in the abstract. But once it's trained, the machine can sort those photos on its own'."

Qualcomm Incorporated (NASDAQ: QCOM) today announced at the Embedded Vision Summit in Santa Clara, Calif. The SDK, called the Qualcomm Snapdragon Neural Processing Engine, is powered by the Qualcomm Zeroth Machine Intelligence Platform and is optimized to utilize Snapdragon's heterogeneous compute capabilities to provide OEMs a powerful, energy efficient platform for delivering intuitive and engaging deep learning-driven experiences on device. This SDK is the latest software addition to Snapdragon 820 and demonstrates Qualcomm Technologies' continued leadership by adding value for our customers to the Snapdragon portfolio. Qualcomm Technologies, with the introduction of the Snapdragon Neural Processing Engine, is the first mobile SOC provider to offer a deep learning toolkit optimized for mobile. This SDK will allow OEMs to run their own neural network models on Snapdragon 820 devices such as smart phones, security cameras, automobiles and drones, all without a connection to the cloud.

Everyone has been hearing about chatbots these past couple of weeks. As more companies introduce chatbots as a customer service option, it has quickly become a new convenient form of customer interaction. But what exactly is the hype all about? According to TechInAsia.com a chatbot "is a service, powered by artificial intelligence, that you interact with via a chat interface." Chatbots allow customers to receive basic answers, place orders, make purchases, and more.

Qualcomm has a vested interest in smarter mobile devices: it wants to power even more of them with its own Snapdragon chips. To that end, today sees the launch of the Qualcomm Snapdragon Neural Processing Engine an SDK for the Snapdragon 820 chipset that promises a simpler path to deep-learning software and artificial intelligence on phones, tablets, wearables, and even in cars. It's all powered by Qualcomm Zeroth Machine Intelligence Platform, and while Zeroth may sound like an enemy for the Power Rangers to fight, it's actually a way to leverage formally cloud-trapped processing entirely on a local device. Traditional machine learning - the ability to assess the world and categorize elements within it - generally requires masses of processing grunt and a big database against which to check the potential results. What Zeroth does is condense that down into a single chipset, taking advantage of the various components of Snapdragon like the Hexagon DSP, Adreno graphics, and more.

Nightmare scenarios involving Artificial Intelligence typically involve computers that become too smart for their own good and turn against their creators. Well, now we have an entirely different cause to be wary of AI, and the culprit is human rather than machine. Dave Gershgorn reports in Popular Science that a risk more imminent and more worrisome than HAL comes with everyday devices using even rather basic versions of AI: Siri (the virtual assistant on Apple iPhones), Alexa (Amazon's assistant) and Google Now (for Android phones). The problem isn't that the computer programs are too smart; it's that they're too gullible. Like the prisoners chained in Plato's cave who mistake manufactured shadows on the wall with reality, Siri and her virtual posse tend to believe what they hear.

Supervised Machine learning requires all data to be accompanied with metadata, i.e., it is tagged and labeled. So, it is largely restricted to structured data with rows and columns.