This undated image provided by Amazon.com, Inc. shows an Amazon Fire TV Cube. This year could be the year of the streaming service. Netflix, Amazon Prime, and Hulu have comfortably held the top three spots for streaming services, but this year they're going to see some serious brands looking to take a bite out of the streaming pie. IMDb, AT&T, Disney, Apple, and Warner have all announced or hinted at plans to launch streaming services in 2019.

WASHINGTON - If you see a video of a politician speaking words he never would utter, or a Hollywood star improbably appearing in a cheap adult movie, don't adjust your television set -- you may just be witnessing the future of "fake news." "Deepfake" videos that manipulate reality are becoming more sophisticated due to advances in artificial intelligence, creating the potential for new kinds of misinformation with devastating consequences. As the technology advances, worries are growing about how deepfakes can be used for nefarious purposes by hackers or state actors. "We're not quite to the stage where we are seeing deepfakes weaponized, but that moment is coming," said Robert Chesney, a University of Texas law professor who has researched the topic. Chesney argues that deepfakes could add to the current turmoil over disinformation and influence operations.

Machine learning is a growing area of computer science and several programming languages support ML framework and libraries. Among all of the programming languages, Python is the most popular choice followed by C, Java, JavaScript, and C#.

"He helped us build our core tech and helped us recruit talent," said D'Souza. The ProNavigator team, based in the Velocity Garage in the Tannery building in downtown Kitchener, used Google Home and Amazon's Alexa when building the smart software. Both devices transcribe spoken words into printed text. Software programmers spoke into the devices as if they were consumers looking for quotes or asking questions about their policy. The text was used to build the platform's understanding of spoken language.

Are the trains running on time today or might the bus be quicker? Is that new restaurant near work any good for lunch? Is the latest blockbuster movie worth the price of a ticket? Sarcasm is very hard for computers to detect as sarcastic comments use many of the same words and language structures as positive comments. Above is a map that the Crystalace team have produced.

We describe a machine-learning approach to pitch correcting a solo singing performance in a karaoke setting, where the solo voice and accompaniment are on separate tracks. The proposed approach addresses the situation where no musical score of the vocals nor the accompaniment exists: It predicts the amount of correction from the relationship between the spectral contents of the vocal and accompaniment tracks. Hence, the pitch shift in cents suggested by the model can be used to make the voice sound in tune with the accompaniment. This approach differs from commercially used automatic pitch correction systems, where notes in the vocal tracks are shifted to be centered around notes in a user-defined score or mapped to the closest pitch among the twelve equal-tempered scale degrees. We train the model using a dataset of 4,702 amateur karaoke performances selected for good intonation. We present a Convolutional Gated Recurrent Unit (CGRU) model to accomplish this task. This method can be extended into unsupervised pitch correction of a vocal performance, popularly referred to as autotuning.

The majority of conversations a dialogue agent sees over its lifetime occur after it has already been trained and deployed, leaving a vast store of potential training signal untapped. In this work, we propose the self-feeding chatbot, a dialogue agent with the ability to extract new training examples from the conversations it participates in. As our agent engages in conversation, it also estimates user satisfaction in its responses. When the conversation appears to be going well, the user's responses become new training examples to imitate. When the agent believes it has made a mistake, it asks for feedback; learning to predict the feedback that will be given improves the chatbot's dialogue abilities further. On the PersonaChat chit-chat dataset with over 131k training examples, we find that learning from dialogue with a self-feeding chatbot significantly improves performance, regardless of the amount of traditional supervision.

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Be warned: Artificial Intelligence is NOT your friend.