AutoML might be the answer to such circumstances.

This is the creator of gpt2-client here. Inspired by Hugging Face's Transformers, I built and launched Sightseer, a TensorFlow package that allows anyone to access SOTA Computer Vision and Object Detection models in 10 lines of code or less. In less than 10 lines of code, you can now access general-purpose SOTA models!!! For now, the Beta release supports YOLOv3 (Darknet by Joseph Redmon) and enables you to quickly load images and ingest them into the model. In the next release (coming very soon!), I'll be adding Facebook AI's Mask R-CNN model with support for video, webcam footage, and screen recordings and tools for data annotation and inter-format conversion (XML/CSV/JSON/TFRecords).

It's not unusual today to see people talking about artificial intelligence (AI). When I was a kid in the 1980s, AI was depicted in Hollywood movies, but its real-world use was unimaginable given the state of technology at that time. While we don't have robots or androids that can think like a person or are likely to take over the world, AI is a reality now, and to understand what we mean when we talk about AI today we have to go through a -- quick, I promise -- introduction on some important terms. Simply put, AI is anything capable of mimicking human behavior. From the simplest application -- say, a talking doll or an automated telemarketing call -- to more robust algorithms like the deep neural networks in IBM Watson, they're all trying to mimic human behavior.

CBS' streaming service, CBS All Access, credits a trio of high-profile events -- including the premiere of its new Star Trek series, "Star Trek: Picard," as well as the 62nd annual Grammy Awards, not to mention a busy month of football -- with helping it to achieve a new record for subscriber sign-ups in a given month. The company says January 2020 surpassed the service's previous record in February 2019 for subscriber sign-ups. In addition, last week was the second-best sign-up week ever, closely behind the week of the 2019 Super Bowl. Much of the record-setting had to do with the launch of the highly anticipated show, "Star Trek: Picard," which brings back fan-favorite Patrick Stewart as Jean-Luc Picard, now a retired Starfleet Admiral whose quiet life on his family's vineyard is about to be disrupted. The show, set 18 years after the events of the final "Star Trek: The Next Generation" movie, "Star Trek: Nemesis," not only capitalizes on Stewart's draw, it also brings back previous "Star Trek" actors including Brent Spiner (Data), Jeri Ryan (Seven of Nine), Marina Sirtis (Troi), and Jonathan Frakes (Riker). But unlike other reboots, which hope nostalgia alone will bring the viewers, "Picard's" creators have actually given thought to the story the show is trying to tell, resulting in a 95% critics score on Rotten Tomatoes.

Eye on A.I. tracks new developments in artificial intelligence research, hosted by longtime New York Times journalist Craig S. Smith. In each episode, Craig will discuss aspects of AI with some of the people making a difference in the space, putting incremental advances into a broader context. AI is about to change your world, so pay attention.

"You start off with business intelligence, analytics, big data … and then add things like AI, machine learning, [and] deep learning into that analytics …

While some call AI and ML overhyped themes that are nothing more than if statements, or just programming stuff, I offer you to come face to face with all pieces of evidence to check this out. In this post, I will contrast these terms and also showcase the difference between the specialists who are involved in these two spheres: who are they? Software engineer, software developer, machine learning expert, data scientist…some people even use a programmer or coder, and some even go as far as a ninja, guru, or rock star! But, are they really the same? And if so, is there a line between Machine Learning and Traditional Programming?

Recent work in the domain of misinformation detection has leveraged rich signals in the text and user identities associated with content on social media. But text can be strategically manipulated and accounts reopened under different aliases, suggesting that these approaches are inherently brittle. In this work, we investigate an alternative modality that is naturally robust: the pattern in which information propagates. Can the veracity of an unverified rumor spreading online be discerned solely on the basis of its pattern of diffusion through the social network? Using graph kernels to extract complex topological information from Twitter cascade structures, we train accurate predictive models that are blind to language, user identities, and time, demonstrating for the first time that such "sanitized" diffusion patterns are highly informative of veracity. Our results indicate that, with proper aggregation, the collective sharing pattern of the crowd may reveal powerful signals of rumor truth or falsehood, even in the early stages of propagation.