Google search, Facebook news feed, Amazon product recommendations are obvious examples of digital services used by billions of consumers everyday that successfully leverage Machine Learning (ML)¹. In fact you could say that the stellar growth these companies have experienced over the last decade or more just would not be possible without it. The internet giants have each conquered specific segments of consumers' daily digital lives and are now an ever-present habit for billions of people around the world. Google enables people to discover knowledge and information about products, places and things. Facebook enables people to engage with friends who have similar interests and stories.
Twitter's recent acquisition spree continues today as the company announces it has acqui-hired the team from news aggregator and summary app Brief. The startup from former Google engineers launched last year to offer a subscription-based news summary app that aimed to tackle many of the problems with today's news cycle, including information overload, burnout, media bias and algorithms that promoted engagement over news accuracy. Twitter declined to share deal terms. Before starting Brief, co-founder and CEO Nick Hobbs was a Google product manager who had worked on AR, Google Assistant, Google's mobile app, and self-driving cars, among other things. Co-founder and CTO Andrea Huey, meanwhile, was a Google senior software engineer, who worked on the Google iOS app and had a prior stint at Microsoft.
Language modeling -- that is, predicting the probability of a word in a sentence -- is a fundamental task in natural language processing. It is used in many NLP applications such as autocomplete, spelling correction, or text generation. Currently, language models based on neural networks, especially transformers, are the state of the art: they predict very accurately a word in a sentence based on surrounding words. However, in this project, I will revisit the most classic language model: the n-gram models. In this project, my training data set -- appropriately called train -- is "A Game of Thrones", the first book in the George R. R. Martin fantasy series that inspired the popular TV show of the same name.
What is powering the onslaught of Artificial Intelligence in every industry across the world? In very simple words, you teach the machine how to derive results. The results purely depend on algorithms used and the data that is poured to train/teach the machine. Machine learning is being used to power recommendation systems, audio/video classification software, autonomous driving, and many more industrial processes. There are 97 million songs in the world, now these are just the songs that are documented.
During training, the squared L2 error between the clean spectrogram and the predicted spectrogram is used as a loss function to train the network. At inference time, our separation model can be applied to arbitrarily long segments of video and varying numbers of speakers. The latter is achieved by either directly training the model with multiple-input visual streams (one for speaker), or simply by feeding the visual features of the desired speaker to the visual stream. For full details about the architecture and training process, see our full paper.15
Sense8 was an eight-hour Netflix Original series created by Lana and Andy Wachowski, and J. Michael Straczynski. The science fiction series starred eight characters worldwide, connected by a bond that can be felt through every sense. Sense8 follows the inhabitants of Chicago, who are all connected by more than just two or three senses; they are experiencing everything that their counterparts are seeing, sensing, hearing, and feeling. The series is a love story between two characters, and as they become more connected to their sense counterparts, they begin to feel their partners' pain. They also carry the responsibility of protecting their loved ones that are constantly in danger and fighting for freedom from some sort of outside threat.