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Deep learning is helping Facebook draw value from a larger portion of its unstructured datasets created by almost 2 billion people updating their statuses 293,000 times per minute. Most of its deep learning technology is built on the Torch framework that focuses on deep learning technologies and neural networks. One of the most useful tools used by Facebook is Deeptext Deep Text uses unsupervised machine learning to understand humans and interpret what they say. Deeptext, which decodes the meaning of the content posted to find the relative meaning. Facebook then helps brands to generate leads with this tool by directing people to advertisers based on the conversations they are having.
Facebook says that it will expand an online course in deep learning to more students to help improve the diversity of its AI division. After a successful pilot program at Georgia Tech, the company will roll out this graduate-level course in deep learning to more colleges across 2021. The focus will be on offering the system to universities that serve large numbers of Black and Latinx students. It's hoped that, by improving the diversity of the people building these systems, some of the more odious biases will be weeded out. This is part of a broader program to encourage people to enter the computer science field even if their undergraduate training is in another area.
Researchers at Facebook AI recently introduced and open-sourced a new framework for self-supervised learning of representations from raw audio data known as wav2vec 2.0. The company claims that this framework can enable automatic speech recognition models with just 10 minutes of transcribed speech data. Neural network models have gained much traction over the last few years due to its applications across various sectors. The models work with the help of vast quantities of labelled training data. However, most of the time, it is challenging to gather labelled data than unlabelled data.
Understanding individuals' feelings are fundamental for organizations since clients can communicate their feelings and sentiments more transparently than ever before. By automatically analyzing customer feedback, from study reactions to social media discussions, brands can listen mindfully to their clients, and tailor products and services to address their issues. Sentiment analysis is a machine learning method that recognizes polarity (for example a positive or negative thought) within the text, whether a whole document, paragraph, sentence, or clause. Marketing is ending up being one of the artworks most disrupted by the digital revolution. A lot to the aversion of customary marketing proponents and maybe to the pleasure of technologists, it is presently a lot about codifying the whole knowledge chain – catching the abundance of digital data, sorting out it, applying algorithms to process it and taking care of back noteworthy decisions to different functions– all in real-time, with end to end automation, and at lightening quick speed.
Facebook's artificial intelligence researchers have a plan to make algorithms smarter by exposing them to human cunning. They want your help to supply the trickery. Thursday, Facebook's AI lab launched a project called Dynabench that creates a kind of gladiatorial arena in which humans try to trip up AI systems. Challenges include crafting sentences that cause a sentiment-scoring system to misfire, reading a comment as negative when it is actually positive, for example. Another involves tricking a hate speech filter--a potential draw for teens and trolls.
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If ever there was a time for mentorship, it's now. Even back in the days of'business as usual', employees valued having someone to lean on – an experienced colleague who could offer support and advice on tricky situations, business decisions and career progression. It was easier then; all you had to do was knock on their office door or drop them an email requesting a catch up over coffee. But since COVID-19 rocked up, two things have happened: traditional avenues for accessing mentors have all but vanished, and the trend of remote working has stripped employees of vital support networks and structure, making mentoring more important than ever. Some people roll their eyes at the very mention of mentorships; they write them off as a box-ticking exercise for interns or a pastime for chief executives with too much time on their hands (as if such a thing existed). For those people, let me set the record straight.
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