The graph represents a network of 1,100 Twitter users whose tweets in the requested range contained "iiot bigdata", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 20 November 2020 at 12:00 UTC. The requested start date was Friday, 20 November 2020 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 16-hour, 59-minute period from Tuesday, 17 November 2020 at 07:37 UTC to Friday, 20 November 2020 at 00:37 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.
Huyen, aka Huyen Chip, ranked fifth in the annual Top Voices list released this week by the U.S. professional networking site. It compiles the list by examining all sharing activity on its platform from October 1, 2019 through September 30, 2020, and using a combination of quantitative and qualitative signals including engagement (comments, reactions and shares), follower growth and posting cadence. It said: "Having worked at prominent tech companies including Netflix and NVIDIA, Huyen joined the AI startup Snorkel last December. A Stanford graduate, Huyen turned to LinkedIn to find reviewers for the course she'll start teaching there in January next year, Machine Learning Systems Design." Before coming to the U.S., Chip helped launch Vietnam's second most popular web browser, Coc Coc.
The graph represents a network of 7,204 Twitter users whose tweets in the requested range contained "(Artificial Intelligence) OR #AI", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 01 November 2020 at 04:57 UTC. The requested start date was Thursday, 29 October 2020 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 2-day, 0-hour, 45-minute period from Monday, 26 October 2020 at 15:20 UTC to Wednesday, 28 October 2020 at 16:05 UTC.
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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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