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How is bias built into algorithms? Garbage in, garbage out.

#artificialintelligence

In facial recognition and AI development, computers are trained on massive sets of data, millions of pictures gathered from all over the web. There are only a few publicly available datasets, and a lot of organizations use them. He and Abeba Birhane, at University College Dublin, published a paper recently examining these academic datasets. Most of the pictures are gathered without consent, people can be identified in them and there are racist and pornographic images and text. And even the idea of labeling someone a lawyer or a woman or a criminal based on appearance?


Top 10 Artificial Intelligence YouTube Channels in 2020

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We know that Artificial Intelligence (AI) is the main force moving the society into the future described in the movies in the past couple of decades. There are new heights achieved every day in different fields using Artificial Intelligence methods. Artificial Intelligence is a huge field and contains a lot of subfields, that contain a lot of subfields themselves. There are huge amounts of sources that claim that can help you learn Artificial Intelligence. These sources come in different forms and types, from books, blogs, projects, videos, etc. Today we are going to talk about the video sources, more precisely YouTube videos.


51+ Data Sets for Beginner Data Science and Machine Learning Projects

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Description -- This database, updated daily, contains ads that ran on Facebook and were submitted by thousands of ProPublica users from around the world. We asked our readers to install browser extensions that automatically collected advertisements on their Facebook pages and sent them to our servers. We then used a machine learning classifier to identify which ads were likely political and included them in this dataset.


Computer vision(CV): Leading public companies named

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CV is a nascent market but it contains a plethora of both big technology companies and disruptors. Technology players with large sets of visual data are leading the pack in CV, with Chinese and US tech giants dominating each segment of the value chain. Google has been at the forefront of CV applications since 2012. Over the years the company has hired several ML experts. In 2014 it acquired the deep learning start-up DeepMind. Google's biggest asset is its wealth of customer data provided by their search business and YouTube.


Facebook AI Research Is A Game-Changer

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For decades, computer programmers have been trying to beat multiplayer games by finding reliable patterns in data. Researchers at Facebook and Carnegie Mellon University published a whitepaper in Science Journal in July that flips this switch. Their software embraces randomness, and it is reliably beating humans at games. Smart bearded person in a classic gray suit is playing poker at casino in smoke sitting at the table... [ ] with chips and cards on it . He is holding a glass of whiskey in his hand and looking away.


Blender Bot -- Part 3: The Many Architectures

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We have been looking into Facebook's open-sourced conversational offering, Blender Bot. In Part-1 we went over in detail about the DataSets used in the pre-training and fine-tuning of it and the failure cases as well as limitations of Blender. And in Part-2 we studied the more generic problem setting of "Multi-Sentence Scoring", the Transformer architectures used for such a task and learnt about the Poly-Encoders in particular -- which will be used to provide the encoder representations in Blender. In this 3rd and final part, we return from our respite with Poly-Encoders, back to Blender. We shall go over the different Model Architectures, their respective training objectives, the Evaluation methods and performance of Blender in comparison to Meena.


How to Use AI & Machine Learning to Make Social Media Marketing Decisions

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Northern Light CEO C. David Seuss presented a virtual session at The Market Research Event (TMRE) Digital Week on June 24, about the value of new, AI-driven tools for "decision-oriented analysis" of social media posts to help set and refine an organization's product marketing strategy. Seuss' talk, entitled "Using Machine Learning to Make Social Media Marketing Decisions," focused on analyzing Twitter – the most text content-rich social media platform – for the specific purpose of gleaning business insights valuable to marketing professionals. "Assessing simple co-occurrence of Twitter hashtags is insufficient, and often downright misleading, for marketers of complex products," Seuss asserted in his presentation. "Understanding the context of the social media conversation is vital to derive a truly meaningful analysis of hashtag and keyword overlaps." Seuss explained that using AI and machine learning techniques to measure the semantic similarity of hashtags leads to far more accurate analysis that gets at the importance, from a business perspective, of seemingly related terms.


The Convergence of AI and Structural Engineering

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Technology is supposed to have a positive effect on humanity. That was the initial vision, correct? But for some reason this artificial intelligence hype has become a controversy and the new space race all in one. On one hand, Elon Musk, CEO of Tesla, says he's taking a cautious approach to the emerging technology. Musk says it's the most serious threat to the survival of the human race [1].


Facebook's MARGE AI summarizes and translates documents without fine-tuning

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In a paper published on the preprint server Arxiv.org, Facebook researchers describe Multilingual Autoencoder that Retrieves and Generates (MARGE). It's a language model that generates words, sentences, and paragraphs by retrieving related words, sentences, and paragraphs in different languages and identifying patterns within them. The researchers claim MARGE learns to paraphrase, translate, and summarize text without any fine-tuning, a potential step toward systems that can perform any text task from pretraining alone. In machine learning, pretraining involves training an AI model on a vast amount of data before it's fine-tuned on a narrow data set tailored to particular tasks, like summarization.


The Racist Roots of New Technology

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Race After Technology opens with a brief personal history set in the Crenshaw neighborhood of Los Angeles, where sociologist Ruha Benjamin spent a portion of her childhood. Recalling the time she set up shop on her grandmother's porch with a chalkboard and invited other kids to do math problems, she writes, "For the few who would come, I would hand out little slips of paper…until someone would insist that we go play tag or hide-and-seek instead. Needless to say, I didn't have that many friends!" As she gazed out the back window during car rides, she saw "boys lined up for police pat-downs," and inside the house she heard "the nonstop rumble of police helicopters overhead, so close that the roof would shake." The omnipresent surveillance continued when she visited her grandmother years later as a mother, her homecomings blighted by "the frustration of trying to keep the kids asleep with the sound and light from the helicopter piercing the window's thin pane." Benjamin's personal beginning sets the tone for her book's approach, one that focuses on how modern invasive technologies--from facial recognition software to electronic ankle monitors to the metadata of photos taken at protests--further racial inequality.