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The Role of Knowledge Graphs in Artificial Intelligence

#artificialintelligence

Representing knowledge and the reasoning for the conclusions drawn has remained a cornerstone of artificial intelligence (AI) for decades. A knowledge graph (KG) is a powerful data structure that represents information in a graphical format. DBpedia, an open source knowledge graph defines a knowledge graph as "a special kind of database which stores knowledge in a machine-readable form and provides a means for information to be collected, organised, shared, searched and utilised." Formally, a KG is a directed labeled graph which represents relations between data points. A node of the KG represents a data point.


Just What You're Looking For: Recommender Team Suggests Winning Strategies

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The final push for the hat trick came down to the wire. Five minutes before the deadline, the team submitted work in its third and hardest data science competition of the year in recommendation systems. Called RecSys, it's a relatively new branch of computer science that's spawned one of the most widely used applications in machine learning, one that helps millions find what they want to watch, buy and play. The team's combination of six AI models packed into the contest's limit of 20 gigabytes all of the smarts it culled from studying 750 million data points. An unusual rule in the competition said the models had to run in less than 24 hours on a single core in a cloud CPU.


Artificial Intelligence Stocks: The 10 Best AI Companies

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AI could be the 21st century's biggest new industry. Long a focal point of science fiction, AI is no longer a curious theme to ponder for the distant future -- it's here. The best artificial intelligence stocks to buy already use processes like machine learning and neural networks on a daily basis. In fact, if you took away AI capabilities from the following companies, their profitability would instantly plummet -- and five of them are trillion-dollar businesses, underscoring the new technology's importance to 2021's economy. The growth of artificial intelligence is also enviable.


Is there any way out of Clearview's facial recognition database?

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In March 2020, two months after The New York Times exposed that Clearview AI had scraped billions of images from the internet to create a facial recognition database, Thomas Smith received a dossier encompassing most of his digital life. Using the recently enacted California Consumer Privacy Act, Smith asked Clearview for what they had on him. The company sent him pictures that spanned moments throughout his adult life: a photo from when he got married and started a blog with his wife, another when he was profiled by his college's alumni magazine, even a profile photo from a Python coding meetup he had attended a few years ago. "That's what really threw me: All the things that I had posted to Facebook and figured, 'Nobody's going to ever look for that,' and here it is all laid out in a database," Smith told The Verge. Clearview's massive surveillance apparatus claims to hold 3 billion photos, accessible to any law enforcement agency with a subscription, and it's likely you or people you know have been scooped up in the company's dragnet.


Facebook's BlenderBot chat AI no longer has the mental capacity of a goldfish

Engadget

Last April, Facebook's AI research lab (FAIR) announced and released as open source its BlenderBot social chat app. While the neophyte AI immediately proved far less prone to racist outbursts than previous attempts, BlenderBot was not without its shortcomings. For one, the system had the recollection capacity of a goldfish -- any subject or data point the AI wasn't initially trained simply didn't exist in its online reality, as evidenced by the OG BB's continued insistence that Tom Brady still plays for the New England Patriots. For another, due to its limited knowledge of current events, the system had a strong tendency to hallucinate knowledge, like a digital Dunning-Kruger effect. But the advancements BlenderBot 2.0 displays, which FAIR debuted on Friday, should make the AI far more sociable, knowledgeable, and capable.


Facebook is trying to make AI fairer by paying people to give it data

#artificialintelligence

Artificial intelligence systems are often criticized for built-in biases. Commercial facial-recognition software, for instance, may fail when attempting to classify women and people of color. In an effort to help make AI fairer in a variety of ways, Facebook (FB) is rolling out a new data set for AI researchers that includes a diverse group of paid actors who were explicitly asked to provide their own ages and genders. Facebook hopes researchers will use the open-source data set, which it announced Thursday, to help judge whether AI systems work well for people of different ages, genders, skin tones, and in different types of lighting. Facebook also released the data set internally for use within Facebook itself; the company said in a blog post that it is "encouraging" teams to use it.


Consumer Protection and AI--7 Expert Tips To Stay Out Of Trouble

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As more and more companies adopt AI, a question arises--can the federal government keep up with all the changes from a regulatory perspective? In some cases, the federal government is behind (see Why Are Technology Companies Quitting Facial Recognition?). In a recent blog post, the FTC has warned companies that they have sufficient laws to enforce truth, fairness, and equity when enforcing the developers and users of AI. The FTC essentially says that companies need to hold themselves accountable for their AI, or the FTC will take enforcement action against them. The FTC's primary focus is on consumer protection.


Artificial intelligence will change our world more profoundly than fire

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I don't suppose you're the kind of person who, amid a pandemic and the Euros, generally spends Monday morning wondering about the latest ruminations of Judge James Boasberg of Washington, DC. I'm paid to know about these things, and I'd never heard of him. Until, that is, a fortnight ago, when he issued a legal opinion with such potentially profound implications for you, me, and the epoch into which we have stumbled that suddenly I took a rather close interest in this estimable man of law. The learned judge dismissed two anti-trust cases against Facebook, one from the Federal Trade Commission and another from an alliance of US states. They had argued that the social media giant is in effect a monopoly.


Euro 2020: What could social media companies actually do about racist abuse – and would it work?

The Independent - Tech

Penalties once again brought heartbreak for England after an otherwise triumphant campaign in the Euros, as Italy won in the Euro 2020 final. Marcus Rashford, Jadon Sancho and Bukayo Saka – three young, black players – missed the penalties that would have put the cup into England's hands. In the moments that followed, a vocal minority took to social media to direct racist abuse to the players: monkey and banana emojis were commented on Saka's Instagram page, and multiple uses of the n-word were seen on Twitter, among other slurs. Not for the first time legislators, campaigners, and social media companies condemned this behaviour and proposed various solutions: a crackdown against anonymous accounts, an increased use of artificial intelligence or moderation techniques, as well as the government's controversial Online Harms bill. There are many positives and negatives to each of the various suggestions.


Facebook's AI researchers are teaching robots to walk like humans

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In a blog, Facebook says that the solution -- Rapid Motor Adaptation -- is a "breakthrough" in artificial intelligence (AI). It allows robots to walk on sand, in mud, hiking trails, tall grass and over a dirt pile "without a single failure" during trials. "It successfully navigated a cement pile and a pile of pebbles in 80 percent of the trials, despite never seeing the unstable or sinking ground, obstructive vegetation, or steps during training," the post said. "It also maintained its height with a high success rate when moving with a 12 kg payload, which amounted to 100 percent of its body weight," it added. To do this, the researchers used two branches of AI, called supervised learning and reinforcement learning.