Twitter And Facebook To Debunk Fake Coronavirus News
With nearly 2.6 billion...
We take a look at how social media platforms like Twitter and Facebook are combating the rise of fake news regarding COVID-19.
With nearly 2.6 billion monthly active users, Facebook needs to knuckle down to stop the vast spread of fake news and it sees AI as a tool that can take the "drudgery out" of tasks that would take humans a lot of time to complete.
Facebook has been doubling down on artificial intelligence to detect coronavirus misinformation and hate speech, but the social network is finding machines can have a tough time identifying offensive content online.
On Tuesday, the world's largest social network laid out several challenges its AI systems face when trying to find copies of posts that contain coronavirus misinformation or detect hateful memes. Like other social networks, Facebook uses a mix of human reviewers and technology to detect content that violates its rules before users report it. While AI has made progress, misinformation and hate speech keep resurfacing on Facebook and other social networks.
Facebook Chief Technology Officer Mike Schroepfer said in a press call that he knows that AI isn't the answer to every single problem.
"These problems are fundamentally human problems about life and communication," he said. "So we want humans in control and making the final decisions especially when the problems are nuanced."
In April, Facebook put warning labels on about 50 million posts related to COVID-19. Since March, Facebook has removed more than 2.5 million posts about the sale of masks, sanitizers, surface disinfecting wipes and COVID-19 test kits -- items the social network temporarily banned to prevent price gouging and other types of exploitation.
Another platform that is trying to curb the spread of misinformation is Twitter. Twitter said Monday that in some cases it'll add labels and warning messages to tweets that contain misleading or disputed information about the novel coronavirus, a sign that the company is stepping up its efforts to crack down on misinformation.
Misinformation about COVID-19, the respiratory illness caused by the coronavirus, has been an ongoing challenge for social networks. False claims that encourage people to drink bleach or not wear masks can be harmful to people's health. Despite these efforts, conspiracy theories and hoaxes, including that the virus is caused by 5G, continue to spread on social media sites.
If the information in the tweet is false or misleading but has a likelihood of causing "moderate" harm, Twitter will label the tweet rather than remove it. The company will add a label and a warning to claims that are contested or unknown but have a likelihood of causing severe harm. Twitter won't take any action if the information hasn't been confirmed as true or false.
In the warning notice, Twitter users will see a message that says, "Some or all of the content shared in this Tweet conflicts with guidance from public health experts regarding COVID-19." It'll be followed by a link to learn more. A label displayed underneath the tweet has a link that says, "Get the facts about COVID-19," which will direct users to more information.
Video sharing app, TikTok, has not taken such drastic measures, however, if a user uses the hashtag “coronavirus” a warning shows up at the bottom, urging users to check the latest information regarding coronavirus from official sources, such as WHO or the NHS website.
Google AI Designs Next-Gen Chips In Under 6 Hours
In a Google-Nature paper published on Wednesday, the company announced that AI will be able to design chips in less than six hours. Humans currently take months to design and layout the intricate chip wiring. Although the tech giant has been working in silence on the technology for years, this is the first time that AI-optimised chips have hit the mainstream—and that the company will sell the result as a commercial product.
“Our method has been used in production to design the next generation of Google TPU (tensor processing unit chips)”, the paper’s authors, Azalea Mirhoseini and Anna Goldie wrote. The TPU v4 chips are the fastest Google system ever launched. “If you’re trying to train a large AI/ML system, and you’re using Google’s TensorFlow, this will be a big deal”, said Jack Gold, President and Principal Analyst at J.Gold Associates.
Training the Algorithm
In a process called reinforcement learning, Google engineers used a set of 10,000 chip floor plans to train the AI. Each example chip was assigned a score of sorts based on its efficiency and power usage, which the algorithm then used to distinguish between “good” and “bad” layouts. The more layouts it examines, the better it can generate versions of its own.
Designing floor plans, or the optimal layouts for a chip’s sub-systems, takes intense human effort. Yet floorplanning is similar to an elaborate game. It has rules, patterns, and logic. In fact, just like chess or Go, it’s the ideal task for machine learning. Machines, after all, don’t follow the same constraints or in-built conditions that humans do; they follow logic, not preconception of what a chip should look like. And this has allowed AI to optimise the latest chips in a way we never could.
As a result, AI-generated layouts look quite different to what a human would design. Instead of being neat and ordered, they look slightly more haphazard. Blurred photos of the carefully guarded chip designs show a slightly more chaotic wiring layout—but no one is questioning its efficiency. In fact, Google is starting to evaluate how it could use AI in architecture exploration and other cognitively intense tasks.
Major Implications for the Semiconductor Sector
Part of what’s impressive about Google’s breakthrough is that it could throw Moore’s Law, the axion that the number of transistors on a chip doubles every five years, out the window. The physical difficulty of squeezing more CPUs, GPUs, and memory on tiny silicon die will still exist, but AI optimisation may help speed up chip performance.
Any chance that AI can help speed up current chip production is welcome news. Though the U.S. Senate recently passed a US$52bn bill to supercharge domestic semiconductor supply chains, its largest tech firms remain far behind. According to Holger Mueller, principal analyst at Constellation Research, “the faster and cheaper AI will win in business and government, including with the military”.
All in all, AI chip optimisation could allow Google to pull ahead of its competitors such as AWS and Microsoft. And if we can speed up workflows, design better chips, and use humans to solve more complex, fluid, wicked problems, that’s a win—for the tech world and for society.