On Friday, Elon Musk announced he was pausing his $45bn purchase of Twitter because he had only just discovered some of the accounts on the site were fake. But that's not the strangest thing that has happened to the beleaguered social media platform this week. Because on Tuesday the current top brass, perhaps trying to demonstrate their vision for the site, released a Super Nintendo-style browser game that recaps Twitter's private policy. The platform unveiled Twitter Data Dash, which plays like a vintage side-scrolling platformer that's been draped with a healthy dose of disinformation anxiety. You take control of a blue-hued puppy named Data and are tasked with retrieving five bones hidden in each of the game's day-glo urban environments.

A senior director at Apple has quit his job in protest at the company demanding staff return to the office three days a week. Ian Goodfellow, the director of machine learning, is believed to be the most senior employee to resign so far as a result of the plan. On April 11, the company began mandating one day a week in the office - a requirement that rose to two days on May 2. By May 23, all staff had to be at their desks three days a week. A survey of Apple workers from April 13-19 found 67 percent saying they were dissatisfied with the return-to-office policy, Fortune reported. And Goodfellow, in his resignation note, said he would not do it.

Popular gay dating app Grindr has agreed to go public through a blank-cheque firm whose founder was part of a consortium that bought the company in 2020, according to a filing with the US Securities and Exchange Commission on Monday. The deal with Tiga Acquisition Corporation will raise $384m including $284m of the special-purpose acquisition company's (SPAC) cash in trust plus up to $100m in a forward purchase agreement, valuing the company at $2.1bn including debt, according to the filing. The dating app was valued at $620m when it was sold in 2020 by its Chinese owner. Tiga Acquisition Corp went public in November 2020 to raise $240m, a few months after the Grindr sale. The SPAC would have to liquidate later this month if it failed to reach a deal with a potential merger target, after several extensions of the liquidation deadline.

There are so many great applications of Artificial Intelligence in daily life, by using machine learning and other techniques in the background. AI is everywhere in our lives, from reading our emails to receiving driving directions to obtaining music or movie suggestions. Don't be scared of AI jargon; we've created a detailed AI glossary for the most commonly used Artificial Intelligence terms and the basics of Artificial Intelligence. Now if you're ready, let's look at how we use AI in 2022. Artificial intelligence (AI) appears in popular culture most often as a group of intelligent robots bent on destroying humanity, or at the very least a stunning theme park. We're safe for now because machines with general artificial intelligence don't yet exist, and they aren't expected to anytime soon. You can learn the risk and benefits of Artificial Intelligence with this article.

A joint research collaboration between US universities and IBM has formulated a proof-of-concept adversarial attack that's theoretically capable of causing stock market losses, simply by changing one word in a retweet of a Twitter post. In one experiment, the researchers were able to hobble the Stocknet prediction model with two methods: a manipulation attack and a concatenation attack. The attack surface for an adversarial attack on automated and machine learning stock prediction systems is that a growing number of them are relying on organic social media as predictors of performance; and that manipulating this'in-the-wild' data is a process that can, potentially, be reliably formulated. Besides Twitter, systems of this nature ingest data from Reddit, StockTwits, and Yahoo News, among others. The difference between Twitter and the other sources is that retweets are editable, even if the original tweets are not.

Over the course of her nine-year tenure at LinkedIn, Ya Xu has held technology roles with increasing responsibility. Today, she heads the data function for the online professional networking platform. Ya Xu has been a driving force in transforming LinkedIn into a data-first company since she first joined the organization in 2013. As head of data, she leads a global team of about 1,000 data scientists and AI engineers whose work is at the core of delivering economic opportunities to LinkedIn's members and customers. Xu's emphasis on responsible AI and data science ensures that LinkedIn's AI systems put people first and enables the company to empower its members, better serve its customers, and benefit society. In addition to her work at LinkedIn, Xu has coauthored the book Trustworthy Online Controlled Experiments (Cambridge University Press, 2020), has been named to Fortune's 40 under 40 in tech, and was nominated for VentureBeat's Women in AI Awards. She has delivered countless speeches, including a commencement speech to Stanford's class of 2019 in mathematics, statistics, and mathematical and computational science. Previously, Xu worked at Microsoft and earned a Ph.D. in statistics from Stanford University. Ya joins hosts Sam Ransbotham and Shervin Khodabandeh in this episode of the Me, Myself, and AI podcast, where she discusses AI's essential role in helping LinkedIn create the best "matches" -- content creators with content consumers, job seekers with employers, and buyers with sellers -- within its three key marketplaces. Ya also describes how the company has fostered a data-first culture from the top down, and how its vast amount of economic activity data is helping governments and policy makers worldwide.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. The past 12 months have taught us that the metaverse is regarded by many as the next major frontier for tech. The furor caused by Facebook's conspicuous rebranding to Meta has shown that the 21st century's biggest technological space race will be fought on the battlegrounds of Wall Street -- but the stock market has also helped to identify some of the forgotten players that will be integral to the mechanics of this brave new world. The race for the metaverse is a unique prospect for the 21st century, primarily because there is little understanding of what such a revolutionary mixed reality digital space will actually look like in practice -- or how businesses will be looking to capitalize on the new technology. So, who will succeed in profiting the most from the metaverse?

The lure of machine learning isn't always about big new features; often, what it does best are small tweaks that subtly improve user experience. So it is with Twitter's use of neural networks to automatically crop picture previews to their most interesting part. The company's been working on this tool for a while, but described its methods in detail in a blog post yesterday. It's an interesting little read, with ML engineer Lucas Theis and ML lead Zehan Wang explaining how they started just using facial recognition to crop images to faces, but found that this method didn't work with pictures of scenery, objects, and, most importantly, cats. Their solution was "cropping using saliency" (saliency here meaning whatever's most interesting in a picture -- faces or no).

Meta AI, the research arm of Facebook’s parent company, studies how the human brain processes language. Researchers are looking at how the brain and AI language models respond to the same spoken or written sentences. The work is part of Meta AI’s broader focus on human-level AI that learns with little to no human supervision. Meta AI is working with NeuroSpin (CEA), a Paris-based research center for innovation in brain imaging, and the French National Institute for Research in Digital Science (INRIA). Meta AI and NeuroSpin are studying how language models are trained to predict the next word in a sentence. Such prediction based on partially observable inputs is at the core of AI self-supervised learning. Researchers are creating an original neuroimaging dataset to further hone their study. King said that neuroscientists could help improve AI models by studying long-range forecasting capability more in-depth. He said that enhancing algorithms with such capability can help them become more correlated with the brain. He added that progress requires bringing together the disciplines of neuroscience and AI.

Tesla CEO Elon Musk has laid out some bold, if still vague, plans for transforming Twitter into a place of "maximum fun" once he buys the social media platform for $44 billion and takes it private. But enacting what at the moment are little more than a mix of vague principles and technical details could be considerably more complicated than he suggests. Here's what might happen if Musk follows through on his ideas about free speech, fighting spam and opening up the "black box" of artificial intelligence tools that amplify social media trends. Musk's feistiest priority -- but also the one with the vaguest roadmap -- is to make Twitter a "politically neutral" digital town square for the world's discourse that allows as much free speech as each country's laws allow. He's acknowledged that his plans to reshape Twitter could anger the political left and mostly please the right.