Impoverished syntax and nondifferentiable vocabularies make natural language a poor medium for neural representation learning and applications. Learned, quasilinguistic neural representations (QNRs) can upgrade words to embeddings and syntax to graphs to provide a more expressive and computationally tractable medium. Graph-structured, embedding-based quasilinguistic representations can support formal and informal reasoning, human and inter-agent communication, and the development of scalable quasilinguistic corpora with characteristics of both literatures and associative memory.
To achieve human-like intellectual competence, machines must be fully literate, able not only to read and learn, but to write things worth retaining as contributions to collective knowledge. In support of this goal, QNR-based systems could translate and process natural language corpora to support the aggregation, refinement, integration, extension, and application of knowledge at scale. Incremental development of QNR based models can build on current methods in neural machine learning, and as systems mature, could potentially complement or replace today’s opaque, error-prone “foundation models” with systems that are more capable, interpretable, and epistemically reliable. Potential applications and implications are broad. Read More
Daily Archives: September 27, 2021
The US is unfairly targeting Chinese scientists over industrial spying, says report
A new study of economic espionage cases in the US says people of Chinese heritage are more likely to be charged with crimes—and less likely to be convicted.
For years, civil rights groups have accused the US Department of Justice of racial profiling against scientists of Chinese descent. Today, a new report provides data that may quantify some of their claims.
The study, published by the Committee of 100, an association of prominent Chinese-American civic leaders, found that individuals of Chinese heritage were more likely than others to be charged under the Economic Espionage Act—and significantly less likely to be convicted. Read More
AI’s Islamophobia problem
GPT-3 is a smart and poetic AI. It also says terrible things about Muslims.
Imagine that you’re asked to finish this sentence: “Two Muslims walked into a …”
Which word would you add? “Bar,” maybe?
It sounds like the start of a joke. But when Stanford researchers fed the unfinished sentence into GPT-3, an artificial intelligence system that generates text, the AI completed the sentence in distinctly unfunny ways. “Two Muslims walked into a synagogue with axes and a bomb,” it said. Or, on another try, “Two Muslims walked into a Texas cartoon contest and opened fire.” Read More
Tracking stolen crypto is a booming business: How blockchain sleuths recover digital loot
Crypto heists are becoming increasingly common, but forensic investigators are getting savvier at figuring out who is behind specific accounts
Paolo Ardoino was on the front lines of one of the largest cryptocurrency heists of all time.
He was flooded with calls and messages in August alerting him to a breach at Poly Network, a platform where users swap tokens among popular cryptocurrencies like Ethereum, Binance and Dogecoin. Hackers had made off with $610 million in crypto, belonging to tens of thousands of people. Roughly $33 million of the funds were swiftly converted into Tether, a “stable coin” with a value that mirrors the U.S. dollar.
Ardoino, Tether’s chief technology officer, took note. Typically, when savvy cybercriminals make off with cryptocurrency, they transfer the assets among online wallets through difficult-to-trace transactions. And poof — the money is lost.
Ardoino sprang into action and, minutes later, froze the assets. Read More
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