Nov 17, 2021 07:40 PM
(This post was last modified: Nov 17, 2021 08:11 PM by C C.)
Partner predictions fare better than either AI or humans alone
https://www.eurekalert.org/news-releases/935208
INTRO: Artificial intelligence (AI) can assess far more data far more quickly than any single human can do. With such immense pools of information, AI should be able to consider past data, process all the implications and produce a reliable prediction better than a human — right? That may not always be the case, according to a multi-institution research team who examined the synergies between how humans and AI make predictions. They publish their results in Journal of Social Computing, issued through Tsinghua University Press... (MORE)
Friend or foe: Do online recommender tools really improve decision making?
https://www.eurekalert.org/news-releases/934942
INTRO: Artificial intelligence (AI) recommender tools are widely used by industries such as e-commerce, media, banking and utilities. The tool’s algorithm uses website visitors’ past online activity and other data, both implicit and explicit, to predict what that visitor would like to view or buy next, and then presents those options to them.
This can be highly beneficial; for example, for customers, the recommender can save them time by suggesting options tailored to their tastes and needs. While, for companies, it can encourage consumers to spend more via their website and increase customer loyalty: in the case of streaming giant Netflix, it’s estimated that its recommendation engine generates $1 billion annually.
A group of researchers from China, Finland and Korea set out to uncover the potential negative effects of these AI tools. As co-author Sihua Chen, from China’s Jiangxi University of Finance and Economics, explains: “Every coin has two sides, and with the penetration of AI recommenders in our lives, the downsides are becoming more apparent.” The research was first published online August 10, 2021 in the journal, Journal of Management Science and Engineering, and is scheduled to be published in print December 2021.
[...] According to author Jian Mou, of Korea’s Pusan National University, “we found that AI recommendation augmented the information cocoon effect. In other words, people see what they want to see, and then the website’s AI recommender algorithm selects content for them based on those preferences. This negatively impacts the quality of the customer’s purchasing decisions.” (MORE - missing details)
IBM clears the 100-qubit mark with its new processor
https://arstechnica.com/science/2021/11/...processor/
EXCERPTS: IBM has announced it has cleared a major hurdle in its effort to make quantum computing useful: it now has a quantum processor, called Eagle, with 127 functional qubits. This makes it the first company to clear the 100-qubit mark, a milestone that's interesting because the interactions of that many qubits can't be simulated using today's classical computing hardware and algorithms.
But what may be more significant is that IBM now has a roadmap that would see it producing the first 1,000-qubit processor in two years. And, according to IBM Director of Research Darío Gil, that's the point where calculations done with quantum hardware will start being useful.
[...] In terms of practical uses, the Eagle processor doesn't change things dramatically. There are probably some interesting things you can do on it more easily than you could on a smaller processor, but we've not fundamentally reached the point when we can regularly do useful calculations that would be difficult to impossible on traditional computers. In many ways, Eagle is most important as a mile marker on IBM's roadmap. That roadmap foresees a processor with 400-plus qubits next year and something with over 1,000 qubits in 2023. (It shifts to a vague "and beyond" after 2023.)
[...] This doesn't mean that, overnight, everything will work better—or at all—if it's run on quantum hardware. Instead, Gil argued that the transition out of our current state (a small number of error-prone qubits) will be gradual... (MORE - missing details)
https://www.eurekalert.org/news-releases/935208
INTRO: Artificial intelligence (AI) can assess far more data far more quickly than any single human can do. With such immense pools of information, AI should be able to consider past data, process all the implications and produce a reliable prediction better than a human — right? That may not always be the case, according to a multi-institution research team who examined the synergies between how humans and AI make predictions. They publish their results in Journal of Social Computing, issued through Tsinghua University Press... (MORE)
Friend or foe: Do online recommender tools really improve decision making?
https://www.eurekalert.org/news-releases/934942
INTRO: Artificial intelligence (AI) recommender tools are widely used by industries such as e-commerce, media, banking and utilities. The tool’s algorithm uses website visitors’ past online activity and other data, both implicit and explicit, to predict what that visitor would like to view or buy next, and then presents those options to them.
This can be highly beneficial; for example, for customers, the recommender can save them time by suggesting options tailored to their tastes and needs. While, for companies, it can encourage consumers to spend more via their website and increase customer loyalty: in the case of streaming giant Netflix, it’s estimated that its recommendation engine generates $1 billion annually.
A group of researchers from China, Finland and Korea set out to uncover the potential negative effects of these AI tools. As co-author Sihua Chen, from China’s Jiangxi University of Finance and Economics, explains: “Every coin has two sides, and with the penetration of AI recommenders in our lives, the downsides are becoming more apparent.” The research was first published online August 10, 2021 in the journal, Journal of Management Science and Engineering, and is scheduled to be published in print December 2021.
[...] According to author Jian Mou, of Korea’s Pusan National University, “we found that AI recommendation augmented the information cocoon effect. In other words, people see what they want to see, and then the website’s AI recommender algorithm selects content for them based on those preferences. This negatively impacts the quality of the customer’s purchasing decisions.” (MORE - missing details)
IBM clears the 100-qubit mark with its new processor
https://arstechnica.com/science/2021/11/...processor/
EXCERPTS: IBM has announced it has cleared a major hurdle in its effort to make quantum computing useful: it now has a quantum processor, called Eagle, with 127 functional qubits. This makes it the first company to clear the 100-qubit mark, a milestone that's interesting because the interactions of that many qubits can't be simulated using today's classical computing hardware and algorithms.
But what may be more significant is that IBM now has a roadmap that would see it producing the first 1,000-qubit processor in two years. And, according to IBM Director of Research Darío Gil, that's the point where calculations done with quantum hardware will start being useful.
[...] In terms of practical uses, the Eagle processor doesn't change things dramatically. There are probably some interesting things you can do on it more easily than you could on a smaller processor, but we've not fundamentally reached the point when we can regularly do useful calculations that would be difficult to impossible on traditional computers. In many ways, Eagle is most important as a mile marker on IBM's roadmap. That roadmap foresees a processor with 400-plus qubits next year and something with over 1,000 qubits in 2023. (It shifts to a vague "and beyond" after 2023.)
[...] This doesn't mean that, overnight, everything will work better—or at all—if it's run on quantum hardware. Instead, Gil argued that the transition out of our current state (a small number of error-prone qubits) will be gradual... (MORE - missing details)