Computers will be able to read on their own, understanding and modeling what they read, by the second decade of the 21st century.
Mostly false False in substance, right in a detail.
Prediction: Statements about events outside the person's control, checked once they fell due: did they come true?
What would count as true or false
Judge at the end of 2020. True: by the end of 2020, documented computer systems could read unrestricted natural-language text on their own (without human hand-coding of its content) and show understanding by building usable models of it. That understanding would show in answering questions, summarizing or drawing inferences about general text at roughly human level on broad evaluations. Mostly true: such systems existed by 2020, with only minor caveats, such as known failure modes that do not undercut general reading ability. Mostly false: by 2020 machines matched humans only on narrow reading-comprehension benchmarks or restricted domains, without general autonomous reading and understanding. False: by the end of 2020 no system came close to autonomous reading with understanding of general text.
Set before the research began.
Evidence (3)
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E1 Machines Beat Humans on a Reading Test. But Do They Understand? | Quanta Magazine Quanta Magazine · 17 October 2019
“But it's definitely not understanding English in a comprehensive and robust way.”
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E2 Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data ACL Anthology (Bender and Koller, ACL 2020) · 2020
“we argue that a system trained only on form has a priori no way to learn meaning.”
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E3 Measuring Massive Multitask Language Understanding arXiv (Hendrycks et al.) · September 2020
“However, on every one of the 57 tasks, the best models still need substantial improvements before they can reach human-level accuracy.”
Reasoning
E1 (Quanta, 2019) reports machines beating humans on a reading test but quotes an expert saying such a system is 'definitely not understanding English in a comprehensive and robust way.' E3 (Hendrycks et al., September 2020) finds that on a broad 57-task language-understanding evaluation the best models still needed substantial improvement to reach human-level accuracy on every task. E2 (ACL 2020) argues that systems trained only on form cannot learn meaning. Together these show human-level performance only on narrow reading benchmarks by 2020, without general autonomous reading with understanding, which matches the mostly-false criterion.
Cite this claim
Ray Kurzweil, “Computers will be able to read on their own, understanding and modeling what they read, by the second decade of the 21st century.” (said January 2001). Verdict: mostly false. Reliability API, claim c_3685c9e4bfb5, method 0.1.0+28da144b, dataset release v1.1.2. https://epistacklabs.com/claims/c_3685c9e4bfb5
This claim is in dataset release v1.1.2, with the same verdict.
Reply to this claim
More prediction claims by Ray Kurzweil
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Mostly false
“This is where combining AI with biotechnology is headed, and within several years all vaccine and medication development could be done this way.”
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Mostly false
“We're going to gradually merge and enhance ourselves.”
Kurzweil predicted that humans will gradually merge with technology and enhance themselves.
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Resolved true
“Ten or 15 years from now we’ll go to the third dimension.”
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Mostly false
“In line with my earlier predictions, supercomputers will achieve one human brain capacity by 2010, and personal computers will do so by around 2020.”
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Mostly false
“my prediction is that indeed these views will prevail, but only over time, as more and more evidence of the exponential nature of technology and its impact on the economy becomes apparent.”
Verdicts are made automatically; how verdicts are made.