The deep learning approach to language would never work, as argued in a chapter of Rebooting AI.

The claim is that the deep-learning approach to language described in the Rebooting AI chapter (large statistical models trained on text, with no explicit cognitive or world models) would 'never work', meaning it would never reach genuine, robust language understanding. Judge it against the chapter's own tests and examples, such as understanding simple stories, drawing inferences about the events and entities in a text, and answering questions that need common sense, as performed by the best pure deep-learning language systems at the time of assessment. True: the best pure deep-learning systems still fail, systematically, the kinds of understanding tasks the chapter used as its examples, and independent evaluations attribute those failures to the approach itself. Mostly true: they still show the core failures the chapter described, but have overcome a minority of its specific examples. Mixed: they now handle many of the chapter's examples reliably but still fail other central ones, so the evidence is split on whether the approach 'works' in the chapter's sense. Mostly false: they reliably handle most of the chapter's examples and tasks, with only scattered or edge-case failures left. False: pure deep-learning language systems reliably pass the chapter's examples and comparable new tests of the same kinds of understanding, as shown by independent evaluations. Systems whose success depends on added symbolic or explicit-model components do not count as the pure deep-learning approach working. Because the claim says 'never', a true verdict can only mean the claim holds as of the assessment date.

Set before the research began.

1. E1 clean version arXiv (Shultz, Wise, Nobandegani; McGill University and Mila) · 18 January 2026

   “On this test, GPT-4 performs slightly, but not statistically significantly, better than humans given the very high level of human performance.”

2. E2 Reading Subtext: Evaluating Large Language Models on Short Story Summarization with Writers Transactions of the Association for Computational Linguistics (MIT Press) · 23 December 2024

   “We find that all three models make faithfulness mistakes in over 50% of summaries and struggle with specificity and interpretation of difficult subtext.”

3. E3 Winograd schema challenge - Wikipedia Wikipedia · 13 November 2014

   “The challenge is considered "defeated" since 2019, when a number of transformer-based language models achieved accuracies of over 90%.”

4. E4 If Computers Are So Smart, How Come They Can't Read? Chapter 4 of Rebooting AI (Marcus and Davis), hosted on NYU site · 2019 Own record

   “Deep learning can’t even begin to tackle this sort of problem.”

The evidence is split: large language models matched human performance on one test (E1) and passed the Winograd Schema Challenge (E3), but made faithfulness mistakes in over 50% of short-story summaries (E2). None of the items shows which examples the chapter used (E4) or whether the systems tested are purely deep learning, so the evidence cannot show that they reliably handle most of the chapter's examples.