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"Cancer diagnosis and management depend upon the extraction of complex information from microscopy images by pathologists, which requires time-consuming expert interpretation prone to human bias. Supervised deep learning approaches have proven powerful, but are inherently limited by the cost and quality of annotations used for training. Therefore, we present Histomorphological Phenotype Learning, a self-supervised methodology requiring no labels and operating via the automatic discovery of discriminatory features in image tiles. Tiles are grouped into morphologically similar clusters which constitute an atlas of histomorphological phenotypes (HP-Atlas), revealing trajectories from benign to malignant tissue via inflammatory and reactive phenotypes. These clusters have distinct features which can be identified using orthogonal methods, linking histologic, molecular and clinical phenotypes. Applied to lung cancer, we show that they align closely with patient survival, with histopathologically recognised tumor types and growth patterns, and with transcriptomic measures of immunophenotype. These properties are maintained in a multi-cancer study."

"LeCun argues that even self-supervised learning and learnings from neurobiology won't be enough to achieve artificial general intelligence (AGI), or the hypothetical intelligence of a machine with the capacity to understand or learn from any task. That's because intelligence - even human intelligence - is very specialized, he says. "AGI does not exist - there is no such thing as general intelligence," said LeCun. "We can talk about rat-level intelligence, cat-level intelligence, dog-level intelligence, or human-level intelligence, but not artificial general intelligence.""

""Robots are a great educational tool for children. It inspires them to learn about science and engineering," Sharkey told the Guardian in March. "But there are significant dangers in having robots mind our children. They do not have the sensitivity or understanding needed for childcare." "

"ChatGPT can also give entirely wrong answers and present misinformation as fact, writing "plausible-sounding but incorrect or nonsensical answers", the company concedes. OpenAI says that fixing this issue is difficult because there is no source of truth in the data they use to train the model and supervised training can also be misleading "because the ideal answer depends on what the model knows, rather than what the human demonstrator knows"."