While the pandemic is still raging, the chaos of the past 18 months has calmed a bit, and the dust is starting to settle. Now the time has come for healthcare CIOs and other health IT leaders to look forward and plan their IT investments – shaped, in no small part, by the lessons of the recent past.
1:42 Are we on the wrong train to AGI?
4:20 Marvin Minsky and AI generalization problem.
11:57 Defining intelligence in AI
17:17 Is AI masquerading as a trendy statistical analysis tool?
23:35 AI systems lack our most basic intuitions.
27:38 The public not wanting to face Reality.
29:36 Equipping AI with Kant’s categories of the mind (Time, Space, Causality)
33:40 Neural nets VS traditional tools.
34:50 Causality in AI
37:14 Lack of interdisciplinary learning.
45:54 How can we achieve human level of understanding in AI?
49:21 More limitations.
59:35 Motivation in inanimate systems.
1:01:31 Lack of body and transcendent consciousness.
1:05:55 What interdisciplinary learning would you encourage?
1:06:49 Book recommendations.
Gary Marcus is CEO and Founder of Robust AI, well-known machine learning scientist and entrepreneur, author, and Professor Emeritus at New York State University.
From cryogenics to drugs the dream of immortality is closer than ever and now the world’s richest men including Richard Branson, Jeff Bezos and Elon Musk are putting their weight behind the research.
A Long March 2F rocket launched the Shenzhou 13 spacecraft carrying astronauts Zhai Zhigang (commander), Wang Yaping and Ye Guangfu to the Tianhe core module of China’s new space station on Oct. 15 2021. [Full Story](https://www.space.com/china-launches-shenzhou-13-astronauts-to-space-station)
Credit: China Central Television
A Long March 2F rocket launched the Shenzhou 13 spacecraft carrying astronauts Zhai Zhigang (commander), Wang Yaping and Ye Guangfu to the Tianhe core module of China’s new space station on Oct. 15 2021 at 12:23pm ET (00:23 Oct. 16 Beijing time). Full Story: https://www.space.com/china-launches-shenzhou-13-astronauts-to-space-station.
Broadcast feed from the Jiuquan Satellite Launch Center in the Gobi Desert courtesy China Central Television (CCTV)
BEIJING, Oct 18 (Reuters) — China tested a space vehicle in July, not a nuclear-capable hypersonic missile as reported by the Financial Times, the Chinese foreign ministry said on Monday.
Quoting five people familiar with the matter, the Financial Times reported on Saturday that China had tested a nuclear-capable hypersonic missile that flew through space, circling the globe before cruising down toward its target, which it missed by about two dozen miles. read more. The paper said the feat had “caught U.S. intelligence by surprise”.
“It was not a missile, it was a space vehicle,” ministry spokesman Zhao Lijian told a regular press briefing in Beijing when asked about the report, adding it had been a “routine test” for the purpose of testing technology to reuse the vehicle.
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A conceptual NASA study explores the idea of a lunar Wi-Fi network to help fix inadequate internet services in American cities, including Cleveland, Ohio.
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Convolutional neural networks running on quantum computers have generated significant buzz for their potential to analyze quantum data better than classical computers can. While a fundamental solvability problem known as “barren plateaus” has limited the application of these neural networks for large data sets, new research overcomes that Achilles heel with a rigorous proof that guarantees scalability.
“The way you construct a quantum neural network can lead to a barren plateau—or not,” said Marco Cerezo, co-author of the paper titled “Absence of Barren Plateaus in Quantum Convolutional Neural Networks,” published today by a Los Alamos National Laboratory team in Physical Review X. Cerezo is a physicist specializing in quantum computing, quantum machine learning, and quantum information at Los Alamos. “We proved the absence of barren plateaus for a special type of quantum neural network. Our work provides trainability guarantees for this architecture, meaning that one can generically train its parameters.”
As an artificial intelligence (AI) methodology, quantum convolutional neural networks are inspired by the visual cortex. As such, they involve a series of convolutional layers, or filters, interleaved with pooling layers that reduce the dimension of the data while keeping important features of a data set.
