Using machine learning, a computer model can teach itself to smell in just a few minutes. When it does, researchers have found, it builds a neural network that closely mimics the olfactory circuits that animal brains use to process odors.
Animals from fruit flies to humans all use essentially the same strategy to process olfactory information in the brain. But neuroscientists who trained an artificial neural network to take on a simple odor classification task were surprised to see it replicate biology’s strategy so faithfully.
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When asked to classify odors, artificial neural networks adopt a structure that closely resembles that of the brain’s olfactory circuitry.
Yahoo Finance’s Ines Ferre reports on LinkedIn shutting down its app in China with plans to launch a jobs-only platform later this year. Don’t Miss: Valley of Hype: The Culture That Built Elizabeth Holmes. WATCH HERE:
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If the properties of materials can be reliably predicted, then the process of developing new products for a huge range of industries can be streamlined and accelerated. In a study published in Advanced Intelligent Systems, researchers from The University of Tokyo Institute of Industrial Science used core-loss spectroscopy to determine the properties of organic molecules using machine learning.
The spectroscopy techniques energy loss near-edge structure (ELNES) and X-ray near-edge structure (XANES) are used to determine information about the electrons, and through that the atoms, in materials. They have high sensitivity and high resolution and have been used to investigate a range of materials from electronic devices to drug delivery systems.
However, connecting spectral data to the properties of a material—things like optical properties, electron conductivity, density, and stability—remains ambiguous. Machine learning (ML) approaches have been used to extract information for large complex sets of data. Such approaches use artificial neural networks, which are based on how our brains work, to constantly learn to solve problems. Although the group previously used ELNES/XANES spectra and ML to find out information about materials, what they found did not relate to the properties of the material itself. Therefore, the information could not be easily translated into developments.
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.
Dr. Marcus attended Hampshire College, where he designed his own major, cognitive science, working on human reasoning. He continued on to graduate school at Massachusetts Institute of Technology, where his advisor was the experimental psychologist Steven Pinker. He received his Ph.D. in 1993.
His books include The Algebraic Mind: Integrating Connectionism and Cognitive Science, The Birth of the Mind: How a Tiny Number of Genes Creates the Complexities of Human Thought, Kluge: The Haphazard Construction of the Human Mind, a New York Times Editors’ Choice, and Guitar Zero, which appeared on the New York Times Bestseller list. He edited The Norton Psychology Reader, and was co-editor with Jeremy Freeman of The Future of the Brain: Essays by the World’s Leading Neuroscientist, which included Nobel Laureates May-Britt Moser and Edvard Moser. Together with Ernie Davis, he authored Rebooting AI and is well known to deconstruct myths of the AI community.
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 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.
