From robots that flip burgers in California to ones that serve up bratwursts in Berlin, we are starting to see how machines can play sous-chef in kitchens around the world. But scientists at the University of Cambridge have been exploring how these culinary robots might not only do some of the heavy lifting but actually elevate the dining experience for the humans they serve, demonstrating some early success in a robot trained to cook omelettes.
The research project is a collaboration between the University of Cambridge researchers and domestic appliance company Beko, with the scientists setting out to take robotic cooking into new territory. Where robot chefs have been developed to prepare pizzas, pancakes and other items, the team was interested in how it might be possible to optimize the robot’s approach and produce a tastier meal based on human feedback.
“Cooking is a really interesting problem for roboticists, as humans can never be totally objective when it comes to food, so how do we as scientists assess whether the robot has done a good job?” says Dr Fumiya Iida from Cambridge’s Department of Engineering, who led the research.
Tesla has managed to attract the best artificial intelligence specialists to its Autopilot team who are committed to developing software that makes full self-driving possible. The company recently published two patents that relate to improvements in this area.
Pleased to have been the guest on this most recent episode of Javier Ideami’s Beyond podcast. We discuss everything from #spaceexploration to #astrobiology!
In this episode, we travel from Ferdinand Magellan’s voyage to the first mission to Mars with Bruce Dorminey. Bruce is a science journalist and author who primarily covers aerospace, astronomy and astrophysics. He is a regular contributor to Astronomy magazine and since 2012, he has written a regular tech column for Forbes magazine. He is also a correspondent for Renewable Energy World. Writer of “Distant Wanderers: The Search for Planets Beyond the Solar System”, he was a 1998 winner in the Royal Aeronautical Society’s Aerospace Journalist of the Year Awards (AJOYA) as well as a founding team member of the NASA Astrobiology Institute’s Science Communication Focus Group.
EPISODE LINKS: Bruce web: https://www.forbes.com/sites/brucedorminey/#47e297264d03 Distant Wanderers Book: https://www.amazon.es/Distant-Wanderers-Search-Planets-Beyond/dp/1441928723 Renewable Energy World: https://www.renewableenergyworld.com/author/bruce-dorminey/#gref Bruce’s Twitter: https://twitter.com/bdorminey
INFO: Podcast website: https://volandino.com Spotify: https://open.spotify.com/show/3O74ctu6Hv5zZdHYT9Ox3Z Apple Podcasts: https://podcasts.apple.com/us/podcast/beyond/id1509949724 RSS: https://volandino.com/feed/podcast Full episodes playlist:
OUTLINE: 01:21 — Magellan’s journey to the indies; first circumnavigation of the earth — Risk: today vs previous centuries. 02:15 — On route to the Spice Islands — Moluccas — Treaty of Tordesillas. 03:07 — Spain and Portugal on top of the world. 03:41 — Reaching philippines and the wrong side of things. 05:20 — Killed in the Philippines. 06:08 — The reasons behind the expedition: trade and religion. 07:23 — Casualties — Magellan’s expedition vs today. 07:58 — Early astronauts, challenging missions — minimal computing power. 08:40 — Mission to Mars and tolerance to risk today. 10:03 — First Mars mission attempt — the odds. 10:37 — Watching the Apollo launches live. 11:23 — The uniqueness of the moment — Apollo 8. 12:12 — Putting risk in perspective: astronauts of the Apollo program vs today. 13:05 — Psychological risks of space missions — Harrison Hagan “Jack” Schmitt (last person that walked on the moon) — the impact of being on the moon. 15:54 — Psychological factors on a trip to Mars — can we predict them? — Experiences on the International Space Station. 17:03 — Shortening the trip to Mars. 19:02 — The drive to do these missions today vs the Apollo times. 20:00 — The lost time in the moon — natural resources, astronomy, practicing for future missions to mars. 20:37 — Terraforming Mars 22:33 — Second homes, platforms in space (example: at Lagrange points). 23:43 — Exoplanets — detecting signs of life. 26:18 — Methods of detection & verification vs going there (detecting microbial life through analysis of color, surface reflectivity and other means) 27:50 — Enceladus: plumes of gas and liquid — potential insitu analysis by probes. 28:43 — microfossils on Mars. 29:00 — Impact of finding life in another planet of our solar system, even if microbial. 29:54 — Intelligent life — David Kipping, Columbia University — 3:2 odds that intelligence is rare. 30:31 — Probability of finding life — 400 billion stars in our galaxy. 33:24 — Facing the discovery of new forms of intelligent life. 35:50 — People’s resilience and attention spans / Inter-species communication. 38:26 — Could we miss new kinds of lifeforms due to them having different structures, chemical arrangements, etc? 40:30 — What is life — lack of agreement. 41:48 — Scratching the surface on any topic — a neverending search for an ultimate truth. 43:50 — ALH 84001 Allan Hills meteorite 47:26 — Asteroid mining — natural resources — Planetary Resources startup (acquired by ConsenSys). 48:52 — Commercializing space travel — trips to go around the moon — translunar flights. 51:22 — Progress since the Apollo era and next steps. 52:55 — Spending a weekend on the moon. 54:00 — Next decade in Space — putting a crew on mars, robotic sample return missions, permanent or semi-permanet settlements on the lunar surface, optical and radio-based astronomy on the far side of the moon, space tourism, space based interferometers, etc 56:22 — will other intelligent life forms want to communicate? gregarious vs non-gregarious civilizations. 57:35 — Consequences of the pandemic. 59:06 — conclusion — “Distant Wanderers — search for planets beyond the solar system”
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The COVID-19 pandemic will have a profound impact on robotics, as more companies look to automation as a way forward. While wide-scale automation had long seemed like an inevitability, the pandemic is set to accelerate the push as corporations look for processes that remove the human element from the equation.
Of course, Locus Robotics hasn’t had too much of an issue raising money previously. The Massachusetts-based startup, which raised $26 million back in April of last year, is adding a $40 million Series D to its funds. That brings the full amount to north of $105 million. This latest round, led by Zebra Technologies, comes as the company looks to expand operations with the launch of a European HQ.
“The new funding allows Locus to accelerate expansion into global markets,” CEO Rick Faulk said in a release, “enabling us to strengthen our support of retail, industrial, healthcare, and 3PL businesses around the world as they navigate through the COVID-19 pandemic, ensuring that they come out stronger on the other side.”
Over the last few years, the size of deep learning models has increased at an exponential pace (famously among language models):
And in fact, this chart is out of date. As of this month, OpenAI has announced GPT-3, which is a 175 billion parameter model—or roughly ten times the height of this chart.
As models grow larger, they introduce new infrastructure challenges. For my colleagues and I building Cortex (open source model serving infrastructure), these challenges are front and center, especially as the number of users deploying large models to production increases.
When Plato set out to define what made a human a human, he settled on two primary characteristics: We do not have feathers, and we are bipedal (walking upright on two legs). Plato’s characterization may not encompass all of what identifies a human, but his reduction of an object to its fundamental characteristics provides an example of a technique known as principal component analysis.
Now, Caltech researchers have combined tools from machine learning and neuroscience to discover that the brain uses a mathematical system to organize visual objects according to their principal components. The work shows that the brain contains a two-dimensional map of cells representing different objects. The location of each cell in this map is determined by the principal components (or features) of its preferred objects; for example, cells that respond to round, curvy objects like faces and apples are grouped together, while cells that respond to spiky objects like helicopters or chairs form another group.
The research was conducted in the laboratory of Doris Tsao (BS ‘96), professor of biology, director of the Tianqiao and Chrissy Chen Center for Systems Neuroscience and holder of its leadership chair, and Howard Hughes Medical Institute Investigator. A paper describing the study appears in the journal Nature on June 3.
Neural networks enable learning of error correction strategies for computers based on quantum physics
Quantum computers could solve complex tasks that are beyond the capabilities of conventional computers. However, the quantum states are extremely sensitive to constant interference from their environment. The plan is to combat this using active protection based on quantum error correction. Florian Marquardt, Director at the Max Planck Institute for the Science of Light, and his team have now presented a quantum error correction system that is capable of learning thanks to artificial intelligence.
