Toggle light / dark theme

Where does the mind end and the world begin? Is the mind locked inside its skull, sealed in with skin, or does it expand outward, merging with things and places and other minds that it thinks with? What if there are objects outside—a pen and paper, a phone—that serve the same function as parts of the brain, enabling it to calculate or remember?

In their famous 1998 paper “The Extended Mind,” philosophers Andy Clark and David J. Chalmers posed those questions and answered them provocatively: cognitive processes “ain’t all in the head.” The environment has an active role in driving cognition; cognition is sometimes made up of neural, bodily, and environmental processes.

From where he started in cognitive science in the early nineteen-eighties, taking an interest in A.I., professor Clark has moved quite far. “I was very much on the machine-functionalism side back in those days,” he says. “I thought that mind and intelligence were quite high-level abstract achievements where having the right low-level structures in place didn’t really matter.”

Each step he took, from symbolic A.I. to connectionism, from connectionism to embodied cognition, and now to predictive processing, took Clark farther away from the idea of cognition as a disembodied language and toward thinking of it as fundamentally shaped by the particular structure of its animal body, with its arms and its legs and its neuronal brain. He had come far enough that he had now to confront a question: If cognition was a deeply animal business, then how far could artificial intelligence go?

Clark knew that the roboticist Rodney Brooks had recently begun to question a core assumption of the whole A.I. project: that minds could be built of machines. Brooks speculated that one of the reasons A.I. systems and robots appeared to hit a ceiling at a certain level of complexity was that they were built of the wrong stuff—that maybe the fact that robots were not flesh made more of a difference than he’d realized.

Clark couldn’t decide what he thought about this. On the one hand, he was no longer a machine functionalist, exactly: he no longer believed that the mind was just a kind of software that could run on hardware of various sorts. On the other hand, he didn’t believe, and didn’t want to believe, that a mind could be constructed only out of soft biological tissue. He was too committed to the idea of the extended mind—to the prospect of brain-machine combinations, to the glorious cyborg future—to give it up. In a way, though, the structure of the brain itself had some of the qualities that attracted him to the extended-mind view in the first place: it was not one indivisible thing but millions of quasi-independent things, which worked seamlessly together while each had a kind of existence of its own.

Tesla (NASDAQ: TSLA) may be slowly making its way toward Ark Invest’s Golden Goose scenario, which involves a $22,000 price target pre-split.

At the beginning of 2020, ARK Invest released its updated TSLA valuation based on new research it had collected at the time. ARK analysts described ten difference scenarios Tesla could take leading up to 2024 and gave each one a price target.

Tesla seems to be on track to hit the scenario ARK Invest labeled “The High Functioning EV Company” which has a price target of $3,400. Keep in mind that ARK released these estimates before Tesla announced the stock split. In this scenario, Tesla manages to lower costs and build factories efficiently, but doesn’t launch its autonomous network.

Esla has managed to reduce costs further this year and could continue to do so in the coming years. In the third-quarter earnings call, CFO Zachary Kirkhorn stated that Tesla would continue to reduce manufacturing and operational costs in the future.

“We are also seeing benefits from the ongoing upward trend of locally built and delivered cars, which has increased from under 50% at the beginning of last year to over 70% most recently, which is a core component of our cost reduction strategy,” he added.

Gigafactory Shanghai showed that Tesla could build factories efficiently.

Tesla China has been an integral part of the company’s profitable quarters this year. The construction of Giga Berlin and Giga Texas are not moving at quite the same speed as Giga Shanghai. However, Tesla has stated that the construction of Gigafactory Berlin and Texas are on schedule and could still start production by 2021. — Adam (@AdamHoov) November 26, 2020 These numbers don’t seem so crazy anymore (we’re at $2875 post split) $tsla @ARKInvest pic.twitter.com/PFkJMaOOvF

The California PUC granted Waymo a permit to operate 24 hours/day in San Francisco taking select members of the public for rides with no safety driver in the vehicle. Waymo says it will begin this shortly. This comes on the heels of them expanding such service in Phoenix, as reported in my article on how the death of self-driving cars has been greatly exaggerated earlier this week.

This service will be with “trusted testers” rather than members of the broad public that can ride in Chandler and Phoenix, Arizona. Limiting ridership can be useful when demand is higher than supply, and also to learn from people who ride it repeatedly, but it has another less noble purpose, namely that riders can be under NDA and not exposing any problems to the public. (Waymo previously has required this of “trusted testers” but says they will very quickly transition to not needing an NDA.) Once a service allows members of the general public to ride with no conditions, it’s a declaration that “we’re confident we are not going to do anything embarrassing.” Cruise, which is Waymo’s rival in SF, has been serving the public for some time, and on Nov 16 announced they will be doing daytime rides with GM employees. Cruise had previously only been operating with passengers after 10pm on much calmer streets.

Waymo also announced it will deploy in Los Angeles soon (it has been testing with staff there.) In LA they also revealed their new robotaxi model, built by Geely under the Zeekr brand in China. I will have more comment when I get to see it, though I am disappointed the front seats don’t swivel to allow more social settings when a group of more than 2 go for a ride. (Facing backwards is less comfortable for some, but also safer in a forward crash.) California Public Utilities Commission (CPUC), in ongoing efforts to support transportation innovation, today authorized Waymo LLC to participate in California’s pilot program to provide “driverless” autonomous vehicle (AV) passenger service to the public. Waymo joins the CPUC’s Driverless Pilot program, in which passengers can ride in a test AV that operates without a driver in the vehicle. Waymo may not charge passengers for any rides in test AVs.

The National Robotarium at Heriot-Watt University is focused on the development and testing of robotics and AI solutions By Hollie Tye Designing and manufacturing assisted living technologies, Pressalit were asked to contribute to the work being carried out by the Ambient Assisted Living Lab (AAL) at Heriot-Watt University Demonstrating how assisted living technologies can help transform lives, solutions […].

Galactica was supposed to help “organize science.” Instead, it spewed misinformation.

In the first year of the pandemic, science happened at light speed. More than 100,000 papers were published on COVID in those first 12 months — an unprecedented human effort that produced an unprecedented deluge of new information.

It would have been impossible to read and comprehend every one of those studies. No human being could (and, perhaps, none would want to).

Spiraling costs, closed facilities, capacity issues, staff burnout, staff shortages, lots of chaos — sounds like an ailing industry — and that industry is healthcare. Can artificial intelligence help mend some of the problems faced by hospitals and healthcare providers? There has been progress on that front — not fast enough, but progress nonetheless.

While interest in healthcare AI is high, “the level of acculturation of C-level executives is lagging, especially for organizations that would need it the most — pharmas, medtechs and hospitals,” a recent Capgemini report relates. The problem, the study’s authors relate, is data. “Enhancing the patient care pathway and improving care delivery remain on the top of the organizations’ agendas,” according to the report’s team of coauthors, led by Charlotte Pierron-Perlès. However, only about a third of healthcare organizations surveyed by Capgemini prioritize the availability of patient information. “We do not see major progress from 2021 [the year of the previous study].”

The good news is that many healthcare providers are stepping up their AI work. “The healthcare industry is now starting to implement AI and machine learning solutions at increased scale and sophistication,” says Tony Ambrozie, CIO at Baptist Health South Florida. “AI and machine learning will augment their ability to make sense of the vast amounts of data available.”

A new AI system reconstructs images from MRI data two-thirds more accurately than older systems. This is made possible by more data and diffusion models.

Can AI models decode thoughts? Experiments with large language models, such as those by a Meta research group led by Jean-Remi King, attempt to decode words or sentences from MRI data using language models.

Recently, a research group demonstrated an AI system that decodes MRI data from a person watching a video into text describing some of the visible events.