Inside The Curious Question Of The Great Emancipator’s Race
During Lincoln’s presidency from 1860 to 1865, some suggested that Lincoln had — in their view — an unnaturally close bond with African-Americans. They printed political cartoons, passed around pamphlets, and even dubbed Lincoln “Abraham Africanus I.”
A.I. Is Getting Better at Mind-Reading
In a recent experiment, researchers used large language models to translate brain activity into wordsPerson's thoughts ---------------------- AI translating thoughtsOn Monday, scientists from the University of Texas, Austin, made another step in [toward mind reading by machines]. In a study published in the journal Nature Neuroscience, the researchers described an A.I. that could translate the private thoughts of human subjects by analyzing fMRI [functional magnetic resonance imaging] scans, which measure the flow of blood to different regions in the brain.Scientists recorded M.R.I. data from three participants as they listened to 16 hours of narrative stories to train the model to map between brain activity and semantic features that captured the meanings of certain phrases and the associated brain response. .... The researchers used a large language model to match patterns in brain activity to the words and phrases that the participants had heard.Already, researchers have developed language-decoding methods to pick up the attempted speech of people who have lost the ability to speak, and to allow paralyzed people to write while just thinking of writing. But the new language decoder is one of the first to not rely on implants. In the study, it was able to turn a person’s imagined speech into actual speech and, when subjects were shown silent films, it could generate relatively accurate descriptions of what was happening onscreen.Large language models like OpenAI’s GPT-4 and Google’s Bard are trained on vast amounts of writing to predict the next word in a sentence or phrase. In the process, the models create maps indicating how words relate to one another. A few years ago, Dr. Huth noticed that particular pieces of these maps — so-called context embeddings, which capture the semantic features, or meanings, of phrases — could be used to predict how the brain lights up in response to language.
In a basic sense, said Shinji Nishimoto, a neuroscientist at Osaka University who was not involved in the research, “brain activity is a kind of encrypted signal, and language models provide ways to decipher it.”
In their study, Dr. Huth and his colleagues effectively reversed the process, using another A.I. to translate the participant’s fMRI images into words and phrases. The researchers tested the decoder by having the participants listen to new recordings, then seeing how closely the translation matched the actual transcript.
Almost every word was out of place in the decoded script, but the meaning of the passage was regularly preserved. Essentially, the decoders were paraphrasing.
Original transcript: “I got up from the air mattress and pressed my face against the glass of the bedroom window expecting to see eyes staring back at me but instead only finding darkness.”
Decoded from brain activity: “I just continued to walk up to the window and open the glass I stood on my toes and peered out I didn’t see anything and looked up again I saw nothing.”
‘The Godfather of A.I.’ Leaves Google and Warns of Danger Ahead
For half a century, Geoffrey Hinton nurtured the technology at the heart of chatbots like ChatGPT. Now he worries it will cause serious harm.
Geoffrey Hinton was an artificial intelligence pioneer. In 2012, Dr. Hinton and two of his graduate students at the University of Toronto created technology that became the intellectual foundation for the A.I. systems that the tech industry’s biggest companies believe is a key to their future.
On Monday, however, he officially joined a growing chorus of critics who say those companies are racing toward danger with their aggressive campaign to create products based on generative artificial intelligence, the technology that powers popular chatbots like ChatGPT.
Dr. Hinton said he has quit his job at Google, where he has worked for more than a decade and became one of the most respected voices in the field, so he can freely speak out about the risks of A.I. A part of him, he said, now regrets his life’s work.
“I console myself with the normal excuse: If I hadn’t done it, somebody else would have,” Dr. Hinton said during a lengthy interview last week in the dining room of his home in Toronto, a short walk from where he and his students made their breakthrough.
"In this research we found higher-order cognitive function, which we normally associate with the human brain, can be emulated in non-biological hardware," Dr Loeffler said.
"This work builds on our previous research in which we showed how nanotechnology could be used to build a brain-inspired electrical device with neural network-like circuitry and synapse-like signalling.
"Our current work paves the way towards replicating brain-like learning and memory in non-biological hardware systems and suggests that the underlying nature of brain-like intelligence may be physical."
Nanowire networks are a type of nanotechnology typically made from tiny, highly conductive silver wires that are invisible to the naked eye, covered in a plastic material, which are scattered across each other like a mesh. The wires mimic aspects of the networked physical structure of a human brain.
Advances in nanowire networks could herald many real-world applications, such as improving robotics or sensor devices that need to make quick decisions in unpredictable environments.
To test the capabilities of the nanowire network, the researchers gave it a test similar to a common memory task used in human psychology experiments, called the N-Back task.
For a person, the N-Back task might involve remembering a specific picture of a cat from a series of feline images presented in a sequence. An N-Back score of 7, the average for people, indicates the person can recognise the same image that appeared seven steps back.
When applied to the nanowire network, the researchers found it could 'remember' a desired endpoint in an electric circuit seven steps back, meaning a score of 7 in an N-Back test.
"What we did here is manipulate the voltages of the end electrodes to force the pathways to change, rather than letting the network just do its own thing. We forced the pathways to go where we wanted them to go," Dr Loefflersaid.
"When we implement that, its memory had much higher accuracy and didn't really decrease over time, suggesting that we've found a way to strengthen the pathways to push them towards where we want them, and then the network remembers it.
"Neuroscientists think this is how the brain works, certain synaptic connections strengthen while others weaken, and that's thought to be how we preferentially remember some things, how we learn and so on."
The researchers said when the nanowire network is constantly reinforced, it reaches a point where that reinforcement is no longer needed because the information is consolidated into memory.
"It's kind of like the difference between long-term memory and short-term memory in our brains," Professor Kuncic said.
This study is the first to demonstrate a nontrivial cognitive task— inspired by the WM n-back task—in a physical non–CMOS (complementary metal-oxide semiconductor) substrate with native neuromorphic properties (i.e., not requiring implementation of neuromorphic algorithms).In a previous study, Neftci and colleagues (53) demonstrated a simple cognitive task by emulating spiking neurons in a CMOS system. Their method used an intermediate computational layer in which silicon neurons are configured as soft winner-take-all (WTA) networks (54).
In conclusion, by applying supervised and reinforcement learning strategies similar to those operating in the brain, we have demonstrated working memory and memory consolidation in nanowire networks. These higher-order cognitive functions were achieved by implementing a nontrivial cognitive task routinely applied to human subjects. Results reveal that neuromorphic learning paradigms implemented in nanowire networks leverage similar mechanisms to the brain, namely, synaptic metaplasticity and synaptic strengthening and pruning, to optimize working memory and memory consolidation.
These discrepant views—[e.g., differing personal] concepts of penguins—are the kind of information researchers at the University of California, Berkeley, elicited from participants in a study that was published last month. The team’s results show that even the plainest of nouns can invoke dozens of distinct concepts in individuals’ mind. “People have wondered for a long time how to put a number on how much overlap there is, and it’s really low. It blows my mind,” says psychologist Celeste Kidd of the U.C. Berkeley, who was senior author of the study.
To make matters worse, the researchers found that people are usually oblivious to these differences and believe that most other people think like they do even when almost nobody does. This may be one reason people so often are at loggerheads. “We think it can explain a lot of disagreements people have,” Kidd says. “It’s an approach to understanding why people talk past each other.” Being more aware of how often we might not be comprehending one another may help us “get on the same page when it matters,” she adds.
It is well known that abstract, high-concept words such as “knowledge” and “fairness”** provoke frequent debates about exactly what is meant. But researchers have struggled to formally characterize how people’s concepts differ and to quantify how often that happens. Past efforts have stumbled, Kidd says, because we do not fully understand what concepts consist of.
** And, one of my favorites, “fascism.”
Participants believed around two thirds would agree with them when the actual proportion was usually much smaller. In some cases, people believed they were in the majority when virtually nobody agreed. This shows that people are typically oblivious to the extent to which others share their concepts. “That was cool,” Gelman says, “and may have implications for when we think we’re communicating but aren’t.”
The study may offer hope that we can sometimes overcome our differences by simply becoming aware of them. “When people disagree, it might not be for the reasons they think,” Kidd says. “It could just be because their concepts aren’t aligned.” Her advice: “Hash it out,” she says. “Questions like ‘What do you mean?’ can go a long way toward preventing a dispute going off the rails.”
Many social and legal conflicts hinge on semantic disagreements. Understanding the origins and implications of these disagreements necessitates novel methods for identifying and quantifying variation in semantic cognition between individuals. .... Our results show at least ten to thirty quantifiably different variants of word meanings exist for even common nouns. Further, people are unaware of this variation, and exhibit a strong bias to erroneously believe that other people share their semantics. This highlights conceptual factors that likely interfere with productive political and social discourse.
Even when two individuals use the same word, they do not necessarily agree on its meaning. Disagreements about meaning are common in debates about terms like “species” (Zachos, 2016), “genes” (Stotz et al., 2004), or “life” (Trifonov, 2011) in biology; “curiosity” (Grossnickle, 2016), “knowledge” (Lehrer, 2018), or “intelligence” (Sternberg, 2005) in psychology; and “measurement” in physics (Wigner, 1995). Ernst Mach and Albert Einstein even disagreed about what constitutes a “fact” (de Waal & ten Hagen, 2020). In contemporary society, social issues often hinge on the precise meaning of terms like “equity” (Benjamin, 2019), “pornography” (Stewart, 1964), “peace” (Leshem & Halperin, 2020), or the “right to bear arms” (Winkler, 2011). Sometimes these debates are settled by fiat—for example, the U.S. Supreme court decided that a tomato counted as a vegetable (not a fruit) for tax purposes because the law should follow the “ordinary meaning” of words rather than their botanical meaning (see Goldfarb, 2021; Nix v. Hedden, 149 U.S. 304, 1893).
Despite the frequency of such terminological debates, these conflicts have not been characterized using cognitive psychology methods.
