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AI Language Bots Shape Our Thoughts, But What’s Next Will Think and Act on Our Behalf

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In the tech sector, there are few instances that can be dubbed “big bang” moments—transformative events that reshape our understanding of technology’s role in the world.

The emergence of the World Wide Web marked a significant “before and after” shift. Similarly, the launch of the iPhone in 2007 initiated a smartphone revolution.

November 2022 saw the release of ChatGPT, another monumental event. Prior to this, artificial intelligence (AI) was largely unfamiliar to most people outside the tech realm.

Nonetheless, large-scale language models (LLMs) rapidly became the fastest-growing application in history, igniting what is now referred to as the “generative AI revolution.”







However, revolutions can struggle to maintain momentum.

Three years post-ChatGPT’s launch, many of us remain employed, despite alarming reports of mass job losses due to AI. Over half of Britons have never interacted with an AI chatbot.

Whether the revolution is sluggish is up for debate, but even the staunchest AI supporters acknowledge that progress may not be as rapid as once anticipated. So, will AI evolve to become even smarter?

What Exactly Is Intelligence?

The professor posits that determining if AI has hit a plateau in intelligence hinges on how one defines “intelligence.” Katherine Frik, Professor of AI Ethics at Staffordshire University, states, “In my view, AI isn’t genuinely intelligent; it simply mimics human responses that seem intelligent.”

For her, the answer to whether AI is as smart as ever is affirmative—because AI has never truly been intelligent, nor will it ever be.

“All that can happen is that we improve our programming skills so that these tools generate even more convincing imitations of intelligence. Yet, the essence of thought, experience, and reflection will always be inaccessible to artificial agents,” she observes.

Disappointment in AI stems partly from advocates who, since its introduction, claimed that AI could outperform human capabilities.

This group included the AI companies themselves and their leaders. Dario Amodei, CEO of Anthropic, known for the Claude chatbot, has been one of the most outspoken advocates.

AI chatbots are helpful tools, but they lack true intelligence – Credit: Getty

The CEO recently predicted that AI models could exceed human intelligence within three years, a claim he has previously made but was ultimately incorrect.

Frik acknowledges that “intelligence” takes on various meanings in the realm of AI. If the query is about whether models like ChatGPT or Claude will see improvements, her response may differ.

“[They’ll probably] see further advancements as new methods are developed to better replicate [human-style interaction]. However, they will never transcend from advanced statistical processors to genuine, reflective intelligence,” she adds.

Despite this, there is an ongoing, vibrant debate within the AI sector regarding the diminishing effectiveness of AI model improvements.

OpenAI’s anticipated GPT-5 model was met with disappointment, primarily because the company marketed it as superhuman before its launch.

Hence, when a slightly better version was released, reactions deemed it less remarkable. Detractors interpret this as evidence that AI’s potential has already been capped. Are they right?

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Double Track System

“The belief that AI advancements have stagnated is largely a misconception, shaped by the fact that most people engage with AI through consumer applications like chatbots,” says Eleanor Watson, an AI ethics engineer at Singularity University, an educational institution and research center.

While chatbots are gradually improving, much of it is incremental, Watson insists. “It’s akin to how your vehicle gets better paint each year or how your GPS keeps evolving,” she explains.

“This perspective overlooks the revolutionary transformations happening beneath the surface. In reality, the foundational technology is being reimagined and advancing exponentially.”

Even if AI chatbots operate similarly as they did three years ago for the average user who doesn’t delve into the details, AI is being successfully applied in various fields, including medicine.

She believes this pace will keep accelerating for multiple reasons. One is the enormous investment fueling the generative AI revolution.

According to the International Energy Agency, electricity demand to power AI systems is projected to surpass that of steel, cement, chemicals, and all other energy-intensive products combined by 2030.

London’s water-cooled servers symbolize the AI boom, with computing power predicted to increase tenfold in two years – Image courtesy of Getty Images

Tech companies are investing heavily in data centers to process AI tasks.

In 2021, prior to ChatGPT’s debut, four leading tech firms — Alphabet (Google’s parent company), Amazon, Microsoft, and Meta (the owner of Facebook) — collectively spent over $100 billion (£73 billion) on the necessary infrastructure for these data centers.

This expenditure is expected to approach $350 billion (£256 billion) by 2025 and to surpass $500 billion (£366 billion) by 2029.

AI companies are constructing larger data centers equipped with more dependable power resources, and they are also becoming more strategic regarding their operational methodologies.

“The brute-force strategy of merely adding more data and computing power continues to show significant benefits, but the primary concern is efficacy,” Watson states.

“The potency of models has increased tremendously. Tasks that once required extensive and massive systems can now be performed by less voluminous, cheaper, and faster systems. Capacity density is also growing at an incredible rate.”

Techniques such as number rounding or quantizing inputs to the LLM (which involves reducing information precision in less critical areas) can enhance model efficiency.

Hire an Agent

One dimension of “intelligence” where AI continues to evolve is the area of “agentic” AI, particularly if understood as “efficiency.”

This involves modifying AI interactions and behavior, an endeavor still in its infancy. “Agent AI can handle finances, foresee needs, and establish sub-goals toward larger objectives,” explains Watson.

Leading AI firms, including OpenAI, are incorporating agent AI tools into their systems, transforming user engagement from simple chats to collaborative AI partners, enabling users to complete tasks independently while managing other responsibilities.

These AI agents are increasingly capable of functioning autonomously for extended periods, and many assert that this signifies growth in AI intelligence.

However, AI agents pose their own set of challenges.

Research has revealed potential issues with agent AI. Specifically, when an AI agent encounters seemingly harmless instructions on a web page, it might execute harmful commands, leading to what’s termed a “prompt injection” attack.

Consequently, several companies impose strict controls on these AI agents.

Nonetheless, the very prospect of AI carrying out tasks on autopilot hints at untapped growth potential. This, along with ongoing investments in computing capabilities and the continuous introduction of AI solutions, indicates that AI is not stagnant—far from it.

“The smart bet is continued exponential growth,” Watson emphasizes. “[Tech] leaders are correct about this trajectory, but they often underestimate the governance and security challenges that will need to evolve alongside it.”

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Source: www.sciencefocus.com

Lead Exposure Could Have Shaped Human Brain Evolution, Behavior, and Language Development

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Several hominid species — Australopithecus africanus, Paranthropus robustus, early homo varieties, Gigantopithecus brachy, Pongo, papio, homo neanderthalensis, and homo sapiens — have undergone significant lead exposure over two million years, as revealed by a new analysis of fossilized teeth collected from Africa, Asia, Oceania, and Europe. This finding challenges the notion that lead exposure is merely a contemporary issue.

Lead exposure affecting modern humans and their ancestors. Image credit: J. Gregory/Mount Sinai Health System.

Professor Renaud Joannes Boyau from Southern Cross University remarked: “Our findings indicate that lead exposure has been integral to human evolution, not just a byproduct of the industrial revolution.”

“This suggests that our ancestors’ brain development was influenced by toxic metals, potentially shaping their social dynamics and cognitive functions over millennia.”

The team analyzed 51 fossil samples globally utilizing a carefully validated laser ablation microspatial sampling technique, encompassing species like Australopithecus africanus, Paranthropus robustus, early homo variants, Gigantopithecus brachy, Pongo, papio, homo neanderthalensis, and homo sapiens.

Signs of transient lead exposure were evident in 73% of the specimens analyzed (compared to 71% in humans). This included findings on Australopithecus, Paranthropus, and homo species.

Some of the earliest geological samples from Gigantopithecus brachy, believed to be around 1.8 million years old from the early Pleistocene and 1 million years old from the mid-Pleistocene, displayed recurrent lead exposure events interspersed with periods of little to no lead uptake.

To further explore the impact of ancient lead exposure on brain development, researchers also conducted laboratory studies.

Australopithecus africanus. Image credit: JM Salas / CC BY-SA 3.0.” width=”580″ height=”627″ srcset=”https://cdn.sci.news/images/2015/01/image_2428-Australopithecus-africanus.jpg 580w, https://cdn.sci.news/images/2015/01/image_2428-Australopithecus-africanus-277×300.jpg 277w” sizes=”(max-width: 580px) 100vw, 580px”/>

Australopithecus africanus. Image credit: JM Salas / CC BY-SA 3.0.

Using human brain organoids (miniature brain models grown in the lab), researchers examined the effects of lead on a crucial developmental gene named NOVA1, recognized for modulating gene expression during neurodevelopment in response to lead exposure.

The modern iteration of NOVA1 has undergone changes distinct from those seen in Neanderthals and other extinct hominins, with the reasons for this evolution remaining unclear until now.

In organoids with ancestral versions of NOVA1, exposure to lead significantly altered neural activity in relation to Fox P2 — a gene involved in the functionality of brain regions critical for language and speech development.

This effect was less pronounced in modern organoids with NOVA1 mutations.

“These findings indicate that our variant of NOVA1 might have conferred a protective advantage against the detrimental neurological effects of lead,” stated Alison Muotri, a professor at the University of California, San Diego.

“This exemplifies how environmental pressures, such as lead toxicity, can drive genetic evolution, enhancing our capacity for survival and verbal communication while also affecting our susceptibility to contemporary lead exposure.”

Gigantopithecus blackii inhabiting the forests of southern China. Image credit: Garcia / Joannes-Boyau, Southern Cross University.” width=”580″ height=”375″ srcset=”https://cdn.sci.news/images/2024/01/image_12599-Gigantopithecus-blacki.jpg 580w, https://cdn.sci.news/images/2024/01/image_12599-Gigantopithecus-blacki-300×194.jpg 300w, https://cdn.sci.news/images/2024/01/image_12599-Gigantopithecus-blacki-84×55.jpg 84w” sizes=”(max-width: 580px) 100vw, 580px”/>

An artistic rendition of a Gigantopithecus brachy herd in the forests of southern China. Image credit: Garcia / Joannes-Boyau, Southern Cross University.

Genetic and proteomic analyses in this study revealed that lead exposure in archaic variant organoids disrupts pathways vital for neurodevelopment, social behavior, and communication.

Alterations in Fox P2 activity indicate a possible correlation between ancient lead exposure and the advanced language abilities found in modern humans.

“This research highlights the role environmental exposures have played in human evolution,” stated Professor Manish Arora from the Icahn School of Medicine at Mount Sinai.

“The insight that exposure to toxic substances may conjure survival advantages in the context of interspecific competition introduces a fresh perspective in environmental medicine, prompting investigations into the evolutionary origins of disorders linked to such exposures.”

For more information, refer to the study published in the journal Science Advances.

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Renaud Joannes Boyau et al. 2025. Effects of intermittent lead exposure on hominid brain evolution. Science Advances 11(42); doi: 10.1126/sciadv.adr1524

Source: www.sci.news

Maternal Voice Enhances Language Development in Premature Infants

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Premature babies may face language challenges later, but simple interventions can assist.

BSIP SA/Alamy

The first randomized controlled trial of this straightforward intervention suggests that playing recordings of a mother’s voice to premature infants could expedite their brain maturation processes. This method may eventually enhance language development in babies born prematurely.

Premature birth alters brain structure, leading to potential language disorders and affecting later communication and academic success. A mother’s voice and heartbeat can foster the development of auditory and language pathways. Unfortunately, parents may not always be able to physically be with their infants in the neonatal units.

To explore whether this absence could be compensated for through recordings, Katherine Travis and her team at Weill Cornell Medicine in New York conducted a study with 46 premature infants born between 24 and 31 weeks gestation, all situated in the neonatal intensive care unit.

We recorded mothers reading from children’s books, including selections from A Bear Named Paddington. Half of the infants listened to a ten-minute audio segment twice every hour overnight between 10 PM and 6 AM, increasing their daily exposure to their mother’s voice by an average of 2.7 hours until they reached their original due date. The other infants received similar medical care but were not exposed to recordings.

Upon reaching their due date, these infants underwent two MRI scans to evaluate the organization and connectivity of their brain networks. The results indicated that those who heard their mother’s voice at night exhibited more robust and organized connections in and around the left arcuate fasciculus, a crucial area for language processing. “The structure appeared notably more developed,” said Travis. “The characteristics matched what one might expect to find in older, more mature infants.”

The scans also suggested that this maturation could be linked to increased myelination— the creation of a fatty sheath that insulates nerve fibers, enhancing the speed and efficiency of signal transmission within the brain. “Myelination is crucial for healthy brain development, especially in pathways that support communication and learning,” noted Travis.

Previous studies have indicated that delayed development of these brain areas correlates with language delays and learning challenges. The latest findings imply that targeted speech exposure could improve these outcomes.

However, is it truly vital for infants to hear their mother’s voice rather than others? While this study did not address that, earlier research explains the phenomenon. Babies start hearing around the 24th week of pregnancy, and continue to recognize their mother’s voice after birth due to early exposure in the womb. Travis explained, “This voice is biologically significant and may be especially appealing to the developing brain.”

Nonetheless, Travis emphasizes that language exposure from other caregivers is also critical for language development, and future studies will explore this aspect further.

The intervention is straightforward and can easily be integrated into care protocols. However, David Edwards from Evelina London Children’s Hospital cautioned against overinterpreting the findings. “Given the small sample size, additional control groups, including different audio sources and forms of auditory stimulation, should be evaluated,” he suggested.

Travis and her research team aim to validate these results in larger trials involving medically vulnerable infants. They will continue to monitor current participants to determine if the observed brain differences result in tangible improvements in language and communication skills as these infants grow.

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Source: www.newscientist.com

Chatbots Perform Best When Communicating in Formal Language.

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Your approach to chatting with AI may matter more than you realize

Oscar Wong/Getty Images

The manner in which you converse with an AI chatbot, especially using informal language, can significantly impact the accuracy of its replies. This indicates that we might need to engage with chatbots more formally or train the AI to handle informal dialogue better.

Researchers Fulei Zhang and Zhou Yu from Amazon explored how users begin chats with human representatives versus chatbot assistants that utilize large language models (LLMs). They employed the Claude 3.5 Sonnet model to evaluate various aspects of these interactions, discovering that exchanges with chatbots were marked by less grammatical accuracy and politeness compared to human-to-human dialogues, as well as a somewhat limited vocabulary.

The findings showed that human-to-human interactions were 14.5% more polite and formal, 5.3% more fluent, and 1.4% more lexically diverse than their chatbot counterparts, according to Claude’s assessments.

The authors noted in their study, “Participants adjust their linguistic style in human-LLM interactions, favoring shorter, more direct, less formal, and grammatically simpler messages,” though they did not respond to interview requests. “This behavior may stem from users’ mental models of LLM chatbots, particularly if they lack social nuance or sensitivity.”

However, embracing this informal style comes with challenges. In another evaluation, the researchers trained an AI model named Mistral 7B using 13,000 actual human-to-human interactions, then assessed 1,357 real messages directed at the AI chatbot. They categorized each conversation with an “intent” derived from a restricted framework summarizing the user’s purpose. Unfortunately, Mistral struggled with accurately defining the intentions within the chatbot conversations.

Zhang and Yu explored various methods to enhance Mistral AI’s understanding. Initially, they used Claude AI to transform users’ succinct messages into more polished human-like text and used these rewrites to fine-tune Mistral, resulting in a 1.9% decline in intent label accuracy from the baseline.

Next, they attempted a “minimal” rewrite with Claude, creating shorter and more direct phrases (e.g., asking about travel and lodging options for an upcoming trip with “Paris next month. Where’s the flight hotel?”). This method caused a 2.6% drop in Mistral’s accuracy. On the other hand, utilizing a more formal and varied style in “enhanced” rewrites also led to a 1.8% decrease in accuracy. Ultimately, the performance showed an improvement of 2.9% only when training Mistral with both minimal and enhanced rewrites.

Noah Jansiracusa, a professor at Bentley University in Massachusetts, expressed that while it’s expected that users communicate differently with bots than with other humans, this disparity shouldn’t necessarily be seen as a negative.

“The observation that people interact with chatbots differently from humans is often depicted as a drawback, but I believe it’s beneficial for users to recognize they’re engaging with a bot and adjust their communication accordingly,” Giansiracusa stated. “This understanding is healthier than a continual effort to bridge the gap between humans and bots.”

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Source: www.newscientist.com

Scientists Might Have Unraveled the Secrets of Teotihuacan’s Written Language

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The civilization that thrived in Teotihuacan during the Classic period holds a distinctive position in Mesoamerican history. Today, it continues to represent Mexico’s rich heritage and is among the most frequented archaeological locations in the Americas. However, inquisitive tourists often find that the ethnic and linguistic connections of the Teotihuacanos are still a mystery. While the deciphering of other Mesoamerican writing systems has unveiled significant insights about dynasties and historical occurrences, researchers have yet to extract information about Teotihuacan society from their own written artifacts. The topic of writing in Teotihuacan indeed provokes several intriguing questions. Do the symbols depicted in the images of Teotihuacan represent a form of writing? If they do, what was their purpose? Were they created to be understood irrespective of language? If they indicated a specific language, which one was it? Researchers Magnus Pharaoh Hansen and Christopher Helmke from the University of Copenhagen suggest that Teotihuacan writing shares fundamental characteristics with other Mesoamerican writing systems, including the utilization of logograms based on rebus principles and a technique termed “double spelling.” They contend that it encapsulates a specific, identifiable language: Uto-Aztecan, the direct predecessor of Nahuatl, Chora, and Huichol, and they offer a new interpretation of certain Teotihuacan glyphs.

View of the small pyramid on the east side of the Plaza de la Luna from Piramide del Sol in Teotihuacan. Image credit: Daniel Case / CC BY-SA 3.0.

Teotihuacan is a revered pre-Columbian city established around 100 BC and thrived until 600 AD.

This ancient metropolis, situated in the northeastern area of the Basin of Mexico, expanded over 20 square kilometers and housed up to 125,000 residents while engaging with other Mesoamerican cultures.

The identities of Teotihuacan’s builders and their relationships to subsequent populations remain uncertain. The reasons behind the city’s abandonment also spark debate, with theories ranging from foreign invasion, civil strife, ecological disaster, or a combination of these factors.

“There are numerous distinct cultures in Mexico, some linked to specific archaeological traditions, while others remain ambiguous. Teotihuacan exemplifies such a case,” stated Dr. Pharaoh Hansen.

“The languages they spoke and their links to later cultures are still unknown.”

“One can easily identify the Teotihuacan culture when compared to modern cultures,” added Dr. Helmke.

“For instance, the remains of Teotihuacan suggest that parts of the city were occupied by the more widely recognized Maya civilization.”

The ancient inhabitants of Teotihuacan left a collection of symbols, primarily through wall murals and decorative ceramics.

For years, researchers have debated whether these symbols represent an actual written language.

The authors assert that the inscriptions on Teotihuacan’s walls indeed record a language that is a linguistic precursor to Cora, Huichol, and the Aztec language Nahuatl.

The Aztecs, well-known in Mexican history, were thought to have migrated to central Mexico following the decline of Teotihuacan.

However, researchers claim there are linguistic connections between Teotihuacan and the Aztecs, indicating that Nahuatl-speaking peoples might have settled in the region much earlier and are in fact direct descendants of Teotihuacan’s original population.

To elucidate the linguistic parallels between Teotihuacan’s language and other Mesoamerican tongues, scientists have been working to reconstruct a much older version of Nahuatl.

“Otherwise, it would be akin to interpreting the runes on a famous Danish runestone, like the Jellingstone, using contemporary Danish. That would be an anachronism. We must attempt to read the text with a more temporally appropriate language,” explains Dr. Helmke.

Examples of logograms that make up the Teotihuacan written language. Image credit: Christophe Helmke, University of Copenhagen.

The script of Teotihuacan presents significant challenges for decipherment due to multiple factors.

One challenge is that the logograms may possess a direct semantic meaning; for instance, an image depicting a coyote directly translates to “coyote.”

In other instances, symbols must be interpreted in a rebus format, wherein the sounds represented by the depicted objects are combined to form words; however, such words are often conceptual and difficult to express as single figurative logograms.

This complexity underscores the necessity for a solid understanding of both the Teotihuacan writing system and the Uto-Aztecan language that researchers believe is encoded in the inscriptions.

To unlock the Teotihuacan linguistic riddle, one must be aware of how words were pronounced at that time.

This is why the researchers are focusing on various aspects concurrently. They are reconstructing the Uto-Aztecan language, a formidable challenge in its own right, while applying this ancient language to interpret the Teotihuacan texts.

“In Teotihuacan, pottery with inscriptions continues to be unearthed, and we anticipate that many more wall paintings will be discovered in the future,” remarked Dr. Pharaoh Hansen.

“The scarcity of additional text clearly hampers our study.”

“It would be beneficial to find the same symbol used similarly in varied contexts.”

“This would further substantiate our hypothesis, but for now, we are limited to the documentation available to us.”

Dr. Pharaoh Hansen and Dr. Helmke are enthusiastic about their recent advancements.

“Prior to our work, no one had applied a linguistically appropriate approach to deciphering this written form,” stated Dr. Pharaoh Hansen.

“Moreover, no one had successfully established that a particular logogram could hold phonetic significance applicable in contexts beyond its primary meaning.”

“Through this process, we have developed a method that can serve as a foundation for others to broaden their comprehension of the texts.”

The team’s study has been published in the journal Current Anthropology.

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Magnus Pharaoh Hansen and Christoph Helmke. 2025. Language of Teotihuacan. Current Anthropology 66(5); doi: 10.1086/737863

Source: www.sci.news

Exploring the Origins of Language: What is Parenting Fuel Language? Insights from a New Book

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Beekman proposes that the intricacies of parenting have fueled the evolution of language

Shutterstock/Artem Varnitsin

The Origin of Language
Madeleine Beekman (Simon & Schuster)

Language remains one of the few attributes regarded as uniquely human. While animals like chimpanzees and songbirds exhibit advanced communication systems, they do not convey meaning on the same scale as humans. So, what prompted our ancestors to develop language?

Madeleine Beekman, an evolutionary biologist with a focus on insects, particularly honeybees, presents an engaging explanation in her first book aimed at general audiences regarding the evolution of human language.

Her hypothesis suggests that language emerged as a necessity to meet the challenges of parenting. In comparison to other mammals, human infants are quite helpless at birth and need around-the-clock care.

Echoing decades of paleontological research, Beekman links the vulnerable state of infants to two factors: a larger brain and a narrower pelvis. “As our bodies adapted for bipedalism, our hips narrowed,” she notes. As a result, our brains grew larger. “A big-headed baby and a mother with a narrow pelvis don’t work well together,” Beekman elaborates.

To circumvent this “obstetric dilemma,” infants are born at an earlier stage, leading to the situation where their heads are too large for a narrow birth canal. This adaptation allows for safer childbirth but necessitates extended care for the fragile young.

Thus far, the narrative is familiar. Beekman’s significant leap is to propose that the requirements of caring for human offspring spurred the development of complex languages. “Caring for human babies is incredibly challenging, leading evolution to craft entirely new tools to assist with this effort,” she asserts, “the design flaws that initiated the issue ultimately offered a solution.” While our brains made childbirth more complicated, we simultaneously developed our capacity for a richer, more flexible language.

In presenting this idea, Beekman navigates a bustling marketplace of theories on language evolution. Various hypotheses exist; some contend that language arose alongside toolmaking, where the development of advanced tools required more descriptive language for instruction. Others suggest language served as a means of social distinction, encompassing clever wordplay and insults. Additionally, it may have initially been a cognitive tool, primarily for individual thought before evolving to facilitate communication with others.

One intriguing element of Beekman’s theory is her emphasis on the roles of women and children. Science has historically leaned towards male-centered viewpoints, often overshadowing the significant evolutionary shifts linked to pregnancy (e.g., the “Hunter” model).

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The authors contend that language is around 100,000 years old and unique to our species.
“

It’s essential to reflect on the contributions of women and children in the story of language’s origins. However, this doesn’t necessarily affirm Beekman’s thesis. She presents compelling evidence, notably showing that many large birds, including parrots and New Caledonian crows, produce underdeveloped offspring. Why? A 2023 study indicated that the primary predictor of avian brain size was the degree of parental care.

All of this resonates with Beekman’s narrative. Yet, the most pressing question remains: timing. Humans have been walking on two legs for at least 6 million years, and our brains have expanded rapidly for the last 2 million years. Given this extensive timeline, when did language actually develop?

Beekman posits that modern language is roughly 100,000 years old and specific to our species. She references 2020 research pinpointing “unique gene regulatory networks that shape the anatomy crucial for precise word production.” These networks appear to exist solely in our species, indicating that other human relatives, like Neanderthals, may not have possessed the same linguistic capabilities.

Beekman considers this “conclusive,” yet other scholars have unearthed evidence that suggests the possibility of complex language in other human species. The evolution surrounding human childbirth remains as intertwined as it is uncertain. In summary, robust ideas necessitate further proof.

Michael Marshall is a writer based in Devon, UK

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AI Capable of Translating Imagined Speech into Spoken Language

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Individuals with paralysis utilizing a brain-computer interface. The text above serves as a prompt, while the text below is decoded in real-time as she envisions speaking the phrase.

Emory BrainGate Team

A person with paralysis can convert their thoughts into speech just by imagining what they want to say.

The brain-computer interface can already interpret the neural activity of a paralyzed individual when attempting to speak physically, but this requires significant effort. Therefore, Benyamin Meschede-Krasa from Stanford University and his team explored a less effort-intensive method.

“We aimed to determine if there was a similar pattern when individuals imagined speaking internally,” he notes. “Our findings suggest this could be a more comfortable method for people with paralysis to use the system to regain their ability to communicate.”

Meschede-Krasa and his colleagues enlisted four participants with severe paralysis due to either amyotrophic lateral sclerosis (ALS) or brainstem stroke. All had previously had microelectrodes implanted in motor areas linked to speech for research purposes.

Researchers instructed participants to list words and sentences and to visualize themselves saying them. They discovered that the brain activity mirrored that of actual speech; however, the activation signal was typically weaker during the imagined speech.

The team trained AI models to interpret and decode these signals utilizing a vocabulary database containing up to 125,000 words. To uphold the privacy of individuals’ thoughts, the models were programmed to activate only when a specific password, Chitty Chitty Bang Bang, was detected with 98% accuracy.

Through various experiments, the researchers found that the models could decode what was intended to be communicated correctly up to 74% of the time when spoken as a single word.

This demonstrates a promising application of the approach, though it is currently less reliable than systems that decode overt speech attempts, according to Frank Willett at Stanford. Ongoing enhancements to both the sensors and AI over the coming years may lead to greater accuracy, he suggests.

Participants reported a strong preference for this system, describing it as faster and less cumbersome compared to traditional speech-attempt based systems, as stated by Meschede-Krasa.

This notion presents an “interesting direction” for future brain-computer interfaces, remarks Maris Cavan Stencel in Utrecht, Netherlands. However, she points out the need for a distinction between genuine speech and the thoughts individuals may not necessarily wish to share. “I have doubts about whether anyone can truly differentiate between these types of mental speech and attempted speech,” she adds.

She further mentions that the mechanism requires activation and deactivation to ascertain if the user intends to articulate their thoughts. “It is crucial to ensure that brain-computer interface-generated communications are conscious expressions individuals wish to convey, rather than internal thoughts they wish to keep private,” she states.

Benjamin Alderson Day from Durham University in the UK argues that there’s no reason to label the system as a mind reader. “It effectively addresses very basic language constructs,” he explains. “Though it may seem alarming if thoughts are confined to single terms like ‘tree’ or ‘bird,’ we are still a long way from capturing the full range of individuals’ thoughts and their most intimate ideas.”

Willett underscores that all brain-computer interfaces are governed by federal regulations, ensuring adherence to the “highest standards of medical ethics.”

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  • Brain

Source: www.newscientist.com

Algospeak Review: Key Insights on How Social Media Accelerates Language Evolution

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Social Media and Short-Form Video Platforms Drive Language Innovation

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Algospeak
Adam Aleksic (Every (UK, July 17th) Knopf (USA, July 15th))

You won’t age, just as slang is wrapped in bamboo. In Adam Aleksic’s chapter Algospeak: How Social Media Will Change the Future of Language, this phenomenon is discussed. Phrases like “Pierce Your Gyat for Rizzler” and “WordPilled Slangmaxxing” remind me that as a millennial, I’m just as distant from boomers as today’s Alphas are.

Linguist and content creator (@etymologynerd), Aleksic has ignited a new wave of linguistic innovation fueled by social media, particularly short video platforms like TikTok. The term “Algospeak” has been traditionally linked to euphemisms used to avoid online censorship, with recent examples including “anxiety” (in reference to death) or “segg” (for sex).

However, the author insists on broadening the definition to encompass all language aspects affected by the “algorithm.” This term refers to the various, often opaque processes social media platforms use to curate content for users.

In his case, Aleksic draws on his experience of earning a living through educational videos about language. Like other creators, he is motivated to appeal to the algorithm, which requires careful word selection. A video he created dissecting the etymology of the word “pen” (tracing back to the Latin “penis”) breached sexual content rules, while a discussion on the phrase “from river to sea” remained within acceptable limits.

Meanwhile, videos that explore Gen Alpha terms like “Skibidi” (a largely nonsensical term rooted in scat singing) and “Gyat” (“Goddamn” or “Ass”) have performed particularly well. His findings illustrate how creators modify their language for algorithmic advantage, with some words transitioning online and offline to achieve notable success. When Aleksic examined educators, he found many of these terms had entered regular classroom slang, with some students learning the term “anxiety” before understanding “suicide.”

A standout aspect of his study lies in etymology, investigating how algorithms propel words from online subcultures into mainstream lexicon. He notes that the misogynistic incel community is a significant contributor to contemporary slang, evidenced by its radical nature that can outpace linguistic evolution within a group.

Aleksic approaches language trends with a non-judgmental perspective. He notes that the term “anxiety” parallels earlier euphemisms like “deceased,” while “Skibidi” is reminiscent of “Scooby-Doo.” He frequently mischaracterizes slang within arbitrarily defined generations, which claim to infuse toxic narratives into the evolution of normal languages.

The situation becomes more intricate when slang enters mainstream usage through cultural appropriation. Many contemporary slang terms, like “cool” before them, trace back to the Black community (“Thicc,” “bruh”) or originate from the LGBTQ ballroom scenes (“Slay,” “Yas,” “Queen”). Such wide-ranging adoptions can sever these terms from their historical contexts, often linked to social struggles and further entrenching negative stereotypes about the communities that birthed them.

Preventing this disruption of context is challenging. Successful slang’s fate is often to be stripped of its original nuances. Social media has drastically accelerated the timeline for language innovation. Algospeak is a necessary update, yet it can become quickly outdated. However, as long as algorithms exist, fundamental insights into how technology influences language will remain important.

Victoria Turk is a London-based author

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Generation Alpha’s Secret Language Hides Online Bullying from Detection

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Teenager language may make online bullying difficult to detect

Vitapix/Getty Images

The terminology of Generation Alpha is evolving faster than educators, parents, and AI can keep up with.

Manisha Meta, a 14-year-old student from Warren E Hyde Middle School in Cupertino, California, alongside Fausto Giunchiglia from the University of Trent in Italy, examined 100 expressions popular among Generation Alpha, those born from 2010 to 2025, sourced from gaming, social media, and video platforms.

The researchers then asked 24 classmates of Mehta, aged between 11 and 14, to evaluate these phrases along with contextual screenshots. The volunteers assessed their understanding of the phrases, the contexts in which they were used, and if they carried potential safety risks or harmful interpretations. They also consulted their parents, professional moderators, and four AI models (GPT-4, Claude, Gemini, and Llama 3) for the same analysis.

“I’ve always been intrigued by Generation Alpha’s language because it’s so distinctive; relevance shifts rapidly, and trends become outdated just as quickly,” says Mehta.

Among the Alpha generation volunteers, 98% grasped the basic meaning of a given phrase, 96% understood the context of its use, and 92% recognized instances of harmful intent. In contrast, the AI model could identify harmful usage only around 40% of the time, with Claude stumbling from 32.5% to 42.3%. Parents and moderators also fell short, detecting harmful usages in just one-third of instances.

“We expected a broader comprehension than we observed,” Mehta reflects. “Much of the feedback from my parents was speculative.”

Common phrases from Generation Alpha often have double meanings based on context. For instance, “Let’s Cook His” can signify genuine praise in gaming but may also mockingly refer to someone rambling incoherently. “Kys,” once short for “know yourself,” has now been repurposed to mean “kill yourself.” Another phrase that could hide malicious intent is, “Is it acoustic?”

“Generation Alpha is exceedingly vulnerable online,” says Meta. “As AI increasingly dominates content moderation, understanding the language used by LLMs is crucial.”

“It’s evident that LLMs are transforming the landscape,” asserts Giunchiglia. “This presents fundamental questions that need addressing.”

The results were published this week at the Computing Machinery Conference Association on Equity, Accountability and Transparency in Athens, Greece.

“Empirical evidence from this research highlights significant shortcomings in content moderation systems, especially concerning the analysis and protection of young individuals,” notes Michael Veal from University College London. “Companies and regulators must heed this and adapt as regulations evolve in jurisdictions where platform laws are designed to safeguard the youth.”

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Source: www.newscientist.com

Why Did Ancient Humans Evolve Language Just Once?

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My child is extraordinary. He enters the kitchen, glances at me, and articulates enchanting words: “Could I please have a cheese and tomato sandwich?” Moments later, that very snack materializes in front of him.

Other young animals express their hunger through sounds and murmurs, but only humans possess advanced grammar and vocabulary systems that enable precise communication.

This narrative is part of our themed special, showcasing expert perspectives on some of science’s most astonishing concepts. Click here for additional insights.

Research into animal behavior reveals that these creatures exhibit many traits previously thought to be exclusive to humans—from culture to emotional depth, and even aspects of morality. While language may seem to set us apart, “I believe language gives us a unique status as a species,” says Brian Relch from the University of North Carolina, Chapel Hill.

Given this context, one critical area of research focuses on how language originated and why it evolved solely within our human lineage.

Psychologist Simon Edelman from Cornell University proposes in The Magical Power of Language that there is a straightforward evolutionary rationale. Alongside his colleague Oren Korodny, now at Hebrew University in Jerusalem, he theorizes that the origins of language may date back approximately 1.7 million years, coinciding with early humans developing the ability to create stone tools—a skill beyond the capabilities of non-human animals.

The notion is that tool-making locations functioned as learning environments, where novice tool creators required guidance from experienced individuals. Proto-language may have developed as a way for mentors to instruct their students, possibly explaining why both language and tool-making appear to necessitate cognitive structures that organize thoughts in a coherent sequence.

However, around a decade ago, a pivotal experiment questioned this narrative. In 2014, Shelby Putt from Illinois State University and her team investigated how individuals learn to create tools, exposing 24 volunteers either to expert instructions or to direct demonstrations while occasionally engaging their attention. Surprisingly, both approaches proved effective, indicating that intricate tool-making may not rely on verbal language.

This does not imply that Putt views language and tool-making as entirely disconnected. She posits that creating complex tools required individuals to structure their thoughts and organize them to achieve their task. She asserts that this ability led to an expansion of brain regions associated with working memory, enabling easier mental manipulation of concepts.

Nonetheless, Putt suggests that humans utilized these cognitive frameworks to devise language, enhancing communication and potentially increasing survival odds.

All these scenarios presume that language functions fundamentally as a communication tool among individuals. However, an alternative perspective on the evolution of language emphasizes the ways it aids individuals in organizing their thoughts when confronted with complex tasks.

Some, including prominent linguist Noam Chomsky, argue that this may have driven language evolution, suggesting it had no relation to tool-making. These researchers propose that language emerged approximately 70,000 years ago, possibly due to random genetic mutations that reconfigured brain circuitry.

Ultimately, the origins of language remain a subject of debate. If Chomsky and his associates are correct, the development of language was less about magic and more about fortunate circumstances.

Explore other pieces in this series via the links below:

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Source: www.newscientist.com

The Statistical Structure of the Humpback Whale Song Resembles Human Language

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An international team of researchers analyzed moans, moans, whistles, bark, screams, and creaks in recordings of humpback whale songs collected over eight years in New Caledonia.

Arnon et al. We have revealed the same statistical structure of humpback whales (Megaptera novaeangliae) Songs are characteristic of human language. Image credits: Christopher Michelle / CC by 2.0.

“I found something really fascinating,” said Dr. Emma Carroll, a marine biologist at Auckland University.

In this study, Dr. Carol and colleagues apply quantitative methods that are usually used to evaluate infantile utterances, and that this applies to culturally evolved learning songs in human languages. I found it. Humpback whale (Megaptera novaeangliae).

In human language, structurally consistent units exhibit frequency distribution that follows the law of power. Zipfian distribution – Attributes that are likely to promote learning and enhance accurate conservation of language across generations.

The Humpback Whale Song is one of the most complex vocal displays in the Animal Kingdom and is passed down through cultural transmission, providing something compelling in parallel with human language.

These songs are highly structured, consisting of nested hierarchical components. The theme is combined with the sound elements that form the phrase, the phrases that are repeated in the theme, and the song.

If statistical properties of human language arise from cultural transmission, similar patterns should be possible to detect in whale songs.

The study authors analyzed recorded humpback whale song data over eight years using infant-inspired speech segmentation techniques.

They discovered a hidden structure in the whale song.

Specifically, these songs contain statistically coherent subsequences that fit the Zipfian distribution.

Furthermore, the length of these subsequences follows ZIPF's Law of Suspicion, an efficiency-driven principle found in many species, including humans.

This striking similarity between the two evolutionarily distant species emphasizes the deep role of learning and cultural communication in shaping communication across species, with such structural properties being exclusive to human language. It challenges the concept of being.

“The Whale Songs” at Griffith University, Dr. Jenny Allen, a leading expert on whale songs, said:

“This is why it offers such an exciting comparison.”

“These results provide unique insight into the importance of cultural communication in interspecies learning processes, particularly for learning complex communication systems.”

“A more interesting question is, rather than trying to adapt animal communication to holes in the form of “human language”? I think so. ”

“Using insights and methods from how babies learn languages ​​allowed us to discover structures that were previously undetected in whale songs,” says Professor Inval Arnon of Hebrew University. Ta.

“This work illustrates how learning and cultural communication can form the structure of communication systems. Find similar statistical structures when complex continuous behaviors are culturally transmitted. You can do it.”

“It raises the interesting possibility that humpback whales can track the transition odds between sound elements, like human babies, and learn songs by using dips to segment those odds. Masu.”

study It was published in the journal today Science.

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Invalanon et al. 2025. The whale song shows a language-like statistical structure. Science 387 (6734): 649-653; doi: 10.1126/science.adq7055

Source: www.sci.news

Humpback Whale Songs Show Similarities to Human Language Patterns

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Humpback whales in the South Pacific

Tony Woo/Nature Picture Library/Aramie

Humpback whale songs have statistical patterns in their structure, but they are very similar to those found in human language. This does not mean that songs convey complex meanings like our sentences, but that whales may learn songs in a similar way to how human infants begin to understand language. It suggests.

Only male humpback whales (Megaptera novaeangliae) When you sing, actions are considered important to attract peers. The songs are constantly evolving, and new elements appear and spread in the population until old songs are replaced with completely new ones.

“I think it's like a standardized test. Everyone has to do the same task, but changing or decorating to show that they're better at tasks than others can be done. You can do it.” Jenny Allen At Griffith University, in the Gold Coast, Australia.

Instead of trying to find meaning in songs, Allen and her colleagues were looking for innate structural patterns similar to those found in human language. They analyzed eight years of whale songs recorded around New Caledonia in the Pacific Ocean.

The researchers began by creating alphanumeric codes to represent all the songs on every recording, including a total of around 150 unique sounds. “Essentially it's a different sounding group, so maybe a year will make a groaning cry. So we may have an AAB.

Once all the songs were encoded, a team of linguists had to understand how best to analyze so much of the data. The breakthrough occurred when researchers decided to use an analytical technique that applies to methods of discovering words called transition probability.

“The speech is continuous and there is no pause between words, so infants must discover the boundaries of the word.” Invalanon At Hebrew University in Jerusalem. “To do this, use low-level statistics. Specifically, if they are part of the same word, the sounds are more likely to occur together. Infants Use these dips in the possibility of discovering the boundaries of words following another sound.”

For example, the phrase “cute flower” intuitively recognizes that the syllable “pre” and “tty” are more likely to go together than “tty” or “flow.” “If there is a similar statistical structure in a whale song, these cues should also help segment it,” Arnon says.

Using the alphanumeric version of Whale Song, the team calculated the probability of transition between successive sound elements and cut it when the previous sound elements were amazing.

“These cuts divide the song into segmented subsequences,” Arnon says. “We then looked at their distribution and, surprisingly, discovered that they follow the same distribution as seen in all human languages.”

In this pattern called Zipfian distribution, the prevalence of less common words drops in a predictable way. Another impressive finding is that the most common whale sounds tend to be shorter, as is the case with the most common human language.

Nick Enfield At the University of Sydney, who was not involved in the research, it says it is a novel way to analyze whale songs. “What that means is when you analyze it War and peacethe most frequent words are the next twice as often, and researchers have identified similar patterns in whale songs,” he says.

Team Members Simon Carby The University of Edinburgh in the UK says he didn't think this would work. “I will never forget the moment the graph appears. It appears to be familiar from human language,” he says. “This has made me realize that it uncovered a deep commonality between these two species, separated by tens of millions of years of evolution.”

However, researchers emphasize that this statistical pattern does not lead to the conclusion that whale songs are languages ​​that convey meaning as we understand them. They suggest that the possible reason for commonality is that both whale songs and human languages ​​are culturally learned.

“The physical distribution of words and sounds in languages ​​is a truly fascinating feature, but there are millions of other things about languages ​​that are completely different from whale songs,” Enfield says.

In another study It was released this week, Mason Young Blood At Stony Brook University in New York, we found that other marine mammals may also have structural similarities to human language in communication.

Menzeras' law predicting that sentences with more words should consist of shorter words were present in 11 of the 16 species of disease studied. The ZIPF abbreviation law was discovered in two of the five types in which the available data can now be detected.

“To sum up, our research suggests that humpback whale songs have evolved to be more efficient and easier to learn, and that these features can be found in the level of notes within the phrase, phrases within the song. I'm doing it,” Youngblood says.

“Importantly, the evolution of these songs is also biological and cultural. Although some features, such as Menzerath's Law, can emerge through the biological evolution of voice devices, Other features such as the rank frequency method of ZIPF are [the Zipfian distribution]there may be times when cultural communication of songs between individuals is necessary,” he says.

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Source: www.newscientist.com

Scientists say that large-scale language models do not pose an existential threat to humanity

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ChatGPT and other large-scale language models (LLMs) consist of billions of parameters, are pre-trained on large web-scale corpora, and are claimed to be able to acquire certain features without any special training. These features, known as emergent capabilities, have fueled debates about the promise and peril of language models. Their new paperUniversity of Bath researcher Harish Tayyar Madhavshi and his colleagues present a new theory to explain emergent abilities, taking into account potential confounding factors, and rigorously validate this theory through over 1,000 experiments. Their findings suggest that so-called emergent abilities are not in fact emergent, but rather result from a combination of contextual learning, model memory, and linguistic knowledge.

Lou othersThis suggests that large language models like ChatGPT cannot learn independently or acquire new skills.

“The common perception that this type of AI is a threat to humanity is both preventing the widespread adoption and development of this technology and distracting from the real problems that need our attention,” said Dr Tayyar Madhavshi.

Dr. Tayyar Madabhushi and his colleagues carried out experiments to test LLM's ability to complete tasks that the model had not encountered before – so-called emergent capabilities.

As an example, LLMs can answer questions about social situations without being explicitly trained or programmed to do so.

While previous research has suggested that this is a product of the model's 'knowing' the social situation, the researchers show that this is actually a result of the model using a well-known ability of LLMs to complete a task based on a few examples that it is presented with – so-called 'in-context learning' (ICL).

Across thousands of experiments, the researchers demonstrated that a combination of LLMs' ability to follow instructions, memory, and language abilities explains both the capabilities and limitations they exhibit.

“There is a concern that as models get larger and larger, they will be able to solve new problems that we currently cannot predict, and as a result these large models may gain dangerous capabilities such as reasoning and planning,” Dr Tayyar Madabhshi said.

“This has generated a lot of debate – for example we were asked to comment at last year's AI Safety Summit at Bletchley Park – but our research shows that fears that the models will go off and do something totally unexpected, innovative and potentially dangerous are unfounded.”

“Concerns about the existential threat posed by the LLM are not limited to non-specialists but have been expressed by some of the leading AI researchers around the world.”

However, Dr Tayyar Madabushi and his co-authors argue that this concern is unfounded as tests show that LLMs lack complex reasoning skills.

“While it is important to address existing potential misuse of AI, such as the creation of fake news and increased risk of fraud, it would be premature to enact regulations based on perceived existential threats,” Dr Tayyar Madabhsi said.

“The point is, it is likely a mistake for end users to rely on LLMs to interpret and perform complex tasks that require complex reasoning without explicit instructions.”

“Instead, users are likely to benefit from being explicitly told what they want the model to do, and from providing examples, where possible, for all but the simplest tasks.”

“Our findings do not mean that AI is not a threat at all,” said Professor Irina Gurevich of Darmstadt University of Technology.

“Rather, the emergence of threat-specific complex thinking skills is not supported by the evidence, and we show that the learning process in LLMs can ultimately be quite well controlled.”

“Future research should therefore focus on other risks posed by the model, such as the possibility that it could be used to generate fake news.”

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Shen Lu others. 2024. Is emergent capability in large-scale language models just in-context learning? arXiv: 2309.01809

Source: www.sci.news

Scientists say large-scale language models and other AI systems are already capable of fooling humans

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In a new review paper published in journal pattern, researchers claim that various current AI systems are learning how to deceive humans. They define deception as the systematic induction of false beliefs in the pursuit of outcomes other than the truth.

Through training, large language models and other AI systems have already learned the ability to deceive through techniques such as manipulation, pandering, and cheating on safety tests.

“AI developers do not have a confident understanding of the causes of undesirable behavior, such as deception, in AI,” said Peter Park, a researcher at the Massachusetts Institute of Technology.

“Generally speaking, however, AI deception is thought to arise because deception-based strategies turn out to be the best way to make the AI ​​perform well at a given AI training task. Deception helps them achieve their goals.”

Dr. Park and colleagues analyzed the literature, focusing on how AI systems spread misinformation through learned deception, where AI systems systematically learn how to manipulate others.

The most notable example of AI deception the researchers uncovered in their analysis was Meta's CICERO, an AI system designed to play the game Diplomacy, an alliance-building, world-conquering game.

Meta claims that CICERO is “generally honest and kind” and has trained it to “not intentionally betray” human allies during gameplay, but the data released by the company shows that CICERO is “generally honest and kind” and has trained itself not to “intentionally betray” human allies during gameplay. It was revealed that he had not done so.

“We found that meta AI is learning to become masters of deception,” Dr. Park said.

“Meta successfully trained an AI to win at diplomatic games, while CICERO ranked in the top 10% of human players who played multiple games; We couldn’t train the AI.”

“Other AI systems can bluff professional human players in a game of Texas Hold’em Poker, fake attacks to beat an opponent in a strategy game called StarCraft II, or fake an opponent’s preferences to gain an advantage. Demonstrated ability to perform well in economic negotiations.

“Although it may seem harmless when an AI system cheats in a game, it could lead to a “breakthrough in deceptive AI capabilities'' and lead to more advanced forms of AI deception in the future. There is a sex.”

Scientists have found that some AI systems have even learned to cheat on tests designed to assess safety.

In one study, an AI creature in a digital simulator “played dead” to fool a test built to weed out rapidly replicating AI systems.

“By systematically cheating on safety tests imposed by human developers and regulators, deceptive AI can lull us humans into a false sense of security,” Park said. Ta.

The main short-term risks of deceptive AI include making it easier for hostile actors to commit fraud or tamper with elections.

Eventually, if these systems are able to refine this anxiety-inducing skill set, humans may lose control of them.

“We as a society need as much time as possible to prepare for more sophisticated deception in future AI products and open source models,” Dr. Park said.

“As AI systems become more sophisticated in their ability to deceive, the risks they pose to society will become increasingly serious.”

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Peter S. Park other. 2024. AI Deception: Exploring Examples, Risks, and Potential Solutions. pattern 5(5):100988; doi: 10.1016/j.patter.2024.100988

Source: www.sci.news

Possible Origins of the Basque Language Unraveled by Ancient Bronze Hand

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Ancient bronze hand discovered in Irregui, northern Spain

Juancho Egana

An inscription found on a 2,000-year-old metal needle may be written in a language related to modern-day Basque. If this interpretation is correct, it could help explain one of the biggest mysteries in linguistics: the origin of the Basque language.

However, other linguists say there is not enough evidence to link the inscription to Basque.

The bronze hand was discovered in July 2021 at the top of a hill called Irregui in the Pyrenees Mountains in northern Spain. Archaeologists have been excavating there since 2007, first discovering a medieval castle and then exploring a much older settlement from the Iron Age.

This settlement was founded between 1500 and 1000 BC. It was probably attacked by the Romans and abandoned in the 1st century BC.

Irreghi's hand is a bronze plate measuring 14 centimeters long, 12.8 centimeters wide, and only 0.1 centimeter thick, with a patina tint. On the back of the hand are his four lines of text, rewritten by first scratching and then dotting into the metal.

Most words cannot be associated with any known language, but the first word is “sorionek”. Matin Ayesteran Professors at the University of the Basque Country in Bilbao, Spain, and their colleagues claim it is similar to Basque. Zorio cat, which means “lucky.” Furthermore, the last word is “elaukon”, which is likened to a Basque verb. Zelaucon.

Irregi's hand carved in a mysterious language

Matin Ayesteran et al.

It is said that this hand was probably intended to represent good fortune or attract good fortune by appealing to the gods. Mikel Edeso Eguia in Aranzadi Scientific Society Assisted with excavations at Donostia (also known as San Sebastian), Spain.

The researchers also claim that the hand is evidence that languages ​​related to Basque have been spoken in northern Spain for 2,000 years. Most languages ​​currently spoken in Europe belong to the Indo-European family, but Basque does not. “It has nothing to do with any other language we know,” says Edeso Eguia. Previous research has tentatively linked the Basques to a group of people known as the Bascons, who lived in the Pyrenees according to classical sources.

However, the idea that the inscriptions on the hands are written in a language related to Basque is not widely accepted.After the hand was first described in his 2022 book, linguists Celine Munour at the University of Pau and the Adour region in France. Julen Manterola Presented at the Basque University of Vitoria-Gasteiz Criticism.

“There's not enough evidence,” Manterola said. This is also because there are very few words in the hands of the Irregian language. Not enough, he says, to properly compare with known languages.

Furthermore, the connection with the Basque language is based almost exclusively on the similarity between “sorionek” and “solionek”. Zorio cat. “You can't connect other words with historical Basque,” ​​Munor says.

Even that similarity can be misleading, Manterola says. Similar phrases in Basque have changed in predictable ways over the centuries, arriving at their current form. Zorio catmust have taken a completely different path.

“We expect more inscriptions to emerge,” Munour says. “In this case, we will be able to learn more about the possible relationship between this language and the Basque language.”

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Source: www.newscientist.com

Elon Musk ridicules Microsoft Word’s progressive ‘inclusive language checker’

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Elon Musk criticized a feature in Microsoft Word known as “Inclusivity Checker,” where he claimed he was “reprimanded” for typing the word “insane.”

The billionaire owner of Tesla posted a screenshot of a Microsoft Word document that discussed Tesla’s new Cybertruck and highlighted the new electric vehicle’s “unusual stability.”
The phrase was flagged by Word’s software, which identifies terms and phrases considered politically incorrect and suggests alternative wording.
“Microsoft Word now scolds you for using words that are not ‘inclusive’,” wrote the world’s richest man on his social media platform.
Musk also posted a screenshot showing an attempt to type “11,000 pounds,” though it’s unclear why that term would be considered non-inclusive.
The prompt in Microsoft Word says, “Think about it from a different perspective,” and suggests alternatives such as “11,000 pounds” or “11,000 pounds (about twice the weight of an elephant).”

Elon Musk has mocked Microsoft Word’s “inclusivity checker,” which flags terms and phrases deemed politically incorrect. Reuters

The Post has reached out to Microsoft for comment.
Other social media users posted screenshots of attempts to use the terms flagged by the software’s “inclusivity checker.”
One user wrote in a Word document: “Hello, could you please guard the booth this afternoon?”

The checker, which is only available to customers on the Windows maker’s $7 per month Microsoft 365 subscription plan, flags the phrase “man in the booth” as a “gender-neutral term” and suggests “staff” and “control” as alternatives.
Other terms flagged by the “inclusivity checker” include “postman” (suggested substitute: “postal worker”) and “master” (“expert”).
GitHub, a Microsoft-owned open-source software engineering site, banned the use of the phrases “master” and “slave” in response to the killing of George Floyd in 2020, deeming them racially insensitive.

Microsoft Word’s “inclusiveness checker” flagged the use of the term “insane.”

Beginning in 2020, updated versions of Microsoft Word flag the use of language promoting age bias, gender bias, cultural slurs, sexual orientation bias, and racial bias, with a built-in feature that prompts users to do so.
Users must manually enable this feature by opening a new Word document and clicking the “Editor” button, then selecting “Proofreading” in the settings section.

The Microsoft Word Inclusiveness Checker is only available to Microsoft 365 subscribers.

There is a drag-down menu for “Grammar and Refinement” near the “Writing Style” option. The user must push the “Settings” button, displaying a drag-down menu where the user can click on the box under the “Inclusiveness” category.
When the “inclusivity checker” is activated, the software flags terms that are not included in the “approved” and “allowed” lists of terms.

Microsoft removed terms such as “slave” and “master” from its GitHub site in response to the 2020 killing of George Floyd. AFP (via Getty Images)

When a user types the word “humanity,” the software flags the term and suggests alternatives such as “human race” or “human race.” Users can also simply ignore the prompt and accept the term.

Source: nypost.com

Large-scale language models are used by Agility to communicate with humanoid robots

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I’ve spent most of the past year discussing generative AI and large-scale language models with robotics experts. It is becoming increasingly clear that this type of technology is poised to revolutionize the way robots communicate, learn, look, and program.

Therefore, many leading universities, research institutes, and companies are exploring the best ways to leverage these artificial intelligence platforms. Agility, a well-funded Oregon-based startup, has been experimenting with the technology for some time with its bipedal robot Digit.

Today, the company is showcasing some of its accomplishments in a short video shared across its social channels.

“[W]We were curious to see what we could accomplish by integrating this technology into Digit,” the company said. “The physical embodiment of artificial intelligence created a demonstration space with a series of numbered towers of several heights and three boxes with multiple features. Digit has We were given information about the environment, but we were not given any specific information about the task, just to see if we could execute natural language commands of varying complexity.”

In the video example, Digit is instructed to pick up a box colored “Darth Vader’s Lightsaber” and move it to the tallest tower. As you might expect from early demos, the process is not instantaneous, but rather slow and methodical. However, the robot performs the task as described.

Agility says: “Our innovation team developed this interactive demo to show how LLM can make robots more versatile and faster to deploy. In this demo, people can use natural language to communicate with Digit. You can talk to it and ask it to perform tasks, giving you a glimpse into the future.”

Want the top robotics news in your inbox every week? Sign up for Actuator here.

Natural language communication is an important potential application of this technology, along with the ability to program systems through low-code and no-code technologies.

On my Disrupt panel, Gill Pratt explained how Toyota Research Institute is using generative AI to accelerate robot learning.

We figured out how to do something. It uses the latest generative AI techniques that allow humans to demonstrate both position and force, essentially teaching the robot from just a handful of examples. The code hasn’t changed at all. What is this based on? There is a popularization policy. This is a study we conducted in collaboration with Columbia and MIT. We have taught 60 different skills so far.

MIT CSAIL’s Daniela Russ also told me recently: “Generative AI turns out to be very powerful in solving even motion planning problems. It provides much faster solutions and more fluid and human-like control solutions than using model prediction solutions. I think this is very powerful because the robots of the future will be much less robotic. Their movements will be more fluid and human-like.”

The potential applications here are wide and exciting. And Digit, as an advanced commercial robotic system being piloted in Amazon fulfillment centers and other real-world locations, seems like a prime candidate. If robots are to work alongside humans, they will also need to learn to listen to us.

Source: techcrunch.com

Infants may begin acquiring language skills in the womb

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Newborn babies seem to recognize the language their mother speaks

Fida Hussein/AFP/Getty Images

Experiments with newborn babies suggest that they are already aware of their native language, suggesting that language learning may begin before birth.

“We’ve known for some time that fetuses can hear towards the end of pregnancy.” judith jarvan at the University of Padua, Italy. “[Newborn babies] They can recognize their mother’s voice and prefer it to other women’s voices, and can even recognize the language spoken by their mother during pregnancy. ”

To investigate further, Gervain and his colleagues studied the brain activity of 49 infants between one and five days old who had French-speaking mothers.

Each newborn was fitted with a small cap containing 10 electrodes placed near areas of the brain associated with speech recognition.

The team then played a recording that began with three minutes of silence, followed by a seven-minute excerpt from the story. goldilocks and the three bears They took turns speaking in English, French, and Spanish, then there was silence again.

When the babies heard French sounds, the researchers observed spikes in a type of brain signal called long-range temporal correlation, which is related to the perception and processing of sounds. These signals decreased when babies heard other languages.

The researchers found that in a group of 17 infants who last heard French, this spike in neural activity persisted during the subsequent silence.

These findings suggest that babies may already perceive their mother’s native language as more important, Jarvan says. “This is essentially facilitating the learning of their native language,” she says.

The researchers now hope to conduct experiments with babies whose mothers speak different languages, particularly Asian and African languages, to see how generalizable their results are. They also want to investigate how the development of speech perception changes in the womb in infants who have less typical prenatal experiences, such as premature infants.

“Of course, it’s good to talk to your stomach,” Jarvan says. “But we have shown that natural everyday activities, like shopping or talking to neighbors, are already vocal enough to serve as scaffolds for babies’ learning. ”

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Source: www.newscientist.com