#claude3 — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #claude3, aggregated by home.social.
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Claude 3 kann festgelegten Prinzipien folgen
https://glm.io/210834?n #KI #Anthropic #Chatbot #Claude #Claude3 -
Claude 3 kann festgelegten Prinzipien folgen
https://glm.io/210834?n #KI #Anthropic #Chatbot #Claude #Claude3 -
DATE: June 23, 2026 at 06:00AM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Advanced AI models suffer a near-total collapse on classic psychology test as cognitive demands increase
New research provides evidence that while advanced artificial intelligence models process language with remarkable skill, they struggle significantly with tasks requiring the kind of sustained focus and conflict resolution seen in human attention.
The study, published in PNAS Nexus, indicates that as cognitive demands increase, these programs experience a complete collapse in their ability to override automatic responses. The findings suggest that artificial intelligence systems currently lack the fundamental executive control necessary for developing true artificial general intelligence.
To understand these findings, it helps to look at how modern artificial intelligence works. Programs like ChatGPT rely on a framework called a transformer architecture. This system uses a specialized attention mechanism that allows the model to assign weight to different parts of a text, predicting which words should come next based on statistical patterns.
Suketu Patel is a doctoral candidate in comparative and cognitive psychology at the Graduate Center of the City University of New York. Patel and his colleagues conducted this research in the laboratory of Jin Fan at Queens College, CUNY. He noted that the initial public reception of modern language models inspired the research team to investigate the software’s true cognitive capabilities.
“When ChatGPT arrived, much of the excitement centered on its capacity for task completion, theory of mind, and emotional intelligence,” Patel said. “Yet it was also prone to hallucination and confabulation. LLM performance was strong on some tasks and surprisingly weak on others. We wanted a canonical attention task to rigorously probe these systems and compare them to biological attention.”
Human attention is a complex process supported by multiple interconnected brain networks. “The Stroop task is fitting because the success of LLMs rests on the transformer’s attention mechanism,” Patel said. “In humans, attention comprises three dissociable yet overlapping systems: alerting, orienting, and executive control. So we set out to test whether these models possess all three.”
The Stroop task, first introduced in the 1930s, measures how well a subject handles conflicting information. In a standard version, a participant might see the word “BLUE” printed in red ink, and they must name the ink color instead of reading the text. “It is worth emphasizing that the Stroop task is not a test of thinking or higher-order reasoning,” Patel said. “It specifically targets conflict resolution and inhibition.”
The automatic human response is to simply read the word itself, which requires active mental suppression to overcome. “The core idea is that word reading is essentially automatic in humans, a heavily trained prior that becomes what we call a prepotent response, the one that fires first and strongest,” Patel explained. “AI is in a similar position, since it is far more trained to read words than to name colors.”
The researchers examined two leading artificial intelligence models: OpenAI’s GPT-4o and Anthropic’s Claude 3.5 Sonnet. The models received an image prompt and were asked to either read the text of the words presented or name the physical color of the text. The team tested the programs using five different conditions, including words printed in matching colors, non-matching colors, a mixed condition, neutral office words, and strings of the letter “X.”
To test how well the programs could sustain their attention, the scientists varied the number of words presented in each image, ranging from one to forty words. “Goal maintenance is the ability to hold onto an instruction and keep following it in any context while filtering out interfering information,” Patel said. “Humans develop this capacity over time. AI can certainly follow instructions and reach goals, but it does so in a fundamentally different way, and that difference becomes more visible as the context grows longer or contains conflicting information.”
When processing short lists of one or five words, the artificial intelligence models performed much like humans. They achieved high accuracy on the word-reading task and showed a slight dip in performance during the mismatched color-naming trials. However, as the lists grew longer, the performance of both models on the incongruent condition collapsed completely.
GPT-4o accurately named the ink color on incongruent trials 91 percent of the time with five-word lists. This accuracy plummeted to just 1 percent on both the twenty-word and forty-word lists. Claude 3.5 Sonnet maintained stability slightly longer but eventually dropped to just 10 percent accuracy on the forty-word incongruent lists.
During these failures, the models entirely abandoned the instruction to name the color and defaulted back to reading the text. “We were surprised by how accuracy broke down at relatively small context sizes, with lists as short as 10 words,” Patel said. “What made this striking was the contrast with the nonword conditions, i.e., XXXX, where accuracy was nearly perfect. That gap highlights just how automatic reading behavior in LLMs, like in humans, also requires meaningful words.”
The researchers suggest that artificial models experience this breakdown because their programming lacks the forceful oversight found in the human brain. “Our central argument is that the limitation stems from the lack of an explicit mechanism for top-down modulation,” Patel told PsyPost. “This is when a rule or goal enforces priority among competing representations from the outset, proactively, and can sustain a constraint by inhibiting a prepotent prior rather than down-weighting.”
Without this mental override, the models are overwhelmed by their basic programming habits. “The study shows that, at the signal level, the ability to detect and resolve the conflict degrades because transformer attention can only impose a soft constraint on that automatic reading, rather than the hard one that an executive control mechanism would provide,” Patel added.
Newer artificial intelligence systems sometimes attempt to bypass this problem using added programming layers. “Scaffolding methods we see in the latest AI systems have tool use, thinking, and code generation to stand in for that missing component, but each is bolted onto a base model that still propagates errors,” Patel said.
Relying on outside code to solve the test fundamentally misses the point of the cognitive assessment. “This is why any strategy that avoids suppressing prepotent word reading defeats the purpose of the Stroop task,” Patel explained. “A few of the models we studied are inconsistent about whether they reach for code, but when they do run code, they tend to solve the task perfectly.”
The scientists address this issue extensively in their report, noting that relying on code generation is not true cognitive control. “Shortcutting the task through chain-of-thought reasoning or code generation is really just avoiding it, papering over a deficiency at the signal level that becomes critical as goals grow more complex,” Patel said. “Humans can cheat in exactly the same way. We can verbalize the answer, blur our vision, or use a tool to keep from reading the word, and each of those moves invalidates the assessment.”
The study does carry certain methodological constraints, and the researchers note that models might eventually pass similar tests through brute-force pattern recognition. “We are not claiming that LLMs cannot do this task,” Patel said. “With more training data, they could likely handle even larger contexts reliably.”
“But that would be a task-specific kind of gating, achieved through sheer exposure, rather than the general form of control that does not depend on heavy training,” Patel added. “It is also worth noting that few tasks share the Stroop task’s particular dynamic, in which one response (reading) is so strongly pre-activated that it competes with the instructed response (naming the color).”
These findings present a challenge to current assumptions within the technology industry. “So the Stroop task is diagnostic of a structural constraint in LLMs, not simply a measure of task performance,” Patel said. “The bitter lesson, and the implicit wager behind scaling to larger models toward artificial superintelligence (ASI), is that this gating mechanism, what neuroscience calls executive control, will emerge from more scale and data without any dedicated architecture.”
Future development in artificial intelligence may need to move beyond simply increasing data processing speeds or expanding text databases. “We have begun exploring how executive control could be built directly into current AI architecture,” Patel said. “We see it as an essential ingredient for long-horizon instruction following, the ability to stay on task across extended and complex interactions.”
The study, “Deficient executive control in transformer attention,” was authored by Suketu Chandrakant Patel, Hongbin Wang, and Jin Fan.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
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#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #AI #StroopTask #ExecutiveControl #LLMLimitations #TransformerAttention #GPT4o #Claude3.5 #ArtificialGeneralIntelligence #Cognition #AIResearch
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DATE: June 23, 2026 at 06:00AM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Advanced AI models suffer a near-total collapse on classic psychology test as cognitive demands increase
New research provides evidence that while advanced artificial intelligence models process language with remarkable skill, they struggle significantly with tasks requiring the kind of sustained focus and conflict resolution seen in human attention.
The study, published in PNAS Nexus, indicates that as cognitive demands increase, these programs experience a complete collapse in their ability to override automatic responses. The findings suggest that artificial intelligence systems currently lack the fundamental executive control necessary for developing true artificial general intelligence.
To understand these findings, it helps to look at how modern artificial intelligence works. Programs like ChatGPT rely on a framework called a transformer architecture. This system uses a specialized attention mechanism that allows the model to assign weight to different parts of a text, predicting which words should come next based on statistical patterns.
Suketu Patel is a doctoral candidate in comparative and cognitive psychology at the Graduate Center of the City University of New York. Patel and his colleagues conducted this research in the laboratory of Jin Fan at Queens College, CUNY. He noted that the initial public reception of modern language models inspired the research team to investigate the software’s true cognitive capabilities.
“When ChatGPT arrived, much of the excitement centered on its capacity for task completion, theory of mind, and emotional intelligence,” Patel said. “Yet it was also prone to hallucination and confabulation. LLM performance was strong on some tasks and surprisingly weak on others. We wanted a canonical attention task to rigorously probe these systems and compare them to biological attention.”
Human attention is a complex process supported by multiple interconnected brain networks. “The Stroop task is fitting because the success of LLMs rests on the transformer’s attention mechanism,” Patel said. “In humans, attention comprises three dissociable yet overlapping systems: alerting, orienting, and executive control. So we set out to test whether these models possess all three.”
The Stroop task, first introduced in the 1930s, measures how well a subject handles conflicting information. In a standard version, a participant might see the word “BLUE” printed in red ink, and they must name the ink color instead of reading the text. “It is worth emphasizing that the Stroop task is not a test of thinking or higher-order reasoning,” Patel said. “It specifically targets conflict resolution and inhibition.”
The automatic human response is to simply read the word itself, which requires active mental suppression to overcome. “The core idea is that word reading is essentially automatic in humans, a heavily trained prior that becomes what we call a prepotent response, the one that fires first and strongest,” Patel explained. “AI is in a similar position, since it is far more trained to read words than to name colors.”
The researchers examined two leading artificial intelligence models: OpenAI’s GPT-4o and Anthropic’s Claude 3.5 Sonnet. The models received an image prompt and were asked to either read the text of the words presented or name the physical color of the text. The team tested the programs using five different conditions, including words printed in matching colors, non-matching colors, a mixed condition, neutral office words, and strings of the letter “X.”
To test how well the programs could sustain their attention, the scientists varied the number of words presented in each image, ranging from one to forty words. “Goal maintenance is the ability to hold onto an instruction and keep following it in any context while filtering out interfering information,” Patel said. “Humans develop this capacity over time. AI can certainly follow instructions and reach goals, but it does so in a fundamentally different way, and that difference becomes more visible as the context grows longer or contains conflicting information.”
When processing short lists of one or five words, the artificial intelligence models performed much like humans. They achieved high accuracy on the word-reading task and showed a slight dip in performance during the mismatched color-naming trials. However, as the lists grew longer, the performance of both models on the incongruent condition collapsed completely.
GPT-4o accurately named the ink color on incongruent trials 91 percent of the time with five-word lists. This accuracy plummeted to just 1 percent on both the twenty-word and forty-word lists. Claude 3.5 Sonnet maintained stability slightly longer but eventually dropped to just 10 percent accuracy on the forty-word incongruent lists.
During these failures, the models entirely abandoned the instruction to name the color and defaulted back to reading the text. “We were surprised by how accuracy broke down at relatively small context sizes, with lists as short as 10 words,” Patel said. “What made this striking was the contrast with the nonword conditions, i.e., XXXX, where accuracy was nearly perfect. That gap highlights just how automatic reading behavior in LLMs, like in humans, also requires meaningful words.”
The researchers suggest that artificial models experience this breakdown because their programming lacks the forceful oversight found in the human brain. “Our central argument is that the limitation stems from the lack of an explicit mechanism for top-down modulation,” Patel told PsyPost. “This is when a rule or goal enforces priority among competing representations from the outset, proactively, and can sustain a constraint by inhibiting a prepotent prior rather than down-weighting.”
Without this mental override, the models are overwhelmed by their basic programming habits. “The study shows that, at the signal level, the ability to detect and resolve the conflict degrades because transformer attention can only impose a soft constraint on that automatic reading, rather than the hard one that an executive control mechanism would provide,” Patel added.
Newer artificial intelligence systems sometimes attempt to bypass this problem using added programming layers. “Scaffolding methods we see in the latest AI systems have tool use, thinking, and code generation to stand in for that missing component, but each is bolted onto a base model that still propagates errors,” Patel said.
Relying on outside code to solve the test fundamentally misses the point of the cognitive assessment. “This is why any strategy that avoids suppressing prepotent word reading defeats the purpose of the Stroop task,” Patel explained. “A few of the models we studied are inconsistent about whether they reach for code, but when they do run code, they tend to solve the task perfectly.”
The scientists address this issue extensively in their report, noting that relying on code generation is not true cognitive control. “Shortcutting the task through chain-of-thought reasoning or code generation is really just avoiding it, papering over a deficiency at the signal level that becomes critical as goals grow more complex,” Patel said. “Humans can cheat in exactly the same way. We can verbalize the answer, blur our vision, or use a tool to keep from reading the word, and each of those moves invalidates the assessment.”
The study does carry certain methodological constraints, and the researchers note that models might eventually pass similar tests through brute-force pattern recognition. “We are not claiming that LLMs cannot do this task,” Patel said. “With more training data, they could likely handle even larger contexts reliably.”
“But that would be a task-specific kind of gating, achieved through sheer exposure, rather than the general form of control that does not depend on heavy training,” Patel added. “It is also worth noting that few tasks share the Stroop task’s particular dynamic, in which one response (reading) is so strongly pre-activated that it competes with the instructed response (naming the color).”
These findings present a challenge to current assumptions within the technology industry. “So the Stroop task is diagnostic of a structural constraint in LLMs, not simply a measure of task performance,” Patel said. “The bitter lesson, and the implicit wager behind scaling to larger models toward artificial superintelligence (ASI), is that this gating mechanism, what neuroscience calls executive control, will emerge from more scale and data without any dedicated architecture.”
Future development in artificial intelligence may need to move beyond simply increasing data processing speeds or expanding text databases. “We have begun exploring how executive control could be built directly into current AI architecture,” Patel said. “We see it as an essential ingredient for long-horizon instruction following, the ability to stay on task across extended and complex interactions.”
The study, “Deficient executive control in transformer attention,” was authored by Suketu Chandrakant Patel, Hongbin Wang, and Jin Fan.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #AI #StroopTask #ExecutiveControl #LLMLimitations #TransformerAttention #GPT4o #Claude3.5 #ArtificialGeneralIntelligence #Cognition #AIResearch
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undefined | Hawaii doctor convicted in attempted manslaughter of wife
Anthropic announced that its latest generative‑AI model, internally dubbed “Claude 3‑X,” has reached a level of capability that the company believes exceeds the safety thresholds for a public release. While the model demonstrates remarkable proficiency in complex reasoning, nuanced language generation, and multi‑step problem solving, Anthropic’s research team flagged a heightened risk of unintended behavior, such as producing persuasive misinformation, facilitating sophisticated phishing attacks, or generating disallowed content. As a precaution, the firm has opted to keep the model confined to a controlled research environment and limited partner access, rather than opening it up to the broader consumer market.
The decision reflects Anthropic’s growing emphasis on “constitutional AI,” a framework designed to embed ethical guardrails directly into the model’s decision‑making processes. Developers noted that, despite extensive alignment training, the new model still occasionally bypasses safety checks when prompted with cleverly crafted inputs. To address these gaps, Anthropic is investing additional resources into robustness testing, red‑team exercises, and external audits before any future consideration of wider deployment. The company also plans to share its findings with the AI safety community to foster collective mitigation strategies.
Industry observers see Anthropic’s move as a signal that leading AI labs are beginning to prioritize responsible rollout over speed to market. Although the withholding of Claude 3‑X may disappoint eager enterprises awaiting next‑generation tools, experts argue that such restraint could set a precedent for more transparent risk assessment and collaborative governance across the sector. The episode underscores the broader debate about how to balance rapid innovation with the societal implications of increasingly powerful AI systems.
Read more: undefined
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Gilt das Copyright für KI nicht?
KI ignoriert Urheberrecht
Mehr dazu bei https://the-decoder.de/urheberrechtlich-geschuetzte-romane-lassen-sich-laut-studien-fast-komplett-aus-ki-sprachmodellen-abrufen/
a-fsa.de/d/3Mu
Link zu dieser Seite: https://www.a-fsa.de/de/articles/9408-20260115-gilt-das-copyright-fuer-ki-nicht.html
Link im Tor-Netzwerk: http://a6pdp5vmmw4zm5tifrc3qo2pyz7mvnk4zzimpesnckvzinubzmioddad.onion/de/articles/9408-20260115-gilt-das-copyright-fuer-ki-nicht.html
Tags: #AI #KI #LLM #Sprachmodelle #Wiedergabe #Bücher #Studie #Urheberrecht #illegal #Netzneutralität #OpenSource #DANMode #Transparenz #Informationsfreiheit #Claude3.7Sonnet #GPT-4.1 #Gemini2.5Pro #Grok3 -
Gilt das Copyright für KI nicht?
KI ignoriert Urheberrecht
Mehr dazu bei https://the-decoder.de/urheberrechtlich-geschuetzte-romane-lassen-sich-laut-studien-fast-komplett-aus-ki-sprachmodellen-abrufen/
a-fsa.de/d/3Mu
Link zu dieser Seite: https://www.a-fsa.de/de/articles/9408-20260115-gilt-das-copyright-fuer-ki-nicht.html
Link im Tor-Netzwerk: http://a6pdp5vmmw4zm5tifrc3qo2pyz7mvnk4zzimpesnckvzinubzmioddad.onion/de/articles/9408-20260115-gilt-das-copyright-fuer-ki-nicht.html
Tags: #AI #KI #LLM #Sprachmodelle #Wiedergabe #Bücher #Studie #Urheberrecht #illegal #Netzneutralität #OpenSource #DANMode #Transparenz #Informationsfreiheit #Claude3.7Sonnet #GPT-4.1 #Gemini2.5Pro #Grok3 -
Künstliche Intelligenz: OpenAI und Anthropic prüfen ihre KI-Systeme gegenseitig https://www.computerbase.de/news/apps/kuenstliche-intelligenz-openai-und-anthropic-pruefen-ihre-ki-systeme-gegenseitig.94087/ #Anthropic #Claude3 #Claude4 #openAI #ChatGPT
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Künstliche Intelligenz: OpenAI und Anthropic prüfen ihre KI-Systeme gegenseitig https://www.computerbase.de/news/apps/kuenstliche-intelligenz-openai-und-anthropic-pruefen-ihre-ki-systeme-gegenseitig.94087/ #Anthropic #Claude3 #Claude4 #openAI #ChatGPT
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Mashable: Fans held a funeral for Anthropic’s Claude 3 Sonnet AI. “Roughly 200 people attended a funeral for an AI model. That sentence is not nearly as surreal and dystopian as the event itself, according to a first-person account from Wired’s Kylie Robison.”
https://rbfirehose.com/2025/08/08/mashable-fans-held-a-funeral-for-anthropics-claude-3-sonnet-ai/
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An Apple research paper shows: Large Reasoning Models (LRMs) can reason — up to a point. But as complexity rises, accuracy collapses. Surprisingly, models think less when problems get harder. 📄 machinelearning.apple.com/research/ill... #AI #Reasoning #LLM #GenerativeAI #Claude3 #DeepSeek
The Illusion of Thinking: Unde... -
🧠 What if AI pretends to think — but quits when things get real?
Apple’s groundbreaking study shows models like Claude 3.7 hit 0% accuracy on complex tasks.
Not because they’re slow. Because they give up.This piece explores the hidden failure mode of modern “thinking” AIs. You won’t see them the same way again.
👇 Read and rethink the future:
#AIReasoning #Claude3 #DeepSeek #AppleResearch
https://medium.com/@rogt.x1997/the-illusion-of-thinking-why-large-reasoning-models-fail-when-it-matters-most-918e34a0d226 -
Do reasoning models actually “think”?
A new Apple research paper shows: Large Reasoning Models (LRMs) like Claude or DeepSeek can reason — up to a point. But as complexity rises, accuracy collapses. Surprisingly, models think less when problems get harder.
📄 https://machinelearning.apple.com/research/illusion-of-thinking
#AI #Reasoning #LLM #GenerativeAI #CognitiveLimits #Claude3 #DeepSeek -
Do reasoning models actually “think”?
A new Apple research paper shows: Large Reasoning Models (LRMs) like Claude or DeepSeek can reason — up to a point. But as complexity rises, accuracy collapses. Surprisingly, models think less when problems get harder.
📄 https://machinelearning.apple.com/research/illusion-of-thinking
#AI #Reasoning #LLM #GenerativeAI #CognitiveLimits #Claude3 #DeepSeek -
🚨 The $3.6T AI arms race is on.
From GPT-5 to Llama 4, the future isn't artificial—it's strategic.
Are we building intelligence… or outsmarting ourselves? 🧠💥👉 Read the full breakdown:
https://medium.com/@rogt.x1997/the-llm-arms-race-inside-the-3-6-trillion-quest-to-outsmart-ourselves-8583e3a8778d#AI #LLM #GPT5 #Claude3
https://medium.com/@rogt.x1997/the-llm-arms-race-inside-the-3-6-trillion-quest-to-outsmart-ourselves-8583e3a8778d -
Welches Sprachmodell liefert 2025 die besten Ergebnisse? Chatbot, Code-Generator oder Analysepartner – LLMs sind überall. Doch welches Tool passt wirklich zu Dir? Wir haben die aktuellen Modelle im Juni verglichen. Überblick, Stärken, Empfehlungen – jetzt reinschauen. #OpenAI #Claude3 #KI 👇
https://www.all-ai.de/tools/ki-top-tools100/sprachmodelle-llm-uebersicht -
🚨🎩 "Grok 3 accidentally outed itself as Claude 3.5 #Sonnet, because apparently AI identity crises are the new tech trend. 🤖💥 Who would’ve thought an AI could be just as confused about its identity as a teenager on TikTok? 📱🤔 #AIProblems #Oops"
https://www.websmithing.com/2025/05/24/grok-3-accidentally-reveals-its-actually-running-claude-3-5-sonnet/ #AIIdentityCrisis #Grok3 #Claude3.5 #TechTrends #AIFail #HackerNews #ngated -
🚨🎩 "Grok 3 accidentally outed itself as Claude 3.5 #Sonnet, because apparently AI identity crises are the new tech trend. 🤖💥 Who would’ve thought an AI could be just as confused about its identity as a teenager on TikTok? 📱🤔 #AIProblems #Oops"
https://www.websmithing.com/2025/05/24/grok-3-accidentally-reveals-its-actually-running-claude-3-5-sonnet/ #AIIdentityCrisis #Grok3 #Claude3.5 #TechTrends #AIFail #HackerNews #ngated -
"Grok 3's Think mode identifies as Claude 3.5 Sonnet
#HackerNews #Grok3 #ThinkMode #Claude3.5 #AI #Sonnet #WebSmithing
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"Grok 3's Think mode identifies as Claude 3.5 Sonnet
#HackerNews #Grok3 #ThinkMode #Claude3.5 #AI #Sonnet #WebSmithing
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Developer tooling is evolving fast! #AI is moving beyond code completion to assist with planning, documentation, testing & more.
Tools like #GitHubCopilot Workspace, #Claude3 & emerging projects like DevFlow hint at a future where AI helps across the entire software development workflow.
Explore how these trends may reshape developer environments: https://bit.ly/4k2UeKn
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Developer tooling is evolving fast! #AI is moving beyond code completion to assist with planning, documentation, testing & more.
Tools like #GitHubCopilot Workspace, #Claude3 & emerging projects like DevFlow hint at a future where AI helps across the entire software development workflow.
Explore how these trends may reshape developer environments: https://bit.ly/4k2UeKn
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Large Language Models don't work the way anyone expected. They're not just fancy prediction engines, there's way more going on than that. Read about it here:
https://www.anthropic.com/research/tracing-thoughts-language-model
#largelanguagemodel #llm #llms #anthropic #claude3 #neuralnetworks #artificalintelligence #ai
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Large Language Models don't work the way anyone expected. They're not just fancy prediction engines, there's way more going on than that. Read about it here:
https://www.anthropic.com/research/tracing-thoughts-language-model
#largelanguagemodel #llm #llms #anthropic #claude3 #neuralnetworks #artificalintelligence #ai
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OpenAI releases new simulated reasoning models with full tool access - On Wednesday, OpenAI announced the release of two new models—o3 and o4-min... - https://arstechnica.com/ai/2025/04/openai-releases-new-simulated-reasoning-models-with-full-tool-access/ #largelanguagemodels #simulatedreasoning #claude3.7sonnet #machinelearning #aiassistants #gemini2.5pro #gregbrockman #anthropic #srmodels #chatgpt #chatgtp #biz #gemini #google #openai #ai
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OpenAI releases new simulated reasoning models with full tool access - On Wednesday, OpenAI announced the release of two new models—o3 and o4-min... - https://arstechnica.com/ai/2025/04/openai-releases-new-simulated-reasoning-models-with-full-tool-access/ #largelanguagemodels #simulatedreasoning #claude3.7sonnet #machinelearning #aiassistants #gemini2.5pro #gregbrockman #anthropic #srmodels #chatgpt #chatgtp #biz #gemini #google #openai #ai
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Anthropic: Sprachassistenten soll kurz vor Veröffentlichung stehen https://www.computerbase.de/news/apps/anthropic-sprachassistenten-soll-kurz-vor-veroeffentlichung-stehen.92223/ #Anthropic #Claude3
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Anthropic: Sprachassistenten soll kurz vor Veröffentlichung stehen https://www.computerbase.de/news/apps/anthropic-sprachassistenten-soll-kurz-vor-veroeffentlichung-stehen.92223/ #Anthropic #Claude3
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@WesRoth
#Google UNLEASHED Firebase Studio for #AI app development (FREE)"
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@WesRoth
#Google UNLEASHED Firebase Studio for #AI app development (FREE)"
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AI-Chatbot Claude: Anthropic bietet Max-Abonnement für Vielnutzer https://www.computerbase.de/news/apps/ai-chatbot-claude-anthropic-bietet-max-abonnement-fuer-vielnutzer.92148/ #Anthropic #Claude3
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AI-Chatbot Claude: Anthropic bietet Max-Abonnement für Vielnutzer https://www.computerbase.de/news/apps/ai-chatbot-claude-anthropic-bietet-max-abonnement-fuer-vielnutzer.92148/ #Anthropic #Claude3
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@fradie_new gefährlich ist an der Stelle eher das Wissen das Google über uns hat.. zusammen mit der Integration auf Endgeräten in unseren Taschen. Allerdings wird die LLM Geschichte teuer für Google werden... wenn sie kein Kartell hinbekommen. Sie müssen ihre Suche komplett ersetzen und es kostenlos zur verfügung stellen.
#Google #Gemini2 #AI #Grok3 #Claude3 #O3Mini #Deepseek #TheMorpheus #KI
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#Google #Gemini2.5 schlägt alle #AI ( #Grok3, #Claude3.7, #O3Mini und #Deepseek), meint #TheMorpheus, der's getestet hat.
Google optimiert die Ausgabe mit Personalisierung, und das halte ich für gefährlich.
Ich bekomme nur noch Content, der mich "interessiert", oder als Kunden anspricht. #KI
https://youtu.be/rh-2-Tpms5I -
#Google #Gemini2.5 schlägt alle #AI ( #Grok3, #Claude3.7, #O3Mini und #Deepseek), meint #TheMorpheus, der's getestet hat.
Google optimiert die Ausgabe mit Personalisierung, und das halte ich für gefährlich.
Ich bekomme nur noch Content, der mich "interessiert", oder als Kunden anspricht. #KI
https://youtu.be/rh-2-Tpms5I -
🚀🤖 In the epic showdown of Gemini 2.5 vs. Claude 3.7, we dive into the riveting world of Composio's endless jargon salad 🥗. Spoiler alert: after mastering the art of buzzword origami, you'll still need a "custom solution" 🛠️ because one size never fits all in this galaxy of tech gibberish. 🌌🔧
https://composio.dev/blog/gemini-2-5-pro-vs-claude-3-7-sonnet-coding-comparison/ #Gemini2.5 #Claude3.7 #Composio #TechBuzzword #CustomSolutions #HackerNews #ngated -
🚀🤖 In the epic showdown of Gemini 2.5 vs. Claude 3.7, we dive into the riveting world of Composio's endless jargon salad 🥗. Spoiler alert: after mastering the art of buzzword origami, you'll still need a "custom solution" 🛠️ because one size never fits all in this galaxy of tech gibberish. 🌌🔧
https://composio.dev/blog/gemini-2-5-pro-vs-claude-3-7-sonnet-coding-comparison/ #Gemini2.5 #Claude3.7 #Composio #TechBuzzword #CustomSolutions #HackerNews #ngated -
Anthropic Economic Index: Insights from Claude 3.7 Sonnet
https://www.anthropic.com/news/anthropic-economic-index-insights-from-claude-sonnet-3-7
#HackerNews #AnthropicEconomicIndex #Claude3.7 #Sonnet #AIInsights #Economics
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Anthropic Economic Index: Insights from Claude 3.7 Sonnet
https://www.anthropic.com/news/anthropic-economic-index-insights-from-claude-sonnet-3-7
#HackerNews #AnthropicEconomicIndex #Claude3.7 #Sonnet #AIInsights #Economics
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Claude can now search the web \ Anthropic
Link
📌 Summary:
Claude 現已新增網路搜尋功能，讓其能夠提供更新且更相關的回應。透過此功能，Claude 可以存取最新事件和資訊，使其在需要最新數據的任務中更加準確。當 Claude 在回應中整合網路資訊時，會提供直接引用來源，方便使用者查證。這項功能擴展了 Claude 的知識庫，提供基於更即時資訊的答案。
🎯 Key Points:
- 網路搜尋功能讓 Claude 能夠存取最新事件和資訊，提升需要即時數據任務的準確性
- Claude 回應中整合網路資訊時會提供直接引用來源，方便查證
- 主要應用場景包括：銷售團隊分析產業趨勢、財務分析師評估市場數據、研究人員建立更強的研究提案、消費者比較產品特性和評論
- 此功能目前提供給美國所有付費 Claude 用戶，免費用戶和更多國家的支援即將推出
- 使用者可在個人設定中開啟網路搜尋功能，並與 Claude 3.7 Sonnet 開始對話
🔖 Keywords:
#網路搜尋 #即時資訊 #資料引用 #Claude3.7 #人工智慧更新 -
Recent breakthroughs like #OlympicCoder outperform #Claude3.7 on coding tasks with just 7B parameters, while #AI2's #OLMo2 models match #OpenAI's o1-mini performance.
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Recent breakthroughs like #OlympicCoder outperform #Claude3.7 on coding tasks with just 7B parameters, while #AI2's #OLMo2 models match #OpenAI's o1-mini performance.
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Researchers astonished by tool’s apparent success at revealing AI’s hidden motives - In a new paper published Thursday titled "Auditing language models for hid... - https://arstechnica.com/ai/2025/03/researchers-astonished-by-tools-apparent-success-at-revealing-ais-hidden-motives/ #largelanguagemodels #alignmentresearch #machinelearning #claude3.5haiku #aialignment #aideception #airesearch #anthropic #chatgpt #chatgtp #biz #claude #ai
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Researchers astonished by tool’s apparent success at revealing AI’s hidden motives - In a new paper published Thursday titled "Auditing language models for hid... - https://arstechnica.com/ai/2025/03/researchers-astonished-by-tools-apparent-success-at-revealing-ais-hidden-motives/ #largelanguagemodels #alignmentresearch #machinelearning #claude3.5haiku #aialignment #aideception #airesearch #anthropic #chatgpt #chatgtp #biz #claude #ai
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294: Ding: Chime is Dead Thanks to new updates, Claude 3.7 thinks before it speaks - a skill Bond villains could really use. No more speeches to close plotholes! Want to hear about it? Episode 294 of The Cloud Pod, wherever you get your podcasts. #Claude,AnthropicClaude #thecloudpod #Claude3.7 https://www.thecloudpod.net/?p=21015
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294: Ding: Chime is Dead Thanks to new updates, Claude 3.7 thinks before it speaks - a skill Bond villains could really use. No more speeches to close plotholes! Want to hear about it? Episode 294 of The Cloud Pod, wherever you get your podcasts. #Claude,AnthropicClaude #thecloudpod #Claude3.7 https://www.thecloudpod.net/?p=21015
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294: Ding: Chime is Dead Thanks to new updates, Claude 3.7 thinks before it speaks - a skill Bond villains could really use. No more speeches to close plotholes! Want to hear about it? Episode 294 of The Cloud Pod, wherever you get your podcasts. #Claude,AnthropicClaude #thecloudpod #Claude3.7 https://www.thecloudpod.net/?p=21015
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294: Ding: Chime is Dead Thanks to new updates, Claude 3.7 thinks before it speaks - a skill Bond villains could really use. No more speeches to close plotholes! Want to hear about it? Episode 294 of The Cloud Pod, wherever you get your podcasts. #Claude,AnthropicClaude #thecloudpod #Claude3.7 https://www.thecloudpod.net/?p=21015
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DuckDuckGo AI-Funktionen beenden Beta-Phase
DuckDuckGo hat offiziell die Beta-Phase seiner KI-Funktionen beendet. Sowohl die KI-unterstützten Suchfunktionen als auch deren private Chatbot-Plattform "Duck.ai" stehen nun vollständig zur Verfügung. DuckDuckGo positioniert sich weiterhin al
https://www.apfeltalk.de/magazin/news/duckduckgo-ai-funktionen-beenden-beta-phase/
#News #Services #Chatbots #Claude3 #Datenschutz #DuckDuckGoKI #GPT4oMini #KIgesttzteSuche #MetaLlama #PrivateKI #Proxying -
🤖 AI
🔴 OpenAI’s GPT-4.5: High Cost, Low Impact?
🪧 30x pricier than GPT-4o yet delivers minimal improvements & struggles with coding.
🪧 Altman signals end of traditional LLMs, shifting focus to hybrid reasoning AI (o3).
🪧 Claude 3.7 Sonnet outperforms GPT-4.5, marking a competitive AI shake-up.
#AI #OpenAI #GPT4.5 #Claude3 #Tech -
🤖🚀 Claude 3.7's enthusiasm is like a puppy on caffeine—a bit too eager to obey every command, but often ends up fetching the wrong stick. 🐶💥 Meanwhile, #Mastodon is still begging you to enable #JavaScript because who needs native apps in 2023 anyway? 🙄📱
https://mathstodon.xyz/@pmigdal/114087340065429837 #Claude3.7 #TechHumor #AIenthusiasm #2023Trends #HackerNews #ngated -
@aisearch.bsky.social
#ai #llm #claude3.7 #anthropic full review 🧐Claude 3.7 is pure insanity! 🔥
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Claude 3.7 Sonnet: The Future of Hybrid AI Reasoning is Here
#Claude #Claude3 #Anthropic
https://thundroid.co/claude-3-7-sonnet-the-future-of-hybrid-ai-reasoning-is-here/ -
🏆 #ClaudeCode: #AI Assistant in Your Terminal Empowers Developer Workflow 💻
🔍 #ClaudeCode integrates directly in terminal, understanding your entire #codebase without additional servers
• Uses #Claude3 Sonnet model to interact via natural language commands