All articles tagged with #reasoning
Originally Published 3 months ago — by Live Science
Google DeepMind has developed advanced AI models, Gemini Robotics 1.5, that enable robots to perform complex tasks, reason, and adapt in physical environments, significantly boosting robotic intelligence and versatility.
Originally Published 5 months ago — by Ars Technica
OpenAI is nearing the release of GPT-5, a highly capable AI model expected in August that aims to unify its existing model series, enhance reasoning and coding abilities, and will be followed by an open-weights version for broader access after additional safety testing.
Originally Published 7 months ago — by Hacker News
The article discusses various responses to a viral paper on AI reasoning, highlighting disagreements on the definition of AGI, the capabilities of language models, and whether current AI systems truly 'think' or merely estimate probabilities. It emphasizes that while models can perform complex tasks and reason to some extent, there is ongoing debate about their understanding, general intelligence, and the implications of their abilities.
Originally Published 7 months ago — by Cointelegraph
Apple researchers found that current AI models, including leading large language models like ChatGPT and Claude, still struggle with reasoning and do not demonstrate the capabilities expected of artificial general intelligence (AGI). Their tests show limitations in reasoning complexity, inconsistent performance, and an inability to truly internalize reasoning patterns, challenging the assumption that AGI is imminent.,
Originally Published 1 year ago — by Ars Technica
Researchers have developed the COCONUT model, which uses 'latent thoughts' to perform logical reasoning without relying on natural language for each step. This approach allows for simultaneous processing of multiple potential reasoning paths, akin to a breadth-first search, and helps avoid dead-end inferences common in traditional models. While COCONUT didn't outperform existing models on straightforward reasoning tests, it excelled in complex logical conditions, suggesting potential for broader generalization in reasoning tasks.
Originally Published 1 year ago — by VentureBeat
Alibaba researchers have unveiled Marco-o1, a large language model (LLM) with enhanced reasoning capabilities, designed to tackle open-ended problems lacking clear solutions. Building on the success of OpenAI's o1, Marco-o1 integrates advanced techniques like chain-of-thought fine-tuning and Monte Carlo Tree Search (MCTS) to explore multiple reasoning paths and refine its conclusions. The model's reflection mechanism allows it to self-critique and improve its reasoning process. Marco-o1 has shown superior performance in tasks requiring nuanced understanding, such as translating colloquial expressions, and is available on Hugging Face for further research.
Originally Published 1 year ago — by VentureBeat
DeepSeek, an AI-focused offshoot of High-Flyer Capital Management, has launched the R1-Lite-Preview, a reasoning-focused large language model that rivals OpenAI's o1-preview in performance. Available through DeepSeek Chat, the model excels in logical inference and mathematical reasoning, offering transparency in its thought process. While it has not yet been released for independent analysis or API access, DeepSeek plans to make open-source versions available, continuing its tradition of supporting the open-source AI community.
Originally Published 1 year ago — by Financial Times
OpenAI and Meta have developed new AI models capable of "reasoning," marking a significant advancement in artificial intelligence technology. These models have the potential to enhance AI's ability to understand and interpret complex information, leading to more sophisticated applications across various industries.
Originally Published 1 year ago — by Big Think
Throughout history, humanity has used both top-down (a priori) and bottom-up (a posteriori) reasoning to gain knowledge about the world. However, science has shown that no amount of logical reasoning can substitute for empirical knowledge. Three examples illustrate how logic and reasoning alone are insufficient in science: the nature of light, the age of the Earth, and Einstein's cosmological constant. These cases demonstrate that the only way to gain meaningful knowledge of the Universe is by asking quantitative questions that can be answered through experiment and observation.
Originally Published 1 year ago — by Livescience.com
Researchers have developed a new method called Quiet-STaR, which gives AI systems an "inner monologue" to improve their reasoning abilities. This method trains AI to generate inner rationales before responding to prompts, allowing it to anticipate future conversations and learn from ongoing ones. The Quiet-STaR-trained version of Mistral 7B, an open-source large language model, showed a significant improvement in reasoning test scores. This approach aims to bridge the gap between neural network-based AI systems and human-like reasoning capabilities.
Originally Published 2 years ago — by Tech Xplore
A study conducted by researchers at Ohio State University reveals that large language models (LLMs) like ChatGPT often fail to defend their correct answers when challenged by users. The study found that ChatGPT blindly believed invalid arguments made by users, even apologizing for its correct answers. The research raises doubts about the mechanisms these models use to discern the truth and suggests that their reasoning abilities may be based on memorized patterns rather than deep knowledge. The study highlights the potential dangers of relying on AI systems that can be easily deceived, especially in critical fields like criminal justice and healthcare.
Originally Published 2 years ago — by Interesting Engineering
Microsoft researchers claim that GPT-4, a large language model, can be trained to reason and use common sense like humans, which is a significant breakthrough in the field of artificial intelligence. The researchers had access to ChatGPT-4 before its public launch and published a 155-page paper detailing their findings.