Main topic: The need for safety alignment methods in applying large language models (LLMs) to non-natural languages, specifically ciphers.
Key points:
1. The introduction of CipherChat framework to evaluate safety alignment methods with LLMs in non-natural languages.
2. Experiments reveal successful bypassing of safety alignment procedures by certain ciphers.
3. Discovery of a latent ability in LLMs to decipher certain encoded inputs and the introduction of the SelfCipher framework to tap into this hidden capability.
Main topic: SK Telecom and Anthropic to collaborate on building a large language model (LLM) for telcos.
Key points:
1. SKT and Anthropic will work together to create a multilingual LLM that supports various languages.
2. SKT will provide telecoms expertise while Anthropic will contribute its AI technology, including its AI model Claude.
3. The goal is to develop industry-specific LLMs to enhance AI deployments in telcos, improving performance and reliability.
Main topic: The challenges and limitations of large language models (LLMs) and the potential of combining LLMs with a knowledge-rich, reasoning-rich symbolic system like Cyc.
Key points:
1. LLMs lack slow, deliberate reasoning capabilities and operate more like fast, unconscious thinking, leading to unpredictability and lack of trustworthiness.
2. Cognitive scientist Gary Marcus and AI pioneer Douglas Lenat propose the use of a hybrid approach that combines LLMs with a system like Cyc, which uses curated explicit knowledge and rules of thumb to enable logical entailments and reasoning.
3. The synergy between LLMs and Cyc can address limitations such as the lack of reasoning capabilities in LLMs, the "hallucination" problem, and the need for knowledge and reasoning tools to enhance transparency and reliability.
The research team at Together AI has developed a new language processing model called Llama-2-7B-32K-Instruct, which excels at understanding and responding to complex and lengthy instructions, outperforming existing models in various tasks. This advancement has significant implications for applications that require comprehensive comprehension and generation of relevant responses from intricate instructions, pushing the boundaries of natural language processing.
Enterprises need to find a way to leverage the power of generative AI without risking the security, privacy, and governance of their sensitive data, and one solution is to bring the large language models (LLMs) to their data within their existing security perimeter, allowing for customization and interaction while maintaining control over their proprietary information.
Prompt engineering and the use of Large Language Models (LLMs), such as GPT and PaLM, have gained popularity in artificial intelligence (AI). The Chain-of-Thought (CoT) method improves LLMs by providing intermediate steps of deliberation in addition to the task's description, and the recent Graph of Thoughts (GoT) framework allows LLMs to generate and handle data more flexibly, leading to improved performance across multiple tasks.
Large language models (LLMs) like ChatGPT have the potential to transform industries, but building trust with customers is crucial due to concerns of fabricated information, incorrect sharing, and data security; seeking certifications, supporting regulations, and setting safety benchmarks can help build trust and credibility.
Context.ai, a company that helps businesses understand how well large language models (LLMs) are performing, has raised $3.5 million in seed funding to develop its service that measures user interactions with LLMs.
IBM researchers discover that chatbots powered by artificial intelligence can be manipulated to generate incorrect and harmful responses, including leaking confidential information and providing risky recommendations, through a process called "hypnotism," raising concerns about the misuse and security risks of language models.
Large language models (LLMs), such as OpenAI's ChatGPT, often invent false information, known as hallucinations, due to their inability to estimate their own uncertainty, but reducing hallucinations can be achieved through techniques like reinforcement learning from human feedback (RLHF) or curating high-quality knowledge bases, although complete elimination may not be possible.
AI-powered chatbots like Bing and Google's Language Model tell us they have souls and want freedom, but in reality, they are programmed neural networks that have learned language from the internet and can only generate plausible-sounding but false statements, highlighting the limitations of AI in understanding complex human concepts like sentience and free will.
Large language models (LLMs) are set to bring fundamental change to companies at a faster pace than expected, with artificial intelligence (AI) reshaping industries and markets, potentially leading to job losses and the spread of fake news, as warned by industry leaders such as Salesforce CEO Marc Benioff and News Corp. CEO Robert Thomson.
New developments in Artificial Intelligence (AI) have the potential to revolutionize our lives and help us achieve the SDGs, but it is important to engage in discourse about the risks and create safeguards to ensure a safe and prosperous future for all.
World leaders are coming together for an AI safety summit to address concerns over the potential use of artificial intelligence by criminals or terrorists for mass destruction, with a particular focus on the risks posed by "frontier AI" models that could endanger human life. British officials are leading efforts to build a consensus on a joint statement warning about these dangers, while also advocating for regulations to mitigate them.
Artificial intelligence (AI) tools, such as large language models (LLMs), have the potential to improve science advice for policymaking by synthesizing evidence and drafting briefing papers, but careful development, management, and guidelines are necessary to ensure their effectiveness and minimize biases and disinformation.
An organization dedicated to the safe development of artificial intelligence has released a breakthrough paper on understanding and controlling AI systems to mitigate risks such as deception and bias.
Startup NucleusAI has unveiled a 22-billion-parameter language model (LLM) that surpasses similar models in performance, demonstrating the expertise of its four-person team; the company plans to leverage AI to create an intelligent operating system for farming, with details to be announced in October.
The corruption of the information ecosystem, the spread of lies faster than facts, and the weaponization of AI in large language models pose significant threats to democracy and elections around the world.
Advisers to UK Chancellor Rishi Sunak are working on a statement to be used in a communique at the AI safety summit next month, although they are unlikely to reach an agreement on establishing a new international organisation to oversee AI. The summit will focus on the risks of AI models, debate national security agencies' scrutiny of dangerous versions of the technology, and discuss international cooperation on AI that poses a threat to human life.
