- Startups and developers are questioning the trustworthiness of large-language models (LLMs) like OpenAI's GPT-4.
- Recent research suggests that while LLMs can improve over time, they can also deteriorate.
- Evaluating the performance of LLMs is challenging due to limited information from providers about their training and development processes.
- Some customers are adopting a unique strategy of using other LLMs to assess the reliability of the models they are using.
- Researchers at companies like OpenAI are becoming less forthcoming at industry forums, making it harder for startups to gain insights.
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.
The author discusses the potential threat of large language models (LLMs) like ChatGPT on the integrity and value of education in the humanities, arguing that the no-fence approach, which allows students to use LLMs without restrictions or guidance, may be detrimental to intellectual culture and the purpose of education.
The struggle between open-source and proprietary artificial intelligence (AI) systems is intensifying as large language models (LLMs) become a battleground for tech giants like Microsoft and Google, who are defending their proprietary technology against open-source alternatives like ChatGPT from OpenAI; while open-source AI advocates believe it will democratize access to AI tools, analysts express concern that commoditization of LLMs could erode the competitive advantage of proprietary models and impact the return on investment for companies like Microsoft.
Large language models like ChatGPT, despite their complexity, are actually reliant on human knowledge and labor, as they require humans to provide new content, interpret information, and train them through feedback. They cannot generate new knowledge on their own and depend on humans for improvement and expansion.
Meta has introduced Code Llama, a large language model (LLM) designed to generate and debug code, making software development more efficient and accessible in various programming languages. It can handle up to 100,000 tokens of context and comes in different parameter sizes, offering trade-offs between speed and performance.
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.
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.
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.
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.
Large language models (LLMs) like GPT-4 are capable of generating creative and high-quality ideas, surpassing human performance on creativity tests and outperforming humans in idea generation tasks, making them valuable tools in various domains.
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.
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.
