Podcast Summary
New developments in AI industry: Stay informed about latest news and best practices in AI industry to maximize potential of large language models
There are common mistakes being made when it comes to using large language models, and it's essential to be aware of them to maximize their potential. Apple is reportedly developing new chips for AI software and data centers, which could lead to more efficient and powerful AI processing. OpenAI, the company behind ChatGPT, is moving closer to launching a search engine to compete with Google, and they have recently moved all chat data to chatgpt.com to prepare for this launch. Additionally, the mysterious GBT-2 chatbot model has been re-released. These developments demonstrate the growing importance and competition in the AI industry. To make the most of these tools, it's crucial to stay informed about the latest news and best practices. Sign up for Everyday AI's free daily newsletter to stay updated and learn practical advice for using large language models to boost your career, business, and everyday life.
New Developments in AI: OpenAI's New Chatbots and Microsoft's MAI one: OpenAI releases smaller, powerful chatbot versions, Microsoft unveils a larger language model as competition, both signaling continued advancements in AI technology
There are new developments in the world of AI models, with OpenAI releasing two new versions of their GPT 2 chatbot and Microsoft announcing the creation of their own large language model, MAI one. OpenAI's new models, named "I'm a good GPT 2 chatbot" and "I'm also a good GPT 2 chatbot," are likely smaller, powerful versions that could be used for future free versions of chatGPT and search. Microsoft's MAI one, with around 500,000,000,000 parameters, is significantly larger than previous models and is a direct competition to OpenAI, Google, Anthropic, and Meta's state-of-the-art AI models. Microsoft's investment in MAI one could be seen as an attempt to prove their independence from OpenAI and reduce reliance on the company for future AI developments. The development is being overseen by Mustafa Suleiman, the ex-Google DeepMind co-founder and former CEO and co-founder of Inflexion AI. The future implications for popular tools like Copilot are yet to be seen, as users may have the option to choose between different models. It's an exciting time in the world of AI, with companies continuously pushing the boundaries of what's possible.
Understanding the knowledge cutoff in large language models: Being aware of the knowledge cutoff in large language models is crucial to avoid inaccuracies and ensure the information's relevancy and accuracy.
It's crucial to understand the knowledge cutoff when working with large language models. These models gather data from the internet, which can be good or bad, and humans fine-tune them. However, there's a point where the models stop gathering new data, and this date is important to know. Older data can lead to inaccurate or outdated outputs. With the increasing use of large language models in daily work, failing to recognize the knowledge cutoff can result in publishing or sharing incorrect information. It's essential to be aware of this expiration date and the model's functioning to avoid inaccuracies. Before large language models, we relied on manual research and checking the date of sources. Similarly, when using large language models, it's necessary to consider the data's age and relevancy.
Understanding Knowledge Cutoff Dates in Large Language Models: Ensure language models have up-to-date information by checking their knowledge cutoff dates. Outdated models can lead to inaccurate or irrelevant information.
When working with large language models like chatbots, it's essential to consider the models' knowledge cutoff dates to ensure the information you receive is up-to-date. Different models have varying cutoff dates, with some being more recent than others. For instance, OpenAI's GPT 4 has a cutoff date of December 2023, while Google's Gemini reportedly has a November 2023 cutoff. Meta's Llama has a December 2023 cutoff for its newer versions. However, it's important to note that some free versions of these models may have older cutoff dates, such as the free version of ChatGPT, which has a January 2022 cutoff. Using outdated models can lead to inaccurate or irrelevant information, making it crucial to understand and investigate the knowledge cutoff dates before using a language model. Additionally, models with unclear or undisclosed cutoff dates can negatively impact trust and transparency.
Impact of Internet connectivity on large language models: Large language models without real-time Internet connectivity may provide inaccurate or outdated information due to lack of access to up-to-date data.
While large language models like ChatGPT, Google's Gemini, and Microsoft Copilot can provide answers to queries, their level of Internet connectivity and access to real-time information significantly impact their accuracy and usefulness. During the discussion, it was highlighted that while models like ChatGPT and Microsoft Copilot have some level of Internet connectivity through Bing and Microsoft, respectively, Anthropic's Claude does not. Perplexity, on the other hand, is an answer engine that uses models like GPT or Opus, but it's not a model itself. The examples given during the discussion demonstrated this point. When asked to list the largest companies in the US by market cap, the default version of ChatGPT gave an inaccurate and outdated answer. However, when an Internet-connected version of ChatGPT was used, the answer was more accurate. Google Gemini, another Internet-connected large language model, essentially told the user to use Google instead of providing an answer. The lack of real-time Internet connectivity and access to up-to-date information can result in a model giving incorrect or outdated answers. Therefore, it's crucial to consider a model's level of Internet connectivity when evaluating its usefulness in providing accurate and up-to-date information.
Understanding context window limitations: Large language models can provide accurate responses initially but forget important details as conversation progresses due to limited context window.
While large language models like Copilot, ChatGPT, and others can provide accurate and useful information, they have limitations, specifically in terms of memory or context window. These models can only remember and process a certain amount of information before they start forgetting. For instance, ChatGPT's context window is currently limited to 32,000 tokens or approximately 28,000 words. As users interact with these models, they might experience a "love affair" due to the initial accurate responses, but as the conversation progresses and more information is shared, the models may start forgetting important details. This can lead to inaccurate or irrelevant responses. It's crucial for users to understand the context window limitations of different models and manage the flow of information accordingly. Additionally, as the cost of compute continues to decrease and models become more powerful, the context window limitation may become less of a concern.
Misleading information from language models through screenshots: Avoid sharing or consulting solely based on screenshots from language models, ensure to share the URL or link to the original interaction or output for transparency and accountability.
Relying solely on screenshots from large language models like ChatGPT for information or making decisions is a common yet significant mistake. Sharing or consulting based on screenshots alone can lead to inaccurate or misleading results, as anyone can manipulate a model to produce a desired output and share a screenshot of it. The New York Times made this mistake in a high-profile lawsuit, failing to provide the public URL to verify the authenticity of the screenshots they shared. To avoid this pitfall, it's essential to share the URL or link to the original interaction or output from the language model. This ensures transparency and accountability, allowing others to verify the results for themselves.
Large language models are generative, not deterministic: Even with the same prompt, large language models can generate different responses due to their generative nature and contextual understanding, which can be beneficial in drug discovery.
Large language models are generative, not deterministic. This means that even with the same prompt, you could receive different responses every time you use a large language model. The models don't understand words in the way humans do, but rather use context and vast amounts of training data to generate responses. The generative nature of these models is a feature, not a bug, and is intended to provide unique and different responses each time. So, if you're expecting consistent, identical results from your prompts, that might not be the case. The generative nature depends on the specific prompt and its context. Additionally, there are settings like top p and temperature that can influence the next token prediction. Eli Lilly, a major company, even sees the "hallucinations" or unexpected responses as a feature in drug discovery. So, remember, large language models are designed to generate new and potentially surprising responses, not just repeat the same ones.
Understanding Few-Shot Learning for Better ChatGPT Results: Few-shot learning involves providing multiple input-output pairings or engaging in back-and-forth conversations with a model to improve its responses.
Large language models like ChatGPT are generative models, not deterministic like search engines. They're designed to predict the next token with a level of randomness and generate unique responses based on given prompts. Copy-pasted prompts don't work effectively with these models. Instead, providing a few examples of input and output during prompting can lead to better results. This concept is known as few-shot learning. Research consistently shows that the more input-output pairings or back-and-forth conversations you have with a model, the better the outputs will be. So, be cautious of individuals who claim to have magical solutions or sell prompt books, as they may not have a solid understanding of large language models. Instead, consider joining communities or workshops where you can learn prompt engineering techniques firsthand. Remember, the more interaction and examples you provide, the better the model's responses will be.
Leveraging Large Language Models for Business: Understand the importance of prompting techniques for optimal results with large language models. Companies like Microsoft, Amazon, IBM, OpenAI, and others are leading the way in generative AI, and businesses should integrate these tools into their strategies to stay competitive.
When working with large language models like ChatGPT, copy and paste prompts will not yield optimal results. Instead, it's crucial to understand that these models are the future of work and invest time in teaching them through proper prompting. Prime prompt polishing and prompt engineering are essential techniques to elicit the best outputs. Additionally, businesses of all sizes, from startups to Fortune 500 companies, are already implementing these technologies on a massive scale to save costs and stay competitive. Microsoft Copilot, Amazon Q, IBM Watson, OpenAI, Anthropics Claude, Google, and Meta are just a few examples of companies leading the way in generative AI and large language models. Therefore, it's essential to start integrating these tools into your business strategy to remain competitive and adapt to the changing business landscape.
Misunderstanding large language models: Avoid common mistakes like misjudging their knowledge cutoff, neglecting internet connectivity, mismanaging memory, and forgetting they're generative systems when working with large language models to maximize productivity and collaboration.
The future of work involves integrating large language models into our daily tasks as knowledge workers. These models will become our constant collaborators, and we'll be prompting them every day, hour, and minute. However, there are common mistakes people make when working with large language models. These include misunderstanding their knowledge cutoff, neglecting their internet connectivity, mismanaging their memory, and not realizing they're generative systems, among others. It's crucial to understand these aspects to effectively work with these models. In essence, the future of work is about humans and large language models collaborating closely, with the latter acting as intelligent assistants.
