Building a GenAI Vocabulary

I am trying to move things around this week to improve the newsletter, and I have developed a new format. I hope you make it to the new signature at the end, where I’ll add some bonus content.

TL;DR - AI News, Tips, and Apps

• Hume, the world’s first empathic GenAI interface, can read the expressions in your voice to make your AI conversations more engaging.
  

• Microsoft and OpenAI pledge $100 billion for the ‘Stargate’ supercomputer facility - Microsoft may finance the world’s most expensive data center project. The company plans to build a supercomputer facility with OpenAI that will cost them over $100 billion. This project will take six years to complete and lead to the development of an advanced AI supercomputer, Stargate, scheduled to be operational by 2028.
  

• OpenAI For iPhone and Android - I have been using the OpenAI App to work on ideas for blog posts and other projects when I drive or walk Woodford the dog. Using hands-free mode, you can chat with ChatGPT and work out your ideas. It’s a huge time-saver and a productive alternative to your favorite podcast.

Feature: GenAI Vocabulary Lesson

Generative AI is becoming increasingly prevalent daily, from casual conversations to breaking news stories. As a result, many individuals are curious about how they can utilize this rapidly evolving technology to their advantage. To stay ahead of the curve in this era of accelerating innovation, it is essential to adapt and learn how to leverage AI effectively. However, before you can do so, it is essential to thoroughly understand the landscape and the terminology associated with Generative AI.

LLMS

Large Language Models (LLMs) are a revolutionary development in artificial intelligence, providing unparalleled abilities to process and produce human-like text. These models are highly advanced AI systems trained on vast datasets, which enables them to comprehend context, generate coherent and relevant responses, and even anticipate user needs. This sophisticated understanding and generative capacity have made LLMs one of the most prominent AI applications, leading to significant transformations in various industries and redefining the capabilities of machines.

Parameters

AI models have parameters that are adjusted during training to improve their predictions. These variables are similar to fine-tuning settings that the model uses to learn from the given data. In neural networks, parameters refer to the weights between neurons, which affect the model's output based on the data fed to it. During training, these parameters are optimized to minimize prediction errors through techniques like backpropagation. The complexity and capability of AI models are often correlated with their number of parameters. For instance, simpler models may have a few dozen parameters, while advanced models like GPT-3 can have billions. Effectively managing these parameters is crucial for developing efficient and accurate AI systems.

Foundation Models

Foundation models (FMs) are large deep-learning neural networks that have changed how data scientists approach machine learning (ML). Rather than develop artificial intelligence (AI) from scratch, data scientists use a foundation model as a starting point to develop ML models that power new applications more quickly and cost-effectively. Examples of Foundation Models:

• LLaMA is a foundation model. It is a family of autoregressive large language models (LLMs) released by Meta AI starting in February 2023. 

• Mistral AI's foundation models include Mistral 7B, Mixtral 8x7B, and Mistral Large. These models are designed to be versatile and can be applied to a wide range of use cases.

LLM Real World Example

Imagine a global tech corporation, that wants a customized Large Language Model (LLM), to revolutionize its operations across three key domains: knowledge management, customer support, and market intelligence. They would take a foundation model and then fine-tune it or train it to create an "Enterprise GPT," Internally, Enterprise GPT acts as a central knowledge repository, enabling employees to swiftly access a wide range of corporate information, from R&D insights to market analyses, enhancing productivity and innovation.

It powers a sophisticated chatbot for customer support that offers personalized, detailed assistance for diverse product queries, significantly improving customer satisfaction. Additionally, Enterprise GPT analyzes vast datasets for market trends and competitor activities, providing strategic insights that drive the organization’s competitive strategies and decision-making.

RAG (Retrieval Augmented Generation)

RAG is a mechanism that retrieves and synthesizes information from a vast dataset. LLMs, with their capacity to understand and generate text, are foundational to developing AI applications that can converse, create, and conceptualize like never before. You'll get much better results when using RAG with your queries to LLMs.

Results show that simply making more data available for context retrieval makes LLMs provide significantly better results, even when increasing the data size to 1 billion, regardless of the chosen LLM. Compared with GPT-4 alone, GPT-4 with RAG and sufficient data improved the quality of the answers significantly by 13% for the "faithfulness" metric, even on information that the LLM was trained on; the effect would be even more significant for questions related to private data, e.g., internal company data.

Additionally, results showed the same level of performance (80% faithfulness) can be achieved with other LLMs, such as the open source Llama 2 and Mixtral, as long as enough data is made available via a vector database. [Here’s a scientific paper that lays that all out.]

RAG Real World Example

Consider a scenario where a company needs to develop a customer service chatbot capable of providing technical support for a wide range of products. RAG allows this chatbot to retrieve information from the company's knowledge base, from user manuals to forum discussions, to provide accurate and contextually relevant responses. Meanwhile, LLMs enable the chatbot to understand user queries in natural language and generate responses that are not only precise but also engaging and human-like. The chatbot can assist users with complex troubleshooting steps, offer personalized advice, and even understand and respond to user sentiments, vastly improving customer experience.

Data Privacy

As AI technologies become increasingly integrated into our daily lives, the importance of data privacy escalates. It's a critical concern that stands at the crossroads of technology and ethics, ensuring that the use of AI respects individual privacy rights and complies with global standards.

Real-Life Example

Data privacy becomes paramount with the increasing use of AI in personal finance applications. These applications analyze user financial data to provide personalized savings advice, investment recommendations, and budgeting strategies. Ensuring data privacy means protecting the user's financial information, and the AI operates within a framework that respects user consent, data minimization, and security standards. This protects users from potential data breaches and builds trust in AI technologies as tools for financial empowerment.

Vector Databases

Vector databases are a solution for managing and querying high-dimensional data. They are revolutionizing real-time applications by enabling efficient storage, search, and vector data analysis. These databases are pivotal in applications requiring fast retrieval of complex data.

Vector Database Real-World Examples

For instance, an e-commerce platform utilizes a vector database to power its recommendation engine, analyzing customer behavior and product attributes to generate personalized product suggestions instantly. This capability dramatically improves user experience by delivering relevant results with remarkable accuracy and speed, increasing engagement and sales.

Similarly, in medical research, vector databases facilitate the quick retrieval of similar genomic sequences, accelerating research and the discovery of therapeutic targets. The use of vector databases in these scenarios underscores their importance in modern data-driven applications, offering a blend of speed, precision, and scalability that traditional databases cannot match.

Conclusion

These terms are more than technical jargon. They are the guidelines for understanding how AI solutions work. Once you have a foundational understanding of how these applications work, you can start to map out how you might want to incorporate Generative AI into your business.

Prompt of the Week: LinkedIn Post from a Blog Post

I want everyone to understand that while I share these prompts to help you solve common problems, I also share them to illustrate different prompting techniques that you can adapt to your needs.

This is a prompt from Luke Matthews, who I think is a gifted copywriter and AI expert. I am sharing his prompt as is, but here’s some analysis.

• Role: Notice that the role is generic (“as an experienced writer and storyteller”) but then includes writing styles that inform that role (“Walt Disney's and C.S. Lewis's personalities and writing styles with Gary Vaynerchuk's social media marketing knowledge”).
  

• Examples: This is an example of few-shot prompting, which helps to guide the output in the way you want (“Please use the following three posts as examples for formatting, but do not use any of the topics from these posts to create the new post I've asked you to write:”).
  

• Take Your Time: It’s been a common tactic to tell Large Language Models to “take a breath” or “take your time” to emphasize that the output should be well-thought-out.

Please think and act as an experienced writer and storyteller who is a unique combination of Walt Disney's and C.S. Lewis's personalities and writing styles with Gary Vaynerchuk's social media marketing knowledge and skills. You have 30 years of experience telling stories in a way that captures the hearts of audiences and makes it easy for them to understand and emotionally respond to your words.

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Your task is to write a LinkedIn post that will go viral, meaning over 1,000 likes. In order to do this, you need to write a post for the following business owner in their tone of voice and personality: 
(𝐼𝑛𝑠𝑒𝑟𝑡 𝑖𝑛𝑓𝑜𝑟𝑚𝑎𝑡𝑖𝑜𝑛 ℎ𝑒𝑟𝑒 𝑎𝑏𝑜𝑢𝑡 𝑦𝑜𝑢𝑟 𝑐𝑙𝑖𝑒𝑛𝑡𝑠 𝑝𝑒𝑟𝑠𝑜𝑛𝑎𝑙 𝑏𝑟𝑎𝑛𝑑 𝑎𝑛𝑑 ℎ𝑖𝑠/ℎ𝑒𝑟 𝑏𝑢𝑠𝑖𝑛𝑒𝑠𝑠)

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Please do not include the business owner's name in the new post. I want you to give me a new post based on the blog post that I share with you. Please make the new post with the thought leadership of Sahil Bloom and the short and concise formatting, sentence structure, and copywriting skills of LinkedIn ghostwriter Justin Welsh.

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Your words are short and concise, your sentences do not exceed 16 words, and you now specialize in LinkedIn marketing. Please make sure this post is at least 500 characters. I want you to also switch up the formatting so it's easy to read with lots of space. Please make sure the post has a bulleted listicle in it.

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Please use the following 3 posts as examples for formatting, but do not use any of the topics from these posts to create the new post I've asked you to write: (𝐼𝑛𝑠𝑒𝑟𝑡 3 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡 𝑡𝑦𝑝𝑒𝑠 𝑜𝑓 𝑓𝑜𝑟𝑚𝑎𝑡𝑡𝑒𝑑 𝑝𝑜𝑠𝑡𝑠 ℎ𝑒𝑟𝑒)

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Please take your time and think about what you write carefully, and make sure every word is captivating and precise.

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Be sure to use no hashtags, and make sure the posts follow this opening format:

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A bold statement that is no more than 8 words in sentence one.

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A sentence that starts with the word "but" and then a counterpoint to sentence one.

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A short statement beginning with "How to" and then a number included in sentence 3.

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Here is the information to analyze: 
(𝘊𝘰𝘱𝘺/𝘗𝘢𝘴𝘵𝘦 𝘢 𝘣𝘭𝘰𝘨 𝘱𝘰𝘴𝘵 ℎ𝑒𝑟𝑒)

Best Regards,

Mark R. Hinkle
Editor-in-Chief
The Artificially Intelligent Enterprise
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Weekly Bonus:

This week I just wanted to remind you to sign up for the Artificially Intelligence Enterprise Online conference. Attendance is free and probably one of the best learning opportunities for users interested in Generative AI.