Operationalizing Generative AI, AI Agents Workforce, & Hallucinations
Most CEOs are asking their teams and themselves how they can incorporate generative AI into their companies to increase business value. There has been a lot of buzz around generative AI and its many tasks, such as generating code, marketing content, realistic images, music, and automating many tasks. Sequoia Capital’s recent blog post spoke about how generative AI is entering its Act 2 and will be focused on solving human problems from beginning to end. What is different about Act 2 is that foundational models will be part of a more comprehensive solution, rather than the entire solution. Nonetheless, many are questioning why adoption by enterprises has been so slow. There are many variables, but two of the most important ones are hallucinations and data privacy.
Operationalizing Generative AI
Enterprises need to start by focusing on low-hanging fruit and start with problems that can be fixed today. Enterprises need to start by clearly defining the business problem and then figure out how to use generative AI. Compared to the old classical ML lifecycle, we can now store a bunch of data in a vector store and get analysis on that data very quickly. We do need to be mindful about what data we are inserting in the vector database to avoid the garbage in and garbage out problem. Thus, a great amount of time needs to be spent on creating a highly curated knowledge base.
This past week Microsoft just announced their AI Copilot, your everyday AI companion that will help you work faster and smarter. It is integrated into Microsoft 365 apps, such as Word, Excel, PowerPoint, Outlook, and Teams, and provides real-time assistance with tasks such as:
- Writing and editing: Copilot can help you write and edit documents, emails, and presentations by suggesting relevant content, correcting grammar and spelling errors, and improving your writing style.
- Research and analysis: Copilot can help you research and analyze data by extracting key insights from documents, translating languages, and creating charts and graphs.
- Collaboration and communication: Copilot can help you collaborate and communicate more effectively by summarizing key points from meetings, generating talking points, and translating languages.
Microsoft Copilot is still under development, but it has the potential to revolutionize the way we work. By providing real-time assistance with a wide range of tasks, Copilot can help us to be more productive, efficient, and creative.
ChatDev — Communicative Agents for Software Development
I am very excited and bullish on the future of multiple AI agents communicating with one another to complete complex tasks. It is a new framework and does not always work as intended currently, but it is going to change the way all knowledge work is done. This past week, I was experimenting with ChatDev, a virtual software company where multiple intelligent agents holding different roles communicate with one another to complete the task assigned. Some of the roles include Chief Executive Officer, Chief Technology Officer, Chief Product Officer, Programmer, Code Reviewer, Tester, and more. These are some of the roles provided in the original repository, but roles can be customized. I was testing its limits and was surprised that with just this simple command, I was able to generate a pingpong game, and it produced all of the documentation and instructions on how to play the game.
python3 run.py --task "Develop a simple pingpong game." --name "pingpong"Results may vary on each generation. I had some fun with prompt engineering!
An AI agent workforce has the potential to revolutionize the way we work by automating tasks, making better decisions, and fostering creativity and innovation.
By working together, AI agents can accomplish tasks more quickly, accurately, and efficiently than humans alone. They can also analyze large amounts of data to identify patterns and trends that would be difficult for humans to see. This information can then be used to inform decisions and make better predictions. Additionally, AI agents can be used to generate new ideas and solutions to problems, helping us to be more creative and innovative.
As AI technology continues to develop, AI agent workforces are poised to play an increasingly important role in our lives.
Hallucinations
What do hallucinations even mean? As Marc Benioff explained during Salesforce’s Dreamforce keynote, “These things are good, but they’re not great,” Benioff said. “You get a lot of answers that aren’t exactly true. They call them hallucinations. I call it lies … and they can turn very toxic very quickly.”
Based on conversations I have had with executives recently, hallucinations are the main thing that is preventing many CEOs from deploying LLM solutions into production. Hallucinations are false or misleading statements that LLMs can generate, often due to a lack of understanding of the underlying reality that language describes.
Hallucinations can be a serious problem for enterprises, as they can lead to inaccurate information being provided to customers or investors, or to decisions being made based on false premises. For example, an LLM used to generate marketing copy could hallucinate about product features that do not exist, or an LLM used to generate financial reports could hallucinate about financial data that is inaccurate.
There are a number of reasons why LLMs are prone to hallucinations. First, LLMs are trained on massive datasets of text and code, but these datasets often contain errors and inconsistencies. This means that LLMs can learn to generate text that is factually incorrect or misleading. Second, LLMs are statistical models, and they do not have any understanding of the real world. This means that they can generate text that is grammatically correct and semantically coherent, but that is still false or misleading.
While there are a number of techniques that can be used to mitigate the risk of hallucinations in LLMs, these techniques are not perfect. As a result, enterprises are hesitant to deploy LLMs in production environments.
Overall, the risk of hallucinations is a major barrier to the adoption of LLMs by enterprises. However, as research into hallucination mitigation techniques continues, it is likely that LLMs will become more widely deployed in enterprise environments in the near future.
Let’s Chat!
If you’re interested in chatting more about enterprise use cases for LLMs, AI agents, and AI safety, or if you have any questions or feedback, please feel free to send me a message. I’m always happy to chat about these topics.
Cheers!
Aditya Bahl
