Max Townsend’s Post

Everyone talks about AI. In fact, we also use #AI extensively in #VTDocs. But before delving into the nuances of AI and related technologies, it's important to understand the business context and the challenges it addresses. Let’s consider an example: When reviewing multiple documents, business analysts, project managers, and other professionals typically examine them for essential requirements: They identify dependencies within a document—perhaps paragraph three relates to paragraph 52 on page 19.  These interdependencies occur both within individual documents and across multiple ones.  Examples include standard operating procedures with specific policies, terms and conditions, or supplier agreements.  How does their process look like today? The analyst opens a document and skims through it from start to finish, noting any critical points or elements of interest.  They use the search function (Ctrl + F) to scan for terms like "warranty" or "indemnifications" in the contract. This method is time-consuming and ad-hoc. While experienced professionals might navigate quickly, those less experienced or new to the business need much more time and might overlook crucial details. VT Docs and its AI models offer a different approach: When uploading multiple documents into VT Docs, users instantly see common themes across documents – powered by AI.  Imagine a matrix where each column represents a document, and on the left, thematic frequencies are highlighted.  This visualization allows users to spot patterns quickly. For instance, if references to "indemnification" or variations of that appear across several documents, then VT Docs shows them automatically and provides an aggregated view. VT Docs utilizes an AI model specially designed for business and technical communications.  So, there's no "hallucination" or inaccuracy— the AI doesn't use a generative model (like ChatGPT) but relies on a model that can trace back to the precise text, ensuring 100% accuracy. This precision is non-negotiable and pivotal. Another consideration, especially concerning generative AI, is security and data integrity.  VT Docs and its AI model run completely isolated in the most secure locked-down environments. This ensures no data leaks or unauthorized learning from your data. AI has the potential to transform industries, but only if it maintains reliability and security and seamlessly integrates into existing workflows. At @VisibleThread, we aim to pioneer solutions that embody these principles, ensuring not just innovation but secure and dependable results. #documentreview #artificialintelligence #enterprise

Everyone talks about AI. In fact, we also use #AI extensively in #VTDocs. But before delving into the nuances of AI and related technologies, it's important to understand the business context and the challenges it addresses. Let’s consider an example: When reviewing multiple documents, business analysts, project managers, and other professionals typically examine them for essential requirements: They identify dependencies within a document—perhaps paragraph three relates to paragraph 52 on page 19.  These interdependencies occur both within individual documents and across multiple ones.  Examples include standard operating procedures with specific policies, terms and conditions, or supplier agreements.  How does their process look like today? The analyst opens a document and skims through it from start to finish, noting any critical points or elements of interest.  They use the search function (Ctrl + F) to scan for terms like "warranty" or "indemnifications" in the contract. This method is time-consuming and ad-hoc. While experienced professionals might navigate quickly, those less experienced or new to the business need much more time and might overlook crucial details. VT Docs and its AI models offer a different approach: When uploading multiple documents into VT Docs, users instantly see common themes across documents – powered by AI.  Imagine a matrix where each column represents a document, and on the left, thematic frequencies are highlighted.  This visualization allows users to spot patterns quickly. For instance, if references to "indemnification" or variations of that appear across several documents, then VT Docs shows them automatically and provides an aggregated view. VT Docs utilizes an AI model specially designed for business and technical communications.  So, there's no "hallucination" or inaccuracy— the AI doesn't use a generative model (like ChatGPT) but relies on a model that can trace back to the precise text, ensuring 100% accuracy. This precision is non-negotiable and pivotal. Another consideration, especially concerning generative AI, is security and data integrity.  VT Docs and its AI model run completely isolated in the most secure locked-down environments. This ensures no data leaks or unauthorized learning from your data. AI has the potential to transform industries, but only if it maintains reliability and security and seamlessly integrates into existing workflows. At @VisibleThread, we aim to pioneer solutions that embody these principles, ensuring not just innovation but secure and dependable results. #documentreview #artificialintelligence #enterprise

Ghosts in the machine: Peril of hallucinations in GenAI chatbots https://lnkd.in/gJiT8UV2 Hallucination in Gen AI applications could have much more damaging impact than we probably understand. It is said that "Speak a lie enough number of times and it becomes the truth" With World Wide Web being the default and cheapest source of all information, ensuring the factual correctness of that information before using it for making decisions or even forming opinions that impact the society, industries, policies, judgements, etc - becomes extremely critical. Else, before long, we may loose track of what was a fact and what was not. While this is a really impactful use-case for regulatory and policy level interventions, having a Gen AI system in conjunction with some sort of information vetting and filtration systems could offer a way forward. I am inclined to believe that our Tech brains have solved the "relevance" problem before - when we were starting to catalogue the internet for Search. Google made the PageRank that improved the quality of results and information presented. And THAT could be an inspiration for technology models utilising a similar filteration or weighing paradigm while compiling a GenAI response. GenAI is powerful - but now it's imperative to address its inherent NOISE! Would be keen to listen to ideas and thoughts from the geeks and the researchers! #GenAI #hallucinations #RAG #chatbots #GPT #LLM

Want to understand Generative AI better, but not a technical person? I highly recommend the book, "Co-Intelligence: Living and Working with AI" by Ethan Mollick. https://loom.ly/7IOOstg Everything is explained in an easy-to-understand approach. Filled with examples, as well as prompts you can try, he clearly articulates why GenAI has the potential to be such a huge-leap forward for business and society. Ethan clearly explains why, for now, GenAI works best as a co-intelligence, a tool you can use to augment your own performance. AI can perform some tasks better than humans, but many others it cannot. You just need to experiment. It doesn't matter what tool you start with (Microsoft Copilot, ChatGPT, Claude, Gemini, Perplexity, etc.), just start and experiment to see how you can benefit. Here's a real-world example for you. We ran an AI strategy workshop a few weeks ago. The intent of the workshop was for attendees to get their hands "dirty" by using GenAI throughout the entire day (Microsoft Copilot for this workshop). In a structured process, attendees used Copilot by starting with the prompts we provided. They could see first-hand where AI was adding value, and where they needed to work alongside AI to get the best result. We worked through: ↳ Identifying challenges facing their industries - in addition to the ideas they brought to the workshop ↳ Brainstorming ideas of how AI can help their organizations face those challenges to drive revenue, improve productivity and increase profitability ↳ Determining the impact (high/low) and effort (high/low) for each of the ideas ↳ Creating a high-level AI roadmap by placing ideas along McKinsey's 3-horizon framework ↳ Examining what it would take from a people, process and technology perspective to implement an idea ↳ Creating a high-level project plan for that idea ↳ Creating an executive summary to pitch that idea In the span of one day, with the help of GenAI, attendees were able to get hands-on experience using AI to help their organization identify where AI can add value and drill into their most promising idea to see what it would take to build out a pilot solution.

Everyone talks about AI. In fact, we also use #AI extensively in #VTDocs. But before delving into the nuances of AI and related technologies, it's important to understand the business context and the challenges it addresses. Let’s consider an example: When reviewing multiple documents, business analysts, project managers, and other professionals typically examine them for essential requirements: They identify dependencies within a document—perhaps paragraph three relates to paragraph 52 on page 19. These interdependencies occur both within individual documents and across multiple ones. Examples include standard operating procedures with specific policies, terms and conditions, or supplier agreements. How does their process look like today? The analyst opens a document and skims through it from start to finish, noting any critical points or elements of interest. They use the search function (Ctrl + F) to scan for terms like "warranty" or "indemnifications" in the contract. This method is time-consuming and ad-hoc. While experienced professionals might navigate quickly, those less experienced or new to the business need much more time and might overlook crucial details. VT Docs and its AI models offer a different approach: When uploading multiple documents into VT Docs, users instantly see common themes across documents – powered by AI. Imagine a matrix where each column represents a document, and on the left, thematic frequencies are highlighted. This visualization allows users to spot patterns quickly. For instance, if references to "indemnification" or variations of that appear across several documents, then VT Docs shows them automatically and provides an aggregated view. VT Docs utilizes an AI model specially designed for business and technical communications. So, there's no "hallucination" or inaccuracy— the AI doesn't use a generative model (like ChatGPT) but relies on a model that can trace back to the precise text, ensuring 100% accuracy. This precision is non-negotiable and pivotal. Another consideration, especially concerning generative AI, is security and data integrity. VT Docs and its AI model run completely isolated in the most secure locked-down environments. This ensures no data leaks or unauthorized learning from your data. AI has the potential to transform industries, but only if it maintains reliability and security and seamlessly integrates into existing workflows. At VisibleThread, we aim to pioneer solutions that embody these principles, ensuring not just innovation but secure and dependable results. #documentreview #artificialintelligence #enterprise

Everyone talks about AI. In fact, we also use #AI extensively in #VTDocs. But before delving into the nuances of AI and related technologies, it's important to understand the business context and the challenges it addresses. Let’s consider an example: When reviewing multiple documents, business analysts, project managers, and other professionals typically examine them for essential requirements: They identify dependencies within a document—perhaps paragraph three relates to paragraph 52 on page 19.  These interdependencies occur both within individual documents and across multiple ones.  Examples include standard operating procedures with specific policies, terms and conditions, or supplier agreements.  How does their process look like today? The analyst opens a document and skims through it from start to finish, noting any critical points or elements of interest.  They use the search function (Ctrl + F) to scan for terms like "warranty" or "indemnifications" in the contract. This method is time-consuming and ad-hoc. While experienced professionals might navigate quickly, those less experienced or new to the business need much more time and might overlook crucial details. VT Docs and its AI models offer a different approach: When uploading multiple documents into VT Docs, users instantly see common themes across documents – powered by AI.  Imagine a matrix where each column represents a document, and on the left, thematic frequencies are highlighted.  This visualization allows users to spot patterns quickly. For instance, if references to "indemnification" or variations of that appear across several documents, then VT Docs shows them automatically and provides an aggregated view. VT Docs utilizes an AI model specially designed for business and technical communications.  So, there's no "hallucination" or inaccuracy— the AI doesn't use a generative model (like ChatGPT) but relies on a model that can trace back to the precise text, ensuring 100% accuracy. This precision is non-negotiable and pivotal. Another consideration, especially concerning generative AI, is security and data integrity.  VT Docs and its AI model run completely isolated in the most secure locked-down environments. This ensures no data leaks or unauthorized learning from your data. AI has the potential to transform industries, but only if it maintains reliability and security and seamlessly integrates into existing workflows. At @VisibleThread, we aim to pioneer solutions that embody these principles, ensuring not just innovation but secure and dependable results. #documentreview #artificialintelligence #enterprise

AI Agents: What Are They, Really? With all the buzz around AI, terms like “agents” get thrown around a lot, but what does it actually mean for an AI to be an “agent”? 🤔 Simply put, an AI agent is a system that makes decisions on its own, using an AI model like ChatGPT to figure out the next step in a process. Imagine you have a virtual assistant that’s not just following your instructions but actually deciding the best way to reach a goal. That’s what we mean by an agent—something that can decide its own actions in real-time, without needing constant guidance. Here’s a straightforward definition: 𝐀𝐧 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭 𝐢𝐬 𝐚 𝐬𝐲𝐬𝐭𝐞𝐦 𝐭𝐡𝐚𝐭 𝐮𝐬𝐞𝐬 𝐚𝐧 𝐀𝐈 𝐦𝐨𝐝𝐞𝐥 𝐭𝐨 𝐝𝐞𝐜𝐢𝐝𝐞 𝐭𝐡𝐞 𝐜𝐨𝐧𝐭𝐫𝐨𝐥 𝐟𝐥𝐨𝐰 𝐨𝐟 𝐚𝐧 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧. So, instead of a developer pre-programming every step, the AI agent can make some of those choices itself. The level of autonomy can vary: 1. 𝐁𝐚𝐬𝐢𝐜 𝐇𝐞𝐥𝐩 (𝐥𝐢𝐤𝐞 𝐚 𝐜𝐚𝐥𝐜𝐮𝐥𝐚𝐭𝐨𝐫) – You give it one task, and it simply does that task. 2. 𝐒𝐢𝐧𝐠𝐥𝐞-𝐒𝐭𝐞𝐩 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 – Imagine asking the AI to summarize a document. It reads and summarizes in one go, deciding on the best summary based on its understanding. 3. 𝐌𝐮𝐥𝐭𝐢-𝐒𝐭𝐞𝐩 𝐂𝐡𝐚𝐢𝐧𝐬 – You ask it to research a topic. It decides to search, gather sources, and summarize in multiple steps, choosing each step on its own. 4. 𝐒𝐦𝐚𝐫𝐭 𝐑𝐨𝐮𝐭𝐢𝐧𝐠 – In a customer service scenario, it might analyze your question and decide which department or person it should go to without needing human help. 5. 𝐅𝐮𝐥𝐥𝐲 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 – In a complex task, like planning an event, it might manage the entire process: finding vendors, comparing prices, booking, and updating you as it progresses—all without your input. Think of an AI agent as more than just a tool. It’s like a co-pilot that doesn’t need to be told every little detail. The goal is to empower AI to handle certain tasks on its own, freeing us to focus on bigger things. #ai #llm #aiagent #agents

Interesting and insightful post about AI, there's so many ways it's incredibly useful. I've been using Copilot quite frequently and have posted before on here about AI, now looking forward to seeing the possibilities in Smartsheet.

🪜 What is an AI Agent? Are they the replacement for LLMs? 👀AI Agents are the next generation connected to LLMs to integrate tasks into your legacy systems. This means a chatbot can update your database, modify an order, and much more—all autonomously! 👀 AI agents represent a paradigm shift in how we approach tasks in the realm of artificial intelligence. While Large Language Models (LLMs) like ChatGPT, Gemini, and others have revolutionized the way we interact with data, AI agents are designed to act, not just generate. 🌟 Key Differences: LLMs are powerful at generating text, insights, and responses based on vast data sets. They excel at understanding context, answering questions, and assisting with knowledge-based tasks. AI Agents, on the other hand, are task-driven entities, capable of taking actions based on the information they gather. They integrate with systems, automate processes, and handle specific tasks autonomously. 💡 LLMs in action: AI agents leverage LLMs to query and update legacy systems, ensuring smooth integration and enhancing operational efficiency. By keeping your systems up to date and responsive, AI agents help serve stakeholders better—turning insights into action. Are they replacements for LLMs? No, they are complements. AI agents build on the natural language capabilities of LLMs, adding a layer of execution. The future of AI is collaborative—and AI agents are here to take action where LLMs leave off! #AI #LLMs #ArtificialIntelligence #AIAgents #Automation #LegacySystems #FutureOfWork

Tl;dr: next generation of AI agents will have dynamic tool generation capabilities. Current agentic AI systems (e.g. ChatGPT, Claude) are awesome, and we've just started to scratch the surface of their real-world usefulness.  However, they are still inherently constrained by their pre-defined set of "tools", i.e., the functions that the agent can execute. In other words, the ceiling of an agentic system is always determined by the existence and quality of the tools it has access to. In this sense, an agent is an entity that is designed to operate within a specific context/space which is narrowed down by its toolset. This leads me to think that the next generation of AI agents will likely be able to generate their own tools on-demand, per situation. Such a system would unlock endless possibilities by increasing its autonomy dramatically and eliminating the need for defining tools in advance. Now, from an engineering perspective, how can we build such systems?  To answer this, we need to figure out two things:  a. what are the components of a tool?  b. can it be automatically generated by AI? Generally speaking, a tool is enabled by the following: 1. Code: Representing the tool's logic and functionality, wrapped as a function. 2. Schema: Defining the function's parameters. 3. Function/Tool Calling mechanism: Enabling tool execution by extracting the function's arguments from the agent's context. Can we automatically generate these? The answer is simple, yes: - LLMs are not only capable of but excel at generating code from natural language descriptions. - The schemas can be derived directly from the function's code (or be generated using LLMs in trickier cases). - Function calling mechanisms are now supported in most LLMs. In fact, there is nothing essential that prevents us from building such systems today (which is a bit scary). The technology is already here, waiting to be unleashed. Interesting times ahead... #NextGenAIAgents