Akooda的动态

Using Artificial Intelligence for tendering is a hot topic - here's what my expert bid support team at Intend Business Development think.. https://lnkd.in/e7DaueAM

?? Discovering Innovation with Nolita! ?? I recently came across Nolita, an impressive AI-driven platform that's revolutionizing how we approach technology and data. ?? Explore Nolita here:?https://nolita.ai Nolita offers cutting-edge AI solutions that can transform your business operations and decision-making processes. Check it out and see how it can elevate your business to new heights! #Nolita #AI #Innovation #TechUpdates #BusinessTransformation

Thanks to AI Advances for sharing my recent article on Use-case based evaluation of?#LLMs, where we do a deep dive into #evaluation strategies for #GenAI use-cases in the enterprise. We are at a critical juncture in the #GenerativeAI adoption journey, where we are have started hearing conflicting views reg. the transformative potential of Gen AI. Large Language Model (#LLM) providers, e.g., Open AI, Mistral, Google, Meta, etc. are rolling out one LLM after another?—?with every iteration smaller and more efficient than the previous one. But these are generic pre-trained LLMs without a clear business use-case in mind, or let’s say the business specific use-cases still need to be developed on top of these foundational LLMs. So these LLMs are only an enabler and not a measure of business impact by any means. On the other hand, we are seeing enterprises / experts start to take a more “pessimistic” view on Gen AI. For example, the recent report by Goldman Sachs is a case in point. The title Gen AI: Too much Spend, Too little Benefit? is self-explanatory and I won’t go into details?—?suffice it to say that while nobody is dismissing the future potential of Gen AI, they are not seeing Gen AI (as of now) solve any complex business #strategic problems. One of the problems here is clearly that there is a lot of exploration / #PoCs happening?—?without the PoCs moving into Production. According to some studies (e.g. Forbes, Everest, Gartner), the percentage of Gen AI PoCs failing is as high as 50%. We argue that that one of the key reasons for this failure is a lack of a comprehensive LLM evaluation strategy for the PoCs, with targeted success #metrics specific to the use-cases. The situation seems very similar to that of the seminal #MLOps paper 'Hidden Technical Debt in Machine Learning Systems' where researchers highlighted that training ML models forms only a small part of the overall ML training-to-deployment lifecycle. In the same way, assessing capabilities of the foundational LLMs is only a small part of performing use-case specific LLM evaluation of enterprise use-cases. In this article, we take the first steps towards defining a comprehensive LLM evaluation strategy focused on enterprise #usecases. It is a multi-faceted problem with the need to design use-case specific validation tests covering both functional and non-functions metrics, taking into account the underlying LLM, solution architecture (#RAG, fine-tuning), applicable regulations and enterprise #ResponsibleAI guidelines / policies.

#LLMs are powerful and enable new possibilities! But, if you are building an Enterprise product / application, the sheer number of options can leave you confused. Do you go open-source or rely on the proprietary models? Do you fine-tune a model or rely on prompt engineering? How do you manage the latency? Most importantly - how do you build responsibly? These can be intimidating - specially when there are new models releasing every day / week. This is what I discussed with Rohan Rao in latest episode of "Leading with Data". As always - extremely insightful to hear his experience and the framework he has built to navigate these challenges. Listen to this insightful episode here: https://lnkd.in/g7xVBmjz #generativeai #ai

Compliance with AI is a challenge with LLM hallucinations and generative AI "creativity." In this blog post series, you will learn how to benefit from AI's amazing power while controlling the answers LLM models give to meet your organization's strict compliance policies.

Are you (or your organization) thinking of integrating knowledge graphs and LLMs at the enterprise level? Steve Hedden shares a practical overview of the available methods.

This time, I’m exploring Agents from a high-level business perspective, setting the stage for implementation down the line. Before we get there, I’m looking into orchestration—how I plan to make these agents work together toward clear goals. https://lnkd.in/gsM-yAwA

With the rise of generative AI and Large Language Models (LLMs), a myriad of possibilities emerge, ranging from basic supervised learning tasks to intricate endeavors such as developing software engineering workflows using multi-agent LLM frameworks. As someone deeply passionate about NLP, I've been focused on extracting feature representations from text documents for various purposes over the past year. It's fascinating to ponder the potential value these web representations could offer to organizations and businesses. This post delves into the utilization of NLP techniques I've experimented with, starting from simple count vectors to more sophisticated LLM-driven methods like PEFT, aiming to efficiently capture features from business websites. Stay tuned for more insights as the project progresses! #NLP #AI #BusinessInsights

Curious about simplifying UNSPSC classification? Check out our short video to find out how AICA aids this process. https://lnkd.in/eZkpmapP #AICA #Data #AI #ML #UNSPSC #Classification #LLM #Automation

Curious about simplifying UNSPSC classification? Check out our short video to find out how AICA aids this process. https://lnkd.in/eZkpmapP #AICA?#Data?#AI?#ML?#UNSPSC?#Classification?#LLM?#Automation