Donna Amos’ Post

Interesting piece here on how training new LLM models on information created by Generative AI can cause the models to collapse. "Specifically, the models lose track of the less-common facts over generations, becoming more and more generic. As they do so, the answers they produce become totally irrelevant to the questions they ask, turning into effectively gibberish. 'Models start forgetting improbable events over time, as the model becomes poisoned with its own projection of reality.' https://lnkd.in/eS8q98CD

How can we use AI responsibly in the generative era? 🤔 This article from @AWSCloud has insights on fairness, intellectual property, toxicity, and more. If you'd like additional insights, feel free to contact ETI.

During my #LLMsinProductionIII presentation last week, I focused on the added necessary MLOPs complexity and cost required to increase reliability of production large language models. I predict GenAI hype will be replaced not just by cost and complexity of operationalizing these models but an erosion of data trust from developers cutting corners, building AI for the sake of chasing trends, and as a result putting out unsafe, unreliable, and unguarded AI apps out in the market. This article attacks my thesis from a different angle where they say generative Al will experience a reality check in 2024 as costs rise and risks emerge amid fading hype.

Generative AI platforms have three compounding problems: 1) It generates more questionable data 2) You are uncertain about the output 3) You need more technical FTEs to support. They aren't cheap. If you want to bet on something that's sustainable, this isn't it. It's as if you'd bet on "fuzzy logic" as your main data architecture. As much as generative AI appears like a powerful tool with FOMO use cases, it turns into a rather expensive power tool that you may use for drilling a wall for a frame once a year. Nail and a hammer (existing tool) generally can do that, if that's all you need.

This article sums up many of the thoughts I’ve had about the business model / utility problem I have with generative AI and LLMs. “the reality is that you can't build a hundred-billion-dollar industry around a technology that's kind of useful, mostly in mundane ways, and that boasts perhaps small increases in productivity if and only if the people who use it fully understand its limitations” Added irony: LinkedIn asking me to “rewrite with AI” - no thanks, I’ll make my own mistakes.

Great primer on AI agents...."Today, we mostly use LLMs in zero-shot mode, prompting a model to generate final output token by token without revising its work. This is akin to asking someone to compose an essay from start to finish, typing straight through with no backspacing allowed, and expecting a high-quality result. Despite the difficulty, LLMs do amazingly well at this task!" 

Audrey wrote a thought provoking blog on the topic of Generative AI. What are some of the good aspects of it, ethics around using it, and potential pitfalls. In the blog, "How to leverage Generative AI without getting your butt kicked", they write: "The best GAI system is going to augment what your team is already rocking — not conflicting with their creativity or growth, but wrangling things that computers never complain about, like sorting apples from oranges or generating large datasets for testing. There are interesting opportunities for ChatOps too. Prediction algorithms can do fairly well with mapping a range of possible queries to the information a user is looking for." Check it out here for more insights. https://lnkd.in/gZgMkmW2

Here it is!! Check out Audrey's rad GAI blog post. Let us know what you think.

Coda AI is Crushing It And by "it", I mean code and complex formulas. Here are two examples demonstrating why an AI-first attitude is required as we see generative AI emerge in no/low-code platforms. https://lnkd.in/gK-r_3T2

Choosing the right generative AI components is crucial for success. 🤖 From LLMs to embedding models, a thoughtful approach is needed to consider interoperability, obsolescence, and factors like open-source vs. commercial options. What keeps you up at night when choosing generative AI tools? #AI #machinelearning #discussion #GenAi #DataRobot