Weston Stacey’s Post

AI and ML technologies have the power to revolutionize government functions such as education, health care, and defense. By leveraging computer vision and machine learning, we can improve efficiency, reduce costs, and enhance decision-making processes. Let's explore the potential of these tools and how they can benefit society. #AI #ML #Machinevision

Second WP this week: What really changed with Gen-AI compared to "classic" ML systems: a technical perspective This White Paper delves into the nuanced distinctions between Generative AI (Gen-AI) systems and traditional machine learning (ML) algorithms, emphasizing that Gen-AI brings its own set of unique challenges and an endless number of opportunities. It highlights the low barrier to entry for Gen-AI systems, allowing for rapid proof of concept development, yet underscores the complexity and effort required for scalable deployment due to the intricacies of system components and their interactions. Reliability emerges as a significant concern, particularly given the non-deterministic nature of LLM-based systems and their propensity for "hallucinating" or producing inaccurate outputs, necessitating innovative approaches to ensure system reliability. Interpretability, or understanding why an AI system makes certain decisions, is identified as a critical challenge, impacting trust and the ability to improve models. The post also touches on the complexity introduced by the myriad of parameters and hyperparameters in Gen-AI systems, from prompting strategies to agent learning techniques, highlighting the ongoing need for deep engineering work to normalize data and clarify semantics. https://lnkd.in/ewq8PbhF

World of Generative AI 🤖 Interesting White Paper which delves into the nuanced distinctions between Generative AI (Gen-AI) systems and traditional machine learning (ML)

Is this AI's next big leap? A potential blueprint for perfect recall within LLMs has been proposed by Princeton University and Meta AI. If you've ever tried using LLMs to really integrate long documents you will know it has a short attention span. In a world drowning in data we're not just wanting AI to skim tweets; we're tasking it with undersatnding legal papers, scientific research, long form company documents and endless unstructured data. The demand for AI that can process lengthy sequences of information is peaking, and here's how the latest research is stepping up to the plate: > AI's current kryptonite? 'Context windows'. It loses the thread in sprawling texts. > Enter the memory tree: a proposed structure which could endow AI with the ability to retain and navigate entire documents. > It's a blueprint, not yet reality. But it's a peek into an AI future where full comprehension isn't just a wish it's a reality. This is aligned to the requirements and projects we're working on with clients right now. Will this approach solve it? Who knows, but one thing you can be sure of is that it will be solved. AI is evolving quickly becuase the real world use cases, with actual ROI, are emerging. If you're interested in the research paper the link is in the comments. And if you need help with your AI strategy let us know. #AI #innovation #research #insights

Happy to announce the completion of my Generative AI Concepts Course from #datacamp It was a really exciting course and i look forward to diving more deeply into the new world of Generative AI. A few key points I learnt: ⬜ The Evolution and Factors driving Generative AI. ⬛ Model design and data collection(Model design and research, data preparation , model training and model evaluation) ⬜ Evaluating and mitigating social bias, Legal and privacy consideration, Responsible Generative AI applications, AGI(Artificial General Intelligence) ⬛ Bringing New AI into old workflows, Progress in Generative AI, The future with Generative AI. #datascience #machinelearningalgorithms #llm #generativeartificialintelligence

☕️💡A very Good morning read: 1- “AI” or “a robot” is never, ever going to take your job. It can’t. It’s not sentient, or capable of making decisions. 2- In some fields, the disruption wrought by generative AI may be temporary. The tech might not pan out. 3- AI scientists like Melanie Mitchell repeatedly caution that we need to be intensely skeptical of claims that, say, LLMs are better than humans at basic tasks. And it appears, as noted by the cognitive scientist Gary Marcus and others, that the AI models aren’t improving as fast as they had in the past — they may in fact be plateauing. If that’s the case, then a lot of these models will be stuck at reliability rates of around 80% — enough for noncritical text and image generation, but nothing a serious business would want to rely on for important documents or public-facing materials. 👀⚠️Let’s be realistic and discuss the why and the how about AI/ML for solving real business problems. https://lnkd.in/dEZPP2Ke

Is your AI startup dedicating a third of your funds to AI risk management?

Readers otherwise unaware there are other versions of AI than the GPT model variety hard at work today should take a few moments to read Bruce Davie's "Forget the AI doom and hype, let's make computers useful", https://lnkd.in/exBMvfnK Reading Davie's story should also provide an historical context for today's AI craze - the scale of today's contagion may be much bigger, but echoes of an earlier AI summer, this one back in the early 1980s, might help one tamp down the lure of irrational exuberance. If nothing else, Davie's story does position GPT models as one of several iterations of machine learning solutions. For readers who also invest and trade publicly traded stocks (like me) the story highlights reasons why otherwise promising pockets of trading opportunities based on businesses running on machine learning offers fail to materialize. Without the GPT model brand so-called "whales" have strongly resisted any temptation to invest. What might change the story for businesses like C3.ai, or Palantir (disclaimer: I am trading Palantir call options at present)? #machinelearning #ai #gptmodels #historyofai

1. Open up a Google search 2. Search 'Teacher' images 3. Search 'Professor' images How can AI/ML operators resolve this? With over 50,000 known variations of fairness, I'm eager to understand more about how organizations are countering data bias, model bias, and learning objective bias within their ML models. If you're aware of organizations making strides, particularly at the intersection of Human Capital Management and Higher Learning, please drop a note in the comments or DM me. #LeadingSafe #MakingAIWork #ArtificialIntelligence