Ryanne Dolan

Great work complementing benchmarks which focus on output quality. Speed and cost effectiveness are equally crucial in productization to deliver customer value and business value!

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insightful diagram, and how as you move up the ladder you have to scale from hard skill to shift skills and business skills, on both sides you have to learn how to scale through others

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Longhorn Electric Racing Is Building the Future of Electric Vehicles with Cadence Tools. Longhorn Electric Racing is a student-run racing team at the University of Texas, Austin. Not only are they building the best race cars, but they’re also building the future generation of engineers. Their electric vehicle team is one of their most recent teams, founded in 2016. Some of the challenges they face in designing an electric car are making sure it can go as fast as possible, making sure it has sufficient cooling, and making models effectively. With the help of Cadence CFD and Cadence OnCloud, they’re reaching their goals much more efficiently. https://ow.ly/phqj50RuqK4

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This new little product demo is awesome! Our new Rev AI Transcript Assistant will save you TONS of time. Easily pull out nuggets of info from your transcripts, making valuable content more accessible than ever. https://lnkd.in/gzzFpcDa #transcription #asr #accessibility #transcript #summarization #ai #assistant

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Learn how to build an autonomous research assistant using LangGraph with a team of specialized AI agents #llms #chatgpt4 #gemini #aiml #llmops https://lnkd.in/d6TUbZNj

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New post! Probably the most desired enhancement for database privilege management is finally here in 23ai. #database #sql https://lnkd.in/gXkXGWWH

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Large language models (LLMs) are known to generate plausible sounding factually incorrect responses, especially when faced with unfamiliar concepts, queries. Recent research suggests that strategic fine-tuning and in-context learning, retrieval augmentation can significantly reduce these hallucinations. #llms #chatgpt4 #llmops #aiml #gemini #phi #mlops https://lnkd.in/duRrC3yg

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Generative AI and LLMs trained specifically for coding have allowed me to create with code again, and I'm so grateful. ❤️ 🤖 You see, my programming skills have been rusty for over a decade since I left an IC software engineering role. 😕 I've been using ChatGPT with Code Analyzer for data analysis, writing shell scripts, and simple Python with fewer iterations. Today, I wanted to tackle a bigger (for me) project and try out Github CoPilot. I prompted CoPilot to write code in the same way I would write code—in small pieces, focusing on adding complexity iteratively and validating each step. And y'all - coding was actually delightful again. So get back out there all you rusty programmers! ps - I'm building a script that will query ArXiv for recently published LLM-related research papers and posting a summary and a link into my GenAI Slack community. It's been a fun project to play with the OpenAI API, get experience with Google Cloud, test CoPilot's quality, and optimize summarization prompts. And once done, it will make it easier to stay on top of research 😍

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When *open* isn't *open source* 🙋🏼‍♂️👀 Reflecting on the open-source landscape for Large Language Models, I explore the essentials: permissive licensing, transparency in training data and model architecture & more #LLM #LLMs #AI #GenerativeAI #opensource https://lnkd.in/gMxbN4Nb

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This weekend I had the chance to finally read "The Engineering Executive's Primer : Impactful Technical Leadership" by Will Larson. Like his previous books, this was yet again another masterpiece. https://lnkd.in/e3JMHRTc The book targets individuals who are more mid to senior level managers, and have executive responsibilities where it times it could be that you have multiple functions within your area of scope. As a Staff+ engineer myself who reports into an executive leader, this helped me gain a different level of context. Here are some of the areas I found useful - Planning : I found the frameworks around what are the different levels of planning and connecting it to headcounts very useful. - There is a very nice mention of different kind of leadership stypes. I found that very insightful and now helps me form some abstract mental models around it. - Navigating the early part of an executive's career to understand an organization's challenges is something which is very unique to individuals who have a broad scope. That is also very helpful to individuals who do a Staff+ role as more often or not they take over a very wide scope. - I found the patterns in building network quite new to me. I was struggling personally a lot on how much time would I like to invest in Linkedin and forming a public image and if it is really necessary to be projected as a good leader. I am glad I found some answers which resonated to my thoughts in the book and something I would reflect on. - There are quite a lot of tips on how to not be projected as a micromanager even though you would need to evaluate the trust by going to certain details. This is something I learnt from working with Ofer Eisenberg who had a similar style when it comes to onboarding and I picked a lot from him . - And last call out for Hiring and recruitment. As someone without people responsibilities, I felt my knowledge on Hiring was fragmented where it would be heavily swayed towards the actual content of the interviews and the quality and evaluation techniques. Finally I saw a good concise content on wrapping my head around Hiring and connecting it with headcounts as well.

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If you are creating an LLM based agent/system and you find yourself creating many small rules to align the model behavior with your expectations, you quite possibly need to question the fundamental context you have given the model. Defining a clear role and task are 90% of the battle. This is because they essentially set the baseline behavior profile for the LLM prompt. Always be ready to question whether your role or task definition are mis-directing the model. "The LLM, a polymorphic chorus, harmonizes beautifully with the right notes but falters on the wrong ones. — ChatGPT"

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I think one of the strangest things in the #data world is the discrepancy between how important #dimensionalmodeling still is and how much it is taught (or not taught) to engineers and scientists. We are using some nifty modern tools at Abre to enable a new feature. Despite how modern and sophisticated these tools are and how much they try to simplify the process of extracting value from data, they still require knowledge of fact tables, dimension tables, chasm gaps, etc. Yet, these are often completely foreign concepts to many #softwareengineers and even many #dataengineers. I know Zach Wilson prioritizes data modeling skills in his classes. More instructors should mirror his example.

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Pydantic is sponsoring Frank Hoffmann because inline-snapshots is transformative to how we write unit tests in Python! https://lnkd.in/en6HtXCF If you're using pytest, and you're not using inline-snapshots, you're wasting your time; simple as that.

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sharpening writing skill is one of killer skill!

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I just read this quick article on Bias in AI and how to tackle this problem. I definitely agree that many of the models out there are simply trained on "tons of data" that is not necessarily thoroughly vetted. The article suggests that the problem with Bias in AI will be tackled by understanding the data we feed the models, cleaning/filtering it, etc. In other words "Bias In, Bias Out". That's definitely something I like about how we are training our internal models at Rev. Our dataset is perfectly aligned with the problem we are trying to solve, thus directly vetted and filtered by the amazing people working on our platform. Bias in AI is one of the three biggest issues with AI today. And Sasha Luccioni presented this in a TED talk I shared recently (Look it up "AI is dangerous, but not for the reasons you think"): * Emitting carbon * Infringing copyrights * And spreading biased information Another thing I like to ponder with this... Are we even trying to solve the right problems with some of these GenAI models? Let's keep educating ourselves on the problems and do our best to leave things better than we found them. https://lnkd.in/g_hmP428 #biasinai #ted #bias #asr #ai #genai

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This week, in the Developer Keynote at Google I/O '24 we revealed and launched Data Science Agent, a new Labs experiment on labs.google/code Data Science Agent is powered by Gemini and designed using agentic flows, planning and orchestration to tackle open-ended data science tasks and data explorations. DSA uses Gemini for planning, task decomposition, reasoning and code generation for solving complex tasks and explicitly relies on code execution for feedback and error correction. Some features and capabilities: ◆ Automates and front-loads data cleaning and data wrangling for downstream tasks and analysis. ◆ Gets you started with exploratory data analysis, statistical analysis and predictive modeling.  ◆ Generates plots and visuals to bootstrap your data insights and data summaries.  ◆ Speeds-up your working with libraries for data science.  ◆ Offers an end-to-end path to querying your data in natural language.  ◆ Outputs a fully AI-generated Colab notebook that you can share, fork, and come back to later. Thrilled to have worked with the extremely talented AI coding team and leads at Labs on this fun and ambitious project: Kathy K. Shrestha Basu Mallick Xin Xie Jiahao Cai Beichen Zhang Preetham Mysore Edwin Lee Mitchell A. Gordon Hongxu Ma Roykrong Sukkerd George Zhang Yu Feng Siqi Zuo Owen Lewis Ravin Kumar Ilai Soloducho Ale Jakse Hartman Xiang Deng Jasmine George Vincent Nallatamby Wes Dyer Bin Ni Kelly Schaefer 🇺🇦 Scott Huffman. Catch the reveal here: https://lnkd.in/gC3rpJyN and try it out at: labs.google/code. #google #googleio2024 #developers #datascience #googlegemini #GoogleIO #google #gemini #aiagents #agents #llms #ai

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