For the past few years, we've seen the importance of data literacy and why organizations must invest in a data-driven culture, mindset, and skillset. However, as generative AI tools like ChatGPT have risen to prominence in the past year, AI literacy has never been more important. But how do we begin to approach AI literacy? Is it an extension of data literacy, a complement, or a new paradigm altogether? How should you get started on your AI literacy ambitions?  Cindi Howson is the Chief Data Strategy Officer at ThoughtSpot and host of The Data Chief podcast. Cindi is a data analytics, AI, and BI thought leader and an expert with a flair for bridging business needs with technology. As Chief Data Strategy Officer at ThoughtSpot, she advises top clients on data strategy and best practices to become data-driven, speaks internationally on top trends such as AI ethics, and influences ThoughtSpot’s product strategy.

Cindi was previously a Gartner Research Vice President, the lead author for the data and analytics maturity model and analytics and BI Magic Quadrant, and a popular keynote speaker. She introduced new research in data and AI for good, NLP/BI Search, and augmented analytics, bringing both BI bake-offs and innovation panels to Gartner globally. She’s frequently quoted in MIT, Harvard Business Review, and Information Week. She is rated a top 12 influencer in big data and analytics by Analytics Insight, Onalytca, Solutions Review, and Humans of Data.

In the episode, Cindi and Adel discuss how generative AI accelerates an organization’s data literacy, how leaders can think beyond data literacy and start to think about AI literacy, the importance of responsible use of AI, how to best communicate the value of AI within your organization, what generative AI means for data teams, AI use-cases in the data space, the psychological barriers blocking AI adoption, and much more. 

Links Mentioned in the Show: The Data Chief Podcast  ThoughtSpot Sage  BloombergGPT  Radar: Data & AI Literacy Course: AI Ethics  Course: Generative AI Concepts Course: Implementing AI Solutions in Business 

We talked about:

Aleksander's background Aleksander as a Causal Ambassador Using causality to make decisions Counterfactuals and and Judea Pearl Meta-learners vs classical ML models Average treatment effect Reducing causal bias, the super efficient estimator, and model uplifting Metrics for evaluating a causal model vs a traditional ML model Is the added complexity of a causal model worth implementing? Utilizing LLMs in causal models (text as outcome) Text as treatment and style extraction The viability of A/B tests in causal models Graphical structures and nonparametric identification Aleksander's resource recommendations

Links:

The Book of Why: https://amzn.to/3OZpvBk Causal Inference and Discovery in Python: https://amzn.to/46Pperr Book's GitHub repo: https://github.com/PacktPublishing/Causal-Inference-and-Discovery-in-Python The Battle of Giants: Causality vs NLP (PyData Berlin 2023): https://www.youtube.com/watch?v=Bd1XtGZhnmw New Frontiers in Causal NLP (papers repo): https://bit.ly/3N0TFTL

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

Jerry Liu is the CEO and co-founder of LlamaIndex. LlamaIndex is an open-source framework that helps people prep their data for use with large language models in a process called retrieval augmented generation. LLMs are great decision engines, but in order for them to be useful for organizations, they need additional knowledge and context, and Jerry discusses how companies are bringing their data to tailor LLMs for their needs. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.  The Analytics Engineering Podcast is sponsored by dbt Labs.

Gen AI, LLMs, AI assistants and intelligent agents are powering next-generation customer experiences, transforming every business. But there is no AI without Data. And only the right data delivers accurate, relevant results, with the context, scale and security you need.

"I Love AI" will unlock the power of Generative AI for you, with unique insights into the data platform and AI solutions you need, delivered by experts with real-world experience making AI a reality. This virtual event will help application architects, software developers, practitioners and CTOs learn how to:

• Deliver AI outcomes with extreme accuracy and relevance
• Build Generative AI apps with scale, governance and data security
• Overcome the biggest obstacles keeping Gen AI from being enterprise ready
• Deploy powerful vector search capabilities at a fraction of the cost
• Use cutting edge innovations in the biggest, most powerful vector database

Gen AI, LLMs, AI assistants and intelligent agents are powering next-generation customer experiences, transforming every business. But there is no AI without Data. And only the right data delivers accurate, relevant results, with the context, scale and security you need.

"I Love AI" will unlock the power of Generative AI for you, with unique insights into the data platform and AI solutions you need, delivered by experts with real-world experience making AI a reality. This virtual event will help application architects, software developers, practitioners and CTOs learn how to:

• Deliver AI outcomes with extreme accuracy and relevance
• Build Generative AI apps with scale, governance and data security
• Overcome the biggest obstacles keeping Gen AI from being enterprise ready
• Deploy powerful vector search capabilities at a fraction of the cost
• Use cutting edge innovations in the biggest, most powerful vector database

Session 1 (Americas/EMEA): August 23, 2023, 10AM PDT / 1PM EDT. Gen AI, LLMs, AI assistants and intelligent agents powering next-generation customer experiences. This session will cover data platforms, AI solutions, vector search capabilities, governance and data security.

Session 2 (APAC/EMEA): August 24, 2023, 10AM CEST / 1:30PM IST / 4PM SGT / 6PM AEST. Gen AI, LLMs, AI assistants and intelligent agents powering next-generation customer experiences. This session will cover data platforms, AI solutions, vector search capabilities, governance and data security.

Whenever Kevin and I get together, we "nerd snipe" each other. This conversation is no different, and it's a wide-ranging conversation about how the data landscape evolves alongside LLMs, education, startup mentorship, and the possible (looming?) startup mass extinction.

Kevin's LinkedIn: https://www.linkedin.com/in/kevinzenghu/

Metaplane: https://metaplane.dev/

Send us a text "Insights from Luke Arrigoni, CEO of Arricor, on AI Innovations and Business Impact" Description: Welcome to an enlightening episode of our podcast as we dive into the fascinating world of Generative AI, Vision AI, and Natural Language Processing (NLP) with the esteemed Luke Arrigoni. In this Part 1 interview, Luke, Chief Executive Officer at Arricor, takes us on a journey through AI's transformative potential. Discover the minds behind AI advancements as we delve into topics like facial recognition for privacy, Arricor's mission, Prompt engineering, and the myriad use cases that these technologies unlock. Gain valuable insights into Large Language Models (LLMs) and the role of prompt engineering in optimizing AI's capabilities. Luke Arrigoni shares his expertise on avoiding AI hallucinations, the unique differentiation of Arricor, and the remarkable business impact of Generative AI. Join us to explore the present and future of AI through this engaging discussion. Don't miss this opportunity to gain insights from a visionary in the AI field. Connect with Luke Arrigoni on LinkedIn [https://www.linkedin.com/in/lukearrigoni/] and learn more about Arricor's work on their website [http://arricor.com/]. Stay tuned for Part 2 as we continue our conversation on AI's groundbreaking potential.

01:40 Meet Luke Arrigoni04:13 Facial recognition for privacy06:21 Arricor mission08:39 More on LLMs10:29 Prompt engineering13:30 Use cases16:30 Arricor differentiation20:59 Avoiding hallucinations26:13 Business impact of GenAILinkedIn:  https://www.linkedin.com/in/lukearrigoni/ Website: http://arricor.com/ Want to be featured as a guest on Making Data Simple?  Reach out to us at [email protected] and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. 

Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Talk on LLMs for Security Compliance Assessment.

Talk on LLM Live Book - Open Source Project Update.

Talk on Accelerating Model Development with LLMs.

Talk on LLMs and Data Privacy.

Generative AI is here to stay—even in the 8 months since the public release of ChatGPT, there are an abundance of AI tools to help make us more productive at work and ease the stress of planning and execution of our daily lives among other things.  Already, many of us are wondering what is to come in the next 8 months, the next year, and the next decade of AI’s evolution. In the grand scheme of things, this really is just the beginning. But what should we expect in this Cambrian explosion of technology? What are the use cases being developed behind the scenes? What do we need to be mindful of when training the next generations of AI? Can we combine multiple LLMs to get better results? Bal Heroor is CEO and Principal at Mactores and has led over 150 business transformations driven by analytics and cutting-edge technology. His team at Mactores are researching and building AI, AR/VR, and Quantum computing solutions for business to gain a competitive advantage. Bal is also the Co-Founder of Aedeon—the first hyper-scale Marketplace for Data Analytics and AI talent. In the episode, Richie and Bal explore common use cases for generative AI, how it's evolving to solve enterprise problems, challenges of data governance and the importance of explainable AI, the challenges of tracking the lineage of AI and data in large organizations. Bal also touches on the shift from general-purpose generative AI models to more specialized models, fascinating use cases in the manufacturing industry, what to consider when adopting AI solutions in business, and much more. Links mentioned in the show: PulsarTrifactaAWS Clarify[Course] Introduction to ChatGPT[Course] Implementing AI Solutions in Business[Course] Generative AI Concepts

We talked about:

Sandra's background Making a YouTube channel to break into the LLM space The business cases for LLMs LLMs as amplifiers The befits of keeping a human in the loop when using LLMs (AI limitations) Using LLMs as assistants Building an app that uses an LLM Prompt whisperers and how to improve your prompts Sandra's 7-day LLM experiment Sandra's LLM content recommendations Finding Sandra online

Links:

LinkedIn: https://www.linkedin.com/in/sandrakublik/ Twitter: https://twitter.com/sandra_kublik Youtube: https://www.youtube.com/@sandra_kublik

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

'Software is eating the world’ is a truism coined by Mark Andreesen, General Partner at Andreesen Horowitz. This was especially evident during the shift from analog mediums to digital at the turn of the century. Software companies have essentially usurped and replaced their non-digital predecessors. Amazon was the largest bookseller, Netflix was the largest movie "rental" service, Spotify or Apple were the largest music providers. Today, AI is starting to eat the world. However, we are still at the early start of the AI revolution, with AI set to become embedded in almost every piece of software we interact with. An AI ecosystem that touches every aspect of our lives is what today’s guest describes as ‘Ambient AI’. But what can we expect from this ramp up to Ambient AI? How will it change the way we work? What do we need to be mindful of as we develop this technology? Daniel Jeffries is the Managing Director of the AI Infrastructure Alliance and former CIO at Stability AI, the company responsible for Stable Diffusion, the popular open-source image generation model. He’s also an author, engineer, futurist, pro blogger and he’s given talks all over the world on AI and cryptographic platforms. In the episode, Adel and Daniel discuss how to define ambient AI, how our relationship with work will evolve as we become more reliant on AI, what the AI ecosystem is missing to rapidly scale adoption, why we need to accelerate the maturity of the open source AI ecosystem, how AI existential risk discourse takes away focus from real AI risk, and a lot lot more.

Links Mentioned in the Show Daniel’s Writing on MediumDaniel’s SubstackAI Infrastructure AllianceStability AIFrancois CholletRed Pajama DatasetRun AIWill Superintelligent AI End the World? By Eliezer Yudkowsky Nick Bostrom’s Paper Clip MaximizerThe pessimist archive [Course] Introduction to ChatGPT[Course] Implementing AI Solutions in Business

We talked about:

Meryam's background The constant evolution of startups How Meryam became interested in LLMs What is an LLM (generative vs non-generative models)? Why LLMs are important Open source models vs API models What TitanML does How fine-tuning a model helps in LLM use cases Fine-tuning generative models How generative models change the landscape of human work How to adjust models over time Vector databases and LLMs How to choose an open source LLM or an API Measuring input data quality Meryam's resource recommendations

Links:

Website: https://www.titanml.co/ Beta docs: https://titanml.gitbook.io/iris-documentation/overview/guide-to-titanml... Using llama2.0 in TitanML Blog: https://medium.com/@TitanML/the-easiest-way-to-fine-tune-and-inference-llama-2-0-8d8900a57d57 Discord: https://discord.gg/83RmHTjZgf Meryem LinkedIn: https://www.linkedin.com/in/meryemarik/

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

Data and AI are revolutionizing industries and transforming businesses at an unprecedented pace. These advancements pave the way for groundbreaking outcomes such as fresh revenue streams, optimized working capital, and captivating, personalized customer experiences.

Join Hugh Burgin, Luke Pritchard and Dan Diasio as we explore a range of real-world examples of AI and data-driven transformation opportunities being powered by Databricks, including business value realized and technical solutions implemented. We will focus on how to integrate and leverage business insights, a diverse network of cloud-based solutions and Databricks to unleash new business value opportunities. By highlighting real-world use cases we will discuss:

• Examples of how Manufacturing, Retail, Financial Services and other sectors are using Databricks services to scale AI, gain insights that matter and secure their data
• The ways data monetization are changing how companies view data and incentivizing better data management
• Examples of Generative AI and LLMs changing how businesses operate, how their customers engage, and what you can do about it

Talk by: Hugh Burgin and Luke Pritchard

Here’s more to explore: State of Data + AI Report: https://dbricks.co/44i2HBp The Data Team's Guide to the Databricks Lakehouse Platform: https://dbricks.co/46nuDpI

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksin