Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. DataTopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. In this special one-year anniversary episode, we reminisce about our journey and dive into some intriguing tech stories: WordPress Governance Drama: We discuss recent issues with WordPress. Find out what’s behind the Automattic and WP Engine tension.Astral’s Business Model: Charlie Marsh shares insights into how Astral plans to balance open-source ideals with profitability.Deno 2.0 Release: Deno 2.0 claims to be a “Cargo for JavaScript.” Check out its new features and see how it compares to Node.js.OpenAI’s Soaring Valuation: OpenAI has hit a staggering $150 billion valuation after raising $6.5 billion in new funding.Adobe’s GenAI Policy: Adobe clarified their stance on GenAI, ensuring Firefly is only trained on stock images to support creators.Instructor Library for LLMs: Discover the Instructor library for turning unstructured data into structured outputs with ease.Repo2txt Tool: Convert your GitHub repo into a single text file using Repo2txt for easy analysis.Retro PC Fonts Galore: Explore a treasure trove of vintage fonts with the Ultimate Old-School PC Font Pack.Bop Spotter – Cultural Surveillance: Bop Spotter uses Shazam to capture the music trends and cultural vibes of San Francisco’s Mission District.

Former co-host Julia Schottenstein returns to the show to go deep into the world of LLMs. Julia joined LangChain as an early employee, in Tristan's words, to "Basically solve all of the problems that aren't specifically in product and engineering." LangChain has become one of, if not the primary frameworks for developing applications using large language models. There are over a million developers using LangChain today, building everything from prototypes to production AI applications.

Welcome to the Data Engineering Central Podcast —— a no-holds-barred discussion on the Data Landscape. Welcome to Episode 02 In today’s episode, we will talk about the following topics from the Data Engineering perspective … * Using OpenAI’s o1 Model to do Data Engineering work * Lord Save us from more ETL tools * Rust for the small things * Hosted (SaaS) vs Build

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit dataengineeringcentral.substack.com/subscribe

John Gleeson, COO of Storj, joins us on this episode of the Data Unchained podcast live from NAB! John talks with us about how bringing together organizations availale bandwidth and storage at lower costs with lower carbon footprints while also unifying data sets and getting the most value out of your data.

data #datascience #dataanalytics #AI #artificialintelligence #storage #genai #LLM #podcast #datastorage #technology #innovation #bandwidth #carbonfootprint #carbonfootprintreduction

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Está no ar, o Data Hackers News !! Os assuntos mais quentes da semana, com as principais notícias da área de Dados, IA e Tecnologia, que você também encontra na nossa Newsletter semanal, agora no Podcast do Data Hackers !!

Aperte o play e ouça agora, o Data Hackers News dessa semana !

Para saber tudo sobre o que está acontecendo na área de dados, se inscreva na Newsletter semanal:

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Conheça nossos comentaristas do Data Hackers News:

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⁠Matérias/assuntos comentados:

CTO da OpenAI, Mira Murati anuncia saída da empresa;

Meta anuncia Orion, primeiro par de óculos de realidade aumentada;

Google lança novos Chromebooks com IA.

Baixe o relatório completo do State of Data Brazil e os highlights da pesquisa :

Demais canais do Data Hackers:

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Send us a text More on GenAI, Hallucinations, RAG, Use Cases, LLMs, SLMs and costs with Armand Ruiz, Director watsonx Client Engineering and John Webb, Principal Client Engineering.  With this and the previous episode you'll be wiser on AI than 98% of the world.

00:12 Hallucinations02:33 RAG Differentiation06:41 Why IBM in AI09:23 Use Cases11:02 The GenAI Resume13:37 watson.x 15:40 LLMs17:51 Experience Counts20:03 AI that Surprises23:46 AI Skills26:47 Switching LLMs27:13 The Cost and SLMs28:21 Prompt Engineering29:16 For FunLinkedIn: linkedin.com/in/armand-ruiz, linkedin.com/in/john-webb-686136127 Website: https://www.ibm.com/client-engineering

Love what you're hearing? Don't forget to rate us on your favorite platform! 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. 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.

Episode Summary: In this episode, we dive into the exciting world of AI and Large Language Models (LLMs) and how they're revolutionizing marketing. Gone are the days of generic campaigns and guesswork. With AI, marketing is becoming highly personalized, insight-driven, and responsive to individual customer needs—all in real-time. Key Points Covered: * The Shift from Data-Driven to Insight-Driven MarketingDiscover how marketing is evolving from simply collecting data to understanding the "why" behind customer behavior. AI allows marketers to predict customer preferences, making campaigns more targeted and effective. * AI-Powered Personalization at ScaleLearn how AI can dig into customer data to deliver hyper-personalized experiences, like suggesting a product based on your previous purchases, time of day, or even the weather in your location. * Customer Journey Mapping with AIAI is now capable of mapping every step of a customer’s interaction with a brand, from the first website visit to the final purchase, helping marketers identify friction points and optimize the entire journey. * The Power of Real-Time AI DashboardsForget the overwhelming spreadsheets! AI-powered dashboards are the new standard, delivering clear, actionable insights in real-time across all marketing channels. * Ethical Considerations in AI-Driven MarketingWith great power comes great responsibility. We explore how marketers can walk the fine line between personalization and privacy, and why transparency and trust are critical in this AI-powered era. * The Future of AI in Customer ExperienceFrom chatbots that truly understand your needs to online shopping experiences that adapt to you, AI is poised to make our everyday interactions with brands smoother and more enjoyable. Memorable Quote:"It’s like having a dedicated marketing team for every single customer." Ethical Discussion:We discuss the responsibility marketers have in ensuring AI respects data privacy and builds trust with consumers. Regulations like GDPR are setting important standards, but it’s up to each brand to find the balance between personalization and privacy. Final Thought:As AI continues to reshape the marketing landscape, it's crucial for brands and customers alike to stay informed, ask questions, and participate in the conversation about how these technologies are used. Have thoughts on how AI is transforming marketing? Share your insights with us, and stay curious for the next episode as we dive deeper into the world of AI, marketing, and beyond. Send me an email at [email protected] This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit mukundansankar.substack.com

Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. We dive into conversations smoother than your morning coffee (but let’s be honest, just as caffeinated) where industry insights meet light-hearted banter. Whether you’re a data wizard or just curious about the digital chaos around us, kick back and get ready to talk shop—unplugged style! In this episode: Farewell Pandas, Hello Future: Pandas is out, and Ibis is in. We're talking faster, smarter data processing—featuring the rise of DuckDB and the powerhouse that is Polars. Is this the end of an era for Pandas?UV vs. Rye: Forget pip—are these new Python package managers built in Rust the future? We break down UV, Rye, and what it all means for your next Python project.AI-Generated Podcasts: Is AI about to take over your favorite podcasts? We explore the potential of Google’s Notebook LM to transform content into audio gold.When AI Steals Your Voice: Jeff Geerling’s voice gets cloned by AI—without his consent. We dive into the wild world of voice cloning, the ethics, and the future of AI-generated media.Hacking AI with Prompt Injection: Could you outsmart AI? We share some wild strategies from the game Gandalf that challenge your prompt injection skills and teach you how to jailbreak even the toughest guardrails.Jony Ive’s New Gadget Rumor: Is Jony Ive plotting an Apple killer? Rumors are swirling about a new AI-powered handheld device that could shake up the smartphone market.Zero-Downtime Deployments with Kamal Proxy: No more downtime! We geek out over Kamal Proxy, the sleek HTTP tool designed for effortless Docker deployments.Function Calling and LLMs: Get ready for the next evolution in AI—function calling. We discuss its rise in LLMs and dive into the Gorilla project, the leaderboard testing the future of smart APIs.

Retrieval is the process of searching for a given item (image, text, …) in a large database that are similar to one or more query items. A classical approach is to transform the database items and the query item into vectors (also called embeddings) with a trained model so that they can be compared via a distance metric. It has many applications in various fields, e.g. to build a visual recommendation system like Google Lens or a RAG (Retrieval Augmented Generation), a technique used to inject specific knowledge into LLMs depending on the query. Vector databases ease the management, serving and retrieval of the vectors in production and implement efficient indexes, to rapidly search through millions of vectors. They gained a lot of attention over the past year, due to the rise of LLMs and RAGs.

Although people working with LLMs are increasingly familiar with the basic principles of vector databases, the finer details and nuances often remain obscure. This lack of clarity hinders the ability to make optimal use of these systems.

In this talk, we will detail two examples of real-life projects (Deduplication of real estate adverts using the image embedding model DinoV2 and RAG for a medical company using the text embedding model Ada-2) and deep dive into retrieval and vector databases to demystify the key aspects and highlight the limitations: HSNW index, comparison of the providers, metadata filtering (the related plunge of performance when filtering too many nodes and how indexing partially helps it), partitioning, reciprocal rank fusion, the performance and limitations of the representations created by SOTA image and text embedding models, …

Transformers are everywhere: NLP, Computer Vision, sound generation and even protein-folding. Why not in forecasting? After all, what ChatGPT does is predicting the next word. Why this architecture isn't state-of-the-art in the time series domain?

In this talk, you will understand how Amazon Chronos and Salesforece's Moirai transformer-based forecasting models work, the datasets used to train them and how to evaluate them to see if they are a good fit for your use-case.

In the rapidly evolving landscape of Artificial Intelligence (AI), open source and openness AI have emerged as crucial factors in fostering innovation, transparency, and accountability. Mistral AI's release of the open-weight Mistral 7B model has sparked significant adoption and demand, highlighting the importance of open-source and customization in building AI applications. This talk focuses on the Mistral AI model landscape, the benefits of open-source and customization, and the opportunities for building AI applications using Mistral models.

The first episode of The Pragmatic Engineer Podcast is out. Expect similar episodes every other Wednesday. You can add the podcast in your favorite podcast player, and have future episodes downloaded automatically. Listen now on Apple, Spotify, and YouTube. Brought to you by: • Codeium: ​​Join the 700K+ developers using the IT-approved AI-powered code assistant. • TLDR: Keep up with tech in 5 minutes — On the first episode of the Pragmatic Engineer Podcast, I am joined by Simon Willison. Simon is one of the best-known software engineers experimenting with LLMs to boost his own productivity: he’s been doing this for more than three years, blogging about it in the open. Simon is the creator of Datasette, an open-source tool for exploring and publishing data. He works full-time developing open-source tools for data journalism, centered on Datasette and SQLite. Previously, he was an engineering director at Eventbrite, joining through the acquisition of Lanyrd, a Y Combinator startup he co-founded in 2010. Simon is also a co-creator of the Django Web Framework. He has been blogging about web development since the early 2000s. In today’s conversation, we dive deep into the realm of Gen AI and talk about the following:  • Simon’s initial experiments with LLMs and coding tools • Why fine-tuning is generally a waste of time—and when it’s not • RAG: an overview • Interacting with GPTs voice mode • Simon’s day-to-day LLM stack • Common misconceptions about LLMs and ethical gray areas  • How Simon’s productivity has increased and his generally optimistic view on these tools • Tips, tricks, and hacks for interacting with GenAI tools • And more! I hope you enjoy this episode. — In this episode, we cover: (02:15) Welcome (05:28) Simon’s ‘scary’ experience with ChatGPT (10:58) Simon’s initial experiments with LLMs and coding tools (12:21) The languages that LLMs excel at (14:50) To start LLMs by understanding the theory, or by playing around? (16:35) Fine-tuning: what it is, and why it’s mostly a waste of time (18:03) Where fine-tuning works (18:31) RAG: an explanation (21:34) The expense of running testing on AI (23:15) Simon’s current AI stack  (29:55) Common misconceptions about using LLM tools (30:09) Simon’s stack – continued  (32:51) Learnings from running local models (33:56) The impact of Firebug and the introduction of open-source  (39:42) How Simon’s productivity has increased using LLM tools (41:55) Why most people should limit themselves to 3-4 programming languages (45:18) Addressing ethical issues and resistance to using generative AI (49:11) Are LLMs are plateauing? Is AGI overhyped? (55:45) Coding vs. professional coding, looking ahead (57:27) The importance of systems thinking for software engineers  (1:01:00) Simon’s advice for experienced engineers (1:06:29) Rapid-fire questions — Where to find Simon Willison: • X: https://x.com/simonw • LinkedIn: https://www.linkedin.com/in/simonwillison/ • Website: https://simonwillison.net/ • Mastodon: https://fedi.simonwillison.net/@simon — Referenced: • Simon’s LLM project: https://github.com/simonw/llm • Jeremy Howard’s Fast Ai: https://www.fast.ai/ • jq programming language: https://en.wikipedia.org/wiki/Jq_(programming_language) • Datasette: https://datasette.io/ • GPT Code Interpreter: https://platform.openai.com/docs/assistants/tools/code-interpreter • Open Ai Playground: https://platform.openai.com/playground/chat • Advent of Code: https://adventofcode.com/ • Rust programming language: https://www.rust-lang.org/ • Applied AI Software Engineering: RAG: https://newsletter.pragmaticengineer.com/p/rag • Claude: https://claude.ai/ • Claude 3.5 sonnet: https://www.anthropic.com/news/claude-3-5-sonnet • ChatGPT can now see, hear, and speak: https://openai.com/index/chatgpt-can-now-see-hear-and-speak/ • GitHub Copilot: https://github.com/features/copilot • What are Artifacts and how do I use them?: https://support.anthropic.com/en/articles/9487310-what-are-artifacts-and-how-do-i-use-them • Large Language Models on the command line: https://simonwillison.net/2024/Jun/17/cli-language-models/ • Llama: https://www.llama.com/ • MLC chat on the app store: https://apps.apple.com/us/app/mlc-chat/id6448482937 • Firebug: https://en.wikipedia.org/wiki/Firebug_(software)# • NPM: https://www.npmjs.com/ • Django: https://www.djangoproject.com/ • Sourceforge: https://sourceforge.net/ • CPAN: https://www.cpan.org/ • OOP: https://en.wikipedia.org/wiki/Object-oriented_programming • Prolog: https://en.wikipedia.org/wiki/Prolog • SML: https://en.wikipedia.org/wiki/Standard_ML • Stabile Diffusion: https://stability.ai/ • Chain of thought prompting: https://www.promptingguide.ai/techniques/cot • Cognition AI: https://www.cognition.ai/ • In the Race to Artificial General Intelligence, Where’s the Finish Line?: https://www.scientificamerican.com/article/what-does-artificial-general-intelligence-actually-mean/ • Black swan theory: https://en.wikipedia.org/wiki/Black_swan_theory • Copilot workspace: https://githubnext.com/projects/copilot-workspace • Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems: https://www.amazon.com/Designing-Data-Intensive-Applications-Reliable-Maintainable/dp/1449373321 • Bluesky Global: https://www.blueskyglobal.org/ • The Atrocity Archives (Laundry Files #1): https://www.amazon.com/Atrocity-Archives-Laundry-Files/dp/0441013651 • Rivers of London: https://www.amazon.com/Rivers-London-Ben-Aaronovitch/dp/1625676158/ • Vanilla JavaScript: http://vanilla-js.com/ • jQuery: https://jquery.com/ • Fly.io: https://fly.io/ — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

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For some natural language processing (NLP) tasks, based on your production constraints, a simpler custom model can be a good contender to off-the-shelf large language models (LLMs), as long as you have enough qualitative data to build it. The stumbling block being how to obtain such data? Going over some practical cases, we will see how we can leverage the help of LLMs during this phase of an NLP project. How can it help us select the data to work on, or (pre)annotate it? Which model is suitable for which task? What are common pitfalls and where should you put your efforts and focus?

Graph Retrieval Augmented Generation (Graph RAG) is emerging as a powerful addition to traditional vector search retrieval methods. Graphs are great at representing and storing heterogeneous and interconnected information in a structured manner, effortlessly capturing complex relationships and attributes across different data types. Using open weights LLMs removes the dependency on an external LLM provider while retaining complete control over the data flows and how the data is being shared and stored. In this talk, we construct and leverage the structured nature of graph databases, which organize data as nodes and relationships, to enhance the depth and contextuality of retrieved information to enhance RAG-based applications with open weights LLMs. We will show these capabilities with a demo.

Retrieval-augmented generation (RAG) has become a key application for large language models (LLMs), enhancing their responses with information from external databases. However, RAG systems are prone to errors, and their complexity has made evaluation a critical and challenging area. Various libraries (like RAGAS and TruLens) have introduced evaluation tools and metrics for RAGs, but these evaluations involve using one LLM to assess another, raising questions about their reliability. Our study examines the stability and usefulness of these evaluation methods across different datasets and domains, focusing on the effects of the choice of the evaluation LLM, query reformulation, and dataset characteristics on RAG performance. It also assesses the stability of the metrics on multiple runs of the evaluation and how metrics correlate with each other. The talk aims to guide users in selecting and interpreting LLM-based evaluations effectively.

In the last year there hasn’t been a day that passed without us hearing about a new generative AI innovation that will enhance some aspect of our lives. On a number of tasks large probabilistic systems are now outperforming humans, or at least they do so “on average”. “On average” means most of the time, but in many real life scenarios “average” performance is not enough: we need correctness ALL of the time, for example when you ask the system to dial 911.

In this talk we will explore the synergy between deterministic and probabilistic models to enhance the robustness and controllability of machine learning systems. Tailored for ML engineers, data scientists, and researchers, the presentation delves into the necessity of using both deterministic algorithms and probabilistic model types across various ML systems, from straightforward classification to advanced Generative AI models.

You will learn about the unique advantages each paradigm offers and gain insights into how to most effectively combine them for optimal performance in real-world applications. I will walk you through my past and current experiences in working with simple and complex NLP models, and show you what kind of pitfalls, shortcuts, and tricks are possible to deliver models that are both competent and reliable.

The session will be structured into a brief introduction to both model types, followed by case studies in classification and generative AI, concluding with a Q&A segment.

How is data playing a part of the future of AI security? Where is private data hidden? Where should your company start when thinking about integrating AI and Gen AI into their technologies? Thomas Ryan, Chief Executive Officer and Founder of Bigly Sales Inc. joins us on this episode to discuss the status of data privacy with the advent of AI.

data #datascience #dataanalytics #AI #artificialintelligence #security #genai #LLM #podcast #datastorage #technology #innovation

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This session explores Gemini's capabilities, architecture, and performance benchmarks. We'll delve into the significance of its extensive context window and address the critical aspects of safety, security, and responsible AI use. Hallucination, a common concern in LLM applications, remains a focal point of ongoing development. This talk will highlight recent advancements aimed at mitigating the risk of hallucination to enhance LLMs utility across various applications.

Learn how to automatically scale LLMs in production, optimize your resource usage, and improve performance for your AI-driven applications.