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🌟 Session Overview 🌟

Session Name: Beyond the Hype: Realistic Expectations of AI Speaker: Andreas Buhlmeier, Atita Arora, Stephen Batifol, Peter Farkas, Thomas Schmidt, Zamina Ahmad Session Description: Panel Discussion will focus on separating the myths from the realities surrounding Artificial Intelligence. Expert panelists will provide a balanced perspective on what AI can truly achieve today, addressing both its capabilities and limitations.

🚀 About Big Data and RPA 2024 🚀

Unlock the future of innovation and automation at Big Data & RPA Conference Europe 2024! 🌟 This unique event brings together the brightest minds in big data, machine learning, AI, and robotic process automation to explore cutting-edge solutions and trends shaping the tech landscape. Perfect for data engineers, analysts, RPA developers, and business leaders, the conference offers dual insights into the power of data-driven strategies and intelligent automation. 🚀 Gain practical knowledge on topics like hyperautomation, AI integration, advanced analytics, and workflow optimization while networking with global experts. Don’t miss this exclusive opportunity to expand your expertise and revolutionize your processes—all from the comfort of your home! 📊🤖✨

📅 Yearly Conferences: Curious about the evolution of QA? Check out our archive of past Big Data & RPA sessions. Watch the strategies and technologies evolve in our videos! 🚀 🔗 Find Other Years' Videos: 2023 Big Data Conference Europe https://www.youtube.com/playlist?list=PLqYhGsQ9iSEpb_oyAsg67PhpbrkCC59_g 2022 Big Data Conference Europe Online https://www.youtube.com/playlist?list=PLqYhGsQ9iSEryAOjmvdiaXTfjCg5j3HhT 2021 Big Data Conference Europe Online https://www.youtube.com/playlist?list=PLqYhGsQ9iSEqHwbQoWEXEJALFLKVDRXiP

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🌐 Website: https://bigdataconference.eu/, https://rpaconference.eu/ 👤 Facebook: https://www.facebook.com/bigdataconf, https://www.facebook.com/rpaeurope/ 🐦 Twitter: @BigDataConfEU, @europe_rpa 🔗 LinkedIn: https://www.linkedin.com/company/73234449/admin/dashboard/, https://www.linkedin.com/company/75464753/admin/dashboard/ 🎥 YouTube: http://www.youtube.com/@DATAMINERLT

** Important RSVP here. (Due to room capacity and building security, it's required to pre-register at the link for admission).

Welcome to the monthly in-person AI meetup in Berlin, in collaboration with Thoughtworks. Join us for deep dive tech talks on AI, GenAI, LLMs and machine learning, networking with speakers and fellow developers.

Agenda: * 6:00pm\~7:00pm: Checkin and networking * 7:00pm\~9:00pm: Tech talks and Q&A * 9:00pm: Open discussion and Mixer

Tech Talk: Building an LLM agent to chat with the Berlin Parliament Speaker: Stephen Batifol (Zilliz) Abstract: Lot of things are happening in the Berlin Parliament and you might not be aware of it. By using LLMs and a Vector DB, I'll show you how you can chat and interact with the data of the Berlin Parliament.

Tech Talk: Improving the Usefulness of LLMs with RAG Speaker: Kristian Aune (Vespa) Abstract: LLMs like GPT can give useful answers to many questions, but there are also well-known issues with their output: The responses may be outdated, inaccurate, or outright hallucinations. And they don’t know anything about you or your organization private data (we hope). RAG can help reduce the problems with “hallucinated” answers, and make the responses more up-to-date, accurate, and personalized - by injecting related knowledge, including non-public data. In this talk, we’ll go through what RAG means, demo some ways you can implement it - and warn of some traps you still have to watch out for.

Tech Talk: Boosting Automation - An AI Engineer’s Open Source Toolbox Speaker: Thomas Kranzkowski (CLOUDETEER) Abstract: TBD

Speakers/Topics: Stay tuned as we are updating speakers and schedules. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics

Sponsors: We are actively seeking sponsors to support AI developers community. Whether it is by offering venue spaces, providing food, or cash sponsorship. Sponsors will not only speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 8,000+ AI developers in Berlin or 350K+ worldwide.

Community on Slack/Discord - Event chat: chat and connect with speakers and attendees - Sharing blogs\, events\, job openings\, projects collaborations

• Join Slack/Discord (link is at the bottom of the page) *

This Meetup is presented by our friends from AI Camp Berlin. For more information and to help us keep track, please register via the event page of AICamp here: Link

Welcome to the monthly in-person AI meetup in Berlin! Join us for deep dive tech talks on AI, GenAI, LLMs and machine learning, food/drink, networking with speakers and fellow developers.

On the Agenda: 6:00pm\~7:00pm: Checkin, Food/drink and Networking 7:00pm\~9:00pm: Tech talks and Q&A 9:00pm: Open discussion and Mixer

About the evening

Building an LLM agent to chat with the Berlin Parliament by Stephen Batifol (Zilliz) Lot of things are happening in the Berlin Parliament and you might not be aware of it. By using LLMs and a Vector DB, I'll show you how you can chat and interact with the data of the Berlin Parliament.

Improving the Usefulness of LLMs with RAG by Kristian Aune (Vespa) LLMs like GPT can give useful answers to many questions, but there are also well-known issues with their output: The responses may be outdated, inaccurate, or outright hallucinations. And they don’t know anything about you or your organization private data (we hope). RAG can help reduce the problems with “hallucinated” answers, and make the responses more up-to-date, accurate, and personalized - by injecting related knowledge, including non-public data. In this talk, we’ll go through what RAG means, demo some ways you can implement it - and warn of some traps you still have to watch out for.

Boosting Automation - An AI Engineer’s Open Source Toolbox by Thomas Kranzkowski (CLOUDETEER) The world of data technologies and AI is moving faster and faster, which means that the profile of an AI engineer is constantly changing. Find out which open-source tools and skills you need to keep up with the times in this talk.

------ Important note: To keep the overview we close the registration on this page - please register via the AI Camp page here: Link

------ Speakers/Topics: Stay tuned as we are updating speakers and schedules. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics

AICamp Community on Slack/Discord - Event chat: chat and connect with speakers and attendees - Sharing blogs\, events\, job openings\, projects collaborations Join Slack (search and join the #berlin channel) \| Join Discord

------ Code of Conduct We adhere to the Berlin Code of Conduct to ensure a welcoming and respectful environment for all participants. The event space operates under largely compatible Thoughtworks Meetups & Events CoC.

Accessibility The Location is accessible for wheelchair users. This includes the entrance (no steps to get into the location), toilets and the stage.

Today I’m chatting with returning guest Tom Davenport, who is a Distinguished Professor at Babson College, a Visiting Professor at Oxford, a Research Fellow at MIT, and a Senior Advisor to Deloitte’s AI practice. He is also the author of three new books (!) on AI and in this episode, we’re discussing the role of product orientation in enterprise data science teams, the skills required, what he’s seeing in the wild in terms of teams adopting this approach, and the value it can create. Back in episode 26, Tom was a guest on my show and he gave the data science/analytics industry an approximate “2 out of 10” rating in terms of its ability to generate value with data. So, naturally, I asked him for an update on that rating, and he kindly obliged. How are you all doing? Listen in to find out!

Highlights / Skip to:

Tom provides an updated rating (between 1-10) as to how well he thinks data science and analytics teams are doing these days at creating economic value (00:44) Why Tom believes that “motivation is not enough for data science work” (03:06) Tom provides his definition of what data products are and some opinions on other industry definitions (04:22) How Tom views the rise of taking a product approach to data roles and why data products must be tied to value (07:55) Tom explains why he feels top down executive support is needed to drive a product orientation (11:51) Brian and Tom discuss how they feel companies should prioritize true data products versus more informal AI efforts (16:26) The trends Tom sees in the companies and teams that are implementing a data product orientation (19:18) Brian and Tom discuss the models they typically see for data teams and their key components (23:18) Tom explains the value and necessity of data product management (34:49) Tom describes his three new books (39:00)

Quotes from Today’s Episode “Data science in general, I think has been focused heavily on motivation to fit lines and curves to data points, and that particular motivation certainly isn’t enough in that even if you create a good model that fits the data, it doesn’t mean at all that is going to produce any economic value.” – Tom Davenport  (03:05)

“If data scientists don’t worry about deployment, then they’re not going to be in their jobs for terribly long because they’re not providing any value to their organizations.” – Tom Davenport (13:25)

“Product also means you got to market this thing if it’s going to be successful. You just can’t assume because it’s a brilliant algorithm with capturing a lot of area under the curve that it’s somehow going to be great for your company.” – Tom Davenport (19:04)

“[PM is] a hard thing, even for people in non-technical roles, because product management has always been a sort of ‘minister without portfolio’ sort of job, and you know, influence without formal authority, where you are responsible for a lot of things happening, but the people don’t report to you, generally.” – Tom Davenport (22:03)

“This collaboration between a human being making a decision and an AI system that might in some cases come up with a different decision but can’t explain itself, that’s a really tough thing to do [well].” – Tom Davenport (28:04)

“This idea that we’re going to use externally-sourced systems for ML is not likely to succeed in many cases because, you know, those vendors didn’t work closely with everybody in your organization” – Tom Davenport (30:21)

“I think it’s unlikely that [organizational gaps] are going to be successfully addressed by merging everybody together in one organization. I think that’s what product managers do is they try to address those gaps in the organization and develop a process that makes coordination at least possible, if not true, all the time.” – Tom Davenport (36:49)

Links Tom’s LinkedIn: https://www.linkedin.com/in/davenporttom/ Tom’s Twitter: https://twitter.com/tdav All-in On AI by Thomas Davenport & Nitin Mittal, 2023 Working With AI by Thomas Davenport & Stephen Miller, 2022 Advanced Introduction to AI in Healthcare by Thomas Davenport, John Glaser, & Elizabeth Gardner, 2022 Competing On Analytics by Thomas Davenport & Jeanne G. Harris, 2007