Au choix : codez votre première page web avec HTML & CSS ou mettez-vous dans la peau d’un(e) data analyst pour Airbnb avec Python

Reflection on an Almost Two-Year Journey of Generative AI in Industry – Maria Sukhareva

​About the speaker:

​Maria Sukhareva is a principal key expert in Artificial Intelligence in Siemens with over 15 years of experience at the forefront of generative AI technologies. Known for her keen eye for technological innovation, Maria excels at transforming cutting-edge AI research into practical, value-driven tools that address real-world needs. Her approach is both hands-on and results-focused, with a commitment to creating scalable, long-term solutions that improve communication, streamline complex processes, and empower smarter decision-making. Maria's work reflects a balanced vision, where the power of innovation is met with ethical responsibility, ensuring that her AI projects deliver impactful and production-ready outcomes.

We talked about:

00:00 DataTalks.Club intro

02:13 Career journey: From linguistics to AI

08:02 The Evolution of AI Expertise and its Future

13:10 AI vulnerabilities: Bypassing bot restrictions

17:00 Non-LLM classifiers as a more robust solution

22:56 Risks of chatbot deployment: Reputational and financial

27:13 The role of AI as a tool, not a replacement for human workers

31:41 The role of human translators in the age of AI

34:49 Evolution of English and its Germanic roots

38:44 Beowulf and Old English

39:43 Impact of the Norman occupation on English grammar

42:34 Identifying mushrooms with AI apps and safety precautions

45:08 Decoding ancient languages ​​like Sumerian

49:43 The evolution of machine translation and multilingual models

53:01 Challenges with low-resource languages ​​and inconsistent orthography

57:28 Transition from academia to industry in AI

Join our Slack: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

🌟 Session Overview 🌟

Session Name: Leveraging Azure PaaS for Real-time Social Media Analysis by Building Streaming Dashboard Speaker: Raghav Matta Session Description: In this session, Raghav and Sundar will delve into a practical business scenario focusing on real-time social media analysis using Azure PaaS offerings.

1. They will begin by addressing a prevalent business challenge concerning social media sentiment analysis.

2. Next, speakers explore a range of Azure services including Azure Functions, Logic Apps, Cognitive Services, Stream Analytics, PowerBI, and Azure Databricks.

3. Moving forward, they will demonstrate how to gather live data in real-time utilizing Azure Cognitive Services Bing Web Search API. Subsequently, they will analyze the data using Azure Stream Analytics and visualize insights using PowerBI.

This course combines hands-on labs with theoretical curriculum aligned with the 'Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution'.

For further information and resources, please refer to: https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-twitter-sentiment-analysis-trends https://microsoftlearning.github.io/AI-102-AIEngineer/Instructions/05-analyze-text.html 🚀 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

💡 Stay Connected & Updated 💡

Don’t miss out on any updates or upcoming event information from Big Data & RPA Conference Europe. Follow us on our social media channels and visit our website to stay in the loop!

🌐 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

We talked about:

00:00 DataTalks.Club intro

00:00 Large Hadron Collider and Mentorship

02:35 Career overview and transition from physics to data science

07:02 Working at the Large Hadron Collider

09:19 How particles collide and the role of detectors

11:03 Data analysis challenges in particle physics and data science similarities

13:32 Team structure at the Large Hadron Collider

20:05 Explaining the connection between particle physics and data science

23:21 Software engineering practices in particle physics

26:11 Challenges during interviews for data science roles

29:30 Mentoring and offering advice to job seekers

40:03 The STAR method and its value in interviews

50:32 Paid vs unpaid mentorship and finding the right fit

​About the speaker:

​Anastasia is a particle physicist turned data scientist, with experience in large-scale experiments like those at the Large Hadron Collider. She also worked at Blue Yonder, scaling AI-driven solutions for global supply chain giants, and at Kaufland e-commerce, focusing on NLP and search. Anastasia is a mentor for Ml/AI, dedicated to helping her mentees achieve their goals. She is passionate about growing the next generation of data science elite in Germany: from Data Analysts up to ML Engineers.

Join our Slack: https://datatalks .club/slack.html

We talked about:

00:00 DataTalks.Club intro

02:34 Career journey and transition into MLOps

08:41 Dutch agriculture and its challenges

10:36 The concept of "technical debt" in MLOps

13:37 Trade-offs in MLOps: moving fast vs. doing things right

14:05 Building teams and the role of coordination in MLOps

16:58 Key roles in an MLOps team: evangelists and tech translators

23:01 Role of the MLOps team in an organization

25:19 How MLOps teams assist product teams

27 :56 Standardizing practices in MLOps

32:46 Getting feedback and creating buy-in from data scientists

36:55 The importance of addressing pain points in MLOps

39:06 Best practices and tools for standardizing MLOps processes

42:31 Value of data versioning and reproducibility

44:22 When to start thinking about data versioning

45:10 Importance of data science experience for MLOps

46:06 Skill mix needed in MLOps teams

47:33 Building a diverse MLOps team

48:18 Best practices for implementing MLOps in new teams

49:52 Starting with CI/CD in MLOps

51:21 Key components for a complete MLOps setup

53:08 Role of package registries in MLOps

54:12 Using Docker vs. packages in MLOps

57:56 Examples of MLOps success and failure stories

1:00:54 What MLOps is in simple terms

1:01:58 The complexity of achieving easy deployment, monitoring, and maintenance

Join our Slack: https://datatalks .club/slack.html

We talked about:

00:00 DataTalks.Club intro 01:56 Using data to create livable cities 02:52 Rachel's career journey: from geography to urban data science 04:20 What does a transport scientist do? 05:34 Short-term and long-term transportation planning 06:14 Data sources for transportation planning in Singapore 08:38 Rachel's motivation for combining geography and data science 10:19 Urban design and its connection to geography 13:12 Defining a livable city 15:30 Livability of Singapore and urban planning 18:24 Role of data science in urban and transportation planning 20:31 Predicting travel patterns for future transportation needs 22:02 Data collection and processing in transportation systems 24:02 Use of real-time data for traffic management 27:06 Incorporating generative AI into data engineering 30:09 Data analysis for transportation policies 33:19 Technologies used in text-to-SQL projects 36:12 Handling large datasets and transportation data in Singapore 42:17 Generative AI applications beyond text-to-SQL 45:26 Publishing public data and maintaining privacy 45:52 Recommended datasets and projects for data engineering beginners 49:16 Recommended resources for learning urban data science

About the speaker:

Rachel is an urban data scientist dedicated to creating liveable cities through the innovative use of data. With a background in geography, and a masters in urban data science, she blends qualitative and quantitative analysis to tackle urban challenges. Her aim is to integrate data driven techniques with urban design to foster sustainable and equitable urban environments. 

Links: - https://datamall.lta.gov.sg/content/datamall/en/dynamic-data.html

00:00 DataTalks.Club intro 01:56 Using data to create livable cities 02:52 Rachel's career journey: from geography to urban data science 04:20 What does a transport scientist do? 05:34 Short-term and long-term transportation planning 06:14 Data sources for transportation planning in Singapore 08:38 Rachel's motivation for combining geography and data science 10:19 Urban design and its connection to geography 13:12 Defining a livable city 15:30 Livability of Singapore and urban planning 18:24 Role of data science in urban and transportation planning 20:31 Predicting travel patterns for future transportation needs 22:02 Data collection and processing in transportation systems 24:02 Use of real-time data for traffic management 27:06 Incorporating generative AI into data engineering 30:09 Data analysis for transportation policies 33:19 Technologies used in text-to-SQL projects 36:12 Handling large datasets and transportation data in Singapore 42:17 Generative AI applications beyond text-to-SQL 45:26 Publishing public data and maintaining privacy 45:52 Recommended datasets and projects for data engineering beginners 49:16 Recommended resources for learning urban data science

Join our slack: https: //datatalks.club/slack.html

We talked about:

00:00 DataTalks.Club intro

00:00 DataTalks.Club anniversary "Ask Me Anything" event with Alexey Grigorev

02:29 The founding of DataTalks .Club

03:52 Alexey's transition from Java work to DataTalks.Club

04:58 Growth and success of DataTalks.Club courses

12:04 Motivation behind creating a free-to-learn community

24:03 Staying updated in data science through pet projects

26 :37 Hosting a second podcast and maintaining programming skills

28:56 Skepticism about LLMs and their relevance

31:53 Transitioning to DataTalks.Club and personal reflections

33:32 Memorable moments and the first event's success

36:19 Community building during the pandemic

38:31 AI's impact on data analysts and future roles

42:24 Discussion on AI in healthcare

44:37 Age and reflections on personal milestones

47:54 Building communities and personal connections

49:34 Future goals for the community and courses

51:18 Community involvement and engagement strategies

53:46 Ideas for competitions and hackathons

54:20 Inviting guests to the podcast

55:29 Course updates and future workshops

56:27 Podcast preparation and research process

58:30 Career opportunities in data science and transitioning fields

1:01 :10 Book recommendations and personal reading experiences

About the speaker:

Alexey Grigorev is the founder of DataTalks.Club.

Join our slack: https://datatalks.club/slack.html

We talked about:

00:00 DataTalks.Club intro

08:06 Background and career journey of Katarzyna

09:06 Transition from linguistics to computational linguistics

11:38 Merging linguistics and computer science

15:25 Understanding phonetics and morpho-syntax

17:28 Exploring morpho-syntax and its relation to grammar

20:33 Connection between phonetics and speech disorders

24:41 Improvement of voice recognition systems

27:31 Overview of speech recognition technology

30:24 Challenges of ASR systems with atypical speech

30:53 Strategies for improving recognition of disordered speech

37:07 Data augmentation for training models

40:17 Transfer learning in speech recognition

42:18 Challenges of collecting data for various speech disorders

44:31 Stammering and its connection to fluency issues

45:16 Polish consonant combinations and pronunciation challenges

46:17 Use of Amazon Transcribe for generating podcast transcripts

47:28 Role of language models in speech recognition

49:19 Contextual understanding in speech recognition

51:27 How voice recognition systems analyze utterances

54:05 Personalization of ASR models for individuals

56:25 Language disorders and their impact on communication

58:00 Applications of speech recognition technology

1:00:34 Challenges of personalized and universal models

1:01:23 Voice recognition in automotive applications

1:03:27 Humorous voice recognition failures in cars

1:04:13 Closing remarks and reflections on the discussion

About the speaker:

Katarzyna is a computational linguist with over 10 years of experience in NLP and speech recognition. She has developed language models for automotive brands like Audi and Porsche and specializes in phonetics, morpho-syntax, and sentiment analysis.

Kasia also teaches at the University of Warsaw and is passionate about human-centered AI and multilingual NLP.

Join our slack: https://datatalks.club/slack.html

Understanding the effectiveness of various marketing channels is crucial to maximise the return on investment (ROI). However, the limitation of third-party cookies and an ever-growing focus on privacy make it difficult to rely on basic analytics. This talk discusses a pioneering project where a Bayesian model was employed to assess the marketing media mix effectiveness of WeRoad, the fastest-growing Italian tour operator.

The Bayesian approach allows for the incorporation of prior knowledge, seamlessly updating it with new data to provide robust, actionable insights. This project leveraged a Bayesian model to unravel the complex interactions between marketing channels such as online ads, social media, and promotions. We'll dive deep into how the Bayesian model was designed, discussing how we provided the AI system with expert knowledge, and presenting how delays and saturation were modelled.

We will also tackle aspects of the technical implementation, discussing how Python, PyMC, and Streamlit provided us with the all the tools we needed to develop an effective, efficient, and user-friendly system.

Attendees will walk away with:

• A simple understanding of the Bayesian approach and why it matters.
• Concrete examples of the transformative impact on WeRoad's marketing strategy.
• A blueprint to harness predictive models in their business strategies.

Atelier d’initiation au code avec HTML/CSS.

In this podcast episode, we talked with Guillaume Lemaître about navigating scikit-learn and imbalanced-learn.

🔗 CONNECT WITH Guillaume Lemaître LinkedIn - https://www.linkedin.com/in/guillaume-lemaitre-b9404939/ Twitter - https://x.com/glemaitre58 Github - https://github.com/glemaitre Website - https://glemaitre.github.io/

🔗 CONNECT WITH DataTalksClub Join the community - https://datatalks-club.slack.com/join/shared_invite/zt-2hu0sjeic-ESN7uHt~aVWc8tD3PefSlA#/shared-invite/email Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/u/0/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ Check other upcoming events - https://lu.ma/dtc-events LinkedIn - https://www.linkedin.com/company/datatalks-club/ Twitter - https://twitter.com/DataTalksClub Website - https://datatalks.club/

🔗 CONNECT WITH ALEXEY Twitter - https://twitter.com/Al_Grigor Linkedin - https://www.linkedin.com/in/agrigorev/

🎙 ABOUT THE PODCAST At DataTalksClub, we organize live podcasts that feature a diverse range of guests from the data field. Each podcast is a free-form conversation guided by a prepared set of questions, designed to learn about the guests’ career trajectories, life experiences, and practical advice. These insightful discussions draw on the expertise of data practitioners from various backgrounds.

We stream the podcasts on YouTube, where each session is also recorded and published on our channel, complete with timestamps, a transcript, and important links.

You can access all the podcast episodes here - https://datatalks.club/podcast.html

📚Check our free online courses ML Engineering course - http://mlzoomcamp.com Data Engineering course - https://github.com/DataTalksClub/data-engineering-zoomcamp MLOps course - https://github.com/DataTalksClub/mlops-zoomcamp Analytics in Stock Markets - https://github.com/DataTalksClub/stock-markets-analytics-zoomcamp LLM course - https://github.com/DataTalksClub/llm-zoomcamp Read about all our courses in one place - https://datatalks.club/blog/guide-to-free-online-courses-at-datatalks-club.html

👋🏼 GET IN TOUCH If you want to support our community, use this link - https://github.com/sponsors/alexeygrigorev

If you're a company and want to support us, contact at [email protected]

Links:

LinkedIn:https://www.linkedin.com/company/frontline100/ Ba Linh Le's LinkedIn: https://www.linkedin.com/in/ba-linh-le-/ Sabrina's LinkedIn: https://www.linkedin.com/in/sabina-firtala/ Twitter: https://x.com/frontline_100?mx=2 Website: https://www.frontline100.com/

Free LLM course: https://github.com/DataTalksClub/llm-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We stream the podcasts on YouTube, where each session is also recorded and published on our channel, complete with timestamps, a transcript, and important links.

You can access all the podcast episodes here - https://datatalks.club/podcast.html

📚Check our free online courses ML Engineering course - http://mlzoomcamp.com Data Engineering course - https://github.com/DataTalksClub/data-engineering-zoomcamp MLOps course - https://github.com/DataTalksClub/mlops-zoomcamp Analytics in Stock Markets - https://github.com/DataTalksClub/stock-markets-analytics-zoomcamp LLM course - https://github.com/DataTalksClub/llm-zoomcamp Read about all our courses in one place - https://datatalks.club/blog/guide-to-free-online-courses-at-datatalks-club.html

👋🏼 GET IN TOUCH If you want to support our community, use this link - https://github.com/sponsors/alexeygrigorev

If you’re a company, support us at [email protected]

Create stunning web-based data visualizations with D3.js. This totally-revised new edition of D3.js in Action guides you from simple charts to powerful interactive graphics. Chapter-by-chapter you’ll assemble an impressive portfolio of visualizations—including intricate networks, maps, and even a complete customized visualization layout. Plus, you'll learn best practices for building interactive graphics, animations, and integrating your work into frontend development frameworks like React and Svelte. In D3.js in Action, Third Edition you will learn how to: Set up a local development environment for D3 Include D3 in web development projects, including Node-based web apps Select and append DOM elements Size and position elements on screen Assemble components and layouts into creative data visualizations D3.js in Action, Third Edition has been extensively revised for D3.js version 7, and modern best practices for web visualizations. Its brand new chapters dive into interactive visualizations, cover responsiveness for dataviz, and show you how you can improve accessibility. About the Technology With D3.js, you can create sophisticated infographics, charts, and interactive data visualizations using standard frontend tools like JavaScript, HTML, and CSS. Granting D3 its VIS Test of Time award, the IEEE credited this powerful library for bringing data visualization to the mainstream. You’ll be blown away by how beautiful your results can be! About the Book D3.js in Action, Third Edition is a roadmap for creating brilliant and beautiful visualizations with D3.js. Like a gentle mentor, it guides you from basic charts all the way to advanced interactive visualizations like networks and maps. You’ll learn to build graphics, create animations, and set up mobile-friendly responsiveness. Each chapter contains a complete data visualization project to put your new skills into action. What's Inside Fully revised for D3.js v7 Includes 12 complete projects Create data visualizations with SVG and canvas Combine D3 with React, Svelte, and Angular About the Reader For web developers with HTML, CSS, and JavaScript skills. About the Authors Elijah Meeks was a data visualization pioneer at Stanford and the first Senior Data Visualization Engineer at Netflix. Anne-Marie Dufour is a Data Visualization Engineer. The technical editor on this book was Jon Borgman. Quotes Guides readers through the intricate world of D3 with clarity and practical insight. Whether you’re a seasoned expert or just starting, this book will be invaluable. - Connor Rothschild, Data Visualization Engineer, Moksha Data Studio Amazing job of explaining the core concepts of D3 while providing all you need to learn other fundamental concepts. - Lindsey Poulter, Visualization Engineer, New York Mets A navigation tool to explore all possible paths in the world of D3. Clear schematics and nicely selected examples guide the readers through D3’s possibilities. - Matthias Stahl, Head Data & Visualizations, Der SPIEGEL

Ben Shneiderman is a leading figure in the field of human-computer interaction (HCI). Having founded one of the oldest HCI research centers in the country at the University of Maryland in 1983, Shneiderman has been intently studying the design of computer technology and its use by humans. Currently, Ben is a Distinguished University Professor in the Department of Computer Science at the University of Maryland and is working on a new book on human-centered artificial intelligence.

I’m so excited to welcome this expert from the field of UX and design to today’s episode of Experiencing Data! Ben and I talked a lot about the complex intersection of human-centered design and AI systems.

In our chat, we covered:

Ben's career studying human-computer interaction and computer science. (0:30) 'Building a culture of safety': Creating and designing ‘safe, reliable and trustworthy’ AI systems. (3:55) 'Like zoning boards': Why Ben thinks we need independent oversight of privately created AI. (12:56) 'There’s no such thing as an autonomous device': Designing human control into AI systems. (18:16) A/B testing, usability testing and controlled experiments: The power of research in designing good user experiences. (21:08) Designing ‘comprehensible, predictable, and controllable’ user interfaces for explainable AI systems and why [explainable] XAI matters. (30:34) Ben's upcoming book on human-centered AI. (35:55)

Resources and Links: People-Centered Internet: https://peoplecentered.net/ Designing the User Interface (one of Ben’s earlier books): https://www.amazon.com/Designing-User-Interface-Human-Computer-Interaction/dp/013438038X Bridging the Gap Between Ethics and Practice: https://doi.org/10.1145/3419764 Partnership on AI: https://www.partnershiponai.org/ AI incident database: https://www.partnershiponai.org/aiincidentdatabase/ University of Maryland Human-Computer Interaction Lab: https://hcil.umd.edu/ ACM Conference on Intelligent User Interfaces: https://iui.acm.org/2021/hcai_tutorial.html Human-Computer Interaction Lab, University of Maryland, Annual Symposium: https://hcil.umd.edu/tutorial-human-centered-ai/ Ben on Twitter: https://twitter.com/benbendc

Quotes from Today’s Episode The world of AI has certainly grown and blossomed — it’s the hot topic everywhere you go. It’s the hot topic among businesses around the world — governments are launching agencies to monitor AI and are also making regulatory moves and rules. … People want explainable AI; they want responsible AI; they want safe, reliable, and trustworthy AI. They want a lot of things, but they’re not always sure how to get them. The world of human-computer interaction has a long history of giving people what they want, and what they need. That blending seems like a natural way for AI to grow and to accommodate the needs of real people who have real problems. And not only the methods for studying the users, but the rules, the principles, the guidelines for making it happen. So, that’s where the action is. Of course, what we really want from AI is to make our world a better place, and that’s a tall order, but we start by talking about the things that matter — the human values: human rights, access to justice, and the dignity of every person. We want to support individual goals, a person’s sense of self-efficacy — they can do what they need to in the world, their creativity, their responsibility, and their social connections; they want to reach out to people. So, those are the sort of high aspirational goals that become the hard work of figuring out how to build it. And that’s where we want to go. - Ben (2:05)  

The software engineering teams creating AI systems have got real work to do. They need the right kind of workflows, engineering patterns, and Agile development methods that will work for AI. The AI world is different because it’s not just programming, but it also involves the use of data that’s used for training. The key distinction is that the data that drives the AI has to be the appropriate data, it has to be unbiased, it has to be fair, it has to be appropriate to the task at hand. And many people and many companies are coming to grips with how to manage that. This has become controversial, let’s say, in issues like granting parole, or mortgages, or hiring people. There was a controversy that Amazon ran into when its hiring algorithm favored men rather than women. There’s been bias in facial recognition algorithms, which were less accurate with people of color. That’s led to some real problems in the real world. And that’s where we have to make sure we do a much better job and the tools of human-computer interaction are very effective in building these better systems in testing and evaluating. - Ben (6:10)

Every company will tell you, “We do a really good job in checking out our AI systems.” That’s great. We want every company to do a really good job. But we also want independent oversight of somebody who’s outside the company — someone who knows the field, who’s looked at systems at other companies, and who can bring ideas and bring understanding of the dangers as well. These systems operate in an adversarial environment — there are malicious actors out there who are causing trouble. You need to understand what the dangers and threats are to the use of your system. You need to understand where the biases come from, what dangers are there, and where the software has failed in other places. You may know what happens in your company, but you can benefit by learning what happens outside your company, and that’s where independent oversight from accounting companies, from governmental regulators, and from other independent groups is so valuable. - Ben (15:04)

There’s no such thing as an autonomous device. Someone owns it; somebody’s responsible for it; someone starts it; someone stops it; someone fixes it; someone notices when it’s performing poorly. … Responsibility is a pretty key factor here. So, if there’s something going on, if a manager is deciding to use some AI system, what they need is a control panel, let them know: what’s happening? What’s it doing? What’s going wrong and what’s going right? That kind of supervisory autonomy is what I talk about, not full machine autonomy that’s hidden away and you never see it because that’s just head-in-the-sand thinking. What you want to do is expose the operation of a system, and where possible, give the stakeholders who are responsible for performance the right kind of control panel and the right kind of data. … Feedback is the breakfast of champions. And companies know that. They want to be able to measure the success stories, and they want to know their failures, so they can reduce them. The continuous improvement mantra is alive and well. We do want to keep tracking what’s going on and make sure it gets better. Every quarter. - Ben (19:41)

Google has had some issues regarding hiring in the AI research area, and so has Facebook with elections and the way that algorithms tend to become echo chambers. These companies — and this is not through heavy research — probably have the heaviest investment of user experience professionals within data science organizations. They have UX, ML-UX people, UX for AI people, they’re at the cutting edge. I see a lot more generalist designers in most other companies. Most of them are rather unfamiliar with any of this or what the ramifications are on the design work that they’re doing. But even these largest companies that have, probably, the biggest penetration into the most number of people out there are getting some of this really important stuff wrong. - Brian (26:36)

Explainability is a competitive advantage for an AI system. People will gravitate towards systems that they understand, that they feel in control of, that are predictable. So, the big discussion about explainable AI focuses on what’s usually called post-hoc explanations, and the Shapley, and LIME, and other methods are usually tied to the post-hoc approach.That is, you use an AI model, you get a result and you say, “What happened?” Why was I denied a parole, or a mortgage, or a job? At that point, you want to get an explanation. Now, that idea is appealing, but I’m afraid I haven’t seen too many success stories of that working. … I’ve been diving through this for years now, and I’ve been looking for examples of good user interfaces of post-hoc explanations. It took me a long time till I found one. The culture of AI model-building would be much bolstered by an infusion of thinking about what the user interface will be for these explanations. And even the DARPA’s XAI—Explainable AI—project, which has 11 projects within it—has not really grappled with this in a good way about designing what it’s going to look like. Show it to me. … There is another way. And the strategy is basically prevention. Let’s prevent the user from getting confused and so they don’t have to request an explanation. We walk them along, let the user walk through the step—this is like Amazon checkout process, seven-step process—and you know what’s happened in each step, you can go back, you can explore, you can change things in each part of it. It’s also what TurboTax does so well, in really complicated situations, and walks you through it. … You want to have a comprehensible, predictable, and controllable user interface that makes sense as you walk through each step. - Ben (31:13)

We talked about:

Erum's Background Omdena Academy and Erum’s Role There Omdena’s Community and Projects Course Development and Structure at Omdena Academy Student and Instructor Engagement Engagement and Motivation The Role of Teaching in Community Building The Importance of Communities for Career Building Advice for Aspiring Instructors and Freelancers DS and ML Talent Market Saturation Resources for Learning AI and Community Building Erum’s Resource Recommendations

Links:

LinkedIn: https://www.linkedin.com/in/erum-afzal-64827b24/

Twitter:  https://twitter.com/Erum55449739

Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about:

Vincent’s Background SciKit Learn’s History and Company Formation Maintaining and Transitioning Open Source Projects Teaching and Learning Through Open Source Role of Developer Relations and Content Creation Teaching Through Calm Code and The Importance of Content Creation Current Projects and Future Plans for Calm Code Data Processing Tricks and The Importance of Innovation Learning the Fundamentals and Changing the Way You See a Problem Dev Rel and Core Dev in One Why :probabl. Needs a Dev Rel Exploration of Skrub and Advanced Data Processing Personal Insights on SciKit Learn and Industry Trends Vincent’s Upcoming Projects

Links:

probabl. YouTube channel: https://www.youtube.com/@UCIat2Cdg661wF5DQDWTQAmg Calmcode website: https://calmcode.io/ probabl. website: https://probabl.ai/

Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

Links:

Biodiversity and Artificial Intelligence pdf: https://www.gpai.ai/projects/responsible-ai/environment/biodiversity-and-AI-opportunities-recommendations-for-action.pdf

Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

Do Beats ao Qubits, descubra como a Computação Quântica está redefinindo e moldando o futuro da computação. Junte-se a nós para desvendar os mistérios do universo quântico, e entender o que empresas como o Itaú, vem aplicando no seu dia a dia. 

Neste episódio do Data Hackers — a maior comunidade de AI e Data Science do Brasil-, conversamos com a Samuraí Brito — Head of Quantum Technologies no Itaú, que tem artigos reconhecidos mundialmente sobre Computação Quântica, que revoluciona a forma como pensamos, e deu uma aula, sobre este universo paralelo.

Lembrando que você pode encontrar todos os podcasts da comunidade Data Hackers no Spotify, iTunes, Google Podcast, Castbox e muitas outras plataformas. Caso queira, você também pode ouvir o episódio aqui no post mesmo!

Conheça nossa convidada:

Samuraí Brito — Head of Quantum Technologies (Arq. IT Specialist II) at Itaú

Nossa Bancada Data Hackers:

Monique Femme — Head of Community Management na Data Hackers Paulo Vasconcellos — Co-founder da Data Hackers e Principal Data Scientist na Hotmart.

Referências:

Baixe o relatório completo do State of Data Brazil 2023 : https://stateofdata.datahackers.com.br/ Inscreva-se na Newsletter Data Hackers: https://www.datahackers.news/ Medium da Samuraí: https://samuraigab.medium.com/ Link artigo Samuraí: https://arxiv.org/abs/1911.05445 Livro: Quantum Computing: An Applied Approach: https://a.co/d/5TlQXRT Livro: Quantum Computing for the Quantum Curious: https://a.co/d/8vKUXdP Rede Quântica China: https://phys.org/news/2021-01-world-quantum-network.html

Vagas no Itaú:

Link portal carreiras Itaú: https://carreiras.itau.com.br/tecnologia Vagas — Banco de talentos dados: https://carreiras.itau.com.br/vaga/sao-paulo/faca-sua-carreira-de-dados-no-itau/35299/52511644368

We talked about:

Anahita's Background Mechanical Engineering and Applied Mechanics Finite Element Analysis vs. Machine Learning Optimization and Semantic Reporting Application of Knowledge Graphs in Research Graphs vs Tabular Data Computational graphs Graph Data Science and Graph Machine Learning Combining Knowledge Graphs and Large Language Models (LLMs) Practical Applications and Projects Challenges and Learnings Anahita’s Recommendations

Links:

GitHub repo: https://github.com/antahiap/ADPT-LRN-PHYS/tree/main

Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html