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We would like to put back PyData Warsaw to the landscape of data-related meetups for good, so here is another event we would like you join with us. This time we also meet at Politechnika Warszawska.

18.00 - Oleg Żero - "How can you reach an AGI in your garage?"

About Topic: Today's AI models are becoming a commodity, allowing us to automate simple and repeatable tasks. But how does the situation look when problems require creativity? To what extent can creative thinking be delegated to AI, and how much would it cost to work around it to achieve satisfactory results?During my presentation, I will discuss the issue of "bringing the pieces together". Given a rather simple example of creative production, whose quality I will leave to you to evaluate..., I am going to show you my approach and what challenges I have been facing. In particular, we'll cover the topics of: * automating AI models' inference via agent programming, * the choice of selecting frameworks and tools, * approaching programming that is experimental but also produces usable code, * setting up the workstation.

About Speaker: Data scientist and machine learning engineer by profession. As I say, I like to extract meaning from data. In my day to day life, I deliver practical solutions to various businesses that are based on machine-learning and artificial intelligence. I have graduated from Royal Technical Academy (KTH) in Stockholm as a Photonics Engineer. Throughout my career, I participated in both industrial, as well as academic and start-up settings. Privately, I am a father and husband, passionate about near and far travelling and all kinds of garage made-up technology of my own production.

18:45 - Adrian Boguszewski, Intel - "Beyond the Continuum: The Importance of Quantization in Deep Learning"

About Topic: Quantization is a process of mapping continuous values to a finite set of discrete values. It is a powerful technique that can significantly reduce the memory footprint and computational requirements of deep learning models, making them more efficient and easier to deploy on resource-constrained devices. In this talk, we will explore the different types of quantization techniques and discuss how they can be applied to deep learning models. In addition, we will cover the basics of NNCF and OpenVINO Toolkit, seeing how they collaborate to achieve outstanding performance - everything in a Jupyter Notebook, which allows you to try it at home.

About Speaker: AI Software Evangelist at Intel. Adrian graduated from the Gdansk University of Technology in the field of Computer Science 8 years ago. After that, he started his career in computer vision and deep learning. As a team leader of data scientists and Android developers for the previous two years, Adrian was responsible for an application to take a professional photo (for an ID card or passport) without leaving home. He is a co-author of the LandCover.ai dataset, creator of the Debug Image Viewer Plugin, and a Deep Learning lecturer occasionally. His current role is to educate people about OpenVINO Toolkit. In his free time, he’s a traveler. You can also talk with him about finance, especially investments.

20:00 - After Party in "Pizza przy Politechnice"

Venue: Centrum Innowacji Politechniki Warszawskiej, ul. Rektorska 4 Room 3.12 (3rd Floor)

Welcome to the PyData Berlin May meetup!

We would like to welcome you all starting from 18:45. There will be food and drinks. The talks begin around 19.30.

Please provide your first and last name for the registration because this is required for the venue's entry policy. If you cannot attend, please cancel your spot so others are able to join as the space is limited.

Host: Bonial is excited to welcome you for this month's version of PyData. ************************************************************************** The Lineup for the evening

Talk 1: Beyond the Continuum: The Importance of Quantization in Deep Learning Abstract: Quantization is a process of mapping continuous values to a finite set of discrete values. It is a powerful technique that can significantly reduce the memory footprint and computational requirements of deep learning models, making them more efficient and easier to deploy on resource-constrained devices. In this talk, we will explore the different types of quantization techniques and discuss how they can be applied to deep learning models. In addition, we will cover the basics of NNCF and OpenVINO Toolkit, seeing how they collaborate to achieve outstanding performance - everything in a Jupyter Notebook, which allows you to try it at home.

Speaker: Adrian Boguszewski is an AI Software Evangelist at Intel. Adrian graduated from the Gdansk University of Technology in the field of Computer Science 7 years ago. After that, he started his career in computer vision and deep learning. As a team leader of data scientists and Android developers for the previous two years, Adrian was responsible for an application to take a professional photo (for an ID card or passport) without leaving home. He is a co-author of the LandCover.ai dataset, creator of the OpenCV Image Viewer Plugin, and a Deep Learning lecturer occasionally. His current role is to educate people about OpenVINO Toolkit. In his free time, he’s a traveler. You can also talk with him about finance, especially investments.

Talk 2: A Meat-ing of Minds Abstract: In this far wandering talk we consider the big meaty questions: What can AI learn from Neuroscience? What can Neuroscience learn from AI? Do LLMs "understand" language? What does "meaning" mean? Am I just a robot made of meat? These huge questions and more... will remain completely unanswered, but we will have a good time talking about them! AI, Neuroscience, Philosophy, Semantics. What more could you want?

Speaker: Andy Kitchen is the co-founder of Cortical Labs, a synthetic intelligence company growing live biological neurons inside computer chips and teaching them inside a matrix world. He is a 10+ year start-up veteran and turbo nerd. Beer, bad philosophy and type theory will be the death of him.

Lightning talks There will be slots for 2-3 Lightning Talks (3-5 Minutes for each). Kindly let us know if you would like to present something at the start of the meetup :)

*** NumFOCUS Code of Conduct THE SHORT VERSION Be kind to others. Do not insult or put down others. Behave professionally. Remember that harassment and sexist, racist, or exclusionary jokes are not appropriate for NumFOCUS. All communication should be appropriate for a professional audience including people of many different backgrounds. Sexual language and imagery are not appropriate. NumFOCUS is dedicated to providing a harassment-free community for everyone, regardless of gender, sexual orientation, gender identity, and expression, disability, physical appearance, body size, race, or religion. We do not tolerate harassment of community members in any form. Thank you for helping make this a welcoming, friendly community for all. If you haven't yet, please read the detailed version here: https://numfocus.org/code-of-conduct ***

We are enormously happy to welcome you to our first event in 2024!!!

Where: Gdańsk Science and Technology Park, Building B, Room 002 When: 20th February 2024 at 18:00

Agenda: 18:00 - 18:05 - Meeting boarding 18:05 - 18:10 - A few words about PyData 18:10 - 18:55 - Beyond the Continuum: The Importance of Quantization in Deep Learning by Adrian Boguszewski 18:55 - 19:40 - Building an in-the-wild gaze estimation solution by Cezary Polak 19:40 - Pizza & networking!

Talk #1 Beyond the Continuum: The Importance of Quantization in Deep Learning

Quantization is a process of mapping continuous values to a finite set of discrete values. It is a powerful technique that can significantly reduce the memory footprint and computational requirements of deep learning models, making them more efficient and easier to deploy on resource-constrained devices. In this talk, we will explore the different types of quantization techniques and discuss how they can be applied to deep learning models. In addition, we will cover the basics of NNCF and OpenVINO Toolkit, seeing how they collaborate to achieve outstanding performance - everything in a Jupyter Notebook, which allows you to try it at home.

About Adrian Boguszewski: Adrian is an AI Software Evangelist at Intel. He graduated 7 years ago from Informatics at Gdańsk University of Technology. Then his career in Computer Vision and Deep Learning has took off as team leader. Adrian was responsible for implementing in-home-use app allowing you to take a professional photo later used in ID or Passport just at home, without going out. He is the coauthor of LandCover.ai dataset, and creator of OpenCV Image Viewer Plugin, and gives lectures from time to time. At Intel he educates others on OpenVINO Toolkit capabilities. During his free time he travels and improves his financing / investing skills.

Talk #2 Building an in-the-wild gaze estimation solution Problem: In my presentation, I would like to talk about our efforts to build a web-service-based AI-powered gaze estimation solution that can work using the video feed from the built-in user-facing camera of a mobile device, e.g. a phone. This can be used to tell where on the screen a user is looking at a given moment.

Methodology: Several different methodologies have been combined to solve this problem. We acquired thousands of user videos using a crowd-sourcing platform to build our training datasets. We are using deep learning models to estimate a coarse gaze vector, we further fine-tune it using a calibration procedure. In order to get to know camera intrinsics, we use auto-calibration techniques also based on deep learning. To extract information about the physical phone parameters, we combine databases that we acquired by purchase and computer vision algorithms to extract vital information.

Conclusions: We can achieve an average accuracy of about 13 mm across a wide range of conditions.

Implications: We are developing this solution to enable attention analysis research on content viewed by users on their mobile devices. However, the list of use cases can be expanded to other areas, e.g. smart vehicles.

About Cezary Polak:

Cezary Polak works as a Machine Learning Researcher at DAC.digital in Gdansk, Poland. He graduated from the Faculty of Electronics, Telecommunications, and Informatics at the Gdansk University of Technology. He is interested in using deep learning in biomedical engineering and also in generating synthetic data as photos and texts.