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Tag: Machine Learning

Expert Group Meeting – Machine Learning Clinic: Behind the Hype GenAI – Real Applications and Benefits for Industry?

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Dear ML Clinic experts, 

I am happy to inform you that we are organizing our next ML Clinic workshop which will be hosted by Swisscom. We will have presentations from Swisscom and CSEM followed by an interactive panel discussion to develop further on this exciting topic:

“Behind the Hype GenAI” – Real applications and benefits for industry?

Please find attached the flyer with all the details about the event.

Flyer

Agenda

16:00 – 17:00 – Impulse Presentations

From Idea to Impact – Identifying and Developing Scalable AI Use Cases
Mattmann Carole – Senior Data & AI Consultant, Swisscom AG
An approach to develop AI-driven solutions, guided by a structured framework that supports scalable development and provides a clear methodology for identifying and defining impactful use cases. Through real-world examples across various industries, this presentation will demonstrate how our framework helps us navigate the complexities of AI deployment, including model scalability, data integration, and operationalization, to achieve meaningful business results.

Discover the Swiss AI Platform
Levy Sarah – Stream Lead AI Strategy & Offerings, Swisscom AG
Switzerland’s AI landscape requires balancing innovation with strict data governance. The Swiss AI Platform, a Swiss Army knife for AI, meets this need by offering GPU-as-a-Service, an AI Workhub for collaboration, and a GenAI Studio for generative AI, all underpinned by Swiss data governance and Nvidia’s scalable technology. Discover how this all-in-one platform enables secure, high-performance AI solutions, driving new possibilities for Swiss innovation.

The impact of Generative AI in Industrial Applications
Iason Kastanis – Group leader Predictive Analytics, CSEM SA
This talk will describe CSEM projects in the area of multimodal industrial data with examples from real world applications. Many years of experience have highlighted the idiosyncrasies of the digitalisation process taking place on shop floors across the industry. The rise of GPT-based large language models presents an opportunity to use Generative AI to address the issues faced in the development of AI-driven analytics for the industry.

Defining the boundaries of AI From fundamental limitations to resource constraints
Nadim Maamari – Group Leader Edge AI and Vision System, CSEM
Defining the boundaries of AI involves recognizing its intrinsic limitations, such as lack of true understanding and dependence on data, while balancing its immense computational power needs, which pose sustainability challenges. As AI grows more powerful, ethical considerations and regulatory measures become essential to ensure responsible and energy-efficient advancements, guiding its impact on society and the environment

17:00 – 17:45 Panel Discussion and Q&A

Challenges and opportunities for the Swiss industry
Mattmann Carole, Levy Sarah, Iason Kastanis, Nadim Maamari, and Philipp Schmid (Head Manufacturing Industries, Swisscom AG)
Moderator: Francesco Crivelli, Head of Research & Business Development for Industry 4.0 and ML, CSEM

17:45 – 19:00 Apéro Riche and Networking

This event is open to the public. If you are not a member but would like to attend this workshop, please register by sending an email to: francesco.crivelli@csem.ch

OPEN EVENT: Machine Learning in IoT

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Save the Date: The Machine Learning Clining Expert Group will meet in the Technopark in Zurich with four exciting presentations with a focus on industry aimed at experts working with or having background knowledge in data science. Guests are welcome to join!

Speakers:

Christof Zogg, Swisscom, Head of Business Transformation: Competing in the age AI
Guillaume Noël, Heliot Group, Chief Strategy Officer: Data analytics in industrial packaging
Raamadaas Krishnadas, Inspire AG, Research Engineer: Trajectory optimization for embedded systems with Bayesian linear regression
Florentin Marty, SCS Supercomputing Systems, Department Head: Person counting on the Edge

Followed by an apéro.

Please use the form below to register.

Challenges in Applied Computer Vision

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By Philipp Schmid, CSEM, Andrea Dunbar, CSEM and Jakob Olbrich, PwC

Meeting of Expert Group Machine Learning Clinic, February 11 2022

What have expensive mechanical watches, sand, e-waste and cockpits in common? All areas have tough challenges in computer vision. Human eyes are very hard to outperform with cameras and image processing. What people perform with their visual sense every day is just amazing and and creating these capabilities remains a complex challenge for computer vision.

At this first in person meeting this year the expert group focused on various real world vision problems.
The event was hosted by PwC in their inspiring location in Oerlikon. Four speakers set the floor for great discussions followed by a lively sitting Apéro.

Lukas Schaupp, PwC «Detecting e-Waste»
The amount of electronic devices people dispose is growing exponentially. Not just talking about smartphones, laptops and earphones but as well larger household items like dishwashers, toasters and vacuum cleaners. As prices for raw materials are rocketing off automated recycling of e-waste is becoming attractive. Lukas demonstrated strategies to localize and classify different electronic devices in bulk on a conveyor belt.

Andrea Dunbar, CSEM «AI at the Edge – Safety in the next generation Cockpits»
There are multiple reasons and advantages to process at the edge. Andrea demonstrated this impressively in the use-case: next generation cockpits. Pilot drowsiness detection and more important high accuracy eye gaze detection (±1°) with rates of up to 60 frames per second are only possible at the edge. What is today already reality in the flight simulator will soon be introduced in each car for the safety of our roads.

Francesco Cicala, PwC «Automatic image thresholding for semantic segmentation»
The quality of concrete depends heavily on the right mixture of sand and pebbles. In the future a smartphone app should be able to classify the correct mix by assessing the size of the sand and pebbles. Francesco introduced a powerful method to extend Otsu’s thresholding technique into a locally adaptive threshold map for the whole image. This method is robust, fully explainable and there are no labels needed. In a next phase it will be extended with a U-Net algorithm to improve accuracy.

David Honzatko, CSEM «Photometric stereo in defect detection»
Swiss Made symbolizes perfect quality. Especially in the watch industry requirements are demanding. The small parts are highly reflective, complex shaped and defects can appear randomly at any position. The key to an automated defect detection solution is photometric stereo. David presented a dome setup which can project up to 108 illumination directions. To reduce the hardware requirements whilst keeping the performance David presented a new data augmentation technique, which boosts the training of any deep learning architecture processing the images.

A full evening of new insights and tough challenges in the field of computer vision. Thanks to everyone
for the great participation and especially to the host for the amazing location and the local Apéro.

Expert Group Meeting – Machine Learning Clinic

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The Expert Group will meet in Zurich for the first time in 2022 to discuss Challenges in applied computer vision!

Location: PwC, Birchstrasse 160, 8050 Zurich

Agenda
16:00 Welcome
16:10 Lukas Schaupp, Object classification in e-recycling
16:30 Fran Peric, Image Embeddings for Semantic Search
16:50 Francesco Cicala, Particle size distribution in sand
17:10 TBA
17:30 Sitting Apéro
18:00 End

Covid measures: 2G, mask all the time, eating and drinking only seated, presence list -> PwC covid task force

Participation is limited to 14 people. Please register using the form below.

Expert Group Meeting – Machine Learning Clinic

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The meeting will be physical only!
Location: Bern, https://suedland.ch/angebot/forum/

Rare events – a real pain in Machine Learning. How to detect, classify or predict an event you have rarely seen before?

Let’s learn and discuss with experts from natural hazards: earthquake, lightning, flooding and stock market crash.

A great lineup of top experts:

  • Prof. Dr. Stefan Wiemer, Head of Swiss Seismological Service «Extreme events forecasting in seismology»
  • Dr. Thomas Krabichler, OST «Rare Events in Financial Modelling»
  • Dr. Ralf-Peter Mundani, FHGR «Predicting Natural Hazards such as Floods with Parallel Numerical Simulation»
  • Pad Pedram, CSEM «A data-driven approach for lightning nowcasting with deep learning»

followed by a real Apéro

Expert Group Meeting – Machine Learning Clinic

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Ask the expert

New year, new logo, new concept: the expert group “Machine Learning Clinic” is a unique pool of expert knowledge. Our next meeting aims to answer this need. Either you are an expert yourself and happy to share your knowledge or you are in need of an expert to push your project forward.

With the new open-innovation initiative www.databooster.ch and the support from Innosuisse there are many possibilities to support you during your ML-journey.

Our next event is planned to foster ideas and form teams with knowledge and experience. 

Why should you participate?

  • You are an expert, happy to share knowledge and experience, you could offer service, research partner for a potential new Innosuisse project
  • You have a good idea and need some support, a company and need ML-expertise, open for an Innosuisse project

The Expert Group Machine Learning Clinic will have their next meeting on the 22nd of March. Register with the form below. There are some additional questions on it so please make sure to check both of them, thank you!

Expert Group Meeting – Machine Learning Clinic

Written by dsa_admin on .

Registration:
To help us plan the meeting, please register via this Google form:
https://forms.gle/ALHRq47UXPBGkwui7

14:00-14:30 Check-In
14:30-14:50 Interepreting time-series data representations through structured latent spaces
Vincent Fortuin, ETH Zürich
14:50-15:10 GAN’s & the Data Problem
Andres Romero, ETH Zürich
15:10-16:10 Group Discussion
goals and the future for the Machine Learning Clinic in 2020
16:10-16:30 Unsupervised Learning for Feature Representation and Classification in Vision Tasks
Engin Türetken, CSEM
16:30-16:50 Enterprise Data Science – exchange on technical and organizational aspects
Bojan Skerlak, Migros
16:50-18:00 Networking Apero