Get Ready for ODSC Europe 2024 with Free Virtual Highlights from Last Year’s Event

With ODSC Europe 2024 just weeks away, and our virtual schedule packed with dozens of awesome sessions, we figured it might be worth checking out some of last year’s virtual highlights so you can get a better idea of what to expect from a virtual conference. Here are ten sessions from ODSC Europe 2023 that you can watch today that’ll show you how great this year’s virtual conference will be.

You can watch all of the below sessions from ODSC Europe 2023 for free here.

Keynote: Build and Deploy PyTorch models with Azure Machine Learning

Henk Boelman | Senior Cloud Advocate | Microsoft

With machine learning becoming more and more an engineering problem the need to track, work together, and easily deploy ML experiments with integrated CI/CD tooling is becoming more relevant than ever.

In this session, we take a deep dive into Azure Machine Learning, a cloud service that you can use to track as you build, train, deploy, and manage models. We use the Azure Machine Learning Python SDK to manage the complete life cycle of a PyTorch model, from managing the data, to training the model and finally running it into a production Kubernetes cluster.

At the end of this session, you have a good grasp of the technological building blocks of Azure machine learning services and have trained a PyTorch model at scale.

Semantic Analysis and Procedural Language Understanding in the Era of Large Language Models

Dr. Gözde Gül Şahin | Assistant Professor, KUIS AI Fellow | KOC University

In this talk, we will first introduce the field of semantics and the task of semantic analysis, a.k.a, semantic parsing from a multilingual perspective. In particular, we will first discuss the layers of meaning, from morphology to pragmatics, and then define the scope of semantics as a field. We will then discuss the current benchmarks and datasets spanning different meaning representations, such as sentence-level frame representations (e.g., PropBank, VerbNet, FrameNet), semantic trees (e.g., AMRs), first-order logic (FOL) and Discourse Representation Theory (DRS) which allows for document-level representation.

Why GPU Clusters Don’t Need to Go Brrr? Leverage Compound Sparsity to Achieve the Fastest Inference Performance on CPUs

Damian Bogunowicz & Konstantin Gulin | Machine Learning Engineers | Neural Magic

Forget specialized hardware. Get GPU-class performance on your commodity CPUs with compound sparsity and sparsity-aware inference execution. This talk will demonstrate the power of compound sparsity for model compression and inference speedup for NLP and CV domains, with a special focus on the recently popular Large Language Models.

Why the Jagged Edge Matters

Jutta Treviranus | Director and Professor at Inclusive Design Research Centre | OCAD University

Statistical reasoning shapes our collective sense of what is true, what is best, and what should happen next. Even before we mechanized statistical prediction through machine learning, it was a habitual convention that was used as a marker of quality, rigorous science, and democratic fairness.

Apache Kafka for Real-Time Machine Learning Without a Data Lake

Kai Waehner | Global Field CTO, Author, International Speaker | Confluent

This talk compares a cloud-native data streaming architecture to traditional batch and big data alternatives and explains benefits like the simplified architecture, the ability of reprocessing events in the same order for training different models, and the possibility of building a scalable, mission-critical ML architecture for real-time predictions with muss fewer headaches and problems.

The talk explains how this can be achieved by leveraging Apache Kafka, Tiered Storage, and TensorFlow, but also explores when to better combine data streaming with a data lake.

Few-shot Learning for Natural Language Understanding

Helen Yannakoudakis, PhD | Assistant Professor | King’s College London

In this talk, we address the challenge of learning from limited data for a range of natural language understanding tasks and applications. We will present our work on few-shot learning approaches to NLP in both monolingual and cross-lingual settings and present findings in tasks such as word sense disambiguation, syntactic parsing, and text classification. Finally, we will present recent research on approaches that can enable higher levels of data efficiency, and show how they can outperform much more computationally complex counterparts.

Probabilistic Machine Learning for Finance and Investing

Deepak Kanungo | Founder and CEO | Hedged Capital LLC, Advisory Board Member | AIKON

The objective of this session is to make attendees familiar with the reasons why probabilistic machine learning is the next generation of AI in finance and investing.

Here are some of the learning outcomes:

– Why standard ML systems are inherently unreliable and dangerous in finance and investing

– The three types of errors in all financial models and why they are endemic

– The paramount importance of quantifying the uncertainty of model inputs and outputs

– The three types of uncertainty and different approaches to quantifying them

– Deep flaws in conventional statistics for quantifying uncertainty in financial models

– The probabilistic ML framework and its various components

AI and Bias: How to detect it and how to prevent it

Sandra Wachter, PhD | Professor, Technology, and Regulation | Oxford Internet Institute, University of Oxford

Western societies are marked by diverse and extensive biases and inequality that are unavoidably embedded in the data used to train machine learning. Algorithms trained on biased data will, without intervention, produce biased outcomes, and increase the inequality experienced by historically disadvantaged groups.

We provide concrete recommendations including a user-friendly checklist for choosing the most appropriate fairness metric for uses of machine learning under EU non-discrimination law.

The Unfairness of Fair Machine Learning: Levelling Down and Strict Egalitarianism by Default

Brent Mittelstadt, PhD | Associate Professor, Senior Research Fellow, and Director of Research | Oxford Internet Institute, University of Oxford

In this talk, we will examine the causes and prevalence of leveling down across fairML, and explore possible justifications and criticisms based on philosophical and legal theories of equality and distributive justice, as well as equality law jurisprudence. FairML does not currently engage in the type of measurement, reporting, or analysis necessary to justify leveling down in practice. We will propose a first step towards substantive equality in fairML: “leveling up” systems by design through enforcement of minimum acceptable harm thresholds, or “minimum rate constraints,” as fairness constraints. We will likewise propose an alternative harms-based framework to counter the oversimplified egalitarian framing currently dominant in the field and push future discussion more toward substantive equality opportunities and away from strict egalitarianism by default.

On the Engineering of Social Values

Carles Sierra, PhD | Director | Artificial Intelligence Research Institute

Ethics in Artificial Intelligence is a wide-ranging field that encompasses many open questions regarding the moral, legal, and technical issues that arise with the use and design of ethically-compliant autonomous agents. Under this umbrella, the computational ethics area is concerned with the formulation and codification of ethical principles into software components. In this talk, we will take a look at a particular problem in computational ethics: the engineering of moral values into autonomous agents. I will present some results in this area and a vision for future research.

Sign me up for ODSC Europe 2024!

If the sessions above have you itching for more, then you won’t want to miss ODSC Europe 2024 coming this September 5th-6th. You can sign up for it for free here to watch live as it happens.

If you want a more hands-on approach with the latest in AI, such as LLMs, RAG, machine & deep learning, generative AI, and more, then we still have passes available for our in-person conference. Tickets are currently available and are 40% off, so don’t delay!

Originally posted on OpenDataScience.com

Read more data science articles on OpenDataScience.com, including tutorials and guides from beginner to advanced levels! Subscribe to our weekly newsletter here and receive the latest news every Thursday. You can also get data science training on-demand wherever you are with our Ai+ Training platform. Interested in attending an ODSC event? Learn more about our upcoming events here.