Online Edition Session Archives - ML Conference

Welcome & Introduction

datasyncer@sandsmedia.com — Tue, 21 Jul 2020 08:10:50 +0000

Don’t miss the introduction to ML Con & IoT Con with an outlook into the program and to the conference proceedings by Conference Chair Sebastian Meyen.

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Conference Wrap up & Raffle

datasyncer@sandsmedia.com — Tue, 21 Jul 2020 08:10:50 +0000

At the end of the main conference days, we will summarize the sessions and address the most important issues that have emerged during the last days.

This will be followed by a raffle for the chance to win some great prizes.

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No prejudice. No remorse. Unsupervised ML

datasyncer@sandsmedia.com — Wed, 15 Jul 2020 07:14:43 +0000

Let’s be frank. Machine learning is not about algorithms. Number one question in machine learning is data: how to collect and process it. Number two question is labeling. Machine learning engineers love labels and hate labeling. We spend time and resources, we invite humans in the game to say explicitly what the right answer is. But sometimes we want to be cautious and avoid trusting judgments. This helps us to perceive what the data actually mean, and to find hidden rules. Today we’ll look at a few business cases and figure out how to apply unsupervised machine learning in order to create powerful systems.

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Lunch-Talk: The Ethics of AI – dealing with difficult choices in a non-binary world

datasyncer@sandsmedia.com — Fri, 10 Jul 2020 05:48:34 +0000

I started working with user experience (UX) long before the term was even known. Over the past 40 years, I’ve encountered many issues that have disturbed me – from creating purposely addictive programs, sites, and apps, to the current zeitgeist for various design trends at the expense of basic usability. I have seen research that is faked, ignored, or twisted by internal company politics and by the cognitive bias of the design team. And I have seen countless dark patterns that suppress accessibility and diversity by promoting false beliefs and false security.

Whenever we say, “That’s not my problem,” or, “My company won’t let me do that,” we are handing over our ethical responsibility to someone else – for better or for worse. Do innocent decisions evolve so that they promote racism or gender discrimination through inadvertent cognitive bias or unwitting apathy? Far too often they do.

We, as technologists, hold incredible power to shape the things to come. I would like to share my thoughts with you so you can use this power to truly build a better world for those who come after us!

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A Few Million Words Later – Text Analytics for Software Quality Assurance in Practice

datasyncer@sandsmedia.com — Wed, 11 Mar 2020 13:44:32 +0000

With language assistants, automatic translators and new record-breaking language models appearing every week, it seems like anything is possible, doesn’t it?

We have been using Natural Language Processing (NLP) techniques for many years in more than 60 projects in the automotive and insurance sectors for the quality assurance of software. Examples are the automatic checking of requirements, test generation from user stories, and automated traceability analyses.

This results in a somewhat more differentiated picture.

In this presentation we will show what is state of the art, what is not yet possible in practice, and what (in our opinion) will never work. With this we want to show the possibilities of text analysis without falling into unrealistic expectations or even buzzwords.

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AbadIA: How to build an AI from zero to learn to play and solve a tough game

datasyncer@sandsmedia.com — Mon, 02 Mar 2020 10:20:30 +0000

The process of building an AI looks like is so glamorous but is a long process, and at the end of the day, the tasks related to the AI model are just a 5% or less of the project.
We will see how to start an AI project from zero: defining the objectives, creating the architecture, building the game interfaces, massive data pipelines, defining model strategies, how to parallelize everything, etc.
The “the abbey of crime” is an adamant 8-bit game. This game is more complicated than Montezuma Revenge and is a perfect challenge for an AI. Its complexity is about 10^1000 legal moves to solve it.
As AI technology, we will use Reinforcement Learning using Deep Neural Networks and Monte Carlo Tree Search.
The takeaways of this talk will be: understanding all the process involved to create an AI and learning the basics of Reinforcement Learning.

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AI with a devops mindset – experimentation, sharing and easier ML deployment

datasyncer@sandsmedia.com — Mon, 02 Mar 2020 10:20:29 +0000

In this talk, I’ll look at ML & AI from a devops perspective, emphasizing how to shorten the time-to-market of such products. Although ML development is relatively new, paradigms familiar to the devops world can be translated to the ML world: reduced batch sizes, CI/CD, sharing, observability, … The attendees can go home with a...

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Production nightmare of building AI systems at scale – The last mile

datasyncer@sandsmedia.com — Mon, 24 Feb 2020 13:42:50 +0000

AI is no longer a secret ritual performed by digital-native organizations. Indeed, there was a time when legacy industries would brush off the need of AI systems right at the outset. The times have changed! This talk is not about how to build an AI use case, or how to get managements’ buy-in, or how the lack of talent hampers such initiatives. Neither does it focus on how to get the necessary data, nor on building DL models. While a few organizations continue to be challenged by these concerns, most are facing a completely new predicament. A business-driven dilemma of operationalizing AI systems beyond prototypes. That’s what we are going to talk about. I will share my vision, blue print and personal stories across telecom, manufacturing and automotive industries, including both corporate and start-up experiences. I will tell you how to go from a shiny proof-of-concept to AI production systems, what challenges we faced, and the best practices to avoid the pitfalls.

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Protecting AI Solutions From Attacks

datasyncer@sandsmedia.com — Mon, 03 Feb 2020 10:52:55 +0000

Attacks on machine learning systems include a wide range of different approaches and do not end with the notorious Adversarial examples. Attacks can change the logic of the system (Adversarial examples and reprogramming) to obtain data from AI systems (so-called Membership inference or Model Extraction attacks) or, conversely, to inject data into the system (Poisoning, Backdoor, Trojan). Unfortunately, the silver bullet from these attacks has not been invented and is unlikely to be, but we will show you how to approach the security assessment of AI algorithms correctly and what metrics to look at, what approaches to protection can be applied and where is the best place to apply and how to eventually get the maximum protection for reasonable investment of resources.

* AI Security vs traditional Cybersecurity
* Who should care about AI Security: Industries
* Why should we care about AI Security: Threats, Initiatives, Research
* What is AI Security: AI Objects, Applications, ML tasks
* How to break AI: Different attacks
* When to protect AI: Approaches to protect AI
* Step by step AI Security project
* Where are we going?

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An Introduction to Natural Language Generation

datasyncer@sandsmedia.com — Mon, 03 Feb 2020 10:52:55 +0000

The amazing results of OpenAI’s GPT-2 have rekindled interest in Natural Language Generation (NLG), a subfield of Natural Language Processing (NLP). But how does GPT-2 work, how is it trained and how does one interpret its output to generate text? And why, if those new neural network / transformer – based models have such an impressive performance, are rule-based NLG systems still the norm in commercial text generation applications?

This talk will cover the basics of rule based and ML-based NLG systems and their respective advantages and disadvantages. You will learn how Machine Learning systems like GPT-2 learn to generate text and what their strengths and weaknesses are. We will have a look at the latest attempts to better control the output of systems like GPT-2 and what is still necessary for Deep Learning based systems to completely take over one of the last bastions of symbolic/rule-based AI, natural language generation.

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