Michal Trnka
Gerulata Technologies
VOL_11
24 April 2024 at 5 pm
online
Navigating Chaos: How Can AI Help Make Sense of Information Overload
During the 11th volume of Better AI Meetup, we will be talking about how to successfully use AI to identify, qualify and rate online content, spot fraudulent activities and data and make sense of vast amounts of data available.
Our presenters will show us how they use AI & LLMs to spot generated content or misinformation and how LLMs can be harnessed to produce structured, verified, actionable information out of the available data.
_Speakers
Founder and CEO of Gerulata Technologies, a defense-tech startup specializing in AI-powered software tools for countering hostile information operations and hybrid threats. Gerulata serves clients and partners in 14 countries around the world, including government institutions, armed forces, diplomatic missions, as well as research institutions and universities.
Michal is an experienced leader and manager with two decades of experience in the fields of information technology and security. Prior to founding Gerulata Technologies in 2020, he held several leadership positions, including over 10 years as the Chief Marketing Officer at ESET, a global leader in cybersecurity. He is a graduate of the University of Oxford.
Ivan Srba
Kempelen institute of intelligent technologies
Senior researcher in KInIT. Ivan Srba has expertise in artificial intelligence, machine learning and natural language processing, with a focus on low resource AI, learning with limited labelled data, multilinguality and social computing systems, especially social networks.
His current primary research interest is in supporting media professionals in producing credible content and countering online disinformation. He is currently a researcher and an executive board member in three EU-funded projects – CEDMO, vera.ai and AI-CODE.
_Agenda
Language: English
5:00 pm
Welcome
5:10 pm
On Capabilities of Large Language Models: Estimating Credibility and Content Quality - Ivan Srba, KInIT
5:40 pm
Extracting meaning from chaos: Using AI for structured knowledge - Michal Trnka, Gerulata Technologies
6:05 pm
Live Q&A – _Slido #better_ai
_Sign up
Past Meetups
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Our guests will be talking about how to use the AI Act to create trustworthy, responsible AI applications as well as how to proactively prepare companies and research teams for the regulation. There’s no better place for such a discussion than the Better AI Meetup!
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Medicine is still regarded as the domain where humans rule. But AI is proving a valuable tool in their hands. If used correctly, it has the potential to see better than the best-trained physician and can spot problems before they grow too dangerous.
First, we will be talking about the advances AI has already reached in radiology and what opportunities it can bring in the very near future. Next, we will see the challenges of designing a medical AI so that it passes the regulatory approval.
Join us to see how AI will be helping you get healthier in the coming future!
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Having conversations with AI is being touted as the future of our communication with computers. For the conversations to be both meaningful and easy to understand, they need to be properly trained in as many languages as possible, including those with smaller numbers of speakers. Also, the cheaper the training, the better.
During the 8th Better AI Meetup, we will be addressing exactly these challenges: evaluating quality of language models, training those models without breaking the bank and the future expectations from language models in the business and elsewhere.
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In this edition of Better AI Meetup we will be talking about speech processing as well as designing ways to evaluate machine learning algorithms for accuracy and speed and choose a proper one for a particular use case.
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We will present two practical examples on how to use generative AI for improving datasets for machine learning.
Lukáš will show us how to use AI for annotation and labeling of large datasets, while Igor has developed novel ways to generate fingerprints for training fast, accurate models for fragmented fingerprints. In both cases, AI is helping improve the accuracy of models that then help automate administrative processes or even solve crimes.
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Federated Learning refers to machine learning over private data stored across a large number of heterogeneous devices, such as mobile phones or hospitals. In an October 2020 Forbes article, and alongside self-supervised learning and transformers, Federated Learning was described as one of three emerging areas that will shape the next generation of AI technologies. In this talk I will explain how we recently resolved one of the key open algorithmic and mathematical problems in this field.
AI Meets Humans: The Challenges of Introducing ML Models into Everyday Life
What happens when research, its practical applications and human factor intersect? How to train the machine learning models to expect the unpredictable events that are inevitable in real life? This time we’ll look at the challenges the AI and ML technologies face when put to practical use.
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This time, we want to give you insight into how AI and machine learning methods are put to practical use at Innovatrics for training new biometric algorithms and in Swiss Re, one of the largest re-insurance companies in the world, to develop new, innovative products.
Machine learning (ML) helped us to solve such complex tasks as face recognition, however when it is done on a large scale it relies on many high-effort data management tasks. We will describe our winding road from images in folders and labels stored in CSV files to structured versioned datasets with reproducible data processing pipelines. Moreover we mention how we deal with data versioning / validation / quality evaluation, reproducible ML trainings and experiment management.
First ever Slovak language neural model and why AI is becoming mainstream
The opening presentation is centered on the most recent success of AI research in Slovakia – the SlovakBERT model. Creating a neural language model for languages with limited available corpus can be challenging. We will explain what are the main obstacles when creating such model and what its practical applications are. The second presentation will focus on why AI is becoming mainstream and what it means for the future of the technology.
