WeAreDevelopers AI Congress Vienna 2018
2018-12-04..2018-12-05
Vienna, Austria
| title | author | length |
|---|---|---|
| Official After Movie | 02m | |
Event recap montage that shows conference logistics and networking rather than technical depth, with quick cuts of sessions, audience shots and upbeat music… | ||
| Opening | Margarete Schramböck | 16m |
Opening remarks announce a concrete agenda: Austria has created the first ministry in Europe designed digital-first and declares a Year of Digitalization with… | ||
| Self replicating genies - How to democratize and ensure ethics in AI | Christian Heilmann | 40m |
Argument: current ML spawns algorithms that act like "self‑replicating genies" and require ethics, ownership and transparency. Demonstrations include Microsoft… | ||
| How we democratize AI with Linux foundation Open Source | Eyal Felstaine | 49m |
Claims that open source democratizes AI by packaging workflows and a marketplace into Acumos AI which provides a visual designer and an Acumos AI marketplace… | ||
| Snap ML: a highly-accelerated, scalable software library for machine learning | Haris Pozidis | 42m |
Snap ML is a C++/CUDA library with scikit‑learn‑style Python bindings, MPI and Spark APIs that trains generalized linear models using GPU‑accelerated… | ||
| Cracking the code: neuronal networks in the brain | Sabria Lagoun | 30m |
Live recordings from freely moving animals use tetrode clusters and Neuropixels probes and calcium miniscope implants to read single neurons while behavior is… | ||
| AI: Empowering us all | Tim O’Brien | 30m |
Microsoft showcases concrete tools and results: demo of Azure Cognitive Search that ingests SQL, Cosmos DB and blob storage to index and query data; a pilot… | ||
| Automate customer ticket handling: from an idea to a production ready model | Lukas Kölbl | 32m |
End-to-end system classifies incoming customer tickets into operational categories and triggers replies or agent routing, built in ~6–7 weeks then iterated… | ||
| An Ethical Crisis in Computing? | Prof. Moshe Vardi | 48m |
Argues computing shows a crisis of trust driven by surveillance capitalism and corporate steering of society rather than an “ethics course” gap; cites deployed… | ||
| Simplify, speed and improve development with DevOps | Gerwald Oberleitner | 39m |
Argues that DevOps practices can handle AI/ML end-to-end and demonstrates a working pipeline: code and Jupyter notebooks in git, Azure Repos/GitHub for… | ||
| Practicing Data Science in the cloud | Dr. Robert Hoffmann | 37m |
Shows an end-to-end pipeline using Azure Machine Learning Services with Jupyter notebooks to train a one‑class SVM on streamed Raspberry Pi sensor data, store… | ||
| Bias in NLP | Navid Rekabsaz | 28m |
Shows that word2vec trained on English Wikipedia encodes measurable gender skew: embeddings are pulled toward gendered contexts via negative sampling, causing… | ||
| Build a great conversationalist | Lorenzo Barbieri | 41m |
Demonstrates how to assemble a production bot using the Azure Bot Framework with connected channels (web chat, Skype, Facebook Messenger, Slack, Cortana… | ||
| Learning Fast: Home Experimentation and Open Research at Spotify | Ching-Wei Chen | 28m |
Spotify personalizes the default Home for 191 million users across 40 million tracks using contextual bandits to balance exploration and exploitation, plus an… | ||
| What Security Measures Are Necessary to Trust in AI? | Ahmad Haj Mosa, Wolfgang Ebner, Michaela Jungwirth, Axel Puwein, Jost Bernasch | 45m |
Panelists describe concrete measures: supervised ML in finance producing a 90% probability customer score that needs business-in-the-loop explanation; anomaly… | ||
| Artificial Intelligence for Artificial Humanoids | Rodolphe Gelin | 31m |
SoftBank Robotics describes working humanoids Pepper and prototype Romeo for education, retail and senior care, not full home companions yet. Perception uses a… | ||
| Unleash your AI potential | Ramshanker Krishnan | 32m |
Microsoft positions AI as practical today: cognitive services in containers run on-prem for OCR on tens of thousands of hospital records or local retail CCTV… | ||
| Making machine learning accessible using Keras | Pavithra Vijay | 31m |
Keras is a high‑level Python neural‑network API that runs on TensorFlow, CNTK or Theano and claims about 300,000 users with ~754 GitHub contributors; it… | ||
| Deep learning and spark | Teemu Kinnunen | 32m |
I will show how one can use trained deep learning models (fasttext) to classify millions of messages using spark. I quickly explain basic terms such as machine… | ||
| Artificial Intelligence in the Modern Workplace | Kirk Koenigsbauer | 25m |
Microsoft shows concrete AI features inside Office 365: PowerPoint Designer auto-generates layouts and alt text (claims three millionth hit), image APIs tag a… | ||
| Explore Cognitive Services | Roberto Andreoli | 35m |
Microsoft Cognitive Services are demonstrated end to end: Translator real-time subtitles via a PowerPoint add‑in and mobile app, REST calls that return JSON… | ||
| AI and Advanced Analytics applied at | Lubomir Karlik | 35m |
Raiffeisen builds a proof-of-concept virtual call‑center agent for CEE languages that runs as a cloud prototype, ties voice‑to‑text to NER and dialogue logic… | ||
| Augmented Intelligence - A Marriage between Machine and Human | Simon Stiebellehner | 32m |
Argues that augmented intelligence is a necessary intermediate step toward full automation, using explainability and human feedback to build trust… | ||
| On the Importance of Explainability | Holzinger | 32m |
The talk discusses the problem of explainability in AI: modern systems (e.g., face recognition and image classification) can outperform humans but cannot… | ||
| Bixby: A New Take on the Intelligent Assistant | Adam Cheyer | 49m |
Claims a third major assistant paradigm and shows a working system: a downloadable IDE called Bixby Developer Studio where developers define concepts and… | ||
| Data Mining for Security Anomalies | Martin Pirker | 34m |
Covers project TARGET's pipeline for security anomaly detection: collecting streams of security event data from systems, preprocessing and storing those event… | ||
| I need data (for research)! | Dr. Johanna Ullrich | 32m |
Security research and ML need real datasets but public sources are patchy: clouds act as a black box, so teams use ICMP pings then TTL-based probing as side… | ||
| How to be best in conveying the message by using NLP approach (Big Five modeling) | Tomislav Krizan | 32m |
Claims that Big Five scores are predictable from short social text and metadata and that models improve from SVM/KNN to deep learning. Pipeline uses word… | ||
| Develop intelligent apps for the Modern Workplace | Martina Grom | 32m |
Shows concrete demos that wire Office 365 into AI: querying organizational data via Microsoft Graph (Graph Explorer, SDKs, Windows Timeline) to power the Teams… | ||
| The Contradiction bot | Matthias Lichtenthaler | 22m |
A system claims to find concrete inconsistencies by running semantic analytics and AI over structured and unstructured sources to flag literal contradictions… | ||
| Deep Learning for Recommender Systems | Alexandros Karatzoglou | 30m |
Survey of practical deep-learning recipes for recommendation: learns dense item vectors using Prod2Vec / item embeddings (skip‑gram on purchase sequences)… | ||
| Building a Large Music Recommender Leveraging AI, Deep Learning and Human Expertise | Òscar Celma | 34m |
Pandora combines human-curated Music Genome Project annotations with machine listening and deep learning models trained on data from ~70 million monthly users… | ||
| Remaining Challenges in Deep Learning based NLP | Vered Shwartz | 31m |
Deep learning powers word2vec, RNNs and LSTMs but key gaps remain: models need massive domain-specific data (Google Translate defaults to Igbo/Bible defaulting… | ||
| Ocean Protocol - Empowering a decentralised marketplace for the AI ecosystem | Marcus Jones | 30m |
Ocean Protocol is a nonprofit, Singapore-registered decentralized data exchange that already ships a beta smart-contract layer on Ethereum smart contracts and… | ||
| When humans teach machines | Shai Hertz | 31m |
A self‑service pipeline lets domain experts build sparse-topic text classifiers by: creating a visual query to find dense positives, extracting triage terms… | ||
| The hand prosthesis that learns from the user | Sebastian Amsüss | 34m |
A machine‑learning myoelectric prosthesis from Otto Bach records eight EMG channels around the forearm, extracts simple time‑domain features, and trains on a… | ||
| Real Life Customer Stories with AI | Rina Ahmed | 38m |
Built an employee‑attrition classifier that prioritizes recall to avoid missed leavers, comparing scikit‑learn vs Azure ML Studio and matching a boosted… | ||
| Build Your Intelligent Edge | Gerhard Ebinger | 31m |
Brings cloud-trained models down to devices by packaging ML as IoT Edge containers that run locally and sync when connected, demonstrated on a fleet of about… | ||
| Bridging the gap between AI and UI | Liad Magen | 20m |
Reviews concrete model-interpretability tools and shows how they plug into product UIs: uses LIME and SHAP to attribute features, inspects RNN attention with… | ||
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