Agenda

WeAreDevelopers AI Congress Vienna

  4-5 December ’18

  Hofburg Vienna

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Keynote & Talks Meetup & Panel Discussion Break Lunch break

08:30
09:00
09:30
10:00
10:30
11:00
11:30
12:00
12:30
13:00
13:30
14:00
14:30
15:00
15:30
16:00
16:30
17:00
17:30
18:00
18:30
19:00
Stage Black Box
Stage Black Box
Registration & Pre-Congress Coffee
08:30 - 09:30

WeAreDevelopers Introduction

Self replicating genies - How to democratize and ensure ethics in AI
09:45 - 10:30
Christian Heilmann <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/chris-heilmann-enterjs-cropped-1.jpg" alt="Smiley face" height="50" width="50" align="right">
09:45 - 10:30 @ Stage Black Box

Christian Heilmann
Senior Program Manager | Microsoft
Christian Heilmann

There is no question that we are living in the age of automation and machine learning. Humans and sensors create far too much data for humans to comprehend which is why we need machines to make them digestible for us. The problem is that machines don't have any ethics and technology is still seen as magic by people who give away far too much of their personal data without knowing who listens. To make the AI revolution work we need to build ethical systems and install an ownership in users.

Break

How we democratize AI with Linux foundation Open Source
10:45 - 11:30
Eyal Felstaine <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/Eyal-Felstain.jpg" alt="Smiley face" height="50" width="50" align="right">
10:45 - 11:30 @ Stage Black Box

Eyal Felstaine
CTO | Amdocs
Eyal Felstaine

Many Artificial Intelligence tools are difficult to implement and require dependency on closed vendor solutions. Linux Foundation Deep Learning Foundation (LFDL) was established to democratize AI. Acumos AI is one of the projects developed as part of LFDL, it is the first tool to give users a visual workflow for designing AI and machine-learning applications, as well as a marketplace for freely sharing AI models. In this session, Dr. Eyal Felstaine, Governing board member of the Linux foundation Deep Learning will walk you through the LFDL activity and projects, including Acumos AI distributed platform, explaining its benefits, including how it will free up data scientists, developers and model trainers across different industries and fields, (from network and video analytics, to content curation and threat prediction) so they can focus on their core competencies and accelerate innovation.

Break

Snap ML: a highly-accelerated, scalable software library for machine learning
11:45 - 12:30
Haris Pozidis <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/11/Haris-Pozidis.jpg" alt="Haris Pozidis" height="50" width="50" align="right">
11:45 - 12:30 @ Stage Black Box

Haris Pozidis
Manager of Cloud Storage & Analytics | IBM Research
Haris Pozidis

Snap Machine Learning (Snap ML) is a new software framework for high-performance training of generalised linear models. It combines recent advances in machine learning systems and algorithms in a nested manner to reflect the hierarchical architecture of modern computing systems. Snap ML can accelerate training in distributed environments where intra-node communication is cheaper than inter-node communication. We will review the implementation of Snap ML in terms of GPU acceleration, pipelining, communication patterns and software architecture, highlighting aspects that were critical for achieving high performance. We evaluate the performance of Snap ML in both single-node and multi-node environments, quantifying the benefit of the hierarchical scheme and the data streaming functionality, and compare with popular machine learning software frameworks. We will also present a benchmark on a terabyte-scale click-through-rate prediction dataset and show that Snap ML achieves the same test loss an order of magnitude faster than any of the previously reported results.

Lunch Break
12:30 - 13:15

Cracking the code: neuronal networks in the brain
13:15 - 13:45
Sabria Lagoun <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/Sabria-Lagoun-2.jpg" alt="Smiley face" height="50" width="50" align="right">
13:15 - 13:45 @ Stage Black Box

Sabria Lagoun
Co-Founder | The Brainstorms
Sabria Lagoun

If the first artificial neurons were inspired by the brain, A.I soon diverged to become a discipline on its own. However, evolution produced performant, ultra-light, energy-effective biological neuronal networks. Insects and worms only possess a few hundred neurons, but they are able to navigate, make decisions, adapt their behavior and communicate. Through cutting edge techniques, we are now able to explore these natural networks in vivo, during the execution of extremely demanding cognitive tasks. I will describe fantastic encoding solutions that emerged from the brains’ biophysical constraints. I will give examples of bio-inspired A.I. algorithms for learning and spatial navigation, and explain how artificial neuronal networks can spontaneously show a behavior identical to the live brain.

Break

Kirk Koenigsbauer
14:00 - 14:30

Break

An Ethical Crisis in Computing?
14:45 - 15:30
Prof. Moshe Vardi <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/Moshe-Vardi.jpg" alt="Smiley face" height="50" width="50" align="right">
14:45 - 15:30 @ Stage Black Box

Prof. Moshe Vardi
Professor in Computational Engineering | Rice University
Moshe Vardi

Computer scientists think often of "Ender's Game" these days. In this award-winning 1985 science-fiction novel by Orson Scott Card, Ender is being trained at Battle School, an institution designed to make young children into military commanders against an unspecified enemy. Ender's team engages in a series of computer-simulated battles, eventually destroying the enemy's planet, only to learn then that the battles were very real and a real planet has been destroyed. Many of us got involved in computing because programming was fun. The benefits of computing seemed intuitive to us. We truly believe that computing yields tremendous societal benefits; for example, the life-saving potential of driverless cars is enormous! Like Ender, however, we realized recently that computing is not a game--it is real--and it brings with it not only societal benefits, but also significant societal costs, such as labor polarization, disinformation, and smart-phone addiction. The common reaction to this crisis is to label it as an "ethical crisis" and the proposed response is to add courses in ethics to the academic computing curriculum. I will argue that the ethical lense is too narrow. The real issue is how to deal with technology's impact on society. Technology is driving the future, but who is doing the steering?

Break
15:30 - 16:00

Microsoft AI: Empowering us all
16:00 - 16:30
Tim O'Brien <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/0.jpg" alt="Smiley face" height="50" width="50" align="right">
16:00 - 16:30 @ Stage Black Box

Tim O’Brien
General Manager AI Programs | Microsoft
Tim O’Brien

We are inspired by those who endeavor to think big, dream bold, and advance our world. Let us share with you how we can help to --> empower business by helping to build the next breakthrough product, reimagining a customer experience, or transforming business processes. --> empower developers: From advancement in silicon to breakthroughs in speech and image recognition technology, Microsoft is deeply committed to open source communities and putting AI into the hands of every developer and data scientist. Learn how to start building intelligence into your solutions through the AI School. --> empower society: Around the world, Microsoft AI technology and programs are helping to solve previously intractable problems and address public and societal challenges. We are committed to ensuring the ethical and trusted use of AI and are invested in the professional advancement of others by providing skills-based training.

Break

TBA
16:45 - 17:15

Break

Simplify, speed and improve development with DevOps
17:30 - 18:00
Gerwald Oberleitner <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/Gerwald-Gerwaldo-4.jpg" alt="Gerwald Oberleitner" height="50" width="50" align="right">
17:30 - 18:00 @ Stage Black Box

Gerwald Oberleitner
Technology Solutions - Azure | Microsoft
Gerwald Oberleitner

Agile, CI/CD, DevOps and frequent releases are buzz-words for modern software development practices – but does this also apply for Artificial Intelligence (AI) and Machine Learning (ML) projects ? The session will give an introduction to DevOps, popular cross-platform tools like GitHub or Azure DevOps and how DevOps can apply for AI and ML projects.

Break

Practicing Data Science in the cloud
18:15 - 18:45
Dr. Robert Hoffmann <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/csm_Hoffmann_Robert_ad0b66723f.jpg" alt="Robert Hoffmann" height="50" width="50" align="right">
18:15 - 18:45 @ Stage Black Box

Dr. Robert Hoffmann
Solution Architect and Data Scientist | Microsoft
Robert Hoffmann

The data science process has a multitude of steps, and each has its own importance. We will explore how an end-to-end Machine Learning pipeline comes to life in the cloud.

Closing

Stage White Box
Stage White Box
Registration & Pre-Congress Coffee
08:30 - 09:30

WeAreDevelopers Introduction Streaming

Streaming: Self replicating genies - How to democratize and ensure ethics in AI
09:45 - 10:30
Christian Heilmann <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/chris-heilmann-enterjs-cropped-1.jpg" alt="Smiley face" height="50" width="50" align="right">
09:45 - 10:30 @ Stage White Box

Christian Heilmann
Senior Program Manager | Microsoft
Christian Heilmann

There is no question that we are living in the age of automation and machine learning. Humans and sensors create far too much data for humans to comprehend which is why we need machines to make them digestible for us. The problem is that machines don't have any ethics and technology is still seen as magic by people who give away far too much of their personal data without knowing who listens. To make the AI revolution work we need to build ethical systems and install an ownership in users.

Break

Explainable Deep Learning - When, Why and How
10:45 - 11:30
Ahmad Haj Mosa & Fabian Schneider <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/11/Ahmad-Haj-Mosa.jpg" alt="Ahmad Haj Mosa" height="50" width="50" align="right">
10:45 - 11:30 @ Stage White Box

Ahmad Haj Mosa
Manager | PwC
Ahmad Haj Mosa

Deep learning (DL) is one of the fastest-growing fields in artificial intelligence. The importance of deep learning is due to its capability to learn a high level of abstraction of the raw attributes. However, deep learning models are still not that deep enough to explain them self. In my talk, I will focus on when and why explainable AI is important and how to achieve it. The speech will include a review of the recent related developments, the difference between explaining a black-box and training an explainable black-box using Graph Neural Networks. It will also include a use case in the field of Tax and Legal.

Break

Build a great conversionalist
11:45 - 12:30
Lorenzo Barbieri & Roberto Andreoli <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/Lorenzo-Barbieri.jpg" alt="Smiley face" height="50" width="50" align="right"></br> <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/Roberto.jpg" alt="Smiley face" height="50" width="50" align="right">
11:45 - 12:30 @ Stage White Box

Discover how you can leverage the Azure BOT Framework to build, connect, deploy, and manage intelligent bots to naturally interact with your users via your apps or website.

Lunch break
12:30 - 13:15

Bias in NLP
13:15 - 13:45
<img src="https://www.wearedevelopers.com/wp-content/uploads/2018/09/Navid-Rekabsaz-1.png" alt="Smiley face" height="50" width="50" align="right">
13:15 - 13:45 @ Stage White Box

Navid Rekabsaz
Post-doctoral Researcher | Idiap Research Institute
Navid Rekabsaz

Recent advances in Word Embedding models (representation of words in high-dimensional vectors) provide promising results in capturing semantics of language, becoming the essential building blocks of many Natural Language Processing (NLP) applications—from search engines and job recommendation platforms to automatic machine translators. Since these models are often trained on large amount of historical data, they automatically capture the inherent bias in data, which can potentially cause ethical bias in our decision making. In this talk, Navid first explains the fundamentals as well as interesting qualities of the word2vec algorithm, an effective and efficient neural network-based approach to word embedding. He then discusses a recent study to show how the definitions of several occupations, captured by word2vec from the English Wikipedia text, are biased towards either female or male.

Break

Ching-Wei Chen
14:00 - 14:30
<img src="https://www.wearedevelopers.com/wp-content/uploads/2018/11/Ching-Wei-11-1.jpg" alt="Ching-Wei Chen" height="50" width="50" align="right">

Break

What Security Measures Are Necessary to Trust in AI?
14:45 - 15:30
Panel Discussion

Break
15:30 - 16:00

Are we there yet? Remaining Challenges in Deep Learning based Natural Language Processing
16:00 - 16:30
Vered Shwartz <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/Vered_Shwartz_hq.jpg" alt="Vered Shwartz" height="50" width="50" align="right">
16:00 - 16:30 @ Stage White Box

Vered Shwartz
Natural Language Processing Lab | Bar-Ilan University
Vered Shwartz

Deep Learning has changed the face of Natural Language Processing (NLP) over the recent years. The state-of-the-art performance in nearly every NLP task today is achieved by a neural model, often with a large gap from previous models, which were based on domain knowledge. The availability of pre-trained word embeddings (such as word2vec), trained on a massive amount of texts, has given our models a good starting point for meaning representation. The ability to learn vector representations of arbitrarily long texts using Recurrent Neural Networks (RNNs) has facilitated re-using the same architectures to solve various NLP tasks. Relying on latent neural representations obviated the need to hand-engineer features, allowing machine learning practitioners to join the party without any knowledge of the domain. Popular media announces every few days that AI has solved language. But looking beyond the performance metrics, did Deep Learning actually solve NLP? In this talk, I will present some of the remaining challenges in Deep Learning based NLP. Among which, the need for a large amount of training data creates algorithms that only address certain domains and languages well while performing much worse on others. How performance metrics can lie when you don't actually know what your black-box network is learning. The lack of robustness of our models. The shaky ground on which we base our meaning representation, distributional word embeddings. Our unreasonable expectations from sentence and document level representations. And finally, the lack of interpretability, that didn't only kill the debugging, but also causes major fairness issues.

Break

How to infuse your apps with AI
16:45 - 17:15
Guenda Sciancalepore <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/11/Guenda-Sciancalepore.jpg" alt="Guenda Sciancalepore" height="50" width="50" align="right">
16:45 - 17:15 @ Stage White Box

Guenda Sciancalepore
Technical Evangelist | Microsoft
Guenda Sciancalepore

Bringing intelligence in your core business processes is a key to bring organizations to the next level of efficiency and insights. Learn about ready Out-of-the-box AI solutions which unify data and infuse intelligence throughout Microsoft and non-Microsoft business applications by real world examples.

Break

How to make an AI iOS for personal use
17:30 - 18:00
Milan Todorovic <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/09/tosa-480x480.jpg" alt="Smiley face" height="50" width="50" align="right">
17:30 - 18:00 @ Stage White Box

Milan Todorovic
Swift/iOS Trainer and Software Engineer | Crossover
Milan Todorovic

Apple has two great tools to enable full cycle in machine learning development. Create ML is mechanism to prepare and evaluate model. CoreML uses ready-to-use model to get the maximum out of hardware in your app. All together, enables developer to cover full cycle in machine learning app development that will work on device, without sending any data in outer space. I will demonstrate how to develop an end-to-end app for iOS, when you have data, good idea and Mac.

Break

Reinhard Burgmann
18:15 - 18:45

Closing

Keynote & Talks Meetup & Panel Discussion Break Lunch break

08:30
09:00
09:30
10:00
10:30
11:00
11:30
12:00
12:30
13:00
13:30
14:00
14:30
15:00
15:30
16:00
16:30
17:00
17:30
18:00
18:30
19:00
Stage Black Box
Stage Black Box
Registration & Pre-Congress Coffee
08:30 - 09:30

Unleash your AI potential
09:30 - 10:00
Ramshanker Krishnan <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/Ramshanker-Krishnan.png" alt="Smiley face" height="50" width="50" align="right">
09:30 - 10:00 @ Stage Black Box

Ramshanker Krishnan
EMEA Leader AI | Microsoft
Ramshanker Krishnan

AI is the next big disruption that is going to impact every industry, and developers are key to making this impact real and positive. There is a paradigm shift that is required across the board in adopting AI and this is no different for developers. Learn about this paradigm shift, from AI projects across the world and what makes AI projects successful.

Break

Pavithra Vijay
10:15 - 10:45
<img src="https://www.wearedevelopers.com/wp-content/uploads/2018/09/DSC_8326-1.jpg" alt="Smiley face" height="50" width="50" align="right">

Break

Deep learning and spark
11:00 - 11:30
Teemu Kinnunen <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/Teemu-Kinnunen-.jpg" alt="Teemu Kinnunen" height="50" width="50" align="right">
11:00 - 11:30 @ Stage Black Box

Teemu Kinnunen
Data scientist | Futurice
Teemu Kinnunen

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 learning, text classification, fasttext and spark and then show a demo how one can train a model and classify messages.

Break

Explore Cognitive Services
11:45 - 12:15
Lorenzo Barbieri & Roberto Andreoli <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/Lorenzo-Barbieri.jpg" alt="Smiley face" height="50" width="50" align="right"></br> <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/Roberto.jpg" alt="Smiley face" height="50" width="50" align="right">
11:45 - 12:15 @ Stage Black Box

Learn how your applications will be able to see, hear, speak, understand and interpret your user needs through natural methods of communication.

Lunch Break
12:15 - 13:00

AI and Advanced Analytics applied at Raffeisen Bank International AG
13:00 - 13:30
Lubomir Karlik and Georg Köldorfer <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/11/Lubomir-Karlik-1.jpg" alt="Lubomir Karlik" height="50" width="50" align="right"></br> <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/11/Georg-Köldorfer.jpg" alt="Georg Köldorfer" height="50" width="50" align="right">
13:00 - 13:30 @ Stage Black Box

In this talk, practical application of AI and Advanced Analytics will be presented by both speakers. Lubomir Karlik will show insights on Natural Language Processing for Financial Institutions and Georg Köldorfer will present showcases of Advanced Analytics.

Augmented Intelligence - A Marriage between Machine and Human
13:30 - 14:15
Simon Stiebellehner <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/simon_filter-min-1-250x375-1.jpg" alt="Smiley face" height="50" width="50" align="right">
13:30 - 14:15 @ Stage Black Box

Simon Stiebellehnern
Data Scientist | Craftworks
 Simon Stiebellehner

The marriage of human and machine is commonly referred to as “augmented intelligence”. It is a logical and highly valuable intermediate step on our path to complete automation of significant parts of our lives. Augmented intelligence technologies leverage artificial intelligence to support humans’ decision processes. A concrete case of highly evolved augmented intelligence could be detecting cancer on medical images, computing confidence scores of these predictions, forwarding critical/low-confidence cases to a professional together with an explanation of what the system may have found suspicious, the professional then may return his feedback to the system for it to continue learning. The benefits of such systems are twofold. First, augmented intelligence builds trust through supporting humans without taking away their decision-making power. Trust in machine intelligence is an important prerequisite to more extensive automation. Second, it is important to recognize that both, machines and humans, have different strengths. Whilst machines excel at processing data at a high pace and at recognizing patterns they have frequently seen before, humans are able to learn well based on very few samples and are more flexible in their thinking and perception. Therefore, ideally, these strengths are combined to achieve synergies. However, making this marriage of machine and human a happy one is not trivial.

Break

Katharina Holzinger
14:30 - 15:00
<img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/Katharina-Holzinger.jpg" alt="Smiley face" height="50" width="50" align="right">

Break
15:00 - 15:45

Bixby: A New Take on the Intelligent Assistant
15:45 - 16:30
Adam Cheyer <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/Adam-Cheyer.jpg" alt="Smiley face" height="50" width="50" align="right">
15:45 - 16:30 @ Stage Black Box

Adam Cheyer
Co-Founder and VP of Engineering | Viv Labs
Moshe Vardi

Bixby is a new assistant created by Samsung with the goal of providing a unified conversational interface soon accessible across an ecosystem of hundreds of millions of devices. Designed from the ground up with developers in mind, Bixby offers the most sophisticated platform and tools available today, featuring technologies such as: Dynamic Program Generation, where artificial intelligence works with developers to create on-the-fly responses to every unique user request; preference learning and selection learning to help a specific user more efficiently accomplish complex tasks; and advanced natural language, dialog, and conversational contextual support to enable powerful multi-modal interfaces. In this talk, we will discuss why Bixby is a compelling proposition for developers looking to gain additional reach for their services, and through live coding sessions, demonstrate the power of this new approach to building intelligent interfaces.

Break

Data Mining for Security Anomalies
16:45 - 17:15
Martin Pirker <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/Martin-Pirker.jpg" alt="Smiley face" height="50" width="50" align="right">
16:45 - 17:15 @ Stage Black Box

Martin Pirker
Senior Researcher| Josef Ressel Center TARGET
Moshe Vardi

I will talk about the challenges in our work in project TARGET: 1) collecting streams of security "event" data from systems, 2) (pre)processing and storing them, 3) data mine them for anomalies - security incidents - and generation of human-understandable reports/suggestions

Break

I need data (for research)!
17:30 - 18:00
Dr. Johanna Ullrich <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/Johanna-Ullrich.jpg" alt="Smiley face" height="50" width="50" align="right">
17:30 - 18:00 @ Stage Black Box

Dr. Johanna Ullrich
Senior Researcher | SBA Research
Dr. Johanna Ullrich

Artificial intelligence-based applications in security, but also in other areas, need data as their basis. Their eventual quality is highly dependent on the quality of training data. But the challenge is: where do we get this data from, and how do we know that it is good data? Based on her experience in scientific research in IT security, Johanna elaborates on gaining data and the curiosities along the way.

Break

How to be best in conveying the message by using NLP approach (Big Five modeling)
18:15 - 18:45
Tomislav Krizan <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/Tomislav.jpg" alt="Smiley face" height="50" width="50" align="right">
18:15 - 18:45 @ Stage Black Box

Tomislav Krizan
CEO & AI Evangelist | Atomic Intelligence
Tomislav Krizan

With data-driven world where every business entity relies on data to make better decisions, correct interaction with customers becomes imperative. One of key imperatives for successful interaction with customer is applying approach which best suits that customer. Every personality trait will demand different approach for communication, and thus, our intention is to show how interaction over Social Media, text, use of questionnaires or other channels, can help us to model and predict personality traits for every individual who gives appropriate consent. Based on that personality traits prediction, combined with other personal information (demographic, interest in our services, etc.) we can create unique strategy for approaching persons on individual level. Knowledge of personality trait can help any industry to proactively take appropriate action. There is study showing that members of teen population with specific traits are more prone to drug abuse and for example this can help with preventive and corrective actions on school level. Focus of this talk would be on using NLP modeling to predict personality scores along the Big Five dimensions (Norman, 1963), e.g. Extraversion, Emotional stability, Agreeableness, Conscientiousness and Openness to experience

Closing

Stage White Box
Stage White Box
Registration & Pre-Congress Coffee
08:30 - 09:30

Develop intelligent apps for the Modern Workplace
09:30 - 10:00
Martina Grom & Toni Pohl <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/Martina.jpg" alt="Matthias Lichtenthaler" height="50" width="50" align="right">
09:30 - 10:00 @ Stage White Box

Martina Grom
CEO | atwork
 Martina Grom

Line-of-Business Apps require relevant organization data and signals from various services. Additionally, users expect user friendly and intelligent solutions, with built-in knowledge about data and processes and allow simple workflows. In this session, we demonstrate how to consume and use business data and how workflows can be delivered with adaptive cards to deliver a smooth user experience with serverless computing. In the second part, we will show practical demos for image recognition and OCR. Learn how to benefit from using the Modern Workplace in this demo packed session.

Break

The Contradiction bot - retrieving new insights from a vast amount of information.
10:15 - 10:45
Matthias Lichtenthaler <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/matthias-lichtenthaler-foto.256x256.jpg" alt="Matthias Lichtenthaler" height="50" width="50" align="right">
10:15 - 10:45 @ Stage White Box

Matthias Lichtenthaler
Head of Digital Transformation | Austrian Federal Computing Centre
Matthias Lichtenthaler

Context is key - detecting contradiction and similar points of action: >Specifically trained bots – driven by Semantic Analytics and Artifical Intelligence – can identify substantial contradictions and other inconsistencies within tons of structured and unstructured data. > Analysis and Interpretation up to „Next Best Actions” > Potential connection with additional seuring technology – e.g. Blockchain

Break

Building a Large Music Recommender Leveraging AI, Deep Learning and Human Expertise
11:00 - 11:30
Òscar Celma <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/avatar.jpg.320x320px.jpg" alt="Smiley face" height="50" width="50" align="right">
11:00 - 11:30 @ Stage White Box

Òscar Celma
Head of Research | Pandora
Òscar Celma

Pandora began with The Music Genome Project, the most sophisticated taxonomy of musicological data ever collected and an extremely effective content-based approach to music recommendation. But what happens when you have a decade of additional data points, given off by more than 70 million monthly users who have created 12+ billion personalized radio stations and given 90+ billion thumbs? This session will look at how the interdisciplinary team at Pandora goes about making sense of these massive data sets to successfully make large-scale personalized music recommendations to the masses. Following this session the audience will have an in-depth understanding of how Pandora uses Machine Learning (ML) to determine the perfect balance of familiarity, discovery and relevance for each individual listener, measures and evaluates user satisfaction, and how our online and offline ML architecture stack plays a critical role in playing the right song, at the right time, for the right listener.

Break

Alexandros Karatzoglou
11:45 - 12:15

Lunch break
12:15 - 13:00

Bridging the gap between AI and UI
13:00 - 13:30
Liad Magen <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/1-min-300x300-1.jpg" alt="Smiley face" height="50" width="50" align="right">
13:00 - 13:30 @ Stage White Box

Liad Magen
Data Scientist & Deep-Learning Expert | WeAreDevelopers
Liad Magen

Recently, many efforts have been made in order to gain a better understanding of model predictions and the reliance of their input features. Although we still don’t have a satisfying theory for neural networks nor do we know for sure what is hiding in a sentence or a document vector, there are several methods that can be used to gain insights regarding the relation between the model’s input and its output. In this talk, we will review some of these methods, as well as the latest attempts of understanding RNN in NLP. In addition, we will present several novel ideas for incorporating a visualisation of these methods in a product, as a way to enhance the user experience and user retention.

Decentralized marketplace for data and AI services
13:30 - 14:15
Marcus Jones
13:30 - 14:15 @ Stage White Box

Ocean Protocol is a non-profit foundation designing and implementing a decentralized data marketplace. In this talk, I will demo the features available to Data Engineers, how you can partake in the decentralized data and AI ecosystem, and the roadmap until our ethereum mainnet release. Ocean Protocol aims to break down data silos through equitable, universal, and secure access. The problem: Large companies have a data advantage in the ongoing artificial intelligence revolution. With huge amounts data feeding AI models, there is an incentive to keep this data in-house and gain a competitive advantage. Given that more data often yields better AI, there are solutions which smaller companies and innovators can unlock if they had access to more data. The solution: Ocean Protocol tackles this challenge with a decentralized marketplace for data and AI services. Data providers are incentivized to open their data, and data consumers will gain seamless access to a new world of data, be it big data from a large corporation, or an aggregate of small datasets.

Break

When humans teach machines – the algorithmic challenges in creating a Machine Learning DIY tool
14:30 - 15:00
Shai Hertz <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/Shai-Hertz-1-1.jpg" alt="Smiley face" height="50" width="50" align="right">
14:30 - 15:00 @ Stage White Box

Shai Hertz
Algorithms Team Leader| Refinitiv
Shai Hertz

Organizations face a need to identify many topics within huge amounts of unstructured documents. Oftentimes these topics do not occur frequently, so users face the need to find many different types of needles in one big haystack. Take for example interesting topics like “Drug Trafficking” or “Race Relations”; such topics are not very frequently mentioned in the news, but each occurrence of them could be important to a customer. In addition, these topics are complex, and it is hard to build a search query that would bring all relevant documents without too much noise. It is common wisdom to turn to Machine Learning (ML) solutions in such cases, but typical users don’t have the technical skills needed to build a ML solution on their own. On the other hand, data scientists often lack the domain expertise to create and evaluate such classifiers. Our Self-Service Classification solution is an interactive Do-It-Yourself tool that allows a user who is not a data scientist to train and deploy a classifier by herself. Several similar solutions have been suggested; they, however, typically require significant data collection efforts in order to create train & test sets, and these efforts might be a showstopper. Our solution, in distinction, provides a framework for creating a training set with minimal effort (a “labeling tool”), the ability to train an initial model very quickly, and the ability to improve an initial model via an interactive tuning phase. During the development of this tool we came to an understanding that reducing the data collection efforts is key, and built a workflow that simplifies the data collection process and requires much less work from the user. This process is built on the observation that we can identify ‘areas’ in the corpus of documents where the topic at hand is more prevalent. We help the user build and expand a query in order to retrieve highly relevant documents. After she labels an initial set of positive documents we identify keywords that enable us to define Triage terms to discard completely irrelevant documents. The result is that the user is required to tag a rather small set of positive documents, and we automatically identify the negative documents from an uploaded corpus of (unlabeled) production data. Our approach also offers model-tuning capabilities, which allow the user to intervene in the internal algorithmic steps, and customize them to her needs. This system enables domain experts to train a topic classifier in a couple of days. It is used in Thomson Reuters by several teams to expand TR Intelligent Tagging topic tagging capabilities.

Break
15:00 - 15:45

The impact of AI on our private life
15:45 - 16:30
Panel Discussion

Break

Real Life Customer Stories with AI
16:45 - 17:15
Rina Ahmed <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/Rina-Ahmed.jpg" alt="Smiley face" height="50" width="50" align="right">
16:45 - 17:15 @ Stage White Box

Rina Ahmed
Systems Analyst | IAEA
Rina Ahmed

As a former Software Engineer at one of the big global players (Microsoft), I will talk about real customer stories where we developed Machine Learning solutions together with the customers to support specific business scenarios. I will show our approaches on cases from the areas of classical Machine Learning, Computer Vision and Cognitive Services.

Break

Build your intelligent edge
17:30 - 18:00
Tayo Carvalhal <img src="https://www.wearedevelopers.com/wp-content/uploads/2018/10/20863243.png" alt="Matthias Lichtenthaler" height="50" width="50" align="right">
17:30 - 18:00 @ Stage White Box

Tayo Carvalhal
IoT Technology Specialist | Microsoft
Tayo Carvalhal

Increasingly, IoT solutions are tapping on the aggregated data, leveraging massive amounts of data for Machine Learning and AI in the cloud to learn and build a better product. What if we can bring that intelligence that has been trained closer to the device, leveraging the same technology, AI and ML tools and keep improving your products closer to the customer supporting things like offline scenarios? Join this session to find out how.

Break

TBA
18:15 - 18:45

Closing