WeAreDevelopers AI Congress Logo

WeAreDevelopers
AI Congress Vienna

  4-5 December ’18

  Hofburg Vienna

4000

of AI fun

48

Hours

30

Speakers

About the AI Congress

WeAreDevelopers AI Congress Vienna will focus on human-machine interactions and will bring together two sides: The academy and the industry. We will try to answer questions such as: Can we trust computer decisions? How to deal with decisions bias? How can we improve the user experience for machine learning software? And many, many more.

The Artificial Intelligence Congress revolves around the interaction between wo/man and machines. From trusting and rationalizing the black box, to improving the interface through which we communicate with the models.

In partnership with

microsoft logo

AI Congress Topics

Machine vs Human

Trusting Computers’ Decisions

GDPR and Privacy

In the Era of Big Data

Blockchain

Decentralized Artificial Intelligence

The Future of AI

for Personal Usage

Ethical Challenges

of AI

Deep Learning

Demystifying the Black Box

Security and Safety

in AI

Our Partners

Meet AI Congress Speakers

Adam Cheyer

Co-Founder and VP of Engineering, Viv Lab

Adam Cheyer is Co-Founder and VP of Engineering at Viv Labs, an intelligent assistant startup acquired by Samsung in 2016. Prior to starting Viv Labs, Adam was Co-Founder and VP Engineering at Siri, Inc, which was purchased by Apple in 2010. Adam is also a Founding Member and Advisor to Change.org, the premier social network for positive social change, and a Co-founder of Sentient Technologies. He has also authored more than 60 publications and 27 issued patents.

Pavithra Vijay

Software Engineer, Google

Pavithra is a software engineer working on TensorFlow at Google. She contributes primarily to the Keras module in TensorFlow. Keras is high-level Machine Learning API written in Python focusing on user friendliness and fast experimentation. She started exploring the field of Machine Learning less than a year ago by experimenting with models for text classification applications. She authored a text classification guide that covers important best practices and rules of thumb for picking the right model and processing pipelines. In her first role at Google, Pavithra contributed to the data visualization and reporting platform called DataStudio. Before that, Pavithra worked with Microsoft on their ERP software suite, Dynamics AX. Pavithra received a Masters in Computer Science from University of Wisconsin-Madison.

Ching Wei Chen

Engineering Lead, Home Personalization, Spotify

Ching-Wei is the Engineering Lead for Home Personalization at Spotify, where Content, UX, and Machine Learning are combined to give users the best of what Spotify has to offer them, right when they open the app. He's also passionate about building communities around Open APIs, Open Datasets, and Open Research: he started the developer program at Gracenote, and more recently organized the RecSys Challenge 2018. Previously, he's been a Manager, Researcher, and Engineer at SoundCloud, Gracenote, and Dolby, and has published several papers and patents in the areas of Music Recommendation, Music Information Retrieval, Computer Vision, and Sound Spatialization.

Moshe Y. Vardi

Professor, Rice University

Moshe Y. Vardi is the George Distinguished Service Professor in Computational Engineering and Director of the Ken Kennedy Institute for Information Technology at Rice University. He is the author and co-author of over 600 papers, as well as two books. He is the recipient of several scientific awards, is a fellow of several societies and a member of several honorary academies, and he holds multiple honorary doctorates. He is a Senior Editor of Communications of the ACM, the premier publication in computing, focusing on societal impact of information technology.

Haris Pozidis

Manager of Cloud Storage & Analytics, IBM Research

Haris Pozidis manages the Cloud Storage and Analytics group at IBM Research in Zurich, Switzerland. He was with Philips Research, Eindhoven, The Netherlands, before joining IBM. He has worked on read channel design for DVD and Blu-ray Disc with technology transfers to optical drive products at Philips, and played a key role in developing the first scanning probe-based data storage system, the “Millipede”, at IBM. His current focus is on the development of Flash memory controllers for all-flash arrays, on phase change memory technology and system solutions, and on optimized software libraries for machine learning. Haris holds over 100 US patents, has co-authored more than 100 publications, is an IBM Principal Research Scientist, an IBM Master Inventor, and a Senior Member of the IEEE.

Tim O’Brien

General Manager AI Programs, Microsoft Corp.

Tim leads Ethical AI Advocacy at Microsoft and is responsible for programs to drive and promote responsible development and use of technology, inclusive of public policy and the ethics of artificial intelligence. As part of this role, Tim conducts research focused on ethical use of AI across geographies and cultures. Prior to this role, Tim led the Global Communications group for Microsoft, managing communications strategy and operations in over 90 countries around the world, and the Platform Strategy team, where he was responsible for technical evangelism to drive developer adoption of Microsoft platforms and services. Before joining Microsoft in 2003, Tim worked as an engineer, a marketer and a consultant at both startups and Fortune 500 companies.  He has over 25 years of experience in the tech industry. Tim has a BS in engineering from Purdue University and an MBA in marketing from Northwestern University’s Kellogg School.

Sabria Lagoun

Co-Founder, The Brainstorms

After finishing her Neuroscience M.Sc. in Marseille, Sabria moved to Vienna to complete a Ph.D. in Cognitive Neurobiology. Alongside her studies, she is committed to scientific communication and mental illness awareness initiatives. Sabria organizes The Brainstorms, a series of events around brain topics. She is also involved in hands-on workshops, conceptualized Art x Science projects and is regularly speaking to promote neuroscience knowledge.

Reinhard Burgmann

Head of Advanced Analytics & AI, A1 Telekom Austria AG

Reinhard was working in various roles in Business Intelligence for A1 in the last years! Beginning of 2017 he took over the task to setup a Data Lake for A1 and to start experiments with Big Data (technologies). Mid of 2018 he took the lead of the new created group Advanced Analytics and AI (Triple A) in A1 Austria. This group is dedicated to research and operationalize analytical solutions in A1.

Òscar Celma

Head of Research, Pandora

Dr. Òscar Celma is the Head of Research at Pandora, where he leads a team of 80 (40 scientists and 40 musicologists) to provide the best personalized radio and on-demand music experience. Òscar has published a book named “Music Recommendation and Discovery” (Springer, 2010). Òscar holds a few patents from his work on music discovery as well as on Vocaloid, a singing voice-synthesizer bought by Yamaha in 2004. From 2011 till 2014 Òscar was Senior Research Scientist at Gracenote. His work focused on music and video recommendation and discovery. Before that he was co-founder and Chief Innovation Officer at Barcelona Music and Audio Technologies (BMAT). He’s also an avid speaker at conferences such as Strata + Hadoop, QCon, MLConf, SXSW, and Midem.

Rina Ahmed

Systems Analyst, IAEA

Rina Ahmed has been a Software Engineer at Microsoft where she worked together with customers and partners on building solutions in the area of Artificial Intelligence and Machine Learning. In addition, she has experience working with various developer audiences as a Technical Evangelist focused on cloud and mobile technologies. Currently, she is a Systems Analyst at the IAEA.

Ramshanker Krishnan

EMEA Leader AI, Microsoft

Roberto Andreoli

Director Data, Artificial Intelligence and IoT,

Roberto Andreoli is passionate in improving people’s live and supporting organizations from Western Europe in digital transformation through Data and AI. He empowers European organizations in evangelizing and guiding them through all the different AI technologies, supporting in their AI maturity journey and helps to make the implementation of Data and AI a success by applying a 360° approach. Andreoli held several national and international positions at Microsoft across audiences and products. He has been focusing on Public Sector, Small and Medium Business, Technical Evangelism, Dynamics and Microsoft Services organization. Previously he worked at SAP and Cedati, now part of the Altran group.

Lubomir Karlik

RBI AI Tranformation, Raffeisen Bank International AG

Lubomir Karlik is a senior business analyst at AI Transformation Team in Raiffeisen Bank International. Throughout the career he has been participating in a bank’s international projects in the fields of BI, data warehousing, controlling, risk and regulatory. He is currently helping to build up AI capabilities in the banking group combining the banking experience with his passion – conversational artificial intelligence.

Georg Köldorfer

Head of Advanced Analytics Consulting, Raffeisen Bank International AG

Georg Köldorfer studied business administration and business informatics and worked for several years in consulting for banks. In 2017 he took over the leadership of the newly formed Advanced Analytics team, with the goal of reusing data to improve the customer experience and new business opportunities.

Kirk Koenigsbauer

Corporate Vice President, Microsoft

Kirk Koenigsbauer had the privilege of enjoying a diverse and multi-disciplinary career working for two of the world’s most impactful technology companies – Microsoft and Amazon.com. Currently, he is directly responsible for Microsoft 365, a new cloud service that integrates Office 365, Windows 10, and Enterprise Mobility + Security to empower people and businesses to be creative and work together. Over the last 25 years, he has been fortunate to partner with some amazing technology and business professionals and to lead teams across product management, engineering, marketing, and data science disciplines.

Guenda Sciancalepore

Technical Evangelist, Microsoft

Guenda is a Technical Evangelist who works for Microsoft Western Europe helping partners in developing innovative and AI infused software and solutions on Azure. She joined Microsoft two years ago, after graduating in Cognitive Neuroscience at the University of Trento.

Teemu Kinnunen

Data scientist, Futurice

Teemu Kinnunen works as a data scientist at Futurice. He studied unsupervised methods for image categorisation, graduating with a PhD from Lappeenranta University of Technology in 2011, after which he worked as a postdoc researcher at Aalto University for three years. Since joining Futurice in 2015, he has worked on projects for automotive, media and gaming companies, as well as the public sector. Teemu has worked on developing computer vision, text classification and other predictive systems, ranging from prototypes to services used by hundreds of millions of people every day. As a consultant, he has helped companies use their data to solve business problems and run training sessions where corporate executives and managers learn about AI and machine learning.

Tomislav Krizan

CEO & AI Evangelist, Atomic Intelligence

Tomislav spends large part of his working day for mentoring others and sharing knowledge across the globe and trying to show people how data can help them in their everyday life. Also, he's leading a team of Data Science enthusiasts with whom he started Data Science Croatia group and he's proud to work because they are not shy on sharing knowledge to others as well. Besides that, he's participating on different research projects (self-funded, customer-project funded or EU funded through different funding programs which end as Open Source solutions) which are focusing on data itself and processing of data either in motion or data at rest. Tomislav emerged in different computer related areas from 1984 when he started to experiment with building HW components and first SW interactions. Major shift into data driven environment happened mid ’90 when he started with implementation of Stream analytics and Data Warehouse/BI systems which gradually started to embrace DM/ML in everyday life. His focus for the last decade was on Big Data spectrum where he engaged in many custom implementations utilizing simplification and optimization of data ingestion and processing systems, and enriching those with DM/ML/AI algorithms and frameworks/platforms to support custom/customer product or project needs over vast amount of internal and external data (public and private as well). He also tries to incorporate all research into implementation projects so that those are not just words-on-paper.

Martina Grom

CEO, atwork

Martina is a Principal at atwork currently living in Vienna, Austria. Her expertise is related to online technologies, and her specialty is in Microsoft Online Services and Office 365. She helps architecture planning companies with cloud solutions, and provides strategy, consulting and architectural planning of cloud projects. In addition, she is one of the organizational heads of cloudusergroup for Germany, Austria, and Switzerland. She has authored numerous books, articles and blogs. Martina has a Master’s of International Business Administration from the University of Vienna, Austria.

Marcus Jones

Lead Data Scientist at Ocean Protocol, Ocean Protocol

Marcus Jones is the lead Data Scientist at Ocean Protocol, a blockchain decentralized marketplace for data and models supporting the AI ecosystem. Marcus has been hacking in Python development and and open source projects for over 10 years, starting at the national research agency in Vienna Austria, moving to international project management and consulting, and landing now in Berlin at Ocean Protocol. Marcus is focused on the intersection of Artificial Intelligence, the new decentralized Web 3.0 ecosystem, and how it can empower Data Engineers and Data Scientists.

Toni Pohl

Founder & CTO of atwork, Cloud Consultant, Microsoft

Toni is co-founder and CTO at atwork, a software development company in Vienna, Austria. Toni loves playing with new technologies and developing cloud solutions and works as a consultant. Toni is author, blogger, speaker at community events and conferences and is Microsoft MVP for Azure and Office Development.

Gerwald Oberleitner

Technology Solutions - Azure, Microsoft

With 15+ years of experience in the software industry Gerwald is a Certified SCRUM master, strong advocate of the new and open Microsoft and loves to work with agile organizations regardless of platform or language used. He is specializing on Azure DevOps, Visual Studio App Center (mobile DevOps) and topics like API Management, Containers and Serverless with the Azure cloud.

Ahmad Haj Mosa

Manager , PwC

Dr Ahmad is a researcher and AI developer in the team of Tax Technology and Digital Services at PwC Austria. He is a postdoctoral researcher and a lecturer at the Institute for Smart System Technology (IST) in the University of Klagenfurt, Austria. His research area focuses on Neuroscience based AI, NLP, and Human Emotion Recognition. At PWC, Ahmad works on employing the recent AI breakthroughs to the field of Tax and Legal services including, legal documents analysis, fraud/anomaly detection, and advanced data analytics. His main research goal is to build trust in AI by focusing on explainable and accountable AI

Robert Hoffmann

Solution Architect and Data Scientist, Microsoft

Dr. Robert Hoffmann is an experienced Solution Architect and Data Scientist with a history of working in computer software industry and applied research, most recently at the MSKCC in New York and the MIT in Boston. He currently works at Microsoft with leading enterprises in Austria helping them with their challenges around Advanced Analytics & AI, Big Data and IoT – a.k.a. Digital Transformation.

Johanna Ullrich

Senior Researcher, SBA Research

Johanna is a senior researcher at SBA Research, an Austrian research center for Information Security. Her research interests include network security and security and privacy in traditional engineering. At the moment, she works primarily on aspects of IPv6 networking. Being an electrical engineer by training, she is also working on power grid security and how cryptocurrencies affect reliable power grid operation. Johanna received a Bachelor and Master, both in electrical engineering, as well as a PhD in computer science sub auspiciis praesidentis (with highest honors and awarded by the Austrian president), all from TU Wien.

Martin Pirker

Senior Researcher, Josef Ressel Center TARGET

Martin Pirker is a senior researcher in the field of IT security. He currently works at the Josef Ressel Center for Unified Threat Intelligence on Targeted Attacks (TARGET) at St.Pölten University of Applied Sciences. TARGET researches novel ways of intrusion detection and mitigation via analysis of system events streams in a joint research cooperation with industry partner CyberTrap GmbH.

Matthias Lichtenthaler

Head of Digital Transformation, Austrian Federal Computing Centre

Matthias' topic is: "The Contradiction Bot - retrieving new insights from a vast amount of information"

Vered Shwartz

PhD Student, Natural Language Processing Lab, Bar-Ilan University

Vered Shwartz is a final year PhD student at the Natural Language Processing lab at Bar-Ilan University. Her research focuses on recognizing lexical semantic relations between words and phrases. She is also the author of Probably Approximately a Scientific Blog, where she discusses NLP topics for a general audience.

Navid Rekabsaz

Post-Doctoral Researcher, Idiap Research Institute

Navid Rekabsaz is a post-doctoral researcher at the Natural Language Understanding lab of the Idiap Research Institute. He explores the pioneering data-driven methods for natural language processing, and information retrieval, especially the ones based on deep/representation learning. He is curious about deeply understanding such methods and applying them to real-world problems.

Tayo Carvalhal

IoT Technology Specialist, Microsoft

Tayo is on a mission to make sure his customers change the world by connecting anything they want to the cloud (in his case Azure), no matter how small, no matter how many. He has worked with IoT for 18 years, having almost lost his job once for wasting research time connecting a soda vending machine to a GSM mobile network.

Fabian Schneider

Software Developer, PwC

PoC-Engineer, Software Developer and researcher in the team of Tax Technology at PwC Austria.  His main fields of interest include functional programming, formal software verification and searching usecases for emerging technologies in the field of Tax and Legal services. As public speaker he focuses on Posthumanism, the future of Artificial Intelligence and Human Enhancement. Fabians' top priority is to dismantle traditional structures and reimagine how they could look like.

Katharina Holzinger

Cyber Security Unit, Austrian Armed Forces

Katharinas broader field of interest is causality. This fundamental subject is universally applicable in various application domains, ranging from Archaeology to Zoology – from Cyber Defence to Paleontology — whenever we are confronted with questions of explainability, truth, belief and justification. Consequently, her goal is to help the international research community with contributions towards explainable AI – where DARPA recently launched a program.

Andreas Aschauer

Technology Solution Specialist - Cloud Business Applications , Microsoft

Andreas Aschauer studied Software Engineering at Vienna Technical University with a focus on AI, which back then meant deep diving into theory without many practical applications around. To understand what his clients are talking about he decided to add an Economics degree. Working on numerous large scale development projects, he dedicated his dev life to the then rising Microsoft .NET framework. Ever since he strives to build a bridge between technology and business. To build on that he is a regular published author of DotNetPro Magazine and conference speaker. Currently Andreas is a Technology Solution Specialist at Microsoft, with a special focus on “democratizing” AI and software development by combining AI tools, custom development and low-code platforms, to value driving platforms.

Lorenzo Barbieri

Cloud Solutions Architect, Microsoft

Lorenzo is specialized in Cloud Application Development, both Azure and Office 365, Windows and cross-platform applications, Visual Studio and DevOps, and he likes to talk to people and communities about technology (and food). He is also a speaker, a trainer and a public speaking coach. He works for Microsoft Western Europe, in the One Commercial Partner Technical Organization, helping partners (mainly Systems Integrators and ISVs), supporting software development on Microsoft and OSS technologies. Previously he was a Technical Evangelist and a Microsoft MVP.

Alexandros Karatzoglou

Scientific Director, Telefonica Research

Christian Heilmann

Senior Program Manager, Microsoft

Chris Heilmann dedicated the last 20 years of his life to make the web work and thrive. As a lead developer on some of the largest web products he learned that knowledge is not enough without teamwork and good handover. He dedicated most of his time since on educating, writing and sharing, presenting on average at 30 conferences a year. He strives to make code and coders work efficiently and get more done quickly without losing the understanding of what we do. He is the author of several JavaScript books and the Developer Evangelism handbook (http://developer-evangelism.com). He is currently a Senior Program Manager in Microsoft and spends a lot of time pondering how machine learning and AI can aid humans and replace jobs we're too important to do.

Eyal Felstaine

CTO, Amdocs

Eyal Felstaine has co-founded several startups, including SANDARD and Traffix which were acquired by multinational companies, so he’s pretty passionate about entrepreneurship and incubation, both inside and outside of the corporate environment. (And that includes having brainstorming discussions while walking along Tel-Aviv beach.) As a governing board member of Linux Foundation’s networking group, Eyal’s very involved with creating an open-source telecom universe, and in parallel, he’s responsible for planning and developing Amdocs’ technology vision, architecture and roadmap for some rather cool new products. And if he’s got any spare time left after that, you’ll find him on the ski slopes or mountain biking (but obviously not at the same time).

Liad Magen

Data Scientist & Deep-Learning Expert, WeAreDevelopers

Liad Magen, Msc., a published machine learning Megamind who is the AI & Tech Lead at WeAreDevelopers! When he isn't working on NLP based recommendation systems, he's organizing the Keep-Current Meetups in Vienna, which are focused on applied data science.

Milan Todorovic

Swift/iOS Trainer and Software Engineer, Crossover

Milan is an Apple Certified Trainer, Consultant and Software Engineer, with long experience in software development and especially in training of developers of all levels of knowledge. He is deeply focused on Swift since the very beginning of life of this programming language. He works for Apple Authorized Training Centre - Crossover in Belgrade, Serbia. Passionately helping beginners, intermediate and experienced developers to improve their knowledge, in areas of general development, machine learning, augmented reality and other. Milan is also a sailboat skipper, combining development training with sailing with its goal to maximize motivation and knowledge adoption for attendees.

Shai Hertz

Algorithms Team Leader, Refinitiv

Shai Hertz leads the NLP Algorithms team in Refinitiv’s Text Metadata Services. He holds an M.Sc. In computer science from Tel Aviv University.

Simon Stiebellehner

Data Scientist, Craftworks

Simon is a Data Scientist at Craftworks and lecturer in statistics and digital marketing at WU Wien and FH Wien. After having completed his Bachelor in Information Systems, he gained diverse industry experience, ranging from Microsoft to global players of the consulting industry. Subsequently, Simon obtained his Masters degree from University College London (UCL), specializing in Machine Learning and Data Science. Afterwards, he was a doctoral candidate and research associate, conducting research at the intersection of Neural Probabilistic Language Models and Recommendation Systems in a Real-Time Bidding context.

Stay tuned
for Speaker announcements

Get a sneak peek at some of the talks

Bixby: A New Take on the Intelligent Assistant

Adam Cheyer, Co-Founder and VP of Engineering,Viv Labs
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.

Artificial Neurons

Sabria Lagoun, Co-Founder, The Brainstorms
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.

Natural Language Processing

Navid Rekabsaz, Post-doctoral Researcher, Idiap Research Institute
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.

An Ethical Crisis in Computing?

Prof. Moshe Vardi, Professor in Computational Engineering, Rice University
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?

How We Democratized Artificial Intelligence with Acumos AI

Eyal Felstaine, CTO, Amdocs
Many Artificial Intelligence tools today are difficult to implement and require significant domain expertise which is why Acumos AI is going to have such a large impact. As 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 solutions and data models, the Acumos framework is user-centric and simple to explore. In this session, Dr. Eyal Felstaine takes you through the 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.

Are we there yet? Remaining Challenges in Deep Learning based Natural Language Processing

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.

When humans teach machines – the algorithmic challenges in creating a Machine Learning DIY tool

Shai Hertz, Algorithms Team Leader, Refinitiv
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.

How to be best in conveying the message by using NLP approach (Big Five modeling)

Tomislav Krizan, CEO & AI Evangelist, Atomic Intelligence
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

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AI Congress Pass includes

  • Admission to both days and access to all keynotes.
  • Entry to the expo area and admission to the official afterparty.
  • Free meeting area and access to the networking zone.
  • Access to the official recorded talks and congress goodies.
  • Free Coffee, Tea & Water

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