SEPTEMBER 28, 2019 / WARSAW / GMT +2

STATE OF THE ART
Machine Learning Conference

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The place where meets engineering, business and art. Applied machine learning & real use cases.

This year's leitmotif is "STATE OF THE ART", so be ready for the most advanced designs and ML implementations with a strong focus on practice and real-world use cases.

Meet experts who implement cutting-edge machine learning solutions in areas such as
medicine and healthcare, finance, industrial processes, transport and logistics, conversations and communication, design and art.

Join if you are...
ML engineer
Data Scientist
Head of Innovation
CxO who (want to) runs a business based on ML
Developer who wants to start working with ML
Geek-artist who uses ML
Start-up founder
Investor
You're hungry for knowledge of applied ML
You touch ML topic in another way
SEPTEMBER 28, 2019 / GTM +2
IT'S MORE THAN JUST A TYPICAL ONLINE CONFERENCE
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What you'll get...
Live streaming
You can watch the live broadcast, or watch recorded lectures at a convenient time.

By joining the conference live, you have the chance to talk with participants and ask questions to experts via Slack. You will also receive materials after the conference: summary with practical tips and links to tools, libraries, etc. written by experts.

Check our Agenda and Subscribe to the list :)
Practical ML knowledge
& use cases
The presentations will contain:
- a use case,
- a classic way of solving the presented problem,
- the latest production solutions (state of the art in ML),
- success metrics,
- useful technical and business advice.

Each presentation is a 30-minutes intense dose of practical knowledge of Machine Learning and applied AI.
Expert experience & motivating stories
Among our speakers, you can meet experts from small and large companies/organizations, who are actively working on new models and show the most advanced designs and ML implementations that solve real problems.

Don't miss the opportunity and join AMA sessions with Speakers Online!

AMA is a less formal form of Q&A and has one rule: ask me anything! So take a cup of coffee, go to the AMA zone just in time on dedicated Slack Channel, sit comfortably and ask anything you want to know.
Effective game networking
If you want to go somewhere, it is best to find someone who has already been there (R.Kiyosaki).

Unlimited networking including access to dedicated Slack before and after the conference, game-networking session.

We designed #Slack Game Networking to optimize the search for such people. During the hour you will meet people who have similar interests and different experience. You will not have to talk about the weather, don't worry.
The moderator will keep his finger on the pulse and will take care of the topic of conversation and a good atmosphere.
Video pack after the conference

Don't worry if you still couldn't be online with us.

Few weeks after the conference you'll get a pack with all recorded talks.
Post-conference publication
After the conference, you will also get a summary of all lectures with extra practical tips and links to tools, libraries, etc. written by experts.
DWCC2019 in numbers
1
day
2
tracks
30+
experts
1000+
online attendees
Our experts work at...
Meet our speakers
Tomas Mikolov
Research Scientist / Facebook
He is a research scientist at Facebook AI Research since May 2014. Previously he has been a member of Google Brain team, where he developed and implemented efficient algorithms for computing distributed representations of words (word2vec project). He has obtained PhD from Brno University of Technology (Czech Republic) for his work on recurrent neural network based language models (RNNLM). His long term research goal is to develop intelligent machines capable of learning and communication with people using natural language.

Interests:
Machine learning, and natural language processing.

LinkedIn
Emeli Dral
Chief Data Scientist / Mechanica AI
Emeli Dral leads the data science team at Mechanica AI. She is responsible for the development of core products and technologies for the application of artificial intelligence in industrial processes.
Prior to co-founding Mechanica AI, she served as the Chief Data Scientist at Yandex Data Factory. She led a team of accomplished data scientists and oversaw the development of machine learning solutions for various industries - from banking to manufacturing. Emeli is a lecturer at the Yandex School of Data Analysis and Harbour.Space University, where she teaches courses on machine learning and data analysis tools. In addition, she is a co-author of the "Machine Learning & Data Analysis" and "Big Data for Data Engineers" curriculums at Coursera.

LinkedIn
Janusz Marecki
Senior Research Scientist / DeepMind
Janusz Marecki is currently a lead scientist at Google DeepMind and a co-founder of AI Machina. Prior to joining DeepMind, Janusz was a senior research scientist in the cognitive computing division of IBM T.J. Watson Research, a research assistant at the European Laboratory for Nuclear Research (CERN), a research associate at the Ukrainian Academy of Sciences and a full professor at the Academy of Computer Sciences in Poland. An author of over 100 refereed publications and 7 patents and a recipient of a commendation from the Los Angeles Airport Police, a commendation from the Department of Homeland Security and an Invention Award from an IBM CEO, Janusz's current research interests are in reinforcement learning, deep neural networks, and artificial brain computing.

LinkedIn
Mikhail Burtsev
Founder and Leader / DeepPavlov.ai
Mikhail Burtsev is head of Neural Networks and Deep Learning Laboratory at Moscow Institute of Physics and Technology. In 2005, he received the Ph.D. degree from Keldysh Institute of Applied Mathematics of Russian Academy of Sciences. From 2011 to 2016 he was head of the lab of Department of Neuroscience at Kurchatov NBIC Centre. Now he is one of the organizers of NIPS 2017 Conversational Intelligence Challenge, and a head of iPavlov Project.

His research interests are in fields of Natural Language Processing, Machine Learning, Artificial Intelligence, and Complex Systems.

LinkedIn
Aditya Guglani
Data Scientist / Uber
He is currently a data scientist at Uber where he is using deep learning and natural language processing to provide magical support experiences to customers and driver partners. He has a background in engineering and a graduate degree in data science, which enables him to solve some hard problems using data and machine learning. His interests currently include neural networks, document semantics, and language understanding.

LinkedIn
Marta Markiewicz
Head of Data Science / Objectivity
Born in 1988, a graduate in Mathematical Statistics at the Wrocław University of Technology. Her master's thesis entitled "Stable feature selection methods" won the second place in the XLVI competition of the Polish Mathematical Society for the Best Student Work in Probability Theory and Statistics Applications. Professionally, over almost 8 years, she has been discovering the potential of data in various business domains - from medical data, through retail, HR, finance, aviation, real estate, ... She deeply believes in the power of data in every area of life.

LinkedIn
Valentin Malykh
Applied Research Scientist / Huawei
Valentin has hand-on experience working with Information Retrieval, Natural Language Processing and even Self-Driving tasks. He worked as an engineer in Sputnik & Yandex, as an applied research scientist in iPavlov & VK. Now he is employed as a research scientist at Huawei Noah's Ark Lab.

Recently, Valentin defended a PhD on robustness in NLP tasks. His research interests are robustness to noise, dialog systems, summarisation. Valentin co-organized NeurIPS ConvAI challenges for Conversational AI in 2017 & 2018.

LinkedIn
Łukasz Kidziński
CTO / Saliency.ai
Researcher at Stanford
Łukasz Kidziński is a researcher in the Mobilize Center at Stanford, working on the intersection of computer science, statistics, and biomechanics. He is a co-founder of Saliency.ai -- a startup focusing on improving efficacy of clinical trials using artificial intelligence. Previously, Łukasz was a researcher in the CHILI group, Computer-Human Interaction in Learning and Instruction, at the EPFL. He obtained his Ph.D. at Université Libre de Bruxelles in mathematical statistics, working on time series analysis and functional data. He obtained two master degrees in mathematics and computer science, from the University of Warsaw. His research interests include time series analysis, large scale statistics, alternative data, and applications of data science in biological and medical sciences.

LinkedIn
Łukasz Dziekan
Chief Technology Officer / FinAI
For the 10 years he is dealing with advanced analytics and production deployed machine learning systems. He started in Deloitte where he did complex risk analysis systems in credit risk and stress tests. Then he did 2 years in New York Unicorn Zocdoc where he was analytics tech lead and infrastructure senior developer. However he missed home and came back and built 50 person machine learning advisory team in PwC CEE, It was first such case in Poland. At the end of 2016 he found his entrepreneurship vibe and founded FinAi.com and became CTO. In FinAi he deals with simplifying retail finance – using Machine learning methods – in a way where they are designed to help rather than obstruct. They do things like fraud detection and face recognition and credit risk. And it's interesting, but very hard to do right.

LinkedIn
Bartek Skorulski
Senior Data Scientist at Research Team / Alpha
Bartek Skorulski is Senior Data Scientist in Research Team in Alpha (Telefonica), a team that designs and builds machine learning solutions for improving people mental health. He has extensive experience in both industry and academic word. On one hand he was working in Messaging Team in Schibsted (company knows for portals like Fotocasa, Leboncoin, Infojobs, Segundamano) helping building experimentation driven products like, for example, smart replies in chats. In SCRM (digital hub of Lidl) he was setting up data driven environment, building recommender systems, forecasting demand and AB Test tool. In King (Activision/Blizzard) using data he was helping build best games. On the other hand, he was also working as Associate Professor doing research in Dynamical Systems and teaching graduate and undergraduate courses. Last few years he has been also teaching courses on machine learning, deep learning and data management.

LinkedIn
Dariusz Gross
DATAsculptor
DATAsculptor, an artist interested in machine learning and 3D generative networks in VR/AR. He started the myFATHERintheCloud.ai project as a way of studying the idea of giving the artists digital artistic immortality. The founder of #next_top_architects an Architecture Community provides a unique environment for architects around the globe to meet, share and compete. He started D.E.GROSS in 1994 a residential architecture practice designing one-off luxury homes and commercial developments. Innovative residential architecture that transforms clients' living environments.

Since his project "risky player " CASINO GRAND HOTEL Sopot in 1996, he is developing #DATAsculpting

LinkedIn
Anna Anzulewicz
Head of Research / Harimata
Anna is a cognitive scientist with a background in psychology and data science, and a great interest in using new technologies in diagnosis, therapy, and education. She loves working in multidisciplinary projects and developing technology for social good. She combines her academic work at several universities with business.

For six years Anna worked as head of research at Harimata, a startup company that combines psychology and technology to support assessment and treatment of disorders in children and adults. During her time in Harimata, she developed AI-based solutions supporting early detection of autism spectrum disorder in children as well as applications helping adults who suffer from mood disorders.

LinkedIn
Tomasz Jadczyk
CTO and Co-founder / Techmo
Tomasz Jadczyk is a co-founder and CTO of Techmo – a Voice Technologies company, where he is responsible for management and development of speech recognition, speech synthesis, voice biometrics, and voice analytics systems.
During his career, he moved from 'I implement this myself' through the implementation of State-of-the-Art research systems to the production environments, where ASR handles more than 60 000 calls every day. His research interests focus on moving voice processing systems from laboratory to real-life usage.

LinkedIn
Jana Kludas
In the last 5 years she worked as a data science consultant at the Unbelievable Machine Company in Berlin. During this time, she helped a wide range of customers from industry, insurances, media and others to get more business value out of their data. After work, she does the same for NPOs by co-organizing Data Science for Social Good (DSSG) Berlin. Before consulting she pursued a classic academic career: study of computer science and electronic engineering at Technical University Ilmenau in Germany, PhD at University of Geneva in Switzerland on feature selection for multimodal (text and vision) document processing, PostDoc in Bioinformatics at Aalto University in Finland.

Xing
Daniel Baade
Daniel Baade is the founder and current Chairman of Art World Analytics. He holds a Masters and Ph.D. from Hanover University (GER) with further education at INSEAD (FRA) and Stanford (USA). He's holds senior roles in the art and finance world, including Ex-Global Strategy Director at Christie's - the world's largest art business and CEO of Soprano Mergers & Acquisitions.

Linkedin
SATURDAY | SEPTEMBER 28 | GMT +2

Conference Program
09:00 – 09:20
09:00 – 09:20
Conference Opening
09:20 - 09:50
09:20 - 09:50
State of the ML ART : The end of human creativity?
Dariusz Gross / DATAsculptor / MLearning.ai
"Creatio ex nihilo" has always been ascribed to gods, yet throughout the last 5 centuries humans have granted themselves an exclusivity for creativity; are we then witnesses of the historic moment at the end of human creativity?
The goal of myFATHERintheCloud.ai is the creation of Artificial Sculptor Siegfried Gross (ASSG); the AI model based on GANs and reinforcement learning will create art pieces as a continuation of Siegfried Gross' 60-year legacy. The usage of training data, e.g. 3D scans, photogrammetry, photos and videos of the sculptor's technique contributes to the autonomous creation of sculptures, images, and social media posts. The ultimate goal of the project is the carving out of a new sculpture by an industrial robot, which can be recognized as one of the late Siegfried Gross' works. With that in mind, the project is a process of granting ASSG autonomous creative immortality.

#deeplearning #reinforcementlearning #gan #creativity #photogrammetry #adain #style_transfer #3dscan #art
09:50 - 10:20
09:50 - 10:20
Towards the Future of AI
Janusz Marecki / Senior Research Scientist / DeepMind
Recent years have seen a rising interest in developing AI algorithms for real world big data domains ranging from autonomous cars to personalized assistants. At the core of these algorithms are architectures that combine deep neural networks, for approximating the underlying multidimensional state-spaces, with reinforcement learning, for controlling agents that learn to operate in said state-spaces towards achieving a given objective.

The talk will first outline notable past and future efforts in deep reinforcement learning as well as identify fundamental problems that this technology has been struggling to overcome. Towards mitigating these problems (and open up an alternative path to general artificial intelligence), I will then summarize a brain computing model of intelligence, rooted in the latest findings in neuroscience. The talk will conclude with an overview of the recent research efforts in the field of multi-agent systems, to provide the future teams of humans and agents with the necessary tools that allow them to safely co-exist.

#deeplearning #reinforcementlearning #multiagent_systems #brain_computing #catastrophic_iInterference
10:20 - 10:40
10:20 - 10:40
Coffee break
10:40 - 11:10
10:40 - 11:10
Efficient algorithms for processing natural language
Tomas Mikolov / Research Scientist / Facebook
Machine learning algorithms are increasingly popular and useful in a wide variety of tasks. However, it is often confusing to understand what type of algorithm is the most suitable for what type of problem. In this talk, I will focus on text classification and representational learning and will describe some efficient solutions based on models optimized for computational efficiency, such as word2vec and fasttext.

#deeplearning #nlp #word2vec #fasttext #wordembeddings
11:10 - 11:40
11:10 - 11:40
Optimising industrial production with machine learning: electrolysis example
Emeli Dral / Chief Data Scientist / Mechanica AI
Using accumulated historical data, Mechanica AI developed a machine learning system able to predict which electrolytic cells are likely to underperform in the near future. The goal was to address the periodic efficiency decrease of electrolytic cells that leads to lowered production output and thus lost revenue.

Pilot results demonstrated that the machine learning system correctly identified at least twice as many underperforming cells compared to the current expert-led approach. Early alerts on unnoticed technical problems allowed plant experts to treat them in time, thus avoiding yield loss. The predictive system is now being deployed at the first chosen plant. The gains will result from the timely prevention of cell inefficiencies and a corresponding increase of production levels by up to 0.7%.

#industrialml #industrialai #sensor_data #gradient_boosting #lightGBM #interpretable_ai #shap_values
11:40 - 12:10
11:40 - 12:10
Running it on prod - how to do deployment for machine learning and not be scared
Łukasz Dziekan / Chief Technology Officer / FinAI
They say a data scientist will either bloom or wilt after he/she deploys her first model to production where real decisions will be made, without human intervention. This presentation will be all about dirty, not so glamorous, getting the job done, but still state of the art part of machine learning engineer job - managing the whole process ending on production. Combining our 2-year experience of running extremely important biometrics model on production for the banking sector I will explain that moving beyond Jupyter, getting this 1% more on AUC is really way more important - because this differentiates between making a real impact and affecting people's lives - and staying in the research part.FinAi has built it's platform over 2 years - and while it is by no means finished, will tell you some stories from the trenches, hoping you will learn, maybe be scared a little (and it's healthy) and move you to the next step - having a model working online.

#reproducible_results #model_versioning #model_monitoring #model_deployment #best_practices
12:10 - 12:30
12:10 - 12:30
Coffee Break
12:30 - 13:40
12:30 - 13:40
Game Networking on #Slack
An effective way to meet new people with similar interests and needs.
13:40 - 14:40
13:40 - 14:40
Lunch
14:40 - 15:10
Track 1
14:40 - 15:10
Track 1
Data augmentation in speech recognition
Tomasz Jadczyk / CTO and Co-founder / Techmo
In order to deliver an Automatic Speech Recognition system capable of handling 50 000 speakers each day, one needs a few thousand hours of transcribed recordings, with the variety of speech types and styles covered. Since transcribing an hour of audio takes at least 8 hours, production of training data is expensive.
The solution comes with the data augmentation, which makes it possible to build reliable models based on the limited data resources. By using it, we have managed to decrease word error rate in production from 30% to stable 5-10%, depending on the case.
Since in machine learning every solution brings with it brand new problems, limitations of the presented methods and compatibility issues in the production environment will also be discussed.

#NLP #data_augmentation #speech_recognition #dnn #overfitting
14:40 - 15:10
Track 2
14:40 - 15:10
Track 2
Artificial Intelligence in Mental Health
Bartek Skorulski / Senior Data Scientist at Research Team / Alpha
One out of four adults experience mental health problems at a certain point in life, including anxiety, depression, sleep disturbance, phobias, etc. Limited access to healthcare services results in that most cases remains undiagnosed and untreated. At Telefonica Alpha (Health Moonshot) we try to solve this problem by building mobile apps that provide personalised coaching, therapy, monitoring, and crisis prediction. These apps collect passive data (from mobile sensors) and active data (through questionnaires). Then, on one hand, have built could-based infrastructure with tools like airflow, spark, pytorch which allows us to deploy and test our ML algorithms like recommender systems and behavioural models. On the other hand, we collaborate with neuroscientists or psychologists from institutions like MGH, UCL, who help us in establishing a hypothesis, interpreting the developed models and testing our products with clinical trials.

#deeplearning #recommender_system #activelearning #mentalhealth #digital_phenotyping
15:10 - 15:40
Track 1
15:10 - 15:40
Track 1
Zero- and Few-Shot Learning for Text Classification
Valentin Malykh / Applied Research Scientist / Huawei
Here, at Huawei, we are solving different real-world tasks. Our team spends most of our time on dialog systems.
One of the problems we are solving is how to choose the right skill in each situation. And more interestingly, what if we have (almost) no training set? Traditionally, for such tasks, TF-IDF and word2vec are used. We use currently more and more popular BERT and its successors. We are integrating our developed solution into large-scale dialog system, which arises some new challenges. In my talk, I'll try to describe to you our approach and demonstrate its results on open data. On it, we are achieving 80% vs 40% for traditional approaches. Hopefully, you could get these ideas and apply them to your problem solution.

#deeplearning #nlp #text_embeddings #fewshot_classification
15:10 - 15:40
Track 2
15:10 - 15:40
Track 2
Deep learning for clinical trials in movement-related diseases
Łukasz Kidziński / CTO at Saliency.ai , Researcher at Stanford
Every second person in the world will be affected by symptomatic movement disorders at some point in their life. Proper treatment of these disorders requires very precise measurements of body mechanics. Collecting data requires sophisticated motion capture hardware and a few hours of work of highly trained medical personnel, bringing the cost to more than $2k per visit. We propose a deep learning approach where expensive hardware and trained personnel is replaced by mobile phone and a neural network. We are now recruiting patients to track their movement at their homes. Measurements derived using our method correlate at r = 0.7 - 0.8 with most of the tested metrics, enabling their clinical use. In this talk, I will present all the steps I had to take to execute this medical data science project, from data sharing agreement with a hospital until deployment. I'll discuss the impact of our solution on drug development.

#deeplearning #reinforcementlearning #computer_vision #drugs #x-ray #mri #healthcare #bioengineering
15:40 - 16:10
Track 1
15:40 - 16:10
Track 1
Developing conversational AI systems with DeepPavlov open-source framework
Mikhail Burtsev / Founder and Leader / DeepPavlov.ai
An open-source DeepPavlov library is specifically tailored for development of dialogue systems. The library prioritizes efficiency, modularity, and extensibility with the goal to make it easier to create dialogue systems from scratch with limited data available. It supports modular as well as end-to-end approaches to implementation of conversational agents. In DeepPavlov framework an agent consists of skills and every skill can be decomposed into components. Components are usually trainable models which solve typical NLP tasks such as intent classification, named entity recognition, sentiment analysis or pre-trained encoders for word or sentence level embeddings. Sequence-to-sequence chit-chat, question answering or task-oriented skills can be assembled from components provided in the library. ML models implemented in DeepPavlov have a performance on par with the current state of the art in the field.

#deeplearning #nlp #chatbots #open-source #deeppavlov
15:40 - 16:10
Track 2
15:40 - 16:10
Track 2
Play.Care: bridging psychology and technology for better autism assessment
Anna Anzulewicz / Head of Research / Harimata
Play.Care, developed by Harimata, analyses movement patterns that children make while using a tablet device and assesses the risk of autism spectrum disorders. The solution uses state of the art CNN architectures to analyse users' behaviour during the gameplay. Additionally, features that characterise movement kinematic characteristics are classified with boosted trees and RNN-s. This new, automated approach to detection of developmental disorders can make a difference in the way in which we learn about children's health and make the initial assessment faster and widely available. The results of three major studies (including 520 participants) conducted so far, has shown that the solution has an overall sensitivity 80% and specificity of 89%. Currently, the solution is undergoing clinical study, which will further assess its effectiveness.

#xgboost #deeplearning #cnn #rnn #healthcare #diagnosis
16:10 - 16:40
Track 1
16:10 - 16:40
Track 1
ML in Industry: Anomaly detection in extruder gears using FFT signals
In the talk, an industry anomaly detection use case that we did for an internationally renowned German mechanical machine engineering company will be presented. They wanted to know if it's possible to detect automatically from Fourier transformed sensor signals whether the extruder gears in the plastic foil producing machine are about to break. Since we had no fully labeled data, but only the point in time that the machine was repaired, we trained an unsupervised Gaussian Mixture Model for detecting the normal state of the machines. We successfully detected all repairs, plus time periods with broken sensors and a repair of a machine in China that was not performed by the company's service team and thus not previously known. Previously, the FFTs were checked by humans and thus systematic checks over longer time spans were infeasible.

#mlinIndustry #anomalydetection #unsupervisedlearning #gaussianmixturemodels
16:10 - 16:40
Track 2
16:10 - 16:40
Track 2
Is prediction STILL difficult (especially if it's about the future)?
Marta Markiewicz / Head of Data Science / Objectivity
Who wouldn't like to know what future holds? Humans were intrigued with future mysteries since the beginning of time, and so are we at Objectivity. Forecasting revenue is of especially high importance for us, so over last few years, we've tried several approaches. The first model was productionized in 2016 and it evolved nicely over the years, with various outcomes, coming closer and closer to the actual values. Currently achieved MAPE is around 3%. In the speech I would like to share the pros and cons of different methods we've tried (classical time series forecasting, Monte Carlo, ML), to facilitate this journey for others.
16:40 - 17:00
16:40 - 17:00
Coffee break
17:00 - 17:30
17:00 - 17:30
Can you use Machine Learning to predict the value of fine art?
Daniel Baade / CEO / Soprano Mergers & Acquisitions and Chairman / Art World Insights
Art is Passion, Culture, Inspiration…But art is also an investment and an asset class. Some pictures sell for $100 others sell for $100million. How do you determine the value of an asset class which doesn't produce cashflows and where the range of values is greater than 1,000,000X? Using time-series analytics might be the obvious answer. However, as the average time between two sales of the same paintings can be more than 30 years, this is not a practical solution. We use a form of data decomposition technology to solve this problem. Combining market knowledge, data analytics and machine learning we are able to value art accurate, fast and based on statistical evidence.

#art #artdata #arttech #awi #artasinvestment #data_decomposition
17:30 - 18:00
17:30 - 18:00
COTA: Improving the customer support experience using Deep Learning
Aditya Guglani / Data Scientist / Uber
As the leading ridesharing platform, Uber receives thousands of support tickets daily. The tickets are in different global languages and range from missing item from an EATS order in India to fare adjustment request in North America. To scale up the ticket resolution process, we built COTA (Customer Obsession Ticket Assistant) an intelligent system based on machine learning (ML) and natural language processing (NLP) techniques that are integrated with Uber's customer support platform. It provides customer support representatives, suggestions for the ticket type, appropriate reply and relevant actions to take based on ticket text and additional context such as trip information. As a result, we were able to reduce the average number of re-routings by 15% and the time an agent spends on a ticket by 5% on average, thus making the support experience a little more magical.

#deeplearning #nlp #embeddings #ai_assistant #customer_care #customer_satisfaction
18:00-18:10
18:00-18:10
Closing Conference
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