Using ML to build an autonomous drone — Greg Lamp
Mar
15
3:00 PM15:00

Using ML to build an autonomous drone — Greg Lamp

They might not be delivering our mail (or our burritos--tacocopter.com) yet but drones are now simple, small, and affordable enough that they can be considered a toy. You can even customize and program some of them! The Parrot AR Drone has an API that let's you control not only the drone's movement but also stream video and images from both of its cameras. I'll show you how you can use Python and node.js to build a drone that moves all by itself.

Greg Lamp is the co-Founder and CTO of Yhat. In this role, Greg leads development of Yhat's core products and infrastructure and is the principal architect of the company's cloud and on-premise enterprise software applications. Greg was previously a product manager at OnDeck, a fintech startup in New York and before that an analyst at comScore. Greg is a graduate of the University of Virginia.

Twitter: @theglamp - LinkedIn

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Speed-up distributed deep learning with Spark on AWS —  Vincent Van Steenbergen
Mar
15
2:30 PM14:30

Speed-up distributed deep learning with Spark on AWS — Vincent Van Steenbergen

Training deep networks is a time-consuming process, with networks for object recognition often requiring multiple days to train. For this reason, leveraging the resources of a cluster to speed up training is an important area of work. In this talk we'll show how to use an AWS Spark cluster to train a model quickly from a laptop at a very little cost (around 10€).

Vincent Van Steenbergen is a R&D and Backend Engineer at IDAaas (Intelligent Data Analysis as a Service) a research and development spin-off from the University Paris 13. IDAaaS develops services in Intelligent Data Analysis such as Data Mining, Knowledge Discovery in Databases and Predictive Analytics. Vincent has been developing all kind of software for as long as he remembers.

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Demystifying Deep Learning — Roberto Paredes Palacios
Mar
15
2:00 PM14:00

Demystifying Deep Learning — Roberto Paredes Palacios

Deep Learning (DL) is becoming a big tsunami in the Machine Learning community. This talk aims at introducing DL, its motivation and main techniques. However, part of this talk is also devoted to demystify DL. What are the main advantages but also the main drawbacks of DL?. And what are the key issues that the practitioners have to consider?

Roberto Paredes is an Associate Professor at Departamento de Sistemas Informáticos y Computación DSIC of the Universidad Poliécnica de Valencia UPV. He belongs to the Pattern Recognition and Human Language Technologies Research Centre PRHLT. Roberto Paredes is the Director of the PRHLT and the President of the Spanish AERFAI Association. His main research interests are around the statistical learning, machine learning and more recently neural networks and deep learning.

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Evolving a data-driven company from MapReduce to Spark — Ferran Galí Reniu
Mar
15
1:30 PM13:30

Evolving a data-driven company from MapReduce to Spark — Ferran Galí Reniu

Trovit in short time became one of the leaders in the online classified advertising industry. We adopted Hadoop and MapReduce in order to manage all our content in a scalable way. However, we faced its limitations: that’s the reason why we looked at Spark. Right now, early 2016 we already adopted it for good and it is constantly bringing fresh solutions to our business. The talk will consist of an introduction to Trovit and its Big Data infrastructure, and we will specifically illustrate how Spark works with a demo.

Ferran Galí i Reniu is passionate about web scale distributed systems. Working on Big Data technologies for several years he gained expertise solving problems that require a massive amount of data processing. Architecting the deployment of Hadoop on a cluster of machines, developing new solutions or playing data scientist to make the business thrilling are some of the day-to-day tasks he has to deal with. Right now he is working in Trovit building the best search engine for classified ads.

Twitter: @ferrangali - Linkedin

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Engineering the Future of Our Choice with General AI — JoEllen Lukavec Koester
Mar
15
12:00 PM12:00

Engineering the Future of Our Choice with General AI — JoEllen Lukavec Koester

What is the future we want to create, and what can we do – starting today – to actively shape that future with general AI? This talk outlines a vision for the future of humankind once AI reaches human or superhuman levels, and leads the audience through the steps one research group is taking to get there. From the economics of smart robots and job replacement, to bionic humans exploring the universe through space travel, the talk offers a window into the work of 30 researchers focused on AI development and safety, and explains what attendees can do themselves to help make that future happen.

JoEllen is the AI Safety Ambassador and Head of PR for GoodAI, a Prague-based general AI research and development company. A high school teacher by trade, she has a bachelor’s degrees in English and Philosophy from Seattle University, a master’s degree in Transatlantic Studies from Charles University in Prague, and is the recipient of Fulbright grant. JoEllen is particularly interested in how AI will affect international government and political relations.

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Past, Present and Future of AI: a Fascinating Journey — Ramon Lopez de Mantaras
Mar
15
11:20 AM11:20

Past, Present and Future of AI: a Fascinating Journey — Ramon Lopez de Mantaras

Possibly the most important lesson we have learned after 60 years of AI research is that what seemed to be very difficult to achieve, such as accurate medical diagnosis to playing chess at the level of a Grand Master, turned out to be relatively easy whereas what seemed easy, such as visual object recognition or deep language understanding, turned out to be extremely difficult. In my talk I will try to explain the reasons for this apparent contradiction by briefly reviewing the past and present of AI and projecting it into the near future.

Ramon Lopez de Mantaras is Research Professor of the Spanish National Research Council (CSIC) and Director of the Artificial Intelligence Research Institute of the CSIC. Technical Engineer EE (Electrical Engineering) from the Technical Engineering School of Mondragón (Spain) in 1973. Master of Sciences in Automatic Control from the University of Toulouse III (France) in 1974, Ph.D. in Physics from the University of Toulouse III (France), in 1977, with a thesis in Robotics (done at LAAS, CNRS). Master of Science in Engineering (ComputerScience) from the University of California at Berkeley (USA) in 1979. Ph.D. in Computer Science, from the Technical University of Catalonia, Barcelona (Spain) in 1981.

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Revolutionizing Offline Retail Pricing & Promotions with ML — Daniel Guhl
Mar
15
10:30 AM10:30

Revolutionizing Offline Retail Pricing & Promotions with ML — Daniel Guhl

Everybody uses price promotions in retail. However, individual pricing is seldom used, particularly in offline retail. Marketing literature has been advocating the use of individual price discrimination for decades. Furthermore, product recommendations, ever-present in e-commerce, are also not often found in offline retail. We show the machine learning driven system behind a new promotion channel that enables retailers and manufacturers alike to target individual customers in offline retail. Lessons learned, technologies used, and machine learning approaches driving our system will be shown.

Daniel Guhl has a background in economics & marketing, and got interested in data modeling during his Ph.D.. Currently, he is working as a data scientist at a Berlin based Start-up and is pursuing a postdoc at Humboldt University. He enjoys learning everyday and focuses on solving real world problems.

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Jacek Dabrowski has a university background in mathematics, computer science and psychology. He is also a startup veteran with experience in financial technology and online advertising. His current focus is on building distributed real-time systems, big data pipelines and machine learning engines. He is also passionate about deep learning applications.

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How to predict the future of shopping — Ulrich Kerzel
Mar
15
10:00 AM10:00

How to predict the future of shopping — Ulrich Kerzel

Shopping, or as the people on the other side of the counter call it, retail has become the number one breeding ground for predictive applications in the enterprise. What started as simple recommendation engines has evolved into a complex and powerful ecosystem of predictive applications that affect core processes such as pricing, replenishment and staff planning. In this talk, Ulrich Kerzel will share impact and experiences from building and operating predictive applications for large retailers, and explain why the future of retail is as much a science as an art.

Dr. Ulrich Kerzel is a Senior data scientists at Blue Yonder and renowned scientist with research experience at the University of Cambridge and CERN. Ulrich Kerzel earned his PhD under Professor Dr Feindt at the US Fermi National Laboratory and at that time made a considerable contribution to core technology of NeuroBayes. After his PhD, he went to the University of Cambridge, were he was a Senior Research Fellow at Magdelene College. His research work focused on complex statistical analyses to understand the origin of matter and antimatter using data from the LHCb experiment at the Large Hadron Collider at CERN, the world’s biggest research institute for particle physics. He continued this work as a Research Fellow at CERN before he came to Blue Yonder as a senior data scientist.

Twitter: @Ukerzel - Linkedin

 

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The emergent opportunity of Big Data for Social Good — Nuria Oliver
Mar
15
9:10 AM09:10

The emergent opportunity of Big Data for Social Good — Nuria Oliver

Nuria Oliver is a computer scientist and Scientific Director at Telefónica. She holds a Ph.D. from the Media Lab at MIT. She is one of the most cited female computer scientist in Spain, with her research having been cited by more than 8900 publications. She is well known for her work in computational models of human behavior, human computer-interaction, intelligent user interfaces, mobile computing and big data for social good.

Twitter: @nuriaoliver - LinkedIn

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Automating ML workflows: a report from the trenches — Jose A. Ortega Ruiz
Mar
14
5:00 PM17:00

Automating ML workflows: a report from the trenches — Jose A. Ortega Ruiz

ML services are quickly becoming a commodity, and they will be taken for granted by developers and computer users alike in the near future. The building blocks for ML as an ubiquitous service are already in place, almost always in the form of remote APIs that provide a first level of abstraction over ML problem-solving and, specially, obviate scalability and resource allocation issues. But that's not enough: those building blocks still leak implementation details inessential to the application developer that needs to provide domain-specific solutions. We need to ascend a couple of rungs in the abstraction ladder and provide domain-specific languages to describe ML solutions without nitty-gritty details unrelated to the problem at hand, offering non-experts the possibility of automating their ML solutions. In this talk, we'll discuss our experience designing and developing BigML's data wrangling and ML workflow DSLs, Flatline and WhizzML, and how they generalize to similar ML services and APIs.

Jose A. Ortega Ruiz is part of the founding team of BigML, a little startup trying to apply machine learning and other AI techniques to big data, and make them accessible to non-specialists. He was hacking for Oblong from 2008 to early 2011. Before that, he worked for Google (from July 2007). From June 2005 to May 2007, he worked on embedded software development for the scientific payload of LISA Pathfinder. He was a theoretical physicist in a previous life, and wrote a Ph. D. thesis on gravitational wave detectors. He also got a bachelor’s degree in computer science. Between 2003 and 2005, he taught courses on programming and computer networks at the Universitat Autonoma of Barcelona, where he was part of the mobile agents research group.

Twitter: @jaotwits - Linkedin

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Machine Learning Services Benchmark: choosing the right tools for your application — Inês Almeida
Mar
14
4:30 PM16:30

Machine Learning Services Benchmark: choosing the right tools for your application — Inês Almeida

Implementing a machine learning solution from scratch requires a lot of resource investment before yielding results. It is tempting to look for off the shelf machine learning solutions that are easy to integrate within one’s product instead. In this talk, you will follow a real case example of how the need to solve a specific problem led to doing a benchmark on a series of machine learning services. You will learn how these services compare, and pick up some tips on how to conduct your own benchmarks along the way.

Inês Almeida is a machine learning enthusiast from Lisbon, Portugal, where she has given several talks on the topic, in particular on neural networks. Her goal is to share knowledge that is useful for newbies and experts alike. Inês has a Physics MSc. degree and currently works as a data scientist at Liquid Data Intelligence.

Twitter: @isbalmeida - Linkedin

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Machine learning performance evaluation: tips and pitfalls — Jose Hernandez Orallo
Mar
14
4:00 PM16:00

Machine learning performance evaluation: tips and pitfalls — Jose Hernandez Orallo

Beginners in machine learning usually presume that a proper assessment of a predictive model should simply comply with the golden rule of evaluation (split the data into train and test) in order to choose the most accurate model, which will hopefully behave well when deployed into production. However, things are more elaborate in the real world. The contexts in which a predictive model is evaluated and deployed can differ significantly, not coping well with the change, especially if the model has been evaluated with a performance metric that is insensitive to these changing contexts. A more comprehensive and reliable view of machine learning evaluation is illustrated with several common pitfalls and the tips addressing them, such as the use of probabilistic models, calibration techniques, imbalanced costs and visualisation tools such as ROC analysis.

Jose Hernandez Orallo, Ph.D. is a senior lecturer at Universitat Politecnica de Valencia. His research areas include: Data Mining and Machine Learning, Model re-framing, Inductive Programming and Data-Mining, and Intelligence Measurement and Artificial General Intelligence.

Website

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Open source Machine Learning and Predictive APIs — Alex Housley
Mar
14
3:00 PM15:00

Open source Machine Learning and Predictive APIs — Alex Housley

IT decision makers now face an unprecedented challenge — and opportunity — to help their organization build a one-to-one relationship with customers and gain actionable insights. Machine learning and deep learning technologies that were previously reserved for companies such as Google and Amazon are now open-source. But open source machine learning is a fast moving target, with game-changing developments even in the six months since PAPIs 2015. To follow on from my talk in Sydney about our journey taking Seldon from a closed predictive API to an open source machine learning platform, I will provide fresh insight with applied examples to help decision makers stay in control, and identify opportunities for value creation.

Alex Housley (CEO of Seldon.io) is a serial entrepreneur on a mission to establish the new open standard for predictive AI, to make the world a more personalised, productive and fun place. He is the co-creator of the Genome Laser, which sequenced the inventors of the laser and high-speed genome sequencing and blasted their DNA into space with an enormous laser.

Twitter: @ahousley - Linkedin

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How to build a successful data team — Florian Douetteau
Mar
14
2:30 PM14:30

How to build a successful data team — Florian Douetteau

As you walk into your office on Monday morning, before you've even had a chance to grab a cup of coffee, your CEO asks to see you. He's worried: both customer churn and fraudulent transactions have increased over the past 6 months. As Data Manager, you have 6 months to solve that. 

As Data Manager, you know the challenges ahead:

  • Multitudes of technology choices to make
  • Building a team and solving the skill-set disconnect
  • Data can be deceiving...
  • Figuring out what the successful data product must be

The goal of this talk is to provide some perspective to these topics

Florian works in the “data” field since 01’, back when it was not yet big. He worked in successful startups in search engine, advertising and gaming industries, holding various data or CTO’s role. He started Dataiku in 2013, his first venture as a CEO, with the goal of alleviating the daily pains from the data enthusiasts and let them express their creativity.

Twitter: @fdouetteau - Linkedin

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Microdecision making in financial services — Greg Lamp
Mar
14
12:30 PM12:30

Microdecision making in financial services — Greg Lamp

Fintech startups are taking business away from traditional institutions like banks, exchanges, and brokerages. One of the reasons that these startups are able to compete with $30B+ behemoths like Credit Suisse and Goldman Sachs is their advanced decision making capabilities. By leveraging new data sources and better predictive analytics, companies like Ferratum Bank can make more accurate decisions in a fraction of the time.

This talk will cover:

  • Types of decisions you can automate
  • Challenges in building predictive, financial apps
  • First-hand, real-world examples

Greg Lamp is the co-Founder and CTO of Yhat. In this role, Greg leads development of Yhat's core products and infrastructure and is the principal architect of the company's cloud and on-premise enterprise software applications. Greg was previously a product manager at OnDeck, a fintech startup in New York and before that an analyst at comScore. Greg is a graduate of the University of Virginia.

Twitter: @theglamp - LinkedIn


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Predictive APIs: What about Banking? — Natalino Busa
Mar
14
11:40 AM11:40

Predictive APIs: What about Banking? — Natalino Busa

The best services have one thing in common: a superb customer experience. Banking services are no exception to this rule, and indeed the quest for an effortless, well informed, and personalized customer experience is one of the main goals of today's innovation in digital banking services. According to what Maslow has described in his "pyramid of needs", customers are seeking a more intimate and meaningful experience where banking services can actively assist the customer in performing and managing their financial life. Predictive APIs have a fundamental role in all this, as they enable a new set of customer journeys such as automatic categorization of transactions, detecting and alerting recurrent payments, pre-approving credit requests or provide better tools to fight fraud without limiting legitimate customer transactions. In this talk, I will focus on how to provide better banking services by using predictive APIs. I will describe the path on how to get there and the challenges of implementing predictive APIs in a strictly audited and regulated domain such as banking. Finally, I will briefly introduce a number of data science techniques to implement those customer journeys and describe how big/fast data engineering can be used to realize predictive data pipelines.

Natalino is currently Enterprise Data Architect at ING in the Netherlands, where leads the strategy, definition, design and implementation of big/fast data solutions for data-driven applications, for personalized marketing, predictive analytics, and fraud/security management. All-round Software Architect, Data Technologist, Innovator, with 15+ years experience in research, development and management of distributed architectures and scalable services and applications.

Twitter: @natbusa - Linkedin

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Predictive Analytics Use Cases in the Manufacturing Industry — David Arnu
Mar
14
10:45 AM10:45

Predictive Analytics Use Cases in the Manufacturing Industry — David Arnu

Predictive analytics has become essential for companies in all sectors, including the industrial sector, to leverage the potential of their data and to stay competitive. A frequent use case is predictive maintenance, i.e. predicting and preventing machine failures. This avoids unplanned production stops and maintenance, lowers costs, and improves planning and reliability. Another important use case is quality assurance. Production issues can be detected very early on, i.e. to optimize product design, or production processes can be optimized. Risk management is important for industry companies to identify potential risks that might impact future productions. Future developments can be predicted accurately and costs can be stabilized. Especially for industry firms, productions losses, i.e. caused by interruptions in the supply chain, lead to great issues and costs, because compliance can’t be fulfilled or production isn’t at full capacity.

The “Funded R&D” team of RapidMiner mainly works on research projects and innovative predictive analytics use cases, either funded by the European Union (e.g. within the “Seventh Framework Programme” and “Horizon 2020”) or the German government. Also the US government recognized the importance of predictive analytics and supports research projects that deal with predictive analytics in the industry. The advantage of research projects is that business and research come together and find solutions for relevant industry challenges using innovative technology and algorithms that are applied on real world scenarios at our industrial partners.

RapidMiner started as a research project at Technical University of Dortmund in 2001 and evolved to a widely used open-source code-free predictive analytics platform. Due to our academic roots, RapidMiner still keeps a strong interest in research and performs numerous joint R&D projects with universities. To gain experience in current topics and solve real issues of industry partners, we founded the “Funded R&D” team, which focusses on research projects with innovative predictive analytics use cases.

Within the ProMondi project we created a solution for predicting assembly plans and costs for new product designs using Data and Text Mining and thereby support product designers and assembly planners, enabling better and faster decision making in product design. This solution was successfully applied to product designs and assembly plans of truck engines, washing machines, and dish washers, making the partner companies more agile and reducing their costs. In the FEE project we work together with partners from academia, the chemical industry and industrial automation to early detect critical situations and to support plant operators in their decision making. We’ll create a solution integrated into existing software that helps plant operators and plant managers to detect and react proactively in critical situations. Therefore we use Big Data technology to analyze time series data from sensor readings and unstructured data from log entries (created automatically as well as manually). The change from reactive to proactive handling of critical situations saves costs that were caused by production losses.

With this presentation we share our experience and best practices with predictive analytics use cases in the manufacturing industry. Which use cases occur frequently? How can typical use cases in the industry best be solved? What role plays predictive analytics in those solutions and how can the solutions be integrated in your daily business?

David Arnu studied at University of Dortmund and holds a Master of Science degree in Computer Science. There he worked as a research assistant at the chair of Artificial Intelligence, later he joined the R&D team of RapidMiner as Software Engineer and Project Engineer. Now, he is a Data Scientist at RapidMiner. He is working on research projects focussing on Predictive Analytics and Big Data.

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Real-world applications of AI — Daniel Hulme
Mar
14
10:15 AM10:15

Real-world applications of AI — Daniel Hulme

This talk will offer answers to the following questions: What is data-driven decision making? What is AI? What is Business Intelligence? Why are these concepts important? What are the biggest challenges and opportunities?

Daniel is the CEO of Satalia that provides AI inspired solutions to solve industries hardest problems. He’s the co-founder of the ASI that transitions scientists into data scientists. Daniel has a MSci and EngD in AI from UCL, and is Director of UCL’s Business Analytics MSc; applying AI to solve business/social problems. Daniel has many Advisory and Executive positions, holds an international Kauffman Global Entrepreneur Scholarship and actively promotes innovation across the globe.

Twitter: @TheSolveEngine - Linkedin

 

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Building a production-ready predictive app for customer service — Alex Ingerman
Mar
14
9:45 AM09:45

Building a production-ready predictive app for customer service — Alex Ingerman

Have you always wanted to add predictive capabilities to your application, but haven’t been able to find the time or the right technology to get started? In this session, learn how a smart application for predictive customer service can be built in the AWS cloud. We will walk through the process of labeling data, setting up a real-time data ingestion pipeline and using machine learning to make real-time predictions for messages arriving via social media channels. You will be able to later replicate everything shown on your own, using the provided sample code and training dataset.

Alex Ingerman leads the product management team for Amazon Machine Learning. He joined Amazon in 2012, after working on products including web-scale search, content recommendation systems, immersive data exploration environments, and enterprise email and content servers. Alex holds a Bachelor of Science degree in Computer Science, and a Master of Science degree in Medical Engineering.

Twitter: @alex_ingerman - Linkedin

 

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