PLANNED TALKS

Learn how to automate your systems, how to build chat bots and the future of deep learning. Explore the applications of machine learning, NLP, and computer vision transferring Neural Network know-how from academia to architects

WORLD'S BIGGEST AI ONLINE
CONFERENCE FOR DEVELOPERS

AI WITH THE BEST

50+ SPEAKERS - 2 DAYS - 3 TRACKS
14-15 October 2017
Location: Online
Access platform

3 TRACKS

Machine Learning AI Online With The Best Conference

1

MACHINE LEARNING RESEARCH
Machine and Deep Learning Fundamentals, implementation and new modelling strategies for your frameworks straight from the labs.
Chatbot online developer AI conference

2

DEEP LEARNING,
NLP & CHATBOTS
Detection, tracking & integration tips for robots, drones & autonomous vehicles. Leverage conversational architecture with bot-builders and a slick UX.
Computer Vision online developer AI conference

3

APPLIED AI: STARTUPS, INDUSTRY & SOCIETY
Discover the applications of AI from startups and industry, what it takes to manage an AI company and the impacts on society with this technology.

FEATURED SPEAKERS

Hugo Larochelle
Research Scientist at Google Brain
He currently leads the Google Brain group in Montreal. His main area of expertise is deep learning. His previous work includes unsupervised pretraining with autoencoders, denoising autoencoders, visual attention-based classification, neural autoregressive distribution models. More broadly, he is interested in applications of deep learning to generative modeling, reinforcement learning, meta-learning, natural language processing and computer vision. 
Ian Goodfellow
Research Scientist at Google Brain
Ian Goodfellow is a staff research scientist at Google Brain. He is the lead author of the MIT Press textbook Deep Learning (www.deeplearningbook.org) and the inventor of generative adversarial networks. He is generally interested in all things deep learning, and usually focuses on generative models, machine learning security, and differential privacy.
Louis Monier
Head of AI Lab at Airbnb

SPEAKERS

Ian Goodfellow
Ian Goodfellow
Researcher, Google Brain
Ian Goodfellow is a staff research scientist at Google Brain. He is the lead author of the MIT Press textbook Deep Learning (www.deeplearningbook.org) and the inventor of generative adversarial networks. He is generally interested in all things deep learning, and usually focuses on generative models, machine learning security, and differential privacy.
Devi Parikh
Devi Parikh
Research Scientist, Facebook
Devi Parikh is an Assistant Professor in the School of Interactive Computing at Georgia Tech, and a Visiting Researcher at Facebook AI Research (FAIR). From 2013 to 2016, she was an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. From 2009 to 2012, she was a Research Assistant Professor at Toyota Technological Institute at Chicago (TTIC), an academic computer science institute affiliated with University of Chicago. She has held visiting positions at Cornell University, University of Texas at Austin, Microsoft Research, MIT, and Carnegie Mellon University. She received her M.S. and Ph.D. degrees from the Electrical and Computer Engineering department at Carnegie Mellon University in 2007 and 2009 respectively. She received her B.S. in Electrical and Computer Engineering from Rowan University in 2005. Her research interests include computer vision and AI in general and visual recognition problems in particular. Her recent work involves exploring problems at the intersection of vision and language, and leveraging human-machine collaboration for building smarter machines. She has also worked on other topics such as ensemble of classifiers, data fusion, inference in probabilistic models, 3D reassembly, barcode segmentation, computational photography, interactive computer vision, contextual reasoning, hierarchical representations of images, and human-debugging. She is a recipient of an NSF CAREER award, an IJCAI Computers and Thought award, a Sloan Research Fellowship, an Office of Naval Research (ONR) Young Investigator Program (YIP) award, an Army Research Office (ARO) Young Investigator Program (YIP) award, an Allen Distinguished Investigator Award in Artificial Intelligence from the Paul G. Allen Family Foundation, four Google Faculty Research Awards, an Amazon Academic Research Award, an Outstanding New Assistant Professor award from the College of Engineering at Virginia Tech, a Rowan University Medal of Excellence for Alumni Achievement, Rowan University's 40 under 40 recognition, and a Marr Best Paper Prize awarded at the International Conference on Computer Vision (ICCV).
Sid J. Reddy
Sid J. Reddy
Chief Scientist, Conversica
Dr. Sid J Reddy designed, developed and contributed to dozens of Natural Language Processing and Machine Learning systems used in production in a wide array of use-cases and industry verticals (healthcare, business intelligence, life sciences, legal and e-commerce). He was most recently a professor at Northwestern University and a principal applied scientist at Microsoft. His research that is featured in over 50 journals and peer-reviewed conferences ranged from acquiring lexical resources through distributed word vector representations learned from big data and applying them to improve state of the art in sequential labeling tasks to using functional theories of grammar for association extraction and question-answering.
Hugo Larochelle
Hugo Larochelle
Research Scientist, Google
I currently lead the Google Brain group in Montreal. My main area of expertise is deep learning. My previous work includes unsupervised pretraining with autoencoders, denoising autoencoders, visual attention-based classification, neural autoregressive distribution models. More broadly, I’m interested in applications of deep learning to generative modeling, reinforcement learning, meta-learning, natural language processing and computer vision. Previously, I was Associate Professor at the Université de Sherbrooke (UdeS). I also co-founded Whetlab, which was acquired in 2015 by Twitter, where I then worked as a Research Scientist in the Twitter Cortex group. From 2009 to 2011, I was also a member of the machine learning group at the University of Toronto, as a postdoctoral fellow under the supervision of Geoffrey Hinton. I obtained my Ph.D. at the Université de Montréal, under the supervision of Yoshua Bengio. My academic involvement includes associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), member of the editorial board of the Journal of Artificial Intelligence Research (JAIR) and program chair for the International Conference on Learning Representations (ICLR) of 2015, 2016 and 2017. I’ve also been an area chair for many editions of the NIPS and ICML conferences. Finally, I have a popular online course on deep learning and neural networks, freely accessible on YouTube.
Matthieu Boussard
Matthieu Boussard
R&D Engineer, Craft AI
Matthieu Boussard has more than 10 years of experience in different fields of AI. In 2015, he helped start craft ai, the AI startup where he is currently Lead Scientist, working on Machine Learning with whitebox constraints. He received his PhD from the Caen University in France on multi-robot coordination. Then, he went to Japan, at Toyohashi University of technology, where he did after extensive experimental research on behaviors for mobile indoor robots for three years. Back to France, he decided to switch from robots to video game AI by joining the MASA Life team to help develop an AI middleware for games and simulations. He is a long time member of the reading committees of several international conferences on AI.
Ruslan Salakhutdinov
Ruslan Salakhutdinov
Director AI Research, Apple
Ruslan Salakhutdinov is a UPMC professor of Computer Science in the Machine Learning Department at Carnegie Mellon University. He received his PhD in machine learning from the University of Toronto in 2009. After spending two post-doctoral years at the Massachusetts Institute of Technology Artificial Intelligence Lab, he joined the University of Toronto as an Assistant Professor in the Department of Computer Science and Department of Statistics. In February of 2016, he moved to Carnegie Mellon University as an Associate Professor. Ruslan's primary interests lie in deep learning, machine learning, and large-scale optimization. His main research goal is to understand the computational and statistical principles required for discovering structure in large amounts of data. He is an action editor of the Journal of Machine Learning Research and served on the senior programme committee of several learning conferences including NIPS and ICML. He is an Alfred P. Sloan Research Fellow, Microsoft Research Faculty Fellow, Canada Research Chair in Statistical Machine Learning, a recipient of the Early Researcher Award, Connaught New Researcher Award, Google Faculty Award, Nvidia's Pioneers of AI award, and is a Senior Fellow of the Canadian Institute for Advanced Research.
Natalia Konstantinova
Natalia Konstantinova
Lead Soft Eng, First Utility
Natalia Konstantinova is a great enthusiast with over 10 years' experience in the application of Natural Language Processing, Artificial Intelligence, IT and machine learning technologies to real world problems. She got her PhD from the University of Wolverhampton and worked in various fields such as machine translation, ontologies, information extraction and currently dialogue systems and chat bots. She is currently a lead software engineer in the R&D department of First Utility, the UK's largest independent energy provider, supplying gas and electricity to around one million UK homes. Natalia is still participating in academia by serving as an Associate editor of the Cambridge Journal of Natural Language Engineering, collaborating over research papers and organising conferences and workshops. She is a strong believer that modern technology can transform businesses and our everyday life.
Silvia Chiappa
Silvia Chiappa
Researcher, Google DeepMind
Silvia is a senior research scientist at DeepMind, where she works on deep predictive models of high-dimensional time-series, and also contributes to the DeepMind's diversity and inclusion initiative. Silvia received a Diploma di Laurea in Mathematics from University of Bologna and a PhD in Statistical Machine Learning from École Polytechnique Fédérale de Lausanne. Before joining DeepMind, she worked in several Machine Learning and Statistics research groups, such as the Empirical Inference Group at the Max-Planck Institute for Biological Cybernetics, the Machine Learning and Perception Group at Microsoft Research Cambridge, and the Statistical Laboratory at University of Cambridge. Silvia's research interests are based around Bayesian and causal reasoning, approximate inference, time-series models, and deep learning.
Rebecca Fiebrink
Rebecca Fiebrink
Senior Lecturer, Goldsmiths
Dr. Rebecca Fiebrink is a Senior Lecturer at Goldsmiths, University of London. Her research focuses on designing new ways for humans to interact with computers in creative practice, including on the use of machine learning as a creative tool. Fiebrink is the developer of the Wekinator, open-source software for real-time interactive machine learning whose current version has been downloaded over 10,000 times. She is the creator of a MOOC titled “Machine Learning for Artists and Musicians,” which launched in 2016 on the Kadenze platform. She was previously an Assistant Professor at Princeton University, where she co-directed the Princeton Laptop Orchestra. She has worked with companies including Microsoft Research, Sun Microsystems Research Labs, Imagine Research, and Smule, where she helped to build the #1 iTunes app "I am T-Pain." She holds a PhD in Computer Science from Princeton University.
Joseph Barr
Joseph Barr
Chief Data Scientist, Auritas
Joe is a data scientist with 20-year track record of providing value to customers in government, healthcare, energy/utilities and finance. • Translating business issues into practical solutions • Applies decision sciences methodology and tools, including data, machine learning, statistics, operations research and econometrics Specialties: Expert in statistical modeling including predictive analytics, regression (OLS, logistic, Cox, generalized & mixed linear models), time series and forecasting, Bayesian methods, data mining, machine learning (boosting, neural nets, support vector machines, decision trees, neural networks and deep learning networks). Enthusiastic about heuristics: dimensionality-reduction, PCA, clustering. Strong business sense and experience. Holds doctorate degree in mathematics from the University of New Mexico and is a Chief Data Scientist at Auritas. Authors of several research articles. Journal editor & referee for World Scientific Semantic Computing and Robotics.
Konstantina Palla
Konstantina Palla
Postdoc Researcher, Oxford
I am a postdoc Researcher in the Oxford statistical machine learning group at the University of Oxford, working with prof. Yee Whye Teh. Before that, I completed my PhD in Machine Learning in the Computational and Biological Learning Lab at the University of Cambridge with Prof. Zoubin Ghahramani. I am interested in the development, theoretical analysis and application of Bayesian nonparametric models, a class of statistical methods able to automatically adapt their complexity to observed data. My recent work includes nonparametric models for relational data, clustering, reversible Markov chains, birth-death feature allocations and sparse graphs. Examples of applications are social networks for relational modelling, gene expression analysis for temporal and non-temporal clustering, ion channel patch clamp recordings for reversible Hidden Markov models, relational data for sparse dynamic graphs and evolving disease symptoms for latent dynamic feature allocations.
Igal Raichelgauz
Igal Raichelgauz
CEO & Founder, Cortica
Igal brings with him extensive experience in algorithms & architectures for high-dimensional and high-volume data processing flows. Prior to founding CORTICA, Igal was CTO of LCB Ltd. and managed Rimon project, developing high-end systems for real-time voice recognition. Prior to LCB, Igal was product engineer and technical leader at Intel Haifa product development division, leading characterization and analysis activities of 90nm Cache arrays. Igal has a B.Sc. in EE from Technion with several awards, and more than 3 years of research activities at the Technion, in the area of non-linear natural dynamical computational systems. Prior to Intel, Igal was a Co-Founder of a start-up company (Figment) providing advanced content services to cellular network providers in Israel, among them to Partner Communications. Igal served in elite intelligence unit in IDF, leading a team of data production and analysis. Igal is part of a forum dealing with Israeli strategic homeland security issues.
Andreas Mueller
Andreas Mueller
Lecturer Data Science, Columbia University
Andreas Mueller is a lecturer at the Data Science Institute at Columbia University and author of the O’Reilly book “Introduction to Machine Learning with Python”, describing a practical approach to machine learning with python and scikit-learn. He is one of the core developers of the scikit-learn machine learning library, and he has been co-maintaining it for several years. He is also a Software Carpentry instructor. In the past, he worked at the NYU Center for Data Science on open source and open science, and as Machine Learning Scientist at Amazon. You can find his full cv here. His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms.
Yao Zhang
Yao Zhang
Founder & CEO, Roboterra
Yao Zhang, founder and CEO of RoboTerra Inc., has been working in the education and technology field for more than a decade. RoboTerra, recognized as the annual "Star Company" at the 2014 World Learning Technology Summit and a "Top 30 Innovations Company in 2015 SVIEF", provides a cloud-based learning solution connecting educational robots built by students and respective course modules, which allows students a rewarding and fun learning experience. A Columbia University alumna, Yao was recognized for her great entrepreneurship, contribution and leadership in robotics and education, as a "Top 25 Women in Robotics in 2015" by RoboHub, and most recently honored as a 2016 Young Global Leader by Davos World Economic Forum, an Innovation Ambassador by the UNCTAD, a Global Future Council AI & Robotics Committee member and one of "7 Women in AI & Robotics" by major tech media.
Roman Yampolskiy
Roman Yampolskiy
Associate Professor, University of Louisville
Dr. Roman V. Yampolskiy is a Tenured Associate Professor in the department of Computer Engineering and Computer Science at the Speed School of Engineering, University of Louisville. He is the founding and current director of the Cyber Security Lab and an author of many books including Artificial Superintelligence: a Futuristic Approach. During his tenure at UofL, Dr. Yampolskiy has been recognized as: Distinguished Teaching Professor, Professor of the Year, Faculty Favorite, Top 4 Faculty, Leader in Engineering Education, Top 10 of Online College Professor of the Year, and Outstanding Early Career in Education award winner among many other honors and distinctions. Yampolskiy is a Senior member of IEEE and AGI; Member of Kentucky Academy of Science, and Research Advisor for MIRI and Associate of GCRI. Roman Yampolskiy holds a PhD degree from the Department of Computer Science and Engineering at the University at Buffalo. He was a recipient of a four year NSF (National Science Foundation) IGERT (Integrative Graduate Education and Research Traineeship) fellowship. Before beginning his doctoral studies Dr. Yampolskiy received a BS/MS (High Honors) combined degree in Computer Science from Rochester Institute of Technology, NY, USA. After completing his PhD dissertation Dr. Yampolskiy held a position of an Affiliate Academic at the Center for Advanced Spatial Analysis, University of London, College of London. He had previously conducted research at the Laboratory for Applied Computing (currently known as Center for Advancing the Study of Infrastructure) at the Rochester Institute of Technology and at the Center for Unified Biometrics and Sensors at the University at Buffalo. Dr. Yampolskiy is an alumnus of Singularity University (GSP2012) and a Visiting Fellow of the Singularity Institute (Machine Intelligence Research Institute). Dr. Yampolskiy’s main areas of interest are AI Safety, Artificial Intelligence, Behavioral Biometrics, Cybersecurity, Digital Forensics, Games, Genetic Algorithms, and Pattern Recognition. Dr. Yampolskiy is an author of over 100 publications including multiple journal articles and books. His research has been cited by 1000+ scientists and profiled in popular magazines both American and foreign (New Scientist, Poker Magazine, Science World Magazine), dozens of websites (BBC, MSNBC, Yahoo! News), on radio (German National Radio, Swedish National Radio, Alex Jones Show) and TV. Dr. Yampolskiy’s research has been featured 250+ times in numerous media reports in 22 languages.
Louis Monier
Louis Monier
Head of AI, AirBnB
Ryan Keisler
Ryan Keisler
Head of ML, Descartes Labs
Ryan Keisler is the head of machine learning at Descartes Labs, a startup at the intersection of geospatial data and machine learning. His work draws on his background in physics, data analysis, and machine learning. Prior to joining Descartes, Ryan received his PhD in physics from the University of Chicago and worked as a cosmologist at Stanford University.
Timnit Gebru
Timnit Gebru
Postdoctoral Researcher, Microsoft
Davide Feltoni Gurini
Davide Feltoni Gurini
CEO & Founder, Datalytics Srl
CEO & Founder of Datalytics S.r.l also hold a Ph.D. in Artificial Intelligence and Computer Engineering. I have a deep passion in new Technologies, Big Data Applications and Databases, and I also love teaching people to share my knowledge. In 2013 I've founded my company, Datalytics, that keeps me focused on emerging Technologies, Business Development techniques and Team Leading. I hold a Ph.D. at Roma Tre University regarding Artificial Intelligence applyed to Recommender Systems in Social Media. I've published and presented papers to the best conferences in Italy, USA, Brazil, Denmark, Chile and China. You can also refer to my linkedin profile for any other info -> https://www.linkedin.com/in/davidefeltoni/
Gunnar Carlsson
Gunnar Carlsson
Co-founder, Ayasdi Inc.
Gunnar Carlsson is a mathematician who has worked in the area of topology, the study of shape, since the 1970's. He has taught at University of Chicago, University of California (San Diego), Princeton University, and since 1991 at Stanford University. He has served has the chair of the Mathematics Department at Stanford, and is one of the leading topologists in the world. In 2000, he initiated a project to apply topological methods to the analysis of large and complex data sets, and from 2005-2010 led a large multi-university initiative around the developing area of topological data analysis (TDA). He is a cofounder of Ayasdi Inc., which is a company which develops artificial intelligence applications based on TDA, and has retired from Stanford to work with Ayasdi.
Lukas Biewald
Lukas Biewald
Founder, Crowdflower
Lukas Biewald is the founder and Chief Data Scientist of CrowdFlower. Founded in 2009, CrowdFlower is a data enrichment platform that taps into an on-demand to workforce to help companies collect training data and do human-in-the-loop machine learning. Following his graduation from Stanford University with a B.S. in Mathematics and an M.S. in Computer Science, Lukas led the Search Relevance Team for Yahoo! Japan. He then worked as a senior data scientist at Powerset, acquired by Microsoft in 2008. Lukas was featured in Inc Magazine’s 30 Under 30 list. Lukas is also an expert level Go player.
Reza Zadeh
Reza Zadeh
Founder & CEO, Matroid
Reza Bosagh Zadeh is Founder CEO at Matroid and an Adjunct Professor at Stanford University. His work focuses on Machine Learning, Distributed Computing, and Discrete Applied Mathematics. Reza received his PhD in Computational Mathematics from Stanford under the supervision of Gunnar Carlsson. His awards include a KDD Best Paper Award and the Gene Golub Outstanding Thesis Award. He has served on the Technical Advisory Boards of Microsoft and Databricks. As part of his research, Reza built the Machine Learning Algorithms behind Twitter's who-to-follow system, the first product to use Machine Learning at Twitter. Reza is the initial creator of the Linear Algebra Package in Apache Spark. Through Apache Spark, Reza's work has been incorporated into industrial and academic cluster computing environments. In addition to research, Reza designed and teaches two PhD-level classes at Stanford: Distributed Algorithms and Optimization (CME 323), and Discrete Mathematics and Algorithms (CME 305).
Harm van Seijen
Harm van Seijen
Research Manager, Microsoft
Harm van Seijen is a Research Manager at Microsoft. His research is centered on scalability of reinforcement learning, with the goal of addressing the fundamental challenges related to natural conversations between humans and artificial agents in real-world setting. Prior to joining Maluuba, Harm was a postdoc at the University of Alberta, working alongside professor Richard Sutton on novel reinforcement learning techniques. He completed his PhD at the University of Amsterdam.
Katherine Heller
Katherine Heller
Assistant Professor, Duke University
Katherine Heller is an Assistant Professor in Statistical Science at Duke University. She is the recent recipient of a Google faculty research award, a first round BRAIN initiative award from the NSF, as well as a CAREER award. She received her PhD from the Gatsby Computational Neuroscience Unit at UCL, and was a postdoc at the University of Cambridge on an EPSRC postdoc fellowship, and at MIT on an NSF postdoc fellowship.
Adelyn Zhou
Adelyn Zhou
CMO, Topbots
Adelyn Zhou is a technology and marketing leader who is regularly recognized as one of the top 10 people in marketing, bots and in artificial intelligence by publications such as Inc, Entrepreneur, Forbes, and teams at IBM Watson. Adelyn is currently the CMO at TOPBOTS, a leading research and strategy firm focused on educating and advising Fortune 500 companies on AI technologies. She is an internationally recognized expert on marketing and business topics, and a keynote speaker to conferences such as SxSW, Singularity University, 500 Startups, Launch Festival, DLD, Inbound, Social Media Week, and more. Adelyn graduated with honors from Harvard University and received her MBA from Harvard Business School.
Claudia Perlich
Claudia Perlich
Chief Scientist, Dstillery
Claudia Perlich currently serves as Chief Scientist at Dstillery (the former Media6Degrees) and in this role design, develop, analyze and optimize the machine learning that drives digital advertising to prospective customers of brands. She is an active industry speaker and frequent contributor to industry publications, she enjoys serving as a guide in world of data and was recently named winner of the Advertising Research Foundation’s (ARF) Grand Innovation Award and was selected as member of the Crain’s NY annual 40 Under 40 list. She published numerous scientific articles, and hold multiple patents in machine learning and won many data mining competitions. Prior to joining m6d in February 2010, she worked in Data Analytics Research at IBM’s Watson Research Center, concentrating on data analytics and machine learning for complex real-world domains and applications. She has a PhD in Information Systems from NYU and an MA in Computer Science from Colorado University and am teaching “Data Mining for Business Intelligence” in the NYU Stern MBA program.
Joanna Bryson
Joanna Bryson
Affiliate, Princeton University
Joanna Bryson is a Reader (tenured Associate Professor) at the University of Bath, and an affiliate of Princeton's Center for Information Technology Policy (CITP). She has broad academic interests in the structure and utility of intelligence, both natural and artificial. Venues for her research range from reddit to Science. She is best known for her work in systems AI and AI ethics, both of which she began during her PhD in the 1990s, but she and her colleagues publish broadly, in biology, anthropology, sociology, philosophy, cognitive science, and politics. Current projects include “Public Goods and Artificial Intelligence”, with Alin Coman of Princeton Psychology and Mark Riedl of Georgia Tech, funded by Princeton's University Center for Human Values. This project includes both basic research in human sociality and experiments in technological interventions. Other current research include understanding the causality behind the correlation between wealth inequality and political polarization, generating transparency for AI systems, and research on machine prejudice deriving from human semantics. She holds degrees in Psychology from Chicago and Edinburgh, and in Artificial Intelligence from Edinburgh and MIT. At Bath she founded the Intelligent Systems research group (one of four in the Department of Computer Science) and heads their Artificial Models of Natural Intelligence.
Sinan Ozdemir
Sinan Ozdemir
Founder & CTO, Kylie.ai
Sinan Ozdemir is a Data Scientist and Machine Learning expert from San Francisco with a Masters in Theoretical Mathematics from Johns Hopkins University where he served as a lecturer of Mathematics, Statistics and Computer Science for sometime. He is author of the book "Principles of Data Science" and the creator of several online courses focused on applying AI and machine learning to enterprises. He is also currently the co-Founder/CTO of Kylie.ai, an AI startup focused on cloning human and brand personalities to automate communications.
Matt Sanchez
Matt Sanchez
Founder and CTO, Cognitive Scale
Cian O'Sullivan
Cian O'Sullivan
Top Dog and Founder, Beagle
Cian O’Sullivan is the Top Dog and Founder of Beagle Inc. Beagle uses artificial intelligence to read contracts, and present the information in an intuitive manner. With embedded real time collaboration, and machine learning tools, Beagle provides immediate assistance to lawyers and businesses consuming contracts. An information technology expert by experience, and law professional by training, Cian has a unique outlook on the law, and technology. After graduating from UCC (Ireland), and passing the New York Bar Exam, Cian’s focus was contract negotiations and business.
Mert Yasin
Mert Yasin
CTO, Applied AI
Mert Yasin has a computer engineering degree, with the addition of an unfinished master's. His focus has been on machine learning and specifically on deep learning. He co-started a deep learning reading group (https://deepboun.github.io/) with other graduate students, and it has been a great source of inspiration for the interested locals here. He worked as an ML consultant at an international bank for several projects like predictive maintenance, credit limit prediction and NPS prediction. The previous year, he gave two public talks: "Ask Me Anything #machinelearning" & "Learning to Learn". He also have other work experience that is unrelated to AI, such as game development, financial reporting, mobile app development, and full stack web development. Now, he is the acting CTO of appliedAI.com and he runs everything technical ranging from full stack development to image recognition in the wild.
Mike Tung
Mike Tung
Founding CEO, Diffbot
Mike Tung is a machine learning and natural language processing researcher and leader. Studied AI at Stanford and UC Berkeley. Past experience at Microsoft, Yahoo, eBay, and various startups.
Durk Kingma
Durk Kingma
Research Scientist, OpenAI
Diederik P. (Durk) Kingma is a Research Scientist at OpenAI, with a focus on scalable machine learning. His contributions include the Adam optimization method, weight normalization, and variational inference and generative modeling approaches such as the variational autoencoder (VAE) and inverse autoregressive flow (IAF).
Scott Clark
Scott Clark
Co-founder & CEO, Sigopt
Scott is co-founder and CEO of SigOpt, a YC and a16z backed "Optimization as a Service" startup in San Francisco. Scott has been applying optimal learning techniques in industry and academia for years, from bioinformatics to production advertising systems. Before SigOpt, Scott worked on the Ad Targeting team at Yelp leading the charge on academic research and outreach with projects like the Yelp Dataset Challenge and open sourcing MOE. Scott holds a PhD in Applied Mathematics and an MS in Computer Science from Cornell University and BS degrees in Mathematics, Physics, and Computational Physics from Oregon State University. Scott was chosen as one of Forbes’ 30 under 30 in 2016.
Yoram Levanon
Yoram Levanon
CSO, Beyond Verbal Communication Ltd.
Chief science officer (CSO ) of Beyond Verbal Communication Ltd., Israel (world leader of Emotions Analytics field and vocal biomarkers field ). With multiple degrees in Physics, Mathematics, Statistics and Operations research-Dr. Levanon has chosen multidisciplinary studies and implementations as his preferred career. Based on that approach and on his rich experience he creates the infrastructure of the Emotion Analytics field , where physical models of brain and voice and decision making (emotional and rational) models have been used by him in the development process from 1995 until now. During this process he has discovered (in addition to Emotions universal mapping through intonation) that specific voice features/distortions are biomarkers of several diseases and has validated these findings with leading institutes (CAD-with Mayo Clinic ,Autism-with Weitzman institute ,Neuro diseases with Hadassah Hospital ,and others ).
Nisheeth Ranjan
Nisheeth Ranjan
Co-founder & CTO, Bright Funnel
Nisheeth is the Co-Founder/CTO of BrightFunnel, a B2B marketing attribution and forecasting platform. He is passionate about applying Machine Learning and AI approaches to practical business problems. He is a tech entrepreneur with 20+ years experience in engineering and management at Netscape (bought by AOL for $4.2B in 1998), Liveops (grew to $125M+ in revenue in 2010), and Trulia (IPO in 2012, bought by Zillow for $3.5B in 2014). He studied Computer Science at Cornell (BS) and Stanford (MS, AI specialization).
Sattisvar Tandabany
Sattisvar Tandabany
Lead NLP Engineer, Botfuel
Sattisvar Tandabany is graduated from École Normale Supérieure of Lyon and has a PhD in AI. With 10 years of experience in the field he collected knowledge that enables him to be lead NLP engineer at botfuel.io
Peter Brodsky
Peter Brodsky
CEO, Hyperscience
Peter has been building businesses in machine learning for over a decade. Prior to founding HyperScience, Peter was a Director at Soundcloud, where he led an engineering team that built audio fingerprinting, genre classification, and audio recommendations based on audio analysis and user behavior. Peter was also founder and CTO of Instinctiv, a startup that provided software solutions for audio content identification and predictive algorithmic recommendations for media content. Instinctiv was acquired by SoundCloud in 2012. Peter graduated magna cum laude from Cornell University.
Phil Syme
Phil Syme
Co-Founder & CTO, Area 1 Security
Phil Syme is CTO and one of the co-founders of Area 1 Security. The company offers a comprehensive product to stop phishing attacks of all forms, with an emphasis on preventing targeted and sophisticated attacks against customers. Phil specializes in building systems that scale to process very large data sets and apply inteligent detection algorithms. He has most recently has been incorporating computer vision and machine learning techniques into the production pipeline to detect phishing attacks in email and on the web.
Sam Liang
Sam Liang
CEO & Co-Founder, AISense, Inc.
Sam is the CEO/Founder of AISense Inc, based in Los Altos, California. Funded by Horizons Ventures (DeepMind, Waze, Zoom, Facebook), Tim Draper, David Cheriton (Stanford Professor Billionaire, the first investor in Google), Slow Ventures, etc. With its core team from Google Speech team, AISense is building Ambient Voice Intelligence™ technologies with deep learning that understand human-to-human conversations and provide highly innovative services. It will digitize all meetings, phone calls, and make everything searchable, as well as providing speech analytics on all the conversations, either on mobile or over teleconfePreviously, Sam was the CEO and Co-Founder of Alohar Mobile Inc in Palo Alto, California, which created world's first mobile location context platform, enabling a new category of context-aware mobile apps, such as intelligent mobile social, mobile shopping, mobile ads, smart personal assistants, etc. Alohar was successfully acquired by Alibaba in 2013. Before that, Sam was the platform architect and lead of Google Map Location Service for four years. Sam also designed Google's mobile location API and early Google Latitude. Sam got his Ph.D in EE from Stanford University, specialized in large scale distributed Internet systems.
Nal Kalchbrenner
Nal Kalchbrenner
Research Scientist, Google DeepMind
Gautham Sastri
Gautham Sastri
President & CEO, Isentium
Archa Jain
Archa Jain
Software Engineer, Calico
I am a Software Engineer with a focus in Machine Learning and Biocomputation. I'm excited to build systems that further our understanding of fundamental biology. Before Calico, I worked at Google for 3 years and at Uber for a year. I studied at Carnegie Mellon and Stanford and I have experience in the entire stack — building ML systems, architecting backends and launching products end to end.
Ingo Mierswa
Ingo Mierswa
Founder & President, RapidMiner Inc.
Dr. Ingo Mierswa is the founder and President of RapidMiner and as such responsible for strategic innovation and execution around the open source machine learning platform. Ingo is an industry-veteran data scientist since starting to develop RapidMiner at the Artificial Intelligence Division of the TU Dortmund University in Germany. Ingo, the scientist, has authored numerous award-winning publications about predictive analytics and big data. Ingo, the entrepreneur, has been leading the company with growth rates of up to 300 percent per year over the first seven years. In 2012, he spearheaded the go-international strategy with the opening of offices in the U.S. as well as the UK and Hungary. After three rounds of fundraising, the acquisition of Radoop, and supporting the positioning of RapidMiner with leading analyst firms like Gartner and Forrester, Ingo takes a lot of pride in bringing the world’s best team to RapidMiner.
Alex Holub
Alex Holub
Co-founder, Vidora
Alex is a Founder and CEO of Vidora. Vidora has built the most advanced real-time machine-learning platform that allows anyone in a business to use AI to optimize marketing and product automation in weeks, not years. Alex studied artificial intelligence throughout his academic career at Cornell University and during his Ph.D at Caltech. He has published over 15 academic papers and holds numerous patents in the areas of machine learning, computer vision, and artificial intelligence. Prior to co-founding Vidora, Alex was a technical and product management lead at Ooyala.
Samuel Kim
Samuel Kim
Speech Scientist, Gridspace
Samuel Kim is a speech scientist at Gridspace. His research focuses on understanding human behaviors and perceptions in human-computer and/or human-human interactions. In that regard, he studies information processing and machine learning algorithms dealing with human-centered multimedia signals including video, audio, and text with applications to classification, detection, and information retrieval. He received his Ph.D. on Electrical Engineering from University of Southern California, Los Angeles, California in 2010 and has been with several research institutes and startups since then.
Rajat Monga
Rajat Monga
Engineering Director, Tensorflow
Rajat Monga leads TensorFlow at Google Brain, powering machine learning research and products worldwide. Prior to this role, he led teams in Google AdWords, built and scaled eBay’s first search engine, built the engineering team at Attributor delivering web scale content-matching, and more. Rajat graduated from IIT Delhi in 1996.
Slater Victoroff
Slater Victoroff
CEO, Indico Data
Slater Victoroff is the CEO of indico (www.indico.io), a Boston-based startup focused on artificial intelligence and deep learning. indico’s platform for text and image analysis enables users to extract meaningful insight from unstructured data at scale regardless of their size or capability. With indico’s platform, users can quickly develop highly accurate new data models that are specific to their needs in a fraction of the time and effort that would be otherwise needed. Slater co-founded indico out of the Olin College of Engineering and Tech Stars Boston. In previous lives, he has been an MMA fighter, a Buddhist monk, and a poet, though the last several years have been spent in solemn dedication to software.
Cliff Click
Cliff Click
CTO, Neurensic
Cliff Click, Founder, Entrepreneur, CTO. A longtime veteran of Silicon Valley, Cliff was the CTO (& architect, vp eng) of Neurensic, a Fin-Tech startup applying AI on Big Data to successfully catch fraudsters in the stock markets. Before that the CTO and Co-Founder of h2o.ai, the makers of H2O an open source math and machine learning engine for Big Data. Cliff wrote his first compiler when he was 15 (Pascal to TRS Z-80!), although Cliff’s most famous compiler is the HotSpot Server Compiler (the Sea of Nodes IR). That compiler showed the world that JIT'd high quality code was possible, and was at least partially responsible for bringing Java into the mainstream. Cliff helped Azul Systems build an 864 core pure-Java mainframe that keeps GC pauses on Tb-sized heaps to under 10ms, and worked on all aspects of that JVM. Cliff is invited to speak regularly at industry and academic conferences and has published many papers about HotSpot technology. He holds a PhD in Computer Science from Rice University and about 20 patents.
Sanchit Arora
Sanchit Arora
Lead Researcher, Axon Research Group
Sanchit Arora is the Lead Researcher in the Axon Research group. Previously, he was the Co-founder and CTO at Dextro building video understanding and analytics solutions. Dextro was acquired by Axon to bring Deep Learning based video analytics to law enforcement challenges. Sanchit completed a Bachelors in Computer Science at the Indian Institute of Technology, Delhi and a Masters in Robotics from the University of Pennsylvania where he worked on multiple machine learning problems including real world visual understanding from robotic vision systems.
Max Yankelevich
Max Yankelevich
CEO, Workfusion
Robert Morris
Robert Morris
Founder & CEO, Terravion
Debo Olaosebikan
Debo Olaosebikan
Founder & CTO, Gigster
Melanie Warrick
Melanie Warrick
Sr. Developer Advocate, Google
Senior Developer Advocate at Google. Previous experience includes work as a founding engineer on DL4J as well as implementing machine learning in production at Change.org. Prior experience also covers business consulting and large enterprise technology implementations for a wide variety of companies. Over the last couple years, spoken at many conferences about artificial intelligence, and her passions are working on machine learning problems at scale.
Feynman Liang
Feynman Liang
Director of Engineering, Gigster
Feynman is an engineering director at Gigster and a researcher at UC Berkeley's RISE lab. His research lies at the intersection between industry and academia, focusing on distributed machine learning and practical systems for deploying machine learning in production. He is a contributor to Apache Spark's MLlib and the lead engineer for Kubernetes integration on Clipper, a low latency prediction serving system.
Dustin Hillard
Dustin Hillard
Vice President Of Engineering, Versive, Inc.
Pierre-Eduouard Lieb
Pierre-Eduouard Lieb
Partnerships Manager, Recast.ai
After graduating from Ecole 42 in Paris Pierre-Edouard joined Recast.AI in 2015 and is now managing partnerships. Building your bot in one hour with Recast.AI 101. This workshop will teach you how to use the Recast.AI platform and the basics of chatbot building. You will also learn how to connect your bot to a messaging platform using the bot connector.
Devon Bernard
Devon Bernard
VP of Engineering, Enlitic
Devon is the VP of Engineering of Enlitic, a startup developing clinical applications of deep learning that automatically interpret medical scans and reports to improve patient outcomes. Previously, he has co-founded numerous B2B start-ups in AI. One of which, Rollio, develops NLP to streamline CRM usage for sales representatives.
Gary Sieling
Gary Sieling
Lead Software Eng, Wingspan
Gary Sieling is a Software Architect at Wingspan Technology, in Blue Bell, PA, with an interests in database technologies, machine learning, and software engineering practices. He is involved in curating talks for a company lunch and learn program and on the organizing committee for a Philadelphia area tech conference (Philly ETE). Building on these experiences, he built a search engine called FindLectures.com to help find great talks and speakers.
Colin Raffel
Colin Raffel
Resident, Google Brain
Colin is a Research Scientist (formerly a resident) at Google Brain, where he is working on unsupervised learning, machine learning security, and models for sequential data. He did his PhD at Columbia University in LabROSA, supervised by Dan Ellis. He also has a Master's from Stanford University's CCRMA and a Bachelor's from Oberlin College.
Maran Nelson
Maran Nelson
Clara Labs, Co-founder & CEO

AGENDA PREVIEW

Generalizing from Few Examples with Meta-Learning

Hugo Larochelle, Google Brain

Visual Question Answering (VQA)

Devi Parikh, Virginia Tech

Conversational AI: What we’ve learned from millions of AI conversations for thousands of customers

Sid J. Reddy, Conversica

Automated Machine Learning

Andreas Mueller, Columbia University

Real estate valuations: machine learning approach.

Joseph Barr, Auritas

Machine Learning in Healthcare

Igal Raichelgauz, Cortica

Artificial Intelligence Safety

Roman Yampolskiy, University of Louisville

Active Learning and Transfer Learning

Lukas Biewald, Crowdflower

Achieving Above-Human Performance on Ms. Pac-Man by Reward Decomposition

Harm van Seijin, Microsoft

Scaling CNNs with Kubernetes and TensorFlow

Reza Zadeh, Matroid

Social Biases: Propagation and Creation through Predictive Models

Claudia Perlich, Dstillery

How to architect AI: transparency in intelligent systems with and without machine learning

Joanna Bryson, Princeton University

Robotics at OpenAI

Wojciech Zaremba, OpenAI

How to teach a machine to read and write

Sinan Ozdemir, Kylie.ai

Using AI to collect (nearly) infinite amounts of training data

Mike Tung, Diffbot

Using Bayesian Optimization to Tune Deep Learning Pipelines

Scott Clark, Sigopt

How Vocal interactions between Humans and AI can improve wellness and prevent diseases.

Yoram Levanon, Beyond Verbal Communication Ltd.

Using computer vision to combat phishing

Phil Syme, Area 1 Security

New Voice AI technologies and applications

Sam Liang, AISense Inc.

How to Ruin your Business with Data Science & Machine Learning

Dr. Ingo Mierswa, RapidMiner, Inc

Toward understanding social signals

Samuel Kim, Gridspace

Machine Learning for Everyone : Automated Machine Learning & Strategic AI at Vidora

Alex Holub, Vidora

Policing The Public Markets with Machine Learning

Cliff Click, Neurensic

Leveraging Deep Learning and Video Analysis in Law Enforcement

Sanchit Arora, Axon Research Group

Clipper: low latency prediction serving on Kubernetes

Feynman Liang, Gigster

Automated Machine Learning

Andreas Mueller, Columbia University

Using Machine Learning to Support Human Creativity

Rebecca Fiebrink, Goldsmiths

Context-awareness, digressions and randomness in modern chatbot design

Sattisvar Tandabany

Variational Inference for Large-scale and Complex Models

Silvia Chiappa, Google DeepMind

Big Data Visualization technology for Social Media Intelligence

Davide Gurini, Datalytics Srl

Machine Learning for Healthcare Data

Katherine Heller, Duke University

How to Create an AI strategy with Winning Results

Adelyn Zhou, Topbots

Building Reproducible ML Models

Archa Jain, Calico

Leveraging Deep Learning and Video Analysis in Law Enforcement

Sanchit Arora, Axon Research Group

Reinforcement Learning

Melanie Warrick, Google

Building a Discovery Engine with Machine Learning

Gary Sieling, Wingspan

Doing Strange Things with Attention

Colin Raffel, Google Brain

Quantifiable AI

Mert Yasin, Applied AI

Bayesian nonparametric modelling for networks and genomics.

Konstantina Palla, Oxford

From production rule based chatbot to machine learning: challenges and discoveries

Natalia Konstantinova, First Utility
Presenting companies include
Clara Labs
Enlitic
Versive, Inc.
Gigster
Wingspan
Enlitic
Terravion
Workfusion
toytalk
Neurensic
Indico
Tensor Flow
Vidora
GridSpace
RapidMiner Inc
Calico
iSentium
AISense
Area 1 Security
Hyperscience
Botfuel
BrightFunnel
Beyond Verbal Communication Ltd.
Sigopt
Diffbot
Applied AI
Beagle
Cognitive Scale
Kylie.ai
OpenAI
Princeton
Dstillery
Topbots
Duke University
Matroid
Microsoft
Datalytics
Stanford
Descartes Labs
Roboterra
Louisville
AirBNB
Columbia University
Cortica
Oxford
HomeUnion
Goldsmiths
Google DeepMind
First Utility
Apple
Craft ai
Google
Kindred.ai
Microsoft
Virginia Tech
Google

AGENDA - EST

9am-6pm EST // 6am-3pm PST // 3pm-12am CEST // 9pm-6am GMT+8
MACHINE LEARNING RESEARCH
DEEP LEARNING, NLP, & CHATBOTS
Applied AI: STARTUPS, INDUSTRY & SOCIETY
9:00 am
9:40 am
9:40 am
10:20 am
Yoram Levanon
CSO, Beyond Verbal Communication
How Vocal interactions between Humans and AI can improve wellness and prevent diseases.

Research suggests there is a mind-body correlation. Stress, depression, loneliness and other emotional conditions affect our health. Beyond Verbal can feel our wellness through 2 ways: 1. monitoring emotions in real time and overtime; 2. identifying vocal biomarkers which can indicate early signs of certain diseases. Based on these discoveries the future (near future) of vocal conversations between AI devices (like VPA, smartphone) and humans could provide real time alerts when high risk patients develop emotions that are linked to increased morbidity, and when anyone's voice can include problematic biomarkers. These indications will be delivered to the doctors, to the family or to the patient himself - as potential alerts. Your smart home will be your "fullfeeling home".

10:20 am
11:00 am
11:00 am
11:40 am
Roman Yampolskiy
Associate Professor, University of Louisville
Artificial Intelligence Safety

Many scientists, futurologists and philosophers have predicted that humanity will achieve a technological breakthrough and create Artificial General Intelligence (AGI). It has been suggested that AGI may be a positive or negative factor in the global catastrophic risk. In order to mitigate any dangerous impact it is important to understand how the system came to be in such a state. In this talk, I will survey, classify and analyze a number of pathways, which might lead to arrival of dangerous AGI.

11:40 am
12:20 pm
Ingo Mierswa
Founder, RapidMiner, Inc
How to Ruin your Business with Data Science & Machine Learning

Everyone talks about how machine learning will transform business forever and generate massive outcomes. However, it’s surprisingly simple to draw completely wrong conclusions from statistical models, and “correlation does not imply causation” is just the tip of the iceberg. The trend of the democratization of data science further increases the risk for applying models in a wrong way. This session will discuss: 1. How highly-correlated features can overshadow the patterns your machine learning model is supposed to find – this leads to models which will perform worse in production than during model building 2. How incorrect cross-validation lead to over-optimistic estimations of your model accuracy, especially we will discuss the impact of data preprocessing on the accuracy of machine learning models 3. How feature engineering can lift simple models like linear regression to the accuracy of deep learning – but comes with the advantages of understandability & robustness

12:20 pm
1:00 pm
Devi Parikh
Faculty, Virginia Tech
Visual Question Answering (VQA)

Wouldn’t it be nice if machines could understand content in images and communicate this understanding as effectively as humans? Such technology would be immensely powerful, be it for aiding a visually-impaired user navigate a world built by the sighted, assisting an analyst in extracting relevant information from a surveillance feed, educating a child playing a game on a touch screen, providing information to a spectator at an art gallery, or interacting with a robot. As computer vision and natural language processing techniques are maturing, we are closer to achieving this dream than we have ever been. Visual Question Answering (VQA) is one step in this direction. Given an image and a natural language question about the image (e.g., “What kind of store is this?”, “How many people are waiting in the queue?”, “Is it safe to cross the street?”), the machine’s task is to automatically produce an accurate natural language answer (“bakery”, “5”, “Yes”). In this talk, I will present our VQA dataset, VQA models, and open research questions in free-form and open-ended Visual Question Answering (VQA). Visual questions selectively target different areas of an image, including background details and underlying context. As a result, a system that succeeds at VQA typically needs a more detailed understanding of the image and complex reasoning than a system producing generic image captions. Answering any possible question about an image is one of the ‘holy grails’ of AI requiring integration of vision, language, and reasoning. I will end with a teaser about the next step moving forward: Visual Dialog. Instead of answering individual questions about an image in isolation, can we build machines that can hold a sequential natural language conversation with humans about visual content?

Joseph Barr
Chief Data Scientist, Auritas
Real estate valuations: machine learning approach

I show a machine learning approach to home price index (HPI). The algorithm estimates home price for every quarter based on comps, starting from Jan 2000, and rolls out prices on the neighborhood level then it incorporates a smoothing algorithm to exhibit price trend, one for each of the 200,000-odd neighborhoods in the USA.  Home price index based machine learning was published by HomeUnion beginning in 2016.

1:00 pm
1:40 pm
1:40 pm
2:20 pm
Reza Zadeh
Founder CEO, Matroid
Scaling CNNs with Kubernetes and TensorFlow

Providing customized computer vision solutions to a large number of users is a challenge. Matroid allows the creation and serving of computer vision models, model sharing between users, and serving infrastructure at scale. Reza will present an overview of Matroid’s pipeline, which uses TensorFlow, Kubernetes, Kafka, and Amazon Web Services, and explains how Matroid allows customization of computer vision neural network models in the browser, followed by building, training, and visualizing TensorFlow models, which are provided at scale to monitor streams of video.

Lukas Biewald
Founder, CrowdFlower
Active Learning and Transfer Learning

This talk will cover best practices in active learning with real-world examples. How search engines use active learning to select the most impactful results to show raters to improve relevance. How self driving car companies can guess the answers and show them to annotators to get a 10x speed up in data collection. This talk will also go over the state of the art of transfer learning. How LSTMs can be trained on one language and applied to another. How image-net was collected and drove the growth of vision algorithms. How neural nets make it easy to fine tune existing algorithms and get great performance with a small amount of training data. This talk will also cover best practices in training data collection. Training data collection strategies are often overlooked but are often the difference between a successful AI deployment and a science experiment.

2:20 pm
3:00 pm
3:00 pm
3:40 pm
Scott Clark
Co-founder, Sigopt
Using Bayesian Optimization to Tune Deep Learning Pipelines

In this talk we introduce Bayesian Optimization as an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time-consuming or expensive. Deep learning pipelines like MXnet and Tensorflow are notoriously expensive to train and often have many tunable parameters including hyperparameters, the architecture, and data pre-processing parameters that can have a large impact on the efficacy of the model. We will motivate the problem by giving several example applications using open source deep learning frameworks and open datasets. We’ll compare the results of Bayesian Optimization to standard techniques like grid search, random search, and expert tuning and show that Bayesian Optimization allows you to get better results in fewer evaluations.

3:40 pm
4:20 pm
Sinan Ozdemir
CTO, Kylie
How to teach a machine to read and write

More and more people are interacting with machines through natural language instead of hard input. Natural Language Processing has seen bursts of advancement in recent years. Deep Learning Architectures, Reinforcement Learning, Word Vectorizations are three of many tools that AI engineers have to teach machines to understand and process text. This talk will focus on these technologies, how they work, and how they are utilized in systems that you are already using.

4:20 pm
5:00 pm
5:00 pm
5:40 pm
5:40 pm
6:20 pm
MACHINE LEARNING RESEARCH
Deep learning, NLP, & CHATBOTS
APPLIED AI: STARTUPS, INDUSTRY & SOCIETY
9:00 am
9:40 am
9:40 am
10:20 am
10:20 am
11:00 am
11:00 am
11:40 am
11:40 am
12:20 pm
12:20 pm
1:00 pm
1:00 pm
1:40 pm
1:40 pm
2:20 pm
2:20 pm
3:00 pm
Alex Holub
Co-Founder, Vidora
Machine Learning for Everyone : Automated Machine Learning & Strategic AI at Vidora

Vidora CEO Alex Holub will discuss how Vidora leverages the latest techniques from the nascent field of Automated Machine Learning to enable anyone in an organization to benefit from real-time machine learning and automation - from data scientists to marketers to the C-suite. In the process he will describe automated techniques ingest billions of real-time behavioral data points, clean data, generate features, and select models. Finally, he will highlight how Vidora makes machine learning automation accessible to everyone within a simple SaaS interface.

3:00 pm
3:40 pm
3:40 pm
4:20 pm
Andreas Mueller
Data Science Lecturer, Columbia
Automated Machine Learning

Recent years have seen a widespread adoption of machine learning in industry and academia, impacting diverse areas from advertisement to personal medicine. As more and more areas adopt machine learning and data science techniques, the question arises on how much expertise is needed to successfully apply machine learning, data science and statistics.  Not every company can afford a data science team, and going your PhD in biology, no-one can expect you to have PhD-level expertise in computer science and statistics. This talk will summarize recent progress in automating machine learning and give an overview of the tools currently available.  It will also point out areas where the ecosystem needs to improve in order to allow a wider access to inference using data science techniques.  Finally we will point out some open problems regarding assumptions, and limitations of what can be automated. The talk will first describe recent process in commodification of machine learning, as witnessed by a wide array of open source packages and commercial solutions.  Then I will discuss the setting of automating supervised learning, and recent progress in automatic model selection and meta learning.

4:20 pm
5:00 pm
5:00 pm
5:40 pm
5:40 pm
6:20 pm

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