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
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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.
Ruslan Salakhutdinov
Director of AI Research at Apple
Ruslan Salakhutdinov is a UPMC professor of Computer Science in the Machine Learning Department at Carnegie Mellon University.  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.
Sid J. Reddy
Chief Scientist at 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.

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, 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.
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. 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.
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
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.
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.
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
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.
Gautham Sastri
Gautham Sastri
President & CEO, Isentium
Gautham Sastri is a seasoned technology entrepreneur and investor with over 30 years of experience in signal processing, cloud computing, and intelligence gathering. The former COO of SGI, Gautham holds 3 patents and has published 40+ papers on signal processing. He has a track record of tackling big data problems in areas that range from seismic processing to weather forecasting to large-scale simulations/data analysis for government agencies. Gautham joined iSentium in 2010, having previously founded Terrascale Technologies (acquired by Rackable Systems, now SGI Corp [NASDAQ:SGI]), and Maximum Throughput (acquired by Avid Technology [NASDAQ:AVID]). Subsequent to the acquisition of Terrascale by Rackable, Gautham was elevated to the position of COO, overseeing a revenue stream of $500MM/annum. He has published numerous technical papers and has been an invited speaker at many conferences. He has co-authored two granted patents in the field of cloud storage, one in the field of sentiment analysis, and has three additional patents pending. Gautham takes his passion for speed and precision onto the race track as well as an avid race car driver.
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.
Robert Morris
Robert Morris
Founder & CEO, Terravion
Debo Olaosebikan
Debo Olaosebikan
Founder & CTO, Gigster
Debo is co-founder and CTO of Gigster. Debo has founded multiple marketplace, energy, and data startups and is on leave from a physics PhD at Cornell where he worked on silicon nanophotonics and theoretical physics.
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
Maran is the co-founder and CEO of Clara Labs, where they design Clara: a human-in-the-loop assistant that schedules meetings. She previously studied Psychology at the University of Texas, where she did research on Intelligence. She loves reading, drinking coffee, and speculating about the future.
Ahmad Abdulkader
Ahmad Abdulkader
CTO, Voicera
Ahmad Abdulkader is CTO of Voicera and a well-renowned industry expert in Machine Learning, Deep Learning, and Neural Networks. Before co-founding Voicera, Abdelkader served as a lead architect for Facebook’s applied AI efforts, which produced platforms like DeepText, a text-understanding engine with near-human accuracy in over 20 languages. Prior to that, he worked at Google, building OCR engines, Machine Learning systems, and computer vision systems. Prior to Google, Abdulkader led a number of teams at Microsoft Ad-Center & Bing. He also built the state of the art 'Handwriting Recognition Technology' which currently powers Microsoft's touch devices including 'Surface'.
Oliver Christie
Oliver Christie
Founder, Foxy Machine
Aishwarya Srinivasan
Aishwarya Srinivasan
Research Assistant, Columbia Business School
Doina Precup
Doina Precup
Associate Professor, McGill University
Geeta Chauhan
Geeta Chauhan
Consulting CTO, Silicon Valley Software Group
Geeta Chauhan is a Consulting CTO at SVSG with 20+ years of experience building new products, leading diverse global teams, scaling and architecting complex distributed systems for companies ranging from nimble startups to Fortune 500s. These days you can find her in the hallways of AI startups building Deep Learning based platforms or tinkering in her garage to convert her electric car to run autonomously. Prior to this, she was the CTO of Data Platforms at Alcatel-Lucent and led the Advanced Technology incubator for Genesys Labs. She is passionate about sustainability and adoption of AI for good
Vishal Srivastava
Vishal Srivastava
Director of Engineering, Clarity Money
Vishal currently leads data science and engineering efforts at Clarity Money. Previously, he developed and contributed to various large scale data systems, recommendation engines and built many data-driven based products in industry verticals spanning investment banking, e-commerce, software and media.
Kevin Mader
Kevin Mader
Co-founder, 4Quant
Presenting companies include
4Quant
Clarity Money
Recast.ai
Ayasdi
CrowdFlower
Foxy machine
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
Cognitive Scale
Kylie.ai
OpenAI
Princeton
Dstillery
Topbots
Duke University
Matroid
Conversica
Datalytics
Stanford
Descartes Labs
Roboterra
Louisville
AirBNB
Columbia University
Oxford
HomeUnion
Goldsmiths
Google DeepMind
First Utility
Apple
Craft ai
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
Hugo Larochelle
Research Scientist, Google
Generalizing from Few Examples with Meta-Learning

Generalizing from Few Examples with Meta-Learning A lot of the recent progress on many AI tasks was enable in part by the availability of large quantities of labeled data. Yet, humans are able to learn concepts from as little as a handful of examples. Meta-learning is a very promising framework for addressing the problem of generalizing from small amounts of data, known as few-shot learning. In meta-learning, our model is itself a learning algorithm: it takes as input a training set and outputs a classifier. For few-shot learning, it is (meta-)trained directly to produce classifiers with good generalization performance for problems with very little labeled data. In this talk, I'll review recent research that has made exciting progress on this topic.​

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".

Mert Yasin
CTO, Appled AI
Quantifiable AI

1. Quick look at AI and what it means for the future of innovation; 

2. How to define and vet a piece of AI work; 

3. Where appliedAI.com stands in the global AI scene; 

4. How we apply AI internally

10:20 am
11:00 am
Harm van Seijen
Research Manager, Microsoft
Achieving Above-Human Performance on Ms. Pac-Man by Reward Decomposition

Ms. Pac-Man is considered by many as one of the hard games of the ALE benchmark set. Standard deep reinforcement learning methods have so far failed to achieve a performance that is close to human performance. That Ms. Pac-Man is a hard game is surprising, because it seems to classify as a reactive game, on which deep reinforcement learning methods typically dominate humans. In this talk, we argue that the reason that Ms. Pac-Man is hard is that the optimal value function for Ms. Pac-Man is very complex – much more so than for other reactive ALE games. Furthermore, we show that by using reward decomposition a complex value function can be decomposed into a set of low-complexity value functions. Using this strategy, we are able to achieve above-human performance on the challenging Ms. Pac-Man game.

Rebecca Fiebrink
Senior Lecturer, Goldsmiths
Using Machine Learning to Support Human Creativity

In 2017, machine learning seems to suddenly be everywhere: playing Go, driving cars, serving us targeted advertising. What do advances in machine learning mean for the future of human creativity? What will the future hold for humanity, beside sitting at home all day listening to algorithmically generated music after robots take our jobs? In this talk, I will challenge you to consider how we can instead use machine learning to better support fundamentally human creative activities. For instance, machine learning can aid human designers engaged in rapid prototyping and refinement of new interactions with sound and media. Machine learning can support greater embodied engagement in the design of those interactions, and it can enable more people to participate in the creation and customisation of new technologies. Furthermore, machine learning is leading to new types of human creative practices with computationally-infused mediums, in which people act not only as designers and implementors, but also as explorers, curators, and co-creators.

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
Silvia Chiappa
Researcher, DeepMind
Variational Inference for Large-scale and Complex Models

Variational methods have long been used in Machine Learning as a faster alternative to approaches such as Markov chain Monte Carlo sampling for performing inference in intractable probabilistic models. However, traditional variational approaches require conjugacy between prior and posterior distributions and do not scale well for large models. Recent work on variational methods has addressed these limitations, making it possible to use probabilistic approaches in large-scale and complex domains, such as deep learning. In this tutorial, we give an introduction to variational methods and review the recent literature on their extension to large-scale and complex models.

Slater Victoroff
CEO, Indico data
Using and abusing text embeddings for few-shot learning and novel user experience

In the last few years, one of the exciting veins of research in the NLP community has centered around text embeddings. From Alec Radford’s inspiring work on discovering unsupervised sentiment to Facebook’s more recent fasttext and Infersent, the number of approaches for creating text embeddings has never been greater. Come learn the basics of embeddings, and how they can enable learning on small datasets, then step through a series of (hopefully) interesting demos showing off strange properties of embeddings that can be used to change the way that users interact with text.

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
Gunnar Carlsson
Co-founder, Ayasdi Inc.
Data Modeling for Machine Intelligence

Artificial intelligence is taking large steps toward "broader AI", i.e. the application of methods of intelligence to the study of problems which humans do not solve (or solve well or inefficiently). Since there is no human template for solving the problems, there is a requirement for methods of discovery and unsupervised analysis. These methods need to satisfy a number of requirements, including that they be genuinely unsupervised, without biases coming from the hypotheses of the investigator, and that they possess transparency, permitting the understanding of how the algorithm works, explanations of results in human terms, and the ability to diagnose problems in the algorithm. I will discuss such a methodology, with examples.

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
Ian Goodfellow
Staff Research Scientist, Google Brain
Numerical Computation for Deep Learning

This presentation covers chapter 4 of the Deep Learning textbook ( www.deeplearningbook.org ). Deep learning algorithms are usually described in terms of real numbers, with infinite precision. We usually implement these algorithms using 32-bit floating point numbers on GPU. While this may seem like an implementation detail, it can have a profound effect on the performance of the algorithm. Knowing how to write numerically precise code can be the difference between success and failure in deep learning. This presentation describes how to avoid the most common pitfalls.

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
Gary Sieling
Lead Software Engineer (R&D), Wingspan
Building a Discovery Engine with Machine Learning

FindLectures.com is a discovery engine for tech talks, historic speeches, and academic lectures. The site rates audio and video content for quality, showing different recommended talks each day on a variety of topics. FindLectures.com crawls conference sites to get talk metadata, such as speaker names and bios, descriptions, and the date a video was recorded. Often these attributes are sparsely populated, or available across multiple websites. Additional attributes are inferred from audio and video content, but require more sophisticated data extraction to be useful in a full-text search engine. This talk will discuss interesting lessons learned from crawling historical videos and demonstrate information extraction with machine learning.

Claudia Perlich
Chief Scientist, Dstillery
Social Biases: Propagation and Creation through Predictive Models

Our world is increasing shaped by the predictions of models that learn from data. Whether it is the ads that we see, who to become friends with on Facebook, your chances of repaying a loan or the likelihood of developing cancer – more and more of our environment is shaped by predictive models. While often beneficial and de facto better at informing our decisions than say human experts, there is an increasing concern that while being better at making predictions, they may be equally limited when it comes to discrimination simply because the data the models were build on data that itself reflected our all too human biases. If there was no (or very few) female data scientist who got hired/invited for an interview in the database, the recommender system would not ever recommend a female for that position. So much has been argued that the training data needs to be assessed for biases and if needed somehow ‘be-biased’ to ensure that models are truly fair and reflect our human values (rather than just the statistics of the data). While this is a important concern, this work looks at a much less easily diagnosed second order effect: even if the first order of the data is unbiased (say 50% of the invited applicants for data science jobs in the data are in fact female), we can show that predictive models can easily create biases in their prediction and as a result, the candidates predicted to be most likely to be invited can deviate strongly from the desired 50% and skew to one or the other subpopulation. This effect is not related to the choice of algorithm but primarily to the relative signal to noise ration in one sub-population over the other. We finally discuss the limitations of trying to detect such model-induced biases.

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.

Samuel Kim
Speech Scientist, Gridspace
Toward understanding social signals

Understanding human conversations is a perennial challenge in designing human-centered artificial intelligence systems. While methodologies to extract what has been said from speech (a.k.a. speech recognition) have been studied intensely, there are many open challenges in understanding how it has been said. In this talk, I’d like to introduce an example of efforts that try to solve the open challenges. It studies social signals - they are produced during social interactions and play an important role in understanding the interactions and their participants. Particularly, in this talk, we focus on detecting conflicts during face-to-face debates. The study analyzes a database from political domain where genuine conflicts exist.

3:40 pm
4:20 pm
Katherine Heller
Assistant Professor of Statistics, Duke University
Machine Learning for Healthcare Data

We will discuss multiple ways in which healthcare data is acquired and machine learning methods are currently being introduced into clinical settings. This will include: 1) Modeling disease trends and other predictions, including joint predictions of multiple conditions, from electronic health record (EHR) data using Gaussian processes. 2) Predicting surgical complications and transfer learning methods for combining databases 3) Using mobile apps and integrated sensors for improving the granularity of recorded health data for chronic conditions and 4) The combination of mobile app and social network information in order to predict the spread of contagious disease. Current work in these areas will be presented and the future of machine learning contributions to the field will be discussed.

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
Doina Precup
Associate Professor
Deep reinforcement learning with temporal abstraction

Reinforcement learning has made great progress in solving large tasks by leveraging the power of deep networks, which allow a system to deal with a large space of states (or observations). In this talk I describe a way for deep reinforcement learning agents to also generalize over multiple time scales of actions. I will discuss the option-critic architecture, which learns how to play Atari games while also learning courses of actions that vary at the appropriate time scale, end-to-end, without prior knowledge

Feynman Liang
Director of Engineering, Gigster
Clipper: low latency prediction serving on Kubernetes

Machine learning is being deployed in a growing number of applications which demand real-time, accurate, and robust predictions under heavy query load. However, most machine learning frameworks and systems only address model training and not deployment. Clipper is a general-purpose low-latency prediction serving system. Interposed between end-user applications and a wide range of machine learning frameworks, Clipper introduces a modular architecture to simplify model deployment across frameworks. Furthermore, by introducing caching, batching, and adaptive model selection techniques, Clipper reduces prediction latency and improves prediction throughput, accuracy, and robustness without modifying the underlying machine learning frameworks.

5:00 pm
5:40 pm
Colin Raffel
Resident, Google Brain
Doing Strange Things with Attention

Recurrent neural network models with an attention mechanism have proven to be extremely effective on a wide variety of sequence-to-sequence problems.  In this talk I will give an overview of attention, provide some intuition as to why it works, and describe some recent improvements to the framework.  Applications will include speech recognition, machine translation, text summarization, and more.

5:40 pm
6:20 pm
Maran Nelson
Co-founder & CEO, Clara Labs
Beyond Human: How Clara Labs bootstrapped its product, data sets, and scheduling expertise

Clara Labs is an email-based scheduling service for busy people. Simply CC Clara on an email to a person you want to meet with, and we'll handle the back and forth game of email-tag for you in accordance with your preferences. To build a robust and accurate system that gracefully handles nuanced requests, we've combined machine learning (ML) with a distributed human labor force. A core thesis of the company is that customer value and product quality reign as top concerns above all else. We've held these ideals fixed as we continue to incrementally automate each subproblem that arises in an email-based scheduling negotiation. In this talk we'll discuss the path Clara took to build it's automation system as the company grew, starting from two founders manually scheduling in a back office, to small team of around 10 ex-executive-assistants, to the ML enabled task-based workforce we employ today. Along the way you'll learn about how we think about conversational interfaces and how our human-in-the-loop virtuous cycle works.

Gautham Sastri
President & CEO, Isentium
HOW TO GAUGE THE WISDOM OF THE CROWD (AND AVOID SPENDING BILLIONS DOING IT

One need look no further than the latest uproar in politics, or the quick spread of a brand’s ill-advised move to know the flash-flood speed of social media’s swell. Social media is the modern Town Crier, multiplied, amplified, and accelerated by the power of the Internet. Traditionalists may bemoan the demise of the Trading Pit. Take heart: The Trading Pit is not dead – it has merely migrated on to the Internet. And Open Outcry has transitioned from a few hundred traders communicating at the speed of sound to millions of traders communicating at the speed of light. At any given second, over 50 Trillion bytes of “stuff” is sent and received over the Internet. How does one extract actionable intelligence from this volume of Open Outcry without incurring billions of dollars of operational expense? Having spent over 25 years at the bleeding edge of cloud computing and intelligence extraction, I hope to provide some pragmatic insights and provoke a spirited debate.

MACHINE LEARNING RESEARCH
Deep learning, NLP, & CHATBOTS
APPLIED AI: STARTUPS, INDUSTRY & SOCIETY
9:00 am
9:40 am
Ruslan Salakhutdinov
Director of AI research, Apple
Recent Advances in Deep Learning

In this talk I will first introduce a broad class of deep learning models and show that they can learn useful hierarchical representations from large volumes of high-dimensional data with applications in information retrieval, object recognition, and speech perception. I will next introduce models that can find semantically meaningful representations of words, learn to read documents and answer questions about their content. I will further introduce the notion of "Memory" as being a crucial part of an intelligent agent’s ability to plan and reason in partially observable environments and demonstrate a deep reinforcement learning agent that can learn to store arbitrary information about the environment over long time lags. I will show that on several tasks these models significantly improve upon many of the existing techniques.

9:40 am
10:20 am
Konstantina Palla
Postdoc, Oxford
Bayesian nonparametric modelling for networks and genomics.

In this talk I will present recent work that includes models that fall in the category of Bayesian nonparametrics; a flexible framework for modelling complex data of unknown latent dimensionality. I will start my talk with a brief overview of Bayesian and nonparametric modelling. I will continue with presenting Bayesian nonparametric models for relational data, e.g. social networks and gene expression analysis. The talk aims to provide an overview of what is Bayesian and nonparametric modelling and how it can be used in real applications.

Sattisvar Tandabany
Lead NLP Engineer, Botfuel
Context-awareness, digressions and randomness in modern chatbot design

Bots can quite easily follow a straightforward dialog about a single topic, asking questions and understanding answers sequentially. Now allowing the bot to extract entities that are not directly requested, or going back to a statement said earlier in the dialog in order to change it, are more difficult tasks. Enabling the bot to handle one or several digressions proves to be even harder. This leads to a paradigm shift in conversation design for chatbot developers. We will review the problems brought about by this shift and how to address them.

Matthieu Boussard
R&D Engineer, craft.ai
Whitebox oneclass classification applied to waste management

Recently, trash bins in the street of Paris have been equipped with chips that send their collection time. From those data we were asked to build a system that learns and predicts the collection time at any address in the 14th district of Paris. With this application, Parisians can now take out their trash at the right time, and thus decrease the presence of trash bins in the streets of Paris. This talk will present this work, starting from the raw data to the final application. It will gives us the opportunity to discuss some theoretical machine learning issues like handling oneclass classification in a whitebox context.

10:20 am
11:00 am
Melanie Warrick
Sr Developer Advocate, Google
Reinforcement Learning

Reinforcement learning is a popular subfield in machine learning because of its success in beating humans at complex games like Go and Atari. The field’s value is in utilizing an award system to develop models and find more optimal ways to solve complex, real-world problems. This approach allows software to adapt to its environment without full knowledge of what the results should look like. This talk will cover reinforcement learning fundamentals and examples to help you understand how it works.

Natalia Konstantinova
Lead Software Engineer (R&D), First Utility
From production rule based chatbot to machine learning: challenges and discoveries

Machine learning is becoming more and more popular and has a great amount of advantages, however, there are some cases in industry where rule based approach seems as a more appealing one. Natalia will discuss the challenges of transitioning a well established rule based chatbot to ML and getting the most of 2 worlds without losing existing accuracy.

Vishal Srivastava
Director of Engineering
Application of AI/ML towards beneficial personal finance advocacy

AI has a tremendous potential in Fintech. This talk specifically talks about the potential impact of machine learning and AI in personal finance industry. Also the pushing need to build and deploy AI agents or ML systems that either favor or at least stay neutral towards customer's interest. The talk also covers a real use case examples of AI and ML uses and dives into technical details about a specific project. Other takeaways include: 1) How a AI project lifecycle looks like. 2) How to build an impactful AI strategy with limited resources at hand. 3) How to analyze transactional data with help of Neural Net libraries.

11:00 am
11:40 am
Phil Syme
Co-Founder & CTO, Area 1 Security
Using computer vision to combat phishing

This talk describes Area 1 Security's use of computer vision techniques to identify malicious and deceptive uses of logos in email messages and web pages, attempting to steal passwords or deliver malware. Area 1 offers a comprehensive product that stops phishing attacks. The product uses a number of machine learning based approaches including computer vision. The first portion of the talk describes the specific computer vision techniques that have been deployed into the production email processing pipeline, as well as other vision techniques under active research. The second part of the talk details some of the system architecture and engineering practices that supports the use of machine learning algorithms in a real time data processing pipeline with stringent performance, latency, and uptime constraints.

Pierre-Eduouard Lieb
Partnerships Manager, Recast.ai
Random Access Navigation bots

How to use Random Access Navigation (RAN) to build smart bots with natural language processing on Recast.AI

11:40 am
12:20 pm
Rajat Monga
Engineering Director, TensorFlow
Deep Learning: Trends and Developments

Deep Learning is having a great impact across products at Google, and in the world at large. We are pushing the limits of AI and deep learning with research across many areas. With integration into many of Google’s products, this research is improving the lives of billions of people. Open source tools like TensorFlow and open publications puts the latest deep learning research at the fingertips of every engineer around the world. This talk begins with what has enabled this field to evolve rapidly over the last few years. We then discuss some of the leading research advances and present some of the current trends that provide us a glimpse of a promising future.

Ahmad Abdulkader
CTO, Voicera
The Rise Of Voice-Activated Assistants In The Workplace

In this talk, Ahmad Abdulkader, the CTO of Voicera, will explain why voice-activated AI is the most important development to come to the workplace. The market is already demonstrating strong value in the home for these types of tools, but the work environment is quickly starting to catch up. He will pull from his experiences creating Eva, the first enterprise voice assistant focused on making meetings more actionable and productive. He will dive specifically into the challenges of automatic speech recognition, natural language processing and neural networks that make it difficult to create these kinds of voice-activated assistants - and more importantly, he will explain how he and his team have overcome these challenges.

Timnit Gebru
Researcher, Microsoft
Fine-Grained Object Recognition in the Wild: A Multi-Task Domain Adaptation Approach

Fine-Grained object recognition is the task of distinguishing between highly similar objects such as cars or bird species. Although it is an important problem in computer vision, current recognition models are unlikely to accurately classify objects in the wild. These fully supervised models need additional annotated images to classify objects in every new scenario, a task that is infeasible. However, sources such as e-commerce websites and field guides provide annotated images for many classes. For example, last year, I spoke about our work using characteristics of cars recognized in Google Street View images to predict neighborhood demographics. In that work, we leveraged many labeled images from e-commerce websites, along with a smaller number of labeled Google street view images, to train a car classifier. In this talk, I'll discuss fine-grained domain adaptation as a step towards overcoming the dataset shift between easily acquired annotated images (such as those found in e-commerce sites) and the real world.

12:20 pm
1:00 pm
Nisheeth Ranjan
Co-founder & CTO, Bright Funnel
Machine Learning Powered Insights For Marketing Spend Optimization

Every marketing organization wants to know the return on investment (ROI) of their marketing campaigns. Ideally, the cost of the marketing campaign should be less than the amount of revenue generated by it. For enterprises with long sales cycles and many online/offline marketing campaigns, it is hard to analyze the linkages between marketing campaigns run many months ago to revenue won today.  This talk will explore how machine learning powered approaches can help B2B marketers optimize spend across all their marketing channels.

Adelyn Zhou
CMO, Topbots
How to Create an AI strategy with Winning Results

Learn how to create an AI strategy that delivers tangible business results rather than hype. We will cover how to structure a team, to pick winning projects, to set achievable KPIs, and to gain buy in across the organization for AI/ML driven initiatives. Takeaways: 1) Corporate executives will learn how to push an AI transformation within their companies. 2) Startups will learn how large corporation evaluate AI projects and solutions, and conversely understand how to sell their services into these companies better.

1:00 pm
1:40 pm
Sid Reddy
Chief Scientist, Conversica
Conversational AI: What we’ve learned from millions of AI conversations for thousands of customers

In this presentation, we will introduce our artificial intelligence approach to creating, deploying, and continuously improving, an automated sales assistant that engages in a genuinely human conversation, at scale, with every one of an organization’s sales leads. We will present empirical data to assess how personalized, responsive and helpful the AI assistant is. We share our observations on the ability of the assistant to help in increasing sales and delve deeply into how an artificial intelligence assistant can free humans to focus on higher-value activities, and even lead businesses to employ more people, not fewer, as a result of implementing AI. We will delve into the development of our systems, which have been trained using state of the art machine learning algorithms over linguistic features computed from millions of real conversations between leads and clients, and share observations regarding the rationale on why the assistant was successful in increasing sales, improving marketing and gathering critical business intelligence.

1:40 pm
2:20 pm
Dustin Hillard
Vice President Of Engineering, Versive, Inc.
Detecting Advanced Cyber Adversaries with Machine Learning

Hype about machine learning and AI might only be matched by hype about the challenges and solutions in cybersecurity today.  Both fields are suffering from talent shortages that result in most large enterprises being unable to benefit from the subject matter expertise and rapid technology improvements necessary to protect their business.  This requires automation that can replicate security expertise while learning robust machine learning models for each specific customer environment.  Finally, a system must also present interpretable results that enable security teams to understand and act.  This presentation will provide a brief overview of the core behaviors necessary to model advanced cyber adversaries, and then describe a machine learning approach to automatically learn behaviors on any large network based on historical log data.

Geeta Chauhan
Consulting CTO
Distributed Deep Learning Optimizations

This talk will cover how to run distributed deep learning models at scale. You will learn how to parallelize your models, and techniques for optimizing your cluster for faster performance for both model training and inference.

Oliver Christie
Founder, Foxy Machine
How To Build an AI Insurance Company

How do you go about building an AI centered insurance company, break all the rules and beat the competition? I will describe how technology, customer experience and business intersect in a regulated market. What the challenges are of building a new insurance service and how (most) insurance thinking is from the dark ages.

2:20 pm
3:00 pm
Cliff Click
CTO, Neurensic
Policing The Public Markets with Machine Learning

Neurensic has built a solution, SCORE, for doing Trade Surveillance using H2O (an open-source pure Java Big Data ML tool), Machine Learning, and a whole lot of domain expertise and data munging.  SCORE pulls in private and public market data and in a few minutes will search it for all sorts of bad behavior: spoofing, wash-trading and cross-trading, pinging, and a lot more.  It then filters down the billions of rows of data down to human scale with some great visualizations - well enough to use as hard legal evidence.  Indeed SCORE and it's underlying tech is not just used by companies to police themselves; it is being used by the public sector to find and prosecute the Bad Guys.  I'll close with a demo of a Real Life bad guy - he was defrauding the markets out of 10's of millions - who got caught via an early alpha version of SCORE.  All data anonymized of course, but he made the front page of the Wall Street Journal.

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
Aishwarya Srinivasan
Research Assistant, Columbia Business School
High Frequency Trading using deep learning

In the talk, emphasis will be made on the proposed deep learning strategies applied to design algorithm for the implementation of High Frequency Trading. The deep learning concept applied was achieved by training the neural network with the current date, hour and minute, time series analysis, standard deviations an predictor indicator for predicting the next minute's stock price. It is seen that the stock prices prediction cannot be made just based on the trend analytics, the prices may vary because of other parameters of the market as well. In order to have a more precise analysis, market situation needs to be understood. For the purpose, text analysis of the Twitter data is done. The prediction model for the HFT involves both quantitative data of the previous minutes' data as well as quantitative data from Twitter. The analysis is done on the Amazon.com Inc (AMZN) data, from March 2017 to August 2017; the prices seem to have a constant increase since March. Soon after the acquisition of Whole Foods during June second week, the Amazon's stock market hit a high of 1082.65 on July 27, 2017; which made Jeff Bezos the richest man in the world with Amazon's stake hit the high of $86.5bn. The research is the first of its kind to take into consideration both qualitative and quantitative data for stock prediction.

Joanna Bryson
Sabbatical Fellow and Affiliate, Center for Information Technology Policy, Princeton University
There Is No AI Ethics

Understanding AI Ethics is difficult because clear definitions of both Intelligence and Ethics are rare. Here I describe Intelligence as generating context-specific action, and Ethics as the behaviours that create (including define) a society. I draw on these definitions to describe scientifically-grounded expectations for the impact of intelligent artefacts on human society.

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.

Sanchit Arora
Lead Researcher, Axon Research Group
Leveraging Deep Learning and Video Analysis in Law Enforcement

Body-worn cameras have proven to strengthen trust and accountability between law enforcement agencies and the communities they serve. However, large scale use of body-worn cameras has generated massive amounts of data, which is practically impossible for these agencies to use effectively. This has led to significant, and unproductive, time spent manually analyzing data. Axon Research is using the latest advances in Deep Learning and GPU acceleration to enable increased efficiency across the body-worn camera continuum by accelerating the many manual, time consuming workflows in public safety, such as redacting footage in response to a public request. Attendees will hear the potential impact of large scale Deep Learning on law enforcement and public safety information management.

Devon Bernard
VP of Engineering, Enlitic
Medical Deep Learning: Technical, Clinical, and Regulatory Challenges and how to Overcome Them

Deep Learning is proving to be a powerful tool that can improve healthcare for both patients and care-providers. In this talk I’ll cover an intro to some of the medical problems currently being solved by deep learning, market adoption, healthcare challenges (e.g regulation, data quality, data acquisition), deep learning challenges (e.g. model stability, training/convergence time, scalable training environment), and tips learned by tackling these problems head-on.

4:20 pm
5:00 pm
Ryan Keisler
Head of ML, Descartes Labs
Exploring Earth with Computer Vision

Our planet is teeming with human activity: agriculture, energy, logistics, and much more.  The ongoing explosion of satellite imagery, combined with computer vision and other forms of machine learning, provides a means to understand these processes at a global scale.  This talk will give a brief overview of satellite imagery (pros & cons, how it differs from normal imagery, some practical considerations), and a tour of applications of computer vision to satellite imagery, such as visual search over the planet.

Robert Morris
Founder & CEO, Terravion
Feeding One Billion People with Food from the Public Cloud

TerrAvion has been, and continues to be, the leader in low cost, high resolution, high-revisit aerial imagery. Aerial imagery represents the richest data set that growers can analyze. Other data points include weather, soil, harvest, and application. TerrAvion is excited about integrating with agricultural AI partners to help create new insights and drive profits for our resellers and growers.

5:00 pm
5:40 pm
Archa Jain
Software Engineer, Calico
Building Reproducible ML Models

This talk draws from software engineering principles to introduce an end-to-end data science workflow that makes it easy to reproduce given results. In a 2016 study conducted by Nature [1], it was found that more than 70% of researchers surveyed were unable to reproduce research published by others, and more than 50% were unable to reproduce their own results. In general, the lack of reproducibility in research holds back other groups from building on existing results, holding back overall progress. Even in industry, it is often extremely hard for data scientists to share analysis methods, and to build on the work done by others in the company. In general, there is a belief that reproducibility is achieved by sharing raw code and data, and while this is a step in the right direction, there are often confounding environmental variables, especially in more complex models and analyses, that still make the results inconsistent. In this talk, I will provide a workflow rooted in Git and Jupyter that makes it easy to share research results, both in academia as well as within a company. I will also share some general principles and tips derived from software engineering that will allow anyone to tailor their work techniques to make their analysis more consistent and sharable. [1] https://www.nature.com/news/1-500-scientists-lift-the-lid-on-reproducibility-1.19970

Kevin Mader
Co-founder
From Pixel Patterns to Better Medicine

The diagnosis and treatment of cancer has been drastically improved by newer imaging methods like PET-CT which generate large number of images where single spots can drastically influence the diagnosis and treatment.For physicians this means a long time must be spent carefully reading thousands images a day and looking at dozens of different regions carefully to check for the possibility of aggressive disease. 4Quant Ltd. together with the University Hospital Basel has demonstrated the potential to radically reduce the physicians reading time without sacrificing quality by using a Deep Learning approach. We present the work we have done towards computer aided staging of Non-Small Cell Lung Cancer (NSCLC) for a more efficient and evidence based precision medicine and illustrate challenges, hurdles and findings while developing an AI-based product for medical use.

5:40 pm
6:20 pm
Yao Zhang
Founder & CEO, RoboTerra
Wiring Empathy into Robots: AI Strategy for a Winning Consumer Electronics Product

In this 30 minutes session, we will first overview the landscape of consumer robotics market, with a focus of robotic products that emphasize values beyond just completing a physical tasks; we will then share RoboTerra's product experiences on more of a modulized robot, the generation of high quality data on microcontroller board with design for data collection in mind, and also how the base of such data on both the hardware and also our cloud accumulated insights for an improved consumer product; we will also share experiences/lessons we learned in incorporating emotional AI such as facial and voice recognitions with clear design guidelines as a response to the demand collected from our user researches.

Debo Olaosebikan
Founder & CTO, Gigster
Opportunities for automation in the creation and delivery of software

Work traditionally handled by humans is increasingly getting automated. Opportunities abound in making cars self driving and in building intelligent assistants that help in disease diagnosis. While there has been major progress in automating low skilled work, we are still in the initial stages of having computers complete more complicated tasks. Through the lens of Gigster — a software development marketplace, we highlight progress and opportunities towards automating the sale, management and development of software. We also share a viewpoint on the impact of increased automation on employment and productivity within the context of software creation.

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