Artemy Malkov, CEO, Data Monsters at The AI Conference 2017
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The document discusses the challenges of building chatbots using natural language. It notes that natural language has insane variability and long tail distributions, meaning scripts do not work. It also discusses the high dimensionality of natural language and techniques for reducing this, such as preprocessing, understanding, and detecting intents. However, it notes there are long tails of intents across business verticals. An ideal chatbot is not a silver bullet and what businesses expect from bots does not always match reality. The best approaches involve statistical analysis of conversations, understanding intent distributions, semi-supervised learning from conversations, and reinforcement learning with an agent-in-the-loop.
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Erik Bernhardsson is the CTO at Better, a small startup in NYC working with mortgages. Before Better, he spent five years at Spotify managing teams working with machine learning and data analytics, in particular music recommendations.
Abstract Summary:
Nearest Neighbor Methods And Vector Models: Vector models are being used in a lot of different fields: natural language processing, recommender systems, computer vision, and other things. They are fast and convenient and are often state of the art in terms of accuracy. One of the challenges with vector models is that as the number of dimensions increase, finding similar items gets challenging. Erik developed a library called “Annoy” that uses a forest of random tree to do fast approximate nearest neighbor queries in high dimensional spaces. We will cover some specific applications of vector models with and how Annoy works.
Mukund Narasimhan, Engineer, Pinterest at MLconf Seattle 2017
Mukund Narasimhan is an engineer at Pinterest where he works on content modeling and recommendations. Prior to Pinterest, he has worked at Google, Facebook, Microsoft and Intel. He has a Ph.D. in Electrical Engineering from the University of Washington and a M.S in Mathematics from Louisiana State University.
Abstract summary
Knowledge at Pinterest:
Pinterest is building the world’s largest catalog of ideas. These ideas are embodied in billions of Pins and well over 100 million Users and Boards which, together, form a complex picture of our users and their interests. The goal of the Knowledge team is to leverage our unstructured data (images, videos, and text), and structured data (user curated data, partner generated content, and engagement signals) to model users and their interests so we can help them discover fresh, diverse, and personalized new ideas. In this talk, we go into some detail on the progress we’ve made so far.
Garrett Goh, Scientist, Pacific Northwest National Lab
Garrett Goh is a Scientist at the Pacific Northwest National Lab (PNNL), in the Advanced Computing, Mathematics & Data Division. He was previously awarded the Howard Hughes Medical Institute fellowship which supported his PhD in Computational Chemistry at the University of Michigan. At PNNL, he was awarded the Pauling Fellowship that supports his research initiative of combining deep learning and artificial intelligence with traditional chemistry applications. His current interests is in AI-assisted computational chemistry, which is the application of deep learning to predict chemical properties and the discovery of new chemical insights, while using minimal expert knowledge.
Abstract summary
A Deep Learning Computational Chemistry AI: Making chemical predictions with minimal expert knowledge:
Using deep learning and with virtually no expert knowledge, we construct computational chemistry models that perform favorably to existing state-of-the-art models developed by expert practitioners, whose models rely on the knowledge gained from decades of academic research. Our findings potentially demonstrates the impact of AI assistance in accelerating the scientific discovery process, where we envision future applications not just in chemistry, but in affiliated fields, such as biotechnology, pharmaceuticals, consumer goods, and perhaps other domains as well.
Jamila Smith-Loud - Understanding Human Impact: Social and Equity Assessments...
Understanding Human Impact: Social and Equity Assessments for AI Technologies
Social and Equity Impact Assessments have broad applications but can be a useful tool to explore and mitigate for Machine Learning fairness issues and can be applied to product specific questions as a way to generate insights and learnings about users, as well as impacts on society broadly as a result of the deployment of new and emerging technologies.
In this presentation, my goal is to advocate for and highlight the need to consult community and external stakeholder engagement to develop a new knowledge base and understanding of the human and social consequences of algorithmic decision making and to introduce principles, methods and process for these types of impact assessments.
Ted Willke - The Brain’s Guide to Dealing with Context in Language Understanding
The Brain’s Guide to Dealing with Context in Language Understanding
Like the visual cortex, the regions of the brain involved in understanding language represent information hierarchically. But whereas the visual cortex organizes things into a spatial hierarchy, the language regions encode information into a hierarchy of timescale. This organization is key to our uniquely human ability to integrate semantic information across narratives. More and more, deep learning-based approaches to natural language understanding embrace models that incorporate contextual information at varying timescales. This has not only led to state-of-the art performance on many difficult natural language tasks, but also to breakthroughs in our understanding of brain activity.
In this talk, we will discuss the important connection between language understanding and context at different timescales. We will explore how different deep learning architectures capture timescales in language and how closely their encodings mimic the brain. Along the way, we will uncover some surprising discoveries about what depth does and doesn’t buy you in deep recurrent neural networks. And we’ll describe a new, more flexible way to think about these architectures and ease design space exploration. Finally, we’ll discuss some of the exciting applications made possible by these breakthroughs.
Justin Armstrong - Applying Computer Vision to Reduce Contamination in the Re...
Applying Computer Vision to Reduce Contamination in the Recycling Stream
With China’s recent refusal of most foreign recyclables, North American waste haulers are scrambling to figure out how to make on-shore recycling cost-effective in order to continue providing recycling services. Recyclables that were once being shipped to China for manual sorting are now primarily being redirected to landfills or incinerators. Without a solution, a nearly $5 billion annual recycling market could come to a halt.
Purity in the recycling stream is key to this effort as contaminants in the stream can increase the cost of operations, damage equipment and reduce the ability to create pure commodities suitable for creating recycled goods. This market disruption as a result of China’s new regulations, however, provides us the chance to re-examine and improve our current disposal & collection habits with modern monitoring & artificial intelligence technology.
Using images from our in-dumpster cameras, Compology has developed an ML-based process that helps identify, measure and alert for contaminants in recycling containers before they are picked-up, helping keep the recycling stream clean.
Our convolutional neural network flags potential instances of contamination inside a dumpster, enabling garbage haulers to know which containers have the wrong type of material inside. This allows them to provide targeted, timely education, and when appropriate, assess fines, to improve recycling compliance at the businesses and residences they serve, helping keep recycling services financially viable.
In this presentation, we will walk through our ML-based contamination measurement and scoring process by showing how Waste Management, a national waste hauler, has experienced 57% contamination reduction in nearly 2,000 containers over six months, This progress shows significant strides towards financially viable recycling services.
Igor Markov - Quantum Computing: a Treasure Hunt, not a Gold Rush
Quantum Computing: a Treasure Hunt, not a Gold Rush
Quantum computers promise a significant step up in computational power over conventional computers, but also suffer a number of counterintuitive limitations --- both in their computational model and in leading lab implementations. In this talk, we review how quantum computers compete with conventional computers and how conventional computers try to hold their ground. Then we outline what stands in the way of successful quantum ML applications.
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Viewers also liked
Venkatesh Ramanathan, Data Scientist, PayPal at MLconf ATL 2017
Large Scale Graph Processing & Machine Learning Algorithms for Payment Fraud Prevention:
PayPal is at the forefront of applying large scale graph processing and machine learning algorithms to keep fraudsters at bay. In this talk, I’ll present how advanced graph processing and machine learning algorithms such as Deep Learning and Gradient Boosting are applied at PayPal for fraud prevention. I’ll elaborate on specific challenges in applying large scale graph processing & machine technique to payment fraud prevention. I’ll explain how we employ sophisticated machine learning tools – open source and in-house developed.
I will also present results from experiments conducted on a very large graph data set containing millions of edges and vertices.
Ashrith Barthur, Security Scientist, H2o.ai, at MLconf 2017
Machine Learning Based Attack Vector Modeling for CyberSecurity:
Connections have behavioural patterns that are unique to protocols, loads, window sizes, bandwidth, and mainly the type of traffic. A CDN enterprise behaves completely differently than how a Cloud service company would behave and they both would be different from a corporation. This also means that attack vectors and attack landscapes are different in all these places. In this talk we speak about modeling different kinds of attacks and build a model that is able to identify these different kinds of attacks using ML.
The method we use is to identify different profiles based on many variables that specifically but robustly identify attacks of different kinds. The variables are specific to business, network profile, traffic. The variables are also high-level i.e. aggregate, and packet-level. This way the models are specifically picking up on constant variations in traffic, and create machine learning models to identify these attacks. Using the power of H2O these analyses are not just limited to a research and analysis of the traffic and concluding with a “OH, this was what it was.” moment but to actually deploy code, besides existing IDS and IPS, or deploying highly optimized, independent programs that can handle high thruputs at the rate of 1.2 Million decisions per second making it one of the fastest implementations of ML to identify, defend and protect critical infrastructure that are potentially under threat.
Alexandra Johnson, Software Engineer, SigOpt at MLconf ATL 2017
Best Practices for Hyperparameter Optimization:
All machine learning and artificial intelligence pipelines – from reinforcement agents to deep neural nets – have tunable hyperparameters. Optimizing these hyperparameters provides tremendous performance gains, but only if the optimization is done correctly. This presentation will discuss topics including selecting performance criteria, why you should always use cross validation, and choosing between state of the art optimization methods.
Hanjun Dai, PhD Student, School of Computational Science and Engineering, Geo...
Graph Representation Learning with Deep Embedding Approach:
Graphs are commonly used data structure for representing the real-world relationships, e.g., molecular structure, knowledge graphs, social and communication networks. The effective encoding of graphical information is essential to the success of such applications. In this talk I’ll first describe a general deep learning framework, namely structure2vec, for end to end graph feature representation learning. Then I’ll present the direct application of this model on graph problems on different scales, including community detection and molecule graph classification/regression. We then extend the embedding idea to temporal evolving user-product interaction graph for recommendation. Finally I’ll present our latest work on leveraging the reinforcement learning technique for graph combinatorial optimization, including vertex cover problem for social influence maximization and traveling salesman problem for scheduling management.
Will Murphy, VP of Business Development & Co-Founder, Talla at The AI Confere...
Startup Showcase Presentation on Talla
Rahul Mehrotra, Product Manager, Maluuba at The AI Conference 2017
Rahul Mehrotra is a Product Manager at Maluuba, a Canadian AI company that’s teaching machines to think, reason and communicate with humans (acquired by Microsoft in January 2017). Based in the AI epicenter of Montréal, Maluuba applies deep learning techniques to solve complex problems in language understanding. Rahul works across Maluuba’s three research areas (Machine Comprehension, Dialogue Systems and Reinforcement Learning) and helps advance breakthrough research by providing real-world problems and use cases. Rahul leads product initiatives to bring cutting-edge academic research to robust product pipelines. Rahul holds a B.ASc in Systems Design Engineering from the University of Waterloo.
Building Literate Machines
Advances in AI research have led to great innovations based on image and voice recognition, and 2017 will see further advances in the field of language, including the creation of more literate machines—those that can comprehend and communicate with humans but also machines that begin to model innate human-like skills.
In this talk, Rahul Mehrotra will explore how advances in deep and reinforcement learning are being applied to solve language understanding problems. You will gain a deeper understanding of the research fundamentals as well as implications and opportunities that language understanding AI services will bring. Rahul will outline how researchers are seeking to equip machines with higher level cognitive skills like common-sense reasoning, information seeking, transfer learning, and decision-making.
He will explain how these capabilities are being applied in enterprise, using practical examples across a range of business functions. These use cases are transformative.
To give just one example, knowledge workers and employees would no longer need to desperately search through an organization’s directories, repositories, emails, and other channels to find a specific document. Instead, the employee would communicate with an AI agent leveraging machine comprehension capabilities. The agent would be capable of answering the question in a security-compliant manner by having a deep understanding of the contents of the organization’s documents instead of simply retrieving based on keywords.
The talk will provide audience with key takeaways on the underlying research as well as the current and future applications of using language understanding AI in enterprise.
Talha Obaid, Email Security, Symantec at MLconf ATL 2017
A Machine Learning approach for detecting a Malware:
The project is to improve the way we detect script based malware using Machine Learning. Malware has become one of the most active channel to deliver threats like Banking Trojans and Ransomware. The talk is aimed at finding a new and effective way to detect the malware. We started with acquiring both malicious and clean samples. Later we performed feature identification, while building on top of existing knowledge base of malware. Then we performed automated feature extraction. After certain feature set is obtained, we teased-out feature which are categorical, interdependent or composite. We applied varying machine learning models, producing both binary and categorical outcomes. We cross validated our results and re-tuned our feature set and our model, until we obtained satisfying results, with least false-positives. We concluded that not all the extracted features are significant, in fact some features are detrimental on the model performance. Once such features are factored-out, it results not only in better match, but also provides a significant gain in performance.
LN Renganarayana, Architect, ML Platform and Services and Madhura Dudhgaonkar...
Lessons Learnt from building ML Products for enterprise SaaS:
Having spent the last 4+ years productizing ML powered enterprise products, we have learnt a lot! Join us to hear the stories of our stumbles (ahem learnings) in applying machine learning to solve business problems for Fortune 500 companies. Our hands-on experience has shaped our product strategy, ML platform design and organization’s operational principles. And the investments we made based on our learnings have helped us drastically improve our time to market for ML products. Come on by to hear the technical and organizational challenges (and some solutions) in building ML products for enterprise SaaS. Hopefully our learnings will be useful in your journey.
Bio: LN leads the architecture and design of Workday’s ML Platform and Services. He is all about building large scale distributed systems and data platforms. Currently his days (and some nights) are spent on solving the challenges in building ML products for Enterprise SaaS. LN’s career spans across HP, IBM Research, Symantec and now Workday. At Symantec, he was the architect and lead of a streaming platform that ingested and processed 2+ billions of events per day. As a Research Staff Member at IBM T.J. Watson Research Center, LN built optimizations for automatic parallelization, techniques for approximate computing, deployment automation for OpenStack, and analytics for large scale cloud services.
LN holds a Ph.D. in Computer Science from Colorado State University and has published more than 40 technical publications / patents. His work has received awards from ACM, IBM, and HP.
Bio:Madhura Dudhgaonkar is responsible for leading Workday’s search, data science and machine learning teams based in San Francisco. Her teams have spent ~4 years building machine learning products used by Fortune 500 companies. Her experience ranges from being a hands-on engineer to leading large engineering organizations. Madhura’s career spans across SUN Microsystems, Adobe and now Workday. During her career, she has been involved with building a variety of products – from developing Java Language to building a version 1.0 consumer product to building enterprise SaaS products.
She holds a bachelor’s degree in electronics and telecommunications and a master’s degree in math and computer science.
Madhura is originally from a small town in India and came to the United States to pursue her passion in technology. She currently calls San Francisco home, and despite nine years here, can’t get enough of its hilly charm, the diversity of people, culture, and experiences.
Tim Chartier, Chief Academic Officer, Tresata at MLconf ATL 2017
Beyond a Bit Fit
An emerging and important avenue of sport analytics is biometric data. From the casual athlete tracking steps and sleep to professional athletes tracking heart rate and impact data, biometric data can improve performance and prevent injury. What can we learn from biometric data? How can it aid athletes and coaches? How can you be a bit fitter by analyzing a body’s data? This talk will discuss the data, analysis and insights available and evolving in sports analytics of biometric data.
Jessica Rudd, PhD Student, Analytics and Data Science, Kennesaw State Univers...
Application of Support Vector Machine Modeling and Graph Theory Metrics for Disease Classification:
Disease classification is a crucial element of biomedical research. Recent studies have demonstrated that machine learning techniques, such as Support Vector Machine (SVM) modeling, produce similar or improved predictive capabilities in comparison to the traditional method of Logistic Regression. In addition, it has been found that social network metrics can provide useful predictive information for disease modeling. In this study, we combine simulated social network metrics with SVM to predict diabetes in a sample of data from the Behavioral Risk Factor Surveillance System. In this dataset, Logistic Regression outperformed SVM with ROC index of 81.8 and 81.7 for models with and without graph metrics, respectively. SVM with a polynomial kernel had ROC index of 72.9 and 75.6 for models with and without graph metrics, respectively. Although this did not perform as well as Logistic Regression, the results are consistent with previous studies utilizing SVM to classify diabetes.
Dr. Bryce Meredig, Chief Science Officer, Citrine at The AI Conference
Startup Showcase Presentation on Citrine
Malika Cantor, Operations Partner, Comet Labs at The AI Conference 2017
Malika is devoted to bringing great ideas to life. She is an Operations Partner at Comet Labs, a cross between a venture fund and experimental research lab that supports AI and robotics startups. She previously worked in investment banking, and oversaw the development and growth of software and hardware startups in the education, healthcare, and telecom fields, in Asia, Europe and North America. She graduated from the University of Cambridge and has an MBA from Tsinghua University and MIT Sloan.
A VC Perspective on AI
Claudia Perlich, Chief Scientist, Dstillery
Claudia Perlich leads the machine learning efforts that power Dstillery’s digital intelligence for marketers and media companies. With more than 50 published scientific articles, she is a widely acclaimed expert on big data and machine learning applications, and an active speaker at data science and marketing conferences around the world.
Claudia is the past winner of the Advertising Research Foundation’s (ARF) Grand Innovation Award and has been selected for Crain’s New York’s 40 Under 40 list, Wired Magazine’s Smart List, and Fast Company’s 100 Most Creative People.
Claudia holds multiple patents in machine learning. She has won many data mining competitions and awards at Knowledge Discovery and Data Mining (KDD) conferences, and served as the organization’s General Chair in 2014.
Prior to joining Dstillery in 2010, Claudia worked at IBM’s Watson Research Center, focusing on data analytics and machine learning. She holds a PhD in Information Systems from New York University (where she continues to teach at the Stern School of Business), and an MA in Computer Science from the University of Colorado.
Abstract Summary:
Predictability and other Predicaments:
In the context of building predictive models, predictability is usually considered a blessing. After all – that is the goal: build the model that has the highest predictive performance. The rise of ‘big data’ has in fact vastly improved our ability to predict human behavior thanks to the introduction of much more informative features. However, in practice things are more differentiated than that. For many applications, the relevant outcome is observed for very different reasons. In such mixed scenarios, the model will automatically gravitate to the one that is easiest to predict at the expense of the others. This even holds if the predictable scenario is by far less common or relevant. We present a number of applications where this happens: clicks on ads being performed ‘intentionally’ vs. ‘accidentally’, consumers visiting store locations vs. their phones pretending to be there, and finally customers filling out online forms vs. bots defrauding the advertising industry. In conclusion, the combination of different and highly informative features can have significantly negative impact on the usefulness of predictive modeling.
Yuri M. Brovman, Data Scientist, eBay
Yuri is a Member of Technical Staff / Data Scientist at eBay in New York City. He is currently focused on developing scalable machine learning algorithms to produce high quality item recommendations. Yuri holds a Ph.D. degree from the Applied Physics and Applied Mathematics department from Columbia University and an undergraduate degree in Physics from UC Berkeley.
Abstract Summary:
Innovations in Recommender Systems for a Semi-structured Marketplace:
eBay has over 1 billion live items on the site at any given time. The lack of structured information about listings as well as variable inventory makes traditional collaborative filtering algorithms difficult to use in eBay’s large semi-structured marketplace. We will discuss approaches to overcome these challenges using machine learning and deep learning (both text and image based models). The details of the sampling strategy, feature engineering, and machine learned ranking model are all important for delivering improved operational metrics in A/B tests. We will cover both system architecture engineering as well as data science and machine learning methods that were developed to generate high quality recommendations.
Byron Galbraith, Chief Data Scientist, Talla, at MLconf NYC 2017
Byron Galbraith is the Chief Data Scientist and co-founder of Talla, where he works to translate the latest advancements in machine learning and natural language processing to build AI-powered conversational agents. Byron has a PhD in Cognitive and Neural Systems from Boston University and an MS in Bioinformatics from Marquette University. His research expertise includes brain-computer interfaces, neuromorphic robotics, spiking neural networks, high-performance computing, and natural language processing. Byron has also held several software engineering roles including back-end system engineer, full stack web developer, office automation consultant, and game engine developer at companies ranging in size from a two-person startup to a multi-national enterprise.
Abstract Summary:
Bayesian Bandits:
What color should that button be to convert more sales? What ad will most likely get clicked on? What movie recommendations should be displayed to keep subscribers engaged? What should we have for lunch? These are all examples of iterated decision problems — the same choice has to be made repeatedly with the goal being to arrive at an optimal decision strategy by incorporating the results of the previous decisions. In this talk I will describe the Bayesian Bandit solution to these types of problems, how it adaptively learns to minimize regret, how additional contextual information can be incorporated, and how it compares to the more traditional A/B testing solution.
Erik Bernhardsson, CTO, Better Mortgage
Erik Bernhardsson is the CTO at Better, a small startup in NYC working with mortgages. Before Better, he spent five years at Spotify managing teams working with machine learning and data analytics, in particular music recommendations.
Abstract Summary:
Nearest Neighbor Methods And Vector Models: Vector models are being used in a lot of different fields: natural language processing, recommender systems, computer vision, and other things. They are fast and convenient and are often state of the art in terms of accuracy. One of the challenges with vector models is that as the number of dimensions increase, finding similar items gets challenging. Erik developed a library called “Annoy” that uses a forest of random tree to do fast approximate nearest neighbor queries in high dimensional spaces. We will cover some specific applications of vector models with and how Annoy works.
Mukund Narasimhan, Engineer, Pinterest at MLconf Seattle 2017
Mukund Narasimhan is an engineer at Pinterest where he works on content modeling and recommendations. Prior to Pinterest, he has worked at Google, Facebook, Microsoft and Intel. He has a Ph.D. in Electrical Engineering from the University of Washington and a M.S in Mathematics from Louisiana State University.
Abstract summary
Knowledge at Pinterest:
Pinterest is building the world’s largest catalog of ideas. These ideas are embodied in billions of Pins and well over 100 million Users and Boards which, together, form a complex picture of our users and their interests. The goal of the Knowledge team is to leverage our unstructured data (images, videos, and text), and structured data (user curated data, partner generated content, and engagement signals) to model users and their interests so we can help them discover fresh, diverse, and personalized new ideas. In this talk, we go into some detail on the progress we’ve made so far.
Garrett Goh, Scientist, Pacific Northwest National Lab
Garrett Goh is a Scientist at the Pacific Northwest National Lab (PNNL), in the Advanced Computing, Mathematics & Data Division. He was previously awarded the Howard Hughes Medical Institute fellowship which supported his PhD in Computational Chemistry at the University of Michigan. At PNNL, he was awarded the Pauling Fellowship that supports his research initiative of combining deep learning and artificial intelligence with traditional chemistry applications. His current interests is in AI-assisted computational chemistry, which is the application of deep learning to predict chemical properties and the discovery of new chemical insights, while using minimal expert knowledge.
Abstract summary
A Deep Learning Computational Chemistry AI: Making chemical predictions with minimal expert knowledge:
Using deep learning and with virtually no expert knowledge, we construct computational chemistry models that perform favorably to existing state-of-the-art models developed by expert practitioners, whose models rely on the knowledge gained from decades of academic research. Our findings potentially demonstrates the impact of AI assistance in accelerating the scientific discovery process, where we envision future applications not just in chemistry, but in affiliated fields, such as biotechnology, pharmaceuticals, consumer goods, and perhaps other domains as well.
Viewers also liked (18)
Venkatesh Ramanathan, Data Scientist, PayPal at MLconf ATL 2017
Venkatesh Ramanathan, Data Scientist, PayPal at MLconf ATL 2017
Ashrith Barthur, Security Scientist, H2o.ai, at MLconf 2017
Ashrith Barthur, Security Scientist, H2o.ai, at MLconf 2017
Alexandra Johnson, Software Engineer, SigOpt at MLconf ATL 2017
Alexandra Johnson, Software Engineer, SigOpt at MLconf ATL 2017
Hanjun Dai, PhD Student, School of Computational Science and Engineering, Geo...
Hanjun Dai, PhD Student, School of Computational Science and Engineering, Geo...
Will Murphy, VP of Business Development & Co-Founder, Talla at The AI Confere...
Will Murphy, VP of Business Development & Co-Founder, Talla at The AI Confere...
Rahul Mehrotra, Product Manager, Maluuba at The AI Conference 2017
Rahul Mehrotra, Product Manager, Maluuba at The AI Conference 2017
Talha Obaid, Email Security, Symantec at MLconf ATL 2017
Talha Obaid, Email Security, Symantec at MLconf ATL 2017
LN Renganarayana, Architect, ML Platform and Services and Madhura Dudhgaonkar...
LN Renganarayana, Architect, ML Platform and Services and Madhura Dudhgaonkar...
Tim Chartier, Chief Academic Officer, Tresata at MLconf ATL 2017
Tim Chartier, Chief Academic Officer, Tresata at MLconf ATL 2017
Jessica Rudd, PhD Student, Analytics and Data Science, Kennesaw State Univers...
Jessica Rudd, PhD Student, Analytics and Data Science, Kennesaw State Univers...
Dr. Bryce Meredig, Chief Science Officer, Citrine at The AI Conference
Dr. Bryce Meredig, Chief Science Officer, Citrine at The AI Conference
Malika Cantor, Operations Partner, Comet Labs at The AI Conference 2017
Malika Cantor, Operations Partner, Comet Labs at The AI Conference 2017
Byron Galbraith, Chief Data Scientist, Talla, at MLconf NYC 2017
Byron Galbraith, Chief Data Scientist, Talla, at MLconf NYC 2017
Mukund Narasimhan, Engineer, Pinterest at MLconf Seattle 2017
Mukund Narasimhan, Engineer, Pinterest at MLconf Seattle 2017
Garrett Goh, Scientist, Pacific Northwest National Lab
Garrett Goh, Scientist, Pacific Northwest National Lab
More from MLconf
Jamila Smith-Loud - Understanding Human Impact: Social and Equity Assessments...
Understanding Human Impact: Social and Equity Assessments for AI Technologies
Social and Equity Impact Assessments have broad applications but can be a useful tool to explore and mitigate for Machine Learning fairness issues and can be applied to product specific questions as a way to generate insights and learnings about users, as well as impacts on society broadly as a result of the deployment of new and emerging technologies.
In this presentation, my goal is to advocate for and highlight the need to consult community and external stakeholder engagement to develop a new knowledge base and understanding of the human and social consequences of algorithmic decision making and to introduce principles, methods and process for these types of impact assessments.
Ted Willke - The Brain’s Guide to Dealing with Context in Language Understanding
The Brain’s Guide to Dealing with Context in Language Understanding
Like the visual cortex, the regions of the brain involved in understanding language represent information hierarchically. But whereas the visual cortex organizes things into a spatial hierarchy, the language regions encode information into a hierarchy of timescale. This organization is key to our uniquely human ability to integrate semantic information across narratives. More and more, deep learning-based approaches to natural language understanding embrace models that incorporate contextual information at varying timescales. This has not only led to state-of-the art performance on many difficult natural language tasks, but also to breakthroughs in our understanding of brain activity.
In this talk, we will discuss the important connection between language understanding and context at different timescales. We will explore how different deep learning architectures capture timescales in language and how closely their encodings mimic the brain. Along the way, we will uncover some surprising discoveries about what depth does and doesn’t buy you in deep recurrent neural networks. And we’ll describe a new, more flexible way to think about these architectures and ease design space exploration. Finally, we’ll discuss some of the exciting applications made possible by these breakthroughs.
Justin Armstrong - Applying Computer Vision to Reduce Contamination in the Re...
Applying Computer Vision to Reduce Contamination in the Recycling Stream
With China’s recent refusal of most foreign recyclables, North American waste haulers are scrambling to figure out how to make on-shore recycling cost-effective in order to continue providing recycling services. Recyclables that were once being shipped to China for manual sorting are now primarily being redirected to landfills or incinerators. Without a solution, a nearly $5 billion annual recycling market could come to a halt.
Purity in the recycling stream is key to this effort as contaminants in the stream can increase the cost of operations, damage equipment and reduce the ability to create pure commodities suitable for creating recycled goods. This market disruption as a result of China’s new regulations, however, provides us the chance to re-examine and improve our current disposal & collection habits with modern monitoring & artificial intelligence technology.
Using images from our in-dumpster cameras, Compology has developed an ML-based process that helps identify, measure and alert for contaminants in recycling containers before they are picked-up, helping keep the recycling stream clean.
Our convolutional neural network flags potential instances of contamination inside a dumpster, enabling garbage haulers to know which containers have the wrong type of material inside. This allows them to provide targeted, timely education, and when appropriate, assess fines, to improve recycling compliance at the businesses and residences they serve, helping keep recycling services financially viable.
In this presentation, we will walk through our ML-based contamination measurement and scoring process by showing how Waste Management, a national waste hauler, has experienced 57% contamination reduction in nearly 2,000 containers over six months, This progress shows significant strides towards financially viable recycling services.
Igor Markov - Quantum Computing: a Treasure Hunt, not a Gold Rush
Quantum Computing: a Treasure Hunt, not a Gold Rush
Quantum computers promise a significant step up in computational power over conventional computers, but also suffer a number of counterintuitive limitations --- both in their computational model and in leading lab implementations. In this talk, we review how quantum computers compete with conventional computers and how conventional computers try to hold their ground. Then we outline what stands in the way of successful quantum ML applications.
Josh Wills - Data Labeling as Religious Experience
Data Labeling as Religious Experience
One of the most common places to deploy a production machine learning systems is as a replacement for a legacy rules-based system that is having a hard time keeping up with new edge cases and requirements. I'll be walking through the process and tooling we used to help us design, train, and deploy a model to replace a set of static rules we had for handling invite spam at Slack, talk about what we learned, and discuss some problems to solve in order to make these migrations easier for everyone.
Vinay Prabhu - Project GaitNet: Ushering in the ImageNet moment for human Gai...
Project GaitNet: Ushering in the ImageNet moment for human Gait kinematics
The emergence of the upright human bipedal gait can be traced back 4 to 2.8 million years ago, to the now extinct hominin Australopithecus afarensis. Fine grained analysis of gait using the modern MEMS sensors found on all smartphones not just reveals a lot about the person’s orthopedic and neuromuscular health status, but also has enough idiosyncratic clues that it can be harnessed as a passive biometric. While there were many siloed attempts made by the machine learning community to model Bipedal Gait sensor data, these were done with small datasets oft collected in restricted academic environs. In this talk, we will introduce the ImageNet moment for human gait analysis by presenting 'Project GaitNet', the largest ever planet-sized motion sensor based human bipedal gait dataset ever curated. We’ll also present the associated state-of-the-art results in classifying humans harnessing novel deep neural architectures and the related success stories we have enjoyed in transfer-learning into disparate domains of human kinematics analysis.
Jekaterina Novikova - Machine Learning Methods in Detecting Alzheimer’s Disea...
Machine Learning Methods in Detecting Alzheimer’s Disease from Speech and Language
Alzheimer's disease affects millions of people worldwide, and it is important to predict the disease as early and as accurate as possible. In this talk, I will discuss development of novel ML models that help classifying healthy people from those who develop Alzheimer's, using short samples of human speech. As an input to the model, features of different modalities are extracted from speech audio samples and transcriptions: (1) syntactic measures, such as e.g. production rules extracted from syntactic parse trees, (2) lexical measures, such as e.g. features of lexical richness and complexity and lexical norms, and (3) acoustic measures, such as e.g. standard Mel-frequency cepstral coefficients. I will present the ML model that detects cognitive impairment by reaching agreement among modalities. The resulting model is able to achieve state of the art performance in both supervised and semi-supervised manner, using manual transcripts of human speech. Additionally, I will discuss potential limitations of any fully-automated speech-based Alzheimer's disease detection model, focusing mostly on the analysis of the impact of a not-so-accurate automatic speech recognition (ASR) on the classification performance. To illustrate this, I will present the experiments with controlled amounts of artificially generated ASR errors and explain how the deletion errors affect Alzheimer's detection performance the most, due to their impact on the features of syntactic and lexical complexity.
Meghana Ravikumar - Optimized Image Classification on the Cheap
Optimized Image Classification on the Cheap
In this talk, we anchor on building an image classifier trained on the Stanford Cars dataset to evaluate two approaches to transfer learning -fine tuning and feature extraction- and the impact of hyperparameter optimization on these techniques. Once we define the most performant transfer learning technique for Stanford Cars, we will double the size of the dataset through image augmentation to boost the classifier’s performance. We will use Bayesian optimization to learn the hyperparameters associated with image transformations using the downstream image classifier’s performance as the guide. In conjunction with model performance, we will also focus on the features of these augmented images and the downstream implications for our image classifier.
To both maximize model performance on a budget and explore the impact of optimization on these methods, we apply a particularly efficient implementation of Bayesian optimization to each of these architectures in this comparison. Our goal is to draw on a rigorous set of experimental results that can help us answer the question: how can resource-constrained teams make trade-offs between efficiency and effectiveness using pre-trained models?
Noam Finkelstein - The Importance of Modeling Data Collection
The Importance of Modeling Data Collection
Data sets used in machine learning are often collected in a systematically biased way - certain data points are more likely to be collected than others. We call this "observation bias". For example, in health care, we are more likely to see lab tests when the patient is feeling unwell than otherwise. Failing to account for observation bias can, of course, result in poor predictions on new data. By contrast, properly accounting for this bias allows us to make better use of the data we do have.
In this presentation, we discuss practical and theoretical approaches to dealing with observation bias. When the nature of the bias is known, there are simple adjustments we can make to nonparametric function estimation techniques, such as Gaussian Process models. We also discuss the scenario where the data collection model is unknown. In this case, there are steps we can take to estimate it from observed data. Finally, we demonstrate that having a small subset of data points that are known to be collected at random - that is, in an unbiased way - can vastly improve our ability to account for observation bias in the rest of the data set.
My hope is that attendees of this presentation will be aware of the perils of observation bias in their own work, and be equipped with tools to address it.
June Andrews - The Uncanny Valley of ML
The Uncanny Valley of ML
Every so often, the conundrum of the Uncanny Valley re-emerges as advanced technologies evolve from clearly experimental products to refined accepted technologies. We have seen its effects in robotics, computer graphics, and page load times. The debate of how to handle the new technology detracts from its benefits. When machine learning is added to human decision systems a similar effect can be measured in increased response time and decreased accuracy. These systems include radiology, judicial assignments, bus schedules, housing prices, power grids and a growing variety of applications. Unfortunately, the Uncanny Valley of ML can be hard to detect in these systems and can lead to degraded system performance when ML is introduced, at great expense. Here, we'll introduce key design principles for introducing ML into human decision systems to navigate around the Uncanny Valley and avoid its pitfalls.
Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection Tasks
Deep Learning Architectures for Semantic Relation Detection Tasks
Recognizing and distinguishing specific semantic relations from other types of semantic relations is an essential part of language understanding systems. Identifying expressions with similar and contrasting meanings is valuable for NLP systems which go beyond recognizing semantic relatedness and require to identify specific semantic relations. In this talk, I will first present novel techniques for creating labelled datasets required for training deep learning models for classifying semantic relations between phrases. I will further present various neural network architectures that integrate morphological features into integrated path-based and distributional relation detection algorithms and demonstrate that this model outperforms state-of-the-art models in distinguishing semantic relations and is capable of efficiently handling multi-word expressions.
Anoop Deoras - Building an Incrementally Trained, Local Taste Aware, Global D...
Building an Incrementally Trained, Local Taste Aware, Global Deep Learned Recommender System Model
At Netflix, our main goal is to maximize our members’ enjoyment of the selected show by minimizing the amount of time it takes for them to find it. We try to achieve this goal by personalizing almost all the aspects of our product -- from what shows to recommend, to how to present these shows and construct their home-pages to what images to select per show, among many other things. Everything is recommendations for us and as an applied Machine Learning group, we spend our time building models for personalization that will eventually increase the joy and satisfaction of our members. In this talk we will primarily focus our attention on a) making a global deep learned recommender model that is regional tastes and popularity aware and b) adapting this model to changing taste preferences as well as dynamic catalog availability.
We will first go through some standard recommender system models that use Matrix Factorization and Topic Models and then compare and contrast them with more powerful and higher capacity deep learning based models such as sequence models that use recurrent neural networks. We will show what it entails to build a global model that is aware of regional taste preferences and catalog availability. We will show how models that are built on simple Maximum Likelihood principle fail to do that. We will then describe one solution that we have employed in order to enable the global deep learned models to focus their attention on capturing regional taste preferences and changing catalog.In the latter half of the talk, we will discuss how we do incremental learning of deep learned recommender system models. Why do we need to do that ? Everything changes with time. Users’ tastes change with time. What’s available on Netflix and what’s popular also change over time. Therefore, updating or improving recommendation systems over time is necessary to bring more joy to users. In addition to how we apply incremental learning, we will discuss some of the challenges we face involving large-scale data preparation, infrastructure setup for incremental model training as well as pipeline scheduling. The incremental training enables us to serve fresher models trained on fresher and larger amounts of data. This helps our recommender system to nicely and quickly adapt to catalog and users’ taste changes, and improve overall performance.
Vito Ostuni - The Voice: New Challenges in a Zero UI World
Vito Ostuni - The Voice: New Challenges in a Zero UI World
The adoption of voice-enabled devices has seen an explosive growth in the last few years and music consumption is among the most popular use cases. Music personalization and recommendation plays a major role at Pandora in providing a daily delightful listening experience for millions of users. In turn, providing the same perfectly tailored listening experience through these novel voice interfaces brings new interesting challenges and exciting opportunities. In this talk we will describe how we apply personalization and recommendation techniques in three common voice scenarios which can be defined in terms of request types: known-item, thematic, and broad open-ended. We will describe how we use deep learning slot filling techniques and query classification to interpret the user intent and identify the main concepts in the query.
We will also present the differences and challenges regarding evaluation of voice powered recommendation systems. Since pure voice interfaces do not contain visual UI elements, relevance labels need to be inferred through implicit actions such as play time, query reformulations or other types of session level information. Another difference is that while the typical recommendation task corresponds to recommending a ranked list of items, a voice play request translates into a single item play action. Thus, some considerations about closed feedback loops need to be made. In summary, improving the quality of voice interactions in music services is a relatively new challenge and many exciting opportunities for breakthroughs still remain. There are many new aspects of recommendation system interfaces to address to bring a delightful and effortless experience for voice users. We will share a few open challenges to solve for the future.
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Using a Bayesian Neural Network in the Detection of Exoplanets
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https://arxiv.org/abs/2306.08302
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https://alandix.com/academic/papers/synergy2024-epistemic/
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Artemy Malkov, CEO, Data Monsters at The AI Conference 2017
- 2. Facebook launches Messenger platform with chatbots Apr 2016 Bots, Messenger and the future of customer service May 2016 Why and how chatbots will dominate social media Jul 2016 Will Facebook be your next call center operator? Jul 2016 The human role in a bot-dominated future Jul 2016 The new paradigm for human-bot communication Aug 2016 THE ROARING 2016
- 3. Facebook’s new chatbots still need work Apr 2016 The dehumanization of Facebook Messenger May 2016 Why do chatbots suck? May 2016 The impending bot backlash Jun 2016 The hidden risks of the bot explosion Jun 2016 Bursting the chatbot bubble Jul 2016 Hard questions about bot ethics Sep 2016 As Messenger’s bots lose steam, Facebook pushes menus over chat Mar 2017 THE PHANTOM MENACE
- 4. Natural Language Insane variability. Long tail distributions. Scripts don’t work. NLP dimensionality reduction.
- 5. Question
- 6. Answer 1 inchSize of the Message
- 7. Answer 1 inchSize of the Message 3 800 000 inchesSize of the Bay Area
- 8. Answer 500 000 000 inchesSize of the Earth 1 inchSize of the Message 3 800 000 inchesSize of the Bay Area
- 9. Answer 54 800 000 000 inchesSize of the Sun 500 000 000 inchesSize of the Earth 1 inchSize of the Message 3 800 000 inchesSize of the Bay Area
- 10. Answer 55 870 000 000 000 000 000 000 inchesSize of the Galaxy 54 800 000 000 inchesSize of the Sun 500 000 000 inchesSize of the Earth 1 inchSize of the Message 3 800 000 inchesSize of the Bay Area
- 11. Answer 34 848 000 000 000 000 000 000 000 000 inchesSize of the Universe 55 870 000 000 000 000 000 000 inchesSize of the Galaxy 54 800 000 000 inchesSize of the Sun 500 000 000 inchesSize of the Earth 1 inchSize of the Message 3 800 000 inchesSize of the Bay Area
- 12. Answer 282 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 Combinations of 59 symbols: 26 letters + “ ” ( 27 = 2.82e+84 )59 34 848 000 000 000 000 000 000 000 000 inchesSize of the Universe 55 870 000 000 000 000 000 000 inchesSize of the Galaxy 54 800 000 000 inchesSize of the Sun 500 000 000 inchesSize of the Earth 1 inchSize of the Message 3 800 000 inchesSize of the Bay Area
- 13. Okay, Some Words Are More Frequent Than Others the + be + to + of + and + a + + in + that + have + I 25% of English language An iceberg is a large piece of freshwater ice that has broken off a glacier or an ice shelf and is floating freely in open water. It may subsequently become frozen into pack ice (one form of sea ice). Almost 91% of an iceberg is below the surface of the water. In this text: 19 of 51 words
- 14. What Is Long Tail (Zipf’s Law) F = a rank
- 15. What Is Long Tail (Zipf’s Law) Williams, J. R. et al. Zipf’s law holds for phrases, not words. Sci. Rep. 5, 12209; doi: 10.1038/srep12209 (2015).
- 17. Why Dimensionality Matters? Buttons UI Natural Language UI 2-10 variations 100 000 000 000 000 000 variations
- 19. Reality
- 20. Reality
- 21. Reality
- 22. Reducing of the Dimensionality of Natural Language An iceberg is a large piece of freshwater ice that has broken off a glacier or an ice shelf and is floating freely in open water. It may subsequently become frozen into pack ice (one form of sea ice). As it drifts into shallower waters, it may come into contact with the seabed, a process referred to as seabed gouging by ice. Almost 91% of an iceberg is below the surface of the water. Sentence splitting Tokenization Spell checking Stemming / Lemmatization Vectorization Word embedding PoS tagging Topic detection Sentiment analysis Named entity recognition Relation extraction Intent detection Natural Text Pre-processing Understanding
- 23. Long tails again! Business verticals. Scripts don’t work again! Human Needs and Intents
- 24. Dozens, Hundreds, And Thousands Of Intents
- 25. Different Business Verticals Have Different Distributions Heavy “head” and short “tail” Heavy and long “tail”
- 26. Different Business Verticals Have Different Distributions Heavy “head” and short “tail” Heavy and long “tail”
- 27. Question Your company has 100 000 chat conversations a month. How many intents should you build in your chatbot to cover 95% of requests?
- 28. Question Your company has 100 000 chat conversations a month. How many intents should you build in your chatbot to cover 95% of requests? 20-50?
- 29. Question Your company has 100 000 chat conversations a month. How many intents should you build in your chatbot to cover 95% of requests? 20-50? NO! 5000-7000
- 31. Oh, no! Long tails again! Business Processes
- 32. Even An Ideal Chatbot Is Not A Silver Bullet
- 33. What Does All This Mean For Business? OR Humans are expensive Let’s replace human agents with chatbots!
- 34. What Businesses Expect From Bots Expectation
- 36. Solution?
- 37. 1. Start with STATISTICAL ANALYSIS of conversations with your customers! 2. Understand what distributions are typical for your business 3. Semi-supervised learning from conversations (Rules and scripts won’t work for mid/low-frequent requests) 4. Retrieval of the “Semantic layer” 5. Reinforcement learning and agent-in-the-loop How To Make It Work
- 38. The right way to augment customer service AND The best “Chatbot” systems are those, that learn fast and augment human agents
- 39. Thank you for your attention! Artemy Malkov, Ph.D. CEO, Data Monsters am@datamonsters.co bots.datamonsters.co