schedule Aug 8th 09:00 - 09:45 AM place Grand Ball Room people 183 Interested

Since we originally proposed the need for a first-class language, compiler and ecosystem for machine learning (ML) - a view that is increasingly shared by many, there have been plenty of interesting developments in the field. Not only have the tradeoffs in existing systems, such as TensorFlow and PyTorch, not been resolved, but they are clearer than ever now that both frameworks contain distinct "static graph" and "eager execution" interfaces. Meanwhile, the idea of ML models fundamentally being differentiable algorithms – often called differentiable programming – has caught on.

Where current frameworks fall short, several exciting new projects have sprung up that dispense with graphs entirely, to bring differentiable programming to the mainstream. Myia, by the Theano team, differentiates and compiles a subset of Python to high-performance GPU code. Swift for TensorFlow extends Swift so that compatible functions can be compiled to TensorFlow graphs. And finally, the Flux ecosystem is extending Julia’s compiler with a number of ML-focused tools, including first-class gradients, just-in-time CUDA kernel compilation, automatic batching and support for new hardware such as TPUs.

This talk will demonstrate how Julia is increasingly becoming a natural language for machine learning, the kind of libraries and applications the Julia community is building, the contributions from India (there are many!), and our plans going forward.

 
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schedule Submitted 5 months ago

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  • Anoop Kulkarni
    By Anoop Kulkarni  ~  5 months ago
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    Viral, thanks for your submission. I have been using Julia for some time now as part of quantum computing.. moving only recently to Julia for machine learning.. looking foward to this talk from you given your pedigree in the area


    ~anoop


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    Naresh Jain - Ethical AI - Fishbowl

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    Naresh Jain
    Founder
    XNSIO
    schedule 3 weeks ago
    Sold Out!
    45 Mins
    Keynote
    Beginner

    There has been a lot of concerns about the black-box nature of AI. People have been asking for a sensible AI guideline with the weight of Law behind it. In April 2019, the EU released it's Ethics guidelines for trustworthy AI. Before that during the Obama administration, the National Science and Technology Council came up with its own set of broad guidelines called "Preparing for the Future of Artificial Intelligence."

    Most of these cover an impressive amount of ground in several major categories:

    • Transparency: Any time an AI system makes decisions on a user's behalf, that person should be aware of it. The reasoning behind decisions should be easily explainable.
    • Safety: AI systems should be designed to withstand attempted hijacking and other attacks performed by hackers.
    • Fairness: Decisions made by AI systems should not be influenced by gender, race or other personal identifiers. They should be as impartial as possible and not reflect human biases.
    • Environmental stewardship: Not all the stakeholders in AI development are human. The development of these platforms and the implications of their decision-making and sustainability should take into account the needs of the larger environment and other forms of life.
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    Workshop
    Intermediate

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    Case Study
    Intermediate

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    Paolo Tamagnini / Kathrin Melcher - Guided Analytics - Building Applications for Automated Machine Learning

    90 Mins
    Tutorial
    Beginner

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    By mixing and matching interaction with automation, we can use Guided Analytics to develop predictive models on the fly. More interestingly, by leveraging automated machine learning and interactive dashboard components, custom Guided Analytics Applications, tailored to your business needs, can be created in a few minutes.

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    Aditya Singh Tomar - Building Your Own Data Visualization Platform

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    Aditya Singh Tomar
    Data Consultant
    ACT Insights
    schedule 3 months ago
    Sold Out!
    45 Mins
    Demonstration
    Beginner

    Ever thought about having a mini interactive visualization tool that caters to your specific requirements. That is the product I created when I started independent consulting. 2 years since, and I have now decided to make it public – even the source code.

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    Anuj Gupta - Continuous Learning Systems: Building ML systems that keep learning from their mistakes

    Anuj Gupta
    Anuj Gupta
    Scientist
    Intuit
    schedule 3 months ago
    Sold Out!
    45 Mins
    Talk
    Beginner

    Won't it be great to have ML models that can update their “learning” as and when they make mistake and correction is provided in real time? In this talk we look at a concrete business use case which warrants such a system. We will take a deep dive to understand the use case and how we went about building a continuously learning system for text classification. The approaches we took, the results we got.

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    Deepak Mukunthu - Automated Machine Learning

    45 Mins
    Talk
    Beginner

    Intelligent experiences powered by AI can seem like magic to users. Developing them, however, is pretty cumbersome involving a series of sequential and interconnected decisions along the way that is pretty time-consuming. What if there was an automated service that identifies the best machine learning pipelines for a given problem/data? Automated Machine Learning does exactly that!

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    Govind Chada - Using 3D Convolutional Neural Networks with Visual Insights for Classification of Lung Nodules and Early Detection of Lung Cancer

    Govind Chada
    Govind Chada
    AI/ML Researcher
    Cy Woods
    schedule 3 months ago
    Sold Out!
    45 Mins
    Case Study
    Intermediate

    Lung cancer is the leading cause of cancer death among both men and women in the U.S., with more than a hundred thousand deaths every year. The five-year survival rate is only 17%; however, early detection of malignant lung nodules significantly improves the chances of survival and prognosis.

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    Gaurav Godhwani / Swati Jaiswal - Fantastic Indian Open Datasets and Where to Find Them

    45 Mins
    Case Study
    Beginner

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    Machine learning and deep learning have been rapidly adopted in various spheres of medicine such as discovery of drug, disease diagnosis, Genomics, medical imaging and bioinformatics for translating biomedical data into improved human healthcare. Machine learning/deep learning based healthcare applications assist physicians to make faster, cheaper and more accurate diagnosis.

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    Dr. C.S.Jyothirmayee / Usha Rengaraju / Vijayalakshmi Mahadevan - Deep learning powered Genomic Research

    90 Mins
    Workshop
    Advanced

    The event disease happens when there is a slip in the finely orchestrated dance between physiology, environment and genes. Treatment with chemicals (natural, synthetic or combination) solved some diseases but others persisted and got propagated along the generations. Molecular basis of disease became prime center of studies to understand and to analyze root cause. Cancer also showed a way that origin of disease, detection, prognosis and treatment along with cure was not so uncomplicated process. Treatment of diseases had to be done case by case basis (no one size fits).

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    Badri Narayanan Gopalakrishnan / Shalini Sinha / Usha Rengaraju - Lifting Up: How AI and Big data can contribute to anti-poverty programs

    45 Mins
    Case Study
    Intermediate

    Ending poverty and zero hunger are top two goals United Nations aims to achieve by 2030 under its sustainable development program. Hunger and poverty are byproducts of multiple factors and fighting them require multi-fold effort from all stakeholders. Artificial Intelligence and Machine learning has transformed the way we live, work and interact. However economics of business has limited its application to few segments of the society. A much conscious effort is needed to bring the power of AI to the benefits of the ones who actually need it the most – people below the poverty line. Here we present our thoughts on how deep learning and big data analytics can be combined to enable effective implementation of anti-poverty programs. The advancements in deep learning , micro diagnostics combined with effective technology policy is the right recipe for a progressive growth of a nation. Deep learning can help identify poverty zones across the globe based on night time images where the level of light correlates to higher economic growth. Once the areas of lower economic growth are identified, geographic and demographic data can be combined to establish micro level diagnostics of these underdeveloped area. The insights from the data can help plan an effective intervention program. Machine Learning can be further used to identify potential donors, investors and contributors across the globe based on their skill-set, interest, history, ethnicity, purchasing power and their native connect to the location of the proposed program. Adequate resource allocation and efficient design of the program will also not guarantee success of a program unless the project execution is supervised at grass-root level. Data Analytics can be used to monitor project progress, effectiveness and detect anomaly in case of any fraud or mismanagement of funds.

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    Dr. Vikas Agrawal - Non-Stationary Time Series: Finding Relationships Between Changing Processes for Enterprise Prescriptive Systems

    45 Mins
    Talk
    Intermediate

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    For instance, we can show a CEO that they would miss their revenue target by over 6% for the quarter, and tell us why i.e. in what ways has their business changed over the last year. Then we provide the prioritized ordered lists of quickest, cheapest and least risky paths to help turn them over the tide, with estimates of relative costs and expected probability of success.

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    Dipanjan Sarkar / Anuj Gupta - Natural Language Processing Bootcamp - Zero to Hero

    Dipanjan Sarkar
    Dipanjan Sarkar
    Data Scientist
    Red Hat
    Anuj Gupta
    Anuj Gupta
    Scientist
    Intuit
    schedule 7 months ago
    Sold Out!
    480 Mins
    Workshop
    Intermediate

    Data is the new oil and unstructured data, especially text, images and videos contain a wealth of information. However, due to the inherent complexity in processing and analyzing this data, people often refrain from spending extra time and effort in venturing out from structured datasets to analyze these unstructured sources of data, which can be a potential gold mine. Natural Language Processing (NLP) is all about leveraging tools, techniques and algorithms to process and understand natural language based unstructured data - text, speech and so on.

    Being specialized in domains like computer vision and natural language processing is no longer a luxury but a necessity which is expected of any data scientist in today’s fast-paced world! With a hands-on and interactive approach, we will understand essential concepts in NLP along with extensive case- studies and hands-on examples to master state-of-the-art tools, techniques and frameworks for actually applying NLP to solve real- world problems. We leverage Python 3 and the latest and best state-of- the-art frameworks including NLTK, Gensim, SpaCy, Scikit-Learn, TextBlob, Keras and TensorFlow to showcase our examples. You will be able to learn a fair bit of machine learning as well as deep learning in the context of NLP during this bootcamp.

    In our journey in this field, we have struggled with various problems, faced many challenges, and learned various lessons over time. This workshop is our way of giving back a major chunk of the knowledge we’ve gained in the world of text analytics and natural language processing, where building a fancy word cloud from a bunch of text documents is not enough anymore. You might have had questions like ‘What is the right technique to solve a problem?’, ‘How does text summarization really work?’ and ‘Which are the best frameworks to solve multi-class text categorization?’ among many other questions! Based on our prior knowledge and learnings from publishing a couple of books in this domain, this workshop should help readers avoid some of the pressing issues in NLP and learn effective strategies to master NLP.

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    • Basics of Natural Language and Python for NLP tasks
    • Text Processing and Wrangling
    • Text Understanding - POS, NER, Parsing
    • Text Representation - BOW, Embeddings, Contextual Embeddings
    • Text Similarity and Content Recommenders
    • Text Clustering
    • Topic Modeling
    • Text Summarization
    • Sentiment Analysis - Unsupervised & Supervised
    • Text Classification with Machine Learning and Deep Learning
    • Multi-class & Multi-Label Text Classification
    • Deep Transfer Learning and it's promise
    • Applying Deep Transfer Learning - Universal Sentence Encoders, ELMo and BERT for NLP tasks
    • Generative Deep Learning for NLP
    • Next Steps

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    Arpit Agarwal - Practitioner's Perspective : How do you accelerate innovation and deliver faster time-to-value for your AI initiative

    Arpit Agarwal
    Arpit Agarwal
    Director of Data Science
    Zoomcar
    schedule 3 weeks ago
    Sold Out!
    20 Mins
    Experience Report
    Beginner

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    Madhan Rajasekkharan - Data Augmentation using GAN to improve Risk Models for New Credit Card customers

    Madhan Rajasekkharan
    Madhan Rajasekkharan
    Director
    American Express
    schedule 3 weeks ago
    Sold Out!
    20 Mins
    Experience Report
    Beginner

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    Experience Report
    Beginner

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    Bargava Subramanian - Anomaly Detection for Cyber Security using Federated Learning

    Bargava Subramanian
    Bargava Subramanian
    Co-Founder
    Binaize Labs
    schedule 1 month ago
    Sold Out!
    20 Mins
    Experience Report
    Beginner

    In a network of connected devices, there are two critical aspects of the system to succeed:

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    • Ability to build more accurate models faster
    • Low latency during inference
    • Privacy-preserving
    • Improved energy efficiency of the devices

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    Rahul Agarwal - Continuous Data Integrity Tracking

    Rahul Agarwal
    Rahul Agarwal
    Vice President
    American Express
    schedule 1 month ago
    Sold Out!
    20 Mins
    Experience Report
    Beginner

    "In God we trust; all others must bring data." - W. E. Deming, Author & Professor

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    At American Express, we have Data getting generated and stored across multiple platforms. For example, in a market like the US, we process more than ~200 transactions every second and make an authorization decision. Given this speed and scale of data generation, ensuring Data quality becomes imperative and a unique challenge in itself. There are hundreds of models running in production platforms within AMEX having thousands of variables. Many variables are created/populated originally in legacy systems (or have components derived from there) which are then passed onto downstream systems for manipulation and creating new attributes. A tech glitch or a logic issue could impact any variable at any point of this process resulting in disastrous consequences in model outputs which can get transformed into real-world customer impact leading to financial and reputational risk for the bank. So how do we catch these anomalies before they adversely impact processes?

    Traditional approaches to anomaly detection have relied on measuring the deviation from the mean of the variable. The more fancy ones employ time series based forecasting. But both these approaches are fraught with high levels of false positives. When every alert generated has to be analyzed by the business which has a cost, high levels of accuracy is desired. In this talk, we will discuss how AMEX has approached and solved this problem.

  • Liked Amit Doshi
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    Amit Doshi - Integrating Digital Twin and AI for Smarter Engineering Decisions

    Amit Doshi
    Amit Doshi
    schedule 3 months ago
    Sold Out!
    45 Mins
    Talk
    Intermediate

    With the increasing popularity of AI, new frontiers are emerging in predictive maintenance and manufacturing decision science. However, there are many complexities associated with modeling plant assets, training predictive models for them, and deploying these models at scale for near real-time decision support. This talk will discuss these complexities in the context of building an example system.

    First, you must have failure data to train a good model, but equipment failures can be expensive to introduce for the sake of building a data set! Instead, physical simulations can be used to create large, synthetic data sets to train a model with a variety of failure conditions.

    These systems also involve high-frequency data from many sensors, reporting at different times. The data must be time-aligned to apply calculations, which makes it difficult to design a streaming architecture. These challenges can be addressed through a stream processing framework that incorporates time-windowing and manages out-of-order data with Apache Kafka. The sensor data must then be synchronized for further signal processing before being passed to a machine learning model.

    As these architectures and software stacks mature in areas like manufacturing, it is increasingly important to enable engineers and domain experts in this workflow to build and deploy the machine learning models and work with system architects on the system integration. This talk also highlights the benefit of using apps and exposing the functionality through API layers to help make these systems more accessible and extensible across the workflow.

    This session will focus on building a system to address these challenges using MATLAB, Simulink. We will start with a physical model of an engineering asset and walk through the process of developing and deploying a machine learning model for that asset as a scalable and reliable cloud service.