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Artificial Intelligence

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  • AI for Innovation and Transformation
    AI for Innovation and Transformation Angelique Mohring, GainX Recorded: Jul 20 2017 40 mins
    Big data, to date, has been focused on external drivers such as customer economics and market intelligence. To compete, market leaders must leverage machine learning and artificial intelligence to aggressively ingest and respond to both external and internal data-centric insights. There is no exception. FSI must both innovate and transform to stay relevant.

    Today, Angelique Mohring discusses why FSIs must leverage AI to master the future by driving greater ROI on multi-million/billion dollar innovation and transformation spend.
  • Radiant, a powerful open source Shiny application for business analytics
    Radiant, a powerful open source Shiny application for business analytics Ali Marami Chief Data Scientist Recorded: Jul 11 2017 58 mins
    Radiant is a robust tool for business analytics and running sophisticated models without any need for code development. It leverages the functions and tools in R and at the same time provides a user-friendly interface. With Radiant, you can manipulate and visualize your data, run different models from simple OLS to decision trees (CART) and neural networks, and evaluate your results.

    The application is based on the Shiny package and can be run locally or on a server. Radiant was developed by Vicent Nijs. In this webinar, we review the tools available in Radiant and explain how easily you can use this tool without any setup or installation on your system.

    Radiant key features:

    • Explore: Quickly and easily summarize, visualize, and analyze your data
    • Run different models: OLS, GLM, Neural Networks, Naïve Bayes and CART.
    • Cross-platform: It runs in a browser on Windows, Mac, and Linux
    • Reproducible: Recreate results and share work with others as a state-file or an Rmarkdown report
    • Programming: Integrate Radiant's analysis functions with your own R-code
    • Context: Data and examples focus on business applications

    After this webinar you will learn:

    • Data manipulation and running different models
    • How to run advanced analytics in a browser on any device even in your tablet or iPad.


    Presenter bio:

    Ali has a Ph.D. in Finance from the University of Neuchatel in Switzerland and a BS in Electrical Engineering. He has extensive experience in financial modeling, quantitative modeling, and financial risk management in several US banks.
  • Binomial and Multinomial Logistic Regressions in R
    Binomial and Multinomial Logistic Regressions in R Ali Marami Chief Data Scientist Recorded: Jun 29 2017 49 mins
    Logistic regressions are the basic of machine learning. In this webinar, we discuss binomial and multinomial logistic regressions, how we implement them in R and test their performance. We will also review few examples of their usage in industry. In addition, you will learn how to use R-Brain advanced IDE when implementing the model.

    - Logistic regressions fundamentals and how to interpret estimates
    - Binomial and Multinomial logistic regressions
    - Implement logistic regressions in R
    - Performance measurement in logistic regressions
    - Generating and understanding ROC curve
    - Building confusion metrics and understanding its elements
    - Examples of model application in industry
    - Learn about new advanced IDE

    Presenter bio:

    Ali has a Ph.D. in Finance from the University of Neuchatel in Switzerland and a BS in Electrical Engineering. He has extensive experience in financial modeling, quantitative modeling, and financial risk management in several US banks.
  • Death to Traffic: How Smart Cities are Changing Transportation
    Death to Traffic: How Smart Cities are Changing Transportation Laura Schewel, CEO, StreetLight Data Recorded: Jun 23 2017 45 mins
    From automated vehicles to ride hailing apps, transportation as we know it is changing - and fast. But new technologies alone won't help communities build the efficient, equitable, and sustainable transportation networks communities want. In fact, these innovative technologies could do just the opposite, especially if they are not deployed wisely. Cities must collect the right data and enact the right policies to ensure they do not exacerbate problems like inequity and traffic, and to hold themselves accountable to the promise of new mobility technologies.

    In this webinar, you will find out why - and how - the smartest cities of tomorrow will be those that adopt data-driven transportation strategies today. Join for Laura Schewel's presentation to gain insights into:

    • Why the status quo for transportation data collection is no longer good enough
    • The types of Massive Mobile Data that are useful for transportation and urban planning
    • Algorithmic processing techniques that are critical for making this data useful
    • Case studies from California and Virginia that demonstrate why Massive Mobile Data drives more effective transportation planning
    • A forward-looking blueprint for using Massive Mobile Data to maximize the potential benefits of new transportation technologies - and minimize negative impacts

    Laura Schewel founded StreetLight Data, a mobility analytics provider, after spending more than a decade as an advanced transportation researcher and statistician at the Rocky Mountain Institute and FERC. She has particular expertise in transportation systems, sustainability and safety, and vehicle/system modeling and analysis.
  • How IoT Will Make Healthcare Healthy, Wealthy, & Wise
    How IoT Will Make Healthcare Healthy, Wealthy, & Wise Jarie Bolander, COO, Lab Sensor Solutions Recorded: Jun 23 2017 46 mins
    IoT is a technology that has the potential to make us healthy, wealthy, and wise especially in healthcare. Healthcare is just now adopting IoT to improve patient outcomes and decrease the cost of care.

    In this webinar, you’ll learn:

    - How to identify if an IoT solution will work for your use case.
    - What others in healthcare are using IoT for.
    - The challenges of IoT in healthcare
  • Panel: Smart Fog and Transaction Management for Cities and Maritime
    Panel: Smart Fog and Transaction Management for Cities and Maritime Moderator: Katalin Walcott (Intel) Panel: Jeff Fedders (OpenFog), Mark Dixon (IBM), Matthew Bailey (Powering IoT) Recorded: Jun 22 2017 61 mins
    Fog computing represents a tectonic shift for the future of transaction management, distributed supply chain and overall experience. It blurs the lines between the edge and the cloud and puts the focus on the systems which manage and balance the delivery of coherent, end-to-end sessions and associated transaction level agreements. As a result, this new technology is pervasive in several industries.

    Join this panel of experts as they discuss solutions with specific industry use cases from smart fog for Cities, Buildings, Ports, and Maritime.

    Moderator: Katalin Walcott, Work Group Chair Manageability at OpenFog Consortium & Principal Engineer - IoT/Fog Computing Orchestration Architecture at Intel

    Panelists:
    - Jeff Fedders, President at OpenFog Consortium & Chief Strategist, IoTG Strategy and Technology Office at Intel
    - Mark Dixon, Senior Architect for Smarter Cities at IBM
    - Matthew Bailey, President, Powering IoT - Smart City advisor and strategist to governments, technology corporations, and economic development agencies
  • Toward Internet of Everything: Architectures, Standards, & Interoperability
    Toward Internet of Everything: Architectures, Standards, & Interoperability Ram D. Sriram, Chief of the Software and Systems Division, IT Lab at National Institute of Standards and Technology Recorded: Jun 21 2017 63 mins
    In this talk, Ram will provide a unified framework for Internet of Things, Cyber-Physical Systems, and Smart Networked Systems and Societies, and then discuss the role of ontologies for interoperability.

    The Internet, which has spanned several networks in a wide variety of domains, is having a significant impact on every aspect of our lives. These networks are currently being extended to have significant sensing capabilities, with the evolution of the Internet of Things (IoT). With additional control, we are entering the era of Cyber-physical Systems (CPS). In the near future, the networks will go beyond physically linked computers to include multimodal-information from biological, cognitive, semantic, and social networks.

    This paradigm shift will involve symbiotic networks of people (social networks), smart devices, and smartphones or mobile personal computing and communication devices that will form smart net-centric systems and societies (SNSS) or Internet of Everything. These devices – and the network -- will be constantly sensing, monitoring, interpreting, and controlling the environment.

    A key technical challenge for realizing SNSS/IoE is that the network consists of things (both devices & humans) which are heterogeneous, yet need to be interoperable. In other words, devices and people need to interoperate in a seamless manner. This requires the development of standard terminologies (or ontologies) which capture the meaning and relations of objects and events. Creating and testing such terminologies will aid in effective recognition and reaction in a network-centric situation awareness environment.

    Before joining the Software and Systems Division (his current position), Ram was the leader of the Design and Process group in the Manufacturing Systems Integration Division, Manufacturing Engineering Lab, where he conducted research on standards for interoperability of computer-aided design systems.
  • Machine Vision for large-scale Aerial Imagery
    Machine Vision for large-scale Aerial Imagery Zbigniew Bogdan Wojna (Cofounder at TensorFlight Inc.; also PhD at UCL; Google, Nvidia, Microsoft) Recorded: Jun 15 2017 33 mins
    Zbigniew will provide insight into how Tensorflight is building a digital brain capable of understanding the world from the sky. The approach is based on deep convolutional neural networks, inspired by visual processing in the human brain.

    The company has partnered with DroneDeploy, the leading platform for collection aerial imagery via drones.

    Tensorflight’s first machine vision solution can count different types of objects such as trees, crops, cars, livestock etc. It is based on state of the art research. Models are easily scalable and deployed on the cloud through almost real-time analysis and a distributed orchestration system. Zbigniew will discuss how they have solved problems of processing maps that are 30.000 x 30.000 pixels in size in almost real time.
  • The Three A (AI-Art-Animation)
    The Three A (AI-Art-Animation) Davide La Sala (3D Generalist at Facebook, Roboticist) Recorded: Jun 13 2017 21 mins
    Davide is going to dive into why Art and Animation are fundamental parts of AI and robotics development, how an artist in your team can help increase dramatically the user experience and emotionally engagements with an AI and/or a robot. His presentation will include a behind the scene of the art choices behind the animation design of the first Olly robot iteration presented at TechCrunch Disrupt London 2015.
  • Jupyter is more than notebooks, JupyterLab and beyond! (Webinar)
    Jupyter is more than notebooks, JupyterLab and beyond! (Webinar) Ali Marami Chief Data Scientist Recorded: Jun 1 2017 48 mins
    Join us to learn about JupyterLab, the new open source computational environment for Jupyter. Increase the performance of your data science projects by working in an integrated environment for your notebooks, editor, terminal and console. We will also discuss R-Brain cloud platform and its new R Python Cloud IDE which is built on JupyterLab.

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