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Artificial Intelligence

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  • Neural Networks/Deep Learning to Transform Modern AI Platform
    Neural Networks/Deep Learning to Transform Modern AI Platform Dr. Umesh Hodeghatta Rao, CTO, Nu-Sigma Analytics Labs Recorded: Feb 22 2018 64 mins
    AI is changing the way organizations do businesses and how they interact with customers. AI continues to drive the change. Deep Learning and Natural Language Processing will become standards in AI solutions. Deep Learning is based on brain simulations and uses deep neural networks. AlphaGo is the first AI system to defeat a professional human Go player, the first program to defeat a Go world champion, and arguably the strongest Go player in history. Baidu improved speech recognition from 89% to 99% using Deep Learning. Every AI and Machine learning scientist is required to know Deep Learning tools in his / her current job scenario.

    In this session, we will be discussing what is Deep Learning and why it is gaining popularity. We will explain AI solutions using Deep Learning with a practical example. Deep Learning has an edge over other machine learning techniques as with the increased volume of data, performance increases with Deep Learning. Further, Deep Learning enables Hierarchical Feature Learning i.e. learning feature hierarchies.
  • Panel Discussion: The Road to a Data-Driven Business
    Panel Discussion: The Road to a Data-Driven Business Jen Stirrup, Gordon Tredgold, Joanna Schloss, & Lyndsay Wise Recorded: Feb 22 2018 62 mins
    Join Jenn Stirrup (Director, DataRelish), Gordon Tredgold (CEO & Founder, Leadership Principles LLC), Joanna Schloss (Data Expert) and Lyndsay Wise (Solution Director, Information Builders) as they discuss what it takes to take a business from needing analytics to leveraging analytics successfully.
  • Artificial Intelligence in FinTech: Taking the Leap
    Artificial Intelligence in FinTech: Taking the Leap Innovate Finance Recorded: Jan 25 2018 60 mins
    Artificial Intelligence and Machine Learning has the potential to transform Financial Services, but so far we have seen few successful use cases. Is 2018 finally the year this technology turns from hype to reality? Which FinTechs are utilising AI best to serve the end consumers and what are the challenges that come with this? Can incumbents see past chatbots and start implementing intelligence products and solution that will make a real impact on their business model? As part of its IFGS 2018 Webinar Series, Innovate Finance will host a discussion looking at the current and potential future impact of AI technology in Financial Services and hear from companies leading the way.

    Roshan Rohatgi, Senior Innovation & Entrepreneurship Professional, RBS

    Clare Flynn Levy, Founder and CEO, Essentia Analytics
    Benedetta Arese Lucini, Co-Founder and CEO, Oval Money
    Dom Barker, CTO and Co-Founder, Fluidly
  • RIDE Containerized Data Science IDE server For Enterprise
    RIDE Containerized Data Science IDE server For Enterprise Ali Marami, Data Science Advisor at R-Brain Recorded: Dec 14 2017 45 mins
    RIDE is an all-in-one, multi-user, multi-tenant, secure and scalable platform for developing and sharing Data Science and Analytics, Machine Learning (ML) and Artificial Intelligence (AI) solutions in R, Python and SQL.

    RIDE supports developing in notebooks, editor, RMarkdown, shiny app, Bokeh and other frameworks. Supported by R-Brain’s optimized kernels, R and Python 3 have full language support, IntelliSense, debugger and data view. Autocomplete and content assistant are available for SQL and Python 2 kernels. Spark (standalone) and Tesnsorflow images are also provided.

    Using Docker in managing workspaces, this platform provides an enhanced secure and stable development environment for users with a powerful admin control for controlling resources and level of access including memory usage, CPU usage, and Idle time.

    The latest stable version of IDE is always available for all users without any need of upgrading or additional DevOps work. R-Brain also delivers customized development environment for organizations who are able to set up their own Docker registry to use their customized images.

    The RIDE Platform is a turnkey solution that increases efficiency in your data science projects by enabling data science teams to work collaboratively without a need to switch between tools. Explore and visualize data, share analyses, all in one IDE with root access, connection to git repositories and databases.
  • Big Data Analytics vs Privacy: Risks and Opportunities
    Big Data Analytics vs Privacy: Risks and Opportunities Rob Anderson, Head of Field Operations (Privitar),Tim Hickman, Associate (White & Case) Recorded: Dec 14 2017 58 mins
    Today's modern businesses gain competitive edge and remain innovative by using advanced analytics and machine learning. Utilising big data can build customer loyalty by improving personalised marketing campaigns; optimises fraud detection; and improves products and services by advanced testing. However, the data sets required for advanced analytics are often sensitive, containing personal customer information, and therefore come with an inherent set of privacy risks and concerns.

    This roundtable will cover a few key questions on data utility and privacy:

    - In what ways advanced analytics help businesses gain competitive edge?

    - What is defined as sensitive data?

    - Will GDPR affect the way you're allowed to use customer data?

    - What opportunities are there to utilise sensitive data?

    Unlocking the data’s true value is a challenge, but there are a range of tools and techniques that can help. This live discussion will focus on the data analytics landscape; compliance considerations and opportunities for improving data utility in 2018 and beyond.

    Key takeaways:

    - A view of the data protection landscape

    - How to remaining compliant with GDPR when using customer data

    - Use cases for advanced analytics and machine learning

    - Opportunities for maximising data utility in 2018
  • AI in VR: Enabling a world of “6 DoF” Analytics
    AI in VR: Enabling a world of “6 DoF” Analytics John Cutter, Product Manager, Watson Developers Labs & AR/VR Labs, IBM Recorded: Dec 13 2017 42 mins
    Augmented Reality and Virtual Reality hold the potential to affect nearly every facet of daily business. Those implications go beyond the disruptive nature of even the mobile phone.

    Explore the business implications of AR & VR with John as he discusses trends through the lens Big Data, Analytics, and AI.

    John, IBM’s Head of AR/VR Labs, will discuss real-world examples of compelling work in these categories, draw on the lessons learned, and expand on future trends that businesses should seriously consider when using AR or VR in their company.
  • Natural Language Processing Exposed: The Art, the Science and the Applications
    Natural Language Processing Exposed: The Art, the Science and the Applications Sid J Reddy, Chief Scientist, Conversica Recorded: Dec 12 2017 62 mins
    In this presentation, we will discuss several applications of NLP such as information extraction, knowledge synthesis, and entity retrieval. We will discuss how these fundamental set of algorithms are applicable for a wide array of use-cases and industry verticals such as healthcare, business intelligence, life sciences, legal, e-commerce, sales, and marketing.

    Additionally, case studies from these areas will be used to provide an intuitive explanation of complex NLP topics such as distributional semantics, computational linguistics, question-answering, conversational AI, and applications of deep learning to text data.

    Dr. Sid J. Reddy's Bio: https://www.conversica.com/sid-j-reddy/
  • Building a Fast, Scalable & Accurate NLP Pipeline on Apache Spark
    Building a Fast, Scalable & Accurate NLP Pipeline on Apache Spark David Talby, CTO, Pacific AI Recorded: Dec 12 2017 62 mins
    Natural language processing is a key component in many data science systems that must understand or reason about text. Common use cases include question answering, paraphrasing or summarization, sentiment analysis, natural language BI, language modeling, and disambiguation. Building such systems usually requires combining three types of software libraries: NLP annotation frameworks, machine learning frameworks, and deep learning frameworks.

    This talk introduces the NLP library for Apache Spark. It natively extends the Spark ML pipeline API's which enabling zero-copy, distributed, combined NLP & ML pipelines, which leverage all of Spark's built-in optimizations.

    The library implements core NLP algorithms including lemmatization, part of speech tagging, dependency parsing, named entity recognition, spell checking and sentiment detection. The talk will demonstrate using these algorithms to build commonly used pipelines, using PySpark on notebooks that will be made publicly available after the talk.

    David Talby has over a decade of experience building real-world machine learning, data mining, and NLP systems. He’s a member of the core team that built and open sourced the Spark NLP library.
  • The Human Role in AI
    The Human Role in AI Peter Bruce, President and Founder, The Institute for Statistics Education at Statistics.com Recorded: Dec 12 2017 36 mins
    Artificial Intelligence (AI) is a hot topic, and there is widespread alarm that AI will replace humans in the analytical process. Adam Selipsky, the CEO of Tableau, terms this a myth, and said recently that AI's role will remain that of an assistant to the analytics professional.

    In this talk we go beyond that, and look at some interesting aspects of the human role as an integral component of machine learning and statistical modeling.

    We discuss how human expertise "supervises" machine learning, how reliance on multiple sources can deliver surprising expertise, and when that system can go wrong.
  • AI in social housing
    AI in social housing Vicki Howe, Head of Product Development, HouseMark; Jason Lee, CEO, illumr Recorded: Dec 12 2017 18 mins
    This session explores what artificial intelligence can add to data analysis in the social housing sector. Is it better than traditional statistical techniques?

    HouseMark is the leading provider of data analysis and insight solutions to social housing providers. Illumr took part in HouseMark’s accelerator programme introducing innovative technology start-ups to the social housing sector. HouseMark have worked with illumr to test out the appetite for AI and explore what insights can be gained using data typically held by social housing providers.

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