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Artificial Intelligence

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  • [Ep.21] Ask the Expert: Ethics in AI
    [Ep.21] Ask the Expert: Ethics in AI Alexey Malanov, Malware Expert, Kaspersky Lab Recorded: Sep 18 2018 49 mins
    This webinar is part of BrightTALK's Ask the Expert series.

    Alexey Malanov joined Kaspersky Lab in 2004, where he began his work as a virus analyst. He has since been appointed Head of Kaspersky's Anti-Malware Team and in 2012, shifted his focus to analyzing technology trends and risks.

    The threat posed by a Strong AI (artificial intelligence where the machine's intellectual capability is functionally equal to a human's) is well-considered in many science fiction films. But the invention of a Strong AI will take many years, so we can afford not to hurry with the study of "laws of robotics."

    However, we are already facing growing concerns. The use of machine learning in various fields has shown that algorithms often make strange, discriminatory and difficult-to-understand decisions. Moreover, it has been clearly demonstrated that a person can cheat algorithms to achieve desired results.

    Alexey addresses some of the looming issues surrounding the AI algorithms that are becoming a part of our daily lives, and will increasingly affect us and our decisions.
  • 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: Sep 4 2018 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.
  • State of the Art Natural Language Understanding in Healthcare
    State of the Art Natural Language Understanding in Healthcare David Talby, CTO, Pacific AI Recorded: Aug 30 2018 60 mins
    The ability of software to reason, answer questions and intelligently converse about clinical notes, patient stories or biomedical papers has risen dramatically in the past few years.

    This talk for practicing data scientists and software engineers covers state of the art natural language processing, deep learning, and machine learning libraries in this space. We'll share benchmarks from industry & research projects on use cases such as clinical data abstraction, patient risk prediction, named entity recognition & resolution, negation scope detection, and cohort selection.
  • Artificial Intelligence & Employee Security: Greasing the Wheels of Productivity
    Artificial Intelligence & Employee Security: Greasing the Wheels of Productivity Ehab Samy, VP Product Management, Plurilock Recorded: Aug 15 2018 32 mins
    Traditional security and authentication methods are designed to put up stumbling blocks for your employees, challenging them at several points throughout their workday to identify themselves. To do this they must enter, remember, and frequently change passwords, and in the expanding world of two- or multi-factor authentication the enterprise seeks greater security at the further expense of productivity. But traditional passwords and other authentication methods can be compromised, so the impact to your user processes does not always deliver proportional security.

    Artificial intelligence and biometrics can change the authentication game, supporting enhanced security by automatically identifying employee identity without impacting workflows and user processes. Join Ehab Samy, VP Product Management, to learn about applications of biometrics and Artificial Intelligence that smooth your employees’ path to completing any task at work.
  • Deploying Complex Stateful Applications with Kubernetes
    Deploying Complex Stateful Applications with Kubernetes Tom Phelan, Chief Architect, BlueData; Yaser Najafi, Big Data Solutions Engineer, BlueData Recorded: Aug 14 2018 59 mins
    Join this webinar to learn about using Kubernetes with stateful applications for AI and Big Data workloads.

    Kubernetes is now the de facto standard for container orchestration. And while it was originally designed for stateless applications and microservices, it's gaining ground in support for stateful applications as well.

    But distributed stateful applications – including analytics, data science, machine learning, and deep learning workloads – are still complex and challenging to deploy with Kubernetes.
    In this webinar, we'll discuss considerations for running stateful applications on Kubernetes:

    -Unique requirements for multi-service stateful workloads including Hadoop, Spark, Kafka, and TensorFlow

    -Persistent Volumes, Statefulsets, Operators, Helm, and other Kubernetes capabilities for stateful applications

    -Technical gaps in Kubernetes deployment patterns and tooling, including security and networking

    -Options and strategies to deploy distributed stateful applications in containerized environments

    Learn about a new open source project focused on deploying and managing stateful applications with Kubernetes.
  • Can your data infrastructure support AI marketing?
    Can your data infrastructure support AI marketing? Rolf Schromgens, CEO, Trivago Recorded: Aug 9 2018 62 mins
    Are you AI-ready? You need to be. Artificial intelligence marketing applications are delivering on their promise to source new insights to grow your business fast, AI technology is becoming available to any company looking for a competitive edge, and now’s the time to move, if you want to stay ahead of the curve.

    But the challenge is not just in defining an AI strategy; it requires developing the infrastructure to support it.

    Should you be developing in-house, or look to available technology and collaborate with outside experts? Keep deployment on-premise, or reach for the cloud? Can your current infrastructure, from server performance to processing power, stand up to the demands of increasingly complex AI applications?

    Get ready to level up your marketing strategy with AI, advance your technological capabilities in smart, strategic ways, learn how to make sure your next infrastructure move helps you exploit the AI advantage and more when you join this VB Live event!

    Register for free!

    Attend this webinar and learn:
    * How to tell if your marketing and IT departments are AI-ready
    * The fundamentals of an AI-driven infrastructure
    * The role of clean, definable data goals in successful AI implementation
    * How to scale the AI workload

    Speakers:
    * Andrew Malinow, PhD., Principal Data Scientist, Zylotech
    * Rolf Schromgens, CEO, Trivago
    * Sherif Mityas, Chief Experience Officer, TGI Friday's
    * Michele Goetz, Principal Analyst, Forrester
    * Rachael Brownell, Moderator, VentureBeat

    Sponsored by Zylotech
  • Fight gaming fraud with AI and machine learning
    Fight gaming fraud with AI and machine learning Jeff Sakasegawa, Trust and Safety Architect, Sift Science Recorded: Jul 31 2018 61 mins
    Globally there are 2.2 billion active gamers, and 47 percent of them shell out cash while they play. And 100 percent of them are at risk from fraudsters who rip off everything from a gamer’s identity to their credit cards, online goods, and trust in your company. With every instance of fraud, your reputation takes a nose dive, driving away customers and directly impacting your bottom line.

    But fraud is notoriously difficult to combat. Legacy rules-based approaches have never been able to keep up with fraudsters, who constantly evolve their techniques using sophisticated technology like automated scripts and bots.

    That’s why machine learning and artificial intelligence are being leveraged to detect fraud before it affects your company and end users. Machine learning can sift through billions of game events and analyze vast streams of data in real time to stop fraud in its tracks.

    To learn more about how machine learning and AI can keep your game and players safe from increasingly aggressive online criminals, don’t miss this VB Live event!

    In this webinar, you'll learn:
    * How the gaming industry can secure gamer data and build trust
    * How account takeover, fake licensing, spam, and scams pose a particular challenge to gamers and gaming platforms
    * What policies your company should have in place around data breach ransom
    * How to combat trolling

    Speakers:
    * Jeff Sakasegawa, Trust and Safety Architect, Sift Science
    * Dean Takahashi, Lead Writer, GamesBeat
    * Scott Adams, CEO FraudPvP.com, Former Director of Fraud & Risk, Riot Games
    * Rachael Brownell, Moderator, VentureBeat

    Sponsored by Sift Science
  • Realizing the ROI of Big Data and AI in Banking: The Foundational Steps
    Realizing the ROI of Big Data and AI in Banking: The Foundational Steps James Dotter, Chief Financial Officer, at MX Recorded: Jul 19 2018 42 mins
    In a changing world where 50% of home loans and 38% of auto loans are provided by a non-FI, how do you compete? The answer lies in how you collect, analyze and act on data. James will walk you through how to lay the foundation for an AI-driven approach to big data.

    What You’ll Learn:

    - Why data-driven financial institutions get better results
    - How to build a foundation for a data-first approach
    - How and when to use AI and Machine Learning in your financial institution

    About the presenter:

    James Dotter, Chief Financial Officer at MX, brings more than 16 years of management experience and financial expertise with industry-leading, billion-dollar technology companies. Previous to joining the MX team, Dotter managed finance and operations at InsideSales.com, one of the fastest growing tech companies in the US. He led InsideSales.com through five years of more than 100 percent revenue growth, directed talent acquisition of more than 100 employees per quarter, and raised more than $140 million in private equity financing, while building and maintaining strategic partnerships.
  • Pragmatic approach to implementing AI for Financial Services
    Pragmatic approach to implementing AI for Financial Services Genady Chybranov, Director of Financial Technology Innovation, Hitachi Vantara Recorded: Jul 19 2018 48 mins
    In this webinar, Genady will talk about implementing AI projects in a financial services environment. More specifically, he will cover the following topic areas:

    - Understanding what Artificial Intelligence can and can’t do
    - What to take into consideration before the implementation and how FSI is different from other industries
    - Step by step solution implementation avoiding common mistakes
    - Choosing right resources for a task in-house vs. partnering vs. buying
  • [Ep.17] Founders' Spotlight: Matthew Lamons & Paul Heirendt, Co-Founders- Skejul
    [Ep.17] Founders' Spotlight: Matthew Lamons & Paul Heirendt, Co-Founders- Skejul Harriet Jamieson, BrightTALK and Matthew Lamons and Paul Heirendt, Skejul Recorded: Jul 17 2018 58 mins
    In this episode of Founders Spotlight, Matthew Lamons and Paul Heirendt, Co-Founders of Skejul, share how they started the AI driven, Blockchain-protected Calendar Assistant application.

    Skejul was named “Cool Vendor for Unified Communications 2017” by Gartner, Inc, and enables its users to set meetings in seconds without sharing calendars, bouncing emails, or disclosing private information. It also lets users see all planned and potential events in one place, on any device and lets you share your time, thoughts and activities with whoever you choose, wherever you are.

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