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AI offers incredible potential for revenue growth and customer engagement. Learn how NexJ's award-winning Intelligent Customer Management products use AI to help you improve customer service, increase productivity, grow assets under management, and increase share of wallet.

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NexJ Nudge - AI Suite: Intelligent Investment Recommendations
What makes NexJ’s Intelligent Investment Recommendations assistant the smart choice? Learn how you can automate compliance, mitigate risk, and optimize portfolio construction with a peek at our Deep Learning-powered digital assistant.

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Conversational AI: Bot Building Tools
Watch to learn about the latest bot building tools that are AI services aware, and support end-to-end bot development workflow to create, build, test, deploy and manage your bot. The tools we provide include a set of rich command line tools that help you create and manage bots and channel registrations, and manage AI connected services. We will showcase how different tools assist you in different stages of the bot development process, helping you with conversational modelling, LUIS, QnA maker and language model dispatching. We will also give a sneak peak of the new and extensible bot emulator, which allows management of bots, connected services and transcripts. All the tools and the emulator are open source – visit our GitHub repos and clone, contribute, comment and be a part of creating great bot tools! https://github.com/Microsoft/botbuilder-tools https://github.com/Microsoft/BotFramework-Emulator

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Applying AI and Machine Learning to Financial Services Using the Google Cloud
Applying AI and Machine Learning to Financial Services Using the Google Cloud Platform. Scott Penberthy, Google. LendIt Fintech USA is the World’s Leading Event in Financial Services Innovation. View more videos at http://www.lendit.com/usa/2018/videos

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Soledad Galli - Machine Learning in Financial Credit Risk Assessment
Filmed at PyData London 2017 Description Risk management is paramount to any lending institution, allowing it to perform well-informed decisions while originating loans. In this talk, I will describe our research and development approach to build our Credit Risk Prediction Model. I will browse over our target definition, feature optimisation, model building and tuning and our experience with model stacking. Abstract Credit Risk assessment is a general term used among financial institutions to describe the methodology used to determine the likelihood of loss on a particular asset, investment or loan. The objective of assessing credit risk is to determine if an investment is worthwhile, what steps should be taken to mitigate risk, and what the return rate should be to make an investment successful. Building a Credit Risk Prediction Model as accurate as possible becomes essential, as it allows the institution to provide fair prices to the customers while ensuring predictable and minimal losses. We build our Credit Risk Model by combining data gathered from the customer’s application on our online platform with their credit history provided by different credit agencies. In this talk, we will cover the research and development behind our recently created Credit Risk Model. We will discuss the definition of the target, the variable selection procedure, the different machine learning models built and how we optimise their hyper-parameters, as well us some of our latest research in model stacking and deep learning. Our development and Modelling pipeline is built in Python, using Pandas, Numpy, Scikit-Learn, XGBboost, Keras, Matplotlib and Seaborn. We combine the use of machine learning algorithms with data visualisation to better understand the variables and our customers, and to convey the message to different stakeholders within and outside the company. Throughout the talk, we will focus both on the intellectual rationale of the research and the utilisation of the different python tools to accomplish each task, highlighting both the problems encountered and the solutions devised. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. We aim to be an accessible, community-driven conference, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps

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How artificial intelligence (AI) will influence customer experience in 6 levels
Artificial Intelligence (AI) will have a huge impact on customer experience. In this video I describe in six levels how artificial intelligence will grow into the customer experience.

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Transform customer service with machine learning (Google Cloud Next '17)
It's easy to analyze one customer conversation, but what if you have thousands of messages all coming in at once? Google Cloud Platform (GCP) has a suite of machine learning products to help you understand customer service data. In this video, you'll learn how to use Cloud Machine Learning, along with the Cloud Natural Language, Speech, and Translation APIs to analyze customer emails, tickets and calls in real time. You'll also hear from a Google Cloud customer on how they're using our machine learning products to streamline and improve their customer service. Missed the conference? Watch all the talks here: https://goo.gl/c1Vs3h Watch more talks about Big Data & Machine Learning here: https://goo.gl/OcqI9k

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Tom Bocklisch - Conversational AI: Building clever chatbots
Description Most chatbots and voice skills are based on a state machine and too many if/else statements. Tom will show you how to move past that and build flexible, robust experiences using machine learning throughout the stack. Abstract Conversational software is everywhere: messaging apps have opened up APIs to bot developers and millions of consumers now own voice controlled speakers. But the tools and frameworks for building these systems are still immature. Tom will talk about Rasa, an open source machine learning framework for building conversational software. The talk will cover the algorithms Rasa uses to build flexible and robust voice and text systems, the trade offs in using supervised versus reinforcement learning, and whether it's really such a good idea to generate text with LSTMs. Outline: Components : NLU , DM , integration , NLG Overview of available tools and frameworks Describe how Rasa does NLU Motivation & a chatbot leading to state machine hell How Rasa does dialogue management. How to advance a bots capabilities - closing the loop and data collection. Current research topics and challenges. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps

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How AI is Transforming Customer Care (Cloud Next '18)
This session covers how latest development in AI technology from Google is empowering customers to transform customer care. MLAI239 Event schedule → http://g.co/next18 Watch more Machine Learning & AI sessions here → http://bit.ly/2zGKfcg Next ‘18 All Sessions playlist → http://bit.ly/Allsessions Subscribe to the Google Cloud channel! → http://bit.ly/NextSub

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Webinar | Intelligent Customer Service With ServiceNow Customer Service Management
#ServiceNow® Customer Service Management (#CSM) goes beyond traditional customer service solutions to serve your customers—consumers or businesses—faster and more effectively. It gives customers their choice of contact options with omni-channel engagement. Customer issues are quickly routed to the appropriately skilled agents. Using an online customer service portal, you can automate recurring requests, deliver solutions via a comprehensive knowledge base, and provide customers with a community of peers and experts. Join Shea Laughlin, Senior Solution Consultant at Alcor Solutions for a 40 minute interactive webinar and demo, as he demystifies the ServiceNow Customer Service Management portal for you to unravel its benefits. - To know more about Alcor Solutions , please visit our website at http://www.alcortech.com - Like Alcor on Facebook at http://www.facebook.com/AlcorSolutions - Join our twitter conversations at http://www.twitter.com/Alcor_tech

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Never Talk to a Customer Service Robot Again Thanks to Digital Genius
Never Talk to a Customer Service Robot Again Thanks to Digital Genius on Disrupt New York '15. Subscribe to TechCrunch today: http://bit.ly/18J0X2e TechCrunch Disrupt is one of the most anticipated technology conferences of the year. From May 4th - May 6th, TechCrunch TV will be airing exclusive coverage from the Manhattan Center in New York City. Disrupt NY has an all new slate of outstanding startups, influential speakers, and celebrity guests.

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Artificial Intelligence (AI) in risk management - IBM, 4th - IR, Microsoft & Google
Imperial College Business School, Trestle Group and the IRM gathered experts from IBM, 4th-IR, Microsoft and Google. - Bernhard Janischowsky (Trestle) - 00:00:05 - Tarun Ramadorai (Imperial) - 00:01:36 - Nicola Crawford (IRM Chair) - 00:04:25 - Raza Sadiq (IRM SIG Chair) - 00:05:47 - Grace Brasington (IBM) - 00:06:50 - Frank Luijckx (4th-IR) - 00:17:45 - Martin Moeller (Microsoft) - 00:26:26 - Matt McNeill (Google) - 00:33:43 - Panel discussion - 00:45:50 Thanks to all the speakers, Imperial, Trestle and to Raza Sadiq, Shiva Keihaninejad, Nousheen Hassan, Markus Krebsz and the IRM ERM in Banking and Finance SIG: www.theirm.org/ermbfs Find out more at: www.theirm.org

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Using AI to Generate Quality Leads and Close More Deals - Kirish

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Top 5 Uses of Neural Networks! (A.I.)
Use my link http://www.audible.com/coldfusion or text coldfusion to 500-500 to get a free book and 30 day free trial. Subscribe here: https://goo.gl/9FS8uF Become a Patron!: https://www.patreon.com/ColdFusion_TV CF Bitcoin address: 13SjyCXPB9o3iN4LitYQ2wYKeqYTShPub8 Hi, welcome to ColdFusion (formerly known as ColdfusTion). Experience the cutting edge of the world around us in a fun relaxed atmosphere. Sources: Let there be Color: http://hi.cs.waseda.ac.jp/~iizuka/projects/colorization/en/ Pixel Enhancing CSI Style: https://arxiv.org/pdf/1702.00783.pdf?xtor=AL-32280680 Generating New Images: https://arxiv.org/pdf/1702.00783.pdf?xtor=AL-32280680 Pix2Pix demo: Image to image DEMO https://affinelayer.com/pixsrv/index.html Lip Reading: https://arxiv.org/abs/1611.01599 Creating a Scene From Scratch: https://arxiv.org/pdf/1612.00005.pdf //Soundtrack// **coming soon** » Google + | http://www.google.com/+coldfustion » Facebook | https://www.facebook.com/ColdFusionTV » My music | http://burnwater.bandcamp.com or » http://www.soundcloud.com/burnwater » https://www.patreon.com/ColdFusion_TV » Collection of music used in videos: https://www.youtube.com/watch?v=YOrJJKW31OA Producer: Dagogo Altraide » Twitter | @ColdFusion_TV

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Deepmind and the Future of the Finance Industry | Matthew Dixon | TEDxIIT
Can Google’s Deepmind predict the future of financial markets? Google has released tensor-flow, an open source software, that provides special types of neural networks that perform well on gene sequencing and speech transcription problems. I show how these networks help us predict the future of financial markets. Matthew Dixon is an Assistant Professor of Finance and Statistics in the Stuart School of Business. His research focuses on the application of advanced computational techniques to financial applications, especially in the areas of algorithmic trading. Matthew's research is currently funded by Intel Corporation and he has been referenced as a computational finance expert in multiple reputed media outlets and trade shows including the Financial Times. Matthew holds a MEng in Civil Engineering from Imperial College London, a MSc in Parallel and Scientific Computation (with distinction) from the University of Reading, and a PhD in Applied Math from Imperial College. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx

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Future Bank Today | Episode 1: Machine Learning
Future Bank Today: Innovations in our Financial Institutions

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Fraud Prevention | AI in Finance
Can AI be used for fraud prevention? Yes! In this video, we'll go over the history of fraud prevention techniques, then talk about some recent AI startups that are helping business reduce credit card fraud. We'll break down what the different AI models that help with fraud prevention look like (decision trees, logistic regression, neural networks) and finally, we'll try it out on a transaction dataset. Code for this video: https://github.com/llSourcell/AI_for_Financial_Data Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More learning resources: https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505541d877 https://www.youtube.com/watch?v=GlV_QO5B2eU https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data https://pythonprogramming.net/python-programming-finance-machine-learning-framework/ https://gist.github.com/yhilpisch/648565d3d5d70663b7dc418db1b81676 https://www.quantopian.com/posts/simple-machine-learning-example Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Sign up for the next course at The School of AI: https://www.theschool.ai And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

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Intro to machine learning on Google Cloud Platform (Google I/O '18)
There are revolutionary changes happening in hardware and software that are democratizing machine learning (ML). Whether you're new to ML or already an expert, Google Cloud Platform has a variety of tools for users. This session will start with the basics: using a pre-trained ML model with a single API call. It'll then look at building and training custom models with TensorFlow and Cloud ML Engine, and will end with a demo of AutoML Vision - a new tool for training a custom image classification model without writing model code. Qwiklabs → https://goo.gle/2YhJz5f Rate this session by signing-in on the I/O website here → https://goo.gl/4n5aYA Watch more GCP sessions from I/O '18 here → https://goo.gl/qw2mR1 See all the sessions from Google I/O '18 here → https://goo.gl/q1Tr8x Subscribe to the Google Cloud Platform channel → https://goo.gl/S0AS51 #io18 event: Google I/O 2018; re_ty: Publish; product: Cloud - General; fullname: Sara Robinson; event: Google I/O 2018;

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AI, Machine Learning and Chatbots Improving Insurance Profitability & CX
From underwriting and customer service to product innovation and customer-centric claims, the race to win the customers’ hearts is tighter than ever and carriers can’t afford to lag on customer-focused innovation. Insurance Nexus talked to MetLife, Chubb and Nationwide about how to prioritize investments and internal resources. Learn which innovations will have the biggest impact on customer experience and improved profitability. If you've enjoyed this webinar, I’d definitely check out the Insurance AI & Analytics USA Summit (June 27 – 28, Chicago), which will be two days packed with discussions like the webinar. http://events.insurancenexus.com/analyticsusa/ An agenda designed to tackle the biggest challenges and opportunities in AI and analytics • From insight to impact: bridge the gap between tech and business needs to achieve growth. Hardwire insights into the core business functions including pricing, marketing, claims and underwriting • Take your AI plans off the ground: build the foundation to deliver future-proof AI and Machine Learning across your organization. Develop a robust business intelligence infrastructure and achieve data integrity and a 360-view of the customer • Discover exactly where and how AI is impacting insurance: from automating the underwriting processes and improving customer experience to delivering a seamless claims experience • Create business efficiencies: discover which processes can be quickly automated to deliver immediate gains to the business. Use new technologies such as chatbots to improve productivity and reduce human errors • Deliver seamless and connected customer experience: transform your marketing, sales, operations and claims to meet and exceed customer expectations

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AI and the future of accountancy
Many argue that more intelligent systems will help accountants to provide more insight and move further up the value chain. More data, better predictions and more intelligent automation will enable accountants to deliver more value, focus on forward-looking analysis and provide greater leadership to businesses. Indeed, in a world of big data, these machine learning technologies will be essential to gaining insight from new and vast sources of data. But there are also many fears that systems will increasingly take over complex decision tasks, leaving little for human accountants to do. While these systems do not replicate human intelligence, increasingly they produce outputs that far exceed the accuracy and consistency of those produced by humans. This is leading to many predictions about the long-term demise of profession such as accountancy. Join Artificial Intelligence expert Professor Moshe Vardi as he discusses the growth of AI and its impact on the accounting profession. If you have questions for the speakers or comments you would like to raise in the live stream, please send them to [email protected] or tweet them to @icaew_itfaculty or #icaewai For more information, visit http://www.icaew.com/techtalk

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Introduction to Google Cloud Machine Learning (Google Cloud Next '17)
Google Cloud is at the forefront of developing cutting-edge machine learning technology. Computer vision, predictive modeling, natural language understanding, and speech recognition are among the most popular business related uses of machine learning so far. In this video, you'll learn how to leverage machine learning for your own business applications. Missed the conference? Watch all the talks here: https://goo.gl/c1Vs3h Watch more talks about Big Data & Machine Learning here: https://goo.gl/OcqI9k

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