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3rd Annual Global Artificial Intelligence Conference - Webinar - Online Warm-Up (Free)

Global Big Data Conference

Thursday, January 17, 2019 from 1:00 PM to 2:15 PM (PST)

3rd Annual Global Artificial Intelligence Conference -...

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Type End Quantity
Webinar Ticket   more info Jan 25, 2019 Free  

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Event Details

We are very excited to organize 3rd Annual Global Artificial Intelligence Conference this month! As we get closer to the conference, we want to invite you to participate in Global Artificial Intelligence Conference  Webinar - Online Warm-Up.

We will features 5 speakers from our upcoming 3rd Annual Global Artificial Intelligence Conference  in Santa Clara each of which will present a 10 minute sessions including:

Schedule:
1:00PM-1:15PM Keynote:  Lessons from Google's Journey to AI-First (Ron Bodkin, Technical Director for Applied AI, Google)

1:15PM-1:25PM Workshop: Bringing Your Machine Learning and Deep Learning Algorithms to Life: From Experiments to Production Use (Nisha Talagala, CTO, ParallelIM)

1:25PM-1:35PM Technical Track: Improving Customer Support with Deep Learning and NLU (Sami Ghoche, Founder, Forethought)

1:35PM-1:45PM Hackathon - Build decentralized applications for IoT, AI@Edge, Blockchain apps to run on our decentralized cloud Geeta Chauhan : Geeta Chauhan (Chief Technology Officer, DeepCloudAI )

1:45PM-1:55PM Business Track: Machine Learning vs. Feature Engineering: What should the Focus be in Attempting to Predict Customer Behaviour  (Richard Boire, CEO, Boire Filler Group)

1:55PM-2:15PM Q&A

Dr KRS Murthy (CEO, I Cubed World) - Moderator

Complete Agenda Details:

Keynote: Lessons from Google's Journey to AI-First (Ron Bodkin, Technical Director for Applied AI, Google)
Abstract
Learn about how ‘deep learning’ is a fundamental shift in the way software is written, the tasks it can perform and how it is deployed for business value. In this talk, Ron Bodkin will show how Deep Learning AI capabilities power many of Google's key decisions, a large digital business growing at double digit rates. This stack is the secret sauce for improving digital marketing, customer care, manufacturing operations, medical diagnosis and more. Hear about the industry disruption from new business processes powered by software 2.0, ranging from marketing and advertising, customer care, and operations. Learn about recent advances in the field and industry applications.

Profile
Ron is Technical Director for Applied Artificial Intelligence team in Google’s Cloud CTO office. Ron leads AI experiments and collaborates with Google Product and Engineering for AI products and engages deeply with Global F500 enterprises to unlock strategic value with AI. Previously, Ron was the founding CEO of Think Big Analytics. Think Big provides end to end support for enterprise Big Data including data science, data engineering, advisory and managed services and frameworks such as Kylo for enterprise data lakes. Think Big was acquired by Teradata in 2014 and was the leading pure play big data services firm. At Teradata, Ron led Think Big to grow globally, to develop the Kylo open source data lake framework, and to expand to architecture consulting. In 2017 Ron led the creation of an Artificial Intelligence incubator at Teradata. Previously, Ron was VP Engineering at Quantcast where he led the data science and engineer teams that pioneered the use of Hadoop and NoSQL for batch and real-time decision making including lookalike models. Prior to that, Ron was Founder of New Aspects, which provided enterprise consulting for Aspect-Oriented Programming. Ron was also Co-Founder and CTO of B2B applications provider C-Bridge, which he led to team of 900 people and a successful IPO. Ron graduated with honors from McGill University with a B.S. in Math and Computer Science. Ron also has a Master’s Degree in Computer Science from MIT, leaving the PhD program after presenting the idea for C-bridge and placing in the finals of the $100k Entrepreneurship Contest.

Workshop Preview : Bringing Your Machine Learning and Deep Learning Algorithms to Life: From Experiments to Production Use (Nisha Talagala, CTO, ParallelIM)
Abstract:
In this hands on workshop, attendees will learn how to take Machine Learning and Deep Learning programs into a production use case and manage the full production lifecycle. This workshop is targeted for data scientists, with some basic knowledge of Machine Learning and/or Deep Learning algorithms, who would like to learn how to bring their promising experimental results on ML and DL algorithms into production success. In the first half of the workshop, attendees will learn how to develop an ML algorithm in a Jupyter notebook and transition this algorithm into an automated production scoring environment using Apache Spark. The audience will then learn how to diagnose production scenarios for their application (for example, data and model drift) and optimize their ML performance further using retraining. In the second half of the workshop, users will perform a similar exercise for Deep Learning. They will learn how to experiment with Convolutional Neural Network algorithms in TensorFlow and then deploy their chosen algorithm into production use. They will learn how to monitor the behavior of Deep Learning algorithms in production and approaches to optimizing production DL behavior via retraining and transfer learning.

Attendees should have basic knowledge of ML and DL algorithm types. Deep mathematical knowledge of algorithm internals is not required. All experiments will use Python. Environments will be provided in Azure for hands on use by all attendees. Each attendee will receive an account for use during the workshop and access to the notebook environments, Spark and TensorFlow engines, as well as an ML lifecycle management environment. For the ML experiments, sample algorithms and public data sets will be provided for Anomaly Detection and Classification. For the DL experiments, sample algorithms and public data sets will be provided for Image Classification and Text Recognition.
 
Bio
Nisha Talagala is Co-founder, CTO/VP of Engineering at ParallelM, a startup focused on Production Machine Learning. As Fellow at SanDisk and Fellow/Lead Architect at Fusion-io, she led advanced technology development in Non-Volatile Memory and applications. Nisha has more than 15 years of expertise in software, distributed systems, machine learning, persistent memory, and flash. Nisha was also technology lead for server flash at Intel - where she led persistent memory/flash technology and storage-memory convergence. Nisha was also the CTO of Gear6, where she designed clustered computing caches for HPC. Nisha also served at Sun Microsystems, where she developed I/O solutions and file system optimization. Nisha earned her PhD at UC Berkeley on distributed systems research. Nisha holds 54 patents in distributed systems, networking, storage, performance and non-volatile memory. Nisha is a frequent speaker at both industry and academic conferences and serves on multiple technical conference program committees.

Technical Track: Improving Customer Support with Deep Learning and NLU (Sami Ghoche, Founder, Forethought)
Abstract
Almost every company that grows beyond a certain size will need to hire customer support representatives to interface with its customers and answer their questions. Unfortunately, information at large enterprises is badly organized, difficult to retrieve, and largely a yet untapped resource. What's more, the expertise accumulated by support reps disappears with them when they move on from their jobs. Forethought is the proud maker of Agatha Answers, an information-retrieval and question answering tool that embeds into your favorite helpdesk, to help supercharge your customer support agents. In this talk, we will go over an overview of where traditional search and NLP techniques are today, then we'll go over some of the recent advances in deep learning for question answering and document understanding, and some of the cutting edge research in those areas that's happening at Forethought today.

Bio
Sami Ghoche is a Co-founder in Forethought AI. Prior to joining Forethought AI, Sami worked on the Feed AI team at LinkedIn and completed his undergraduate and graduate studies at Harvard, where he wrote his thesis on algorithms to speed up unsupervised learning methods.

Hackathon : Build decentralized applications for IoT, AI@Edge, Blockchain apps to run on our decentralized cloud  (Geeta Chauhan, Chief Technology Officer, DeepCloudAI)
Bio
Geeta Chauhan is a Consulting CTO at SVSG with 20+ years of experience building new products, leading diverse global teams, scaling and architecting complex distributed systems for companies ranging from nimble startups to Fortune 500s. These days you can find her in the hallways of AI startups building Deep Learning based platforms or tinkering in her garage to convert her electric car to run autonomously. Prior to this, she was the CTO of Data Platforms at Alcatel-Lucent and led the Advanced Technology incubator for Genesys Labs. She is passionate about sustainability and mentors’ startups at Clean Tech Open the largest clean technology accelerator in the world.

Business Track: Machine Learning vs. Feature Engineering: What should the Focus be in Attempting to Predict Customer Behaviour  (Richard Boire, CEO, Boire Filler Group)
Abstract
The use of machine learning is a common theme in organizations today, yet most people still struggle with its definition given its many different levels. In this session, we attempt to eliminate this confusion by exploring a number of machine learning algorithms ranging from the simple to the more complex. We observe the use of these algorithms across a variety of industries as well as different behaviours such as customer response and customer risk. Alongside the comparison of machine learning algorithms, we also look at the impact of the data and how feature engineering impacts a given solution.

Bio:
Richard Boire's experience in database marketing and predictive analytics dates back to 1983, when he received an MBA from Concordia University in Finance and Statistics. His initial experience at organizations such as Reader’s Digest and American Express allowed him to become a pioneer in the application of predictive modelling technology for all direct marketing programs. This extended to the introduction of models which targeted the acquisition of new customers based on return on investment. With this experience, Richard formed his own consulting company back in 1994 which is now called the Boire Filler Group, a Canadian leader in offering analytical and database services to companies seeking solutions to their existing predictive analytics or database marketing challenges. Richard is a recognized authority on predictive analytics and is among a very few, select top five experts in this field in Canada, with expertise and knowledge that is difficult, if not impossible to replicate in Canada. This expertise has evolved into international speaking assignments and workshop seminars in the U.S. , England, Eastern Europe, and Southeast Asia. Within Canada, he gives seminars on segmentation and predictive analytics for such organizations as Canadian Marketing Association (CMA), Direct Marketing News,Direct Marketing Association Toronto, Association for Advanced Relationship Marketing(AARM.) and Predictive Analytics World(PAW). His written articles have appeared in numerous Canadian publications such as Direct Marketing News, Strategy Magazine, and Marketing Magazine. He has taught applied statistics,data mining and database marketing at a variety of institutions across Canada which include University of Toronto, George Brown College,Seneca College, and currently Centennial College. Richard was Chair at the CMA’s Customer Insight and Analytics Committee and sat on the CMA’s Board of Directors from 2009-2012. . He has chaired numerous full day conferences on behalf of the CMA(the 2000 Database and Technology Seminar as well as the 2002 Database and Technology Seminar and the first-ever Customer Profitability Conference in 2005. He has most recently chaired the Predictive Analytics World conferences in both 2013 and 2014 which were held in Toronto.. He has co-authored white papers on the following topics: ‘Best Practices in Data Mining’ as well as ‘Customer Profitability: The State of Evolution among Canadian Companies’. In Oct. of 2014, his new book on “Data Mining for Managers-How to use Data(Big and Small) to Solve Business Problems” was published by Palgrave Macmillian

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When & Where


Santa Clara Convention Center
5001 Great America Parkway
Santa Clara, CA 95054

Thursday, January 17, 2019 from 1:00 PM to 2:15 PM (PST)


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Global Big Data Conference

Organizer of the Global Career Fair - The Largest AI & Data Science Career Fair of 2019(FREE)

Organizer of the Global Artificial Intelligence Conference - The Largest AI Conference of 2019

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