Tuesday, March 20, 2018


AI World and AI Trends have produced more than 20 webinars with industry leaders covering most of the major areas of emerging AI business and technology solutions for the enterprise.  Click on the appropriate link below to either register for an upcoming webinar or view our complimentary archived webinars and also receive updates on our upcoming 2018 webinars.

Upcoming Webinars

Artificial Intelligence and the 4th Industrial Revolution – Friday, March 16 – 1:00pm et

Today we stand in front of the 4th and largest wave of the industrial revolution, powered by artificial intelligence and data.  The deep Learning model has taken the tech industry by storm. These models require huge amounts of computing power and the latest developments and applied AI systems are pushing the limits of what current chips can support.  Join us for an executive armchair discussion with BootstrapLabs’ Co-Founder, Ben Levy, one of the most active investor in Applied AI software technologies to learn how software and hardware infrastructures will evolve to support the acceleration and adaption of Deep Learning Models in every industry.

Ben Levy is Co-Founder and Partner, Bootstrap Labs. He is a Husband & Father, AI eats Software believer, Tech Investor, Startup Builder, Biz Dev, Funding, M&A Expert, Windsurfer and Snowboarder. In his previous life he was a Technology, Media, and Telecom Investment Banker who advised startup founders and CxOs of Fortune 500 companies on corporate strategy, financing, and M&A. Ben has helped clients raise over $300M from institutional investors and close over $5B in M&A transactions.

Got Data? The Importance of High-Quality Data for Building Effective Machine Learning-Based Solutions – Wed Apr 4 – 1:00pm et

When it comes to annotating data for academic purposes, there are specific industry standards that are commonly used.  However, when it comes to the commercial sector, building a solution that relies on machine learning requires different data annotation standards.  To build a strong solution that can understand and mimic humans, high-quality, human-annotated training data is key.

In this webinar you will learn:

  • Pros and cons of licensable public data vs. building your own datasets
  • Choices and tradeoffs in the level of effort you invest in acquiring and labeling data
  • Why curated crowds yield higher quality data for your machine learning

Speaker: James Lyle is Director of the Custom Linguistic Solutions team at Appen Inc. After earning his Ph.D. in linguistics at the University of Washington, James joined Microsoft in 1999 and spent more than 14 years working on various natural language technologies, including proofing tools, information extraction, and text analytics. Since joining Appen in 2013, he has focused on providing tech industry clients with linguistic consultation and high-quality annotated data for machine-learned NLU solutions.

Previous Webinars

Augmented Intelligence (AI) systems are gaining traction around voice assistants and chat bots. But, making these systems truly impactful requires more than just responding to simple commands and questions; you have to construct them and train them to have meaningful conversations. The state of the technology is evolving rapidly, making it increasingly likely that you can easily construct such systems. But how you think about such systems matters.  In this webinar, Rob High outlines some of the key principles that you need to consider when constructing a conversational agent that engages your clients to really serve their needs.

Speaker: Rob High is one of the world’s leaders in artificial intelligence. He is the Technology Strategist for Watson and Cloud Platform CTO, IBM Fellow and VP, Member, IBM Academy of Technology for IBM Watson.  High leads the IBM team responsible for bringing cognitive computing to the world, supporting the development of deep natural language processing and other cognitive computing capabilities in the areas of speech, language, vision, and reasoning.

Nothing was more talked about in 2017 than the coming age of Artificial Intelligence. As we enter 2018, the race is on between leaders and laggards, leaders – if not already – will establish and make progress on AI initiatives in 2018. In this informative webinar, Paul Hahn, Analytics and AI Marketing Manager for Cray Inc., will be covering five key considerations for business leaders looking to “roll up their sleeves and getting started with AI” in 2018.

Computing for AI is undergoing a major shift, from a cloud-centered paradigm to a more edge-centric one. Edge computing makes low power consumption of devices necessary. In addition, data centers that host cloud servers consume enormous amounts of electricity. Therefore, development of AI hardware that can reduce power consumption both for the cloud and at the edge is pressingly needed. This webinar will pinpoint which component of AI hardware is the most power hungry, and introduce hardware solutions that can effectively reduce its power consumption.

This webinar looks ahead to 2018 and developments we might see in the development and application of artificial intelligence and machine learning. With the pace of change so rapid in this area, predictions may be especially hard, but we will look at overall trends and pinpoint some areas of expected innovation.

We will also examine the use cases we’re seeing now and expect to see in 2018. And while it’s very still very early days in the evolution of AI/ML but we will also look at areas of potential caution as the technology and market for it evolves.

While research continues on artificial general intelligence, the more proven technology of weak or narrow AI, which includes machine learning and deep learning, is creating real business value today.  In this webinar, Jai Malhotra, CTO and co-founder of Kogentix, one of the leaders in the emerging category of machine learning automation software, will offer specific guidance on how to build modern business applications utilizing machine learning applied to big data.   He will also show use case examples enabling the audience to envision how their organizations can benefit from practical AI. What does this mean for the AI community? We now have the ability to propel AI forward faster, make it more scalable for the enterprise and enable it to become more accessible for all.

The explosion of narrowly focused, highly specialized cognitive engines combined with ever-increasing sources of unstructured data will require novel approaches to fully realize the future of artificial intelligence. The best business and technology strategies to accelerate AI deployment and ROI, applicable to virtually any industry or organization, will include the following key components:

  • Aggregation of specialized cognitive engines in a diverse ecosystem
  • Simplicity of access and use for end users via SaaS platforms
  • Continuously learning orchestration systems
  • A spectrum of deployment from centralized (cloud) to highly decentralized (Edge/IoT)
  • The melding of hardware and software
  • Engines building engines

What does this mean for the AI community? We now have the ability to propel AI forward faster, make it more scalable for the enterprise and enable it to become more accessible for all.

In recent years, robotics, AI, augmented reality/virtual reality (AR/VR), and Internet of Things (IoT) technologies have grown by leaps and bounds. To date, many of the underlying enabling computational advancements have been software-based. In order for these technologies to develop further, advances in hardware chips will become necessary. In this webinar, we will begin by providing an overview of important performance measures for hardware chips for these different end applications.  We will also discuss some of the emerging hardware computing paradigms, key players in this space, and the outlook for the future.

This webinar will dive into insights from Forrester’s “TechRadar™: Artificial Intelligence Technologies and Solutions, Q1 2017” report. Attendees will learn about the 12 most prominent artificial intelligence (AI) technologies customer insights (CI) professionals are using to help them understand and interact with their customers. Firms considering investing in AI will learn about the current level of sophistication of each technology and how much business value they are expected to add. Additionally, the webinar will highlight key lessons from early adopters of these disruptive technologies. In this webinar, we’ll cover how to:

  • Embrace new data sources and analytical techniques for AI
  • Apply a framework for identifying the right customer analytics projects
  • Learn how to change your organization to give CI a greater impact

The Cognitive Computing Consortium (CCC) is producing a one day conference at AI World on December 13, 2017 entitled “Directions in Cognitive“.  As part of the Consortium’s leadership webinar program, CCC chair Hadley Reynolds interviewed AI World conference founder and chair and executive editor of AI TrendsEliot Weinman.

Artificial intelligence has become a critical driver for best-in-class predictive analytics and decision making. According to Gartner Inc, advanced machine learning, AI and data science will be among the top new technologies to shape business trends in 2017 and beyond as cited in Gartner’s Top 10 Strategic Technology Trends for 2017. In this webinar, we provide an overview of machine learning business applications in various industries to help you discover possible use cases and define an AI strategy. We will also explain why deep learning is currently the most promising technology in predictive analytics. You will learn:

  • The power of machine learning and deep learning on the basis of various business cases
  • The pros and cons of building custom-tailored solutions, buying a packaged application and outsourcing to a trusted partner
  • How to start developing smart AI solutions

Presenter: Karthik Lalithraj, Principle Solutions Architect, Kinetica Artificial intelligence’s promise is to change how we work and live. With cognitive applications in healthcare, retail, financial services, manufacturing, and transportation, AI is already transforming industries, saving lives, and delivering efficiencies. But deploying AI solutions isn’t easy. Do you optimize for compute, throughput, power, or cost? How do you manage the data? For the various AI frameworks like TensorFlow, Caffe, Torch, would more and faster training of the models be beneficial? What if you could run AI and BI workloads on one platform and deliver faster and better analytics? Karthik Lalithraj explains how a GPU-accelerated database helps you deploy an easy-to-use, scalable, cost-effective, and future-proof AI solution that enables data science teams to develop, test, and train simulations and algorithms while making them directly available on the same systems used by end users. Topics include:

  • The characteristics of AI workloads and requirements for productionizing AI models: Compute, throughput, data management, interoperability, security, elasticity, and usability
  • Considerations for architecting AI pipelines: Data generation (data prep and feature extraction), model training, and model serving
  • How a modern GPU-accelerated database with in-database analytics delivers the ease-of-use, scale, and speed to deploy AI models and libraries such as TensorFlow, Caffe, and Torch pervasively across the enterprise—and allows you to converge AI with BI and more quickly deliver results

Presenter: William Meisel, President, TMA Associates Today, companies have the option to communicate with their customers in human language—“natural language”—using text or speech. The maturing of this branch of AI allows cost-effective mass communication and interaction with customers to answer questions, describe your products and services, and even close sales. This conversational interaction can occur in calls to contact centers, through chatbots on web sites, through messaging services, through home devices, through the general personal assistants—a rapidly evolving number of channels. How can a company take advantage of this opportunity? This webinar will outline the range of options a company has in creating intelligent assistants and bots, both for specific channels and as a branded, independent company digital assistant. It will address the types of resources available to do so and variations in how companies can use those resources.

Modern AI (aka #MachineIntelligence) is changing the way people work and interact with growing acceptance of AI-powered applications for analytics, marketing & sales automation, customer service, human resources, risk intelligence, and much more where user experience is also shaped by AI. VC money is flowing into AI startups but the bar has now raised by established players who have increasingly acquired talent and open sourced software to protect vested interests including bloated organizations and software stacks. In this webinar Steve Ardire, AI startup advisor, will share experiences and lessons learned on how AI startups with terrific product/market fit and smart tactical execution can compete and win against status quo players. Presenter: Steve Ardire – @sardire – software startup advisor

Strategic Directions for Enterprise Artificial Intelligence – February 23, 2017 This webinar is based on a special executive report authored by Mark Bünger who served as conference chair of AI World 2016. The primary mission of AI World is to focus on the state of the practice of AI in the enterprise. This webinar reports on the findings of the event. For most of its existence, the field of artificial intelligence (AI) was confined to academic and highly technical pursuits, but in 2016, AI became the next business imperative. Thanks to the rise of a non-technical AI community, software and services bringing AI to enterprise needs, and enterprise software vendors shifting to “AI-first” from “mobile first” strategies, corporate users now have real offerings to choose from and real decisions to make, like:

  • Which vendor should I choose, for what enterprise needs?
  • How will my industry be affected?
  • What should my company’s strategy be?

Presenter: Mark Bünger, VP of Research, Lux Research 

 January 26, 2017 Turning hype into action for emerging technologies like conversational AI, chatbots, machine learning, and natural language can be confusing and intimidating, but it doesn’t have to be. We will tell you what other vendors won’t to help you break through the noise and get you on your way to successfully leading your company into the conversational era. Presenter: Lindsay Sanchez, Bots evangelist, Kore, Head of Strategic Operations & Chief Marketing Officer, Kore, Inc.

October 6 The AI market may be enabled by technology, but ultimately it is a more practical focus on use cases that will define the business of AI.  Based on Tractica’s comprehensive analysis of more than 300 potential use cases, this webinar will provide details and insights on the top 15 use cases, in terms of revenue potential during the next decade, that represent the industry’s most promising areas for implementation and growth.  The discussion will include real-world case study examples, an assessment of which industry players are pursuing specific applications, and a quantification of the market opportunity for each of the top use cases. Presenters: Clint Wheelock, Managing Director, Tractica and Aditya Kaul, Research Director, Tractica

September 29 Presenter: Caroline Gabriel, Research Director & Co-Founder, Rethink Technology Research Artificial intelligence, machine learning and predictive analytics are poised to transform processes and costs for businesses worldwide. Studies show they will deliver trillions of dollars of new value, yet 60% of companies say they lack the knowledge to implement AI. This webinar will address that issue. It will provide the highlights of a brand new report, ‘Enterprise AI Adoption: Commercial Impact of AI and Machine Learning on Vertical Industries’.  This webinar will provide decision makers with a concise, digestible guide to the essential issues of deploying AI for business impact.

Sept 1, 2016 Dr. Bill Meisel, author of the recently published report: Specialized Digital Assistants and Bots: Vendor Guide and Market Study (available thru AI Trends here) presents this webinar based on the 256 page report covering over 170 Intelligent Assistants & Bots. AI through intelligent assistants and Bots will play an integral role that all companies and app developers must address: providing a natural language interface with customers. General personal assistants that were initially developed for mobile phones are now expanding to homes, cars, and the desktop, have driven the awareness that an individual can ask for what they want in human language. And now, leading messaging services are letting you text outside applications—“bots”—to connect with a company or service.

August 23, 2016 AI World and deepsense.io are pleased to present a webinar focused on the state-of-the-practice in machine learning in the enterprise. Attendees will learn how advanced machine learning and data science technologies are being used to build competitive advantage, drive new business opportunities and accelerate innovation efforts. Michał Iwanowski will introduce attendees to specific machine learning use cases that will help you to understand how machine learning can save your company money by cutting costs, automate business processes and develop new business solutions for problems that previously seemed unsolvable. No programming or statistic knowledge is needed. Presented by Michal Iwanowski, Product Director, deepsense.io

July 20, 2016 AI has relied on energy-hogging fast processors and large datasets for training neural networks – both of which presupposed centralized computing architectures. But today, more powerful chips are letting AI escape from centralized, cloud-based systems and move out to devices at the edge of the network. Among incumbents, Intel spent a whopping $16.7 billion on AI chipmaker Altera; Google is developing an AI chip called Tensor and working with Movidius to put AI on a USB stick; and Nvidia has dropped $2 billion so far on its Tesla graphics chip for machine vision and other AI tasks.

May 2016 AI has long been locked up in the lab, but with enterprise vendors and startups now incorporating it into their offerings, it’s entering industry in more and more ways. Some applications, like predictive maintenance or demand forecasting, are common across nearly all companies, and increasingly a proven business case. Others like route optimization in transportation and logistics, or predicting drug interactions in medicine, are highly specific to one industry – and likely to drive competitive differentiation. Lux Research VP Mark Bünger will describe the shared and specific ecosystems in these and a variety of other fields, explaining where they fit, what matters, who to watch, and what will happen next.