Presenter: Michal Tadeusiak, Senior Data Scientist, Deepsense.ai
Wednesday, August 30th at 10AM PT/1PM ET
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 will 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
Michal is a Senior Data Scientist at deepsense.ai. He holds a joint master’s degree in Complex Systems Science from the University of Warwick and the École Polytechnique. He has experience in data analysis in various disciplines, spanning from medical signal analysis to threat detections in network traffic. He won the AAIA’16 Data Mining Challenge and came in 5th in Kaggle’s What’s Cooking competition.
Previous Webinars Now Available for Download
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.
- 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.
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
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.
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.