Machine Learning, Deep Learning and Artificial Intelligence for Strategic Innovation
Artificial Intelligence (AI) in its multiple forms and guises, has huge potential to change the ways in which we live and work . As we all know, AI, Machine Learning (ML) and Deep Learning(DL) are not new. However, there has been huge investment in the space in recent years and the ability to automatically apply complex mathematical calculations to Big Data – over and over, faster and faster – is a fairly recent development. With steady advances in digitisation and cheap computing power, no wonder people are excited about the possibilities.
Join us for this Post-Conference Focus Day and hear from companies leading the way in this space. Learn about how they are using ML, DL and AI in practical ways within their businesses, to improve internal efficiencies, drive profits, better serve their customers, fight crime and even save lives.
Examining the market and technological and theological advances that have occurred and sparked renewed interest in this space. What is the difference between Artificial Intelligence, Machine Learning, and Deep Learning? Exploring the origins and current ecosystems. What are the opportunities and risks for using Machine Learning within your business and our everyday lives?Speaking
Keynote Presentation: Cognitive Computing, Machine Learning, and applied AI for Improving Humanity, Innovating products, and creating efficiencies
Is it Cognitive? Why human behavior is basis for all strategic activities Addiction Treatment Fraud: ‘bad’ behavior in ‘good recovery’ industry Mapping business challenges and indicators to data science, AI, swarm intelligence, and Cognitive Computing Steps, and results, the discipline of technology can accomplishSpeaking
Is Machine Learning/AI/Cognitive just a passing fad, or is it here to stay? Should you invest in these new technologies and approaches or not? What is the value, and is it tangible and quantifiable? Constantly evolving and learning – exploring the ways in which Machine learning can be used to process huge amounts of unstructured… Read more.Speaking
Prominent Use Cases – What is the low hanging fruit Challenges to implementation Return on InvestmentSpeaking
Organizations are finding an unlimited number of use cases leveraging image analysis, speech recognition, natural language, and geospatial machine learning capabilities. Explore how using time-series data and the cloud can reduce inefficiencies, avoid costly downtime, and generate new revenue. Discuss the practical application of machine learning and how it can revolutionize how you run your business… Read more.Speaking
Feed personalization has become a mainstay of the modern web. Reddit is special in that it doesn’t just have a pile of content that needs to be categorized, it has an engaged active user base which also actively curates the content into Communities (née “subreddits”). This additional metadata provides challenges and opportunities for feed customization…. Read more.Speaking
Background: The role of AI in the future of content creation has received considerable attention recently but the focus has been primarily on content in the context of the arts and entertainment. AI can play a critical role, however, in highly specialized editorial content generation as well. What do we mean by specialized content: a… Read more.Speaking
Discussing approaches for early fraud and misconduct detection – is there a way to stop it before it happens? Examining algorithms that serve as early warning systems predicting and detecting corporate misconduct, What interesting legal and ethical questions do such systems raise?Speaking
Keynote Presentation: Machine Learning for a nimble way to develop services and better serve your customers
Exploring the ways in which leading companies are utilizing Machine Learning and Deep Learning techniques to better serve and understand customers… With the expansion of the IoT and the wealth of data it creates, what are the opportunities for your business and what are the potential benefits for your customers? Keeping up with demand –… Read more.Speaking
It took 20 years to develop the first ever vaccine against dengue disease. What are the obstacles in exploring the wealth of data generated over time? Is AI hype or can it really be a tool to augment researchers’ knowledge of the field? How? What may need to change? How could AI be a force… Read more.Speaking
Best practices in choosing, designing, and implementing from the portfolio of analytical methods including real-time, geospatial, machine learning, statistical, and “big data” Discussing ways to operationalize the results from advanced analytics and models and put them in to action? How do we integrate Internet of Things, pervasive sensing, and the cloud into my companies existing… Read more.Speaking