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?
Keynote Presentation: How Important will Machine Learning, AI and Deep Learning Applications be for Future Economic Growth?
Discussing the future applications for Machine Learning approaches, and what these may mean for your business. Will it live up to the hype? In what ways will these technological changes impact future economic growth and what would the implications be for every day life? With the rise of Artificial Intelligence what does this mean for… Read more.
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.
Exploring the core principles of Cognitive Search, and understanding the factors of success. What is realistic at this stage of the technology’s maturity, and what groundwork needs to be laid for a successful implementation? Product data, customer data and curated content at the Heart of Cognitive Search. Case study examples discussed.
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.
In what ways will machine learning both support and drive today’s data scientists and advanced analytics leaders to the future. What opportunities does Apache Spark present and in what ways is it broadening access to Machine Learning? Discussing practical use cases for machine learning, from cyber security and fraud detection to recommendation engines.
Machine learning algorithms aren’t new, but the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. With steady advances in digitization and cheap computing power, what tangible opportunities does this present for optimizing your business and processes? Human vs. Machine – How… Read more.
Keynote Presentation: Embedding Machine Learning into business processes at scale using a factory approach
Create and train predictive models using automated techniques and enable business analysts to play a key role in the predictive value chain Deploy and maintain thousands of predictive models across your enterprise and allow your data science team to maintain each at peak performance with a minimum of effort Examine the complementary role automated predictive… Read more.
Discussing the ways in which Machine-based learning is now being put to work in data-driven early warning systems for detecting and preventing corporate fraud and other misconduct at Drinker Biddle & Reath LLP
What exactly is Deep Learning? Differences from traditional Machine Learning; current state of technology How does it work? Technical underpinnings; different types of neural networks What are common use cases? Most common applications of DL How can I jump start DL capability in my organization on a shoe string budget? Open source tools, libraries including… Read more.
Using NLP and non financial indicators to help inform investment decisions. ML and AI based risk assessment to inform responsible investment and lending. Semantic language for developing socially informed investment strategies.
Discussing the ways in which Machine Learning can not only generate accurate predictions and models and the ways in which it can it help to better tap the wealth of expertise, insight and knowledge across an organization and in its data stores. Using Machine Learning algorithms to learn from history predict from streaming data and… Read more.
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.