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As the buzz around Artificial Intelligence has increased, so have the issues around trust. There is an increasing polarisation in the discourse around AI, ADS and automation. So what can you do as a tech leader or employee in a company utilising tech, to build trust? Or much more to the point, what can you do to become trustworthy?
An important step is to communicate honestly with your customers and stakeholders about the technology you use. However too often organisations fall at the first hurdle due to the damaging visual misrepresentations of AI that accompany their written communications, promotional material or media coverage. This session investigates the way that organisations and the media represent AI through images, and what this says about what they and customers expect and understand about the benefits of technology. We look at how this exposes the broader implications of anthropomorphising technology, and how to deal with describing the role of humans and human agency in your solutions.
As well as exploring how public perceptions of AI will shape the risks and opportunities faced by your organisation, you will leave this session with some practical suggestions on earning and building trust in your AI solutions, and helping to imagine a more positive AI future.
Cybernetix is not a new discipline. However, it appears being more important than ever before. It is ubiquitous when it comes to AI (Artificial Intelligence). And when AI meets industrial IoT (Internet of Things) and OT (Operational Technology) , it is about the cybernetic model. In contrast to past times, it is about hundreds of signals per second per thing, device, and machine, which needs to be processed and used for optimization. No way doing so without AI.
When looking at public infrastructures such as the ones making connected vehicles drive without (too many, too severe) accidents, when it comes to smart services (cities, utilities,…), it all is about immense amounts of signals that need to be delivered (5G) and stored (blockchains and beyond, e.g. IOTA), and processed (AI again).
All with security in mind, in the context of users and their settings, their consent, their preferences.
Fast and efficient, without too much of latency.
Cybernetix brings together all these technical evolutions that are discussed today.
In his talk, Martin Kuppinger will look at how all this is connected and why we need taking a broader perspective, beyond single innovations, towards what makes the modern world move: Cybernetix.
So far, AI relies totally on human intelligence, in the form of human-written programs in classical AI or the human-provided sample data of deep learning. The pursuit of AI over the last five decades has been caught within a fixed conceptual framework. Given the current level of tremendous attention, investment, technological infrastructure and application potential, maybe we are just a simple fundamental change in perspective away from a tremendous technological explosion.
Over the last year, an unprecedented scale of digital transformation has resulted in exponential growth of organisational data, which could impact decision making. Using machine learning approaches to mine and reason through masses of data is ineffective. In this session you will learn that while the first wave of AI involved many narrow applications, the next wave will help generate a dynamic understanding of relationships and patterns in a corpus of information. This understanding primarily happens through explainable AI. It will become a key part of enterprise digital transformation initiatives that fundamentally change how organizations make sense of real-world information.
Congratulations. Your AI business case is crisp; you already have a data strategy in place; your proof-of-concept looks and feels great; you have the right talent to build the AI product or service which will push your organisation directly into the digital age. Sounds familiar? It is at this stage where most organisations give up on the AI initiatives due to lack of value creation. Why is that, one might ask? The business case was already locked, among other aspects, where's the problem at? One word: Production. AI products and services are notoriously different in terms of production than any other SW, and traditional workflows do not work anymore. This is what we are going to talk about. I will share my vision, blue print and personal stories across telecom, manufacturing and automotive industries, including both corporate and start-up experiences. I will tell you how to go from a shiny proof-of-concept to AI production systems, what challenges we faced, and the best practices to avoid the pitfalls.
Key takeaways: 1) Vision and strategy to create value out of AI - The last mile
2) How to go from a shiny proof-of-concept to AI production systems
3) AI production challenges and pitfalls
4) Multi-industry use cases across corporate and start-up ecosystems