Artificial Intelligence and Machine Learning will be at the core of DIGILITY 2019

AI , Machine learning

As technologies become more interconnected, and certainly not one can be singled out as the most important, Artificial Intelligence (AI) and Machine Learning (ML) play a significant role in a lot of recent technological developments. Industry leaders, entrepreneurs, researchers and tech visionaries worldwide are working on applying the principles of AI and ML to everyday life and industry needs. It’s therefore natural that AI and ML will be at the core of DIGILTY 2019, the European Keynote event exclusively designated to Digital Reality.

Already, AI specialists like Julie Choi (Global Head of AI Marketing, Intel Corporation), Alison B. Lowndes (Artificial Intelligence DevRel, Nvidia), Dr. Anastassia Lauterbach (Advisory Board Member, Nasdaq), Prof. Dr. Christoph von der Malsburg (Senior Fellow, Frankfurt Institute for Advanced Studies), and Dr. Chris Boos (CEO, Arago) have confirmed their participation in the 2019 DIGILITY conference. They all have contributed greatly to advancements through AI in their various fields and will spark the conversation on AI use cases in the real world.

Julie Choi has been part of DIGILITY 2018 and presented some of the use cases Intel is involved in. She is excited about the speed the development of AI technology has picked up in recent years: “It's still very early in terms of where we are with AI application development. It’s only been two or three years since we were able to identify cats! While there is tremendous potential, we need to really be grounded in AI is being applied. That's why I'm always really excited to share real use cases from fields like medicine, transportation, even saving things like the Great Wall of China.”

In case you are wondering how AI can save the Great Wall of China, among other applications, tune in to Julie’s talk from DIGILITY 2018.  

What does AI do?

Today, AI is used in many different ways including speech recognition, learning, planning, problem solving. Automated assistants like Amazon’s Alexa, Apple’s Siri and Google’s Google Home all use aspects of AI. Photo recognition that tags friends and objects automatically, self-driving cars, spam detection, and those “products you might also like” suggestions - these are all examples of AI at work. Scientific research and medical examinations can be made more efficient and accurate. These are all examples of so-called “narrow AI” which solutions are programmed to carry out specific tasks. In addition to narrow AI, there is naturally also “General AI” which attempts to mimic the human intellect and is capable of learning and performing a variety of different tasks.

What is Machine Learning?

Machine learning (ML) was made possible by the realization by Arthur Samuel in 1959 that it was more efficient to let computers learn for themselves, and then later the internet which enabled access to nearly unlimited data. Essentially, if we make machines intelligent enough, they can do their own learning.

There are two kinds of ML - supervised and unsupervised learning. In supervised learning, computers are fed labeled data and then learn to process unlabled data based on the previously learned examples. Essentially, we are training the machines to be able to make decisions about a foreign data set. Unsupervised learning uses algorithms that scan data looking for categories and patterns that can cluster the data.

The development of Neural Networks - computer systems that mimic the structure of the human brain - goes hand in hand with ML. Other important areas are machine perception which deals with sensory inputs and computer vision which focuses specifically on facial and gesture recognition. And of course, robotics is a field which relies online many aspects of AI and ML.  

And what about Deep Learning?

Deep learning is an expansion of ML that allows for extremely complex and layered networks that process huge amounts of data and have made recent leaps in speech recognition and computer vision possible. The successes of Google’s AlphaZero and AlphaGo in defeating Chess and Go players shows the potential of this technology.

If you are curios what will be the next big leap in AI development and why AI expert Patrick Ehlen from Loop AI thinks it might not be in deep learning after all, watch his chat from DIGILITY 2018.

How can AI be applied to xR?

Looking at the xR technologies that DIGILITY has focused on during the past years, AI also enhances their value, as developer and digital evangelist Michael Ludden from Bose knows well: “Building point clouds in the world to understand your absolute positioning is an application of AI for XR. Everything from adding machine learning powered voice interaction to VR and Augmented Reality so you can control and manipulate the world with your voice to other unique ways like foveated rendering. There's a million ways you can apply machine learning specifically to VR and AR. I think we are at the very beginning of real quality use cases for this.“ Here is Michael’s full talk from DIGILITY 2018.

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Photos from Unsplash: https://unsplash.com/license

Sources and further reading:

https://www.techopedia.com/definition/190/artificial-intelligence-ai

https://www.forbes.com/sites/bernardmarr/2016/12/06/what-is-the-difference-between-artificial-intelligence-and-machine-learning/#360b398a2742

http://jmc.stanford.edu/artificial-intelligence/what-is-ai/index.html

https://medium.com/ai-first-design/what-is-ai-really-5a4a7ceb5008

https://snips.ai/content/intro-to-ai/#ai-symphony

https://tdwi.org/articles/2017/04/04/ai-with-augmented-and-virtual-reality-next-big-thing.aspx

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