A Cadet's Introduction to Science Fiction
‘The minute I was bored with a book or a subject I moved to another one, instead of giving up on reading altogether… The trick is to be bored with a specific book, rather than with the act of reading.’ – Nassim Nicholas Taleb
Are we ready for machines to learn and make decisions for us?
LTCOL Jasmin Diab uses the example of her recent work in supporting the International Atomic Energy Agency to ask us to consider the future of machine learning and its ability to support military decision making.
Balancing the Science and Art of Warfare
As technological advances increasing automate the control of weapons, it is timely to review the skills we need in our warfare professionals. Their core skills will increasingly be maintaining SA and making decisions in confusing and evolving circumstances. We need to ensure the ‘science’ and ‘art’ of warfare are balanced.
Augmented Reality and the Future of Learning and Business
Our interaction with the technological world today is changing rapidly. We are no longer limited by screens or even reality as we knew it. David Rapien walks us through the history and differences of Virtual Reality and Augmented Reality and looks towards the future options of these technologies in life, business and education.
How Biased Minds can be the Key to Unbiased AI Systems
The TED Talk examines the origins of Cognitive Bias, its advantages, drawbacks and challenges with Biased AI. The solution might lie within ourselves.
The Future is here! 5th Generation Air Force
The F-35 heralds a revolution in how the ADF will fight, as units learn to integrate with 5th Generation technologies and operate as a network, leveraging stealth and information fusion. This article explores the ‘night versus day’ change, which opens the door for related opportunities including man-machine teaming and the ‘loyal wingman’ concept.
Is AI Really a Threat to Humanity?
This article comments on the impact of Cognitive Bias, found in AI systems, on our future. It provides the examples of the biased systems and asks the fundamental questions on our strategy going forward. The article is suitable for all the levels of JMPE continuum, and will be of interest to those particularly interested in Cognitive computing or Artificial Intelligence.
Catalogue of Technology, Knowledge and Concepts by Best-Selling Author Ray Kurzweil
Kurzweil Network is a small format digest — featuring hand-picked, specially curated stories and resources. This website is also home to the permanent collection of writings and commentary by Ray Kurzweil. It follows progress in the science and technology landscape, with topics including biology, nanotech, materials science, electronics, computation, artificial intelligence, robotics, web, pattern recognition, virtual reality, and prosthetics + body augmentation.
A Beginner's Guide to Artificial Intelligence, Machine Learning, and Cognitive Computing
This article explores some of the important aspects of AI and its subfields, including a brief history of its development. It defines key AI terminology and language and set a useful start-state for further targeted study.
AI-Based Virtual Tutors – The Future of Education?
“This blog post is about the UC Berkeley Virtual Tutor project and the speech recognition technologies that were tested as part of that effort. We share best practices for machine learning and artificial intelligence techniques in selecting models and engineering training data for speech and image recognition.
Glimpse: How Electronic Tattoos Will Change The World — And Ourselves
Scientists have developed a means to 3D print electronics onto the skin as a form of wearable technology akin to a tattoo. It is proposed that further development of this technology will allow these tattoos to monitor our vitals, and feed us personalized health advice in real time.
Artificial intelligence system uses transparent, human-like reasoning to solve problems
This article describes a method by which a computer can recognise objects using Transparency by Design Network (TbD-Net) developed at the MIT Lincoln Laboratory. Researchers have used human-like reasoning to develop an algorithm which they claim can outperform other visual recognition software and algorithms because humans can view its reasoning process to determine where and how it is making mistakes.