Does Consciousness Influence Quantum Mechanics?

It’s not surprising that the profound weirdness of the quantum world has inspired some outlandish explanations – nor that these have strayed into the realm of what we might call mysticism. One particularly pervasive notion is the idea that consciousness can directly influence quantum systems – and so influence reality. Today we’re going to see where this idea comes from, and whether quantum theory really supports it.

The behavior of the quantum world is beyond weird. Objects being in multiple places at once, communicating faster than light, or simultaneously experiencing multiple entire timelines … that then talk to each other. The rules governing the tiny quantum world of atoms and photons seem alien. And yet we have a set of rules that give us incredible power in predicting the behavior of quantum system – rules encapsulated in the mathematics of quantum mechanics. Despite its stunning success, we’re now nearly a century past the foundation of quantum mechanics and physicists are still debating how to interpret its equations and the weirdness they represent.

Quantum Impact: Computing a more sustainable future (Ep. 1)

While quantum computing may seem like the next frontier, its foundations have actually been around for billions of years—in the natural world. This episode of Quantum Impact explores the ways in which we can tap into nature’s organic systems and processes to help solve some of today’s most pressing issues around climate change and environmental sustainability. Join Dr. Julie Love, senior director of quantum business development at Microsoft, and Lucas Joppa, Microsoft’s chief environmental officer, as they discuss the complex problem of land use optimization, one environmental challenge that can be addressed through quantum solutions.

Deep Learning In 5 Minutes

This video on “What is Deep Learning” provides a fun and simple introduction to its concepts. We learn about where Deep Learning is implemented and move on to how it is different from machine learning and artificial intelligence. We will also look at what neural networks are and how they are trained to recognize digits written by hand. We further look at some popular applications of Deep Learning. So, let’s dive into the world of Deep Learning with this video.

Introducing Quantum Impact (Ep. 0)

From early cancer detection to fighting climate change, new advances in quantum computing are inspiring solutions to some of the world’s most pressing issues. Explore the possibilities in our newest series, Quantum Impact, hosted by Dr. Julie Love and Dr. Krysta Svore of Microsoft

EU to unveil proposed regulations for artificial intelligence

The European Union is set to unveil its proposed regulations for artificial intelligence (AI). It is part of a plan to challenge the United States and China’s dominance in the sector. That includes committing billions of dollars in public and private funds to advance the science behind AI.

Part of that investment will focus on bringing data storage back from other areas outside the EU – in particular the US.

And one of the main issues it is focusing on is data privacy. The European Commission is particularly concerned as technologies like smart home systems and facial recognition become more widespread.

Mark Coeckelbergh, a member of the High-Level Expert Group on Artificial Intelligence for the European Commission, talks to Al Jazeera about the development.

What’s the Distinction Between Machine Learning & Artificial Intelligence?

Learn about the distinction between artificial intelligence and machine learning in this short Q&A with Dr. Nicko van Someren, CTO at Absolute. What is artificial intelligence? Artificial Intelligence (AI) refers to the area of computer science tasked with making computers behave in ways that normally require human intelligence. AI is used across industries, for example, to automate repetitive tasks or improve customer experiences. What is machine learning? Machine learning (ML) is a subset of AI and refers to machines that can learn on their own and adjust themselves based on new and historical data. ML algorithms look at vast data sets to determine patterns and identify outliers. With outliers defined, ML finds better ways to detect them more quickly and uses an understanding of the actions that were taken in response to these outliers in the past to proactively propose the same when a similar outlier is detected in the future. In machine learning models, machines learn on their own without being explicitly programmed.