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By Jean-Simon Venne, Chief Technology Officer & Co-Founder at BrainBox AI

The real estate sector is a major energy user. According to the 2018 World Economic Forum CRE Sustainability Report, it uses more energy than any other sector and is a growing contributor to CO2 emissions. 

[Real estate] consumes over 40% of global energy annually; 20% of total global greenhouse gas emissions originate from buildings; and there is a projected 56% increase in building CO2 emissions by 2030.

That impact is only set to increase. By 2060, the number of buildings worldwide will have doubled.

But that also means there’s a major opportunity to improve.

Energy efficiency is a vast and largely untapped energy resource contained within our built environments that is key to meeting our future global energy needs. For over a decade, energy professionals have been talking about this hidden resource and about the magnitude of the potential energy savings for owners of commercial retail spaces, office buildings, manufacturing facilities, and many other conditioned spaces.

Effectively managing the thermal equilibrium of a building using dynamic modulation is a complex process that requires continuously optimizing energy flow to ensure occupant comfort and maximum energy efficiency. The first steps towards maintaining a building at equilibrium are to calculate its energy leak rate and determine the ideal HVAC system settings to ensure optimal levels for the power-to-thermal load relationship.

We are delighted to welcome Celeste Kidd, Assistant Professor of Psychology at UC Berkeley as the opening headliner at World Summit AI Americas this March in Montreal, talking about ‘How to Know’.

Celeste’s research focuses on about how people come to know what they know. The world is a sea of information too vast for any one person to acquire entirely. How then do people navigate the information overload, and how do their decisions shape their knowledge and beliefs?

Do we judge humans and machines equally? Or are our judgments biased depending on who or what is performing an action?

Dozens of experiments have been carried out to compare people's judgments of humans and machines across a variety of situations: from algorithmic bias in hiring decisions, and from autonomous weapons to self-driving vehicles and labor displacement.

Challenges are what make life interesting, and overcoming them is what makes life meaningful - Joshua J. Marine

We're calling out to all experts from our 51,000 community members to help us solve some of medicine's greatest challenges. If you have the tech expertise to work together in multi-disciplinary groups to solve the grand challenges below, join us for practical outcomes:

After practicing medicine for over 30 years, being a 62 year old “disruptor” is as exciting as it is frustrating.  Curiosity about emerging technologies exposed me to many “solutions” while 3 decades of patient interactions presented many problems which could be solved “if I could  only get…..”.   Today’s technologies frequently offer answers only visible to those posing the right questions.  Being 62 one develops a long list of questions.

It wasn’t all that long ago that we considered AI to be an industry that focused solely on robots that could comprehensively speak the same language as us and understand it in return. Nowadays, AI assists us in predicting the weather and answering questions on business websites. It’s this diverse range of possibilities that make it such a unique industry to join, particularly for those who are trained engineers and programmers.