Smart Buildings

How will AI Turn Smart Buildings Into Intelligent Buildings?

Last month we discussed the evolution of pattern recognition, machine learning and how the current deep learning movement could be a precursor to artificial intelligence (AI). This month we consider how deep learning and AI are already becoming part of our society through applications in the smart building sector. Since its first public discussion at a summer conference hosted at Dartmouth University in 1956, AI has both enchanted and perplexed forward thinkers around the world. AI is essentially just an application of computing for the purposes of induction or inference but in reality it has come to be known as a replacement for human cognition in many applications. AI could create machines with the ability to learn and therefore replace humans in many roles within industry and society. This potential has led to a huge increase in investment into AI in recent years. Data analytics firm Quid quotes growth of $1.5bn in 2010 to $5bn […]

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Last month we discussed the evolution of pattern recognition, machine learning and how the current deep learning movement could be a precursor to artificial intelligence (AI).

This month we consider how deep learning and AI are already becoming part of our society through applications in the smart building sector.

Artificial Intelligence

Since its first public discussion at a summer conference hosted at Dartmouth University in 1956, AI has both enchanted and perplexed forward thinkers around the world. AI is essentially just an application of computing for the purposes of induction or inference but in reality it has come to be known as a replacement for human cognition in many applications. AI could create machines with the ability to learn and therefore replace humans in many roles within industry and society.

This potential has led to a huge increase in investment into AI in recent years. Data analytics firm Quid quotes growth of $1.5bn in 2010 to $5bn in 2015, representing more than a threefold increase in just five years. While significant commercial application of this new investment was seen in technologies such as driverless vehicles and medical diagnostics, as much as $1.7bn has gone into the smart building sector. Furthermore, since 2010, deep learning companies with applications for smart buildings have raised $273m, according to Quid.

The recent investment charge seems to have been triggered by the emergence of deep learning, which in turn has come about due to our increased capacity to process Big Data, vital for the “learning” process. “Deep learning is a form of artificial intelligence that relatively mimics how our brain hierarchically understands objects and environments”, explains Ruggero Altair Tacchi, lead data scientist at Quid. “This allows us to approach problems from different scales, for example, in computer vision, where a computer makes sense of an image at different layers”.

The news is generally being welcomed in the smart building sector, where sensor enabled processing units are already recognising patterns such as occupancy in relation to temperature and lighting in order to optimise energy consumption, for example. There has also been a significant uptake within enterprise management, “in retail, this is helping with inventory protection. In offices, we see firms optimising office dynamics by matching people on teams to enhance productivity”, says Tacchi.

The technology is going beyond simple pattern recognition to something we can begin to describe as AI. “Cognitive technologies are more than analytics – they encompass speech recognition, natural language processing, rules-based systems, automated planning, and are combined in robotics,” said David Schatsky, Senior Manager at Deloitte. “These technologies are finding their way into all kinds of consumer and business products and services. These technologies will be ubiquitous”.

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While large tech companies invest billions in creating sophisticated AI engines. Start-ups building AI products will need to stay focused on specific applications to compete against the tech elite. Development is then likely to be exponential as AI systems improve with the more data that is collected, meaning it is possible to create a virtuous flywheel of data network effects. More users --> More data --> Better products --> More users!

In recent years Google has bought 14 AI and robotics companies. Considering “search” still makes up 80% of Google’s revenues it would be logical to assume that Google is improving its AI portfolio to improve its search capabilities. However when you consider the development of AI you realise that Google is actually using search to make its AI better.

Every search and subsequent click on a given result is training Google AI to know the “right” result for any given query. Then when you consider the 3.5 billion searches taking place on Google everyday, you begin to get an idea of the potential speed of upcoming AI developments.

For future start ups the business plan is straightforward: find an application and add AI. Some products represent the start of deep learning in smart buildings and ushers in the era of AI. As such, first movers will make advances in the space, then the growth of investors and AI entrepreneurs will likely follow.

We may still be in the early days of this transformative technology, but through its use in smart buildings, AI will likely soon force us to reinvent how we live and work.

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