I’m Vignesh Kaushik. I curate and write articles on Thank God It’s Computational to help architects, designers, and urban planners leverage cutting-edge technologies on AEC projects.
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Algorithms are now used throughout the public and private sectors, informing decisions on everything from education and employment to criminal justice. But when they turn into “black boxes” that don’t offer up their secrets, we can’t hold them accountable. There is growing evidence that some algorithms and analytics can be opaque, making it difficult to determine when their outputs may be biased or erroneous.
Algorithms and the data that drive them are designed and created by people. Even for techniques such as genetic algorithms that evolve on their own, or machine-learning algorithms where the resulting model was not hand-crafted by a person, results are shaped by human-made design decisions, rules about what to optimize, and choices about what training data to use. “The algorithm did it” is not an acceptable excuse if algorithmic systems make mistakes or have undesired consequences.
This is exactly why a group of professors have come up with five principles to hold algorithms accountable.
Having said that, let’s take a look at some exciting progress in creating algorithms for better buildings and cities. Most of the work in this area are primarily driven by data-analytics and machine learning startups in collaboration with private & public Agencies. As architects and planners, it is extremely important that we understand how the algorithms that impact our buildings and cities are created.
Algorithms & Assisted Creativity
Arranging employees in an office is like creating a 13-dimensional matrix that triangulates human wants, corporate needs, and the cold hard laws of physics. Project Discover generated 10,000 designs, exploring different combinations of high- and low-traffic areas, communal and private zones, and natural-light levels. Then it matched as many of the 300 workers as possible with their specific preferences, all while taking into account the constraints of the space itself.
We live in times, where science fiction authors are struggling to keep up with reality. In recent years, there has been an explosion of research and experiments that deal with creativity and A.I. Almost every week, there is a new bot that paints, writes stories, composes music, designs objects or builds houses: Artificial Intelligence systems performing creative tasks? Science, technology and creativity have a long, intertwined history. Selecting which metaphors to explore is an important research decision. This article explore three metaphors: Augmented Creativity, Computational Creativity and Creative Systems.
Algorithms & Predictions
Cities can use readily-accessible data to get smarter on building safety. It’s not possible to know whether a place is unsafe without an investigation. But it is possible, in advance, to estimate the probability of a problem, given some additional information. Atlanta Fire Department worked with data analysts who trained an algorithm to find how attributes like buildings’ size, condition, location, and age had contributed to the distribution of past fires, and to predict future ones. Their strategy, which they named Firebird, was two and a half times more efficient than the one most inspections departments follow.
Risk Prediction Interface from Atlanta's Firebird Project
Imagine a building that tells you – before it happens – that the heating is about to fail. Some start-ups use sensors and machine learning to do “predictive maintenance”, spotting faults in building systems like heating and air con before they crash. Finnish start-up Leanheat puts a wireless temperature, humidity and pressure sensor into apartments to remotely control heating and monitor appliance health. In the US, start-up Augury installs acoustic sensors in machines to listen for audible changes in function and spot potentially imminent failures.
Algorithms & Behavioural Science
Emotional recognition firm Affectiva believes that in our world of hyper-connected smart technology and appliances, our devices have lots of cognitive intelligence but no emotional intelligence. And by using a variety of sensory technology, such as wearables and video surveillance, we can take the internet of things (IoT) to the next level – sensing human emotion to predict and react to human behaviour.
To keep drivers on the road, Uber has exploited some people’s tendency to set earnings goals — alerting them that they are ever so close to hitting a precious target when they try to log off. Consider an algorithm called forward dispatch that dispatches a new ride to a driver before the current one ends. Forward dispatch shortens waiting times for passengers, who may no longer have to wait for a driver 10 minutes away when a second driver is dropping off a passenger two minutes away. Perhaps no less important, forward dispatch causes drivers to stay on the road substantially longer during busy periods — a key goal for Uber.
Algorithms & Big Data
In the world of smart buildings we collect far more data than we use. But Data is currency, and it has tremendous value beyond its initial application. There are innumerable ways to analyze the same data set. What might not be immediately useful now, could be invaluable in the future. Data you may not be able to process now, would likely be easier to process in the future. And what might never be helpful for you, might be exactly what someone else needs and is willing to pay for.
Nokia is making a comeback of sorts – the Finnish technology firm wants to start building future Smart Cities. in February, Nokia published its smart city framework in a report entitled ’A new world of cities and the future of Australia’. The report outlines a more centralized approach to smart city development, bucking the growing trend towards a citizen-led, bottom-up implementation. Nokia goes so far as to suggest a state and territory government-level approach, working in conjunction with an overarching federal government program leaving cities to concentrate on their specific needs.