Imagine a construction worker has stayed two hours longer than his normal shift, perhaps he is covering for a friend. He’s stressed and tired, his heart rate is up, and his alertness is reduced. It is potentially an accident waiting to happen. Fortunately, your company has invested in the Internet of Things (IoT). An automatic alert has already been sent from the device he wearing to his shift manager, who can pinpoint his exact location and quickly intervene.

This is just one of the many potential uses that the IoT could bring to construction. Interventions like this could prevent mistakes and even save lives. That is why the Internet of Things is one of the most exciting new innovations in construction technology. Consultants at McKinsey believe it could have a global economic impact of nearly $1 trillion on worksites worldwide by 2025. Already, $8 billion has been invested in IoT in construction worldwide and with the sheer variety of applications and some serious benefits, it could be a very promising market indeed.

What is the internet of things?

The definition of the IoT has evolved significantly in the last two decades due to the convergence of multiple technologies and fields. In 1999, it was viewed that radio-frequency identification (RFID) would be essential to its development, but a growing portion of IoT devices such as smartphones, tablets and computers have since been the driving force of the idea. Put simply, the internet of things is the internet for things, it is a way for our devices to connect through the internet in a similar way to how people already do.

IoT in construction, for example, can involve the use of internet-connected sensors which are placed around the construction site or are worn by workers. These devices collect certain types of data about activity, performance and conditions on the building site. They would send these to a central dashboard where the data can be analyzed and used to inform decision-making.

Traditionally, most internet-connected devices have been computers and smartphones. Now however, a huge variety of sensors can easily and cheaply be upgraded with a chip like a SIM card. This could include wristbands that monitor heart rate, temperature sensors and vibration monitors. Connecting these devices to a central database means many more aspects of your sites can be monitored in ‘real time’. This can have huge implications for safety, security, productivity, and cost reduction.

What are its benefits and what are its drawbacks?

Benefits

Improving safe working practices: Wearable IoT devices have real potential for improving safety on building sites. If all staff on a job site wear a wrist band or clip-on device, data about their movements and activity can be used to discover any risky behavior.

Take the example of a New York construction firm who did just that. The device would send an automatic alert to the company’s site safety manager whenever the device physically dropped by three foot or more (the idea being to immediately notify health and safety of any falls). The manager noticed that one worker appeared to be repeatedly falling and went to investigate. As it turned out he was jumping into a pit, rather than using the ladder provided. He was naturally reminded of the dangers of his behavior!

Improve resource management: If all machines, staff and materials are connected to the internet with a chip, you can geolocate them immediately. How many hours get wasted on building sites searching for materials? How many liters of fuel are burned by idle engines? How much time do workers spend underemployed when they could be sent to support other tasks?

The potential cost and time savings here are significant. Take truck monitors by the IoT firm Trimble – their rugged IoT construction solutions can identify location and activity of a wide variety of vehicles and other assets.

Better reporting and cost-efficient maintenance:  IoT devices can continually feed back information about conditions in both completed and under-construction sites. Sensors can monitor for things like unusual vibrations on a piece of machinery that suggests it needs to be fixed. They can detect increases in humidity which can tell your inspection teams about damp issues.

At PlanRadar, this is an area we are particularly excited about. Our app already functions as an IoT solution for construction by providing real-time information about site problems to maintenance teams. Using hardware that users are already familiar with, the app enables users to collect a central bank of evidence that makes oversight and reporting more efficient. With improvements in IoT sensors, this could be achieved even faster and the data passed from person to person could be even more accurate.

Reduce insurance premiums: Having this kind of data could additionally be used to renegotiate insurance premiums. Not only will you be improving safety on-site, but you will also be able to prove it. In the case of IoT building construction company Pillar, these devices can even help prevent fires.

Obstacles

Data security and safety issues: An IoT database could be a real goldmine of sensitive information to organized criminals. If a malicious actor found a way to hack into a company’s IoT database, they could easily access a list of where all your machinery is currently located or where expensive materials are stored. It is essential IoT databases are confidential and unauthorized parties cannot gain access.

Privacy issues: With security issues established, most concerns that exist to do with IoT are framed around the concerns that many people have with tracking the physical movement of workers. Many would object on privacy grounds, as could labor organizations.

Price: While most IoT devices are relatively cheap, many job site owners will need convincing that they are worth the investment. Especially on smaller sites where you can conduct all checks in just a couple of minutes, it might seem unnecessary to spend money on sensors and learn to use a dashboard when it can continue being done manually. For the time being, it is likely that IoT construction solutions will mainly be used on large building and civil engineering projects.

Deployment can be a big learning curve: Like any technology, sensors are only as good as the way they are deployed. There is little sense in spending money on, say, moisture sensors, when the biggest risk on a site is heat. Sensors need to be placed and chosen strategically. Many construction firms will have to follow a big learning curve before being able to truly benefit from these tools. Simply placing IoT sensors around a site will not solve issues on its own.

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Artificial intelligence (AI) is being talked about more than ever before – for both good and bad reasons.

While many see the potential for AI in areas such as medical advance, there are fears that jobs will be lost with more roles being automated as robotics become more advanced.

Rohit Talwar is a futurist with Fast Future, a professional foresight firm specialising in executive education, research, and consulting on the emerging future. Fast Future has studied the seven stages of evolution that AI is likely to go through in the next 20 years and beyond.

Talwar has been a futurist for 20 years and has advised many clients, from big corporations to governments, on shaping the future and planning for different scenarios.

He spoke to Insider about his thoughts on forthcoming, potentially life-changing, developments.

Talwar said: “Right now everyone is fascinated by the power of AI. There is a lot of hype, but there are a lot of possibilities.

“People are trying to understand AI and how it will change businesses, how it might change countries and how it will create new possibilities. It will have an impact on jobs and society.”

In terms of where the world is in the evolution of AI, Talwar said that technology is getting better and smarter, but it isn’t completely perfect.

He gives the example of photographic recognition where a change to a couple of pixels in an image can greatly reduce the accuracy of AI because it isn’t sophisticated enough to realise the person in the picture is the same – a human would realise this, but AI doesn’t yet have that capability.

“It’s evolving and there are lot of applications now,” he said. “It’s mainly in areas such as rule-based systems – processing thousands of rules to work out if you can have a mortgage or get that insurance claim, for example.”

He explained that the next stage can be seen as collaboration or negotiation when AI will start to take on some of the roles filled by sales people and even lawyers and accountants.

“Beyond that, the next stage of evolution in AI will be to get it as smart as us – where it can look and feel like it’s thinking,” he said. “But it’s simulation of thinking rather than real thinking. It’s still basically using millions of rules and statistical probability to make decisions.”

Rohit warns that this type of advance in AI will destroy jobs, and it has already removed thousands of jobs from large organisations. But rather than be alarmed by this prospect, he advises businesses and governments to prepare for such change to be able to manage it.

He said: “We have to start re-skilling people now before it becomes a crisis. We need to teach people job-specific skills but also give them the skills that mean they can move from job to job.

“Such skills are problem solving, collaboration, scenario and design thinking and structured problem solving. These skills allow you to be productive in lots of different jobs and move from role to role quickly.”

He points out that a youngster of 11 years of age today could live to be 120 or 130. This means they are likely to do many jobs in their lifetimes and therefore need to be ‘continuous learners’.

He also believes that more investment has to be made into adult education to build people’s confidence and let them learn new skills.

He said: “We need to fund a massive expansion in university education. We also have to reinvigorate communities and start to think big. Let’s start to think more broadly about what the economy of the future looks like.

While a lot of focus has been on the risk to jobs that AI brings, Talwar said it can bring benefits to many industries and professions.

He said: “If we have the data of millions of people, we can get much better at analysing disease patterns. We can start to target what the issues are from a health perspective and how to treat them. We can start to come up with innovative solutions more easily at a lower cost.”

His advice for business leaders is to take time to understand how game-changing technology works and what it can achieve.

He said: “Business leaders need to get their heads around what it is. The best way of doing that is to do a little bit every day. There are a lot of sites out there that have 30 to 90 second videos explaining all sorts of innovations.

“We recommend people give themselves permission to spend three minutes a day watching one or two videos about what is changing. Over the course of two weeks, your level of understanding of the capability of these technologies will have expanded dramatically.

“Then start speaking to your colleagues about what you can do with this technology and how you can make it work in your organisation. Teach yourself the concepts and look for tools out there to use to create new opportunities for businesses and create more efficiency inside organisations.

“Fear comes from what knowing. The key is education to get past the hype and start to understand the implication and opportunism.”

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Bengaluru: Last year, for the first time ever, an international beauty contest was judged by machines. Thousands of people from across the world submitted their photos to Beauty.AI, hoping that their faces would be selected by an advanced algorithm free of human biases, in the process accurately defining what constitutes human beauty.

In preparation, the algorithm had studied hundreds of images of past beauty contests, training itself to recognize human beauty based on the winners. But what was supposed to be a breakthrough moment that would showcase the potential of modern self-learning, artificially intelligent algorithms rapidly turned into an embarrassment for the creators of Beauty.AI, as the algorithm picked the winners solely on the basis of skin colour.

“The algorithm made a fairly non-trivial correlation between skin colour and beauty. A classic example of bias creeping into an algorithm,” says Nisheeth K. Vishnoi, an associate professor at the School of Computer and Communication Sciences at Switzerland-based École Polytechnique Fédérale de Lausanne (EPFL). He specializes in issues related to algorithmic bias.

A widely cited piece titled “Machine bias” from US-based investigative journalism organization ProPublica in 2016 highlighted another disturbing case.

It cited an incident involving a black teenager named Brisha Borden who was arrested for riding an unlocked bicycle she found on the road. The police estimated the value of the item was about $80.

In a separate incident, a 41-year-old Caucasian man named Vernon Prater was arrested for shoplifting goods worth roughly the same amount. Unlike Borden, Prater had a prior criminal record and had already served prison time.

Yet, when Borden and Prater were brought for sentencing, a self-learning program determined Borden was more likely to commit future crimes than Prater—exhibiting the sort of racial bias computers were not supposed to have. Two years later, it was proved wrong when Prater was charged with another crime, while Borden’s record remained clean.

And who can forget Tay, the infamous “racist chatbot” that Microsoft Corp. developed last year?

Even as artificial intelligence and machine learning continue to break new ground, there is enough evidence to indicate how easy it is for bias to creep into even the most advanced algorithms. Given the extent to which these algorithms are capable of building deeply personal profiles about us from relatively trivial information, the impact that this can have on personal privacy is significant.

This issue caught the attention of the US government, which in October 2016 published a comprehensive report titled “Preparing for the future of artificial intelligence”, turning the spotlight on the issue of algorithmic bias. It raised concerns about how machine-learning algorithms can discriminate against people or sets of people based on the personal profiles they develop of all of us.

“If a machine learning model is used to screen job applicants, and if the data used to train the model reflects past decisions that are biased, the result could be to perpetuate past bias. For example, looking for candidates who resemble past hires may bias a system toward hiring more people like those already on a team, rather than considering the best candidates across the full diversity of potential applicants,” the report says.

“The difficulty of understanding machine learning results is at odds with the common misconception that complex algorithms always do what their designers choose to have them do, and therefore that bias will creep into an algorithm if and only if its developers themselves suffer from conscious or unconscious bias. It is certainly true that a technology developer who wants to produce a biased algorithm can do so, and that unconscious bias may cause practitioners to apply insufficient effort to preventing bias,” it says.

Over the years, social media platforms have been using similar self-learning algorithms to personalize their services, offering content better suited to the preferences of their users—based solely on their past behaviour on the site in terms of what they “liked” or the links they clicked on.

“What you are seeing on platforms such as Google or Facebook is extreme personalization—which is basically when the algorithm realizes that you prefer one option over another. Maybe you have a slight bias towards (US President Donald) Trump versus Hillary (Clinton) or (Prime Minister Narendra) Modi versus other opponents—that’s when you get to see more and more articles which are confirming your bias. The trouble is that as you see more and more such articles, it actually influences your views,” says EPFL’s Vishnoi.

“The opinions of human beings are malleable. The US election is a great example of how algorithmic bots were used to influence some of these very important historical events of mankind,” he adds, referring to the impact of “fake news” on recent global events.

Experts, however, believe that these algorithms are rarely the product of malice. “It’s just a product of careless algorithm design,” says Elisa Celis, a senior researcher along with Vishnoi at EPFL.

How does one detect bias in an algorithm? “It bears mentioning that machine learning-algorithms and neural networks are designed to function without human involvement. Even the most skilled data scientist has no way to predict how his algorithms will process the data provided to them,” said Mintcolumnist and lawyer Rahul Matthan in a recent research paper on the issue of data privacy published by the Takshashila Institute, titled “Beyond consent: A new paradigm for data protection”.

One solution is “black-box testing”, which determines whether an algorithm is working as effectively as it should without peering into its internal structure. “In a black-box audit, the actual algorithms of the data controllers are not reviewed. Instead, the audit compares the input algorithm to the resulting output to verify that the algorithm is in fact performing in a privacy-preserving manner. This mechanism is designed to strike a balance between the auditability of the algorithm on the one hand and the need to preserve proprietary advantage of the data controller on the other. Data controllers should be mandated to make themselves and their algorithms accessible for a black box audit,” says Matthan, who is also a fellow with Takshashila’s technology and policy research programme.

He suggests the creation of a class of technically skilled personnel or “learned intermediaries” whose sole job will be to protect data rights. “Learned intermediaries will be technical personnel trained to evaluate the output of machine-learning algorithms and detect bias on the margins and legitimate auditors who must conduct periodic reviews of the data algorithms with the objective of making them stronger and more privacy protective. They should be capable of indicating appropriate remedial measures if they detect bias in an algorithm. For instance, a learned intermediary can introduce an appropriate amount of noise into the processing so that any bias caused over time due to a set pattern is fuzzed out,” Matthan explains.

That said there still remain significant challenges in removing the bias once discovered.

“If you are talking about removing biases from algorithms and developing appropriate solutions, this is an area that is still largely in the hands of academia—and removed from the broader industry. It will take time for the industry to adopt these solutions on a larger scale,” says Animesh Mukherjee, an associate professor at the Indian Institute of Technology, Kharagpur, who specializes in areas such as natural language processing and complex algorithms.

This is the first in a four-part series. The next part will focus on consent as the basis of privacy protection.

A nine-judge Constitution bench of the Supreme Court is currently deliberating whether or not Indian citizens have the right to privacy. At the same time, the government has appointed a committee under the chairmanship of retired Supreme Court judge B.N. Srikrishna to formulate a data protection law for the country. Against this backdrop, a new discussion paper from the Takshashila Institute has proposed a model of privacy particularly suited for a data-intense world. Over the course of this week we will take a deeper look at that model and why we need a new paradigm for privacy. In that context, we examine the increasing reliance on software to make decisions for us, assuming that dispassionate algorithms will ensure a level of fairness that we are denied because of human frailties. But algorithms have their own shortcomings—and those can pose a serious threat to our personal privacy.

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