Ambient Intelligence as a Multidisciplinary Paradigm

In recent years, advances in artificial intelligence (AI) have opened up new business models and new opportunities for progress in critical areas such as personal computing, health, education, energy, and the environment. Machines are already surpassing human performance of certain specific tasks, such as image recognition.

Artificial intelligence technologies have received $974m of funding as a first half of 2016, set to surpass 2015’s total, with 200 AI-focused companies have raised nearly $1.5 billion in equity funding. These figures will continue to rise as more AI patent applications were filed in 2016 than ever before: more than three thousand patent applications versus just under a hundred in 2015.

Yet the future of artificial intelligence is not so much about direct interaction between humans and machines, but rather indirect amalgamation with the technology that is all around us, as part of our everyday environment. Rather than having machines with all-purpose intelligence, humans will interact indirectly with machines having highly developed abilities in specific roles. Their sum will be a machine ecosystem that adapts to and aids in whatever humans are trying to do.

In that future, the devices might feel more like parts of an overall environment we interact with, rather than separate units we use individually. This is what ambient intelligence is. 

The IST Advisory Group (ISTAG) coined the term in 2001, with an ambitious vision of its widespread presence by 2010. The report describes technologies which exist today, such as wrist devices, smart appliances, driving guidance systems, and ride sharing applications. On the whole it might seem still very futuristic, but nothing in it seems outrageous. At first glance, its systems seem to differ from what we have today in pervasiveness more than in kind.

The scenarios, which ISTAG presents, surpass present technology in a major way, though. The devices they imagine anticipate and adapt to our needs in a much bigger way than anything we have today. This requires a high level of machine learning, both about us and about their environment. It implies a high level of interaction among the systems, so they can acquire information from one another.

Not Quite Turing's vision

Alan Turing thought that advances in computing would lead to intelligent machines. He envisioned a computer that could engage in a conversation indistinguishable from a human's. Time has shown that machine intelligence is poor at imitating human beings, but extremely good at specialized tasks. Computers can beat the best chess players, drive cars more safely than people can, and predict the weather for a week or more in advance. Computers don't compete with us at being human; they complement us with a host of specialties. They're also really good at exchanging information rapidly.

This leads naturally to the scenario where AI-implemented devices attend to our needs, each one serving a specific purpose but interacting with devices that serve other purposes.

We witness this in the Internet of Things. Currently most of its devices perform simple tasks, such as accepting remote direction and reporting status. They could do a lot more, though. Imagine a thermostat that doesn't just set the temperature when we instruct it to, but turns itself down when we leave the house and turns itself back up when we start out for home. This isn't a difficult task, computationally; it just requires access to more data about what we're doing.

Computers perform best in highly structured domains. They “like” to have everything unambiguous and predictable. Ambient intelligence, on the other hand, has to work in what are called "uncertain domains." (Much as in HBO’s Westworld, users (guests) are thrown into pre-determined storylines from which they are free to deviate, however ambient intelligence (hosts) are programmed with script objectives, so even minor deviations or improvisations based on a user’s interference won't totally disrupt their functioning, they adapt.) The information in these domains isn't restricted to a known set of values, and it often has to be measured in probability. What constitutes leaving home and returning home? That's where machine learning techniques, rather than algorithms, come into play.

To work effectively with us, machines have to catch on to our habits. They need to figure out that when we go out to lunch in the middle of the day, we most likely aren't returning home. Some people do return home at noon, though, so this has to be a personal measurement, not a universal rule.

Concerns About Privacy and Control

Giving machines so much information and leeway will inevitably raise concerns. When they gather so much information about us, how much privacy do we give up? Who else is collecting this information, and what are they using it for? Might advertisers be getting it to plan campaigns to influence us? Might governments be using it to learn our habits and track all our moves?

When the machines anticipate our needs, are they influencing us in subtle ways? This is already a concern in social media. Facebook builds feeds that supposedly reflect our interests, and in doing so it controls the information we see. Even without any intent to manipulate us, this leads to our seeing what we already agree with and missing anything that challenges our assumptions. There isn't much to prevent the manipulation of information to push us toward certain preferences or conclusions.

With ambient intelligence, this effect could be far more pervasive than it is today. The machines that we think are carrying out our wishes could herd us without being noticed.

The question of security is important. Many devices on the Internet of Things have almost nonexistent security. (An unknown attacker intermittently knocked many popular websites offline for hours last week, from Twitter to Amazon and Etsy to Netflix, by exploiting the security breach in ordinary household electronic devices such as DVRs, routers and digital closed-circuit cameras.) Devices have default passwords that are easily discovered. In recent months, this has let criminals build huge botnets of devices and use them for denial of service attacks on an unprecedented scale.

If a malicious party could take control of the devices in an ambient intelligent network, the results could be disastrous. Cars could crash, building maintenance systems shut down, daily commerce disintegrate. To be given so high a level of trust, devices will have to be far more secure than the ones of today.

The Convergence of Many Fields

Bringing about wide-scale ambient intelligence involves much more than technology. It will need psychological expertise to effectively anticipate people's needs without feeling intrusive or oppressive. It will involve engineering so that the devices can operate physical systems efficiently and give feedback from them. But mainly it will involve solving non technology-related factors: social, legal and ethical implications of full integration and adaptation of intelligent machines into our everyday life, accessing and controlling every aspect of it.

Unravelling Smart Cities: An Integrative Framework

“Its urbanization, progressing steadily, had finally reached the ultimate. All the land surface of Trantor, 75,000,000 square miles in extent, was a single city. The population, at its height, was well in excess of forty billions. This enormous population was devoted almost entirely to the administrative necessities of Empire, and found themselves all too few for the complications of the task.”

― Isaac Asimov, Foundation

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By 2030 the world’s 750 largest cities will account for 61 percent of global GDP (or just $80,000,000,000,000). Supporting and establishing those future cities as smart cities (with sophisticated sensors, buildings, and appliances everywhere to ensure the management of city infrastructures and the delivery of services to its citizens) will be very different in many ways and thus is already becoming a major priority. Its fundamental solution, the Internet of Things, will create a digital layer of infrastructure that will help citizens access and consume any information they need, no matter where they are.

Components of the Smart City

What the digital skeleton of the smart city might look like is open to dispute, but some elements are sure to emerge as standard:

• Smart energy networks - The city might have numerous small power plants in buildings and between them, with batteries or other energy storage at many locations. Smart sensors will monitor the power plants, wiring, and other component.

• Smart shareable homes—The Internet of Things will make it easier for people to rent out spare rooms in their homes and apartments. At a moment's notice it will be possible to remotely activate lights and heat or air conditioning. In addition to lighting, heating, and cooling a homeowner will be able to control appliances remotely. If you are in a hurry to start baking, you can remotely preheat the stove so it is time to put the pizza in when you arrive.

• Traffic flow will be more efficient and predictable—There will be no need to drive around and around looking for a parking place. Mobile apps will help drivers find parking spaces and route themselves around trouble spots. Smart traffic monitoring systems with networks of wired devices will make it easier to keep a traffic jam from developing at all. Intersections can managed by smart devices that keep drivers safe and streamline traffic flow.

• Shareable and reusable buildings—Smart devices will make it easier to manage commercial and industrial buildings, of course. The WEF suggests that smart devices will make it easy for a business owner to modify space to suit the communication and lighting needs of different clients. Another suite of smart devices will monitor power and water use, to make the most efficient use of each.

The "Killer App" Is Data Accessibility

All cities need telecommunications grids to handle the vast amounts of data racing around, into and out of, a city. Roads, power lines, water lines, sewage systems, traffic flow infrastructures, utilities in large commercial and government buildings—all need to be kept in top shape, monitored and managed efficiently. The data required to improve systems, perform timely maintenance, and do quick repairs has to come from somewhere. Currently, most cities rely on a complex blend of manual inspections, stand-alone electronic devices, and Web-enabled devices. A smart city moves that mix of management and maintenance tools toward a heavy reliance on Web-enabled devices. 

The Internet of Things will help service providers use the full range of data that's potentially available to manage how they deliver services. Government officials will be better equipped to make good strategic decisions about infrastructure, while allowing more transparency to the citizens—service providers and government offices will have to share information on their performance. Are they meeting performance and quality goals? The data from Web-enabled devices will make it easy for decision makers and citizens to find out.

The Smart City's Data-Intensive Infrastructure

The vast number of Web-enabled devices around the smart city will have to communicate over an increasingly dense network of wires, wireless devices, and routers. Creating and managing that digital infrastructure will create new business opportunities for anyone who can create and install elements of that new infrastructure. Data storage and processing on a vast scale are going to be critical. That's the challenge that underlies all of the innovative developments that will create a Smart City. Wireless networks and wired networks will need to be ubiquitous and much faster than the norm today.

Governance

Faced with ongoing budget concerns, city managers are working to create more effective and efficient operating models by moving away from top-down, centralized management systems and breaking down vertical service functions and departments. Today, cities require regular maintenance and repair services to roads, sewer lines, phone lines, and power lines. Whether private companies or the government that will do the work in the future is irrelevant. Whoever performs the monitoring, maintenance, and repair work can look forward to integrate the IoT and software applications to control them in areas like road infrastructure (better monitoring of pavement and bridge conditions by using intelligent sensors and new “big data” computing capabilities), highway traffic management, healthcare, education, and agriculture. Collaborative design of multi-stakeholder ownership and processes calls for new governance and business models, which are essential to aligning all city services. Cities should take that into consideration when designing a coherent deployment plan to ensure synergies and cross-functionalities that optimize the number of sensors and services provided for the wires, wireless devices, and routers that connect all of these smart devices to each other and to their owners, constantly generating more data mining opportunities.  

Privacy Challenges

The smart city depends on collecting and processing a huge amount of data, some concerning residents' daily activities. The connected devices that make their lives easier also expose them to a potential loss of privacy. The proliferation of sensing equipment in society already raises important questions about data security and privacy. Addressing these challenges requires rules of the game that allow the fast-moving technology and market trends to evolve. To address data privacy and security and other challenges related to enabling progress in the circular economy–intelligent assets will require a robust legal framework with adequate innovative enforcement mechanisms. The key challenge for policy-makers lies in stimulating (open) innovation while ensuring data security and generating trust for those who are directly and indirectly linked through city intelligent assets. Companies and policymakers would need a multi-stakeholder approach to create such conditions; if successful they could lay the groundwork for solving several of the core challenges for designing an economy that is truly restorative and regenerative.