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.