Will artificial intelligence remain under human control?

How can one communicate fluently with artificial intelligence? Can one cooperate with artificial intelligence?

The existing artificial intelligence (AI) systems based on machine learning are often independent actors that inform people about their conclusions, but otherwise interact with people in a very limited scale.  AI is being increasingly introduced not only in services accessible via the internet, but also in mobile machines, such as autonomous cars and robots. We should consider how to ensure that AI will always remain under human control, and how humans can and how they should be able to interact with AI.

Verbal and non-verbal communication

In trend analyses of technology, the interactive properties of AI have been identified as the next major step in their development. Dialogical interaction does not require the user to seek and learn commands, but the correct function is negotiated through free dialogue with the machine. Interaction can be supplemented by non-verbal communication in such a manner that the machine identifies and reacts to the person’s emotional state, such as the person being confused. A machine can learn to identify individuals and adjust its operations according to which matters the person is and is not familiar with, and how he or she prefers to operate. Personal virtual assistants, such as Apple’s Siri, strive to establish a relationship with their owner and learn their preferences in such a manner that, with time, they can predict the person’s needs and offer assistance even before the person takes the initiative to ask for it.

In the internet, nowadays you often encounter chatbots. They are already relatively clever, and, when dealing with them, you may not always notice at first that you are not encountered by a real human being. A chatbot’s ability to discuss is based on the fact that it knows very well the limited service area within which it operates. It has learned to predict what kind of questions people may have. Every now and then, a chatbot may feel a little bit rude. This probably derives from the fact that they are programmed by people who transfer their own manners to the robot.

Interest towards AI solutions where a human and AI operate in collaboration with each other is increasing.  Collaborative human power can be used, for example, for collecting data or interpreting images in solutions, where a large group of people and AI form a collectively functioning entity. This kind of collective  intelligence has been used for such purposes as digitalisation of old texts. A human eye is incomparable in recognising words, even when written in strange letters. When AI carries out easy text recognition tasks and lets people deal with any unclear cases, the work will advance quickly with such collective power.

Fluent interaction requires learning and participation

Fluent interaction between humans and AI still requires a lot of development in many areas. In the future, we will see increasing amounts of work teams consisting of humans and robots. A robot can assist humans in many kinds of maintenance and service tasks. Fluent interaction is based on AI, with the help of which the robot interprets its environment and humans. Recognising the intentions of one another plays a key role: a human must be able to anticipate the robot’s actions, and, in the same way, the robot must be able to anticipate human actions. Dialogical interaction solutions are needed in this field as well.

Autonomous cars and other vehicles largely function on their own, but when they encounter a problematic situation, they may easily need human assistance. In such a situation, it is good if the machine has kept the human up to date on what is going on, so that he or she may quickly resolve the problematic situation. Indicating and recognising intentions is important also with a view to bystanders: when pedestrians encounter an autonomous car, how can they be sure that the car has seen them and stops at a pedestrian crossing to give way for them? How do you establish an eye contact with an autonomous car?

Different smart services at home and in offices strive to fulfil people’s wishes and predict their desires. Often such services remain unnoticed by people, in which case it may remain unclear why air conditioning is blowing at full blast or why the temperature does not rise. An easy interaction channel is needed, so that people can find out why things are going the way they are going, and that they can influence matters.

AI is not infallible − it can make mistakes and it may have faults. Once humans learn to understand the limitations of AI, and the way AI draws conclusions and functions, the interaction between them will become easier. When people understand the basics of the way AI functions, they can put themselves on a level with it, in the same manner as people naturally tune into the same level with the person they are talking with.  It is important to develop AI solutions in such a manner that people who will work with AI are allowed to participate in the design of the solutions.

Read more: VTT and Smart City

Kaasinen Eija
Eija Kaasinen
Senior Scientist, VTT



Service innovations derive from dialogue and collaborative learning with users

Modern economies are based on service- and knowledge-intensiveness. In Western countries, services account for more than 70% of the GDP.

The structures of our service society need a reform, but we also need to consider how we could develop our organisations  to be able to produce renewals and service innovations themselves. The focus should be placed on understanding the grassroots level of development: the development of suitable human competencies and interactions that contribute to the development of service innovations.

Service innovations mean new, creative and efficient ways for organising services and business operations. Technological development and digitalization serves as an important driver for the development of new kind of services. However, the innovation itself is often based on its social aspects, such as a new way of organising the service and renewing the roles of the service provider and user in its provision.

Key issue: collaboration and learning from the users

During my dissertation work, I noticed that collaboration and the ability to learn from service users are the most important prerequisites of generating of service innovations. The results of collaborative learning emerge in interaction between the users, employees and the management.

In earlier studies, the active role of users and learning with them have been identified as major contributing factors for service innovations, but this activity has not been described in concrete terms.

The results of my dissertation describe how essential it is for service providers to understand the goals of the service users’ activities and their everyday problems and to create solutions that better meet them. For example, the Uber taxi service has brought a new, cost-efficient transport service on the market.

In addition, service users are an important source of innovation; they are capable of generating ideas and implementing service innovations in practice. The City of Hämeenlinna, for example, empowered its residents to generate ideas and implement the kind of service reforms they wanted. This resulted in hundreds of service reforms, of which we could mention as examples a life-cycle café for older people and school children, and a sports club for young men in danger of social exclusion.

Path towards the generation of service innovations:

To generate service innovations, organisations should invest in their capability to learn from service users and create services that best serve the goals of their activities. In the following, I describe five theses with the help of which organisations can start developing their operations in this direction:

  1. Respect the service users: For an organisation that intends to generate innovations, it is important to understand the goals of their users’ and to develop solutions to better meet them. The understanding of users’ aims is best established in dialogue: by hearing the users and getting to know their operating environment. The organisation should respect the users and their ideas.
  2. Make deliberate efforts to learn from service users: Service innovations often emerge as novel solutions to mundane problems that can be generalised. Make deliberate efforts to involve service users in both practical decision-making and strategic situations that steer the activities.Lead-users, or pioneers within the field, in particular are important collaborators when seeking a strategic direction for an organisation. The organisation may, for example, take advantage of the views of the pioneers in their strategic work, when foresighting changes in their operating environment and evaluating their past operations.
  3. Encourage collaborative experimentation: Innovative service solutions often emerge as a result of practical learning experiments. Service users can also be mandated to come up with new solutions themselves. This requires that the organisation has tolerance to failure and that it conducts several learning experiments with the users.
  4. Make space for joint evaluation: Changed practices often generate new insights. Innovation requires that these insights are put into practice, since an insight without a practical solution is not yet an innovation. Space must be made for joint evaluation, the kind of learning situations where experiences from collaboration with users can be evaluated in a critical manner and these evaluations are used as a basis for changing the organisation’s operations.
  5. Create systematic practices for collaborative learning: The responsibility for creating innovations lies with both the management and the employees. The generation of innovations cannot be left to random ideation and implementation capacity. Invest in building the kind of systematic learning practices through which the employees and the management encounter the service users and collaborative reflect the results of that collaboration.

My dissertation “Collaborative learning with users as an enabler of service innovation (Yhteistoiminnallinen oppiminen käyttäjien kanssa palveluinnovaatioiden mahdollistajana)” is available online.

Katri Kallio,

Research scientist

Katri Kallio’s viva voce examination will be held at Aalto University on 13 November.