List of Key Challenges

The List of Key Challenges identifies cross-cutting issues that combine societal relevance with ICT fields of research and innovation. This resource provides a problem-oriented view, focusing on shared challenges and issues.

  • The key challenges defined that will be the focus of the HubIT stakeholder network activities.
  • Cross-cutting issues that combine societal relevance with ICT fields of research and innovation These resource provides a catalogue of such challenges relevant in ICT and SSH. Each of these key challenges will be expanded and emphasize shared concerns among stakeholders.
Responsibility in AI

The use of intelligent machines represents a challenge in terms of liability: who/what shall be responsible in case something goes wrong? Earlier, it was relatively easy to determine whether an incident was the result of the actions of a user, developer or manufacturer. For example, financial institutions are reluctant to give machines full autonomy because their behavior is not fully foreseeable. They tend to keep a human supervisor to validate the machine’s decisions for critical activities such as releasing/blocking payments or validating trades, partially defeating the purpose of using a machine in the first place. Current compliance and operational security standards are quite strict; I anticipate that they will loosen over time when the technology matures.

Narrow focus

By design, intelligent algorithms are good at solving specific problems and cannot deviate from what they were designed for. An algorithm trained to detect suspicious payments would not be able to detect any other suspicious activity related to trading, for instance. In addition, algorithms are purely rational and lack essential factors such as emotional intelligence and the ability to contextualize information, unlike human beings. That’s why banking chatbots often disappoint: they are “smart” but lack empathy.

Black-box effect

The results of intelligent algorithms are opaque and not verifiable. They deliver statistical truths, meaning that they can be wrong on individual cases. The results could have a hidden bias difficult to identify. The diagnosing and correcting of those algorithms is very complex. The fact that there is no explanation as to why the algorithm provided a positive or negative answer to a specific question can be disturbing for a banker’s rational mind. This is often a blocking point for the use of AI in trading.

Building Trust on AI

Brobst predicts that by 2020 there will be a revolt by a “noisy 10 per cent” against the hold AI has taken over our lives. “The problem is that AI is a black box – people don’t feel comfortable when they don’t understand how the decision was made. “For example algorithms used by banks are mainly linear maths and it’s pretty easy to explain the path from the input to the output – ‘I denied your mortgage application because, you don’t have a job, or whatever…’. “With multi-layer neural networks, the average human doesn’t understand, so now we’re making predictions based on things that people don’t understand and that’s going to make people uncomfortable.”

Neuromorphic Revolution

Biologic brains are not binary based. The feed forward multi-layer perceptron network is a very basic model of true neurons. Biological neurons are based on membrane potential and the difference in voltage between the inside and outside of the cell membrane. As charge builds up they eventually reach a point where the charge passes from the dendrite to the axiom which causes a chemical release the builds potential in neurons near the axiomal ends, aka spiking. Signals pass from one neuron to others connected to it. This is the basic mechanism of a second-generation except that a computing pass starts at the inputs and moves values (not voltage potential) through the network with each value being adjusted by the weight of the connection.

Acceptance of AI

A challenge of AI will be acceptance by humans as not only superior, but fully capable of creativity and emotions, ie “the new hunans”, the next step in human evolution, leaving our biological bodies behind. The will start in small activities such as self-driving cars, then artificial fund managers, computer lawyers and doctors, until eventually when general AI is achieved, a thinking machine fully capable of interacting with humans to advance robotics and genetics to help our bodies as well as build bodies for the new humans.

Brain–computer interfaces (BCI) are direct connections between the brain and a computer.

Regulation of neuroelectrical activity or brain activity is used to replace or improve lost or impaired function.

Medical informatics is the intersection of information science, computer science, and health care.

This field deals with the resources, devices, and methods required to optimize the acquisition, storage, retrieval, and use of information in health and biomedicine.

Invisibility Cloaking Devices

Two research teams have made structures that could help conceal objects from daylight – taking the next step towards making the visible, invisible. Recent progress draws on advances in so-called metamaterials, which are microscopic structures that bend light in unnatural directions.

Increasing influence upon police work.

For police, ICT plays a twofold role: New technologies can support police work but also provide new opportunities for offenders to commit crimes. Open borders, the free flow of people, goods, information, and capital also facilitate the planning and committing of crimes. Politicians and police forces alike are faced with the pressure to address these problems in ways that should alleviate citizens’ fears on the one and, but will not infringe upon civil liberties and human rights, on the other. For European police forces, these major societal changes have triggered ambitious change programmes aiming at modernising and rationalising the way police work is conducted.

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