Artificial intelligence has become an area of strategic importance and a key driver of economic development. This technology can bring solutions to many societal challenges from treating diseases to minimising the environmental impact of farming. However, it also raises concerns (legal, ethical and socioeconomic). The European Union aims to have leadership in this technology to ensure competitiveness and to shape the conditions for its development and use.
Artificial Intelligence in our daily lives
We are already using Artificial Intelligence (AI) every day to:
- search information on the web;
- optimise our daily commuting;
- translate text;
- use voice command to dial phone numbers while driving;
- avoid collision when driving;
- navigate autonomous lawnmowers and vacuum cleaners;
- use fingerprint or face recognition to unlock the phone, etc.
Artificial Intelligence is expected to power a vast number of applications and products, bringing some level of autonomy, to be more efficient or better support its user. It is expected to help people at home and at work, and the business perspectives are promising, in view of the number of new products and services that can be created.
A European approach to Artificial Intelligence
The European Commission will put forward a comprehensive European approach to artificial intelligence and robotics in the first half of this year. It will deal with technological aspects to strengthen the EU’s research and industrial capacity in this area. Importantly, given the concerns, it will also address ethical, legal and socioeconomic aspects to allow the careful management and safe deployment of this technology.
What is Artifical Intelligence?
Artificial intelligence endows systems with the capability to analyse its environment and take decision with some degree of autonomy to achieve its goals.
Machine learning denotes the ability of a software/computer to learn from its environment or from a very large set of representative data, enabling such systems to adapt their behaviour to changing circumstances or to perform tasks for which they have not been explicitly programmed.
To build robust models, additional resources are required:
- Big Data to learn from;
- High Performance computing to analyse huge sets of data;
- Cloud computing to provide distributed computing resources;
- High speed connectivity to link the various sensors, and sources of information.
AI-based systems can be pure software acting in the virtual world - typically on the web on PCs or mobile devices as in the case of:
- conversational agents;
- image analysis;
- search engines;
- decision support systems;
- face recognition.
A wide range of AI-based systems are also embodied in hardware devices such as advanced robots, autonomous vehicles, Internet of Things, cyber-physical systems, typically perceiving their environments through sensors and physically acting or moving in their environment.