Answer ... (a) Healthcare
The state is focusing on regulatory issues and on increasing the accessibility of health data. Making available and exploiting health data assets through modern infrastructure is an explicit regulatory objective. This requires an appropriate regulatory environment. The clear objective is to create the necessary infrastructure to support the use of health data assets. The regulatory environment is intended to facilitate the use of secondary health data.
(b) Security and defence
In the areas of security and defence, the regulatory and public administrative framework designed for automated systems will apply.
Specific security-related areas of focus include:
- the development of border control systems and complex identification systems;
- data-driven law enforcement and crime prevention using complex analysis;
- the introduction of existing AI technologies into the investigative process;
- AI-based mapping of offender contact networks.
Specific defence-related areas of focus include:
- the automation of big data processing, information operations and decision-making systems;
- the implementation and development of predictive supply systems;
- the development of autonomous systems in all relevant operational domains (airspace, surface, space, cyberspace);
- the development of human-machine interaction on both sides;
- protection against AI-supported systems in all relevant operational spaces (including modelling and simulation); and
- developments aimed at protecting and analysing the defence-related elements of national data assets.
(c) Autonomous vehicles
The Hungarian regulatory environment allows for flexibility in the conduct of test operations for self-driving vehicles. The government has made the promotion of such developments an explicit public objective by providing appropriate test tracks and an innovation-friendly legal environment.
(d) Manufacturing
In the manufacturing area, an explicit aim is to create a test environment for the analysis of manufacturing data, in order to:
- facilitate manufacturing-related data management;
- develop cybersecurity and data protection in manufacturing;
- implement data standardisation protocols to facilitate data analytics in manufacturing; and
- introduce manufacturing data to the data marketplace.
To increase the efficiency of manufacturing and promote the development of new manufacturing processes, it is necessary:
- to centralise research and match it to industrial needs; and
- to set up an innovation ecosystem (to be undertaken by the future AI National Laboratory).
Short-term areas of focus: These include:
- parameter control of production processes;
- manufacturing decision support;
- quality control with AI tools;
- online product testing;
- layout and process simulation;
- factory optimisation;
- predictive maintenance;
- high-accuracy indoor and outdoor positioning systems with 5G and AI;
- robot control support with AI solutions;
- artificial vision manufacturing applications;
- open production IT architecture; and
- manufacturing in the city.
Medium-term areas of focus: These include:
- AI use in 6G networks;
- after-sales product tracking;
- AI-based data processing;
- service demand estimation and forecasting;
- drone management in the industrial domain (sample factory, sample area);
- automated management of critical machine-to-machine communication;
- extensive use of Internet of Things devices and private communication devices in the industrial domain (sample area);
- supply chains;
- product tracking;
- optimisation of manufacturing logistics;
- optimisation of manufacturing energy management; and
- manufacturing cybersecurity.
With regard to the small and medium-sized enterprise (SME) sector, which is a key engine of the Hungarian economy, there is a need to implement digital transformation projects to ensure that manufacturing SMEs can remain competitive.
(e) Agriculture
The aim is to implement and disseminate AI technologies in line with the digital transformation of the agricultural sector. Agriculture-related focus areas in the AI Strategy include:
- the development of the Agro-Data Framework by creating a cloud-based data information platform that allows producer (farm-level) and government data related to agriculture to be recorded, processed and stored in a uniform, structured way;
- the establishment of a Digital Agro-Innovation Centre to develop a digital innovation ecosystem and incubate start-ups using AI technologies. This will include the creation of a testing ground for innovation and testing of robots based on the use of AI technology;
- revision of the regulations on the use of drones and autonomous machines in the agriculture sector; and
- the development of a crop forecasting service.
(f) Professional services
Professional services as such are not currently the focus of legislation. In highly regulated sectors such as financial services and insurance, the supervisory authority plays a key role in promoting, implementing and controlling AI-driven technologies. The same applies to the implementation of AI-driven technologies in the public administration. As for other professional services, there is no relevant specific national regulation and none is expected in the near future.
(g) Public sector
With regard to both public administration and case management, the focus is on developing automatic decision making and automatising processes as far as possible.
(h) Other
Other AI-related developments include the following:
-
Energy: The aim is to utilise data assets in the energy sector in the best possible way and to develop personalised services as a result. Among other things, developments include:
-
- the rollout of smart meters;
- smart grid development;
- the development of data-driven energy market models;
- predictive maintenance;
- autonomous operation; and
- the development of smart energy supply and optimisation systems.
-
Banking/insurance: Many AI-related projects have been already implemented in these sectors, such as:
-
- automatic email responses using language processing;
- support of credit analysis;
- identification by analysing transaction patterns;
- preliminary processing of incoming claims; and
- modelling of possible damage events.
-
Telecommunications: Several AI-related projects have been already implemented in this sector, including:
-
- automated customer service with phonebots/chatbots;
- forecasting of failures in the network infrastructure; and
- calibration of network coverage by applying self-learning antennae.