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DIH4AI_OC2
Call identifier: DIH4AI_OC2
Publication date: 2022-10-17
Status: Closed
Opening date: 2022-10-17 09:00:00 (Brussels time)
Closing date: 2022-12-16 17:00:00 (Brussels time)
Call general detailsThematic areasSupporting documentation
Call Summary
The objective of this Open Call is to extend the DIH Network ecosystem to more regions, additionally to the 5 Regional DIHs who participate in the Consortium (Bavaria, South Netherlands, Paris Region, Czechia, Saxony-Anhalt). oreover, the Open Call also aims to establish the most needed link between the DIH Regional engines and the AI4EU Pan-EU service platform power, in order to ignite a virtuous Cross-Sectorial European economy of intelligence for SMEs at scale
Call Keywords
Earth Observation
Finance and Insurance
AI
DIH
Manufacturing
Agrifood
Energy
PublicAdministration
Intra- Regional experiments
Oriented to reinforce the Intra-Regional experiments, in particular, the provision of Skills development and Technology Provision and Data Management services, the mini-consortium shall be formed by:
o An AI Technology Provider SME or start-up, who leads the experiment, and should be the main IPR and exploitation rights owner of the solution, and the coordinator of reporting the Project Consortium by submitting the needed documentation, etc.
o Its Regional DIH, who will provide the SME with the delivery of services related to Skills development and Technical support and mentoring.
o Optionally, an End-User SME, or a Policy Maker can finalise the mini-consortium. This third tentative participant will be the one in charge of validating the developed solution.
Topics:
AgrifoodThe role of AI in the food industry is becoming increasingly important due to its ability to help save food, improve the hygiene of production sites, and support the processing equipment in the preparation of dishes; therefore, there are many cases of use of Artificial intelligence and Machine Learning in the Food Industry. Automated systems can, in a few seconds, collect hundreds of data sets on a single food product and quickly make an assessment of it. A system, for example, can collect and process data from hundreds of individual ingredients, as they move quickly on a conveyor belt; these systems can significantly reduce labour costs and waste . This Sectorial area is not only limited to Food Industry, but also to Precision Farming: AI-driven sensors that will inform farmers (or machines) when is the optimal time to fertilise, irrigate, plant or harvest.
Earth ObservationAI can create pathways to the collection of information about the Earth, through remote sensing techniques, and its eventual analysis, parameter correlation and interpretation. Satellites provide us with vast quantities of data, over 150 TeraBytes per day, which is not always processed efficiently . There are tremendous opportunities that can come from AI research, which has great potential in terms of improving our knowledge of planetary interactions, such as physical, biological, chemical, and anthropological interactions. On the other hand, not only it decisively helps with Meteorology Forecasting and Climatology (with a special focus on Climate Change), but it greatly supports research in areas such as Oceanography, Agriculture, Ecology and the Environment, and Infrastructure.
Finance and InsuranceThe interest in AI in Financial Sector is increasing, like in other industries. It is estimated that 75% of banks with over $100 billion in assets are implementing AI technologies . Another estimation states that banking and other financial service companies can generate more than $250 billion in value by applying AI technologies in their financial processes . There is still a long way for AI models to be widely used in financial services. For example, historical bias can be an issue in automated credit scoring. AI models could take into account variables like gender, race, or profession, which may have been used historically in credit applications. Banks need to monitor models to avoid such situations. Although the integration of AI into finance and insurance needs further development, the benefits definitely outweigh the potential costs. AI technologies will help banks and other institutions accelerate their processes with reduced cost and error while ensuring data security.
Forecast behaviourFinancial processesMachine Learning techniquesAI modelsAIDIHFinance and Insurance
ManufacturingAI and industrial automation have advanced considerably in the recent years. Development in ML techniques, advances in sensors and therefore, the growth of computing capabilities has helped produce a brand-new generation of robots. AI helps allows machines to automatically gather and extract data, acknowledge patterns, learn and adapt to new things or environments through Machine Intelligence, Learning and speech recognition. AI helps manufacturers in:
o Creating rapid, data-driven decisions.
o Facilitating enhanced production outcomes.
o Advancing process effectiveness.
o Minimising operational costs.
o Facilitating superior scalability.
o Supporting product development.
Public AdministrationThe public sector may also make use of Artificial Intelligence technologies, often used to improve efficiency and decision making, foster positive relationships with citizens and business, or solve specific problems in critical fields, such as health, fraud detection and tax evasion, transportation and security, statistics, etc. These AI solutions include the ones that are used to support the public services and engagement – the provision of services to the citizens and businesses, and to facilitate communication with the general public and its participation. Additionally, solutions could also focus on internal management improvement. These AI use cases are used to assist in the management of the internal organisation, such as human resources, procurement, ICT systems or other utilities.
EnergyFor several years now, the energy sector has been developing huge amount of data regarding the consumption of the different energy resources available. It must be pointed out that the AI technologies are increasingly being adopted by all the companies that belong to this sector due to its ability to help energy suppliers and end-consumers to reduce and optimize their consumption of these resources as there is a willingness to contribute to reduce the CO2 emissions and greenhouse effects.
Optimize energy consumptionbig dataenergy transitionMachine Learning techniquesbehaviour forecastDIHAIEnergy
Cross- DIH Inter-Regional experiments
Performed in order to support the DIH network creation (in relation to Cross-DIH Inter-Regional experiments), in which more than one DIH provide at least one service in collaboration, the mini-consortium should be composed of:
o An AI Technology Provider SME or start-up, who performs than leads the experiment, and should be the main IPR and exploitation rights owner of the solution, and the coordinator of reporting the Project Consortium by submitting the needed documentation, etc.
o Its Regional DIH, who provides the SME of services related to Skills development and Technical support and mentoring.
o Another DIH, likely an Experimental Facility owner, who also provides training and technical mentoring, and at the same time, will help with the testing and validation in order to ensure that a fine-tuned, marketable solution has been developed.
Topics:
AgrifoodThe role of AI in the food industry is becoming increasingly important due to its ability to help save food, improve the hygiene of production sites, and support the processing equipment in the preparation of dishes; therefore, there are many cases of use of Artificial intelligence and Machine Learning in the Food Industry. Automated systems can, in a few seconds, collect hundreds of data sets on a single food product and quickly make an assessment of it. A system, for example, can collect and process data from hundreds of individual ingredients, as they move quickly on a conveyor belt; these systems can significantly reduce labour costs and waste . This Sectorial area is not only limited to Food Industry, but also to Precision Farming: AI-driven sensors that will inform farmers (or machines) when is the optimal time to fertilise, irrigate, plant or harvest.
Earth ObservationAI can create pathways to the collection of information about the Earth, through remote sensing techniques, and its eventual analysis, parameter correlation and interpretation. Satellites provide us with vast quantities of data, over 150 TeraBytes per day, which is not always processed efficiently . There are tremendous opportunities that can come from AI research, which has great potential in terms of improving our knowledge of planetary interactions, such as physical, biological, chemical, and anthropological interactions. On the other hand, not only it decisively helps with Meteorology Forecasting and Climatology (with a special focus on Climate Change), but it greatly supports research in areas such as Oceanography, Agriculture, Ecology and the Environment, and Infrastructure.
Finance and InsuranceThe interest in AI in Financial Sector is increasing, like in other industries. It is estimated that 75% of banks with over $100 billion in assets are implementing AI technologies . Another estimation states that banking and other financial service companies can generate more than $250 billion in value by applying AI technologies in their financial processes . There is still a long way for AI models to be widely used in financial services. For example, historical bias can be an issue in automated credit scoring. AI models could take into account variables like gender, race, or profession, which may have been used historically in credit applications. Banks need to monitor models to avoid such situations. Although the integration of AI into finance and insurance needs further development, the benefits definitely outweigh the potential costs. AI technologies will help banks and other institutions accelerate their processes with reduced cost and error while ensuring data security.
Machine Learning techniquesdata securityautomatic processesfinancial servicesAI modelsDIHAIFinance and Insurance
ManufacturingAI and industrial automation have advanced considerably in the recent years. Development in ML techniques, advances in sensors and therefore, the growth of computing capabilities has helped produce a brand-new generation of robots. AI helps allows machines to automatically gather and extract data, acknowledge patterns, learn and adapt to new things or environments through Machine Intelligence, Learning and speech recognition. AI helps manufacturers in:
o Creating rapid, data-driven decisions.
o Facilitating enhanced production outcomes.
o Advancing process effectiveness.
o Minimising operational costs.
o Facilitating superior scalability.
o Supporting product development
Public AdministrationThe public sector may also make use of Artificial Intelligence technologies, often used to improve efficiency and decision making, foster positive relationships with citizens and business, or solve specific problems in critical fields, such as health, fraud detection and tax evasion, transportation and security, statistics, etc. These AI solutions include the ones that are used to support the public services and engagement – the provision of services to the citizens and businesses, and to facilitate communication with the general public and its participation. Additionally, solutions could also focus on internal management improvement. These AI use cases are used to assist in the management of th e internal organisation, such as human resources, procurement, ICT systems or other utilities.
Energyfor several years now, the energy sector has been developing huge amount of data regarding the consumption of the different energy resources available. It must be pointed out that the AI technologies are increasingly being adopted by all the companies that belong to this sector due to its ability to help energy suppliers and end-consumers to reduce and optimize their consumption of these resources as there is a willingness to contribute to reduce the CO2 emissions and greenhouse effects
energy transitionMachine Learningoptimize energy consumptionconsumer behaviour forecastDIHAIEnergy