Carlos Martínez Miguel

Artificial Intelligence allows machines to learn, both supervised and autonomously. The proliferation of Cloud technologies, the digitalisation of images, texts and audios and the development of IoT (Internet of Things) have made it possible to gather the large volumes of information that machines need to learn. In this way, machines, through Artificial Intelligence, acquire the ability to find patterns and relate data, events or variations even imperceptible to the human eye; calculate what is going to happen in a certain area and even provide answers to questions thanks to data analytics, with the potential that this has to help us find solutions to some of the problems of our society. Artificial Intelligence is already present in our lives much more than we realise. Some everyday of Artificial Intelligence examples are the algorithms that recommend content on video-on-demand platforms, those that prevent and detect fraud by identifying anomalous use of bank cards, or those that recalculate the route in the car's GPS based on traffic conditions. Artificial Intelligence has also demonstrated its capacity in areas such as industry, where it prevents failures and breakdowns in machines and systems to avoid incidents or unforeseen stoppages; health, where it has numerous applications in both the diagnosis and treatment of diseases such as Alzheimer's or cancer; or education, where it can anticipate school dropouts, detect talent, or personalise study plans based on the abilities and individual needs of each student. AI of Things Deep Learning: everything you need to know September 6, 2022 Towards a responsible Artificial Intelligence The benefits of Artificial Intelligence are therefore enormous. It allows us to reach far beyond what our human analytical capacity makes possible and opens up great opportunities in the use of data by companies and organisations. The growing importance and influence of data in our lives makes it necessary to develop responsible Artificial Intelligence in which algorithms pivot around three essential principles: ethics, transparency and explainability. Ethics: as algorithms acquire the ability to make or influence decisions, they need to respect social norms so that they are fair, inclusive, diverse and respectful of privacy. Transparency: to avoid algorithms being "black boxes" in which we do not know what happens, we need to know how they are applied and how they work, being able to access the data sources used and the mathematical formulae employed. Explainability: we need to be able to understand the "behaviour" of the algorithm, what results it is generating and why it is generating them, or why it makes a decision or arrives at a particular deduction and not another. Ensuring that the data that will be used to train and teach the algorithm are free of bias and are shaped in a fair manner, aligned with human rights and in line with the rule of law, especially when dealing with personal data, is critical for an algorithm to be ethical. Principles of ethics and transparency In this sense, the European Union's regulatory model is oriented in this direction and public bodies and large companies are focusing their efforts in this direction. One example is Telefónica, which published its Ethical Principles on the Use of Artificial Intelligence in 2018. Children need to be taught about computational thinking, algorithms and Artificial Intelligence. The key to complying with the aforementioned principles is to improve the population's level of knowledge about Artificial Intelligence by investing in education in this area. Children need to be taught about computational thinking, algorithms and Artificial Intelligence, just as they are increasingly trained in programming and computer science. These principles of ethics and transparency are critical to building a responsible and inclusive Artificial Intelligence that fosters equal opportunities and drives economic and social progress. In short, Artificial Intelligence at the service of people, which contributes to building a better society.

We are only in the early stages of AI development, but its impact is already huge When making predictions in the technological field, it is always advisable to start by clarifying some basic concepts. Doing so prevents misinterpretation from leading to exorbitant expectations that will undoubtedly be followed by profound disappointment. In the case of Artificial Intelligence (AI), it is key to distinguish between its three fundamental types according to their capability: Artificial Narrow Intelligence (ANI or Applied AI): this focuses on solving specific problems. For example, predicting when a machine is going to stop working in order to anticipate its failure and avoid it. Artificial General Intelligence (AGI): this is the one that is comparable to human intelligence in all aspects. It would be an artificial intelligence that would have the same capabilities as a human being. Artificial Super Intelligence (ASI): an artificial intelligence that is superior to human intelligence in all aspects. Today we are in the era of Applied AI, which has made great progress in recent years thanks especially to deep learning. However, AGI is still at an early stage of development and expert predictions suggest that it will not become a reality until at least 2040 or even decades later, the biggest obstacle to its development being the lack of knowledge we still have about the human brain. Finally, ASI can still be considered "science fiction". Therefore, to the disappointment of dystopia-loving readers, the possible arrival of super-intelligent robots with the ability to control and subjugate the human race is still a long way off. AI will play a major role in the transformation of all economic sectors by 2022 Fortunately, the age of Applied AI has many more benefits than drawbacks and is enabling a very positive transformation of activity in major economic sectors. For example, the tourism sector, perhaps the sector most affected by the pandemic, is taking advantage of AI to reinvent itself. This reinvention is based on a deeper understanding of the needs and interests of visitors, thus being able to personalise the services on offer in order to attract them and build loyalty. The use of multiple data sources (mobility, card payments, navigation, etc.) allows for the development of advanced analytical models that can predict demand and adapt service capacity dynamically and efficiently. In the mobility sector, by 2022, we will see a consolidation of the use of AI models, powered by data from connected vehicles and other sources, to optimise routes, maximise road safety and minimise environmental impact. Smart logistics will continue to accelerate, spurred by the unstoppable growth of e-commerce, including trials of autonomous delivery vehicles and the consolidation of end-to-end asset traceability, thanks to IoT technologies. In retail, the need to develop a customer experience that seamlessly connects the physical and online worlds will continue to drive the adoption of AI. Models will be developed to maximise the conversion of customer interactions into sales, combining multiple data sources from both worlds. Finally, the industrial sector will undoubtedly be one of the most advanced in its transformation. The massive sensorisation of factories and their connection in minimum latency environments thanks to 5G private networks will be key in the deployment of AI use cases. Predictive maintenance, quality optimisation, minimisation of waste and residues, movement of materials with automated guided vehicles (AGVs), etc. are just a few examples. In Europe, and in Spain in particular, recovery funds will accelerate mass adoption of AI In Europe, and especially in Spain, this transformation will be accelerated with the arrival of funds from the "Recovery, Transformation and Resilience Plan" approved by the EU. A significant part of these funds is aimed precisely at boosting the adoption of AI in all areas of economic activity. These funds will contribute to the financing of projects for the adoption of Big Data and AI infrastructures, the development of use cases, the implementation of data governance models, training and capacity building in this field, etc. In addition, these funds will enable SMEs to start using these technologies, thanks to the Digital Kit programme, which includes modules oriented towards intelligence and analytics. 2022 will undoubtedly be an exciting year in which we will continue to build realities and debunk myths around AI.