Grupo AIA develops algorithms for Quantum Cryptography

AIA’s Innovation Team has completed a software library for post processing in Quantum Key Distribution. With this software, the Institute of Photonic Sciences (ICFO) will be able to implement fully secure communications over conventional optical fiber. Quantum Cryptography is posed as a key technology to replace current cryptographic methods, which face a growing risk of becoming obsolete because of the rapid advances in Quantum Computing.

A thesis transferring neutrino ´s experience to the industrial sector

Last November 27th, Sebastian Piña Otey has successfully defended his doctoral thesis entitled “Deep learning and Bayesian techniques applied to Big Data in the industry and neutrino oscillations”. Although this doctorate thesis is physics centered, there is also an industrial component developed with AIA within the industrial doctorate program framework sponsored by the Catalonian Government called: “Doctorats Industrials de la Generalitat. Sebastian ´s thesis, codirected by Vicens Gaitan, AIA´s representative, along with Thorsten Lux, neutrino´s team main researcher from the Institut de Física d’Altes Energies (IFAE), has developed a project that connects neutrino oscillations (mentioned in a previous blog post) with cutting-edge Deep Learning technology.

The thesis has focused on a new approach for data analysis and extraction of relevant information for the T2K neutrino experiment (Tokai to Kamioka) in Japan. In particle physics experiments, and more particularly, neutrino experiments, data analysis has three main components: detectors´ direct data processing, theoretical model simulation and model parameters determination. These three key aspects are essential for a rigorous and correct scientifically based results´ delivery. Sebastian ´s research had an impact on all three aspects by introducing different proofs of concepts of new technologies.

Firstly, he has successfully implemented an algorithm of Graph Neural Networks to help detecting not physic ambiguous signs during the simulation of a T2K future detector. As a result, the effect on the neutrino can be rebuilt correctly, increasing the performance as compared to traditional techniques. (The paper has been submitted to the Physical Review D and is currently under evaluation. Pre-publication can be download at )

Secondly, Sebastian has combined the normalizing flows technology, invertible neural networks that allow to evaluate and generate data from a probability distribution based on traditional statistical techniques, rejection sampling under the algorithm of Exhaustive Neural Importance Sampling (ENIS). More precisely, along with his supervisors, they have applied ENIS to generate data from a typical neutrino event in T2K, the interaction called charged-current quasi-elastic (CCQE). This paper was published on July 16th in the Physical Review D

Finally, as mentioned in our previous blog post, the normalizing flows were used to determine the parameters of the two neutrino flavors oscillation model. This paper was also published in the Physical Review D on June 2nd

However, the experience gained by the somewhat exotic application of the neutrino oscillations was also transferred to the industrial field. Sebastian and Vicens worked with Red Eléctrica Española through Elewit and could show the impact and value added of these techniques on potential projects of our clients. Thus, Sebastian has successfully achieved our first industrial doctorate, potentiating AIA´s relation with the IFAE and showing the contribution of the industrial sector to pure research.


Grupo AIA takes part in the DNA project developed by Adolfo Domínguez

Adriana Domínguez, as executive president of Adolfo Domínguez, made public in an online press conference last June 19th, the DNA project kick-off in which Grupo AIA has participated as the technological provider. Based on Artificial Intelligence, a recommendation algorithm combined with the experience of Personal Shoppers of Adolfo Domínguez, have allowed them to choose clothe items and accessories, adapted to each of its customers, and send them to their homes without trying them on or going to a shop.

Grupo AIA is pleased to have participated in the project for the retail sector and provided its 30 years´ experience in the development of Artificial Intelligence based applications and data analytics. This Project opens collaboration opportunities between Adolfo Domínguez and Grupo AIA, as the Spanish fashion firm has showed its commitment to innovation as the way to improve its relationship with its customers.

Access to the new DNA service at

Regina Llopis participates in the event «The sum of Intelligences» organized by the UdG

Last May 26th, Regina Llopis, President of Grupo AIA, took part in the online event coordinated by the Universidad de Girona titled «The sum of Intelligences».

More information at and hashtag Twitter  #congrésdigitalSI.

software grupo AIA-ASISA

ASISA and Grupo AIA agree to develop jointly AI-based software

Grupo AIA and ASISA have signed an agreement to develop jointly artificial intelligence-based software aiming at improving efficiency and dynamism of the insurance company´s management processes and ultimately to meet its customers and insurers´ needs.

The project´s kick-off between Grupo AIA and ASISA, a leading health insurance company with the best Net Promoter Score, NPS[i], is aimed at developing innovative software based on basic science (Physics and Mathematics) and Data Science to meet their business needs. This knowledge transfer will enable Grupo AIA to provide intelligence to ASISA´s business processes through advanced analytics methodologies such as Machine Learning.

Grupo AIA focused on solving management efficiency of medical actions at this phase of the collaboration project, will deploy a Machine Learning-based tool that will make medical actions management more efficient and ASISA will be able to make wiser decisions that will benefit its customers. Grupo AIA´ s contribution based on the state-of-the-art Artificial Intelligence technology will help to improve the insurance company´s management and control processes in this business area.

This collaboration project is framed within ASISA´s technological transformation across business processes impacting on both, welfare and management activities´ efficiency and effectivity improvement as well as customers´ satisfaction. The ultimate goal of this process for the company is to provide customers a total digital service including the whole process from services contracts to their management.

[i] Braintrust ´s Observatory of Health Competence.

artificial intelligence drugs

Artificial Intelligence for new drugs discovery

Biomedical science innovation based on AI technology is the long-awaited opportunity for achieving higher effectivity in this industry. New drug development through R&D innovation in a shorter time and at a lower cost is the Holy Grail of the biopharmaceutical industry.

Scientific innovation not only involves finding the molecular mechanism of a disease but also the development of new drugs for the cure, palliation or prevention of diseases.

Innovation in the pharmaceutical industry costs over 2.400,00 million euros, according to Farmaindustria. On the other hand, R&D global investment in the pharmaceutical sector accounts for 30.000,00 million, only in Europe and these figures hikes to 142.000,00 euros worldwide.

From this amount, 57% goes to design, development and clinical tests evaluation phases. The remaining 40% goes to basic research, approval processes and pharmacovigilance.

According to data provided by Biopharmaceutical representatives, developing a new medicine takes about 12 to 13 years from its discovery to its clinical use in patients. However, only a few molecules reach the commercialization phase. Many are left behind along the phases of the drug development process.

It´s precisely in drug targeting discovery and designs that AI-based techniques have cut downtime by half and costs by 25% in the production of new drugs.

Currently, the Spanish biopharmaceutical, Sylentis, has implemented a software based on Neural Networks, SVM and Machine Learning to gather, filter and reinterpret experimental data generated by the pharmaceutical industry. This allows them to enhance and develop the drugs thanks to a  software that trains to generate thousands of specific compounds to deal with a disease in a matter of a few days. The pharmaceutical company reduces the expensive and time- consuming task of candidate´s selection from years to only a few days.

AI for personalized drugs.

A survey conducted by Deloitte and MIT Sloan Management Review last June found out that only 20 % of biopharmaceutical companies are digitally mature enough, and the lack of a clear vision, leadership and financing are slowing down companies´ growth.

According to MarketsandMarkets, AI´s demand in the biopharmaceutical industry is expected to grow from US$ 198.3 million in 2018 to US$ 3.88 billion in 2025.

The four projected areas to drive most of the AI market forward in biopharmacy between 2018 and 2025: drugs discovery, precision medicine, diagnostic imaging and medical diagnosis and research. The report says drugs´ discovery reached a larger market share during the survey period.

These areas that go from the target candidate molecules selection to the production of the new drug provides a unique opportunity to speed up drugs development. The potential improvement of the process includes:

  • Process redesign to speed up new molecules discovery time and is based on expert knowledge.
  • Digitalization of repetitive processes automation and generation of new content and data.
  • Advanced analytics incorporating internal and external sources. Here new predictive model would be included.

A key role for AI algorithms is molecules interaction forecasting to find the disease mechanisms. In turn, these mechanisms could help setting new biomarkers to identify, design, validate and optimize new drugs candidate target and identify existing drugs that could be reused for other indications



Vicens Gaitán, CDS in Grupo AIA: “AI allows us to exploit information better than ever before”

What are AI key points? Are AI-driven companies getting a competitive advantage? At the conference round table held last September at La Salle, “Artificial Intelligence. Why & How to keep your Company alive” were raised these questions and sparked lively discussions thanks to speakers´ participation: Vicens Gaitán, Chief Data Science in Grupo AIA, Pier Paolo Rossi, Advanced Customer Marketing & Analytics Director in Banc Sabadell and Daniel Marco, Department for Digital Policy and Public Administration of Catalonia.

Digital Transformation has become a turning point in companies´ work culture, particularly in data management and AI-based technology to achieve their competitive advantage in their industrial sectors.

However, during the speakers´ interventions at the conference, it was made clear implementing AI was not setting an “automatic pilot”, but instead it involved having essential key elements.

Above all, being able to implement AI to the business to meet specific needs leading to more intelligent decision-making. Vicens Gaitán, Chief Data Science in Grupo AIA, pointed out that “thanks to the implementation of artificial intelligence techniques in companies, these companies can be trained to exploit data as never before”.

In other words, data-driven companies striving for the best and more efficient decision-making must work on these four big areas:

  • Knowing the dataset location and capture it. Data management time must be adequate for accurate and suitable subsequent decision-making fitted to each business case.
  • Dataset creation and assembly
  • Apply the algorithm that will extract information from the data.
  • Make decisions based on results provided by the algorithms.

One of the most sensitive aspects at the conference was the need for customer-centric decision- making for more effective results. Consequently, technology should go hand by hand with the business to lead customer-centric projects.

Artificial intelligence techniques enhance businesses’ capacities to adapt and anticipate strategic and tactic decision making to their real customers´ demand. Therefore, it is essential that the business and Big Data teams work together to achieve business intelligence.

The degree of maturity of Artificial intelligence-driven businesses is based on their capacity to integrate AI as their main structure. The greater artificial intelligence, the greater the corporative intelligence.


data translator

The hidden figure behind a successful AI implementation in the organizations.

The Artificial Intelligence implementation in companies is cross functional: Marketing, Finance, Operations… they all have benefited from the emergence of data driven across business processes in their organizations.

In a recent study published by Fujitsu and Pier Audoin Consultants, shows that the benefits companies have gained through Artificial Intelligence implementation are starting to pay off. This is not a matter of five years´ time. The AI´ s time has come. However, the figures are still low: only 11% of the surveyed companies are implementing AI strategies, 29% have AI projects in progress and 35% expect to do it in the next two years.

Under this classification, they would be defined as innovators, early adopters, followers. In other words, based on the company´s maturity and data adaptation, they will belong to one of the groups before mentioned.

Accordingly, 53% of the companies that have implemented AI or have in mind doing it believe improvement of automation processes depends on it, whereas almost 75% are creating business units for AI´s implementation take-off. The main areas this technology is implemented on is higher production efficiency, maintenance forecasting and above all, in customers´ behavior forecasting for appropriate business actions.

Nevertheless, a survey delivered by MIT Sloan Management Review and Boston Consulting Group a few weeks ago highlighted different data.

Although it claims AI´s rewards promise, these are not risk-free for example, a competitor taking the risk and going a step ahead. These are the innovators that use AI for the company´s across business processes alignment, investment and integration.

Many leading companies see AI not as an opportunity but as a risk strategy.  And this perception has gone up from 37% to 45% from 2017 to 2019 respectively.

Concerning risk management, many AI based initiatives have failed. Seven out of ten of the surveyed companies claimed they have hardly benefited from this technology. And it´s not a trivial matter when almost 90% of companies have invested in AI.

Thus, even if some companies have found out success with AI, most struggle to add value based on it. As a result, many executives face challenges associated with AI: It´s a source of non- exploited opportunities, an inherent risk. But, above all, it´s an urgent issue to tackle. How can executives exploit the opportunities, manage risks and minimize AI associated problems?

Data translator: the hidden figure

Professionals training, not only in technical and scientific areas but also in communication and interpretation, becomes essential for the differential AI value generation. Deep understanding of the business needs and knowing how to convey that to the technical teams in charge of implementing AI is the Holy Grail of all the companies and providers of this service.

On the other hand, Mckinsey says that success results based on AI and data analytics do not depend only on data scientists, data engineers or data analytics teams. A transversal figure is required: a data translator.

Mckinsey believes this figure can ensure the organizations achieve real impact from their analytical initiatives as it can help to understand correctly the business needs and translate them into a scientific -technical language and vice versa.

Data translation experience allows this figure to get deep knowledge of the core business and its value chain in diverse areas: distribution, health, marketing, manufacturing or any other environment.

As the consulting company defines it, in their role, translators help to guarantee deep knowledge generated through sophisticated analytics is translated into impact at every level of the organization. By 2026, The Global McKinsey Institute estimates translators demand will reach two or four million only in the U.S.

Thus, translators take advantage of their insights in AI and analytics to convey these commercial objectives to data professionals who will create the models and the solutions. Finally, the translators ensure the solution produce the insights the company can interpret and execute and ultimately, communicates the benefits of these insights to the businessmen to boost adoption.

One way to reduce the risk strategy companies have taken when they decide to be ahead of their competitors in their sector, is without any doubt, the capacity to interpret the data and offer insights based on them.



marketing ia

The top ten AI uses in Marketing [Infographics].

Nowadays Artificial Intelligence based applications in marketing and sales enable companies to know their customers better and to offer them the best products promotions in real time. Chief Marketing Officers-CMO and their teams need automatic learning and artificial intelligence to stand out and take advantage over their competitors.

In pursuit of customer satisfaction, the best CMOs manage to balance their marketing strategies and elements that make the company brand and experience unique.

Knowing how potential buyers make up their minds on how, when and where to buy, turn marketing strategies more interesting. Advanced analytics enables customers segmentation for better knowledge of their preferences. Thanks to this knowledge, products purchasing propensity or churn prevention in the purchasing process or suitable pricing setting can be estimated, among many other things.

According to a recent survey delivered by Forbes Insights and Quantcast Research, the use of AI allows marketing and sales departments to boost sales by 52% and increase customer retention by 49%.

The infographics shows data of the ten most relevant Artificial Intelligence contributions to marketing teams. In the next two years, according to the reports, the implementation of Artificial Intelligence based technologies and automatic learning will be adopted by the companies that realize their benefits.




Math and music: Advantages of using artificial intelligence

We are used to getting Spotify´s music selection and classification based on what we have listened to and our music taste. Thus, the Swedish company must upload over 20000 new songs or podcasts each day and thanks to artificial intelligence´s help. It provides the music we have listened to the most for some time and its function: “your summer memories” and it creates different music groups depending on what we have listened to lately.

With this help, music gender classification has become obsolete as music lists generation by artificial intelligence do not depend on music gender but on “the good music”. It´s obvious not everybody likes the same music gender, however, specific mathematical patterns which are transversal to music gender do work. Consequently, there are people who like Pop music and still enjoy a song classified as Rock music.

There are different fields in which artificial intelligence can improve music processes. In the 50s, Alan Turing was the first to record computer-generated music. This was the start of an interesting area in which AI-created music through reinforced learning. The algorithm learned what characteristics and patterns created a specific music gender and finally composed.

Thanks to this application, artificial intelligence helps companies to create new music or assist composers in their creations.

Another field in which artificial intelligence has great acceptance is editing. The experience of listening to music with a clear and clean sound is, without any doubt, one of the main features music lovers appreciate the most. Although the creative component is still necessary, AI can train to edit their audios properly to those not having that creative skill

When we talk about applied mathematics to music, we must understand what is involved in diverse areas such as tuning, musical notes, chords, harmonies, rhythm, beat, and nomenclature.

The beginning

In 2002, Polyphonic HMI was founded on the premises of using artificial intelligence and apply it to the music industry. Based on the study of the mathematical components of music, the probabilities of success of a song could be determined. Although an artist´s success depends on many factors, this system helped to simplify the task of finding which song could serve as a launch single of a new album and even of a new artist. Thanks to this, record companies, producers and representatives could allocate resources in a more favorable context. Music commercialization has always been an expensive business and a big challenge to finding promising artists and successful songs.

Fifteen years later, the leading technology companies are investing in this technology focusing on different processes of the music industry. Thanks to mathematics, we can see the impact of artificial intelligence on the music we listen to which enrich our musical experience.