Regina Llopis, FEDEPE 2019 Businesswomen Leadership Award

Regina Llopis, president and CEO of Grupo AIA (Aplicaciones en Informática Avanzada, S.L.), has been awarded “The Businesswomen Leadership Award” granted by the Spanish Federation of Female Directors, Executives, Professionals and Entrepreneurs, FEDEPE. This distinction is a recognition to the efforts, creativity, and motivation in business management best practices for success.

During her speech, Regina Llopis highlighted the impact of the awards and urged the rest of the awardees, many technology leaders among them, to continue as a model for other scientific and technological women. She reminded the reward was a recognition to a company´s steady work on Artificial Intelligence for thirty years. Grupo AIA has developed new methods and algorithms based on Artificial Intelligence and other disciplines for solving complex problems in different industrial sectors and services. Moreover, Grupo AIA ´s corporative strategy is based on investment in R&D as it allows it to ensure its technological leadership and comparative advantage. Likewise, Regina Llopis has set new business partnership models with customers.

On the other hand, the Vice-President of the Acting Government of Spain, Carmen Calvo, recognized the role of Spain as a benchmark for gender equality, however, she admitted many remains to be done by the institutions. During Ana Bujaldón´s speech, FEDEPE´s president, she urged to make progress towards establishing binding rules for equality once for all.

FEDEPE award honors Regina Llopis ´management success in achieving R&D leading position through advanced technological research opportunities with leading prestigious institutions as NASA in the U.S.A and others in the EU. And all this is due to a highly creative and qualified team which has allowed AIA to lead Artificial Intelligence in the last thirty years.

The ceremony was held at the headquarters of the Higher Council for Scientific Research (CSIC) in Madrid, Spain. The awards granted were: the Leadership Women’s Directive Award to Pilar López, president of Microsoft Spain; Professional Woman Leadership Award to Silvia Gil, captain of the Spanish Guardia Civil ,Woman Innovation and Entrepreneurship Award to Arantxa Unda, CEO of Sigesa, a medical software company; Communication Committed to Women Award to the EFE Agency; Boosting the Promotion of Women Award to the sports corporation Adidas; and the International Award to Bisila Bokoko, Founder and CEO of BBES, a business development agency in New York. Susana Griso and Juan Luis Cano in charge of conducting the ceremony created a cheerful and professional atmosphere.

A Special Award was granted for the Promotion of Professional Development of Women with Disabilities to Rosa García, Member of the Board of Directors of Tubacex and Sener.

FEDEPE awards held its 28th annual edition and once again counted on her Majesty the Queen attendance to the ceremony as President of Honor and the support of the Ministry of the Presidency. The ceremony was presided by her Excellency Vice President of the Acting Government of SPAIN Carmen Calvo, and the FEDEPE ´s President and Vice President, Ana Bujaldón and Francisca García Vizcaíno respectively,


Adaptive Machine learning or how to analyze environment´s volatility

The environment is constantly changing. Although we may think the environment is immutable, that the data collection on people and the environment hardly changes, there is nothing further from the truth. Data changes so rapidly that predictions based on Machine learning techniques become soon obsolete. Why?

According to Gartner´s latest trends curve, one of the technologies that will flourish in the next years will be the Adaptive machine learning, also known as Adaptive automatic learning. This technology allows for continuous learning with online machine learning models and in real-time. This capacity allows machine learning models to adapt themselves to the constantly changing world.

Due to this high adaptation capacity, this technology is particularly useful for autonomous cars training. These cars must be able to incorporate new data in real-time, analyze it and make decisions based on this data.

However, the use of this technology goes beyond autonomous cars (which Gartner gives over 10 year-period+ to see one). Adaptive autonomous learning in real-time requires efficient reinforcement learning, in other words, how an algorithm must continually interact with its environment to maximize its reward. These algorithms would work for agriculture, online marketing, smart cities, and even financial institutions or any other industry using IoT.

In these changing environments is not possible to collect all the generated data, organize and quantify it and train a “traditional “machine learning model as it must be retrained and make decisions in real-time.

The idea that emerged in the 50s

Although it may sound very innovative, the truth is back in the 50s B.F. Skinner developed a training machine focused on skills development. The idea was to monitor students´ progress in programmed learning. The machine adapted the questions based on the students´ previous correct answers and provided them the capacity to learn at their own pace.

It was not until 1970 that this idea was applied to artificial intelligence. However, as happened with many other things back then, the development of the adaptive automatic learning was slowed down due to computing capacity, capture and data storing. Simply, they were not ready yet.

Despite all this, Adaptive automatic learning did not die there. In the last decades, these technologies became more agile, scalable and easier to use although not widely used.

Nowadays, there are a myriad of fields for using adaptive machine learning, besides autonomous cars or robots. In the financial field, this technology allows for online data incorporation, for example, to monitor the stock market. It is also being used for location prediction (very useful for autonomous cars), advertising and in insurance field.

According to Gartner, adaptive machine learning is still a challenge as systems based on self-learning and autonomy will require considering aspects such as privacy, ethical aspects, and security. On the other hand, companies will apply continuous learning for autonomous and smart decision-making.

This doesn´t mean current methodologies based on machine learning must be changed as once the off-line algorithms are trained, the adaptive learning allows to improve, contextualize and personalize even more existing machine learning models.


Behavioral Marketing, towards product´s customization

Today we are used to receiving products and services recommendations from certain companies and we usually think these recommendations are made specially for of us. We believe products ads sent to us are personalized, targeted to each person, however, it isn´t quite true.

For most people, personalization and recommendation are the same thing. When a company adapts its services to meet customers´ needs, it can use recommendations to personalize their purchases, although these words may sound like synonyms, they aren´t.

The technological firm´s first approach to what constitutes a personalization engine is defined in its Gartner´s guide for Digital Personalization Engines in 2015 as:

A process that creates a relevant and individualized interaction between two parties designed to improve the customer´s experience. It uses information based on the recipient´s personal and behavioral data as well as behavioral data of similar people to offer an experience to meet specific needs and preferences.

Therefore, personalization engines are solution providers for the personalization process. They determine and provide the customer experience to deliver personalized messages through digital channels (emails, text, automatic notification) and provide analysis and reports to the business user.

Whereas product or content recommendations, involve a small selection of items in the catalogue sent to the user in a variety of ways (banners, messages, recommendation tables), in other words, a recommendation is more like a presentation of items.

However, a recommendation is a form of personalization, but a personalization is not a form of recommendation. Let´s take YouTube for example, Google video platform can suggest related videos based on users previous viewing habits. A recommendation would also be based on what other YouTubers watched. Hence, Facebook personalizes the news feed of your profile based on the activity of your friends but, does not recommend one or the other. This is personalization, as it is based on the individual´s specific habits rather than a general algorithm. The more you know about a person, the better, not only about videos habits. In other words, a recommendation is usually based on elements whereas a personalization is based on individuals.

Therefore, how can we talk about personalized recommendation systems when one refers to a product and the other to a person? Simple: incorporating the behavioral habits of the person in the recommendation engine. The more you know about a customer, the more personalized the products recommendation will be.

Today, there are only a few companies that can really personalize their behavioral marketing strategies as it is necessary to implement not only an algorithm for the recommendation, but also to incorporate all the customers and non-customers data. This is the challenge. Let´s give an example to explain how to overcome this.


Behavioral Marketing in banking

We know how the banking sector is moving ahead at a staggering speed to meet customers´ needs. Gone are the days when financial products ads were targeted to everybody regardless of their personal conditions causing many bad headaches.

Today, the sales and marketing departments of banks need to know better their customers to be able to offer them what they really need at the right time. Banking institutions are using a strategy consisting of incorporating the user´s browsing data.

Behavioral Marketing involves the sales, commercial and ads activities based on the customer´s behavior patterns obtained through cookies, social networks, web analytics, browsing and purchasing history and IPs. In this case, cookies collect anonymous information of browsing habits aiming at targeting ads according to their interests.

Therefore, Behavioral Marketing allows for ads customization based users profile and avoid sending ads to customers which will end up as spammers.

Consequently, when a customer accesses a bank web page it leaves a trace like a “bread trail” showing the yellow brick road of what, how long and what part of the web it has seen, whether it has taken any action like checking its credit. At the same time, if we incorporate all the available information of this customer (browsing activity, purchasing history, IP address, social networks, app data, etc.) we get knowledge of its propensity to certain banking products.

Keeping in mind the user identity will allow for customers characterization and their relationship with the institution through a series of variables that will describe more precisely each user. Using browsing habits as part of the user’s characterization will allow for a broader vision and therefore improve personalization of products recommendation.

Deep Learning and Machine Learning techniques allow to incorporate user´s data which could enhance the personalization model and would allow us to know the propensity of a specific person to buy a certain product. In other words, better conversion rates of their sales departments in their products offers.

Gartner’s hype cycle has change

Like every year, Gartner, the technology firm has released the technology trends for the coming years. CTOs, CIOs, and CEOs worldwide are evaluating these technologies to figure out the technological efforts they should be focused on and how to strategically implement them.

It 1995, Gartner started to publish the top emergent technology trends and since then, its predictions have gained a reputation among the tech community. Since 1995, this model is seen as the new technology adoption guide. The graph is divided according to maturity, in other words, is it an emergent technology or a technological trigger, is there excessive enthusiasm or overestimation, disappointment and gradual adoption of such technology.

Curva gartner

Those who lived those days would remember this assistant was not very successful. It was a nuisance rather than a help. Finally, at Redmon´s decided to eliminate it. Today, almost 25 years later, these virtual assistants are living a second youth thanks to the Chatbots.

And why this? Because researchers, startups and leading technology companies continue to develop this type of technology years after and turn an extravagant idea into something essential for the society.


Primera curva de Gartner 1995

Gartner’s Hype cycle today

This year, the trends curve highlights emergent technologies with a strong impact on the businesses, society and people for the next 10 years. In 2019, the list includes a technology offer ranging from low latency global internet, a virtual map of the real world and imitation of human creativity.

However, this hype has been trending down with “fewer things” on its right side lately, which means Gartner intends to focus more on technologies that can really make it. This is due to a negative opinion of Gartner’s curve during recent years as very few technology trends on the list have been able to overcome the theoretical phase.

As a matter of fact, looking at this curve, there isn´t any blockchain, artificial intelligence, digital twins, deep learning, augmented reality this year as these technologies have been widely implemented. However, analyzing the firm´s lists of recent years, we´ll see that some technologies which had a two-year development period have taken over a decade to start being a reality. For example, in 1995, voice recognition was allegedly at its productivity phase, however, it is now that it has its “place in the world” thanks to the development of deep learning. Two decades later.

Thus, we might think Gartner´s predictions are not very good. None of its 24 curves of emergent technologies included virtualization; NoSQL; open-source, Map Reduce or Hadoop. This is an overestimation of emerging technologies or simply that they do not follow Gartner´s curve rules.

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.



Learn about the state of Quantum computing

In the last quarter of 2018, The E.U. launched the Quantum Flagship megaproject with more than 1000 million euros in funding for a 10 – year period and more than 5000 researchers committed to the development of quantum technologies and to bring their capabilities into the market. Europe has finally made it to the race started by China and the U.S. Both countries want to dominate, or at least, to lead the so- called quantum race. The European Union finally rode on the most powerful research and development program of recent years.

This Quantum Flagship will build an European program network based on quantum technologies which will boost an ecosystem to provide the necessary knowledge, technologies and infrastructure for the development of this industry. The research areas are focused on quantum communication (QComm), quantum computing (QComp), quantum simulation (QSim), meteorology and quantum detection (QMS) and basic science (BSci).

Big technological companies doing research on quantum computing are immersed in a commercial race to see which will be the first to run a quantum computer. However, which are the main features of quantum computing and how can we benefit from it?

Current computers, either portable or big computers, are based on basic binary circuits, in other words, a “yes/no” response. Thus, programmers can create tasks to make that computer work with these sentences “if this/then that”. However, sometimes a computer with these characteristics cannot solve efficiently certain problems. For example, in many mathematical optimization problems, current computers take their time to evaluate individually every possible solution until optimal is found.

In contrast, quantum computers have a completely different concept as they do not use the “yes/no”. binary logic. By their nature, their basic circuits can respond to “yes/no/both (in the same proportion)”. With a quantum system, a developer can implement instructions “if this, then that/not-that/both” and here is what makes the difference. They can explore a great volume of data at the same time, offer very efficient solutions to very complex problems such as transport routes optimization.

Until now, quantum computing has not been developed yet, at least the way we are used to (a computer executing tasks), because one of the problems facing quantum computing is building multipurpose quantum computers. Compared to a normal computer, a quantum machine is extremely complex. The first models available, like the IBM´s recent version, are based on superconductive constituents (including Josephson effect devices), which need to work at a temperature below -273 ºC (almost absolute cero) The necessary cryogenics technology of the components to be able to read and handle steadily these qubits are extremely expensive and complex.

Despite the inherent technical difficulties to quantum-mechanic systems, extensive research has been carried out and confirmed promising applications of quantum computing as soon as the necessary hardware is ready. Artificial Intelligence is among them.


Supervised and unsupervised learning techniques for fraud detection.

Artificial intelligence is redefining fraud prevention techniques as it allows to obtain information based on experience. This information consists of transaction activities, behavior and trends. Before the use of artificial intelligence, the applied methods were based on rules that helped to analyze historical fraud patterns but could not prevent them. Although these models could identify fraud attempts, they did not provide information of the future.

Nevertheless, the technological sophistication of fraud crimes is higher and more precise and efficient attacks have grown during the last few years

As a result, leading companies dealing with potential fraud crimes, mainly in banking and insurance, must increase monitoring accuracy and acuity of customers potential risk for the institution. Decision-making on accepting or rejecting payments, limiting charges refund and reducing operational and reputational risks is much easier now.

Fraud prevention in the future will depend on a combination of supervised and unsupervised automatic learning techniques. Supervised automatic learning finds patters based on historical events, factors, trends, etc., and unsupervised automatic learning looks for relationships and variable links, a combination of both methodologies would help to prevent fraud in the following:

Detection in real time. The use of artificial intelligence enables the detection of attacks in real time, instead of weeks that usually takes to start receiving reverse requests of charges.

Thwarting the most sophisticated attempts of fraud. Fraud techniques get more and more sophisticated. Artificial intelligence would help to prevent and reduce these attacks.

Scoring in real time. Provide analysts a scoring for a better perspective to set the limits to maximize sales and minimize losses in real time.

Immediate transactions. Fraud prevention systems based on AI enable immediate transaction´s approval provided it is within the reverse charges threshold of the main debit and credit cards.

Reduction of false positives. False positives are reduced thanks to supervised and unsupervised automatic learning, whereas current techniques cannot efficiently detect them. Frequently, when a customer pays an unusual amount of money or from a new location, the card is blocked by the system as it interprets it wrongly as a suspicious activity. With artificial intelligence, it is possible to identify more precisely any change in customers expenditure habits.

Profitability in low margin products. AI has allowed insurance companies to continue their profitable business and attract new customers whose historical purchases are not part of the historical supervised learning of fraud systems

Supervised and unsupervised learning should be complemented with experts´ knowledge aiming at a mixed approach to focus their attention in more suspicious cases detected by AI.

analitica avanzada seguros

How advanced analytics is a boon for insurance companies.

Advanced analytics is presented as the best way for insurance companies to prevent fraud, improve user´s experience and avoid churn.

logo-AIA con fondo

Aplicaciones En Informática Avanzada, Sociedad Limitada. Ordinary and extraordinary general shareholder’s meeting

Mrs. Regina LLopis Rivas,  as the Sole Administrator  of the company  APLICACIONES EN INFORMÁTICA AVANZADA, S.L.,