Artificial Intelligence also suffers from coronavirus

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Several companies that offer technological services in Spain have recognized that theirArtificial Intelligence (AI) algorithmsThey have experienced malfunctions due to sudden changes in people’s behavior caused by the coronavirus pandemic, just as AI developers in the rest of the world have done before, and that is seen in the errors of facial recognition with face masks or fraud detection systems.

Last week, an investigation of theMassachusetts Institute of Technology (MIT, United States)warned about this phenomenon. According to international technology providers, AI failures have affected very different sectors, from recommendations of streaming platforms to automated inventory systems in stores.

Now, companies that offer IT services in Spain such as Atos Iberia and Fujitsu have confirmed to Europa Press that these problems have also been registered locally. Its impact has focused onfacial recognition mechanisms when people wear masksand more generally they have also affected sectors such as commerce, transport and logistics.

“All AI algorithms base their ‘intelligence’ on the data that was used for their training”, Carlos Cordero, CTO of Fujitsu, has assured this agency. In the case of facial recognition mechanisms, these have been trained with full-face images, so the use of masks as protection against the Covid-19 implies problems.

To avoid them, there are already some facial recognition systems that work with a mask, such as those developed by the Barcelona company Herta and recently by the SPEC Group, among others, the latteralso able to measure temperature remotely.

For this same reason, other members of the industry at the international level, such asthe American startup Workaround and Wuhan University (China), have begun to develop databases with photographs of the faces of people with masks collected through the Internet, in some cases from images of social networks such as Instagram. In both cases, these data have been published openly on the Github platform.

The problems have also affected iPhone mobiles and their face unlock Face ID, which does not work with a mask. Therefore, the latest version of its mobile operating system, iOS 13.5, has started to automatically show the password screen.

This measure accelerates the access process for people wearing coronavirus masks, sinceavoid waiting secondswhile the Face ID system tries, unsuccessfully, to recognize the user’s covered face.

Fraud detection error

Errors in AI due to changes in human behavior have also affected other different applications, since they are “extrapolated to practically all the AI ​​algorithms that have been trained withinformation that has changed substantially in a few weeks, “has explained the Fujitsu CTO.

Among the AI ​​mechanisms that have failed during this crisis are automatic fraud detection systems such as the use of credit cards, risk calculations in the financial sector, demand predictions and other predictive algorithms that“they have been little or not at all effective due to the sudden change in the behavior of the society confined to their houses”,as Fujitsu has admitted.

How to adapt AI

In this situation, the industry is faced with the difficulty of adapting AI systems to continue operating despite the exceptional context of a pandemic.“The algorithms can be adapted by retraining them with current data, representative of the new situation”, José Esteban, director of Innovation of Atos in Iberia, has assured in statements to Europa Press.

For this process, according to Atos it is necessary that humans supervise the results obtained by the automatic algorithms,reviewing its decisions and annulling them when necessary.

Not all AI mechanisms, however, have received the same impact from the crisis. Although “there is no AI that reliably predicts” phenomena such as the coronavirus,“so the AI ​​doesn’t crash when the environment suddenly changes,One possible solution is systems that continually learn from data, “said Esteban.

In the solutions they useunsupervised continuous learning,systems can adapt on their own to abrupt change like today, with massive use of technology for teleworking, consumption and entertainment.

However, these systems can also have problems, since“they can’t always learn fast enough”, as Esteban has pointed out.

Another drawback derived from unsupervised algorithms is that“if they work very well and learn continuously quicklythey can give rise to systems starting to behave “and, for example, their actions cease to be predictable, as concluded the manager of Atos in Iberia.

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