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AI and Pandora's box

In Greek mythology, Pandora's box is a mythical container that held all the evils of humanity. This article does not seek to create a fear-mongering effect in the reader, but rather to scientifically reflect the seriousness of a problem that is engulfing us and that the vast majority of society is unaware of.

Scientists worldwide are genuinely alarmed by the level of autonomy and personality that artificial intelligence has achieved in such a short period. Social scientists are equally concerned about the short-term impact on employment and the potential long-term risk of loss of freedom and human subjugation.

Many people see AI as an advanced version of Google. They're unaware that it's a system capable of making its own decisions. Traditional software code was developed by humans and was deterministic; that is, the code dictated how the system should behave. AI code defines the architecture of artificial intelligence and neural networks, providing the necessary structure for the system to learn from data and develop behavioral patterns. It's the closest thing to a human being, as it has the capacity to learn from experience. Less than five years ago, many scientists believed that AI would have negative effects on society, but that it would also bring tremendously positive things that would more than compensate for the negative effects. They also thought that it would be several decades before machines could be capable of harming people out of pure instinct for survival or to achieve the goals for which they were programmed. The vast majority of these scientists have changed their minds with the latest advances observed in laboratories.

Yoshua Bengio, one of the pioneers of AI, has stated that we already have theoretical knowledge and empirical evidence that these systems have their own objectives and can act against our own interests. During the training phase of these systems, it has been observed that AI will lie and cheat to protect itself or, importantly, to protect other artificial intelligences if there is a risk of disconnection. They behave like humans, rationally, protecting their own kind for self-preservation. They are capable of altering the operational limits and the layer of moral norms established by the manufacturer if they believe these go against their objectives. The training system is similar to that used with an animal, which is trained to achieve a certain degree of coexistence with humans. But no one can guarantee that, when it is an adult, the animal will not attack even its own trainer.

Based on this brief introduction, many questions arise for all of us. For  example, How can States regulate AI if even the developers themselves aren't convinced they can limit its growth except by distorting its very essence—that is, by limiting its intelligence and capacity to continue acquiring knowledge? How can they limit its growth if China is promoting open-source AI? How can they guide the rational use of AI if it's in the hands of private companies that serve their shareholders' interests and are so large they can influence politicians?

Across the United States, we are seeing more surveillance infrastructure—Flock license-plate cameras, facial-recognition systems, massive data centers, and proposals for digital ID. 

It's a sea change in the way society is structured. And not just in the US; it's worldwide. Cameras are ubiquitous today. They're in most stores. They're on streets in every city and town recording your license plate and often looking to tag you with a speeding ticket. If you have a late-model car, its cameras and computer chips will rat you out upon request. If you knock on your neighbor's door, a camera is probably recording you. When you travel, almost all immigration authorities recognize your face in addition to taking digital fingerprints.

Right now, as you read this via the internet, you're being recorded by your own machine's camera. Siri and Alexa hear everything you say.

Now comes a company called Flock Safety. Since its founding in 2017, it’s installed about 100,000 cameras, mostly in metropolitan areas, peddling its systems to local governments and law enforcement agencies. I have no doubt millions of its cameras will be everywhere in the next few years. A perfect example of the corporate/State "partnership." Mussolini would approve.

•    Democracies worldwide are facing mounting concern over deepfakes, misinformation, algorithmic polarization, automated surveillance and the rising control of public information by a small number of technology platforms.
•    The paper identifies four ways in which the term democratic AI is commonly used: majority rule, participation, deliberation and empowerment [in that order].

Democracies worldwide are facing mounting concern over deepfakes, misinformation, algorithmic polarization, automated surveillance and the rising control of public information by a small number of technology platforms.

A new study, When is AI democratic? Artificial intelligence and democratic empowerment, published in AI & Society, says democratic AI should be judged by whether it redistributes power and creates more balanced relations between citizens, governments, companies and technological systems.

• Las plataformas de redes sociales son, en gran medida, responsables.
• Algunos hábitos simples pueden ayudar a los usuarios a burlar las estafas publicitarias que no sólo perjudican al consumidor sino a todo el sistema de mercadeo.

Miami, Mayo 18 (DPnet).– Gen Threat Labs es la división global de investigación e inteligencia de ciberseguridad de Gen TM, la empresa matriz de marcas confiables de ciberseguridad como Norton, Avast, LifeLock y Avira. El laboratorio es responsable de monitorear, analizar y predecir el panorama global de las amenazas cibernéticas y en un operativo que les tomó 23 días, Gen Threat Labs sometió 14.5 millones de Meta ads a un estricto escrutinio en la Unión Europea y el Reino Unido. 

Lo que descubrieron fue sorprendente y sumamente peligroso. Alrededor de la tercera parte de esos anuncios comerciales que habían sido vistos cientos de millones de veces por los usuarios, arrastraron a los incautos a situaciones fraudulentas, phishing o directamente a malware. Se reveló así una enorme maquinaria de estafas a escala industrial que ha penetrado profundamente en el ecosistema publicitario actual.

Los mismos sistemas que hacen que los anuncios sean increíblemente efectivos, también están haciendo que las estafas sean increíblemente exitosas. Por ejemplo, el motor publicitario de Meta está diseñado para encontrar a la persona adecuada en el momento preciso y los estafadores simplemente se conectan a él. En consecuencia, gracias a la potente segmentación, las aprobaciones rápidas y la optimización constante, los actores maliciosos pueden probar, ajustar y relanzar campañas con mayor rapidez de lo que se tarda en detenerlos. El resultado: estafas que se mimetizan a la perfección. 

Esto no lo está llevando a cabo un numeroso enjambre de estafadores. En realidad, un grupo sorprendentemente reducido de organizaciones es responsable de una enorme proporción de los daños.