Artificial Intelligence

There's a prevailing skepticism regarding whether existing competition laws are equipped to curb the tendency of AI to foster prices above competitive levels, as these laws were originally crafted to target explicit collusion between companies

While increased bureaucratic documentation might make the European AI research landscape less appealing, better documentation will likely result in more thorough and potentially meaningful research.

In the absence of crystal-clear considerations to be drawn on the legal domain, it is advisable to refrain from celebrating the AI Act (as well as its alleged substantial failure) as a turning point or a historical achievement. Let the final legal provisions speak, first.

On October 30, 2023, the Biden Administration issued the Safe, Secure and Trustworthy Development and Use of Artificial Intelligence Executive Order 14110, which marks a key development in the global race to regulate AI

The ongoing conversation among the European Union institutions that started when the Commission released its proposal also features a variety of views on which (and how many) authorities both at EU level and at Member State should lead the governance of AI.

Bite-back occurs when an innovation has unexpected and unintended effects. It is probably the case that in most cases such consequences are negative, but that is not invariably the case. Bite-back happens because a new technology is by definition an exploration into the unknown, and so it is impossible to predict precisely whether it will do more or less of just very different things from what it was meant to do.

The proliferation of advanced generative AI models has unequivocally highlighted what was already foreseeable since the advent of the algorithmic era. The traditional notion of "consent" is no longer a viable proposition in the context of an algorithmic society. Given the increasing sophistication of emerging AI generative models, which legal basis could be a reasonable alternative?

A concise introduction into the workings of large language models is presented. We start with an introduction to the attention mechanism, the core of the transformer architecture, which is then followed by a discussion of the steps needed to engineer the base model, a generatively pretrained transfromers (GPT), to a working chatbot like ChatGPT.

“You can do some pretty impressive things with AI as a technological platform, but I am not necessarily an optimist because there are also some very negative paths that AI could take as a technology. We have a confluence of factors that make the negative use of this technology much more probable than positive use”, MIT economist Daron Acemoglu argues.