Artificial Intelligence is one of the leading technologies in the world, but there are certain risks and dangers it poses for everyone that will use it or the systems that depend on it, according to experts. Now, a legislative act of the European Union is moving towards the'Artificial Intelligence Act' of the region that will focus on protecting the public to counter its negative effects. AI is not what it seems to the world and the EU is to do something about it for its constituents. The European Union is working to push a new law that would protect its citizens regarding the risks and dangers of AI, particularly those that it brings to the world. It is known as the "Artificial Intelligence Act," and the EU says that it is the first in the industry that aims to regulate AI for the many features it brings to the public.

Artificial intelligence has reached a critical turning point in its evolution, according to a new report by an international panel of experts assessing the state of the field. Substantial advances in language processing, computer vision and pattern recognition mean that AI is touching people's lives on a daily basis -- from helping people to choose a movie to aiding in medical diagnoses. With that success, however, comes a renewed urgency to understand and mitigate the risks and downsides of AI-driven systems, such as algorithmic discrimination or use of AI for deliberate deception. Computer scientists must work with experts in the social sciences and law to assure that the pitfalls of AI are minimized. Those conclusions are from a report titled "Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report," which was compiled by a panel of experts from computer science, public policy, psychology, sociology and other disciplines.

Google Director of Research and renowned artificial intelligence (AI) expert Peter Norvig, presented an entirely different side of AI and machine learning at the EmTech Digital conference. He compared traditional software programming to machine learning to highlight the new challenges of debugging and verifying systems programmed with machine learning do what they are designed to do. Traditional software programming uses Boolean-based logic that can be tested to confirm that the software does what it was designed to do, using tools and methodologies established over the last few decades. In contrast, machine learning is a black box programming method in which computers program themselves with data, producing probabilistic logic that diverges from the true-and-false tests used to verify systems programmed with traditional Boolean logic methods. "The problem here is the methodology for scaling this [machine learning verification] up to a whole industry is still in progress. We have been doing this for a while; we have some clues for how to make it work, but we don't have the decades of experience that we have in developing and verifying regular software."