Martina's few words: Last time I wondered: how could we be sure someone is the person she claims to be? We have talked about digital identity services, and I hypothesized that I would surely use some GAN applications if I ever wanted to fool them. Anyway, when Louis suggested talking about GANs in image restoration this week, I was still thinking about the identity problem. I mean, every digital image is an attempt to simulate reality. The human sight itself is subject to bias and errors.

Have you ever pondered about how seamlessly we can drive a car, and why is it so difficult to get a computer to drive a car? It's because our minds are highly evolved and complex and embedding this complexity into a computer is challenging. And today we will cover a tiny fraction of our journey towards achieving self-driving cars. The task I will be discussing in this post is called semantic segmentation. Segmentation as the name suggests is the act of dividing something into separate parts.

The Barcelona city website's blog discusses how the city is implementing artificial intelligence in a way that protects health and safety while also protecting citizens' rights. Each of these projects is highly valuable, but the third project stood out to me for its novelty. The city needed to ensure that its beaches were not overcrowded. They could have used an AI facial recognition algorithm to scan the faces of every person who entered a beach. Instead, they used thermal image processing to assess which parts of the beach were full.

Would you ride in an autonomous vehicle if you knew that it was subject to visual problems? How about undergo cancer treatment based on a computer interpretation of radiological images such as an x-ray, ultrasound, CT, PET, or MRI scan knowing that computer vision could easily be fooled? Computer vision has a problem–it only takes slight changes in data input to fool machine learning algorithms into "seeing" things wrong. Recent advances in computer vision are largely due to the improved pattern-recognition capabilities through deep learning, a type of machine-based learning. Machine learning is a subset of artificial intelligence where a computer is able to learn concepts from processing input data either through supervised learning where the training data is labeled, or not as in unsupervised learning or a combination without explicit programming.

Recent studies by Google Brain have shown that any machine learning classifier can be tricked to give incorrect predictions, and with a little bit of skill, you can get them to give pretty much any result you want. This fact steadily becomes worrisome as more and more systems are powered by artificial intelligence -- and many of them are crucial for our safe and comfortable life. Lately, safety concerns about AI were revolving around ethics -- today we are going to talk about more pressuring and real issues. Machine learning algorithms accept the input in a form of numeric vectors. Designing an input in a specific way to get the wrong result from the model is called an adversarial attack.