Have you heard about fake news, AI, and deep fake? This blog post is going to introduce you to all three of these terms, giving a broad overview of just what is going on. Are we living in a science fiction film? Are computers going to bring about the end of civilization as we know it? This isn't fiction, so I'm not talking about Brave New World (although you should read that book).

A sufficiently complex neural network can result in has 100% accuracy on the data it was trained with, but significant error on any new data. When this occurs, the network is likely overfitting the training data. This means that it makes predictions that are too strongly attached to features it learned in training, but which don't necessarily correlate with the expected results. One way to temper overfitting is by using a process called regularization. Regularization generally works by penalizing a neural network for complexity.

To navigate difficult terrain, humans and animals quite automatically combine the visual perception of their environment with the proprioception of their legs and hands. This allows them to easily handle slippery or soft ground and move around with confidence, even when visibility is low.

The environment is the setting that the agent is acting on and the agent represents the RL algorithm. To understand this better, let's suppose that our agent is learning to play counterstrike. The mathematical approach for mapping a solution in Reinforcement Learning is called Markov's Decision Process (MDP). To briefly sum it up, the agent must take an action (A) to transition from the start state to the end state (S). While doing so, the agent receives rewards (R) for each action he takes.

There is great promise and potential in artificial intelligence (AI), but if such technologies are built and trained by humans, are they capable of bias? Absolutely, says William Wang, the Duncan and Suzanne Mellichamp Chair in Artificial Intelligence and Designs at UC Santa Barbara, who will give the virtual talk "What is Responsible AI," at 4 p.m. Tuesday, Jan. 25, as part of the UCSB Library's Pacific Views speaker series (register here). "The key challenge for building AI and machine learning systems is that when such a system is trained on datasets with limited samples from history, they may gain knowledge from the protected variables (e.g., gender, race, income, etc.), and they are prone to produce biased outputs," said Wang, also director of UC Santa Barbara's Center for Responsible Machine Learning. "Sometimes these biases could lead to the'rich getting richer' phenomenon after the AI systems are deployed," he added. "That's why in addition to accuracy, it is important to conduct research in fair and responsible AI systems, including the definition of fairness, measurement, detection and mitigation of biases in AI systems."

Popularly, art connoisseurs are portrayed as sophisticados who carry themselves with an aura of mystery, in command of an inner portal to truth that the rest of us inexplicably just don't possess. Presented with an unassuming Renaissance painting purchased for $1,000 in New Orleans, for instance, one might be stricken with certainty that the painting was authored by no other than Leonardo da Vinci; another attributes hundreds of paintings to Rembrandt and claims that his genius is obvious to the "experienced eye." The elusive certainty of connoisseurship has always come with raised eyebrows: can you tell a garage sale replica from the real deal, let alone a workshop painting from an Old Master one? Can we trust anyone who claims to know? Recent developments in machine learning applied to photographs of artwork promise to lend more objectivity to processes of attribution when the provenance is uncertain.

A 2022 upgrade will bring that processor total to 16,000. Meta, the tech giant previously known as Facebook, revealed Monday that it's built one of the world's fastest supercomputers, a behemoth called the Research SuperCluster, or RSC. With 6,080 graphics processing units packaged into 760 Nvidia A100 modules, it's the fastest machine built for AI tasks, Chief Executive Mark Zuckerberg says. That processing power is in the same league as the Perlmutter supercomputer, which uses more than 6,000 of the same Nvidia GPUs and currently ranks as the world's fifth fastest supercomputer. And in a second phase, Meta plans to boost performance by a factor of 2.5 with an expansion to 16,000 GPUs this year.

The support vector machine (SVM), developed by the computer science community in the 1990s, is a supervised learning algorithm commonly used and originally intended for a binary classification setting. It is often considered one of the best "out of the box" classifiers. The SVM is a generalization of the simple yet elegant algorithm called the maximal margin classifier. This classifier, however, cannot be applied in every situation since it relies heavily on the assumption that the dataset is linearly separable -- thus, several extensions exist. Note: In the following, we will only cover the maximal margin classifier, purposely avoiding the different extensions.

Infectious diseases pose a threat to human life and could affect the whole world in a very short time. Corona-2019 virus disease (COVID-19) is an example of such harmful diseases. COVID-19 is a pandemic of an emerging infectious disease, called coronavirus disease 2019 or COVID-19, caused by the coronavirus SARS-CoV-2, which first appeared in December 2019 in Wuhan, China, before spreading around the world on a very large scale. The continued rise in the number of positive COVID-19 cases has disrupted the health care system in many countries, creating a lot of stress for governing bodies around the world, hence the need for a rapid way to identify cases of this disease. Medical imaging is a widely accepted technique for early detection and diagnosis of the disease which includes different techniques such as Chest X-ray (CXR), Computed Tomography (CT) scan, etc.

The camera, as the saying goes, does not lie. The ability to see video footage of an alarm event in progress has dramatically reduced false alarms in recent years. And, when that video is supported by the capabilities of artificial intelligence (AI), it even further empowers operators and response teams to respond swiftly, and appropriately. So while it's true that video verification has been enabling better informed responses for several years now, the application of AI for this purpose has proven a real game-changer. Leveraging video to verify intrusion alarms has been gaining some serious momentum recently, as the technology has improved and become less expensive.