Artificial intelligence is passing top law and medical exams, writing children's books in hours and landing job interviews. Now, scientists believe ChatGPT has the power to save humanity from the next pandemic. The current models use mathematical analysis, but researchers at Virginia Tech found they could use the chatbot to simulate how a virus would spread in a town. The team created a fictional US town with 100 people to see how they would react to an outbreak. Experiments showed that agents were more likely to self-quarantine when informed of societal health information, news about the epidemic, and the daily active case count.

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If you're looking for a free introductory AI course for beginner's, Microsoft has got you covered. The aptly-named Artificial Intelligence for Beginners is put together by Microsoft Azure Cloud Advocates, and consists of a 12 week, 24 lesson curriculum designed to introduce learners to the wonderful world of AI. You can see specific course content, laid out lesson by lesson, here. Lessons are taught using a variety of materials. You can find a mind map of the course here. If you are interested in knowing more, you might want to get to know the instructors for the course in the video below.

A language model bigger than GPT-3 has arrived with a bold ambition: freeing AI from Big Tech's clutches. Named BLOOM, the large language model (LLM) promises a similar performance to Silicon Valley's leading systems -- but with a radically different approach to access. While tech giants tend to keep their vaunted LLMs hidden from the public, BLOOM is available to anyone for free. These features could democratize access to technology that's set to make a deep impact on society. Powerful AI models can be trained and released in an open way.

The spotlight is generally on the ripple effects of these technologies; very rarely is it on the people and processes through which these technologies are built and deployed. That's a shame because if we did, we'd have a much greater sense of the opportunities open to us. Whether that's from a personal career perspective or a business one, we'd start to see that we're still very far from mass adoption and usage. These are trends that remain in the hands of the largest companies with the most money to spend.

And the consultation is completely private. It's all thanks to open-source AI that runs inside your web browser–or what happens when insurance companies and big pharma stop making the rules of healthcare, and the age of WebMD self-diagnosis is supercharged with machine learning. The Chester AI radiology assistant was developed in work led by Joseph Paul Cohen, a postdoctoral fellow at Mila (the Quebec AI institute) and the University of Montreal. He used an NIH dataset of chest X-rays and diseases to train software to spot diseases in these scans. Though he is not a clinical doctor, Cohen is focused on the intersection of health and deep learning.

The microcomputer's official magazine is giving away free AI kits as part of a new partnership with Google. The MagPi, a monthly publication offering tips, tricks and tutorials for Raspberry Pi fans, made headlines last year when it gave away a free Raspberry Pi Zero on the cover of every copy to celebrate the device's launch. This month, the magazine is going one better, offering an AI kit with every copy to let users build their own Google voice-powered projects. As part of a freshly announced partnership with Google, dubbed AIY Projects (short for Artificial Intelligence Yourself), The MagPi is launching a new add-on board for the Raspberry Pi called the Voice HAT (Hardware Accessory on Top), allowing makers to integrate artificial intelligence and natural-language processing into their projects. "We are moving to a human interface world, where users will simply have a conversation with a device," said Google's director of AIY Projects, Billy Rutledge.

When someone talks about AI, or machine learning, or deep convolutional networks, what they're really talking about is -- as is the case for so many computing concepts -- a lot of carefully manicured math. At the heart of these versatile and powerful networks is a volume of calculation only achievable by the equivalent of supercomputers. More than anything else, this computational cost is what is holding back applying AI in devices of comparatively little brain: phones, embedded sensors, cameras. If that cost could be cut by a couple orders of magnitude, AI would be unfettered from its banks of parallel processors and free to inhabit practically any device -- which is exactly what XNOR.ai, a breakthrough at the Allen Institute for AI, makes possible. XNOR.ai is, essentially, a bit of clever computer-native math that enables AI-like models for vision and speech recognition to run practically anywhere.

When someone talks about AI, or machine learning, or deep convolutional networks, what they're really talking about is -- as is the case for so many computing concepts -- a lot of carefully manicured math. At the heart of these versatile and powerful networks is a volume of calculation only achievable by the equivalent of supercomputers. More than anything else, this computational cost is what is holding back applying AI in devices of comparatively little brain: phones, embedded sensors, cameras. If that cost could be cut by a couple orders of magnitude, AI would be unfettered from its banks of parallel processors and free to inhabit practically any device -- which is exactly what a breakthrough at the Allen Institute for AI makes possible. XNOR.ai is, essentially, a bit of clever computer-native math that enables AI-like models for vision, speech recognition, to run practically anywhere.