I teach in the computer science department at Vanderbilt University. In my Algorithms class this past spring, I decided to regularly expose my students to ChatGPT so they could see firsthand that it can't replace their critical thinking skills. I did this primarily for selfish reasons--like most instructors, I wanted my students to rely on their own creative problem-solving abilities rather than ChatGPT to answer their homework questions. I hypothesized that demonstrating the fallibility of ChatGPT would be a more effective deterrent than a syllabus policy statement. I just needed to find its weaknesses when it came to our course materials.

After the "deep fake" in image or video, will we see the arrival of "deep fake" sound? It is possible since MicrosoftMicrosoft unveiled a new artificial intelligence (AI) model of speech synthesis called VALLEY. She can imitate and therefore simulate a person's voice with a simple three-second audio sample. Once it has learned a specific voice, this AI can synthesize that person's sound, while preserving its timbre and emotions. At Microsoft, it is believed that VALL-E could be used for applicationsapplications voice synthesis, but also, and this is obviously more worrying, for editing speech in a recording.

A year after it wowed many with its first gameplay video, Chinese developer Game Science Studio is back with an even more impressive look at Black Myth: Wukong. The studio has switched from Unreal Engine 4 to UE5 for the action RPG. According to NVIDIA, it's the first peek at a UE5 game that uses Deep Learning Super Sampling ( DLSS), the company's AI-powered upscaling tech. The 12-minute preview, which you can watch in 4K at 60 frames per second, shows off much more action from the game. It features a couple of stunning boss fights, including one with an electric dragon on a frozen lake.

The Microsoft license is exclusive however, meaning Microsoft's cloud computing competitors cannot access it in the same way. The agreement was seen as important to helping OpenAI with the expense of getting GPT-3 up and running and maintaining it, according to an account in TechTalks. These include an estimated $10 million in expenses to research GPT-3 and train the model, tens of thousands of dollars in monthly cloud computing and electricity costs to run the models, an estimated one million dollars annually to retrain the model to prevent decay, and additional costs of customer support, marketing, IT, legal and other requirements to put a software product on the market.

A novice painter might set brush to canvas aiming to create a stunning sunset landscape -- craggy, snow-covered peaks reflected in a glassy lake -- only to end up with something that looks more like a multi-colored inkblot. But a deep learning model developed by NVIDIA Research can do just the opposite: it turns rough doodles into photorealistic masterpieces with breathtaking ease. The tool leverages generative adversarial networks, or GANs, to convert segmentation maps into lifelike images. The interactive app using the model, in a lighthearted nod to the post-Impressionist painter, has been christened GauGAN. GauGAN could offer a powerful tool for creating virtual worlds to everyone from architects and urban planners to landscape designers and game developers.

In this day and age, everyone has heard about Artificial Intelligence (AI). It's proven to be very controversial, even though it shouldn't have been. The general populace still doesn't understand what it truly represents. In their minds, they may even equate it to Skynet from the Terminator films. But let's avoid jumping to extreme conclusions.

A novice painter might set brush to canvas aiming to create a stunning sunset landscape -- craggy, snow-covered peaks reflected in a glassy lake -- only to end up with something that looks more like a multi-colored inkblot. But a deep learning model developed by NVIDIA Research can do just the opposite: it turns rough doodles into photorealistic masterpieces with breathtaking ease. The tool leverages generative adversarial networks, or GANs, to convert segmentation maps into lifelike images. The interactive app using the model, in a lighthearted nod to the post-Impressionist painter, has been christened GauGAN. GauGAN could offer a powerful tool for creating virtual worlds to everyone from architects and urban planners to landscape designers and game developers.

Algorithmic art is nothing new, but in its early days, it didn't hold a candle to more human art forms. The earliest known generative computergraphik, created by German mathematician and "father of computer art" Georg Nees in the mid-1960s, mostly consisted of black-and-white ink drawings of lines and polygons. The first computer-generated musical composition, Lejaren Hiller and Leonard Isaacson's Illiac Suite for String Quartet, came even earlier in 1957. Both efforts were highly experimental and aimed at academic audiences. And both artistic outputs ring with the rigidity of the strict, rudimentary rules imposed in their algorithms.

Machine learning is breaking down language barriers, fuelling – and fighting – cybercrime, and can even recognise emotions, but the complex processes behind these breakthroughs are often a mystery. Startup Graphcore wants to change this. The Bristol-based firm has created a series of'AI brain scans', using its development chip and software, to produce Petri dish-style images that reveal what happens as processes run. Most machine learning programs – including Google's own systems, and open source frameworks – work by training AI on computational graphs. Put very simply, machine learning systems go through a construction phase, during which a graph showing all the computations needed is created. This is followed by an execution phase where the machine uses the computations (or steps) highlighted in the graph to run through its training processes.

For those of you who don't know who or what Amelia is, she is IPsoft's cognitive agent or, in other words, an Artificial Intelligence agent that can converse with people and act as an electronic call center agent. She can do what I would say is at least 30 percent or more of the work currently performed in today's call centers. When I met Amelia, she read a Wikipedia article and had a conversation about it with us. She effectively operated similar to an eighth-grader's ability to synthesize what was in that article and answer questions. She went on to show how she could converse with us to open bank accounts, help us file insurance claims, or sell us a homeowner's or car insurance policy.