Recent findings reveal that even simple pricing algorithms can make things more expensive. Imagine a town with two widget merchants. Customers prefer cheaper widgets, so the merchants must compete to set the lowest price. Unhappy with their meager profits, they meet one night in a smoke-filled tavern to discuss a secret plan: If they raise prices together instead of competing, they can both make more money. But that kind of intentional price-fixing, called collusion, has long been illegal.

Electricity markets are highly complex, involving lots of interactions and complex dependencies that make it hard to understand the inner workings of the market and what is driving prices. Econometric methods have been developed for this, white-box models, however, they are not as powerful as deep neural network models (DNN). In this paper, we use a DNN to forecast the price and then use XAI methods to understand the factors driving the price dynamics in the market. The objective is to increase our understanding of how different electricity markets work. To do that, we apply explainable methods such as SHAP and Gradient, combined with visual techniques like heatmaps (saliency maps) to analyse the behaviour and contributions of various features across five electricity markets. We introduce the novel concepts of SSHAP values and SSHAP lines to enhance the complex representation of high-dimensional tabular models.

That's because most audio companies sell their top-of-the-line gear around 300- 400. The FoKus Rex5 earbuds, for example, cram in five separate drivers where much of the competition uses two at the most. Noble was also among the first to employ xMEMS drivers in wireless earbuds in a bid to improve bass performance. Enter the FoKus Apollo, a 649 pair of active noise canceling (ANC) headphones with a detachable boom mic and up to 80 hours of battery life. The real star of the show is the driver setup, which Noble says is the first time this configuration appears in wireless headphones.

The G7 has just agreed a (voluntary) code of conduct that AI companies should abide by, as governments seek to minimize the harms and risks created by AI systems. And later this week, the UK will be full of AI movers and shakers attending the government's AI Safety Summit, an effort to come up with global rules on AI safety. In all, these events suggest that the narrative pushed by Silicon Valley about the "existential risk" posed by AI seems to be increasingly dominant in public discourse. This is concerning, because focusing on fixing hypothetical harms that may emerge in the future takes attention from the very real harms AI is causing today. "Existing AI systems that cause demonstrated harms are more dangerous than hypothetical'sentient' AI systems because they are real," writes Joy Buolamwini, a renowned AI researcher and activist, in her new memoir Unmasking AI: My Mission to Protect What Is Human in a World of Machines.

Welcome to a good way to waste most of a working day. Many people think it's magical, which in a sense it is, at least as the magician Robert Neale portrayed it: a unique art form in which the magician creates elaborate mysteries during a performance, leaving the spectator baffled about how it was done. But if the spectator somehow manages to discover how the trick was done, then the magic disappears. So let us examine how Midjourney and its peers do their tricks.

People should not assume a positive outcome from the artificial intelligence boom, the UK's competition watchdog has warned, citing risks including a proliferation of false information, fraud and fake reviews as well as high prices for using the technology. The Competition and Markets Authority said people and businesses could benefit from a new generation of AI systems but dominance by entrenched players and flouting of consumer protection law posed a number of potential threats. The CMA made the warning in an initial review of foundation models, the technology that underpins AI tools such as the ChatGPT chatbot and image generators such as Stable Diffusion. The emergence of ChatGPT in particular has triggered a debate over the impact of generative AI – a catch-all term for tools that produce convincing text, image and voice outputs from typed human prompts – on the economy by eliminating white-collar jobs in areas such as law, IT and the media, as well as the potential for mass-producing disinformation targeting voters and consumers. The CMA chief executive, Sarah Cardell, said the speed at which AI was becoming a part of everyday life for people and businesses was "dramatic", with the potential for making millions of everyday tasks easier as well as boosting productivity – a measure of economic efficiency, or the amount of output generated by a worker for each hour worked.

That was my first thought when I learned about Nomono's portable recording setup last year. Since then, the price has dropped slightly to $2,490. But my main concern remains: Who needs this thing when you can get a small audio recorder and lavalier mics for far less? Nomono's kit includes four wireless lav mics, as well as a space recorder for room tone and spatial data, all of which charge in a portable carrying case. Your recordings are automatically uploaded to Nomono's cloud service, where you can optimize their audio quality (and eventually edit them in your browser).

The field of electricity price forecasting has seen significant advances in the last years, including the development of new, more accurate forecast models. These models leverage statistical relationships in previously observed data to predict the future; however, there is a lack of analysis explaining these models, which limits their real world applicability in critical infrastructure. In this paper, using data from the Belgian electricity markets, we explore a state-of-the-art forecasting model to understand if its predictions can be trusted in more general settings than the limited context it is trained in. If the model produces poor predictions in extreme conditions or if its predictions are inconsistent with reality, it cannot be relied upon in real-world where these forecasts are used in downstream decision-making activities. Our results show that, despite being largely accurate enough in general, even state of the art forecasts struggle with remaining consistent with reality.

Computer monitors that support HDMI 2.1, the latest HDMI standard, are beginning to trickle into online retailers. They sell at extremely high prices (when they're available at all). Even the most affordable HDMI 2.1 monitors, like the Gigabyte Aorus FI32U and Acer Nitro XV282K KV, are priced near $1,000. The high price of HDMI 2.1 implies it's important, but the truth is more nuanced. HDMI 2.1 brings new features to the table, but they're relevant only to people with specific needs.

Computer monitors that support HDMI 2.1, the latest HDMI standard, are beginning to trickle into online retailers. They sell at extremely high prices (when they're available at all). Even the most affordable HDMI 2.1 monitors, like the Gigabyte Aorus FI32U and Acer Nitro XV282K KV, are priced near $1,000. The high price of HDMI 2.1 implies it's important, but the truth is more nuanced. HDMI 2.1 brings new features to the table, but they're relevant only to people with specific needs.