Amazon has disabled two key privacy features in its Alexa smart speakers, in a push to introduce artificial intelligence-powered "agentic capabilities" and turn a profit from the popular devices. Starting from March 28, Alexa devices now send all audio recordings to the cloud for processing, and choosing not to save these recordings will disable personalisation features. A voice assistant works by constantly listening for a "wake word", such as "Alexa". Once woken, it records the command that is spoken and matches it to an action, such as playing a music track. Matching a spoken command to an action requires what computer scientists call natural language understanding, which can take a lot of computer power. Matching commands to actions can be done locally (on the device itself), or sound recordings can be uploaded to the cloud for processing.

Google researchers have built an artificial intelligence that they say can forecast weather and climate patterns just as well as current physics models while also requiring less computer power. Existing forecasts are based on mathematical models run by enormously powerful supercomputers that deterministically predict what will happen in the future. Since they were first used in the 1950s, these models have grown more and more detailed, requiring ever more computer power. How this moment for AI will change society forever (and how it won't) Several projects have aimed to replace these intense calculations with much less demanding AI, including a DeepMind tool to forecast rain locally on short timescales. But like most AI models, these are a "black box" whose inner workings are a mystery, and the inability to explain or replicate their methods is problematic.

For much of last year, knocking OpenAI off its perch atop the tech industry looked all but impossible, as the company rode a riot of excitement and hype generated by a remarkable, garrulous, and occasionally unhinged program called ChatGPT. Google DeepMind CEO Demis Hassabis has recently at least given Sam Altman some healthy competition, leading the development and deployment of an AI model that appears both as capable and as innovative as the one that powers OpenAI's barnstorming bot. Ever since Alphabet forged DeepMind by merging two of its AI-focused divisions last April, Hassabis has been responsible for corralling its scientists and engineers in order to counter both OpenAI's remarkable rise and its collaboration with Microsoft, seen as a potential threat to Alphabet's cash-cow search business. Google researchers came up with several of the ideas that went into building ChatGPT, yet the company chose not to commercialize them due to misgivings about how they might misbehave or be misused. In recent months, Hassabis has overseen a dramatic shift in pace of research and releases with the rapid development of Gemini, a "multimodal" AI model that already powers Google's answer to ChatGPT and a growing number of Google products.

Exploding energy costs have been blamed for the closure of more than 60 public swimming pools across Britain over the past four years. And with the bills for some expected to rise by £100,000 this year, it has left leisure centres scrabbling around for ways to keep the facilities running. It may sound far-fetched, but one leisure centre in Devon is using computer power to heat its swimming pool. The idea works by placing 12 computers inside a white box which is then surrounded by oil to capture the waste heat they produce -- in a similar way to another concept that uses computer servers to heat water in people's homes. Innovative: It may sound far-fetched, but Exmouth Leisure Centre in Devon is using computer power to heat its swimming pool.

Everyone is looking for a way to slash their heating bills amid soaring energy prices and the deepening cost-of-living crisis. Now, a British start-up has come up with a new way of doing so using a method that may seem a little bizarre to some -- by fitting a computer server to a household's hot water tank. Heata claims its shoebox-sized device could help Britons save around £150 a year on their energy bills, while small companies can also make use of the computer power available on the servers rather than them being in a large data centre. As the computer gets hot, the tank takes waste heat away from it and uses this to warm water for showers, baths and washing up. Each unit can deliver up to 4.8kWh of hot water per day, the company says -- approximately 80 per cent of the hot water required in an average UK household. As many people will know, laptops and computers can get very hot when running for long periods, with internal fans used to cool them down.

Most of us assume that the world around us is real. We take it for granted that everything we interact with is the true essence of reality, and not an illusion created by someone else. After all, this world is all we've ever known. We can explain how it works using science and philosophy and other fields of knowledge… can't we? In 2003, philosopher Nick Bostrom introduced his famous "simulation theory" in which he explores the probability that we are all living inside an artificial simulation. Bostrom discusses how a future society could become so technologically advanced that its inhabitants learn how to generate complex artificial worlds using powerful computers.

When Ben Fielding and Harry Grieve met at the kick-off weekend of an accelerator program in March 2020, they were told it would be the last time they'd see each other for some time. Grieve and Fielding had joined Entrepreneur First's six-month program, where would-be founders mix with one another in the hope of launching a startup together. The pair clicked immediately and hunkered down throughout the pandemic while working on their new business Gensyn. The startup connects machine learning researchers with the computing power they need to train AI models. Gensysn has just raised $6.5 million, following on from a previously unannounced $1.1 million pre-seed raise.

Calvin Qi, who works at a search startup called Glean, would love to use the latest artificial intelligence algorithms to improve his company's products. Glean provides tools for searching through applications like Gmail, Slack, and Salesforce. Qi says new AI techniques for parsing language would help Glean's customers unearth the right file or conversation a lot faster. So Glean uses smaller, less capable AI models that can't extract as much meaning from text. "It is hard for smaller places with smaller budgets to get the same level of results" as companies like Google or Amazon, Qi says.

But these AI outcomes don't come cheap: the AI model must be "trained" by recreating countless permutations of those outcomes, and that training eats up enormous amounts of computer power on mammoth GPUs. So they naturally turn to using cloud services like AWS, Microsoft Azure, and Google Cloud to train their AI models. Those wanting compute power can establish an Akash account and bid for compute power. The one providing computer power on the Akash Network is called a Provider. The point is this: By buying more AKT than needed to fund a deployment on Akash Network, the Tenant can recover some of the deployment costs through (1) staking rewards and (2) appreciation of the value of AKT (assuming that value increases).

Will artificial intelligence take over the world? Sometimes it seems that way, for everyone has heard of AI models that can compose entire essays or generate realistic face images of people who don't exist or create images from text descriptions. But these AI outcomes don't come cheap: the AI model must be "trained" by recreating countless permutations of those outcomes, and that training eats up enormous amounts of computer power on mammoth GPUs. XLNet from Google, for example, can cost around $61,000 to train each time, without guaranteed results. Not all AI models are as complicated as the Google one cited above.