Aberystwyth
What does the data tell us about immigration in Wales? Search for your area
What does the data tell us about immigration in Wales? Like many countries, Wales sees a steady flow of people arriving and leaving for other countries each year. The difference between those arriving and those leaving is known as net migration. Focusing on people moving from abroad, latest estimates say Wales' population - which was 3.2 million in June 2024 - had increased by about 23,000 over the previous year as a result of net international migration. A recent YouGov poll found a quarter of people surveyed in Wales believed that immigration, alongside the economy, should be among the issues prioritised by the Welsh government, even though immigration is controlled by the UK government.
- North America > United States (0.15)
- North America > Central America (0.14)
- Africa (0.05)
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What I've learned from 25 years of automated science, and what the future holds: an interview with Ross King
What I've learned from 25 years of automated science, and what the future holds: an interview with Ross King We're excited to launch our new series, where we're speaking with leading researchers to explore the breakthroughs driving AI and the reality of the future promises - to give you an inside perspective on the headlines. Our first interviewee is Ross King, who created the first robot scientist back in 2009. He spoke to us about the nature of scientific discovery, the role AI has to play, and his recent work in DNA computing. Automated science is a really exciting area, and it feels like everyone's talking about it at the moment - e.g. But you've been working in this field for many years now. In 2009 you developed Adam, the first robot scientist to generate novel scientific knowledge. Could you tell me some more about that? So the history goes back to before Adam.
- North America > United States > Texas (0.04)
- Europe > United Kingdom > Wales > Ceredigion > Aberystwyth (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- Europe > Sweden (0.04)
- Health & Medicine (0.95)
- Energy (0.68)
Obtaining Partition Crossover masks using Statistical Linkage Learning for solving noised optimization problems with hidden variable dependency structure
Przewozniczek, M. W., Frej, B., Komarnicki, M. M., Prusik, M., Tinós, R.
In optimization problems, some variable subsets may have a joint non-linear or non-monotonical influence on the function value. Therefore, knowledge of variable dependencies may be crucial for effective optimization, and many state-of-the-art optimizers leverage it to improve performance. However, some real-world problem instances may be the subject of noise of various origins. In such a case, variable dependencies relevant to optimization may be hard or impossible to tell using dependency checks sufficient for problems without noise, making highly effective operators, e.g., Partition Crossover (PX), useless. Therefore, we use Statistical Linkage Learning (SLL) to decompose problems with noise and propose a new SLL-dedicated mask construction algorithm. We prove that if the quality of the SLL-based decomposition is sufficiently high, the proposed clustering algorithm yields masks equivalent to PX masks for the noise-free instances. The experiments show that the optimizer using the proposed mechanisms remains equally effective despite the noise level and outperforms state-of-the-art optimizers for the problems with high noise.
- Europe > Poland > Lower Silesia Province > Wroclaw (0.05)
- North America > United States > Massachusetts > Suffolk County > Boston (0.04)
- North America > Mexico > Quintana Roo > Cancún (0.04)
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- North America > United States > California > San Francisco County > San Francisco (0.04)
- North America > United States > California > Los Angeles County > Los Angeles (0.04)
- Europe > United Kingdom > Wales > Ceredigion > Aberystwyth (0.04)
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- Information Technology > Artificial Intelligence > Vision (1.00)
- Information Technology > Artificial Intelligence > Robots (1.00)
- Information Technology > Artificial Intelligence > Natural Language (0.67)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.46)
- Europe > Switzerland > Zürich > Zürich (0.14)
- Europe > Netherlands > North Holland > Amsterdam (0.04)
- Oceania > Australia > Western Australia (0.04)
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- Europe > Germany > Baden-Württemberg > Stuttgart Region > Stuttgart (0.05)
- Europe > Netherlands > North Holland > Amsterdam (0.04)
- North America > United States > Massachusetts > Hampshire County > Amherst (0.04)
- Europe > United Kingdom > Wales > Ceredigion > Aberystwyth (0.04)
- Research Report > New Finding (1.00)
- Research Report > Experimental Study (1.00)
- Europe > Finland > Northern Ostrobothnia > Oulu (0.05)
- Europe > United Kingdom > Wales > Ceredigion > Aberystwyth (0.04)
- Asia > Japan > Honshū > Chūbu > Nagano Prefecture > Nagano (0.04)
- Asia > Japan > Honshū > Chūbu > Ishikawa Prefecture > Kanazawa (0.04)
The 3,500-mile love story that started in an online horror game
It is an online romance that has overcome a 3,500-mile distance, and also the Covid pandemic - which meant they had to get married virtually. Welsh cheesemaker Lewis Relfe struck up a relationship with Ameila Henderson, from Virginia, USA, while playing the Friday the 13th horror video game in 2017. She made a number of visits across the Atlantic, including one for six months, and he proposed on Aberystwyth Pier, dressed as the game's main character, Jason Voorhees. While they admit to seeing the humour in being the couple that met and married virtually, they now live together in Ceredigion, with daughter Evelyn. But because of parental responsibilities, they no longer get to enjoy the thing that brought them together.
- North America > United States > Virginia (0.25)
- Europe > United Kingdom > Wales > Ceredigion > Aberystwyth (0.25)
- North America > Central America (0.15)
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Ed Zitron on big tech, backlash, boom and bust: 'AI has taught us that people are excited to replace human beings'
Ed Zitron on big tech, backlash, boom and bust: 'AI has taught us that people are excited to replace human beings' His blunt, brash scepticism has made the podcaster and writer something of a cult figure. But as concern over large language models builds, he's no longer the outsider he once was I f some time in an entirely possible future they come to make a movie about "how the AI bubble burst", Ed Zitron will doubtless be a main character. He's the perfect outsider figure: the eccentric loner who saw all this coming and screamed from the sidelines that the sky was falling, but nobody would listen. Just as Christian Bale portrayed Michael Burry, the investor who predicted the 2008 financial crash, in The Big Short, you can well imagine Robert Pattinson fighting Paul Mescal, say, to portray Zitron, the animated, colourfully obnoxious but doggedly detail-oriented Brit, who's become one of big tech's noisiest critics. This is not to say the AI bubble burst, necessarily, but against a tidal wave of AI boosterism, Zitron's blunt, brash scepticism has made him something of a cult figure. His tech newsletter, Where's Your Ed At, now has more than 80,000 subscribers; his weekly podcast, Better Offline, is well within the Top 20 on the tech charts; he's a regular dissenting voice in the media; and his subreddit has become a safe space for AI sceptics, including those within the tech industry itself - one user describes him as "a lighthouse in a storm of insane hypercapitalist bullshit".
- North America > United States > Nevada > Clark County > Las Vegas (0.05)
- North America > United States > California (0.05)
- Europe > Ukraine (0.05)
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- Information Technology (1.00)
- Leisure & Entertainment > Sports (0.69)
- Government > Regional Government (0.69)
- Banking & Finance > Trading (0.68)
- North America > Canada > Ontario > Toronto (0.14)
- North America > United States > Virginia (0.04)
- Europe > United Kingdom > Wales > Ceredigion > Aberystwyth (0.04)
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- Information Technology > Data Science (1.00)
- Information Technology > Artificial Intelligence > Natural Language (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.92)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.67)