'There is no guaranteed outcome with any job,' said Shola West, 25, a media consultant. Working for yourself at least allows you some control over your fate. 'There is no guaranteed outcome with any job,' said Shola West, 25, a media consultant. Working for yourself at least allows you some control over your fate. Facing AI and a tough job market, gen Z turns to entrepreneurship: 'I have to prove myself' When Ashley Terrell graduated from the University of Hawaii in 2024, she planned to find a job in marketing, maybe for a tech company.

Japanese scientist Makoto Kuroda faces felony charges after allegedly poisoning a colleague's water bottle with toxic chemicals at UW-Madison's Influenza Research Institute.

Environment Animals Wildlife Birds Hawaiian forest birds are stealing each other's twigs Kleptoparasitism is a risky crime sweeping the islands' forests. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Bright red iʻiwi birds are among the offenders. Breakthroughs, discoveries, and DIY tips sent six days a week. Birds in Hawaii are stealing from each other, and this bird-on-bird crime even extends to members of the same species.

We prove that conditional diffusion models whose reverse kernels are finite Gaussian mixtures with ReLU-network logits can approximate suitably regular target distributions arbitrarily well in context-averaged conditional KL divergence, up to an irreducible terminal mismatch that typically vanishes with increasing diffusion horizon. A path-space decomposition reduces the output error to this mismatch plus per-step reverse-kernel errors; assuming each reverse kernel factors through a finite-dimensional feature map, each step becomes a static conditional density approximation problem, solved by composing Norets' Gaussian-mixture theory with quantitative ReLU bounds. Under exact terminal matching the resulting neural reverse-kernel class is dense in conditional KL.

The world's most family-friendly landmarks revealed - with six UK spots making the top 50 The UK's best staycations revealed by Daily Mail Travel - from a Gara Rock beach proposal to an £80-a-night mansion retreat This sun-drenched European coast offers great value - and it's just a two-hour flight away Don't get caught out by Ryanair's small bag restrictions - I've tested the carry-on suitcases and underseat bags that beat the strict requirements Why heading to Salcombe, one of Britain's most expensive seaside towns, in the shoulder season is an off-peak treat - and what to do there Tired of fun! Middle class families who turn their noses up at Butlin's are missing out Luxury hotel owner in Cornwall offers to foot British tourists' petrol bills to ease financial pain of staycation With flights disrupted amid Iran war, these are Europe's easiest countries to navigate by train - and how it compares to flying for price and time How to retire to the seaside for as little as £90,000 - and Britain's best hidden beach home spots New business class seats with IMAX-style wrap-around screens revealed - making passengers feel like they're in the cinema How the cost of your staycation REALLY compares with a'cheap' holiday abroad - when you factor in everything from food to fuel Why the Lake District shouldn't introduce tourism tax, says Cumbria tourism boss How Marseille became Europe's Capital of Cool - with 20 degree sunshine, sea views and amazing seafood The world's best food markets revealed - and a UK spot comes in second place READ MORE: The best hotels in the UK for 2026 revealed - does YOUR favourite make the list? Ready to hit the mute button on reality? Deep in the Pembrokeshire countryside lies a cosmic retreat that feels almost light years away from Earth. The awe-inspiring Spodnic UFO is one of three standout stays at Melin Mabes, a four-acre glamping site owned and ran by Martin Johnson and his wife, CarolAnne. 'It looks like it's just landed from outer space and aliens could come out,' Martin notes as he showcases his brainchild during the first episode of Channel's World's Most Secret Hotels.

We initiate the study of language generation in the limit, a model recently introduced by Kleinberg and Mullainathan [KM24], under the constraint of differential privacy. We consider the continual release model, where a generator must eventually output a stream of valid strings while protecting the privacy of the entire input sequence. Our first main result is that for countable collections of languages, privacy comes at no qualitative cost: we provide an $\varepsilon$-differentially-private algorithm that generates in the limit from any countable collection. This stands in contrast to many learning settings where privacy renders learnability impossible. However, privacy does impose a quantitative cost: there are finite collections of size $k$ for which uniform private generation requires $Ω(k/\varepsilon)$ samples, whereas just one sample suffices non-privately. We then turn to the harder problem of language identification in the limit. Here, we show that privacy creates fundamental barriers. We prove that no $\varepsilon$-DP algorithm can identify a collection containing two languages with an infinite intersection and a finite set difference, a condition far stronger than the classical non-private characterization of identification. Next, we turn to the stochastic setting where the sample strings are sampled i.i.d. from a distribution (instead of being generated by an adversary). Here, we show that private identification is possible if and only if the collection is identifiable in the adversarial model. Together, our results establish new dimensions along which generation and identification differ and, for identification, a separation between adversarial and stochastic settings induced by privacy constraints.

Machine learning competitions (MLCs) play a pivotal role in advancing artificial intelligence (AI) by fostering innovation, skill development, and practical problem-solving. This study provides a comprehensive analysis of major competition platforms such as Kaggle and Zindi, examining their workflows, evaluation methodologies, and reward structures. It further assesses competition quality, participant expertise, and global reach, with particular attention to demographic trends among top-performing competitors. By exploring the motivations of competition hosts, this paper underscores the significant role of MLCs in shaping AI development, promoting collaboration, and driving impactful technological progress. Furthermore, by combining literature synthesis with platform-level data analysis and practitioner insights a comprehensive understanding of the MLC ecosystem is provided. Moreover, the paper demonstrates that MLCs function at the intersection of academic research and industrial application, fostering the exchange of knowledge, data, and practical methodologies across domains. Their strong ties to open-source communities further promote collaboration, reproducibility, and continuous innovation within the broader ML ecosystem. By shaping research priorities, informing industry standards, and enabling large-scale crowdsourced problem-solving, these competitions play a key role in the ongoing evolution of AI. The study provides insights relevant to researchers, practitioners, and competition organizers, and includes an examination of the future trajectory and sustained influence of MLCs on AI development.

Dynamical systems describe how a physical system evolves over time. Physical processes can evolve faster or slower in different environmental conditions. We use time-warping as rescaling the time in a model of a physical system. This thesis proposes a new method of transfer learning for Recurrent Neural Networks (RNNs) based on time-warping. We prove that for a class of linear, first-order differential equations known as time lag models, an LSTM can approximate these systems with any desired accuracy, and the model can be time-warped while maintaining the approximation accuracy. The Time-Warping method of transfer learning is then evaluated in an applied problem on predicting fuel moisture content (FMC), an important concept in wildfire modeling. An RNN with LSTM recurrent layers is pretrained on fuels with a characteristic time scale of 10 hours, where there are large quantities of data available for training. The RNN is then modified with transfer learning to generate predictions for fuels with characteristic time scales of 1 hour, 100 hours, and 1000 hours. The Time-Warping method is evaluated against several known methods of transfer learning. The Time-Warping method produces predictions with an accuracy level comparable to the established methods, despite modifying only a small fraction of the parameters that the other methods modify.

How the artists in this year's survey do or, more often, don't acknowledge those who paved the way for them. Machado makes pieces that one might call documents of reverence, excavated burial grounds. If nothing else, the 2026 Whitney Biennial, curated by Marcela Guerrero and Drew Sawyer (at the Whitney Museum through August 23rd), introduces viewers to what I call ChatGPT art--facsimiles of facsimiles by makers who have little if any relationship to what they're putting out there, aside from its being a product in service of a career. Indeed, it's difficult to think of the people who grew up with and apparently condone the use of A.I. sources in the creation of "art" as artists themselves, especially if you define art as a creative expression of thoughts or feelings that have changed, and contributed to the vision of, the artists who made it. It's true that, nearly from the beginning, postmodern art challenged the notion of originality, or, more specifically, the weight of originality--often with great joy and wit and not a little fear.

Spring Break travelers facing TSA hell fume as it's revealed why only certain airports crippled by shutdown Chappell Roan apologises to Jude Law's daughter as she insists she did not ask security guard to approach her and says'I do not hate fans of my music or children' Democratic enclave tears down tent city in its latest'whack-a-mole' move as homeless crisis laid bare Infertile influencer Clavicular's dark fetish is far more alarming than anyone feared, claim high school enemies as they leak unrecognizable photos and humiliating secrets I was a producer on The Bachelor. I've seen what happens when the cameras stop rolling: JANA HOCKING reveals the humiliating crisis talks, sex secrets and forbidden relationships Under fire again: Embattled sheriff in Nancy Guthrie case was accused of'assaulting deputy'...as decades of complaints emerge My daughters begged me not to send them back to their mother... Inside Enya's off-grid life in a £2.5M remote castle with 12 cats and no partner or children after turning her back on fame and admitting she's'dark and difficult' to be around'He just didn't protect him': Insiders reveal REAL reason Justin Bieber and Usher's secret feud hit'boiling point' at Oscars MORE bad news for Austin's housing market as Texan city leads in plummeting prices Hawaii's worst flooding in 20 years caused over $1BILLION in damage as crews desperately search for woman swept away in deluge Princess Beatrice puts on united front with husband Edo during lunch out amid fears her'marriage is in trouble' in wake of Epstein scandal Friends reveal fears Princess Beatrice's'marriage is in trouble' in wake of Epstein scandal. I was the only one JFK Jr and Carolyn Bessette trusted when they burdened me with an extraordinarily intimate secret. Iran war live: Trump threatens to'obliterate' Tehran's power plants if Strait of Hormuz does not'fully open' in next 48 hours Trump's White House ballroom architect'has totally baffled colleagues' by taking on controversial project Oscars PANIC as ratings hemorrhage: Insiders reveal'existential crisis' inside the Academy... and why Hollywood's biggest night was a'big fat dud' My husband's filthy habit is so revolting I don't even want to kiss him: DEAR JANE READ MORE: Britain's best supermarket hot cross buns revealed There's nothing quite like a toasted hot cross bun slathered in butter. Now, experts have suggested an unusual way to make them taste even better - by slicing them into thirds.