Evolutionary algorithms are increasingly recognised as a viable computational approach for the automated optimisation of deep neural networks (DNNs) within artificial intelligence. This method extends to the training of DNNs, an approach known as neuroevolution. However, neuroevolution is an inherently resource-intensive process, with certain studies reporting the consumption of thousands of GPU days for refining and training a single DNN network. To address the computational challenges associated with neuroevolution while still attaining good DNN accuracy, surrogate models emerge as a pragmatic solution. Despite their potential, the integration of surrogate models into neuroevolution is still in its early stages, hindered by factors such as the effective use of high-dimensional data and the representation employed in neuroevolution. In this context, we address these challenges by employing a suitable representation based on Linear Genetic Programming, denoted as NeuroLGP, and leveraging Kriging Partial Least Squares. The amalgamation of these two techniques culminates in our proposed methodology known as the NeuroLGP-Surrogate Model (NeuroLGP-SM). For comparison purposes, we also code and use a baseline approach incorporating a repair mechanism, a common practice in neuroevolution. Notably, the baseline approach surpasses the renowned VGG-16 model in accuracy. Given the computational intensity inherent in DNN operations, a singular run is typically the norm. To evaluate the efficacy of our proposed approach, we conducted 96 independent runs. Significantly, our methodologies consistently outperform the baseline, with the SM model demonstrating superior accuracy or comparable results to the NeuroLGP approach. Noteworthy is the additional advantage that the SM approach exhibits a 25% reduction in computational requirements, further emphasising its efficiency for neuroevolution.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. New Mexico House Majority Leader Sheryl Williams Stapleton resigned Friday amid a criminal investigation involving alleged racketeering and money laundering. The Democratic state lawmaker has "unequivocally" denied the allegations but wrote in a letter to New Mexico's secretary of state that she felt her resignation was in the state's best interest, according to the Santa Fe New Mexican. "This is a decision that weighs heavily on me, and which I have made after a tremendous amount of consideration of the best interest of the people," she wrote.

As promised, Google employees who led the large-scale walkouts in November have held a town hall meeting to share more allegations of a retaliatory culture at the company. Bloomberg understands that Claire Stapleton and Meredith Whittaker provided "more than a dozen" additional stories of reprisals at the gathering, which gave participants a chance to offer input. Details of those extra stories weren't available as we wrote this, but Stapleton described the tales in company email as evidence of "systemic issues" that wouldn't be addressed without collective action. Stapleton and Whittaker started the outcry after explaining their own accusations. Stapleton said she was going to be demoted from her marketing role at YouTube and was asked to take a medical leave she didn't need, saving her position only after she retained a lawyer.

Worker activists at Google held a "town hall" on Friday where they alleged that the company regularly retaliates against employees who speak out about workplace problems and announced plans for a "company-wide day of action" on 1 May. The meeting, livestreamed for Google employees in offices around the world, was announced after two of the organizers of the November 2018 global walkout circulated a letter internally alleging they were being punished for their activism. The two employees, Meredith Whittaker and Claire Stapleton, provided further details of their cases during the Friday event. Their statements, along with anonymous reports of retaliation of 11 other Google employees, were published in internal documents seen by the Guardian. "I didn't walk out because I'm against Google, I walked out because I'm for it – because I wanted to make it better," Stapleton said in her written statement.

Google employees Meredith Whittaker and Claire Stapleton made the allegations in a letter sent to co-workers on Monday. Whittaker says Google dramatically changed her role at the company after the rally by disbanding an artificial intelligence ethics council she co-founded. And despite years of high performance reviews, Stapleton was demoted and told to take leave.

On the first day of November last year, some 20,000 Google employees at more than 40 offices across the world staged a walkout protesting how the company had dealt with serious accusations of sexual assault and harassment and what many employees described as a culture of impunity for executives. The event was planned by a core group of seven organizers who work at Google. On Monday, two of those women, Meredith Whittaker and Claire Stapleton, shared examples of retaliation they've face from the company since on a Google-internal mailing list. Wired first reported the two were facing blowback from Google for helping to organize the protest. Stapleton is a 12-year veteran at Google.

Two of the seven Google employees who organized a massive walkout last November say they've since faced retaliation. After leading the protest, which sought to change Google's handling of sexual misconduct, Meredith Whittaker says she was told her role would be "changed dramatically." Claire Stapleton was told she would be demoted. The two claim they're not alone, and they plan to gather more stories and strategize with colleagues. As Wired reports, Stapleton got news of her demotion just months after the protest.

At its most basic, artificial intelligence (AI) refers to a computer making decisions that typically would have been made by a human. Or put another way, AI is very good at making predictions in which there are a large number of variables and complex interactions at play, Michael Schmidt, chief scientist at DataRobot told CMSWire. Little wonder, then, that companies are eager to have AI applied to their particular business problems. It's new(ish), shiny technology and -- as the hype sometimes goes -- absolutely necessary to remain competitive. But the truth is, it is the rare problem that can only be solved using AI.

Machine learning is the use of computing resources that have the ability to learn without being explicitly programmed -- that is, acquire and apply knowledge and skills that maximize the chance of success. That definition of machine learning, provided courtesy of Rob Clyde, vice-chair of the ISACA board of directors and board director and executive chair to White Cloud Security is a pretty standard explanation. Machine learning is a cognitive system that has the potential to learn from interactions and then deliver evidence-based answers to a problem. But you'd never know that from the many vendors that purport to offer a machine-learning based application that is really something else -- usually a dressed-up business intelligence solution against which SQL queries are run. Such vendors are prevalent, says Padraig Stapleton, VP of Engineering at Argyle Data, "Machine learning and AI are two terms that are abused a lot. You talk to a vendor and it will tell you that it has machine learning or AI but then you talk further to them about how the applications really works and it becomes clear that they do not."

Machine learning in 2017 will become a mainstream tool for communications providers struggling to transform data overload into actionable analytics, according to Argyle Data. "The telecommunications industry is drowning in data," said Padraig Stapleton, VP of engineering at Argyle Data. Stapleton said fraud and financial analysts alike are overwhelmed by the struggle to control and harness this fire-hose of information into actionable analytics. There is just too much IP traffic going across mobile networks for humans to review, detect and respond to fraud in the traditional ways such as discovering fraud and writing preventative rules. Machine learning does all the grunt work for analysts, sifting through data in real time and providing output instantly in understandable, accessible formats," said Stapleton.