Former Hollyoaks Star Says No To Yorkshire BeagleBreeding Facility

first_imgFormer Hollyoaks star Claire Cooper has stayed close to her Yorkshire roots – and that’s why she was appalled to learn from PETA about B&K Universal’s plan to expand its Yorkshire beagle-breeding facility, which would churn out thousands of puppies to be used in deadly experiments.In response, Cooper fired off a letter today calling on Secretary of State for Communities and Local Government, Eric Pickles – who will make the final ruling – to reject the company’s appeal.“Breeding dogs for deadly experiments is a shameful trade”, Cooper writes. “Dogs aren’t commodities or test tubes with tails. Beagles are friendly, loyal and gentle. These qualities – which make them fantastic family companions – are also the very reason why animal experimenters choose to use them for tests, since their trusting nature can be easily exploited. . . . We hope you will do the right thing and reject the appeal for this building extension.”Earlier this year, PETA – whose motto reads, in part, that “animals are not ours to experiment on” – sent a letter, along with more than 25,000 signatures from local residents and other concerned members of the public, urging the UK Planning Inspectorate to refuse permission for the facility. In its letter, PETA warned that in addition to being cruel to dogs, the expansion would disturb residents by generating a high level of noise and an increase in traffic as a result of the transport of dogs from the facility.Cooper was the winner of the Best Actress Award at the 2013 British Soap Awards and is best known for her role as Jacqui McQueen in Hollyoaks from 2006 to 2013. She has also appeared in Strictly Come Dancing and Ready, Steady Cook.last_img read more

San Diego Tax Fighters Richard Rider in studio

first_img Categories: California News, Local San Diego News, Politics FacebookTwitter Posted: September 18, 2018 KUSI Newsroom, KUSI Newsroom September 18, 2018 00:00 00:00 spaceplay / pause qunload | stop ffullscreenshift + ←→slower / faster ↑↓volume mmute ←→seek  . seek to previous 12… 6 seek to 10%, 20% … 60% XColor SettingsAaAaAaAaTextBackgroundOpacity SettingsTextOpaqueSemi-TransparentBackgroundSemi-TransparentOpaqueTransparentFont SettingsSize||TypeSerif MonospaceSerifSans Serif MonospaceSans SerifCasualCursiveSmallCapsResetSave SettingsSAN DIEGO (KUSI) – Here with his reasons on why voters should choose “no” on Prop 1 is Richard Rider from the San Diego Tax Fighters. San Diego Tax Fighters’ Richard Rider in studiolast_img read more

Increase Communication With FarFlung Employees

first_img This hands-on workshop will give you the tools to authentically connect with an increasingly skeptical online audience. Opinions expressed by Entrepreneur contributors are their own. It sounds basic, but the first step in setting up a technology solution for working with offsite employees is to figure out just what you need to do with your extended work force. Most growing businesses with multiple work sites will have two particular needs at the top of their list: the ability for employees to talk with each other on a minute-by-minute basis and the ability to have access to files away from the office. To add to the challenge, these needs have to be met at a price point that won’t strain the budget.Meeting these requirements doesn’t require deep secrets or complex technology. Basically, it’s about e-mail, telephones and IM. For e-mail, outsourcing can provide extra features, higher security and web access. For IM, you can use the many free solutions offered online. And if you want access to customers no matter what IM platform they’re using, try chat clients like Trillian that allow you to cross platforms. And for that pesky file-sharing problem? Look for in-house servers that offer secure web access and online file sharing. 1 min read Enroll Now for Free January 30, 2007 Free Workshop | August 28: Get Better Engagement and Build Trust With Customers Nowlast_img read more

5 Innovative Uses for Machine Learning

first_img Growing a business sometimes requires thinking outside the box. Register Now » January 19, 2018 5 min read Free Webinar | Sept. 9: The Entrepreneur’s Playbook for Going Global Opinions expressed by Entrepreneur contributors are their own. Though its time horizon can’t be predicted, artificial intelligence (AI) promises to foundationally influence modern society, for better or worse. A sub-genre of AI — machine learning — has garnered particular attention from the pundits for its potential impact on the world’s most important industries.Related: Top 3 Ways Machine Learning Will Create JobsDue to the resulting hype, massive amounts of talent and resources are entering this space.But what is machine learning and why should we care about it in the first place? The answer is that, in the broadest sense, machine learning models are an application of AI in which algorithms independently predict outcomes. In other words, these models can process large data sets, extract insights and make accurate predictions without the need for much human intervention.Numerous value-generating implications result from the accelerated development of this technology, and many are poised to radically streamline the business world. Here are five of the most innovative use cases for machine learning. They’ll be coming into your life — at least your business life — sooner than you think.1. Widescale use of autonomous vehiclesThe wide-scale adoption of autonomous vehicles represents a far more efficient future for transportation. Early reports indicate that self-driving cars could reduce traffic-related fatalities by as much as 90 percent.Though we’re probably a few years away from consumer production, the adoption of autonomous vehicles by society is, at this point, inevitable. However, the time scale for adoption of this technology largely depends upon regulatory action, which often lies outside of the tech world’s control.Software engineers developing these self-driving “fleets of the future” are relying heavily upon machine-learning technologies to power the algorithms that enable vehicles to operate autonomously. These models effectively integrate data points from a number of different sensors — lidar (a survey method using lasers), radars and cameras — to operate the vehicle. These deep-learning algorithms become more intelligent over time, leading to safer driving.Related: Automatic Insights: How AI and Machine Learning Improve Customer Service2. A more efficient healthcare networkAlthough a critical part of the economy, the healthcare industry still operates on top of an inefficient legacy infrastructure. A major point of concern is finding ways to preserve sensitive patient details while still optimizing the system.Luckily, we can apply innovative machine learning algorithms (that operate without humans) to process large sets of healthcare data without breaching confidentiality contracts. Furthermore, we can use these models to better analyze and understand diagnoses, risk factors and coefficients of causation.As Dr. Ed Corbett has pointed out: “It’s clear that machine learning puts another arrow in the quiver of clinical decision making.“Machine learning in medicine has recently made headlines,” said Corbett, the medical officer at Health Catalyst. “Google has developed a machine learning algorithm to help identify cancerous tumors on mammograms. Stanford is using a deep learning algorithm to identify skin cancer.”3. Embedded retail-management systemsThe international retail sector has consistently generated over $20 trillion in sales a year for the past few years. This staggering figure comes with an enormous amount of consumer-behavior data (demographics, trends and tastes), compiled from an infinite trough of consumer-shopping patterns and tendencies.However, many retail companies are struggling to implement these valuable insights, since the information often comes from disconnected data warehouses. As a result, there is a massive opportunity to implement machine learning models that enable retailers to better understand their customers and provide a more personalized customer experience.Using previously acquired data, machine learning models can predict everything from which products to recommend to when to give out discounts. Ecommerce retailers, in particular, can combine digital behavior patterns to optimize the entire user journey, from the first point of contact, to the purchase of an item, to follow-up.4. Improved moderating of contentThe moderating of content is a major concern for social media platforms like Facebook and Twitter, as they endeavor to deliver accurate information to their audiences. As the previous election cycle highlighted, the failure to properly overseecontent can have severe repercussions.In response to the public outcry over “fake news,” Facebook recently announced it would hire 3,000 new employees specifically to look after the platform’s newsfeed content. This anxiety, however, extends far beyond social media, owing to how tech conglomerates like Google are pouring significant capital into developing content-monitoring teams of their own to support their fast-growing marketplaces.Emerging machine learning and AI platforms, such as Orions Systems, are providing proprietary systems to “grow and adapt the interactions between humans and artificial intelligence” for tasks like moderating content at scale.Uniquely, these technologies are addressing the task of moderating content with innovative tools and resources (analyzing, for instance, the context and content of every frame of video) so that employees can work more productively. This is an important advancement, as training machine learning to deal with video is notoriously difficult.5. Advanced cybersecurityCybercrime damage costs are estimated to soar past $6 trillion annually by 2021. Experts predict that companies will spend over $1 trillion in cybersecurity services from 2017 to 2021 to counterbalance this growing threat. Clearly, cybersecurity will continue to be a major priority for startups and large enterprises alike.Researchers are developing clever ways to implement machine learning models to detect fraud, prevent phishing and defend against cyberattacks. Defense-mechanism systems are being trained, using past data, to quickly spot and protect against suspicious activity. Unlike humans, these algorithms can run 24 hours a day, seven days a week, without depletion.Related: 5 Reasons Machine Learning Is the Future of MarketingAs these machine learning models become more accessible to developers, they’ll start to gain mass endorsements from consumers and enterprises. And, as that happens, it will be interesting to see which models come  out on top.last_img read more