Tagged With machine learning

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Over the last few weeks, something has been bothering me. One of the recurring themes I'm hearing about, either directly or indirectly, has been around the intersection between technology and trust. While the issues around government access to encrypted communications have received plenty of airplay, the expanding use of machine learning, broad access to vast swathes of data and increased use of social media has made trust the voluble commodity in tech.

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Machine learning holds great promise for helping us to manage vast swathes of data and complementing humans as we try to solve more complex problems in our world. Everything from finding the best route between home and the airport through to finding cures for diseases can be helped by machine learning. But when machine learning is used to make decisions that directly affect people, we need to be able to ask how the models work. Emily Pries, from Lyft, looked at the question of machine learning fairness at the the recent Twilio Signal conference.

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As more tasks become automated through the use of machine learning and AI driven systems, there's been a worry that many people would lose their jobs. On the flipside, there's been optimism that automation will take people away from dreary and repetitive tasks and direct their skills to more complex or rewarding work. But a recent study by the United Nations' International Labor Organization (ILO) says the reality, at least for now, is very different.

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With job-search sites now using software to help filter candidates, it's important to tailor your CV to ensure it has the highest chance of passing that first round of screening. Recruitment agency Hays recently polled over6000 people and learned that over a quarter have already tweaked the CV with another 54% planning to update over the coming year. Just one in five have no plans to bring their CV into the 21st century. So what do you need to do to get your CV algorithm-friendly?

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It feels as though Google has held the market on “point your camera at it to learn more” technology for some time now, first through its Translate app, which let you target signs in foreign languages with your smartphone’s camera and receive translations on the fly, and now via Lens, which expands this technology to give you plenty of information about the objects in photos you’ve taken (or are about to take).

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"It's very easy to get intimidated," says Hamayal Choudhry, the robotics engineer who co-created the smartARM, a robotic hand prosthetic that uses a camera to analyse and manipulate objects. "You have this idea for a project, then think, I don't know a thing about this." Here's how Choudhry and his partner Samin Khan, who programmed the smartARM's machine learning algorithm, used code libraries, college assignments, and sponsored hackathons to find and execute a meaningful project at age 20.

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You probably use machine-learning systems every day without even knowing it. The technology gives us spam filters, our Facebook News Feeds, digital assistants, search engines, Netflix picks, Amazon recommendations, fraud detection systems, chatbots and more. And it's only going to become more pervasive. For forward-looking parents, it's time to get your kids on it.

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Machine learning and artificial intelligence are near the top of the list of items dominating discussions about digital transformation. Chris Bedi is the CIO at ServiceNow and he said, during a briefing at the company's Knowledge 18 event, CEOs are now value in speed over cost. As businesses are changing, he says there's a huge sense of urgency as companies want to ensure they're not left behind.

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Lots of discussions about complex topics start with the premise that there are two types of people. That's where Symantec's Chief Technology Officer Hugh Thompson began his discussion on the challenges facing the security industry. He began his entertaining security keynote at this year's CeBIT event in Sydney telling the story of a bird that flew into a commercial aircraft as the plane was being loaded by ground staff. It was trapped in the passenger cabin, only becoming known when the trans-Atlantic flight was in the air. The reactions to the story are indicative, he said, about differing attitudes to security risks.

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The modern workplace is undergoing a substantial transition. Systems to foster collaboration, automation and machine learning are creating a workplace that is almost unrecognisable from the 1990s. Careers are built by moving between companies and, increasingly, we are expected to be the masters of our own training and development. Where is this leading and what will the workplace look like in another 20 years?

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At the opening of Google's I/O event, the company showed off their new AI tool. In the demonstration, someone told the Google Assistant they wanted to book an appointment. Google found the hairdresser and then phoned them, holding a natural language conversation with a person to make the appointment and add it to a calendar. The party on the other end of the phone didn't know they were talking to a computer (so we're told). This opens up an interesting future.

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If you've ever wanted to have a deeper conversation with the printed page - or scan a library of literature for answers to your many questions - Google's Talk to Books tool is a fun little way to do just that. It isn't a Google search for books, but it does offer more conversational answers for your questions than a traditional search.

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The nature of work is changing. While we've seen increasing levels of automation in workplaces over the last 300 years or so, it's only been over the last decade where we've seen machine learning improve to the point where it can replace humans in tasks that go beyond the repetitive and mechanical. Greg Muller from Gooroo and Jarrad Skeen from Affix are seeing these changes first hand in their roles in recruitment and the development of high performance teams. And while they see different sides to this change, there's one thing they absolutely agree on; being able to adapt to the change will be critical if you want to keep working.

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Machine learning (AKA AI) seems bizarre and complicated. It's the tech behind image and speech recognition, recommendation systems, and all kinds of tasks that computers used to be really bad at but are now really good at. It involves teaching a computer to teach itself. And you can learn to do it in well under a year, according to data scientist Bargava. You'll need to put in a solid 10-20 hours a week, but you will learn a lot along the way.

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Machine learning is changing the way systems are being designed and how we process information. That's true in security as well. But can a ML-based approach protect us when dealing with attack vectors and exploits that haven't been seen before? I spoke with Cylance's VP for engineering, Milind Karnik.

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Artificial intelligence has been big news over the last year or so. While the idea of the technology is not new - Arthur C Clarke's HAL 9000 brought it to the fore back in 1968 when 2001: A Space Odyssey was released - we have reached a tipping point as the amount of data we have access to has exploded and the cloud has made vast amounts of computing power accessible. But we are only now starting to think about the real impacts of AI on business and society.

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Zendesk has developed Answer Bot - a chat bot that can deflect email support requests. And while they're not the first to do this, they are the only company I've found that has been developed a bot Ike this locally that can work at the scale Zendesk delivers. I spoke with Zendesk's Brett Adam, the managing director ANZ and VP of engineering for APAC and data scientist Chris Hausler.

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A panel hosted by the Australian Computer Society (ACS), featuring Liz Bacon (a past President British Computer Society), Marita Cheng (Founder/CEO of Aubot and winner of Young Australian of the Year), Mike Hinchey (from the International Federation for Information Processing) and Anthony Wong (current President of the ACS) discussed what AI is and how it will impact the IT industry and society.

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I have to admit to a little bit of self-interest in this story, as someone I love has Type 1 diabetes (it used to be called Juvenile Diabetes). The Juvenile Diabetes Reseach Foundation (JDRF) and IBM have commenced a new collaboration to apply machine learning in order to identify the factors leading to the onset to this autoimmune disease.