Are robots eating our jobs? Not according to AI

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Automation has been gradually transforming the workplace for years (think Excel spreadsheets or chatbots). As artificial intelligence (AI), machine learning and deep learning systems that can learn from each other become more prevalent and smarter (think Alexa or IBM Watson), they continue to replace more manual, repetitive job tasks. Consequently, automation and robots are changing more jobs globally at breakneck speed.

A McKinsey Global Institute report suggests that between 400 million to 800 million jobs worldwide will be lost due to automation by 2030. The report claims that the U.S. could lose between 16 to 54 million jobs by 2030. The pace at which robots are entering our workforce is staggering. Oxford Economics expects robots and automation to replace 20 million (8.5%) global manufacturing jobs by 2030.

Keep in mind that these predictions came before anyone predicted the Covid-19 pandemic or its impact on our workforce. The pandemic has made the need for digital transformation and automation more urgent as the critical need to work from home, physical distancing and contactless become the new normal.

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Physna aims to be Google of 3D search with geometry-based AI

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Physna, a midwestern U.S. startup founded in 2015, is trying to do for physical object (3D model) search what Google did for text and image search. Using geometric deep-learning technology and proprietary algorithms, Physna is able to understand, map and compare 3D models and index them based on their geometry. While it has been possible to search for 3D models using text, images, tags and more, this is the first time that searching for physical objects based on their fundamental geometry, their physical ‘DNA’ (hence the name PHYSNA according to its founder Paul Powers), has been made possible and available, with the launch of Thangs.com.

“We live in a 3D world, but digital technology is two-dimensional,” said Paul Powers, CEO of Physna. “Over 70% of the economy is centered around physical goods, but less than 1% of software is capable of handling 3D data. Physna was founded on the principle that computers should be taught to “think” in 3D, and accurately describe the real, 3D world around us. By enabling 3D models to be treated and analyzed like other code, Physna’s technology bridges the gap between the physical world and digital world of software. By democratizing the ability to design, interact with and analyze 3D models of the world around them, more people will have the ability to create and drive innovation in product design, 3D printing, augmented and virtual reality, gaming, healthcare and beyond.”

By identifying specific geometry ‘clusters’, the proprietary algorithms characterize and categorize 3D models in a unique way – and directly use this to search for other models that may be similar, different, or exact matches. With this approach of decomposing and linking 3D models by their geometry, Physna is able to capture 10,000 times more data points than a traditional scanned model, by codifying 3D model data for use in software applications. It essentially provides a platform for 3D designers and engineers similar to what software engineers have.

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4 Blockchain projects solving real-world problems

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From wealth management to autonomous robots: four prominent startups at the end of 2018

Investors are no longer interested in ICO projects with no real use. According to Icodata, $150 million were raised in October 2018 through token sales compared to $1.5 billion in January of the same year. “The blockchain space is getting to the point where there’s a ceiling in sight,” claims Ethereum co-founder Vitalik Buterin. The Russian-Canadian programmer believes that the next step will be “real applications of real economic activity.”

Despite the statements and hopes that the end of 2018 will bring about a return to practicality, it is still difficult for applicable projects to break through the information noise. We have picked out four noteworthy blockchain projects that have not yet gained traction in the media, despite featuring a range of out-of-the-box solutions.

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Deepfakes: The dawn of the Post-truth era

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For about 200,000 years, modern humans have relied on our eyes and ears to separate truth from lies and fact from fiction. Even if we ignore the rise of fake news (and how difficult it is to do anything about it), technology (like deep learning) is on the verge of making it impossible to know if what you are seeing and hearing is real or fake.

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Comparing brain vs deep learning and the computational complexity of these processes

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Tim Dettmers: In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets. Thereby we will see that a neuron and a convolutional net are very similar information processing machines. While performing this comparison, I will also discuss the computational complexity of these processes and thus derive an estimate for the brains overall computational power. I will use these estimates, along with knowledge from high performance computing, to show that it is unlikely that there will be a technological singularity in this century.

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The future of Silicon Valley tech is deep learning

Artificial intelligence experts are investigating ways to commercialize “deep learning.”

Scientists are confident about a machine learning technology that can recognize and replicate human activities like seeing and thinking even though it sounds like something out of a dystopian novel.

 

 

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