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AI News Saturday, March 10 Plays gentle music, Solar powered e-windows, Time series & more…

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AI News TLDR / Table of Contents

  • 5 predictions for what life will be like in 2030
    • Driverless cars, smart homes and genetically engineered pets. Here’s how tech is about to change your daily life.
    • plays gentle music, solar powered e-windows, Light Field Displays, specific nutritional needs, Short Palindromic Repeats
  • Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis
    • In this post, we consider different approaches for time series modeling. The forecasting approaches using linear models, ARIMA alpgorithm, XGBoost machine lear…
    • time series, sales time series, , ,
  • Why unemployment isn’t the robots’ fault, it’s ours
    • With robots slowly taking over, it’s easy for us to blame them for unemployment. But if we look a little deeper, the fault is actually our own, writes Shivdeep Dhaliwal.
    • increasingly efficient machines, standard labor practices, people publishing articles, CEO Jerome Pesenti, older age groups

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5 predictions for what life will be like in 2030

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  • Say goodbye to your screens: Todays virtual reality headsets are used for consumer entertainment, yet they are bulky and isolating.
  • In the future, Light Field Displays may eliminate the need for a headset or display altogether, projecting 4D images directly onto your retinas from a point of focus.
  • Should regulatory bodies ban CRISPR technologies in humans, underground labs will flourish worldwide, as parents aim to eliminate congenital genetic disorders or give their kids a heritable advantage in school and life.
  • Biofacturing growing organs and skyscrapers: Perhaps the single most disruptive change will follow developments in genetic engineering, as bacteria, algae and other cells become the factories of tomorrow.
  • The age of implantables: As our world changes, scientists believe that humans brains will continue to get bigger, our lifespans will increase, and our cultures will continue to evolve and merge as we adapt to new environments.

[/vc_column_text][vc_column_text el_class=”topfeed-tags”]Tags: plays gentle music, solar powered e-windows, Light Field Displays, specific nutritional needs, Short Palindromic Repeats[/vc_column_text][/vc_column][vc_column width=”1/2″][vc_separator][vc_column_text el_class=”topfeed-tweet”]

[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]5 predictions for what life will be like in 2030 | World Economic Forum[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”Linear-Machine-Learning-and-Probabilistic-Approaches-for-Time-Series-Analysis”][vc_column width=”1/2″][vc_separator][vc_column_text]

Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis

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  • In this case, we need to take into account sales distributions and dependencies between sales time series features (e.g. day of week, month, average sales, etc.) and external factors such as promo, distance to competitors, etc.
  • Let us consider such features of sales time series as sales (variable logSales), mean sales per day for store (variable meanLogSales) and promo action (variable Promo).
  • As the case study shows, the use of copula make it possible to model stochastic dependencies between different factors of sales time series separately from their marginal distributions.
  • Mean sales for the store (variable meanLogSales) vs sales (variable logSales) obtained for considered Bayesian model are shown on the figure 13.
  • Mean sales for the store (variable meanLogSales) vs sales (variable logSales) – – As the case study shows, the use of Bayesian approach allows us to model stochastic dependencies between different factors of sales time series and receive the distributions for m

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[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis – Data Science Central[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”Why-unemployment-isn-t-the-robots-fault-it-s-ours”][vc_column width=”1/2″][vc_separator][vc_column_text]

Why unemployment isn’t the robots’ fault, it’s ours

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  • So why are there so many jobs left unfilled by both man and machine?
  • So again, it is not the case that robots are stealing jobs from existing workers, they are merely filling existing gaps left by humans.
  • Technology research and analysis company Gartner estimates that by the year 2020 AI field will have created 2.3 million jobs.
  • About this estimate, Svetlana Sicular of Gartner says, unfortunately, most calamitous warnings of job losses confuse AI with automation that overshadows the greatest AI benefit AI augmentation a combination of human and artificial intelligence, where both complement each other.
  • Pew Research published a report titled Automation in Everyday Life which found that U.S. adults are roughly twice as likely to express worry (72%) as enthusiasm (33%) about a future in which robots and computers are capable of doing many jobs that are currently done by humans.

[/vc_column_text][vc_column_text el_class=”topfeed-tags”]Tags: increasingly efficient machines, standard labor practices, people publishing articles, CEO Jerome Pesenti, older age groups[/vc_column_text][/vc_column][vc_column width=”1/2″][vc_separator][vc_column_text el_class=”topfeed-tweet”]

[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]Why unemployment isn’t the robots’ fault, it’s ours | World Economic Forum[/vc_column_text][/vc_column][/vc_row]

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