Get the day's biggest stories sent direct to your inbox so you never miss a thing Artificial Intelligence (AI) has become a staple of our daily lives, from Siri to Google Assistant which can control our phones, computers and even homes. The world of media has explored the advancement and potential dangers of rapidly advancing AI for decades, films such as Blade Runner and 2001: A Space Odyssey have touched on the themes of what happens when AI grows beyond human control. But how much does this affect our perception of AI and its involvement in our daily lives? Using data from Google Search Trends and Linkfluence, new research from Ebuyer has revealed that globally almost 3 million people had searched negative themes around AI online. The research discovered that the biggest search queries included "Can artificial intelligence be dangerous?"

Artificial intelligence is everywhere in our day to day lives. But, with the world's media filled with dystopian tales of computer intelligence taking over humanity; what does the public really think of AI? Using data1 from Google Search Trends, Linkfluence, and Answer the Public, Ebuyer has revealed that over 2.9 million negative posts were generated on AI over the past year! So, does the public really trust AI? And why exactly do people have such a negative perception of the technology that continues to make our lives so much easier? The USA has the largest interest in AI with over 12 million posts over the past year!

This article comes from Algobeans Layman tutorials in analytics. Whenever you spot a trend plotted against time, you would be looking at a time series. The de facto choice for studying financial market performance and weather forecasts, time series are one of the most pervasive analysis techniques because of its inextricable relation to time--we are always interested to foretell the future. One intuitive way to make forecasts would be to refer to recent time points. Today's stock prices would likely be more similar to yesterday's prices than those from five years ago.

These correlations between past and present values demonstrate temporal dependence, which forms the basis of a popular time series analysis technique called ARIMA (Autoregressive Integrated Moving Average). Long short-term memory (LSTM) networks are a type of neural networks that builds models based on temporal dependence. Therefore, google search trends for persimmons could well be modeled by adding a seasonal trend to an increasing growth trend, in what's called a generalized additive model (GAM). The principle behind GAMs is similar to that of regression, except that instead of summing effects of individual predictors, GAMs are a sum of smooth functions.

This article comes from Algobeans Layman tutorials in analytics. Whenever you spot a trend plotted against time, you would be looking at a time series. The de facto choice for studying financial market performance and weather forecasts, time series are one of the most pervasive analysis techniques because of its inextricable relation to time--we are always interested to foretell the future. One intuitive way to make forecasts would be to refer to recent time points. Today's stock prices would likely be more similar to yesterday's prices than those from five years ago.