Why does #AI only rely on Correlation? THIS IS ALSO REFERRED TO AS CAUSE AND EFFECT. There are two types of AI: Statistic AI of ANN, ML and DL, or a fake AI, establishing statistic relationships of input data with the output data to uncover hidden data patterns, correlations and other insights. It is based on data analysis and big data and predictive analytics and machine statistics. Real AI or Causal AI or General AI, establishing causal relationships of input data with the output data to uncover real patterns, causal rules and other intelligence and knowledge.

The graph represents a network of 1,419 Twitter users whose recent tweets contained "iot bigdata", or who were replied to, mentioned, retweeted or quoted in those tweets, taken from a data set limited to a maximum of 5,000 tweets, tweeted between 3/26/2006 12:00:00 AM and 3/14/2023 5:00:36 PM. The network was obtained from Twitter on Wednesday, 15 March 2023 at 11:43 UTC. The tweets in the network were tweeted over the 2136-day, 23-hour, 8-minute period from Monday, 08 May 2017 at 00:51 UTC to Tuesday, 14 March 2023 at 23:59 UTC. There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, an edge for each "retweet" relationship in a tweet, an edge for each "quote" relationship in a tweet, an edge for each "mention in retweet" relationship in a tweet, an edge for each "mention in reply-to" relationship in a tweet, an edge for each "mention in quote" relationship in a tweet, an edge for each "mention in quote reply-to" relationship in a tweet, and a self-loop edge for each tweet that is not from above. The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.

The graph represents a network of 1,692 Twitter users whose recent tweets contained "iot machinelearning", or who were replied to, mentioned, retweeted or quoted in those tweets, taken from a data set limited to a maximum of 5,000 tweets, tweeted between 3/26/2006 12:00:00 AM and 3/14/2023 5:00:36 PM. The network was obtained from Twitter on Wednesday, 15 March 2023 at 12:17 UTC. The tweets in the network were tweeted over the 2072-day, 12-hour, 58-minute period from Tuesday, 11 July 2017 at 11:00 UTC to Tuesday, 14 March 2023 at 23:59 UTC. There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, an edge for each "retweet" relationship in a tweet, an edge for each "quote" relationship in a tweet, an edge for each "mention in retweet" relationship in a tweet, an edge for each "mention in reply-to" relationship in a tweet, an edge for each "mention in quote" relationship in a tweet, an edge for each "mention in quote reply-to" relationship in a tweet, and a self-loop edge for each tweet that is not from above. The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.

Lean principles in manufacturing are focused on reducing waste and lead times. Preventing unplanned equipment downtime to improve throughput is a key activity. There are two primary algorithms for analyzing equipment data - univariate and multivariate anomaly detection. Univariate anomaly detection focuses on analyzing the behavior of a single variable over time, for example, the temperature of a machine. It can be useful for detecting simple patterns of deviation from the normal behavior of a single variable and is relatively straightforward to implement and understand.

The graph represents a network of 1,072 Twitter users whose recent tweets contained "iiot bigdata", or who were replied to, mentioned, retweeted or quoted in those tweets, taken from a data set limited to a maximum of 5,000 tweets, tweeted between 3/26/2006 12:00:00 AM and 3/9/2023 5:00:36 PM. The network was obtained from Twitter on Friday, 10 March 2023 at 12:02 UTC. The tweets in the network were tweeted over the 1827-day, 0-hour, 27-minute period from Friday, 09 March 2018 at 00:30 UTC to Friday, 10 March 2023 at 00:58 UTC. There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, an edge for each "retweet" relationship in a tweet, an edge for each "quote" relationship in a tweet, an edge for each "mention in retweet" relationship in a tweet, an edge for each "mention in reply-to" relationship in a tweet, an edge for each "mention in quote" relationship in a tweet, an edge for each "mention in quote reply-to" relationship in a tweet, and a self-loop edge for each tweet that is not from above. The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.

The graph represents a network of 1,371 Twitter users whose recent tweets contained "iiot machinelearning", or who were replied to, mentioned, retweeted or quoted in those tweets, taken from a data set limited to a maximum of 5,000 tweets, tweeted between 3/26/2006 12:00:00 AM and 3/9/2023 5:00:36 PM. The network was obtained from Twitter on Friday, 10 March 2023 at 12:23 UTC. The tweets in the network were tweeted over the 1827-day, 0-hour, 27-minute period from Friday, 09 March 2018 at 00:30 UTC to Friday, 10 March 2023 at 00:58 UTC. There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, an edge for each "retweet" relationship in a tweet, an edge for each "quote" relationship in a tweet, an edge for each "mention in retweet" relationship in a tweet, an edge for each "mention in reply-to" relationship in a tweet, an edge for each "mention in quote" relationship in a tweet, an edge for each "mention in quote reply-to" relationship in a tweet, and a self-loop edge for each tweet that is not from above. The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.

The graph represents a network of 1,389 Twitter users whose recent tweets contained "iiot machinelearning", or who were replied to, mentioned, retweeted or quoted in those tweets, taken from a data set limited to a maximum of 5,000 tweets, tweeted between 3/26/2006 12:00:00 AM and 2/9/2023 5:00:35 PM. The network was obtained from Twitter on Friday, 10 February 2023 at 12:24 UTC. The tweets in the network were tweeted over the 1474-day, 5-hour, 23-minute period from Sunday, 27 January 2019 at 19:36 UTC to Friday, 10 February 2023 at 01:00 UTC. There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, an edge for each "retweet" relationship in a tweet, an edge for each "quote" relationship in a tweet, an edge for each "mention in retweet" relationship in a tweet, an edge for each "mention in reply-to" relationship in a tweet, an edge for each "mention in quote" relationship in a tweet, an edge for each "mention in quote reply-to" relationship in a tweet, and a self-loop edge for each tweet that is not from above. The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.

The graph represents a network of 1,514 Twitter users whose recent tweets contained "datascientist", or who were replied to, mentioned, retweeted or quoted in those tweets, taken from a data set limited to a maximum of 5,000 tweets, tweeted between 3/26/2006 12:00:00 AM and 2/5/2023 5:00:35 PM. The network was obtained from Twitter on Monday, 06 February 2023 at 20:48 UTC. The tweets in the network were tweeted over the 822-day, 16-hour, 33-minute period from Thursday, 05 November 2020 at 08:27 UTC to Monday, 06 February 2023 at 01:00 UTC. There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, an edge for each "retweet" relationship in a tweet, an edge for each "quote" relationship in a tweet, an edge for each "mention in retweet" relationship in a tweet, an edge for each "mention in reply-to" relationship in a tweet, an edge for each "mention in quote" relationship in a tweet, an edge for each "mention in quote reply-to" relationship in a tweet, and a self-loop edge for each tweet that is not from above. The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.

The graph represents a network of 1,081 Twitter users whose tweets in the requested range contained "iiot machinelearning", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 04 February 2022 at 12:05 UTC. The requested start date was Friday, 04 February 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 21-hour, 2-minute period from Tuesday, 01 February 2022 at 03:57 UTC to Friday, 04 February 2022 at 01:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

What is the understanding about some of the most important conceptions in the philosophy of #AI? Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. My philosophy, science and engineering of #machineintelligence and learning is plain and clear, The basis of MIL is all quantitative sciences, as mathematics and physics, statistics, probability theory and #datascience and #engineering, all within the framework of the data universe ontology, Establishing quantitative structure and relationships between different quantities is the cornerstone of mathematical and physical sciences. Their progress is achieved due to transforming the abstract qualities of entities into quantities, like as postulating that all material bodies marked by quantitative properties or physical dimensions are subject to some measurements and observations.