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 2,186 Twitter users whose tweets in the requested range contained "datascientist", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 12 December 2022 at 20:58 UTC. The requested start date was Monday, 12 December 2022 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 4-day, 1-hour, 36-minute period from Wednesday, 07 December 2022 at 23:23 UTC to Monday, 12 December 2022 at 00:59 UTC.

The graph represents a network of 1,939 Twitter users whose tweets in the requested range contained "datascientist", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 28 November 2022 at 20:51 UTC. The requested start date was Monday, 28 November 2022 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 3-day, 14-hour, 40-minute period from Tuesday, 22 November 2022 at 12:30 UTC to Saturday, 26 November 2022 at 03:10 UTC.

Why Machines fail to reason? I find three of these aspects: An awareness of causal relationships, Good explanations for these causal relationships, The ability to make use of the former in order to achieve motivated goals. Causality One well-known mantra of #statistics is "correlation does not imply causation". It turns out that while there are well-defined statistical measures of correlation, causality is notoriously difficult to define, formalize, or quantify. Good explanations Good explanations are also the reason why we are so convinced that this reasoning can be vastly generalized: you know that you do not want to hide under a tree during a thunderstorm.

This is roughly similar to the brain of humans and animals. Such a computer system is emulating the decision-making ability of a human expert, relying on two interacting modules: the inference engine and the knowledge base.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner What are the Challenges that Preventing Machine Learning to Get Smarter? Every year, machine learning researchers fascinate us with new discoveries and innovations, but there are some challenges and limitations. Here are some key practical issues that are harming machine learning to truly reach its potential. Data Quality Machine learning systems rely on data. That data can be broadly classified into two groups: features and labels.

Art collectors buy ML sculptures to experience them in a new space. Advancement in Artificial Intelligence Virtual art galleries are making art more relatable by showing how it relates to the lives of real people. As a result, it's encouraging more people to buy art they find interesting. How to Take AI Art to the Next Level Virtual Galleries https://lnkd.in/dbgswFFN Become an Author at Machine Learning https://lnkd.in/eYW2t9t

The graph represents a network of 1,177 Twitter users whose tweets in the requested range contained "datascientist", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 20 December 2021 at 20:06 UTC. The requested start date was Monday, 20 December 2021 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 3-day, 9-hour, 42-minute period from Thursday, 16 December 2021 at 15:18 UTC to Monday, 20 December 2021 at 01:00 UTC.

In this episode of SaugaTalks, hosted by Irene Lyakovetsky, I met Stu Bailey, Co-founder and Chief Enterprise AI Architect at ModelOp, to discuss why #ModelOps is a Critical Piece of Enterprise AI Strategy. It is not surprising seeing why #ModelOps is the cornerstone of every #AI initiative. Technology Talk Host: SaugaTalks Chats With Fascinating People In Tech! Follow: bit.ly/SaugaTalksLI and Subscribe For The Full Episodes: bit.ly/SaugaTalks "#ModelOps is a Key #Enterprise Capability for End-to-end #Governance of #AI Initiatives Across the Organization" #SaugaTalks with Giuliano Liguori, Digital Transformation Leader, Innovation Manager, Thought Leader, Board Member CIO Club IT and Stu Bailey, Co-founder and Chief Enterprise AI Architect at ModelOp Full Episode: https://lnkd.in/eVAxm_mt

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner When you need #DataScientists and ML Engineers? Although they certainly work together amicably and enjoy some overlap concerning expertise and experience, the two roles serve quite different purposes. Data Scientists follow the #DataScience Process, Stage 1: Understanding the Business Problem Stage 2: Data Collection Stage 3: Data Cleaning & Exploration Stage 4: Model Building Stage 5: Communicate and Visualize Insights The majority of the work performed by Data Scientists is in the research environment. In this environment, Data Scientists perform tasks to better understand the data so they can build models that will best capture the data's inherent patterns. Once they've built a model, the next step is to evaluate whether it meets the project's desired outcome.