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#cloudcomputing_2020-02-24_06-41-38.xlsx

#artificialintelligence

The graph represents a network of 1,554 Twitter users whose tweets in the requested range contained "#cloudcomputing", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 24 February 2020 at 14:42 UTC. The requested start date was Monday, 24 February 2020 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 1-day, 21-hour, 12-minute period from Saturday, 22 February 2020 at 03:48 UTC to Monday, 24 February 2020 at 01:00 UTC.


Choosing Between Rule-Based Bots And AI Bots

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Until a decade ago, the only option people had to reach out to a company was to call or email their customer service team. Now, companies offer a chat team to provide better round-the-clock customer service. According to a Facebook-commissioned study by Nielsen, 56% of people would prefer to message rather than call customer service, and that's where bots come into play. Bots are revolutionizing the way companies interact with their customers. A decade ago, bots were considered a passing tech fad.


iiot machinelearning_2020-02-21_06-26-07.xlsx

#artificialintelligence

The graph represents a network of 1,157 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, 21 February 2020 at 14:27 UTC. The requested start date was Friday, 21 February 2020 at 01:01 UTC and the maximum number of tweets (going backward in time) was 5,000. The tweets in the network were tweeted over the 6-day, 8-hour, 30-minute period from Friday, 14 February 2020 at 14:32 UTC to Thursday, 20 February 2020 at 23:02 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


InsurTech_2020-02-20_22-31-21.xlsx

#artificialintelligence

The graph represents a network of 2,982 Twitter users whose tweets in the requested range contained "InsurTech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 21 February 2020 at 06:32 UTC. The requested start date was Friday, 21 February 2020 at 01:01 UTC and the maximum number of tweets (going backward in time) was 5,000. The tweets in the network were tweeted over the 7-day, 1-hour, 49-minute period from Thursday, 13 February 2020 at 23:11 UTC to Friday, 21 February 2020 at 01:01 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


#cloudcomputing_2020-02-17_06-41-37.xlsx

#artificialintelligence

The graph represents a network of 2,185 Twitter users whose tweets in the requested range contained "#cloudcomputing", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 17 February 2020 at 14:42 UTC. The requested start date was Monday, 17 February 2020 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 2-day, 0-hour, 29-minute period from Thursday, 13 February 2020 at 12:00 UTC to Saturday, 15 February 2020 at 12:30 UTC.


#iiot_2020-02-10_23-23-34.xlsx

#artificialintelligence

The graph represents a network of 1,790 Twitter users whose tweets in the requested range contained "#iiot", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Tuesday, 11 February 2020 at 07:24 UTC. The requested start date was Tuesday, 11 February 2020 at 01:01 UTC and the maximum number of tweets (going backward in time) was 5,000. The tweets in the network were tweeted over the 2-day, 3-hour, 28-minute period from Saturday, 08 February 2020 at 21:09 UTC to Tuesday, 11 February 2020 at 00:38 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


How to solve 90% of NLP problems: a step-by-step guide

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How you can apply the 5 W's and H to Text Data! Whether you are an established company or working to launch a new service, you can always leverage text data to validate, improve, and expand the functionalities of your product. The science of extracting meaning and learning from text data is an active topic of research called Natural Language Processing (NLP). NLP produces new and exciting results on a daily basis, and is a very large field. While many NLP papers and tutorials exist online, we have found it hard to find guidelines and tips on how to approach these problems efficiently from the ground up.


#futureofwork Twitter NodeXL SNA Map and Report for Saturday, 08 February 2020 at 20:48 UTC

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The graph represents a network of 8,902 Twitter users whose recent tweets contained "#futureofwork", or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 18,000 tweets. The network was obtained from Twitter on Saturday, 08 February 2020 at 21:37 UTC. The tweets in the network were tweeted over the 4-day, 18-hour, 30-minute period from Tuesday, 04 February 2020 at 02:15 UTC to Saturday, 08 February 2020 at 20:46 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods. These tweets may expand the complete time period of the data.


AraNet: New Deep Learning Toolkit for Arabic Social Media

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Arabic is the 4th most-used language on the Internet, and its growing presence on social media is providing ample resources for the study of Arabic-language online communities at scale. There are however few tools currently available that can derive valuable insights from this data for decision making, guiding policies, aiding in responses, etc. Is that about to change? The performance of natural language processing (NLP) systems has dramatically improved on tasks such as reading comprehension and natural language inference, and with these advances have come many new application scenarios for the tech. Unsurprisingly, English is where most NLP R&D has been focused.


Is Tesla Dumping Python For This Programming Language

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Although the neural networks for computer vision models were written in Python, he added, the Tesla team would need people with excellent coding skills, especially in C and C . C/C for building self-driving cars might sound weird, but Musk's tweet does raise some doubts regarding the hype around Python. Our NN is initially in Python for rapid iteration, then converted to C /C/raw metal driver code for speed (important!). Also, tons of C /C engineers needed for vehicle control & entire rest of car. Educational background is irrelevant, but all must pass hardcore coding test. This didn't go well with the developers who pointed out the pitfalls of infrastructure complexity. Tesla researchers authors NNs in python land and rewrite with a bare C implementation when deploying. This feels like a failure of our tooling / infrastructure.