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.

Artificial intelligence is emerging as one of the most powerful agents of change in higher education, presenting the sector with unprecedented academic, ethical and legal challenges. Through its algorithmic ability to adapt, self-correct and learn, AI is pushing the boundaries of human intelligence, making the future of higher education inextricably intertwined with AI. To disentangle the intertwined relationship between AI and higher education, I will briefly discuss the opportunities and the challenges of AI, review some of the emerging applications of AI in higher education, and offer some recommendations for the way forward. As an umbrella term that includes machine learning, deep learning and natural language processing, AI relies on extensive computing power and massive amounts of data processed by algorithms. As it continues to seep into the fabric of our society, AI is being used to solve problems in cybersecurity, health care, agriculture, climate change, manufacturing, banking and fraud detection, among other areas.

MIdJourney's artificial intelligence bot allows for very fast idea iteration for interior designers. Are you feeling stuck on a thorny room planning problem? Are your creative juices running dry? Need help figuring out a solution? MidJourney's artificial intelligence bot can often help you.

Let us start with an easy example, say you are teaching a kid to differentiate dogs from cats. How would you do it? You may show him/her a dog and say "here is a dog" and when you encounter a cat you would point it out as a cat. When you show the kid enough dogs and cats, he may learn to differentiate between them. If he is trained well, he may be able to recognize different breeds of dogs which he hasn't even seen. Similarly, in Supervised Learning, we have two sets of variables.

Machine learning and the world of artificial intelligence (AI) are no longer science fiction. Get started with the new breed of software that is able to learn without being explicitly programmed, machine learning can access, analyze, and find patterns in Big Data in a way that is beyond human capabilities. The business advantages are huge, and the market is expected to be worth $47 billion and more by 2020. In this course, you will implement your own custom algorithm on top of SAP's HANA Database, which is an In-Memory database capable of Performing huge calculation over a large set of Data. We are going to use Native SQL to write the algorithm of Naive Bayes.

Machine learning has enabled tremendously exciting technologies, but at the same time it raises questions as to how it should be deployed in a responsible and trustworthy manner. How can machine learning be made secure, reliable, robust, fair, and private? This workshop will explore the information-theoretic foundations of these aspects of machine learning. The workshop will include invited talks by experts on these topics from both academy and industry, student poster presentations, and time for fruitful discussions. Keynote talks will be given by Tara Javidi, Ilya Mironov, Todd Coleman, and Ayfer Ozgur.

Artificial intelligence (AI) is intelligence -- perceiving, synthesizing, and inferring information -- demonstrated by machines. Today, AI is no longer profound technology in a science lab. Instead, it is at amateurs' fingertips to create decent artwork, generate sophisticated conversation, and perform other intelligent tasks using DALL·E, Stable Diffusion, GPT-3, ChatGPT, Point·E, Whisper, etc. Have you ever wondered how a realistic image is generated by a natural language description? The intelligence comes from Machine Learning (ML), the study of computer algorithms that can improve automatically through experience and by the use of data. These textbook algorithms are publicly available and ready to be used.

With the increase of distance learning, in general, and e-learning, in particular, having a system capable of determining the engagement of students is of primordial importance, and one of the biggest challenges, both for teachers, researchers and policy makers. Here, we present a system to detect the engagement level of the students. It uses only information provided by the typical built-in web-camera present in a laptop computer, and was designed to work in real time. We combine information about the movements of the eyes and head, and facial emotions to produce a concentration index with three classes of engagement: "very engaged", "nominally engaged" and "not engaged at all". The system was tested in a typical e-learning scenario, and the results show that it correctly identifies each period of time where students were "very engaged", "nominally engaged" and "not engaged at all". Additionally, the results also show that the students with best scores also have higher concentration indexes.

This paper focuses on the application of one type of empirical methods, namely statistical causal inference (SCI, see section 2). Such methods have their roots in a number of applied fields (from AI to econometrics) and aim to provide a framework for making valid inferences about causal effects based on interventional or observational data. More specifically, we focus on SCI methods that use graphical models as developed by Pearl and colleagues [1, 2]. This framework has been shown to be equivalent of the potential-outcomes framework (also called the Neyman-Rubin Causal Model [3]) but enriches it by making use of an explicit causal structure called a graphical causal model. Making assumptions about causal effects explicit through a graphical structure has several advantages. First, it helps determine whether causal effects can be estimated and how they might be estimated (see section 2).