Artificial Intelligence is no new concept. The phrase was first coined by John McCarthy in 1956[1], when he invited a group of researchers to discuss the notion of'thinking machines' during a conference at Dartmouth College. Since then, it has been a point of fascination for scientists, academics, software developers, and moviemakers alike. Fast-forward to today where you'll find lots of examples hiding in plain sight. From digital assistants like Amazon's Alexa or Apple's Siri, who use AI to learn from user interactions, to automated email responses and search engines predicting what you're looking for.

Artificial intelligence (AI) is sometimes referred to as a herald of the fourth industrial revolution. That revolution is already here.

Life during the Covid-19 pandemic would be even more difficult without the Internet and automation, making this year timely for the upcoming "Dune" film to portray a distant future where humanity is devastated by our dependence on machines. Director Dennis Villeneuve is set to release his adaptation of the science fiction epic on October 22, both on HBO Max and in movie theaters. The release was delayed from last December to make it safer for people to view and hear the space fantasy in theaters. WarnerMedia aims to distribute Villeneuve's vision of the first "Dune" novel in two films, but has not yet scheduled a release date for the second film after the first half is released. Frank Herbert's "Dune" novel begins in the far distant future, thousands of years after humans were enslaved by robots, fought a revolutionary crusade and banned artificial intelligence with a new anti-tech religion.

The graph represents a network of 1,251 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, 14 September 2021 at 21:00 UTC. The requested start date was Tuesday, 14 September 2021 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 1-day, 16-hour, 41-minute period from Sunday, 12 September 2021 at 07:20 UTC to Tuesday, 14 September 2021 at 00:01 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

Investors in AI-first technology companies serving the defense industry, such as Palantir, Primer and Anduril, are doing well. Anduril, for one, reached a valuation of over $4 billion in less than four years. Many other companies that build general-purpose, AI-first technologies -- such as image labeling -- receive large (undisclosed) portions of their revenue from the defense industry. Investors in AI-first technology companies that aren't even intended to serve the defense industry often find that these firms eventually (and sometimes inadvertently) help other powerful institutions, such as police forces, municipal agencies and media companies, prosecute their duties. Most do a lot of good work, such as DataRobot helping agencies understand the spread of COVID, HASH running simulations of vaccine distribution or Lilt making school communications available to immigrant parents in a U.S. school district.

Music is an indispensable element in film: it establishes atmosphere and mood, drives the viewer's emotional reactions, and significantly influences the audience's interpretation of the story. In a recent paper published in PLOS ONE, a research team at the USC Viterbi School of Engineering, led by Professor Shrikanth Narayanan, sought to objectively examine the effect of music on cinematic genres. Their study aimed to determine if AI-based technology could predict the genre of a film based on the soundtrack alone. "By better understanding how music affects the viewer's perception of a film, we gain insights into how film creators can reach their audience in a more compelling way," said Narayanan, University Professor and Niki and Max Nikias Chair in Engineering, professor of electrical and computer engineering and computer science and the director of USC Viterbi's Signal Analysis and Interpretation Laboratory (SAIL). The notion that different film genres are more likely to use certain musical elements in their soundtrack is rather intuitive: a lighthearted romance might include rich string passages and lush, lyrical melodies, while a horror film might instead feature unsettling, piercing frequencies and eerily discordant notes.

Simply mentioning these fields conjures up images inspired by science fiction. Robots that compose music and write novels. Computers that mimic human emotion and predict the future. Yet despite the futuristic visions that these subjects conjure, the reality of AI/ML, on the operations side, has much in common with the past. In the early days of the internet, web developers, the trailblazers of their time, built almost everything from scratch.

We consider the problem of finding relevant consistent concepts in a conversational AI system, particularly, for realizing a conversational socialbot. Commonsense knowledge about various topics can be represented as an answer set program. However, to advance the conversation, we need to solve the problem of finding relevant consistent concepts, i.e., find consistent knowledge in the "neighborhood" of the current topic being discussed that can be used to advance the conversation. Traditional ASP solvers will generate the whole answer set which is stripped of all the associations between the various atoms (concepts) and thus cannot be used to find relevant consistent concepts. Similarly, goal-directed implementations of ASP will only find concepts directly relevant to a query. We present the DiscASP system that will find the partial consistent model that is relevant to a given topic in a manner similar to how a human will find it. DiscASP is based on a novel graph-based algorithm for finding stable models of an answer set program. We present the DiscASP algorithm, its implementation, and its application to developing a conversational socialbot.

Contrastive explanations for understanding the behavior of black box models has gained a lot of attention recently as they provide potential for recourse. In this paper, we propose a method Contrastive Attributed explanations for Text (CAT) which provides contrastive explanations for natural language text data with a novel twist as we build and exploit attribute classifiers leading to more semantically meaningful explanations. To ensure that our contrastive generated text has the fewest possible edits with respect to the original text, while also being fluent and close to a human generated contrastive, we resort to a minimal perturbation approach regularized using a BERT language model and attribute classifiers trained on available attributes. We show through qualitative examples and a user study that our method not only conveys more insight because of these attributes, but also leads to better quality (contrastive) text. Moreover, quantitatively we show that our method is more efficient than other state-of-the-art methods with it also scoring higher on benchmark metrics such as flip rate, (normalized) Levenstein distance, fluency and content preservation.

… a team from the Spanish fact-checking organisation Maldita created a WhatsApp chatbot, which uses artificial intelligence to provide automated …