On May 26, Image Comics is releasing a new book about artificial intelligence. Rather than asking big existential questions -- Why are we here? The couple purchased the artificial surrogate child in an attempt to grow their family. However, upon the robot Jesse's arrival, she discovers her new parents aren't the only ones who are in need of her talents. Put simply, Made in Korea is like if British TV series Humans were reimagined through the lens of legendary manga artist Osamu Tezuka and fixated on the question of nature vs. nurture.

Shortly after Microsoft announced it was laying off scores of journalists across its news divisions and replacing them with news-skimming artificial intelligence, it's already in hot water after a clear example of racial algorithmic bias. The algorithm doesn't do any original reporting. Instead, it finds articles on the internet and populates MSN with them. Recently, it confused two women of color from the band Little Mix with each other, The Guardian reports, attaching an image of Leigh-Anne Pinnock to an article about singer Jade Thirlwall. It's an unfortunate illustration of how racism continues to persist within AI algorithms, which are notoriously bad at recognizing people of color.

Factorization machine (FM) is a prevalent approach to modeling pairwise (second-order) feature interactions when dealing with high-dimensional sparse data. However, on the one hand, FM fails to capture higher-order feature interactions suffering from combinatorial expansion, on the other hand, taking into account interaction between every pair of features may introduce noise and degrade prediction accuracy. To solve the problems, we propose a novel approach Graph Factorization Machine (GraphFM) by naturally representing features in the graph structure. In particular, a novel mechanism is designed to select the beneficial feature interactions and formulate them as edges between features. Then our proposed model which integrates the interaction function of FM into the feature aggregation strategy of Graph Neural Network (GNN), can model arbitrary-order feature interactions on the graph-structured features by stacking layers. Experimental results on several real-world datasets has demonstrated the rationality and effectiveness of our proposed approach.

This paper explores the task of Difficulty-Controllable Question Generation (DCQG), which aims at generating questions with required difficulty levels. Previous research on this task mainly defines the difficulty of a question as whether it can be correctly answered by a Question Answering (QA) system, lacking interpretability and controllability. In our work, we redefine question difficulty as the number of inference steps required to answer it and argue that Question Generation (QG) systems should have stronger control over the logic of generated questions. To this end, we propose a novel framework that progressively increases question difficulty through step-bystep rewriting under the guidance of an extracted reasoning chain. A dataset is automatically constructed to facilitate the research, on Figure 1: An example of generating a complex question which extensive experiments are conducted to through step-by-step rewriting based on the reasoning test the performance of our method.

No, Tom Hanks didn't just learn fluent Japanese: a bleeding-edge artificial intelligence system has learned to create deepfakes so realistic it looks like your favorite actor is delivering their lines in any language. The resulting dub, the company touts, 'captures all the nuance and emotions of the original material.' TrueSync, an AI platform from director Scott Mann, captures facial movements from both the original actor and the dubber, The data is synthesized to create a 3-D rendering merging the actor's head and the dubber's lip movements. Using deepfakes to perfect film dubbing came from a frustrated filmmaker, of all people. British director Scott Mann is best known for a string of action films, most notably 2015's'Heist,' with Robert DeNiro.

When AI is mentioned, we often think of high level technology and the future of artificial intelligence (AI). The truth is, AI impacts us all right now in many aspects of life. AI used to be the stuff of sci-fi films or dystopian disaster flicks. Technology to be feared in case it got "too intelligent." Films like Terminator and The Matrix told stories of AI run amok. Stephen Hawking once predicted it would be the end of the human race.

In groundbreaking self-service solution from Malong and Dell Technologies, Walmart is showing the power and potential of artificial intelligence at the …

After reading a news article -- whether the subject matter is U.S. politics, a movie review, or a productivity tip -- you can turn to someone else and give them a general idea of what it's about, right? Or if you read a novel, you can classify it as maybe sci-fi, literary fiction, or a romance. Humans tend to be pretty good at classifying texts. And these days, computers can do it, too. For a recent machine learning project, I downloaded consumer complaints from the Consumer Financial Protection Bureau and developed models to classify the complaints into one of five product categories.

When OpenAI demonstrated a powerful artificial intelligence algorithm capable of generating coherent text last June, its creators warned that the tool could potentially be wielded as a weapon of online misinformation. Now a team of disinformation experts has demonstrated how effectively that algorithm, called GPT-3, could be used to mislead and misinform. The results suggest that although AI may not be a match for the best Russian meme-making operative, it could amplify some forms of deception that would be especially difficult to spot. Over six months, a group at Georgetown University's Center for Security and Emerging Technology used GPT-3 to generate misinformation, including stories around a false narrative, news articles altered to push a bogus perspective, and tweets riffing on particular points of disinformation. "I don't think it's a coincidence that climate change is the new global warming," read a sample tweet composed by GPT-3 that aimed to stoke skepticism about climate change.

In March 2018, Peter-Lucas Jones and the ten other staff at Te Hiku Media, a small non-profit radio station nestled just below New Zealand's most northern tip, were in disbelief. In ten days, thanks to a competition it had started, Māori speakers across New Zealand had recorded over 300 hours of annotated audio in their mother tongue. It was enough data to build language tech for te reo Māori, the Māori language – including automatic speech recognition and speech-to-text. The small staff of Māori language broadcasters and one engineer were about to become pioneers in indigenous speech recognition technology. But building the tools was only half the battle. Te Hiku soon found itself fending off corporate entities trying to develop their own indigenous data sets and resisting detrimental western approaches to data sharing.