Slavi Marinov, head of machine learning at Man AHL, part of the $135 billion investment management firm Man Group, told Reuters that NLP was "one of …

Internet Marketing Association Presents Digital Assets, Fintech, Machine Learning for Military Applications and top Thought Leadership in its 10th …

Website development has gotten far too complex for any single individual or team to handle, which is why businesses are turning to artificial intelligence. Artificial Design Intelligence (ADI) is one of these technologies that hasn't fully grown yet, but it's proven that AI is useful in automating routine elements of the website creation process. While some industry professionals regard AI as a hoax, others are concerned that technology may eventually replace their professions. Both perspectives are very extreme. Instead, AI should be viewed as a helpful assistant by developers and designers. Artificial intelligence can even master creative processes such as visual art, poetry, making YouTube videos, composing music, and photography.

Yes, David Chang stands as a giant among chefs with his ever-expanding Momofuku restaurant empire. Last December he was the first celebrity winner of "Who Wants to Be a Millionaire" (the money went to Southern Smoke Foundation, his favorite charity) Now he is co-creator, host and producer of Hulu's six-episode future of food series "The Next Thing You Eat." Provocative and pointed, "Next Thing" examines new terrain in each episode, from the "Rise of the Machines" with robot pizzamakers and drone delivery to asking how will burgers, among the globe's most popular foods, exist in 30 years. Will they be lab-created cells that mimic beef burgers? "This was born out of the pandemic. I talked to Morgan Neville" -- his series' co-creator, the Oscar winning documentary director of "20 Feet from Stardom" and the recent "Roadrunner: A Film About Anthony Bourdain."

Google's latest flagship phones have an impressive set of automated, AI-powered tools to help make your photos look better, with smart blurs, object removal and skin tone exposure. While we'll have to test them out to see if they work as advertised, they could be useful for everyone from pixel peepers to casual snapshot takers. The new cameras themselves are pretty impressive to start with. The main rear camera, shared by the Pixel 6 and Pixel 6 Pro, is a 50-megapixel beast with decent-sized pixel wells and an f/1.85 equivalent aperture (no, it doesn't capture as much light as an f/1.8 on a DSLR, but it's still good). The ultrawide one, also shared, is 12 megapixels and f/2.2 on a smaller sensor, so don't expect mind-blowing image quality.

CEO at Reface, an AI/ML startup shifting from the face-swapping app to the platform for creating personalized content. Last year, 64.2 zettabytes of data were created globally -- enough to fill about 1 trillion 64GB flash drives -- according to IDC. It might be hard to believe, but the total amount of digital data created over the next five years will double the amount of information developed since the birth of digital storage. The percentage of information generated synthetically may be negligible for now, but by 2030 (registration required), synthetic data is expected to completely overshadow real data in AI models. What role will synthetic media play, and what trends are exploding on the market of deep learning products?

Socialbots are software-driven user accounts on social platforms, acting autonomously (mimicking human behavior), with the aims to influence the opinions of other users or spread targeted misinformation for particular goals. As socialbots undermine the ecosystem of social platforms, they are often considered harmful. As such, there have been several computational efforts to auto-detect the socialbots. However, to our best knowledge, the adversarial nature of these socialbots has not yet been studied. This begs a question "can adversaries, controlling socialbots, exploit AI techniques to their advantage?" To this question, we successfully demonstrate that indeed it is possible for adversaries to exploit computational learning mechanism such as reinforcement learning (RL) to maximize the influence of socialbots while avoiding being detected. We first formulate the adversarial socialbot learning as a cooperative game between two functional hierarchical RL agents. While one agent curates a sequence of activities that can avoid the detection, the other agent aims to maximize network influence by selectively connecting with right users. Our proposed policy networks train with a vast amount of synthetic graphs and generalize better than baselines on unseen real-life graphs both in terms of maximizing network influence (up to +18%) and sustainable stealthiness (up to +40% undetectability) under a strong bot detector (with 90% detection accuracy). During inference, the complexity of our approach scales linearly, independent of a network's structure and the virality of news. This makes our approach a practical adversarial attack when deployed in a real-life setting.

Increasing amounts of freely available data both in textual and relational form offers exploration of richer document representations, potentially improving the model performance and robustness. An emerging problem in the modern era is fake news detection -- many easily available pieces of information are not necessarily factually correct, and can lead to wrong conclusions or are used for manipulation. In this work we explore how different document representations, ranging from simple symbolic bag-of-words, to contextual, neural language model-based ones can be used for efficient fake news identification. One of the key contributions is a set of novel document representation learning methods based solely on knowledge graphs, i.e. extensive collections of (grounded) subject-predicate-object triplets. We demonstrate that knowledge graph-based representations already achieve competitive performance to conventionally accepted representation learners. Furthermore, when combined with existing, contextual representations, knowledge graph-based document representations can achieve state-of-the-art performance. To our knowledge this is the first larger-scale evaluation of how knowledge graph-based representations can be systematically incorporated into the process of fake news classification.

We develop the Sparse VAE, a deep generative model for unsupervised representation learning on high-dimensional data. Given a dataset of observations, the Sparse VAE learns a set of latent factors that captures its distribution. The model is sparse in the sense that each feature of the dataset (i.e., each dimension) depends on a small subset of the latent factors. As examples, in ratings data each movie is only described by a few genres; in text data each word is only applicable to a few topics; in genomics, each gene is active in only a few biological processes. We first show that the Sparse VAE is identifiable: given data drawn from the model, there exists a uniquely optimal set of factors. (In contrast, most VAE-based models are not identifiable.) The key assumption behind Sparse-VAE identifiability is the existence of "anchor features", where for each factor there exists a feature that depends only on that factor. Importantly, the anchor features do not need to be known in advance. We then show how to fit the Sparse VAE with variational EM. Finally, we empirically study the Sparse VAE with both simulated and real data. We find that it recovers meaningful latent factors and has smaller heldout reconstruction error than related methods.

… Ina Fried will examine the current inequities within hiring practices, product development and machine learning in the tech industry today, …