The well-groomed young man dressed in a crisp, blue shirt speaking with a soft American accent seems an unlikely supporter of the junta leader of the west African state of Burkina Faso. "We must support … President Ibrahim Traoré … Homeland or death we shall overcome!" he says in a video that began circulating in early 2023 on Telegram. It was just a few months after the dictator had come to power via a military coup. Other videos fronted by different people, with a similar professional-looking appearance and repeating the exact same script in front of the Burkina Faso flag, cropped up around the same time. On a verified account on X a few days later the same young man, in the same blue shirt, claimed to be Archie, the chief executive of a new cryptocurrency platform. They were generated with artificial intelligence (AI) developed by a startup based in east London.

What you're seeing in the image above (middle image, man in blue shirt), as well as the image directly below (left image, man in blue shirt), is not a'real' video into which a small patch of'fake' face has been superimposed, but an entirely synthesized scene that exists solely as a volumetric neural rendering – including the body and background: In the example directly above, the real-life video on the right (woman in red dress) is used to'puppet' the captured identity (man in blue shirt) on the left via RigNeRF, which (the authors claim) is the first NeRF-based system to achieve separation of pose and expression while being able to perform novel view syntheses. The male figure on the left in the image above was'captured' from a 70-second smartphone video, and the input data (including the entire scene information) subsequently trained across 4 V100 GPUs to obtain the scene. Since 3DMM-style parametric rigs are also available as entire-body parametric CGI proxies (rather than just face rigs), RigNeRF potentially opens up the possibility of full-body deepfakes where real human movement, texture and expression is passed to the CGI-based parametric layer, which would then translate action and expression into rendered NeRF environments and videos. As for RigNeRF – does it qualify as a deepfake method in the current sense that the headlines understand the term? Or is it just another semi-hobbled also-ran to DeepFaceLab and other labor-intensive, 2017-era autoencoder deepfake systems?

Everest Group recently conducted a study with 55 banking and financial services firms to evaluate their digital capabilities in areas including strategy, organization and talent, process transformation, technology adoption, and innovation. Here are the primary insights we collected from that study. More than 60 percent of BFS firms have invested in exploring the various use cases in cognitive- and AI-driven technologies. Typical use cases include helpdesk automation using chatbots and other cognitive capabilities for functions such as sales & marketing, data entry, credit assessment, and information gathering. BFS companies are increasingly leveraging AI-enabled transformation in areas where there is significant customer interaction.

How hot has Summer 2018 been around the globe? The speed of evolution in this industry segment is almost without precedent. Firms that had revenues worth tens of millions of U.S. dollars just a couple of years ago are talking about reaching a billion in revenue in just a couple of more years. But the reality is that it's the perfect storm – or heat wave – of innovation and capital intersecting at just the right time. Of course, it doesn't hurt that enterprises have already captured most of the potential value from offshore labor arbitrage. But when you combine the need for a new source of cost savings with the acute shortage of labor in the U.S. and Europe, you have a market condition in which enterprises are screaming for automation that allows continued productivity improvements for less money, with less human labor-based effort.

If I had a penny for every time Artificial Intelligence was mentioned during the recent NASSCOM India Leadership Forum, I could buy a lot of Bitcoins. Both hype and hope abound around AI and its impact on different industries' business models. Let's take a look at AI the healthcare industry. Adoption is increasing, helping solve a number of problems for patients, doctors, and the industry overall. AI engines are helping doctors identify patterns in patient symptoms with data and analytics, improve diagnoses, pick the right treatments, and monitor care.

When trained effectively, the Variational Autoencoder (VAE) is both a powerful language model and an effective representation learning framework. In practice, however, VAEs are trained with the evidence lower bound (ELBO) as a surrogate objective to the intractable marginal data likelihood. This approach to training yields unstable results, frequently leading to a disastrous local optimum known as posterior collapse. In this paper, we investigate a simple fix for posterior collapse which yields surprisingly effective results. The combination of two known heuristics, previously considered only in isolation, substantially improves held-out likelihood, reconstruction, and latent representation learning when compared with previous state-of-the-art methods. More interestingly, while our experiments demonstrate superiority on these principle evaluations, our method obtains a worse ELBO. We use these results to argue that the typical surrogate objective for VAEs may not be sufficient or necessarily appropriate for balancing the goals of representation learning and data distribution modeling.

The message of many things in America is "Like this or die." The camera is a small, white, curvilinear monolith on a pedestal. Inside its smooth casing are a microphone, a speaker, and an eye-like lens. After I set it up on a shelf, it tells me to look straight at it and to be sure to smile! The light blinks and then the camera flashes. A head-to-toe picture appears on my phone of a view I'm only used to seeing in large mirrors: me, standing awkwardly in my apartment, wearing a very average weekday outfit. The background is blurred like evidence from a crime scene. It is not a flattering image. Amazon's Echo Look, currently available by invitation only but also on eBay, allows you to take hands-free selfies and evaluate your fashion choices. "Now Alexa helps you look your best," the product description promises. Stand in front of the camera, take photos of two different outfits with the Echo Look, and then select the best ones on your phone's Echo Look app. Within about a minute, Alexa will tell you which set of clothes looks better, processed by style-analyzing algorithms and some assistance from humans.

Insurers are rethinking their business ethos to become protectors instead of payers. The insurer of the future is aiming to develop a customer-centric value proposition. Carriers are looking at developing innovative products that are contextualized to meet evolving customer needs. And the insurance distribution strategy is shifting to adapt to new product offerings, client needs, and digital technology-led disruption in the ecosystem. Not surprisingly, insurers are adopting AI and related technologies to drive these capabilities.

Artificial intelligence might just spawn a whole new style trend: call it "predictive fashion." In a paper published on the ArXiv, researchers from the University of California, San Diego, and Adobe have outlined a way for AI to not only learn a person's style but create computer-generated images of items that match that style. The system could let retailers create personalized pieces of clothing, or could even be used to help predict broader fashion trends. First, the researchers trained a convolutional neural network (CNN) to learn and classify a user's preferences for certain items, using purchase data scraped from Amazon in six categories: shoes, tops, and pants for both women and men. This type of recommender model is common in the online retail world, usually showing up in an "Other items you might like" area at the bottom of a page.

On November 6 2014, Amazon announced Echo, a smart speaker featuring a voice-controlled assistant service called Alexa. The company didn't exactly go out of its way to whip up excitement for these newcomers. In sharp contrast to the rollout of its ill-fated Fire Phone--which kicked off with a press-conference extravaganza starring Jeff Bezos--it simply disclosed that Echo existed and would be sold, at first, on an invitation-only basis to Prime members. But Echo spoke for itself--and not just literally. People quickly saw that the pint-size cylinder was a genuinely new kind of consumer-electronics device.