The domain of Artificial Intelligence (AI) ethics is not new, with discussions going back at least 40 years. Teaching the principles and requirements of ethical AI to students is considered an essential part of this domain, with an increasing number of technical AI courses taught at several higher-education institutions around the globe including content related to ethics. By using Latent Dirichlet Allocation (LDA), a generative probabilistic topic model, this study uncovers topics in teaching ethics in AI courses and their trends related to where the courses are taught, by whom, and at what level of cognitive complexity and specificity according to Bloom’s taxonomy. In this exploratory study based on unsupervised machine learning, we analyzed a total of 166 courses: 116 from North American universities, 11 from Asia, 36 from Europe, and 10 from other regions. Based on this analysis, we were able to synthesize a model of teaching approaches, which we call BAG (Build, Assess, and Govern), that combines specific cognitive levels, course content topics, and disciplines affiliated with the department(s) in charge of the course. We critically assess the implications of this teaching paradigm and provide suggestions about how to move away from these practices. We challenge teaching practitioners and program coordinators to reflect on their usual procedures so that they may expand their methodology beyond the confines of stereotypical thought and traditional biases regarding what disciplines should teach and how. This article appears in the AI & Society track.

Thaler first brought the image created by his "Creativity Machine" algorithm to the USCO in November 2018, Eileen Kinsella reported for Artnet News. A Recent Entrance to Paradise is part of a series Thaler describes as a "simulated near-death experience," where an algorithm repurposes pictures to create images seen by a synthetic dying brain. Thaler noted to the USCO he was "seeking to register this computer-generated work as a work-for-hire to the owner of the Creativity Machine." Providing this protection is required under current legal frameworks." Thaler has previously tested the limits of patent laws in numerous countries.

A big push is underway to democratize AI, though we need to figure out what this actually means and ... [ ] what it foretells. That phrasing is a well-intended respectful appropriation from Abraham Lincoln's famous 1863 Gettysburg Address in which he memorably stated that our democracy proffers a new birth of freedom entwining an erstwhile government of the people, by the people, and for the people. This same notion of the power of people was also captured in some years earlier speech by Senator Daniel Webster in 1830 in which he exhorted that our government was made for the people, made by the people, and answerable to the people. You could readily assert that those keystone elements are a bedrock of democracy and democratization. The reason that I've leveraged such a famed saying is it seemingly can be purposely applied to Artificial Intelligence. You see, there is a great deal of vocal discussion these days about the democratization of AI. In brief, there is a heartfelt belief that we need to make sure that pretty much anybody at all can craft and deploy AI systems. The ardent view is that we are presently mired in having only techie-focused specialists that can put AI into use. A relatively narrow and devoted clique of AI experts and high-tech entities are dominating which AI systems we are getting and how those AI systems are being devised, the argument goes. We, the people, cannot allow ourselves to become controlled and overseen by a seeming handful of AI gurus. I think you can probably see how there is a claimed analogous pattern between the notion of how people are governed overall and how AI is being fostered upon the world.

Conditional Neural Processes~(CNPs) bridge neural networks with probabilistic inference to approximate functions of Stochastic Processes under meta-learning settings. Given a batch of non-{\it i.i.d} function instantiations, CNPs are jointly optimized for in-instantiation observation prediction and cross-instantiation meta-representation adaptation within a generative reconstruction pipeline. There can be a challenge in tying together such two targets when the distribution of function observations scales to high-dimensional and noisy spaces. Instead, noise contrastive estimation might be able to provide more robust representations by learning distributional matching objectives to combat such inherent limitation of generative models. In light of this, we propose to equip CNPs by 1) aligning prediction with encoded ground-truth observation, and 2) decoupling meta-representation adaptation from generative reconstruction. Specifically, two auxiliary contrastive branches are set up hierarchically, namely in-instantiation temporal contrastive learning~({\tt TCL}) and cross-instantiation function contrastive learning~({\tt FCL}), to facilitate local predictive alignment and global function consistency, respectively. We empirically show that {\tt TCL} captures high-level abstraction of observations, whereas {\tt FCL} helps identify underlying functions, which in turn provides more efficient representations. Our model outperforms other CNPs variants when evaluating function distribution reconstruction and parameter identification across 1D, 2D and high-dimensional time-series.

Here we review them with the help of quantitative linguistics and information theory.

Investigators from Cedars-Sinai have created an artificial intelligence-enabled tool that may make it easier to predict if a person will have a heart attack. The tool, described in The Lancet Digital Health, accurately predicted which patients would experience a heart attack in five years based on the amount and composition of plaque in arteries that supply blood to the heart. Plaque buildup can cause arteries to narrow, which makes it difficult for blood to get to the heart, increasing the likelihood of a heart attack. A medical test called a coronary computed tomography angiography (CTA) takes 3D images of the heart and arteries and can give doctors an estimate of how much a patient's arteries have narrowed. Until now, however, there has not been a simple, automated and rapid way to measure the plaque visible in the CTA images.

Ukraine is using facial recognition software to help identify the bodies of Russian soldiers killed in combat and track down their families to inform them of their deaths, Ukraine's vice-prime minister told the Reuters news service. Mykhailo Fedorov, Ukraine's vice-prime minister who also runs the ministry of digital transformation, told Reuters his country had been using software facial recognition provider Clearview AI to find the social media accounts of dead Russian soldiers. "As a courtesy to the mothers of those soldiers, we are disseminating this information over social media to at least let families know that they they've lost their sons and to then enable them to come to collect their bodies," Fedorov said in an interview, speaking via a translator. Ukraine's Ministry of Defense this month began using technology from Clearview, which scrapes images on the web to match with faces featured in uploaded photos. Reuters first reported Ukraine's use of Clearview earlier this month, but it was not clear at that time how the technology would be used.

The autonomous driving industry in China has enjoyed a boom over the past two years, with both institutional and corporate investors pouring money into a driverless future. Companies thriving in the downstream, those offering robotaxi services, operating robo buses, or dispatching delivery bots, have been particularly popular with investors, raising hundreds of millions of dollars and reaching massive valuations. The prospects of becoming cash-rich and a household name have lured some of their upstream suppliers to start building end solutions as well. One of these ambitious self-driving hardware suppliers is PIX Moving, a Chinese company specializing in automotive skateboards -- a type of chassis that houses the batteries, drive units and other key components, and can be adapted to various kinds of self-driving scenarios because of its modular architecture -- similar to what Canoo does. Founded by former architect Chuan Yu in 2014, PIX recently secured 72 million yuan ($11 million) from a Series pre-A round, lifting its capital raised to $20 million, it told TechCrunch.

It's been years since developers found that Nvidia's main product, the GPU, was useful not just for rendering video games but also for high-performance computing of the kind used in 3D modeling, weather forecasting, or the training of AI models--and it's on enterprise applications such as those that CEO Jensen Huang will focus his attention at the company's GTC 2022 conference this week. Nvidia is hoping to make it easier for CIOs building digital twins and machine learning models to secure enterprise computing, and even to speed the adoption of quantum computing with a range of new hardware and software. Digital twins, numerical models that reflect changes in real-world objects useful in design, manufacturing, and service creation, vary in their level of detail. For some applications, a simple database may suffice to record a product's service history--when it was made, who it shipped to, what modifications have been applied--while others require a full-on 3D model incorporating real-time sensor data that can be used, for example, to provide advanced warning of component failure or of rain. It's at the high end of that range that Nvidia plays.

Isolation Forest is a simple yet incredible unsupervised algorithm that is able to spot outliers or anomalies in a data set very quickly. I should say understanding this tool is a must for any aspiring data scientist. In this article, I will briefly go through the theories behind the algorithm and also its implementation. Its Python implementation from Scitkit Learn has been gaining tons of popularity due to its capabilities and ease of use. But before we jump right into the implementation, it's always best practice for us to study about its use cases and the theory behind it.