Measuring the distance between machine-produced and human language is a critical open problem. Inspired by empirical findings from psycholinguistics on the periodicity of entropy in language, we propose FACE, a set of metrics based on Fourier Analysis of the estimated Cross-Entropy of language, for measuring the similarity between model-generated and human-written languages. Based on an open-ended generation task and the experimental data from previous studies, we find that FACE can effectively identify the human-model gap, scales with model size, reflects the outcomes of different sampling methods for decoding, correlates well with other evaluation metrics and with human judgment scores.

We propose a new learning framework that captures the tiered structure of many real-world user-interaction applications, where the users can be divided into two groups based on their different tolerance on exploration risks and should be treated separately. In this setting, we simultaneously maintain two policies $\pi^{\text{O}}$ and $\pi^{\text{E}}$: $\pi^{\text{O}}$ (``O'' for ``online'') interacts with more risk-tolerant users from the first tier and minimizes regret by balancing exploration and exploitation as usual, while $\pi^{\text{E}}$ (``E'' for ``exploit'') exclusively focuses on exploitation for risk-averse users from the second tier utilizing the data collected so far. An important question is whether such a separation yields advantages over the standard online setting (i.e., $\pi^{\text{E}}=\pi^{\text{O}}$) for the risk-averse users. We individually consider the gap-independent vs.~gap-dependent settings. For the former, we prove that the separation is indeed not beneficial from a minimax perspective. For the latter, we show that if choosing Pessimistic Value Iteration as the exploitation algorithm to produce $\pi^{\text{E}}$, we can achieve a constant regret for risk-averse users independent of the number of episodes $K$, which is in sharp contrast to the $\Omega(\log K)$ regret for any online RL algorithms in the same setting, while the regret of $\pi^{\text{O}}$ (almost) maintains its online regret optimality and does not need to compromise for the success of $\pi^{\text{E}}$.

When Face Recognition Doesn't Know Your Face Is a Face An estimated 100 million people live with facial differences. As face recognition tech becomes widespread, some say they're getting blocked from accessing essential systems and services. Autumn Gardiner thought updating her driving license would be straightforward. After getting married last year, she headed to the local Department of Motor Vehicles office in Connecticut to get her name changed on her license. While she was there, Gardiner recalls, officials said she needed to update her photo.

The long-run behavior of multi-agent online learning -- and, in particular, no-regret learning -- is relatively well-understood in potential games, where players have common interests. By contrast, in general harmonic games -- the strategic complement of potential games, where players have competing interests -- very little is known outside the narrow subclass of 2 -player zero-sum games with a fully-mixed equilibrium. As a first result, we show that the continuous-time dynamics of FTRL are Poincaré recurrent, i.e., they return arbitrarily close to their starting point infinitely often, and hence fail to converge. In discrete time, the standard, "vanilla" implementation of FTRL may lead to even worse outcomes, eventually trapping the players in a perpetual cycle of best-responses. However, if FTRL is augmented with a suitable extrapolation step -- which includes as special cases the optimistic and mirror-prox variants of FTRL -- we show that learning converges to a Nash equilibrium from any initial condition, and all players are guaranteed at most \mathcal{O}(1) regret.

Eightfold's experience offers insight into the potential and the challenge of applying A.I. to high-stakes decisions like hiring, promoting and charting career paths for workers. The company is at the forefront of using A.I. and data to assess a person's potential for success in a job. That assessment is based on measuring skills and experience rather than on university degrees or personal connections. The skills-based perspective has been embraced by labor market and policy experts as a vehicle for broadening opportunity in America, especially for the nearly two-thirds of workers who do not have four-year college degrees. Screening by degrees hits minority workers particularly hard, eliminating 72 percent of Black adults and 79 percent of Latino adults, compared with 58 percent of non-Hispanic white adults.

It was 6:28 am when I woke up to a text from a friend in Shanghai, China. "Hey, Amanda--is this you?" he wrote via WeChat. I hadn't even had my morning coffee yet. I pulled my phone closer to get a better look. "Yes, it's me," I typed back.

The Terminator movie series made it clear that robots would take over our world one day and the massive emergence of technology is bringing about a disruption in the media and entertainment industry. Today we are going to take a look at Deepfake technology and how it is imparting a facelift to the media ecosystem. Deepfake is an AI-based media synthesizing technique that includes manipulating sounds and superimposing human features on another person's face/body to render a real human experience. Deep learning technology is used by the Deepfake app to imitate a person's actions, looks, and mannerisms without requiring them to be present. This results in hyper-realistic audio and video which is impossible to distinguish from the real thing.

This article is part of our series that explores the business of artificial intelligence. Last week, Hugging Face announced a new product in collaboration with Microsoft called Hugging Face Endpoints on Azure, which allows users to set up and run thousands of machine learning models on Microsoft's cloud platform. Having started as a chatbot application, Hugging Face made its fame as a hub for transformer models, a type of deep learning architecture that has been behind many recent advances in artificial intelligence, including large language models like OpenAI GPT-3 and DeepMind's protein-folding model AlphaFold. Large tech companies like Google, Facebook, and Microsoft have been using transformer models for several years. But the past couple of years has seen a growing interest in transformers among smaller companies, including many that don't have in-house machine learning talent.

Last week, Hugging Face announced a new product in collaboration with Microsoft called Hugging Face Endpoints on Azure, which allows users to set up and run thousands of machine learning models on Microsoft's cloud platform. Having started as a chatbot application, Hugging Face made its fame as a hub for transformer models, a type of deep learning architecture that has been behind many recent advances in artificial intelligence, including large language models like OpenAI GPT-3 and DeepMind's protein-folding model AlphaFold. Large tech companies like Google, Facebook, and Microsoft have been using transformer models for several years. But the past couple of years has seen a growing interest in transformers among smaller companies, including many that don't have in-house machine learning talent. This is a great opportunity for companies like Hugging Face, whose vision is to become the GitHub for machine learning.

In this tutorial, we are going to quickly go over how you can detect the age and gender of a face using OpenCV. In computer vision, detecting a face is a very important task. In the past, detecting a face required a lot of time and effort, but today we have pre-trained models that can do it in a few seconds. We will be using a pre-trained model in the OpenCV library to detect a face and return a ground truth label. OpenCV: It is a tool that specializes in the areas of image processing, video analysis, or computer vision. OpenCV can be used to help developers solve lots of problems in your field when it comes down to analyzing images and videos through sophisticated digital algorithms.