Automatic evaluations for natural language generation (NLG) conventionally rely on token-level or embedding-level comparisons with the text references. This is different from human language processing, for which visual imaginations often improve comprehension. In this work, we propose ImaginE, an imagination-based automatic evaluation metric for natural language generation. With the help of CLIP and DALL-E, two cross-modal models pre-trained on large-scale image-text pairs, we automatically generate an image as the embodied imagination for the text snippet and compute the imagination similarity using contextual embeddings. Experiments spanning several text generation tasks demonstrate that adding imagination with our ImaginE displays great potential in introducing multi-modal information into NLG evaluation, and improves existing automatic metrics' correlations with human similarity judgments in many circumstances.

Fair representation learning is an attractive approach that promises fairness of downstream predictors by encoding sensitive data. Unfortunately, recent work has shown that strong adversarial predictors can still exhibit unfairness by recovering sensitive attributes from these representations. In this work, we present Fair Normalizing Flows (FNF), a new approach offering more rigorous fairness guarantees for learned representations. Specifically, we consider a practical setting where we can estimate the probability density for sensitive groups. The key idea is to model the encoder as a normalizing flow trained to minimize the statistical distance between the latent representations of different groups. The main advantage of FNF is that its exact likelihood computation allows us to obtain guarantees on the maximum unfairness of any potentially adversarial downstream predictor. We experimentally demonstrate the effectiveness of FNF in enforcing various group fairness notions, as well as other attractive properties such as interpretability and transfer learning, on a variety of challenging real-world datasets.

Large, deep networks can learn representations that generalize well. While smaller, more efficient networks lack the inductive biases to find these representations from training data alone, they may have the capacity to represent these solutions [e.g., 1, 16, 27, 39]. Influential work on knowledge distillation [19] argues that Bucilă et al. [4] "demonstrate convincingly that the knowledge acquired by a large ensemble of models [the teacher] can be transferred to a single small model [the student]". Indeed this quote encapsulates the conventional narrative of knowledge distillation: a student model learns a high-fidelity representation of a larger teacher, enabled by the teacher's soft labels. Conversely, in Figure 1 we show that with modern architectures knowledge distillation can lead to students with very different predictions from their teachers, even when the student has the capacity to perfectly match the teacher.

The EU is facing a backlash over new AI rules that allow for limited use of facial recognition by authorities -- with opponents warning the carveouts could usher in a new age of biometric surveillance. A coalition of digital rights and consumer protection groups across the globe, including Latin America, Africa and Asia are calling for a global ban on biometric recognition technologies that enable mass and discriminatory surveillance by both governments and corporations. In an open letter, 170 signatories in 55 countries argue that the use of technologies like facial recognition in public places goes against human rights and civil liberties. "It shows that organizations, groups, people, activists, technologists around the world who are concerned with human rights, agree to this call," said Daniel Leufer of U.S. digital rights group Access Now, which co-authored the letter. The use of facial recognition technology is becoming widespread.

Deployed in over 80 per cent of the gaming venues in South Australia, the new solution is part of the state's gambling law reforms to protect the community from the potential harmful effects of gambling. "We had several critical capabilities that our wireless network solution must meet to support our facial recognition devices," said Fraser Larcombe, Vix Vizion. "It must be robust, bullet-proof secure, with the ability to access each machine from anywhere. We got it all from Cradlepoint and LTE." The South Australian state government established a law reform to consolidate banned individuals lists into a single government-managed list.

AI drone may have'hunted down' and killed soldiers in Libya without human input By Charles Q. Choi - Live Science Contributor - June 3, 2021 KARGU a Rotary Wing Attack Drone Loitering Munition System A UN report suggests that at least one autonomous drone operated by artificial intelligence (AI) may have killed people for the first time last year in Libya, without any humans consulted prior to the attack, according to a U.N. report. According to a March report from the U.N. Panel of Experts on Libya, lethal autonomous aircraft may have "hunted down and remotely engaged" soldiers and convoys fighting for Libyan general Khalifa Haftar. It's not clear who exactly deployed these killer robots, though remnants of one such machine found in Libya came from the Kargu-2 drone, which is made by Turkish military contractor STM. Landmines are essentially simple autonomous weapons -- you step on them and they blow up," Zachary Kallenborn, a research affiliate with the National Consortium for the ...

As a Data Scientist, you will lead the charge on building our data science infrastructure and driving insights that lead to step-function improvements in how we operate. We handle millions of tasks for businesses looking to scale their ML development, and we're looking for a talented data science leader to help us understand it all in the service of building better products. In this role, you will apply statistical models, design and interpret experiments, build mission-critical dashboards, and help structure and order our data in the pursuit of transparency over how we operate and how we can improve. Ensure product areas are performing in line with our high expectations and be able to identify, diagnose, and recommend projects to improve our performance. Identify operational efficiencies to the business that will enable Scale to continue growing sustainably.

Scale's customers process millions of tasks through our APIs, and we're looking for a talented Analytics Engineer to build scalable solutions to support this growth. You will have widespread purview, with responsibility for understanding, mining, aggregating, and exposing data across the entire business to support timely and efficient decision-making and data exploration. You will also implement Scale's data warehouse, data mart, and business intelligence reporting environments, and help users transition their workflows to these systems. You will: Work with analytics, infrastructure, finance, and other business partners to drive the development of reporting and analytics platform Establish business intelligence best practices, and build pipelines that provide single-source-of-truth foundational accuracy Partner with operations and sales teams to automate manual workflows Continually improve ongoing data pipelines and simplify self-service support for business stakeholders Perform regular system audits to ensure complete and accurate reporting of data/metrics Design and build visualization dashboards to accelerate information-to-action at scale Ideally you'd have: 5 years of relevant work experience in a role requiring application of data modeling and analytic skills A clear passion for learning new BI skills and techniques independently and continuously Experience with ETL tools and building / maintaining a data warehouse Experience in designing and building data infrastructure/automated reporting tools (Tableau) Ability to create extensible and scalable data schema that lay the foundation for downstream analysis Advanced data analysis knowledge and experience, with strong SQL, and data mining skills Advanced knowledge and hands-on experience leveraging Python, and/or R to perform in-depth data analysis Fluent in written and spoken English Nice to haves: Experience in using highly scalable data engineering technologies such as AWS, Airflow, Dagster, DBT Experience in best practices in table partitioning/data sharding strategies and query optimizationAbout Us:At Scale, we believe that the transition from traditional software to AI is one of the most important shifts of our time. Our mission is to make that happen faster across every industry, and our team is transforming how machine learning can build innovative products.

Uncle Sam doesn't really want a gangly 18-year-old soldier to stand guard outside the gate of a military base, rather he wants a wide-area motion imagery (WAMI) system that provides surveillance, reconnaissance, and intelligence-gathering using specialized software and camera systems to detect and track hundreds of people and vehicles all at the same time over a city-sized area. Uncle Sam doesn't really want a blurry eyed, half asleep and distracted human pilot flying in circles trying to find camouflaged bad guys on the ground, rather he wants a multispectral system, that can see things invisible to human eyes, consisting of four high-definition cameras covering five spectral bands; a three-color diode pump laser designator and rangefinder; laser spot search and track capability; automated sensor and laser bore sight alignment; three-mode target tracker., and MTS sensors that offers multiple fields of view, electronic zoom, and multimode video tracking. Uncle Sam doesn't really want more spies in trench coats that lurk in dark corners vaping, rather he wants persistent surveillance systems that collect and integrate data from specific geographic areas with data on activities that happened there at specific dates and times. This capability requires a spatiotemporal analytic method to recognize trends and patterns from large, diverse data sets. These data sets identify activities: events and transactions conducted by entities (people or vehicles) in an area, while documenting patterns of life and alerting to unusual events.

I have ADHD and am what many would consider "underemployed" so obviously spend most of my time on the internet arguing with people on Twitter and watching TikToks. I live and breathe the internet and unfortunately/sadly haven't been able to stop posting since I first created an account on LiveJournal in the year 2002. Me and the internet have had some crazy times together. Like when my OkCupid page ended up on 200 websites of "insane internet dating profiles", or when my website kept getting hacked and diverted to Russian porn for a year. A few years ago I made an online show called Overshare with my friend Jared Jekyll (edited by my fiance – thanks babe!) purely for discussing random weird stuff we find on the internet.