Artificial Intelligence (AI) has a pivotal role in many K-12 educational systems, providing benefits for both students and teachers. To best utilize AI's potential, it is key for governments to implement policies conducive to AI's adoption within classrooms. We discuss the benefits and limitations AI provides to education as well as the steps needed to responsibly use AI in education in the future. AI has contributed massively to making the American educational landscape much stronger and more stable. Through the use of AI in schools, learning has become much more accessible to all groups.

The education sector was among the hardest hit by the COVID-19 pandemic. Schools across the globe were forced to shutter their campuses in the spring of 2020 and rapidly shift to online instruction. For many higher education institutions, this meant delivering standard courses and the "traditional" classroom experience through videoconferencing and various connectivity tools. The approach worked to support students through a period of acute crisis but stands in contrast to the offerings of online education pioneers. These institutions use AI and advanced analytics to provide personalized learning and on-demand student support, and to accommodate student preferences for varying digital formats.

Moth Flame VR is the leading provider of immersive learning experiences for enterprise scale customers across a range of verticals. At Moth Flame, we are looking for a Senior Software Engineer that's excited about building upon products in the Virtual Reality and Simulation space. You will require strong collaborative skills between 3D artists, our content team, and other Unity Engineers. This role is 100% remote with the focus being towards implementing features for new/existing VR and Mobile applications, eliminating reported bugs, and iterating on designs. Objective: Scope and implement new features utilizing scalable architecture and help build a highly reusable codebase and content pipeline.

Who We AreKensho is a 100-person AI and machine learning company, centered around providing cutting-edge solutions to meet the challenges of some of the largest and most successful businesses and institutions. Our toolkit illuminates insights by helping the world better understand, process, and leverage messy data. Specifically, our solutions largely involve natural language processing (NLP) and include speech recognition (ASR), entity linking (Named Entity Disambiguation), structured document extraction, automated database linking, text classification, and more. We are continuously expanding our portfolio and are looking for passionate researchers to help us build and deploy state-of-the-art models across a variety of domains!At Kensho, we believe in flexibility-first, and give our employees the opportunity to work from where they feel most productive and engaged (must be in the United States). We also value in-person collaboration, so there may be times when travel to one of our Kensho hubs (NY/DC/MA) will be required for team meetings or company events.About

Architect data ingestion and text processing pipelines that will enable the development of useful tools and applications, including literature classification, chatbots, text summarization systems, and more. Qualifications: • Bachelor's Degree in computer science or related field and 4 years of experience; Masters Degree and 2 years experience • Advanced Python skills • Expertise in data structures and principles of optimal algorithm design • Experience working with large-scale data ingestion, both SQL (e.g., Hive, Impala) and NoSQL (e.g., Neo4J) • Some experience with NLP processing pipelines and/or text analysis • Interest in NLP and desire to learn about state-of-the-art NLP systems • Broad knowledge of AI/ML is a bonus, but is not required • Demonstrated ability to work with cloud infrastructure and tools (e.g., AWS, Cloudera) • Proficiency in a secondary programming language (e.g., Scala, Java) is preferred • Ability to multi-task and work within timelines

Role requiring'No experience data provided' months of experience in None Architect data ingestion and text processing pipelines that will enable the development of useful tools and applications, including literature classification, chatbots, text summarization systems, and more. Qualifications: • Bachelor's Degree in computer science or related field and 4 years of experience; Masters Degree and 2 years experience • Advanced Python skills • Expertise in data structures and principles of optimal algorithm design • Experience working with large-scale data ingestion, both SQL (e.g., Hive, Impala) and NoSQL (e.g., Neo4J) • Some experience with NLP processing pipelines and/or text analysis • Interest in NLP and desire to learn about state-of-the-art NLP systems • Broad knowledge of AI/ML is a bonus, but is not required • Demonstrated ability to work with cloud infrastructure and tools (e.g., AWS, Cloudera) • Proficiency in a secondary programming language (e.g., Scala, Java) is preferred • Ability to multi-task and work within timelines Apply Here For Remote NLP Ops Engineer roles, visit Remote NLP Ops Engineer Roles Role requiring'No experience data provided' months of experience in New York Getty Images works with over 496,000 contributors and image partners around the globe who add 8-10 million new assets each quarter to the 495 million assets contained in our catalogue. There are 2 fundamental questions the Getty Images Search & Ranking Team are working to solve: "If an image is worth a thousand words, wouldn't it be nice if we didn't have to type a thousand words to find it?" And: "What do we do when an image is your search query, instead of text entered into a field" To find a solution to these questions we are building a highly efficient, Artificially Intelligent (AI) Image search engine that pushes the boundaries of Natural Language Processing (NLP) and Visual Search Machine Learning (ML) The Search Team at Getty Images is responsible for building something that has never been built before. On a scale that will challenge the best of the best Engineers and Data Scientists alike.

High-quality medical systematic reviews require comprehensive literature searches to ensure the recommendations and outcomes are sufficiently reliable. Indeed, searching for relevant medical literature is a key phase in constructing systematic reviews and often involves domain (medical researchers) and search (information specialists) experts in developing the search queries. Queries in this context are highly complex, based on Boolean logic, include free-text terms and index terms from standardised terminologies (e.g., the Medical Subject Headings (MeSH) thesaurus), and are difficult and time-consuming to build. The use of MeSH terms, in particular, has been shown to improve the quality of the search results. However, identifying the correct MeSH terms to include in a query is difficult: information experts are often unfamiliar with the MeSH database and unsure about the appropriateness of MeSH terms for a query. Naturally, the full value of the MeSH terminology is often not fully exploited. This article investigates methods to suggest MeSH terms based on an initial Boolean query that includes only free-text terms. In this context, we devise lexical and pre-trained language models based methods. These methods promise to automatically identify highly effective MeSH terms for inclusion in a systematic review query. Our study contributes an empirical evaluation of several MeSH term suggestion methods. We further contribute an extensive analysis of MeSH term suggestions for each method and how these suggestions impact the effectiveness of Boolean queries.

Photoactive iridium complexes are of broad interest due to their applications ranging from lighting to photocatalysis. However, the excited state property prediction of these complexes challenges ab initio methods such as time-dependent density functional theory (TDDFT) both from an accuracy and a computational cost perspective, complicating high throughput virtual screening (HTVS). We instead leverage low-cost machine learning (ML) models to predict the excited state properties of photoactive iridium complexes. We use experimental data of 1,380 iridium complexes to train and evaluate the ML models and identify the best-performing and most transferable models to be those trained on electronic structure features from low-cost density functional theory tight binding calculations. Using these models, we predict the three excited state properties considered, mean emission energy of phosphorescence, excited state lifetime, and emission spectral integral, with accuracy competitive with or superseding TDDFT. We conduct feature importance analysis to identify which iridium complex attributes govern excited state properties and we validate these trends with explicit examples. As a demonstration of how our ML models can be used for HTVS and the acceleration of chemical discovery, we curate a set of novel hypothetical iridium complexes and identify promising ligands for the design of new phosphors.

Many applications such as hiring and university admissions involve evaluation and selection of applicants. These tasks are fundamentally difficult, and require combining evidence from multiple different aspects (what we term "attributes"). In these applications, the number of applicants is often large, and a common practice is to assign the task to multiple evaluators in a distributed fashion. Specifically, in the often-used holistic allocation, each evaluator is assigned a subset of the applicants, and is asked to assess all relevant information for their assigned applicants. However, such an evaluation process is subject to issues such as miscalibration (evaluators see only a small fraction of the applicants and may not get a good sense of relative quality), and discrimination (evaluators are influenced by irrelevant information about the applicants). We identify that such attribute-based evaluation allows alternative allocation schemes. Specifically, we consider assigning each evaluator more applicants but fewer attributes per applicant, termed segmented allocation. We compare segmented allocation to holistic allocation on several dimensions via theoretical and experimental methods. We establish various tradeoffs between these two approaches, and identify conditions under which one approach results in more accurate evaluation than the other.

Deep Neural Networks (DNNs) have become an essential component in many application domains including web-based services. A variety of these services require high throughput and (close to) real-time features, for instance, to respond or react to users' requests or to process a stream of incoming data on time. However, the trend in DNN design is toward larger models with many layers and parameters to achieve more accurate results. Although these models are often pre-trained, the computational complexity in such large models can still be relatively significant, hindering low inference latency. Implementing a caching mechanism is a typical systems engineering solution for speeding up a service response time. However, traditional caching is often not suitable for DNN-based services. In this paper, we propose an end-to-end automated solution to improve the performance of DNN-based services in terms of their computational complexity and inference latency. Our caching method adopts the ideas of self-distillation of DNN models and early exits. The proposed solution is an automated online layer caching mechanism that allows early exiting of a large model during inference time if the cache model in one of the early exits is confident enough for final prediction. One of the main contributions of this paper is that we have implemented the idea as an online caching, meaning that the cache models do not need access to training data and perform solely based on the incoming data at run-time, making it suitable for applications using pre-trained models. Our experiments results on two downstream tasks (face and object classification) show that, on average, caching can reduce the computational complexity of those services up to 58\% (in terms of FLOPs count) and improve their inference latency up to 46\% with low to zero reduction in accuracy.