Summarization of electronic health records (EHRs) can substantially minimize 'screen time' for both patients as well as medical personnel. In recent years summarization of EHRs have employed machine learning pipelines using state of the art neural models. However, these models have produced less than adequate results that are attributed to the difficulty of obtaining sufficient annotated data for training. Moreover, the requirement to consider the entire content of an EHR in summarization has resulted in poor performance due to the fact that attention mechanisms in modern large language models (LLMs) adds a quadratic complexity in terms of the size of the input. We propose here a method that mitigates these shortcomings by combining semantic search, retrieval augmented generation (RAG) and question-answering using the latest LLMs. In our approach summarization is the extraction of answers to specific questions that are deemed important by subject-matter experts (SMEs). Our approach is quite efficient; requires minimal to no training; does not suffer from the 'hallucination' problem of LLMs; and it ensures diversity, since the summary will not have repeated content but diverse answers to specific questions.

Access to higher education is critical for minority populations and emergent bilingual students. However, the language used by higher education institutions to communicate with prospective students is often too complex; concretely, many institutions in the US publish admissions application instructions far above the average reading level of a typical high school graduate, often near the 13th or 14th grade level. This leads to an unnecessary barrier between students and access to higher education. This work aims to tackle this challenge via text simplification. We present PSAT (Professionally Simplified Admissions Texts), a dataset with 112 admissions instructions randomly selected from higher education institutions across the US. These texts are then professionally simplified, and verified and accepted by subject-matter experts who are full-time employees in admissions offices at various institutions. Additionally, PSAT comes with manual alignments of 1,883 original-simplified sentence pairs. The result is a first-of-its-kind corpus for the evaluation and fine-tuning of text simplification systems in a high-stakes genre distinct from existing simplification resources.

Dr. Francois Laborie is President of Cognite, supporting the full-scale digital transformation of asset-heavy industries in North America. Artificial intelligence has changed our lives as consumers. Why hasn't it changed our industries? AI in industry requires more than just big data to work, and the solution lies in the world of physics. If a predictive algorithm fails in the consumer industry, it's not the end of the world. Maybe an ad doesn't get clicked or a TV show doesn't get watched.

For all the advances in the development of artificial intelligence algorithms and models, the majority of potential applications never make it to production because of the time and expense of labeling data to train the model. That's a problem Snorkel.ai has set out to automate. "The not-so-hidden secret about AI today is that despite all the technological and tooling enhancements, but 80 to 90%, of the cost, for many use cases, just goes into manually collecting and labeling and curating this data, this training data that the model learns from," said company co-founder and CEO Alex Ratner. Ratner concedes that this is not the first field or even the first decade in which appropriately labeled data has been considered paramount. In a contributed post to TNS last year, Vikram Bahl outlined the challenges of preparing data for machine learning and AI.

As a VP of Product Marketing, I lead a team that focuses on different product marketing activities at M-Files: product positioning, value proposition and customer communications. My team also employs subject-matter experts for different processes and industries. They work closely with product development, marketing and customer success teams to ensure that we address the specific customer needs for various industries and their respective regulations. We use and follow almost all marketing technologies from ABM to Sales Automation to analytics. Marketing organizations typically use M-Files for collaboration, data governance, digital asset management, content management, talent management, customer intelligence and workflow.

It began as all well-meaning ideas do -- as an effort to make life a little bit easier. This year I began building my own stock-picking artificial intelligence (AI) program. I created it to help me invest my savings and gave it a name: AlphaBean. But as AlphaBean became smarter and smarter, I eventually began to wonder: Could it replace me? My job involves studying companies and writing about them for the wider public. If AlphaBean could learn to invest, might it, or something like it, steal my job? Of course, I'm not the first person to create an investing algorithm. Algorithmic thinking about investing has existed for a long time -- much longer, in fact, than people normally think.

Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer. Electronic Health Record (EHRs) systems are now used in 80% of doctors offices and contain a rich source of patient data available to innovate and improve healthcare. A team at New York University's Courant Institute of Mathematical Sciences developed algorithms and a system to extract EHR data to faster diagnose patients and provide a thorough understanding of the patient's health.