Generating high-quality, in-depth textual documents, such as academic papers, news articles, Wikipedia entries, and books, remains a significant challenge for Large Language Models (LLMs). In this paper, we propose to use planning to generate long form content. To achieve our goal, we generate intermediate steps via an auxiliary task that teaches the LLM to plan, reason and structure before generating the final text. Our main novelty lies in a single auxiliary task that does not require multiple rounds of prompting or planning. To overcome the scarcity of training data for these intermediate steps, we leverage LLMs to generate synthetic intermediate writing data such as outlines, key information and summaries from existing full articles. Our experiments demonstrate on two datasets from different domains, namely the scientific news dataset SciNews and Wikipedia datasets in KILT-Wiki and FreshWiki, that LLMs fine-tuned with the auxiliary task generate higher quality documents. We observed +2.5% improvement in ROUGE-Lsum, and a strong 3.60 overall win/loss ratio via human SxS evaluation, with clear wins in organization, relevance, and verifiability.

While learning about advanced computing concepts, machine learning and other technology of the future, Maldonado realized a need to create a …

We decided that this topic is worth covering in depth since any changes to the healthcare system directly impact business leaders in multiple facets such as employee insurance coverage or hospital administration policies. Industry analysts estimate that the AI health market is poised to reach $6.6 billion by 2021 and by 2026 can potentially save the U.S. healthcare economy $150 billion in annual savings. However, no sources have taken a comprehensive look at machine learning applications at America's leading hospitals. This article aims to present a succinct picture of the implementation of machine learning by the five leading hospitals in the U.S. based on the 2016-2017 U.S. News and World Report Best Hospitals Honor Roll rankings. Through facts and figures we aim to provide pertinent insights for business leaders and professionals interested in how these top five US hospitals are being impacted by AI.

We look at some of the most powerful ideas that challenge the status quo and offer some new inspiring ideas for the New Year on how to create and run your company with a passion for people and the way your business treats them. Last month, we took an end-of-the-year look at the most-read articles in this groundbreaking section over the last twelve months. This month, we look at some of the most powerful ideas that challenge the status quo and offer you some new inspiring ideas for the New Year on how to create and run your company with a passion for people and the way your business treats them. When we began the movement to recognize companies that truly cared about doing business the right way and coined it "Social Capital" last year, we knew we were recognizing many old school values. It was based partly on the thoughtful and brilliant ruminations of Adam Smith, often called the Father of Capitalism, in which he explained the philosophical rationality of caring for others and an economic system that actually rewards business done in ethical way.

Technology is a double-edged sword. It can improve our lives, but it can also make our lives much worse. Tech connects us across time and space, while it spreads misinformation and undermines democracy. Tech delivers things right to our door, while it tracks our behavior and sells our data. The headlines tell us what we already know: a generation of tech companies is using us. Social media platforms are engaging, even though they leave us feeling emotionally drained.

Art and artificial intelligence might feel like they're from two different worlds. Today's art world is far removed from most technology, especially artificial intelligence. The truth about AI is that it's hard. It takes a lot of time and data to do it the right way. But art and AI can help one another.

One of the big headaches in deep learning is that models take forever to train. As an ML engineer, waiting hours or days for training to complete makes iteratively improving your model a slow and frustrating process. In this blog post, we show how to accelerate fine-tuning the ALBERT language model while also reducing costs by using Determined's built-in support for distributed training with AWS spot instances. Originally, ALBERT took over 36 hours to train on a single V100 GPU and cost $112 on AWS. With distributed training and spot instances, training the model using 64 V100 GPUs took only 48 minutes and cost only $47! That's both a 46x performance improvement and a 58% reduction in cost!

Artificial Intelligence though having become a common term in today's time, not just to the technologically aware citizens of the world, but even among regular people has the potential to drive humanity forward in an exponential impact index that hasn't surfaced yet. The untapped potential of AI will take years and if not many more decades to come to fruition before its growth comes to a halt. In this article, we talk about Artificial Intelligence and its key elements and the services provided by Microsoft Azure to help innovators build AI Intelligent Systems. Artificial Intelligence (AI) is the branch of computer science with multiple inter-relations to various domains which refers to the creation of intelligence forms that imitate human capabilities and behavior. Artificial intelligence was first ever coined in 1955 and was envisioned for general artificial intelligence during the initial inception but later, progressed into domain-specific and task-based artificial intelligence.

I explain Artificial Intelligence terms and news to non-experts. Is AI the future of video game design? This video is about a paper called Enhancing Photorealism Enhancement. Enhancing Photorealism Enhancement, or EPE, is an AI that can be applied live to the video game and transform every frame to look much more natural. You can find out more about it below.

In the last two events, Apple has been explicit about how serious they are about machine learning-based SoC. In the last two events, Apple has been explicit about how serious they are about machine learning-based SoC.