"companies need to both embrace new technology and retain talented recruiters" AI may have only recently emerged into the popular consciousness, but it is certainly not new. People have been researching AI since the 1950s with famous AI systems making headlines in the decades since, including IBM's Watson, which famously won the TV quiz show Jeopardy! in 2011. But AI technology is now cheaper than ever to develop, opening it up to more businesses. And thanks to its ubiquity, AI is slowly becoming a more recognizable part of our daily lives. This proliferation has inspired scare stories about AI taking work away from humans.

Why is content personalization important? OTT (Over the Top) platforms are transforming the global entertainment scene. The critical players, like Hulu, Netflix, and Disney, are competing in terms of viewership and revenues. With the increasing overlap of content across all these platforms, it is crucial for these services to improve the consumer experience by delivering relevant and engaging content to prevent audience churn. Content personalization is, therefore, vital to acquire more viewing time and improve market share.

The following article is part of a series on Argonne National Laboratory's efforts to use …

Researchers from Harvard University and MIT-IBM Watson Lab have created an AI-powered tool for spotting AI-generated text. Dubbed Giant Language Model Test Room (GLTR), the system aims to detect whether a specific piece of text was generated by a language model algorithm. You can give the tool a spin here. Don't miss Hard Fork Summit in Amsterdam With AI and natural language generation models already employed to produce fake news and spread misinformation, GLTR has the potential to distinguish machine generated text from human-written text to a non-expert reader. According to results shared by the researchers, GLTR improved the human detection-rate of fake text from 54 percent to 72 percent without any prior training.

Today, algorithms that are used to make predictions are everywhere. Netflix recommends new movies and shows based on what you've previously watched, Spotify creates playlists with songs similar to what you've been playing on repeat, and retail brands spam your email with items that can be worn with the new jacket you just ordered. The assumption today is, "with enough data, anything is predictable." However, over the last few years, news headlines have repeatedly illustrated the influence that such algorithms can have on human well-being, along with the inherent biases that affect many of them. With this in mind, before rushing to embrace artificial intelligence (AI) algorithms, it's crucial that we ensure they promote justice and fairness rather than reinforcing already existing inequalities.

Researchers have presented a new artificial intelligence bot that can predict whether a movie will be a critical and financial siccess by simply analyzing its plot. The team behind the AI presented their paper at the 2019 Storytelling Workshop held in Florence, Italy this month. They claimed that the AI can help movie producers decide which movies might be worth the investment. "As the size of investment for movie production grows bigger, the need for predicting a movie's success in early stages has increased," explained the study authors in the paper's abstract. "To enable a more earlier prediction of a movie's performance, we propose a deep-learning based approach to predict the success of a movie using only its plot summary text."

If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA TODAY's newsroom and any business incentives. The spookiest holiday of all is just around the corner--and you're going to need more than a few pumpkins scattered around your porch to properly decorate. Halloween is on Thursday, Oct. 31, which means you've only got a few weeks to add an eerie flair to your home before hoards of trick-or-treaters arrive. From smart plugs to spider webs, we've rounded up 10 essentials you'll need to decorate for this Halloween.

Abstract: We introduce novel communication strategies in synchronous distributed Deep Learning consisting of decentralized gradient reduction orchestration and computational graph-aware grouping of gradient tensors. These new techniques produce an optimal overlap between computation and communication and result in near-linear scaling (0.93) of distributed training up to 27,600 NVIDIA V100 GPUs on the Summit Supercomputer. We demonstrate our gradient reduction techniques in the context of training a Fully Convolutional Neural Network to approximate the solution of a longstanding scientific inverse problem in materials imaging. The efficient distributed training on a dataset size of 0.5 PB, produces a model capable of an atomically-accurate reconstruction of materials, and in the process reaching a peak performance of 2.15(4) EFLOPS$_{16}$.

Artificial Intelligence (AI) has slowly made its way into the business world and has changed its landscape considerably. The technology has the potential to completely revolutionise the way businesses function. It is a broad concept which is generally perceived as being limited to the use of humanoid robots due to its depiction in Hollywood movies. However, there is more to AI than just highly intelligent robots. It encompasses any technique which allows the computer to imitate human intelligence.

Dotscience, the pioneer in DevOps for machine learning (ML), today announced its schedule of presentations at upcoming industry events through the end of 2019. Dotscience will be exhibiting its DevOps for ML platform, which provides collaborative, end-to-end ML data and model management. Dotscience helps simplify, accelerate and control AI and ML models through the entire model development lifecycle. Dotscience makes it possible for ML engineering to be just as simple, fast and safe as modern software engineering when using DevOps techniques. The company sees a parallel between the challenges of software development in the 1990s and the current state of ML model management today.