Technology is at a stage where the engines adapt their guidance to show the patterns that work in the moment; not even two weeks ago. An engine like this can suggest hiring teams to use the words in job posts that will help attract more qualified candidates, regardless of gender, by analysing usage of gendered phrases in job posts. It'll be great addition to -based tools that help build untampered profiles and eliminate the need for multiple skill tests. As a result, there will be more time for conversations with potential aspirants, to accurately evaluate each profile. Firms are shifting towards working as a network of teams.

It has become a sort of tradition for me to try to summarize ML advances at this time of the year (see here for my Quora answer last year, for example). As always, this summary will necessarily be biased by my own interests and focus, but I have tried to keep it as broad as possible. Note that what follows is a blog post version of my Quora answer here. Let's look at all of this in some more detail. If 2017 was probably the cusp of fear mongering and AI hype, 2018 seems to have been the year where we have started to all cool down a bit.

Artificial intelligence (AI) is one of those technologies that excites the public and business imagination alike. Long since a favourite theme in science-fiction, it is now gaining traction in everyday practical scenarios. In 2018, we saw a considerable rise in the adoption of AI around the world and across industries, with businesses using it to improve operations, generate new innovations and boost customer experience. To make artificial intelligence work for a business, leaders need to ensure that employee skills are honed in line with technological investments. With financial services, telecoms and high tech leading the way in bringing AI into the mainstream, and other areas such as automotive, healthcare, energy and retail also embracing it, we expect the rapid growth of AI to continue in 2019 as companies strive to get the most value and competitive advantage from the data they capture.

I am excited to announce the newest course from deeplearning.ai, It will be available on Coursera in early 2019. AI is not only for engineers. This non-technical course will help you understand technologies like machine learning and deep learning, and spot opportunities to apply AI to problems in your own organization. You will see examples of what today's AI can -- and cannot -- do.

Right now, data scientists and humans are trying to create rules for what we can comprehend and then articulate to a machine, enabling machine learning. But in the future, it will be different, "unsupervised deep learning is absolutely the future. I'm 100% convinced of this," she says. We all know about the skills shortage in AI. It is one of the great ironies of AI and automation technologies.

Researchers at Stanford University engineers used a deep learning computer model to identify every solar panel in the continuous U.S. from satellite images. Stanford University engineers have developed a method for locating every solar panel in the contiguous U.S. from a massive satellite image database via a deep learning computer model. The researchers used a pre-trained model called Inception as the basis for the DeepSolar neural network's clustering and classifying of pixels in images. DeepSolar scanned more than 1 billion image "tiles," comprising areas bigger than a neighborhood but smaller than a zip code; each tile had 102,400 pixels, and DeepSolar classified each pixel in each tile, determining whether it was likely part of a solar panel or not. The network completed this task in less than a month, ascertaining that regions with more sun exposure had greater solar panel adoption than areas with less average sunlight.

Prematurity is a significant cause of mortality in neonates. Knowledge of an infant's gestational age is critical in post-delivery treatment plans to reduce neonatal deaths. In high-income countries, prenatal ultrasound scans – the ground truth measure – are the gold-standard method to track gestational aging, but in lower-income countries, access to ultrasound technology and medical experts is limited. "If we could accurately estimate gestational age for newborns using simple, portable technology, we would be able to administer a proper post-treatment plan to reduce the risk of mortality in many under-serviced regions," says Arjun Desai from Duke University, first-author on a new study into an automatic system for gestational aging. The cross-disciplinary team, led by Sina Farsiu, has developed a system based on the previously reported inverse correlation between blood vessel density in the anterior lens capsule region, and gestational age.

Going into 2019, the debates around AI and deep learning will shift from human against machine, to human and machine versus real issues. With IoT, virtual assistants, smart algorithms, automation and advanced robotics, HR firms have begun reshaping the workplace and streamlining manpower. A recent study revealed that 75% of 10 lakh registered companies in India have either invested or are going to invest in either AI- or deep learning-based technologies, or a combination of both. So, HR firms will learn to'stay digital' to revisit three important functions in 2019. Technology is at a stage where the engines adapt their guidance to show the patterns that work in the moment; not even two weeks ago.

Going into 2019, the debates around AI and deep learning will shift from human against machine, to human and machine versus real issues. With IoT, virtual assistants, smart algorithms, automation and advanced robotics, HR firms have begun reshaping the workplace and streamlining manpower. A recent study revealed that 75% of 10 lakh registered companies in India have either invested or are going to invest in either AI- or deep learning-based technologies, or a combination of both. So, HR firms will learn to'stay digital' to revisit three important functions in 2019. Technology is at a stage where the engines adapt their guidance to show the patterns that work in the moment; not even two weeks ago.

Hierarchical text classification, which aims to classify text documents into a given hierarchy, is an important task in many real-world applications. Recently, deep neural models are gaining increasing popularity for text classification due to their expressive power and minimum requirement for feature engineering. However, applying deep neural networks for hierarchical text classification remains challenging, because they heavily rely on a large amount of training data and meanwhile cannot easily determine appropriate levels of documents in the hierarchical setting. In this paper, we propose a weakly-supervised neural method for hierarchical text classification. Our method does not require a large amount of training data but requires only easy-to-provide weak supervision signals such as a few class-related documents or keywords. Our method effectively leverages such weak supervision signals to generate pseudo documents for model pre-training, and then performs self-training on real unlabeled data to iteratively refine the model. During the training process, our model features a hierarchical neural structure, which mimics the given hierarchy and is capable of determining the proper levels for documents with a blocking mechanism. Experiments on three datasets from different domains demonstrate the efficacy of our method compared with a comprehensive set of baselines.