Google announced what they called the most important update in five years. What is BERT and how will it impact SEO? According to Google this update will affect complicated search queries that depend on context. "These improvements are oriented around improving language understanding, particularly for more natural language/conversational queries, as BERT is able to help Search better understand the nuance and context of words in Searches and better match those queries with helpful results. Particularly for longer, more conversational queries, or searches where prepositions like "for" and "to" matter a lot to the meaning, Search will be able to understand the context of the words in your query. You can search in a way that feels natural for you."

The ever-increasing number of Internet of Things (IoT) devices has created a new computing paradigm, called edge computing, where most of the computations are performed at the edge devices, rather than on centralized servers. An edge device is an electronic device that provides connections to service providers and other edge devices; typically, such devices have limited resources. Since edge devices are resource-constrained, the task of launching algorithms, methods, and applications onto edge devices is considered to be a significant challenge. In this paper, we discuss one of the most widely used machine learning methods, namely, Deep Learning (DL) and offer a short survey on the recent approaches used to map DL onto the edge computing paradigm. We also provide relevant discussions about selected applications that would greatly benefit from DL at the edge.

Filmed at Future Decoded 2019. Cloud28 is the world's largest independent community, promoting cloud services and knowledge sharing. It serves end customers, cloud service providers, solution providers, ISVs, systems integrators, distributors, and government entities dedicated to accelerating enterprise Cloud adoption.

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Join the discussion on our Discord server In this episode, I am with Aaron Gokaslan, computer vision researcher, AI Resident at Facebook AI Research. Aaron is the author of OpenGPT-2, a parallel NLP model to the most discussed version that OpenAI d...

Artificial intelligence and machine learning continue to gain a foothold in our everyday lives. Whether for complex tasks like computer vision and natural language processing, or something as basic as an online chatbot, their popularity shows no signs of slowing. Companies have also started to explore deep learning, which is an advanced subset of machine learning. By applying "deep neural networks" deep learning takes inspiration from how the human brain works. Unlike machine learning, deep learning can actually train its processes directly on raw data, requiring little to no human intervention.

A large amount of data is stored in the form of time series: stock indices, climate measurements, medical tests, etc. Time series classification has a wide range of applications: from identification of stock market anomalies to automated detection of heart and brain diseases. There are many methods for time series classification. Most of them consist of two major stages: on the first stage you either use some algorithm for measuring the difference between time series that you want to classify (dynamic time warping is a well-known one) or you use whatever tools are at your disposal (simple statistics, advanced mathematical methods etc.) to represent your time series as feature vectors. In the second stage you use some algorithm to classify your data.

NLP is an interdisciplinary field concerned with the interactions between computers and human natural languages (e.g: English) -- speech or text. Okay, now we get it, NLP plays a major role in our daily computer interactions, let's see more business-related NLP use-cases: NLP is divided into two fields: Linguistics and Computer Science. The Linguistics side is concerned with language, it's formation, syntax, meaning, different kind of phrases (noun or verb) and whatnot. The Computer Science side is concerned with applying linguistic knowledge, by transforming it into computer programs with the help of sub-fields such as Artificial Intelligence (Machine Learning & Deep Learning). Scientific advancements in NLP can be divided into 3 categories (Rule-based systems, Classical Machine Learning models and Deep Learning models).

A new report estimates the cost of waste in the U.S. healthcare system alone ranges as high as $935 billion a year, about 25 percent of total healthcare spending. A growing army of startups and established practitioners sees the inefficiencies as a trillion-dollar opportunity to apply AI. The U.S. spends about 18 percent of its gross domestic product on healthcare, more than any other country. A report published online by the Journal of the American Medical Association surveyed 54 studies to estimate annual waste figures in six broad categories, including failures from choosing ineffective treatments (up to $166 billion), failures from coordinating multiple treatments ($78 billion), fraud and abuse ($84 billion) and administrative complexity ($266 billion). "Implementation of effective measures to eliminate waste represents an opportunity to reduce the continued increases in U.S. health care expenditures," the report concluded.