Deep learning, which is a subset of AI (Artificial Intelligence), has been around since the 1950s. It's focused on developing systems that mimic the brain's neural network structure. Yet it was not until the 1980s that deep learning started to show promise, spurred by the pioneering theories of researchers like Geoffrey Hinton, Yoshua Bengio and Yann Lecun. There was also the benefit of accelerating improvements in computer power. Despite all this, there remained lots of skepticism.

While overall adoption of artificial intelligence (AI) remains low among businesses (about 20 percent upon our last study), senior executives know that AI isn't just hype. Organizations across sectors are looking closely at the technology to see what it can do for their business. As they should--we estimate that 40 percent of all the potential value that can be created by analytics today comes from the AI techniques that fall under the umbrella "deep learning" (which utilize multiple layers of artificial neural networks, so-called because their structure and function are loosely inspired by that of the human brain). In total, we estimate deep learning could account for between $3.5 trillion and $5.8 trillion in annual value. However, many business leaders are still not exactly sure where they should apply AI to reap the biggest rewards.

GIPHY is proud to release our custom machine learning model that is able to discern over 2,300 celebrity faces with 98% accuracy. The model was trained to identify the most popular celebs on GIPHY, and can identify and make predictions for multiple faces across a sequence of images, like GIFs and videos. This project was developed by the GIPHY R&D team with the goal to build a deep learning model that could annotate our most popular content as well as, or hopefully better than, similar models offered by major tech companies. We're extremely proud of our results, and have released this model and training code to the public in hopes that others might build off our work, integrate the model into their own projects, or perhaps learn from our approach. You can play with the model on the demo page, and we've provided a 3D projection of all our celebrity class embeddings along with a list of all celebrities available with the model.

Data scientists are the no. 1 most promising job in America for 2019, according to a Thursday report from LinkedIn. Hence, this comes as no surprise: Data scientist topped Glassdoor's list of Best Jobs in America for the past three years, with professionals in the field reporting high demand, high salaries, and high job satisfaction. Also, with the increase in demand, employers are looking for more skills in modern day data scientists. Furthermore, a modern-day data scientist needs to be a good player in aspects like maths, programming, communication and problem-solving. In this blog, we are going to explore if knowledge of mathematics is really necessary to become good data scientists.

Computer Vision is one of the hottest research fields within Deep Learning at the moment. It sits at the intersection of many academic subjects, such as Computer Science (Graphics, Algorithms, Theory, Systems, Architecture), Mathematics (Information Retrieval, Machine Learning), Engineering (Robotics, Speech, NLP, Image Processing), Physics (Optics), Biology (Neuroscience), and Psychology (Cognitive Science). As Computer Vision represents a relative understanding of visual environments and their contexts, many scientists believe the field paves the way towards Artificial General Intelligence due to its cross-domain mastery. The most obvious answer is that there's a fast-growing collection of useful applications derived from this field of study. I recently finished Stanford's wonderful CS231n course on using Convolutional Neural Networks for visual recognition.

One of the most common applications of machine learning today is in recommendation algorithms. Netflix and YouTube use them to push you new shows and videos; Google and Facebook use them to rank the content in your search results and news feed. While these algorithms offer a great deal of convenience, they have some undesirable side effects. You've probably heard of them before: filter bubbles and echo chambers. Concern about these effects is not new.

The buildup of abnormal tau proteins in the brain in neurofibrillary tangles is a feature of Alzheimer's disease, but it also accumulates in other neurodegenerative diseases, such as chronic traumatic encephalopathy and additional age-related conditions. Accurate diagnosis of neurodegenerative diseases is challenging and requires a highly-trained specialist. Researchers at the Center for Computational and Systems Pathology at Mount Sinai developed and used the Precise Informatics Platform to apply powerful machine learning approaches to digitized microscopic slides prepared using tissue samples from patients with a spectrum of neurodegenerative diseases. Applying deep learning, these images were used to create a convolutional neural network capable of identifying neurofibrillary tangles with a high degree of accuracy directly from digitized images. "Utilizing artificial intelligence has great potential to improve our ability to detect and quantify neurodegenerative diseases, representing a major advance over existing labor-intensive and poorly reproducible approaches," said lead investigator John Crary, MD, PhD, Prof. of Pathology and Neuroscience at the Icahn School of Medicine at Mount Sinai.

Don't look now but Apache Spark is about to turn 10 years old. The open source project began quietly at UC Berkeley in 2009 before emerging as an open source project in 2010. For the past five years, Spark has been on an absolute tear, becoming one of the most widely used technologies in big data and AI. Let's take a look at Spark's remarkable run up to this point, and see where it might be headed next. Apache Spark is best known as the in-memory replacement for MapReduce, the disk-based computational engine at the heart of early Hadoop clusters.

DeepMind, the Alphabet-owned artificial intelligence company, will be moving into a new flagship building in 2020. While a precise date is yet to be determined, the company hopes that the new headquarters – which will feature a double helix staircase descending through a library, a roof garden, lecture theatre and lobby artwork by creatives working with data and artificial intelligence – will be operational in the first half of next year. DeepMind is currently located near to the new site in London's Kings Cross, where it has two floors in the Google building. It also has smaller, satellite offices in Paris, Edmonton, Alberta and Mountain View, California. The new, 11-storey DeepMind headquarters will cement the reputation of the Kings Cross area as London's so-called'knowledge quarter': as well as Google, Facebook has taken office space nearby, as has Samsung.

The year 2018 marked a turning point for the field of Natural Language Processing, with a series of deep-learning models achieving state-of-the-art results on NLP tasks ranging from question answering to sentiment classification. Most recently, Google's BERT algorithm has emerged as a sort of "one model to rule them all," based on its superior performance over a wide variety of tasks. BERT builds on two key ideas that have been responsible for many of the recent advances in NLP: (1) the transformer architecture and (2) unsupervised pre-training. The transformer is a sequence model that forgoes the sequential structure of RNN's for a fully attention-based approach, as described in the classic Attention Is All You Need. BERT is also pre-trained; its weights are learned in advance through two unsupervised tasks: masked language modeling (predicting a missing word given the left and right context) and next sentence prediction (predicting whether one sentence follows another).