A new technology using artificial intelligence detects depressive language in social media posts more accurately than current systems and uses less data to do it. The technology, which was presented during the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, is the first of its kind to show that, to more accurately detect depressive language, small, high-quality data sets can be applied to deep learning, a commonly used AI approach that is typically data intensive. Previous psycholinguistic research has shown that the words we use in interaction with others on a daily basis are a good indicator of our mental and emotional state. Past attempts to apply deep learning techniques to detect and monitor depression in social media posts have been shown to be tedious and expensive, explained Nawshad Farruque, a University of Alberta PhD student in computing science who is leading the new study. He explained that a Twitter post saying that somebody is depressed because Netflix is down isn't really expressing depression, so someone would need to "explain" this to the algorithm.

In our environment, agents play a team-based hide-and-seek game. Hiders (blue) are tasked with avoiding line-of-sight from the seekers (red), and seekers are tasked with keeping vision of the hiders. There are objects scattered throughout the environment that hiders and seekers can grab and lock in place, as well as randomly generated immovable rooms and walls that agents must learn to navigate. Before the game begins, hiders are given a preparation phase where seekers are immobilized to give hiders a chance to run away or change their environment. There are no explicit incentives for agents to interact with objects in the environment; the only supervision given is through the hide-and-seek objective.

Last December, a conference of biologists gathered in Cancun, Mexico, to review a shocking finding. DeepMind, Alphabet's artificial intelligence lab and sister company to Google, had beat a roomful of biologists in a contest to predict the shape of a protein based on its genetic code. That might not sound monumental, but understanding the way proteins fold into three-dimensional shapes is crucial to helping create drugs, which often fight disease by latching onto proteins and altering the way they work in the body. DeepMind was able to predict these proteins' shapes with significantly more accuracy than the many esteemed academics and professionals at the conference. "It dawned on me that this is a field that people have been working in for decades," Mohammed AlQuraishi, a biologist and researcher at Harvard who participated in the contest, told Vox. "The fact that a new group could come in and do so well, so quickly--I felt bad because it demonstrated the structural inefficiency of academia."

Asked what is the biggest misconception about AI, Yoshua Bengio answered without hesitation "AI is not magic." Winner of the 2018 Turing Award (with the other "fathers of the deep learning revolution," Geoffrey Hinton and Yann LeCun), Bengio spoke at the EmTech MIT event about the "amazing progress in AI" while stressing the importance of understanding its current limitations and recognizing that "we are still very far from human-level AI in many ways." Deep learning has moved us a step closer to human-level AI by allowing machines to acquire intuitive knowledge, according to Bengio. Classical AI was missing this "learning component," and deep learning develops intuitive knowledge "by acquiring that knowledge from data, from interacting with the environment, from learning. That's why current AI is working so much better than the old AI."

Along the way, I'll share personal commentary, stories from established deep learning practitioners, and code snippets. Let's start by looking at the common points that can fail a neural network. There are three categorical features here: Sex, Has_Masters, and Has_Bachelors. You may one-hot encode to better represent the relationship or you may just keep them as they are. There are two continuous features in the dataset: Age and Bounties. They vary largely in scale, so you would want to standardize their scales. There are several ways to initialize the weights in a neural network. You can start with all zeros (which isn't advisable, and we will see it a second), you can randomly initialize them, or you can choose a technique like Xavier initialization or He initialization.

I recently caught up with Terry Deem, Product Marketing Manager for Data Science, Machine Learning and Intel Distribution for Python, and David Liu, Software Technical Consultant Engineer for the Intel Distribution for Python*, both from Intel, to discuss the Intel Distribution for Python (IDP): targeted classes of developers, use with commonly used Python packages for data science, benchmark comparisons, the solution's use in scientific computing, and a look to the future with respect to IPD. This Q&A is a follow-up to a previous sponsored post, "Supercharge Data Science Applications with the Intel Distribution for Python." Terry specializes in developer tools and the developer community. Terry has covered a wide variety of tools for Intel from the highly popular XDK to the industry-standard Media Server Studio. He currently covers Intel's machine learning tool such as Intel Data Analytics Acceleration Library (Intel DAAL), Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN) and Intel Distribution for Python.

François Chollet is the creator of Keras, which is an open source deep learning library that is designed to enable fast, user-friendly experimentation with deep neural networks. It serves as an interface to several deep learning libraries, most popular of which is TensorFlow, and it was integrated into TensorFlow main codebase a while back. Aside from creating an exceptionally useful and popular library, François is also a world-class AI researcher and software engineer at Google, and is definitely an outspoken, if not controversial, personality in the AI world, especially in the realm of ideas around the future of artificial intelligence. This conversation is part of the Artificial Intelligence podcast. OUTLINE: 0:00 - Introduction 1:14 - Self-improving AGI 7:51 - What is intelligence?

Automatic age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Nevertheless, performance of existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in performance recently reported for the related task of face recognition. Age and gender, two of the key facial attributes, play a very foundational role in social interactions, making age and gender estimation from a single face image an important task in intelligent applications, such as access control, human-computer interaction, law enforcement, marketing intelligence and visual surveillance, etc. Recently I came across Quividi which is an AI software application which is used to detect age and gender of users who passes by based on online face analyses and automatically starts playing advertisements based on the targeted audience. Another example could be AgeBot which is an Android App that determines your age from your photos using facial recognition.

Applications for machine learning and deep learning have become increasingly accessible. For example, Keras provides APIs with TensorFlow backend that enable users to build neural networks without being fluent with TensorFlow. Despite the ease of building and testing models, deep learning has suffered from a lack of interpretability; deep learning models are considered black boxes to many users. In a talk at ODSC West in 2018, Pramit Choudhary explained the importance of model evaluation and interpretability in deep learning and some cutting edge techniques for addressing it. Predictive accuracy is not the only concern regarding a model's performance.

As Instacart has grown, we've learned a few things the hard way. We're open sourcing Lore, a framework to make machine learning approachable for Engineers and maintainable for Machine Learning Researchers. To address these issues we're standardizing our machine learning in Lore. At Instacart, three of our teams are using Lore for all new machine learning development, and we are currently running a dozen Lore models in production. If you want a super quick demo that serves predictions with no context, you can clone my_app from github. Skip to the Outline if you want the full tour.