Talent search and recommendation systems at LinkedIn strive to match the potential candidates to the hiring needs of a recruiter or a hiring manager expressed in terms of a search query or a job posting. Recent work in this domain has mainly focused on linear models, which do not take complex relationships between features into account, as well as ensemble tree models, which introduce non-linearity but are still insufficient for exploring all the potential feature interactions, and strictly separate feature generation from modeling. In this paper, we present the results of our application of deep and representation learning models on LinkedIn Recruiter. Our key contributions include: (i) Learning semantic representations of sparse entities within the talent search domain, such as recruiter ids, candidate ids, and skill entity ids, for which we utilize neural network models that take advantage of LinkedIn Economic Graph, and (ii) Deep models for learning recruiter engagement and candidate response in talent search applications. We also explore learning to rank approaches applied to deep models, and show the benefits for the talent search use case. Finally, we present offline and online evaluation results for LinkedIn talent search and recommendation systems, and discuss potential challenges along the path to a fully deep model architecture. The challenges and approaches discussed generalize to any multi-faceted search engine.

Ranking is a fundamental and widely studied problem in scenarios such as search, advertising, and recommendation. However, joint optimization for multi-scenario ranking, which aims to improve the overall performance of several ranking strategies in different scenarios, is rather untouched. Separately optimizing each individual strategy has two limitations. The first one is lack of collaboration between scenarios meaning that each strategy maximizes its own objective but ignores the goals of other strategies, leading to a sub-optimal overall performance. The second limitation is the inability of modeling the correlation between scenarios meaning that independent optimization in one scenario only uses its own user data but ignores the context in other scenarios. In this paper, we formulate multi-scenario ranking as a fully cooperative, partially observable, multi-agent sequential decision problem. We propose a novel model named Multi-Agent Recurrent Deterministic Policy Gradient (MA-RDPG) which has a communication component for passing messages, several private actors (agents) for making actions for ranking, and a centralized critic for evaluating the overall performance of the co-working actors. Each scenario is treated as an agent (actor). Agents collaborate with each other by sharing a global action-value function (the critic) and passing messages that encodes historical information across scenarios. The model is evaluated with online settings on a large E-commerce platform. Results show that the proposed model exhibits significant improvements against baselines in terms of the overall performance.

Humans are able to make rich predictions about the future dynamics of physical objects from a glance. On the other hand, most existing computer vision approaches require strong assumptions about the underlying system, ad-hoc modeling, or annotated datasets, to carry out even simple predictions. To tackle this gap, we propose a new perspective on the problem of learning intuitive physics that is inspired by the spatial memory representation of objects and spaces in human brains, in particular the co-existence of egocentric and allocentric spatial representations. We present a generic framework that learns a layered representation of the physical world, using a cascade of invertible modules. In this framework, real images are first converted to a synthetic domain representation that reduces complexity arising from lighting and texture. Then, an allocentric viewpoint transformer removes viewpoint complexity by projecting images to a canonical view. Finally, a novel Recurrent Latent Variation Network (RLVN) architecture learns the dynamics of the objects interacting with the environment and predicts future motion, leveraging the availability of unlimited synthetic simulations. Predicted frames are then projected back to the original camera view and translated back to the real world domain. Experimental results show the ability of the framework to consistently and accurately predict several frames in the future and the ability to adapt to real images.

I implemented the Neural Processes paper in Pytorch, and found a few interesting things not discussed elsewhere as far as I can see. For example, the posterior is liable to collapse to a near point estimate, and this appears to be somewhat related to the formulation they use. I wrote this up in an informal blog post, thoughts/comments welcome!

LinkedIn heavily relies on artificial intelligence to deliver content and create economic opportunities for its 575 million members. Following recent rapid advances of deep learning technologies, our AI engineers have started adopting deep neural networks in LinkedIn's relevance-driven products, including feeds and smart-replies. Many of these use cases are built on TensorFlow, a popular deep learning framework written by Google. In the beginning, our internal TensorFlow users ran the framework on small and unmanaged "bare metal" clusters. But we quickly realized the need to connect TensorFlow to the massive compute and storage power of our Hadoop-based big data platform.

Welcome to part 8 of the Deep Learning with Python, Keras, and Tensorflow series. In this tutorial, we're going to work on using a recurrent neural network to predict against a time-series dataset, which is going to be cryptocurrency prices. Whenever I do anything finance-related, I get a lot of people saying they don't understand or don't like finance. If you want, feel free to adapt this tutorial to a dataset you like. Any example where you have a sequence of features will suffice.

Big pharma is taking an AI-first approach. Apple is revolutionizing clinical studies. We look at the top artificial intelligence trends reshaping healthcare. Healthcare is emerging as a prominent area for AI research and applications. And nearly every area across the industry will be impacted by the technology's rise. Image recognition, for example, is revolutionizing diagnostics. Recently, Google DeepMind's neural networks matched the accuracy of medical experts in diagnosing 50 sight-threatening eye diseases. Even pharma companies are experimenting with deep learning to design new drugs. For example, Merck partnered with startup Atomwise and GlaxoSmithKline is partnering with Insilico Medicine.

In January 2014, Google splashed £400 million on buying the London-based artificial intelligence firm DeepMind. At the time, it wasn't clear what Google, and now parent company Alphabet, would get for its money. Four years later, DeepMind's team that focuses on developing AI for Google is starting to pay off. Google's launch of its latest mobile operating system, Android Pie, involves DeepMind's largest real-world machine learning roll-out to date. It's looking to solve one of the modern smartphone's most frustrating features: poor battery life.

The next big breakthroughs in artificial intelligence may depend on exploring our own minds. So says Josh Tenenbaum, who leads the Computational Cognitive Science lab at MIT and is the head of a major new AI project called the MIT Quest for Intelligence. The project brings computer scientists and engineers together with neuroscientists and cognitive psychologists to explore research that might lead to fundamental progress in artificial intelligence. Tenenbaum outlined the project, and his vision for advancing AI, at EmTech, a conference held at MIT this week by MIT Technology Review. "Imagine we could build a machine that starts off like a baby and learns like a child," he said.

He said the main part of its RL policy is implemented by a 4,000 dimensional LSTM network, which has roughly 100 million (10 9) parameters .He also said that in terms of Flops, it is like a honeybee brain. This means the OpenAI Dota2 bot has roughly the same number of parameters as honeybee brain (Assumption: real synapse weight can be represented by 1 Byte of memory, which is often the case in artificial neural network systems). If the former is more intelligent, it suggests that modern machine learning algorithm is more capable than natural selection at turning a certain size of parameters into intelligence. It is probable that the same algorithm can turn them into super-mice or super-cat intelligence.