Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems. In this paper, we propose the Global-Locally Self-Attentive Dialogue State Tracker (GLAD), which learns representations of the user utterance and previous system actions with global-local modules. Our model uses global modules to share parameters between estimators for different types (called slots) of dialogue states, and uses local modules to learn slot-specific features. We show that this significantly improves tracking of rare states and achieves state-of-the-art performance on the WoZ and DSTC2 state tracking tasks. GLAD obtains 88.1% joint goal accuracy and 97.1% request accuracy on WoZ, outperforming prior work by 3.7% and 5.5%. On DSTC2, our model obtains 74.5% joint goal accuracy and 97.5% request accuracy, outperforming prior work by 1.1% and 1.0%.

Reinforcement learning (RL) algorithms have made huge progress in recent years by leveraging the power of deep neural networks (DNN). Despite the success, deep RL algorithms are known to be sample inefficient, often requiring many rounds of interaction with the environments to obtain satisfactory performance. Recently, episodic memory based RL has attracted attention due to its ability to latch on good actions quickly. In this paper, we present a simple yet effective biologically inspired RL algorithm called Episodic Memory Deep Q-Networks (EMDQN), which leverages episodic memory to supervise an agent during training. Experiments show that our proposed method can lead to better sample efficiency and is more likely to find good policies. It only requires 1/5 of the interactions of DQN to achieve many state-of-the-art performances on Atari games, significantly outperforming regular DQN and other episodic memory based RL algorithms.

A recent Bloomberg article dives into the achievements of Jürgen Schmidhuber. In 1997, Schmidhuber's came up with long short-term memory, or LSTM, a tenet of Artificial General Intelligence (AGI). He states "You can write it down in five lines of code. It can learn to put the important stuff in memory and ignore the unimportant stuff. LSTM can excel at many really important things in today's world, most famously speech recognition and language translation but also image captioning, where you see an image and then you write out words which explain what you see."

Heat maps represent areas of the radiograph that were activated by the deep-learning network and are used to determine the prediction class (large, moderate, or small pleural effusion). In addition to highlighting the meniscus of the effusion, the heat map also includes the lung parenchyma far above the meniscus. The network may be using the degree of aerated lungs to differentiate these classes of effusion, which in this case is a moderate effusion.

I recently wrote about how deep learning is making machine smarter. The big question though, is can deep learning help machines think like humans? Does deep learning do enough to ensure artificial intelligence acts somewhat similar to human intelligence? If somebody from the Seventies were to fast forward to the present time, they would notice that we have made remarkable progress with our intelligent automation. We may not yet have intelligent robots of the sophistication that Star War's C3PO portrayed, but we are far beyond anything that the average Seventies citizen would have imagined.

Derivatives are simple with PyTorch. Like many other neural network libraries, PyTorch includes an automatic differentiation package, autograd, which does the heavy lifting. But derivatives seem especially simple with PyTorch. One of the things I wish I had when first learning about how derivatives and practical implementations of neural networks fit together were concrete examples of using such neural network packages to find simple derivatives and perform calculations on them, separate from computation graphs in neural networks. PyTorch's architecture makes such pedagogical examples easy.

See you in the class! Please note that some of our lectures are marked with (COMING SOON) - as we are still adding new, interesting videos.

When Greg Corrado, Ph.D., an artificial intelligence researcher, took the stage at the TedMed Conference last year, he was frank. "Doctors who partner with artificial intelligence as a decision-making aid will see their healing powers expand more than they have in the past 100 years," he told the audience of medical professionals. Corrado is a principal scientist at Google AI and an expert in machine learning. "To practice medicine today," he continued, "is to weather an information hurricane...AI [and machine learning] is our best opportunity to tame the data beast and actually scale care to meet demand." Companies are creating algorithms to sort medical records, determine treatments, diagnose sepsis in as little as 12 hours, and even predict who will skip their next doctor's appointment.

Demand for compute to train artificial intelligence models has shot up enormously over the past six years and is showing no signs of slowing down. Not for profit research firm OpenAI - which is sponsored by Peter Thiel, Elon Musk, Microsoft and Amazon Web Services, among others - published an analysis that showed the amount of compute used for the largest AI training runs has doubled every three-and-a-half months since 2012. This means compute amounts have grown by more than 300,000 times over the past six years, OpenAI said. In comparison, the well-known Moore's Law, which observed the number of transistors in an integrated circuit would double every year-and-a-half, would yield only a twelve-fold increase in performance over the same period. Part of the reason AI models still have enough compute is because of the use of massively parallel video cards or graphics processing units (GPUs) that can have thousands of cores per unit. Furthermore, over the past two years, optimisations such as huge batch sizes, architecture search and expert iteration using improved and specialised hardware such as Tensor processing units (TPUs) and fast data interconnects have increased past limits for algorithmic parallelism.

The best job to have in 2017 according to Glassdoor? The #1 skill you need to start a career in Data Science? So, if you are interested in a career in data science, algorithmic trading, robotics, or any industry where human labor is getting replaced by machines, you have come to the right place! We have prepared an amazing course not only to get you acquainted with, but help you understand how deep machine learning works! Worried you have no experience?