Automated machine learning has been drawing lots of attention lately, and is software that automatically selects and trains ML models. Some think it replaces the job of a data scientist however there are limitations as to what it can do. Presently, Microsoft's AutoML is able to build a set of ML models automatically, intelligently select models for training, then recommend the best one for you based on the ML problem and data type. Typically a data scientist with their domain expertise would manually produce and compare dozens of models. In a nutshell, it selects the right algorithm and helps to tune hyperparameters.

At the intersection of neuroscience and artificial intelligence (AI) is an alternative approach to deep learning. Evolutionary algorithms (EA) are a subset of evolutionary computation--algorithms that mimic biological evolution to solve complex problems. Published this week on Tuesday in Cell Reports, IBM researchers took an innovative approach using evolutionary algorithms to create a state-of-the-art cloud-based neuroscience model for studying neurodegenerative disorders. The origins of artificial intelligence goes back to the 1950s. The recent global resurrection of AI from its hibernation is largely due to advances in machine learning pattern recognition, namely deep learning.

Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics. Authors Tom Hope, Yehezkel Resheff, and Itay Lieder provide a hands-on approach to TensorFlow fundamentals for a broad technical audience--from data scientists and engineers to students and researchers. You'll begin by working through some basic examples in TensorFlow before diving deeper into topics such as neural network architectures, TensorBoard visualization, TensorFlow abstraction libraries, and multithreaded input pipelines. Once you finish this book, you'll know how to build and deploy production-ready deep learning systems in TensorFlow.

Artificial intelligence, machine learning, and deep learning are arguably the most significant innovations taking the 21stcentury by storm. Differentiating between the three terminologies is no easy feat, which explains why they are often used interchangeably. Artificial intelligence is the art of trying to integrate human intelligence into machines. Put, it is a science or approach of developing a robot or device that works like a human being. Such a device, when adequately configured, would be able to learn, make mistakes, and compensate for the mistakes made as well as gain experience that leads to learning.

Artificial intelligence is spreading through our lives. As it moves from new feeds to productivity tools, the boundary between what is human-made and machine-made is becoming almost invisible, resulting in vast shifts in how we perceive and interact with the world. AI has surprised us with how it can move, see and hear, but also, with what it can create. Once thought of as a solely human trait, creativity is now a debated topic. Or is a thought process constrained by what us humans define it as? In 2016, Google DeepMind's AlphaGo system trounced the world's reigning champion, Lee Sedol at Go – an ancient Chinese game – by using its creative intelligence to devise a winning strategy.

Abstract: Natural language is hierarchically structured: smaller units (e.g., phrases) are nested within larger units (e.g., clauses). When a larger constituent ends, all of the smaller constituents that are nested within it must also be closed. While the standard LSTM architecture allows different neurons to track information at different time scales, it does not have an explicit bias towards modeling a hierarchy of constituents. This paper proposes to add such an inductive bias by ordering the neurons; a vector of master input and forget gates ensures that when a given neuron is updated, all the neurons that follow it in the ordering are also updated. Our novel recurrent architecture, ordered neurons LSTM (ON-LSTM), achieves good performance on four different tasks: language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference.

In this tutorial, you will learn how to perform transfer learning with Keras, Deep Learning, and Python on your own custom datasets. You're just hired by Yelp to work in their computer vision department. Yelp has just launched a new feature on its website that allows reviewers to take photos of their food/dishes and then associate them with particular items on a restaurant's menu. Certain nefarious users aren't taking photos of their dishes…instead, they are taking photos of… (well, you can probably guess). Figure out how to create an automated computer vision application that can distinguish between "food" and "not food", thereby allowing Yelp to continue with their new feature launch and provide value to their users. So, how are you going to build such an application? The answer lies in transfer learning via deep learning. Today marks the start of a brand new set of tutorials on transfer learning using Keras.

In this tutorial, you will learn how to perform transfer learning with Keras, Deep Learning, and Python on your own custom datasets. You're just hired by Yelp to work in their computer vision department. Yelp has just launched a new feature on its website that allows reviewers to take photos of their food/dishes and then associate them with particular items on a restaurant's menu. Certain nefarious users aren't taking photos of their dishes…instead, they are taking photos of… (well, you can probably guess). Figure out how to create an automated computer vision application that can distinguish between "food" and "not food", thereby allowing Yelp to continue with their new feature launch and provide value to their users. So, how are you going to build such an application? The answer lies in transfer learning via deep learning. Today marks the start of a brand new set of tutorials on transfer learning using Keras.

Neuromorphic Computing is a nascent research field in which models and devices are designed to process information by emulating biological neural systems. Thanks to their superior energy efficiency, analog neuromorphic systems are highly promising for embedded, wearable, and implantable systems. However, optimizing neural networks deployed on these systems is challenging. One main challenge is the so-called timescale mismatch: Dynamics of analog circuits tend to be too fast to process real-time sensory inputs. In this thesis, we propose a few working solutions to slow down dynamics of on-chip spiking neural networks. We empirically show that, by harnessing slow dynamics, spiking neural networks on analog neuromorphic systems can gain non-trivial performance boosts on a battery of real-time signal processing tasks.