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The Google Assistant is going global
Over the past year and a half, the Google Assistant has grown from being available on just one device in one language to across many types of devices, including speakers…
Topics /
AI & Machine Learning
The most popular blog posts about ai & machine learning on Hacker News. 188 posts.
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Self driving cars and beyond
The story of technology is not unlike what often happens in dramatic stories and plays, where a “deus ex machina” or character drops down magically from the skies onto our world stage. But in reality, technology never comes so suddenly, or in such an isolated manner: There’s an entire ecosystem that grows up around…
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Translating Between Statistics and Machine Learning
This SEI Blog post explores the differences between statistics and machine learning and how to translate statistical models into machine learning models.
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Guide to Speech Recognition with Python
An in-depth tutorial on speech recognition with Python. Learn which speech recognition library gives the best results and build a full-featured "Guess The Word" game with it.
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Nematode Neural System Uploaded to a Computer and Trained to Balance a Pole
TU Wien News. Press releases. Calendar of Events. University Gazette.
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Reptile: A Scalable Meta-Learning Algorithm
Stay up to speed on the rapid advancement of AI technology and the benefits it offers to humanity.
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Learning by playing
Getting children (and adults) to tidy up after themselves can be a challenge, but we face an even greater challenge trying to get our AI agents to do the same. Success depends on the mastery of several core visuo-motor skills: approaching an object, grasping and lifting it, opening a box and putting things inside of…
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Real time numbers recognition (MNIST) on an iPhone with CoreML
Learn how to build and train a deep learning network to recognize numbers (MNIST),how to convert it in the CoreML format to then deploy it on your iPhoneX and …
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Impala: Scalable Distributed Deep Reinforcement Learning
Deep Reinforcement Learning (DeepRL) has achieved remarkable success in a range of tasks, from continuous control problems in robotics to playing games like Go and Atari. The improvements seen in these domains have so far been limited to individual tasks where a separate agent has been tuned and trained for each task.
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MLflow: An Open Source Machine Learning Platform
MLflow is an open-source platform to manage the ML lifecycle, offering tools for data preparation, model training, and deployment at scale.
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Reproducible machine learning with PyTorch and Quilt
In this article, we'll use Quilt to transfer versioned training data to a remote machine. We'll start with the Berkeley Segmentation Dataset, package the dataset, then train a PyTorch model for super-resolution imaging.
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Open-Source Machine Learning Repos to Inspire Your Next Project
In the last couple of years, machine learning has opened up new horizons in a wide range of industries, with advanced use cases emerging: Facebook’s facial recognition, Netflix’s recommended movies, PrismaAI’s image style transfer, Siri’s voice recognition, Google Allo’s natural… Continue reading 25 Open-Source…
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Nevergrad: A Python library for performing derivative-free ML optimization
Most machine learning tasks — from natural language processing to image classification to translation and many others — rely on derivative-free optimization to tune parameters and/or hyperparameter…
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Interpretable Machine Learning Through Teaching
Stay up to speed on the rapid advancement of AI technology and the benefits it offers to humanity.
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Optimizing Siri on HomePod in Far‑Field Settings
The typical audio environment for HomePod has many challenges — echo, reverberation, and noise. Unlike Siri on iPhone, which operates close…
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Introduction to Apple WatchKit with Core Motion – Tracking Jumping Jacks
Like many developers exploring the world of wearables, I’m an avid fan of apps that track your exercise. Companies like Strava & Runkeeper have paved the way for what we can do with motion and health data, creating new and… Continue reading Introduction to Apple WatchKit with Core Motion — Tracking Jumping Jacks
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Improving Deep Learning Performance with AutoAugment
Posted by Ekin Dogus Cubuk, Google AI Resident and Barret Zoph, Research Scientist, Google Brain Team The success of deep learning in computer vi...
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Growing up with AI
By Stefania Druga Graduate Student, Personal Robots Group LEGO Papert Fellow 2018
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Learning to Predict Depth on the Pixel 3 Phones
Posted by Rahul Garg, Research Scientist and Neal Wadhwa, Software Engineer Portrait Mode on the Pixel smartphones lets you take professional-looki...
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Self-Supervised Tracking via Video Colorization
Posted by Carl Vondrick, Research Scientist, Machine Perception Tracking objects in video is a fundamental problem in computer vision, essential to...
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Measuring abstract reasoning in neural networks
Neural network-based models continue to achieve impressive results on longstanding machine learning problems, but establishing their capacity to reason about abstract concepts has proven difficult. Building on previous efforts to solve this important feature of general-purpose learning systems, our latest paper sets…
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Preserving Outputs Precisely while Adaptively Rescaling Targets
Multi-task learning - allowing a single agent to learn how to solve many different tasks - is a longstanding objective for artificial intelligence research. Recently, there has been a lot of excellent progress, with agents like DQN able to use the same algorithm to learn to play multiple games including Breakout and…
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NGraph: A New Open Source Compiler for Deep Learning Systems
Intel Search Results Page
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Shrinking Machine Learning Models for Offline Use
Get the latest news and features about smart home, Alexa Built-in, and Alexa Gadgets.