WHY IT MATTERS: Digital Transformation

There’s an old saying that the first fifty years of the car industry were about creating car companies and working out what cars should look like, and the second fifty years were about what happened once everyone had a car - they were about McDonalds and Walmart, suburbs and the remaking of the world around the car, for good and of course bad. The innovation in cars became everything around the car. One could suggest the same today about smartphones - now the innovation comes from everything else that happens around them.

A curated list of awesome Deep Learning tutorials, projects and communities.

The child AI is really only capable of a specific task – image recognition. Using its AutoML AI, Google’s AI-building AI created its child AI using a technique called reinforcement learning. This works just like machine learning, except it’s entirely automated where AutoML acts as the neural network for its task-driven AI child.

In May, J.P. Morgan’s quantitative investing and derivatives strategy team, issued the most comprehensive report ever on big data and machine learning in financial services.
Titled, ‘Big Data and AI Strategies’ and subheaded, ‘Machine Learning and Alternative Data Approach to Investing’, the report says that machine learning will become crucial to the future functioning of markets. Analysts, portfolio managers, traders and chief investment officers all need to become familiar with machine learning techniques. If they don’t they’ll be left behind: traditional data sources like quarterly earnings and GDP figures will become increasingly irrelevant as managers using newer datasets and methods will be able to predict them in advance and to trade ahead of their release.

Far from being incomprehensible, the principles behind neural networks are surprisingly simple. Here's a gentle walk through how to use deep learning to categorize images from a very simple camera.

The AI Index is an open, not-for-profit project to track activity and progress in AI. It aspires to be a comprehensive resource of data and analysis for policymakers, researchers, executives, journalists and others to rapidly develop intuitions about the complex field of AI.
AI is the new electricity, and is transforming multiple industries. The AI Index will help current generations track and navigate this societal transformations. It will also help future generations look back and understand the AI's rise.

This guide is for anyone who is curious about machine learning but has no idea where to start. I imagine there are a lot of people who tried reading the wikipedia article, got frustrated and gave up wishing someone would just give them a high-level explanation. That’s what this is. The goal is be accessible to anyone — which means that there’s a lot of generalizations. But who cares? If this gets anyone more interested in ML, then mission accomplished.

What AI can — and cannot — do for your organization.

Google cloud vision API allows developers to analyze the content of images.

Find out how you can make use of Google's machine learning expertise to power your applications. Google Cloud Platform (GCP) offers five APIs that provide access to pre-trained machine learning models with a single API call: Google Cloud Vision API, Cloud Speech API, Cloud Natural Language API, Cloud Translation API and Cloud Video API. Using these APIs, you can focus on adding new features to your app rather than building and training your own custom models. In this session we'll share an overview of each API and dive into code with some live demos.

The AlphaGo Zero agent destroyed the original AlphaGo that beat Lee Sedol 100-0 after just three days of training.

Creating and maintaining a platform for reliably producing and deploying machine learning models requires careful orchestration of many components—-a learner for generating models based on training data, modules for analyzing and validating both data as well as models, and finally infrastructure for serving models in production. This becomes particularly challenging when data changes over time and fresh models need to be produced continuously. Unfortunately, such orchestration is often done ad hoc using glue code and custom scripts developed by individual teams for specific use cases, leading to duplicated effort and fragile systems with high technical debt. We present the anatomy of a general-purpose machine learning platform and one implementation of such a platform at Google. By integrating the aforementioned components into one platform, we were able to standardize the components, simplify the platform configuration, and reduce the time to production from the order of months to weeks, while providing platform stability that minimizes service disruptions. We present the case study of one deployment of the platform in the Google Play app store, where the machine learning models are refreshed continuously as new data arrive. Deploying the platform led to reduced custom code, faster experiment cycles, and a 2% increase in app installs resulting from improved data and model analysis.

A new idea is helping to explain the puzzling success of today’s artificial-intelligence algorithms — and might also explain how human brains learn.

Explore TensorFlow Playground demos. See how they explain the mechanism and power of neural networks, which extract hidden insights and complex patterns.

Instacart is saving minutes per delivery by sorting shopping lists using deep learning. Emojis help to define the problem and outline both a simple and a more complex deep learning architecture.

Teachable Machine, an experiment that makes it easier for anyone to explore machine learning. Teach a machine using your camera – live in the browser, no coding required.

Tech giants including Baidu and Google spent between $20B to $30B on AI in 2016, with 90% of this spent on R&D and deployment, and 10% on AI acquisitions. AI investment has turned into a race for patents and intellectual property (IP) among the world’s leading tech companies.

ARK Invest aims to identify large-scale investment opportunities resulting from technological change. We believe innovation is key to growth. From a broad spectrum of disruptive innovations, Big Ideas represents our annual breakout of technologies that we believe will accelerate significantly in the months ahead.

In just its first year, TensorFlow has helped researchers, engineers, artists, students, and many others make progress with everything from language translation to early detection of skin cancer and preventing blindness in diabetics. We're excited to see people using TensorFlow in over 6000 open-source repositories online.

Second-generation Tensor Processing Units (TPUs) are coming to Google Cloud to accelerate a wide range of machine learning workloads, including both training and inference. We call them Cloud TPUs, and they will initially be available via Google Compute Engine.

https://blog.google/topics/google-cloud/google-cloud-offer-tpus-machine-learning/ 

How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid obstacles? One approach is to use a small dataset collected by human experts: however, high capacity learning algorithms tend to overfit when trained with little data. An alternative is to use simulation. But the gap between simulation and real world remains large especially for perception problems. The reason most research avoids using large-scale real data is the fear of crashes! In this paper, we propose to bite the bullet and collect a dataset of crashes itself! We build a drone whose sole purpose is to crash into objects: it samples naive trajectories and crashes into random objects. We crash our drone 11,500 times to create one of the biggest UAV crash dataset. This dataset captures the different ways in which a UAV can crash. We use all this negative flying data in conjunction with positive data sampled from the same trajectories to learn a simple yet powerful policy for UAV navigation. We show that this simple self-supervised model is quite effective in navigating the UAV even in extremely cluttered environments with dynamic obstacles including humans. For supplementary video see: https://youtu.be/u151hJaGKUo 

An oil refinery is an industrial cathedral, a place of power, drama and dark recesses: ornate cracking towers its gothic pinnacles, flaring gas its stained glass, the stench of hydrocarbons its heady incense. Data centres, in contrast, offer a less obvious spectacle: windowless grey buildings that boast no height or ornament, they seem to stretch to infinity.

Also read: http://www.economist.com/news/leaders/21721656-data-economy-demands-new-approach-antitrust-rules-worlds-most-valuable-resource 

Have you looked at Google Trends? It’s pretty cool — you enter some keywords and see how Google Searches of that term vary through time. I thought — hey, I happen to have this arxiv-sanity database of 28,303 (arxiv) Machine Learning papers over the last 5 years, so why not do something similar and take a look at how Machine Learning research has evolved over the last 5 years?

Thanks to machine learning, it’s becoming easy to generate realistic video, and to impersonate someone.

Systems able to recognize sounds familiar to human listeners have a wide range of applications, from adding sound effect information to automatic video captions, to potentially allowing you to search videos for specific audio events. Building Deep Learning systems to do this relies heavily on both a large quantity of computing (often from highly parallel GPUs), and also – and perhaps more importantly – on significant amounts of accurately-labeled training data. However, research in environmental sound recognition is limited by currently available public datasets.
In order to address this, we recently released AudioSet, a collection of over 2 million ten-second YouTube excerpts labeled with a vocabulary of 527 sound event categories, with at least 100 examples for each category.