We’re right in the thick of baseball season here in the US, which makes this video on ML particularly timely. Baseball is known for it’s sometimes ridiculous hand signals to relay messages to players in the field. Can AI decode these signals? Spoiler alert: yes.

You have to wonder how long it will be before this technology will be deployed at the major league level, if it hasn’t been already.

Federated Learning (FL) is a distributed approach to machine learning that enables training on a large corpus of decentralized data residing on devices like mobile phones.  FL employs the approach of “bringing the code to the data, instead of the data to the code.” Additionally, it addresses the fundamental problems of privacy, ownership, and locality of data.

Here’s a more in depth look at the approach.

There’s a good high-level overview of federated learning on Google’s AI blog. Devices download the current model, improve it by learning using data local to the phone, and then send a small focused model update back to the cloud, where it is averaged with other user updates to improve the shared model. No individual updates are stored in the cloud, and no training data leaves the device.

Machine learning is no longer just for data science whiz kids. Now, front end developers need to have a basic handle on this technology. Here’s a great talk by Charlie Gerard on “Practical Machine Learning for Front End Developers.”

From the abstract:

Machine learning can have some pretty complicated concepts to grasp if you’re not a data scientist. However, recent developments in tooling make it more and more accessible for developers and people with little or no experience. One of these advancements is the ability to now train and run machine learning algorithms and models in the browser, opening this world to front-end developers to learn and experiment. In this presentation, we will talk about the different applications, possibilities, tools and resources, as well as show a few examples and demos, so you can get started building your own experiments using machine learning in JavaScript.

Here’s a great explanation of Reinforcement Learning, AlphaGo Zero, and how it compares to other forms of machine learning.

For example, AlphaGo, in order to learn to play (the action) the game of Go (the environment), first learned to mimic human Go players from a large data set of historical games (apprentice learning). It then improved its play through trial and error (reinforcement learning), by playing large numbers of Go games against independent instances of itself.

Here’s a great overview of the top AI tools and essential machine learning libraries.

Nonetheless, AI has facilitated the rapid transformation of the technological sphere. Several revolutionary AI technologies have cemented its position as the most trending technology of this century; for instance, Keras, Torch, Caffe, TensorFlow, Theano, and Microsoft Cognitive Toolkit, among others.

Python has conquered the machine learning and AI world. Here’s an interesting article from Analytics India Magazine about why Python is on top.

According to the Stack Overflow Survey 2018, Python is the most wanted language for the second year in a row, which means it is the language that developers who do not yet use it most often say they want to learn. It is also claimed to be the fastest-growing major programming language. Developers and pioneers around the globe are implementing this language for machine learning projects.

Here’s an interesting look at what could be the next frontier of machine learning.

What is Meta-Learning? In traditional Machine Learning domains, we usually take a huge dataset which is specific to a particular task and wish to train a model for regression/classification purposes using this dataset. That’s radically far from how humans take advantage of their past experiences to learn very quickly […]

This talk from io19 is for people who know how to code, but who don’t necessarily know machine learning.

Watch this video to learn the ‘new’ paradigm of machine learning, and how models are an alternative implementation for some logic scenarios, as opposed to writing if/then rules and other code.

Here’s a great review of the most common machine learning algorithms by InfoWorld.

Recall that machine learning is a class of methods for automatically creating predictive models from data. Machine learning algorithms are the engines of machine learning, meaning it is the algorithms that turn a data set into a model. Which kind of algorithm works best (supervised, unsupervised, classification, regression, etc.) depends on the kind of problem you’re solving, the computing resources available, and the nature of the data.