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Tyler Renelle is a contractor and developer who has worked in various web technologies like Node, Angular, Rails, and much more. He's also build machine learning backends in Python (Flask), Tensorflow, and Neural Networks.

The JavaScript Jabber panel dives into Machine Learning with Tyler Renelle. Specifically, they go into what is emerging in machine learning and artificial intelligence and what that means for programmers and programming jobs.

This episode dives into:

• Whether machine learning will replace programming jobs
• Economic automation
• Which platforms and languages to use to get into machine learning
• and much, much more...

Links:

• Raspberry Pi
• Arduino
• Hacker News
• Neural Networks (wikipedia)
• Deep Mind
• Shallow Algorithms
• Genetic Algorithms
• Crisper gene editing
• Wix
• thegrid.io
• Codeschool
• Codecademy
• Tensorflow
• Keras
• Machine Learning Guide
• Andrew Ng Coursera Course
• Python
• R
• Java
• Torch
• PyTorch
• Caffe
• Scikit learn
• Tensorfire
• DeepLearn.js
• The Singularity is Near by Ray Kurzweil
• Tensorforce
• Super Intelligence by Nick Bostrom

Picks:

Aimee

• Include media
• Nodevember
• Phone cases

AJ

• Data Skeptic
• Ready Player One

Joe

• Everybody Lies

Tyler

• Ex Machina
• Philosophy of Mind: Brains, Consciousness, and Thinking Machines

Gant Laborde is the Chief Innovation Officer of Infinite Red who is working on a course for beginners on machine learning. There is a lot of gatekeeping with machine learning, and this attitude that only people with PhDs should touch it. In spite of this, Gant thinks that in the next 5 years everyone will be using machine learning, and that it will be pioneered by web developers. One of the strong points of the web is experimentation, and Gant contrasts this to the academic approach. 

They conversation turns to Gant’s course on machine learning and how it is structured. He stresses the importance of understanding unicode, assembly, and other higher concepts. In his course he gives you the resources to go deeper and talks about libraries and frameworks available that can get you started right away. His first lesson is a splashdown into the jargon of machine learning, which he maps over into developer terms. After a little JavaScript kung fu, he takes some tools that are already out there and converts it into a website.

Chris and Gant discuss some different uses for machine learning and how it can improve development. One of the biggest applications they see is to train the computers to figure monotonous tasks out while the human beings focus on other projects, such as watching security camera footage and identifying images. Gant restates his belief that in the next 5 years, AI will be everywhere. People will grab the boring things first, then they will go for the exciting things. Gant talks about his creation NSFW.js, an open source train model to help you catch indecent content. He and Chris discuss different applications for this technology.

Next, the panel discusses where machine learning can be seen in everyday life, especially in big companies such as Google. They cite completing your sentences in an email for you as an example of machine learning. They talk about the ethics of machine learning, especially concerning security and personal data. They anticipate that the next problem is edge devices for AI, and this is where JavaScript really comes in, because security and privacy concerns require a developer mindset. They also believe that personal assistant devices, like those from Amazon and Google, will become even more personal through machine learning. They talk about some of the ways that personal assistant devices will improve through machine learning, such as recognizing your voice or understanding your accent. 

Their next topic of discussion is authenticity, and how computers are actually incredibly good at finding deep fakes. They discuss the practice of placing passed away people into movies as one of the applications of machine learning, and the ethics surrounding that. Since developers tend to be worried about inclusions, ethics, and the implications of things, Gant believes that these are the people he wants to have control over what AI is going to do to help build a more conscious data set. 

The show concludes with Gant talking about the resources to help you get started with machine learning. He is a panelist on upcoming DevChat show, Adventures in Machine Learning. He has worked with people with all kinds of skill sets and has found that it doesn’t matter how much you know, it matters how interested and passionate you are about learning. If you’re willing to put the pedal to the metal for at least a month, you can come out with a basic understanding. Chris and Gant talk about Tensorflow, which helps you take care of machine learning at a higher level for fast operations without calculus. Gant is working on putting together a course on Tensorflow. If you’re interested in machine learning, go to academy.infinite.red to sign up for Gant’s course. He also announces that they will be having a sale on Black Friday and Cyber Monday.

Panelists

• Christopher Buecheler

With special guest: Gant Laborde

Sponsors

• React Round Up

• Sentry use the code “devchat” for 2 months free on Sentry’s small plan

• Adventures in Angular

Links

• Machine Learning: How To go from Zero to Hero

• NSFW.js

• Tensorflow.js

• PyTorch

• Keras

• Academy.infinite.red

• Gantlaborde.com 

Follow DevChatTV on Facebook and Twitter

Picks

Christopher Buecheler:

• Next.js

• Big Wreck, But For The Sun

Gant Laborde: 

• Nicornot.com

Free 5 day mini course on academy.infinite.red

Vitale, Joe, 1953-

Scherer, Manual

Hayden Library - TP248.25.M645 Z63 2018

Online Resource

Lately I've been using variables to plan out pure CSS timelines for complex animations. I built an SVG and CSS Rube Goldberg machine to put this technique to the test!

Manu Bhaker was sure in her mind that the Olympics will take place next year despite the uncertainty in the world of sports due to the COVID-19 pandemic.

Dewey Library - UG1242.D7 C435 2020

Rotch Library - Z251.A6 N46 2017