Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
Video – How to Use AI Ethically from Natalia Modjeska of Omdia
 Event: Machine Learning Week 2021 Keynote: How to Use AI...
Video – Alexa On The Edge – A Case Study in Customer-Obsessed Research from Susanj of Amazon
 Event: Machine Learning Week 2021 Keynote: Alexa On The Edge...
Why AI Isn’t Going to Replace Data Scientists Any Time Soon
 Should data scientists consider AI a threat to their...
“Doing AI” Is a Mistake that Detracts from Real Problem-Solving
  A note from Executive Editor Eric Siegel: Richard...

Deep Learning

The First Rule of Machine Learning: Start without Machine Learning

 Originally published in Eugeneyan. Applying machine learning effectively is tricky. You need data. You need a robust pipeline to support your data flows. And most of all, you need high-quality labels. As a result, most of the time, my first iteration doesn’t involve machine learning at all. Wait—start without machine learning? I’m not alone in saying this.

Microsoft Predicts Weather for Individual Farms

 Originally published in IEEE Spectrum, Oct 4, 2021. Imagine you’re a farmer in the northern United States. It’s early spring, and nighttime temperatures are just starting to rise above freezing. You need to fertilize your newly-planted crops,...

Deep Learning’s Diminishing Returns

 Originally published in IEEE Spectrum, Sept 24, 2021. Deep Learning is now being used to translate between languages, predict how proteins fold, analyze medical scans, and play games as complex as Go, to name just a few applications of a technique...

How Can We Fix the Data Science Talent Shortage?

 Originally published in Springboard Blog, Jan 22, 2021. Data science might just be the most buzzed-about job in tech right now, but its pop culture sheen conceals some of the harsh realities of being a fresh graduate in...

Gradient Descent Models Are Kernel Machines (Deep Learning)

 Originally published in infoproc.blogspot.com, Feb 7, 2021. This paper shows that models which result from gradient descent training (e.g., deep neural nets) can be expressed as a weighted sum of similarity functions (kernels) which measure the similarity...

The Computational Limits of Deep Learning Are Closer Than You Think

 Originally posted to DiscoverMagazine, July 24, 2020. Deep learning eats so much power that even small advances will be unfeasible give the massive environmental damage they will wreak, say computer scientists. Deep in the bowels of the...

Inside TikTok’s Killer Algorithm

 TikTok Wednesday revealed some of the elusive workings of the prized algorithm that keeps hundreds of millions of users worldwide hooked on the viral video app. Why it matters: The code TikTok uses to pick your next...

Traffic Prediction With Advanced Graph Neural Networks

  By partnering with Google, DeepMind is able to bring the benefits of AI to billions of people all over the world.  From reuniting a speech-impaired user with his original voice, to helping users discover personalised apps,...

Can GPT-3 Make Analogies?

 Originally published in Medium, Aug 5, 2020. In the early 1980s, Douglas Hofstadter introduced the “Copycat” letter-string domain for analogy-making. Here are some sample analogy problems: If the string abc changes to the string abd, what does...

Dealing with Overconfidence in Neural Networks: Bayesian Approach

 Originally published in Jonathan Ramkissoon Blog, July 29, 2020. I trained a multi-class classifier on images of cats, dogs and wild animals and passed an image of myself, it’s 98% confident I’m a dog. The problem isn’t...

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