Deep Learning

Deep learning has its origins in the early days of artificial intelligence, when researchers began to explore the use of artificial neural networks to learn from data. However, it wasn't until the early 2000s that deep learning began to gain popularity as a field of study. This was due in part to the development of new algorithms that made it possible to train deep neural networks on large datasets. Additionally, the availability of high-performance computing resources made it possible to train deep neural networks in a reasonable amount of time.

In 2012, Geoffrey Hinton and his team at the University of Toronto used deep learning to achieve a breakthrough in image recognition. Their algorithm, called AlexNet, won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) by a significant margin. This victory helped to spark a renewed interest in deep learning, and the field has since exploded in popularity.

Today, deep learning is used in a wide variety of applications, including image recognition, natural language processing, speech recognition, and machine translation. Deep learning is also being used to develop new drugs, create self-driving cars, and improve the accuracy of weather forecasts.

As deep learning continues to develop, it is likely to have a major impact on a wide range of industries. It is already being used to solve some of the world's most challenging problems, and it is only going to become more powerful in the years to come.

The Age of Automation

I have mixed feelings about automation. The word even appears in my job title from time to time. The myth about automation is that somehow doing so will allow us to have more time to do other things. I've encountered many scenarios where a consultant or subject matter expert is brought into a workplace situation to help a company build out its marketing automation, only to not retain that talent for the long term.

But, this post isn't about marketing automation or my aforementioned rant about companies that fail to use it to build 1-to-1 relationships with their customers. Instead, I'd like to point out the concerns addressing automation's impact on the US trucking industry.

Hello Alexa

Not sure how long ago this feature was added, but it looks like someone just replaced the default microphone app with Alexa's voice and mannerisms on the Amazon app. You'd think that if you were accessing Alexa from within Amazon's shopping app, that the default search would be for items listed in Amazon's eCommerce ecosystem. Sadly, this is not the case.

Screenshot of Alexa's Intro Screen on Amazon App

My first query: "weather tracking for the home", followed by "weather tracking apps"

I don't like Alexa's color bar acknowledgement followed by its electronic beep. For the few seconds it takes to execute these robotic response commands, it is an unnecessary feature. Alexa responds by verbally giving me the weather forecast for Salem Oregon.

The response is puzzling because I was just adding/removing items from my wish lists in the app which one could assume that I am already logged into my account which has my mailing address in it (and I don't live in Oregon). Even if location services were turned on for this app, surely the developers would have programmed that into Alexa -- to be able to give regional information based on already known criteria.

My next query: "search Amazon for home weather tracking"

That brought up a relevant search list on Amazon's store.

Artificial Intelligence is only as good as the team that builds it.

I can just visualize the disconnect between the business user story and what got implemented by the development team. Maybe I'm just disappointed because I'm so used to Google search providing accurate, relevant results from text or voice queries.

At least Alexa can tell jokes (Siri cannot):

"Tell me a funny cat joke"

Alexa: What does a cat say when it gets hurt? Me-ow.