Startup Weekend Vancouver happened at the start of June. People came out from all across the west coast to attend, as far north as Vancouver, BC and as far south as San Luis Obispo, CA. I'd like to say that it was a blast and fun to put on this event but it wasn't. Like most non-profit events, we had a lot of moving parts and at one point had setup a GoFundMe page because we thought we had more expenses than actual revenue from sponsors and ticket sales. Fortunately we were in the black with some budget money leftover for future events.
Hello Alexa, part 2
It's been a while since I last posted about Amazon's Alexa being accessible through the Amazon retail shopping app. And while Amazon's generic web search is fine and comes nowhere near the voice-to-search recognition that Google search offers, Amazon is missing the point about monetizing the index system that they have for the millions of products listed on their shopping exchange.
Wouldn't it be better if instead of matching to keywords (mostly nouns) in a user's speech search, that Amazon served up relevant recommendations instead.
Say for example, you ask Alexa (in the Amazon app):
"recommended wines for dinner" or
"recommended red wines" or
"recommended fruity wines"
Alexa currently offers no recommended product searches for any of the wines or wineries that sell on Amazon. Well, it certainly can't recommend wines that's for sure. But it could if Amazon incorporated product label text, certified wine reviews, or wine manufacturer descriptions in what can be searched. The words "recommend" and "recommended" are not in Alexa's lexicon of search knowledge. Perhaps this is too advanced a concept for Amazon's AI.
You can still just say "red wine" or "white wine" and those options will show up with valid results in the Amazon app.
Voice searching the Amazon product engine should be no different than typing in the search query.
The results are mixed, however.
You can say "services for window washing near me" and Amazon's app will show for "Hire a Window Cleaner" (Amazon Home Services) as the top result. That's spot on. The third result (same screen on a smartphone) shows "Window Cleaning" (Amazon Home Services), also a valid result to what I was voice searching for.
Maybe this is a phased rollout for voice search queries.
Wouldn't it be better if instead of matching to keywords (mostly nouns) in a user's speech search, that Amazon served up relevant recommendations instead.
Say for example, you ask Alexa (in the Amazon app):
"recommended wines for dinner" or
"recommended red wines" or
"recommended fruity wines"
Alexa currently offers no recommended product searches for any of the wines or wineries that sell on Amazon. Well, it certainly can't recommend wines that's for sure. But it could if Amazon incorporated product label text, certified wine reviews, or wine manufacturer descriptions in what can be searched. The words "recommend" and "recommended" are not in Alexa's lexicon of search knowledge. Perhaps this is too advanced a concept for Amazon's AI.
You can still just say "red wine" or "white wine" and those options will show up with valid results in the Amazon app.
Voice searching the Amazon product engine should be no different than typing in the search query.
The results are mixed, however.
You can say "services for window washing near me" and Amazon's app will show for "Hire a Window Cleaner" (Amazon Home Services) as the top result. That's spot on. The third result (same screen on a smartphone) shows "Window Cleaning" (Amazon Home Services), also a valid result to what I was voice searching for.
Maybe this is a phased rollout for voice search queries.
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.
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.
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.
 |
| 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.
Big Bird says "So long" to Big Brother
The Children's Television Workshop lost its government funding in 1981; but was supplemented by grants.
In 1998, the producers of Sesame Street, Sesame Workshop (formerly Children's Television Workshop) opened their brand to corporate sponsorships and product licensing. By 2004, 68% of their annual revenue came from this. Their TV episodes are syndicated worldwide and have been running for 48 seasons so far. If you think that this long running childrens' media brand will be hurt by government cutting of public broadcast funding, sadly you are mistaken about how large this franchise really is.
In 2005, their annual revenue grew 4% to $96m and that was due to international licensing.
Sesame Street on HBO is why you won't find the show running on Netflix or Amazon Prime.
Non-profit status does not mean you don't rake in millions of dollars in revenues; it simply means how you run the organization and its tax liability is different than a for-profit one.
I can see the financial rationale behind cutting government support of "public" broadcasting, as purely a budgetary numbers game.
Sesame Workshop has both non-profit and for-profit subsidiaries. It has built a very successful business model from an initial $8 million investment (this is how Sesame Street got created) to the more than $120m in assets the production company owns today. It is likely that Sesame Workshop will be unaffected by any measure of government cuts to public broadcasting.
Did you know that in the City of Portland, Oregon, there's an arts tax and funding comes out of the pockets of its adult residents earning more than $1,000 per year. The city tax benefits everyone who visits Portland's downtown area with all the public art displays.
Read more:
Sesame Workshop financials
Public Broadcasting Act of 1967
Children's Television Workshop Origins
Trump's Proposed Cuts of Funding For Arts, Humanities And Public Media
In 1998, the producers of Sesame Street, Sesame Workshop (formerly Children's Television Workshop) opened their brand to corporate sponsorships and product licensing. By 2004, 68% of their annual revenue came from this. Their TV episodes are syndicated worldwide and have been running for 48 seasons so far. If you think that this long running childrens' media brand will be hurt by government cutting of public broadcast funding, sadly you are mistaken about how large this franchise really is.
In 2005, their annual revenue grew 4% to $96m and that was due to international licensing.
Sesame Street on HBO is why you won't find the show running on Netflix or Amazon Prime.
 |
| Sesame Workshop: Funding by Source, 2015 |
Non-profit status does not mean you don't rake in millions of dollars in revenues; it simply means how you run the organization and its tax liability is different than a for-profit one.
I can see the financial rationale behind cutting government support of "public" broadcasting, as purely a budgetary numbers game.
Sesame Workshop has both non-profit and for-profit subsidiaries. It has built a very successful business model from an initial $8 million investment (this is how Sesame Street got created) to the more than $120m in assets the production company owns today. It is likely that Sesame Workshop will be unaffected by any measure of government cuts to public broadcasting.
Did you know that in the City of Portland, Oregon, there's an arts tax and funding comes out of the pockets of its adult residents earning more than $1,000 per year. The city tax benefits everyone who visits Portland's downtown area with all the public art displays.
Read more:
Sesame Workshop financials
Public Broadcasting Act of 1967
Children's Television Workshop Origins
Trump's Proposed Cuts of Funding For Arts, Humanities And Public Media
A public transit commuter's podcast recommendations
Five years ago I acquiesced and got a smartphone, upgrading from nearly a decade of using an old flip phone whose only purpose was to send and receive calls. Anyhow, fast forward to today. The smartphone is also a media player, calendar, ebook/RSS reader, fitness logger, camera, video recorder, stopwatch, etc.
On a typical commute into downtown Portland, an activity that typically eats up to two hours per day, I could catch up on some Z's, listen to music, something else relaxing, or learn something new via podcasts. Apple's Podcast app is one of the best default installed apps I have used.
I get burned out on genres and I almost never listen to the same podcast episode twice, unless I happened to have fallen asleep and forgot what happened in the podcast. These are sorted in order of what you should listen to first before the others. Not everyone that I admire from their non-radio works is good at both podcast scripting for audio listeners and are a good podcast host. These are just suggestions of what I think make for pretty good commuter listening.
Non-fiction
Short podcasts (<30 minutes per episode):
Longer podcasts (>30 minutes per episode):
Short podcasts (<30 minutes per episode):
Longer podcasts (>30 minutes per episode):
On a typical commute into downtown Portland, an activity that typically eats up to two hours per day, I could catch up on some Z's, listen to music, something else relaxing, or learn something new via podcasts. Apple's Podcast app is one of the best default installed apps I have used.
I get burned out on genres and I almost never listen to the same podcast episode twice, unless I happened to have fallen asleep and forgot what happened in the podcast. These are sorted in order of what you should listen to first before the others. Not everyone that I admire from their non-radio works is good at both podcast scripting for audio listeners and are a good podcast host. These are just suggestions of what I think make for pretty good commuter listening.
Non-fiction
Short podcasts (<30 minutes per episode):
- Planet Money
Longer podcasts (>30 minutes per episode):
- Freakonomics Radio
- How I Built This
- This American Life
- Agent Marketing Syndicate (for real estate but mostly business)
- The Tim Ferriss Show (mostly business and life-hacking topics)
- The Art of Manliness (really funny if you are a woman listening to this)
- The Moth
- S-Town (crime investigative reporting)
- Crimetown (crime investigative reporting)
Fiction / Fantasy / SciFi
Short podcasts (<30 minutes per episode):
- MarsCorp
- The Bright Sessions
- Lore (by Aaron Mahnke)
- Darkest Night
- The Deep Vault
- Welcome to Night Vale
- Terms
- Star Trek: Lost Enterprise
Longer podcasts (>30 minutes per episode):
- Star Trek: Outpost
- The Penumbra Podcast
- The Leviathan Chronicles
- The Cleansed
- Archive 81
- The Dark Tome
- The Bunker
- Drabblecast Audio Fiction
- Clarksworld Magazine
- Lightspeed Magazine
- The Black Tapes / Tanis / Rabbits
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