Photo credit National Geographic Magazine, November 2016.
Earlier this week I blogged about 5 simple questions you can ask to determine AI hype.
Today I saw something with more hype than I would expect from a respected tech company. “eBay determines this price through a machine learned model of the product’s sales prices within the last 90 days.”
In my opinion this price prediction is not machine learning. It’s just math. It’s not a machine learned anything. It fails 1-5 of Stephen’s tests.
As Machine Learning captures our imagination, it’s important to separate the material from the hype. Here are 5 simple questions you should ask to help reduce AI hype:
- “How much training data is required?”
- “Can this work unsupervised (= without labelling the examples)?”
- “Can the system predict out of vocabulary names?” (i.e. Imagine if I said “My friend Rudinyard was mean to me” – many AI systems would never be able to answer “Who was mean to me?” as Rudinyard is out of its vocabulary)
- “How much does the accuracy fall as the input story gets longer?”
- “How stable is the model’s performance over time?”
source: Stephen Merity of Salesforce
My commentary on Stephen’s article. Stephen is placing the bar for ML high. AFAIK, Google DeepMind’s AlphaGo fulfills these five requirements, but Google Photos does not, since Google photo uses human crowdsourced labeling of what a cat is to determine cat videos. The fifth requirement, “How stable is the model’s performance over time?”, seems to be the least interesting and the most interesting at the same time. It’s the least interesting requirement because the evolution of a cat will indeed eventually change the way the cat looks and untrain even the most sophisticated ML model, unless the model adapts as the cat evolves. It’s the most interesting requirement because I can imagine how a model would fail to understand meme’s such as “Bye Felicia”, “On Fleek”, “idk my BFF Jill” or another human-created that seems like gibberish to machines or people not in the know.
Skip to 3:01 to learn how the automobile differential allows a vehicle to turn a corner while keeping the wheels from skidding. It’s brilliant product marketing, using language and concepts to conceptualize the product features into something that everyone understands and wants to buy.
Here’s the recorded video of a 5-minute talk I gave at Battery San Francisco about Fix Maps. The talk format is called “Ignite” which calls for 20 slides that auto-advance every 15 seconds.
Here’s the slides from the talk.
Snow caves are fun to build and provide warm places to sleep and take shelter from a storm. This is a brief how-to guide to building a snow cave.
The snow cave in this example was built by 4 people near Skinner Hut at the edge of the timberline in late-December 2015 at 11,620 feet. Builders were Brett Poulin, Chris, Nick, and me, Neal Mueller. The cave we built was large enough to sleep and provide eating quarters and shelter for 4 people. It included a vapor escape for cooking.
Step 1. Find snow drift, not cornice. We found our snow drift nearby Skinner Hut at 11,620 feet in Colorado. It had a gorgeous view and was large enough for the snow cave.
Step 2. Use shovel or hoe to excavate snow cave. It helps to have just one-person inside and a team outside to ferry loads of snow away from the entrance. TIME: Our snow cave took 4 athletic people about 2 hours to excavate.
Step 3. Use snow saw to create snow cave benches or sleeping bunks, save blocks. Keep the bunks above the height of the door entrance, or allow for a heat pocket. Heat rises. TIME: Our snow cave took 4 athletic people about 1 hour to deepen.
Step 4. Use snow saw to raise ceiling height of snow cave, save blocks. TIME: Our snow cave took 4 athletic people about 1 hour to raise the ceiling.
Step 5. Line and narrow snow cave entrance with sawed blocks. TIME: Our snow cave took 4 athletic people about 1 hour to narrow the entrance and finalize.
That’s it. You’re done. Now you can use snow cave for shelter or fun. Below are photos of our snow cave as it was excavated. Remember to keep a shovel inside the cave, in case you get snowed in.
The snow cave pictured above was my second snow cave.
My first snow cave was at 15,000 feet just above the head-wall on Mt. Denali with Mike Wood, Jed Workman, and Evan Howe of AMS. Here’s a picture of that snow cave. You can see me in the far back, second from the left.
Here is a snow cave diagram (book source).
Have fun. Stay warm.
This guy analyzed 250 SaaS pricing pages — here’s what he found:
- The average number of packages is three and a half
- 50% highlight a package as the best option
- 69% of companies sell the benefits
- 81 percent organize prices low to high
- 38 percent list their most expensive package as ‘Contact us’
- The most common call to action is ‘Buy Now’
- 36 percent don’t use a contrasting CTA color
- 63 percent offer a free trial
- 4% of companies offer pricing on a sliding scale
- 81 percent of packages are named
- 6% show a money back guarantee on-page
Read the full report.
Best definition I’ve seen of growing products on the web.
A growth hacker is someone who has thrown out the traditional marketing playbook and replaced it with only what is testable, trackable, scalable…while their marketing brethren track vague notions like branding and mindshare, growth hackers relentlessly pursue users and growth (source: Ryan Holiday).