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?”
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 a brilliant product break-down using language and concepts to conceptualize the product features into something that everyone understands and wants to buy.
Want to know how to spell “Red Bull gives you wiiings.” in hexadecimal? Check out these two red bull advertisement in silicon valley. Here’s a hex to text converter to see for yourself. It’s not a recruiting ad, because this is the only job they have in SF. Must be just advertising to thirsty programmers. I love it.
Video of internal 1997 meeting 2-months after Jobs returned to Apple. Sets the vision as something other than speeds and feeds. Launches the brand for the next 15-years, which you can see even today. The 1997 ‘Think Different’ billboards are reminiscent of the 2015 ‘Shot on Iphone 6’ billboards.
Worth the 16 minutes if you’re interested in product strategy.
Excerpt from Y-Combinator article on ideas for new startups. [source: YCombinator Blog]
Enterprise Software – Software used by large companies is still awful and still very lucrative.
Category-defining enterprise software companies will emerge to solve problems for every vertical, every business size, and every job function. Here are 3 specific areas we think are particularly interesting:
Making The Expensive Cheap: Because of the cost of traditional enterprise software, many categories of solutions were previously cost prohibitive for small or even medium sized businesses to benefit from.
The Next Billion Workers: Traditionally office-based knowledge workers have been the users of enterprise software. Mobile phones and tablets turn every type of employee – from the retail store associate to the field services team – into a knowledge worker.
Digitizing Every Industry: Every industry is going through some form of information-based disruption; this is causing businesses to modernize their practices, leveraging new data, accelerating key processes, and delivering digitally-enabled experiences in the process.
We have our own domain, industrial design CAD, point-of-sale packaging CAD, logo, trademark, patent-pending, working prototypes, and are currently under due diligence by some of the largest watch and fitness companies in the world. We *even* broke down and created a Facebook Page. Now you can “like” us.
Lapview is a sensor that counts laps for swimmers. In the future it will communicate with a watch to count your laps.