10/16/19 • 22 min
We show how the normal software development cycle does not work with AI and how the modified dev model needs attention from UX at every step
Music: The Pirate And The Dancer by Rolemusic
TranscriptsHere is the scenario for this episode: The boss gives you access to the companies data and asks you to come up with a model that uses it. With all this data it’s got to be good for making something the users will use right? You buckle down, work with data scientists and make a lot of tweaks to the data come up with something, but no matter how much you advertise it no one wants to use it. Back to the drawing board. This time you find out what the users do want, more tweaks to the data and get a model that is accurate. People love it, tons of users flood in and flood the server. The servers crash from too large of a model. The IT guys say they can fix it and bring in a bunch of new hardware. It all seems to be going fine until you notice every review of your app laughs at how inaccurate it is. This can’t be, it’s the same model, just running on different hardware, right? Lets make sure this doesn’t ever happen. Today we are covering the development cycle for AI This podcast is called design for AI It is here to help define the space where Machine learning intersects with UX. Where we talk to experts and discuss topics around designing a better AI. music is by Rolemusic Im your host Mark Bailey Lets get started music Machine learning up to this point has been more on the research side. So much so that it really doesn’t fit in to the normal software development cycle. There are all these gotchas that won’t let you fit into the normal cyclic agile sprints that most people are used to. This affects getting in good design. A big part of UX design not slowing down the software development cycle is to have a regular process so UX can run in parallel to development. It is possible with machine learning development, the cycle just looks a little different. The normal software development process is building a machine. It’s a really complicated machine, but in development terms it is still stateful, so development is done to by writing to the test case. For the updated process, instead of a machine, think of it like you are hiring an employee. There are 5 stages to hiring an employee.
- This is laying the groundwork
- lay out the job listing – what are the requirements?
- Find Objectives, why are you hiring them?
- Job posting
- What is the purpose & design
- Set your goals
- Define benchmarks
- Build On Expertise
- Collect representative data
- Build the model
- Data scientists train the model
- Train – The model is watching how you do things
- Reinforce Education
- Subject matter experts train the model
- Shadow – You are standing over their shoulder.
- Build Trust
- AI leads task
- Subject matter expert manages AI
- Set your goal
- Define benchmarks
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