I recently retweeted an interesting article in the Economist that talks about Otto's incorporation of AI into its core business and how they allow AI models to automatically order product in anticipation of demand:
It's an interesting piece, and I recommend everyone pop over to the economist to read it. This type of AI deployment is an autonomous form of what my colleague at Dell EMC, Bill Schmarzo, calls Prescriptive Analytics:
However, instead of merely suggesting the proper course of action (deciding which products to order in advance), Otto has decided that for its purposes, the model they have deployed is accurate enough to forego the final human check. Now, the AI model dictates what items get ordered ahead of time.
For some, this may seem like an absolutely terrifying proposition. Like a rogue computer trading stocks or being able to play Global Thermonuclear War, the popular culture has ingrained in us this idea of a computer program run amok, and that there should always be a human - behind the scenes, pulling the levers - who is responsible for ultimately executing the plan.
Otto realized that the quality of decisions being made by their model was at least as good, if not better, than those of the people who used to be responsible for pre-ordering product, and decided to hire the AI to do that job, instead. The results have been extremely positive for Otto:
It was a bold, forward-thinking move. And it paid off. Plus, Otto didn't fire anyone because they deployed machine learning models within their business. In fact, they hired more. It's a great example of the sorts of business operations which can not only be prescribed by data analytics and artificial intelligence, but also the sort of streamlining that is made possible by allowing artificial intelligence to take control of the process.
Plus, if you ever become uncomfortable with the decisions your artificial intelligence is making, you can always take it out of production for retraining. Or, even better, retrain it while it's still doing the job (something that is very difficult to do with humans!). Machine learning models are only as good as their training, and every decision you allow your model to make is another potential data point to make it better at its job - just like every day on the job is a learning experience for the rest of us!
As always, feel free to leave comments or connect with me on Twitter and LinkedIn. Also, you can now subscribe to The Professional Programmer by adding your email address to the subscription form in the right-hand sidebar!
"The AI system has proved so reliable—it predicts with 90% accuracy what will be sold within 30 days—that Otto allows it automatically to purchase around 200,000 items a month from third-party brands with no human intervention." #ArtificialIntelligence https://t.co/WJDLeVxgsQ
— Lucas A. Wilson (@lucasawilson) March 17, 2018
It's an interesting piece, and I recommend everyone pop over to the economist to read it. This type of AI deployment is an autonomous form of what my colleague at Dell EMC, Bill Schmarzo, calls Prescriptive Analytics:
What’s the difference between descriptive, predictive and prescriptive analytics? https://t.co/fax7HhggJJ pic.twitter.com/BITNXwXBJL
— Dean of Big Data (@schmarzo) March 8, 2018
However, instead of merely suggesting the proper course of action (deciding which products to order in advance), Otto has decided that for its purposes, the model they have deployed is accurate enough to forego the final human check. Now, the AI model dictates what items get ordered ahead of time.
For some, this may seem like an absolutely terrifying proposition. Like a rogue computer trading stocks or being able to play Global Thermonuclear War, the popular culture has ingrained in us this idea of a computer program run amok, and that there should always be a human - behind the scenes, pulling the levers - who is responsible for ultimately executing the plan.
Otto realized that the quality of decisions being made by their model was at least as good, if not better, than those of the people who used to be responsible for pre-ordering product, and decided to hire the AI to do that job, instead. The results have been extremely positive for Otto:
Overall, the surplus stock that Otto must hold has declined by a fifth. The new AI system has reduced product returns by more than 2m items a year. Customers get their items sooner, which improves retention over time, and the technology also benefits the environment, because fewer packages get dispatched to begin with, or sent back.
It was a bold, forward-thinking move. And it paid off. Plus, Otto didn't fire anyone because they deployed machine learning models within their business. In fact, they hired more. It's a great example of the sorts of business operations which can not only be prescribed by data analytics and artificial intelligence, but also the sort of streamlining that is made possible by allowing artificial intelligence to take control of the process.
Plus, if you ever become uncomfortable with the decisions your artificial intelligence is making, you can always take it out of production for retraining. Or, even better, retrain it while it's still doing the job (something that is very difficult to do with humans!). Machine learning models are only as good as their training, and every decision you allow your model to make is another potential data point to make it better at its job - just like every day on the job is a learning experience for the rest of us!
As always, feel free to leave comments or connect with me on Twitter and LinkedIn. Also, you can now subscribe to The Professional Programmer by adding your email address to the subscription form in the right-hand sidebar!