Other commenters have of course pointed out the constraints around mass production. I wonder if there is a product strategy that captures extra value from limited production. This seems like a fairly obvious play for sneakers (thinking Yeezys) where rarity commands extra value. I would imagine that this is the strategy that Adidas is taking. How can Nike stay one step ahead of this strategic move? I can see the product strategy going one of two directions a. charging a premium for high levels of user customization and offering this customization en masse and very close to the consumer, or b. making it economical to make more frequent small runs of highly exclusive endorsed sneakers that have some “ephemerality” to them – meaning they will never be produced again. My bet is that Adidas will go for strategy a and Nike will go for strategy b.
Reading this I’m reminded of another user-driven viral media site that some of us may remember: Gawker. Gawker was of course sued over the Hulk Hogan debacle and forced to shut down. Buzzfeed could benefit greatly from tempering some of its open innovation with machine learning focused on detecting potentially controversial or legally problematic content and preventing it from being published. One actually real product (unlike watson) that IBM has is a natural language sentiment analysis tool – it would be capable of reading any given article and scoring likely consumer sentiment towards the article. Articles that score negatively could be forwarded to BuzzFeed editors for review eliminating a lot of the human quality control risk. Again this would be a human-augmentation product rather than a full automation product.
The shift from their core product to the experiences platform is a great example of the “what business are you really in?” conundrum and a great example of this company moving with consumer tastes. I’m concerned however with the company’s ability to actually monetize the best ideas from this platform. How is airbnb monitoring and augmenting the ideas that enter it’s pipeline through this product? I wonder if airbnb could take the youtube model and assign staff to some of the best experience ideas to help their creators develop businesses around these ideas that further leverage the airbnb platform and create more data for the company.
Absolutely fascinating. I think you need to go sell the idea of building a popularity analysis tool to spotify and pandora. I can see a few different ways to build that product. Firstly – you need consumption data around a large bank of music (spotify and pandora), secondly you would require the master tracks to any given song or at least musical notation so that melody and rhythm can be analyzed. AI would be able to recognize patterns in popular songs on a variety of components, this should probably be segmented by genre. Spotify has the added machine learning component of being able to measure the popularity of songs analyzed by it, then deployed on the spotify application.
Leaving aside the prophetic capabilities of Watson for a moment, I think there are a few things that DHL needs to seriously consider with this use case. It can attempt to use machine learning all it wants to optimize flow of goods, but this system will be for naught without a centralized asset management system with a form of authentication that is capable of teeing off automated processes every time a good reaches a subsequent destination in the supply chain (e.g. has been authenticated as reaching the “next destination”). Know where I’m headed? – Blockchain. I think DHL is a great use case for moving on from one tech buzz product to another. AI is really only a good use case here for analyzing the overall performance of a supply chain – the core use case of managing the supply chain itself is much better suited to blockchain. Here’s why: an asset (a package) can be moved from one node (location) to another in the blockchain network every time a package is transferred from one location to another. This hand-off will be authenticated by the blockchain. Automated processes (payment, communication, restocking) can be teed off by the blockchain protocol every time an asset moves to a new node without any kind of watsonesque intelligence.
Great essay! I think we’ll start to see a few different classes of trading oriented AI emerge over the next 5 years. You talk about current AI’s latency challenges – I don’t see AI ever being quicker than current intraday algos. I think AI will bifurcate into tools that are centered around developing intelligence around general equity analysis (of the buy,hold,sell variety) and intraday specific tools. For the intraday tools, rather than something that is actively managing trading – I see AI as a tool that can augment the capabilities of a trader. At IBM we were working on a voice to code product, meaning a piece of AI that would translate natural speech (you talking to it) into code. I thought a great application of this would be enabling non-tech savvy traders to write trading algos on the fly.