If you’re like most companies, your data has become a crucial asset to your marketing department — and losing access to it is a huge compromise to make when shopping for an AI tool.
In an article recently published in the MIT Technology Review, Senior Editor Will Knight wrote of AI that “no one really knows how the most advanced algorithms do what they do.” According to Knight, that could turn out to be a big problem for widespread AI adoption.
He goes on to cite the example of Nvidia’s unique self-driving car that, rather than following a programmer’s instructions, relies entirely on an algorithm that taught itself how to drive through observation. The one issue? Due to the car’s deep learning technology, no one can understand why it makes the choices it does. “The mysterious mind of this vehicle,” Knight notes, “points to a looming issue with artificial intelligence.” Without the ability to understand and analyze the machine’s choices, it will be near impossible to predict when failures will occur — or to ensure that they won’t happen again.
Understanding Deep Learning
Essentially, deep learning AIs are programmed to act as a neural network. Because this technology so closely resembles the brain’s own complex processes, it is often better able to solve real-world problems than its less autonomous counterparts.
The downside is that, as much as the precise machinations of the brain are still stumping neuroscientists everywhere, these systems are opaque even to the programmers who designed them. Instead of storing the data they’ve gathered into an easily-readable form of digital memory, black box AI solutions diffuse information in a way that’s almost impossible to unscramble.
The Disappearing Data Problem
For businesses, this opacity becomes a serious analytics problem. According to information management guru Brian McKenna, “In buying a black box solution, you lose control of your data… You are not sure what it’s telling you, you allow others to benefit from it for free, and you may not be able to access it in the future.” After all, as McKenna points out, analytics is not a “plug and play solution.” If you don’t have the data, you can’t perform effective analysis — simple as that.
Access to your data is a crucial function for most companies, and losing access to it is a huge compromise to make when shopping for an AI platform, particularly for your marketing department.
Seeking Transparent Solutions
Luckily, there are plenty of tools that avoid this lack of transparency altogether. Known as glassbox solutions, these platforms are ideal for those who understand the true value of their data. Plenty of big tech companies (Google and Facebook, for instance) offer artificial intelligence APIs that can help you build a more transparent model every bit as sophisticated as many black box AIs already on the market.
That’s why AI solutions like Albert™ are designed with transparency in mind. Described as a “glassbox” tool, rather than a black box one, Albert is more than just another machine — he’s a co-worker you can collaborate with, drawing from his data to learn precisely how your marketing strategy could be improved.
The worlds of big data and artificial intelligence are still very much developing and in flux — those who are able to work out the kinks in both fields will win significant competitive advantages. Companies should be careful to avoid trading one benefit for another, and they can do so by selecting an AI tool that allows them to retain full control over their data.