IDC has predicted that as of this year, 75 percent of developer teams will include artificial intelligence functionality in one or more applications or services.
While the potential for AI is enormous, we’re still testing the waters of how exactly it will help (and sometimes hurt) the way we interact and complete everyday tasks. While no prediction is certain, it’s safe to say that AI will transform our daily lives. What we can say with certainty is that the two key areas of AI that will gain momentum are governance and algorithms.
Here’s why: As of 2018, six billion connected devices per year will be automatically asking for support, without human involvement. With an increased reliance on AI-bots assisting humans in our daily lives, we need to work on building trust.
Yes, bots can help with customer service and deliver packages to our doorstep, but they can also cause serious problems if the right precautions aren’t put in place. For example, AI bots have shown subtle gender bias caused by the all-too-human data used to train them. Consumers, enterprises and educational institutions alike are investing in the improvement and research of AI to ensure that it’s ethical and unbiased.
As we accelerate AI implementations in the enterprise, governance and standards will help ensure AI lives up to its potential as a force for good. In 2018, as we rely more on non-human interactions, the first thing we need to do is understand that we can’t just outsource our responsibilities to machines. Someone needs to be clearly responsible for decisions made by algorithms, with the power and resources to make changes when problems arise.
Another critical component of AI governance is full disclosure. Organizations should clearly explain to consumers what data they have about them and what they are doing with it. Organizations might be reluctant to disclose this information because of negative consumer reaction.
Another key trend we’ll see this year is the emergence of “algorithm whisperers.”
An algorithm whisperer’s job is to have a deep understanding of the context of algorithm use. It’s about understanding the data and the algorithms that are being used, and interpreting the results. At the end of the day, bad data means bad results – it’s critical to have someone with the skills to tell what data has been collected, when it doesn’t make sense and why, and who understands the impact this will have on results.
But, what really distinguishes an algorithm whisperer is creativity. For example, data scientists working on the 9/11 memorial in New York determined that it was impossible to achieve the level of adjacency that had been requested to memorialize all of the people impacted. Yet, data artist Jer Thorpe managed it by using the physical characteristics of the place and length of the names.
Algorithm whisperers can also use their deep expertise to figure out what the results of predictive studies mean.
For example, a subway authority’s algorithm was trained to predict every time a machine stopped working. It turned out that every time a single machine broke down, the machine next to it would break down shortly afterwards, too. It was almost as if they were “catching” the breakage from each other, like a common cold. It didn’t seem to be logical. Was it a data quality problem with the same machine counted twice? Maybe machines were installed together and tended to break down together after a certain amount of time?
The answer is that this was a result of repair teams with strict service level agreements. If they missed the window for fixing a machine they were paid less. So, if there was a broken part, they would order a new one, but replace it immediately using a part from the machine next to it. They would go back the next day to fix the “new” breakage. It took an algorithm whisperer with a full understanding of the context of the data to successfully and correctly interpret what was actually going on.
The future of AI will rely on human intuition and creativity paired with algorithms.
It’s been predicted that 13.6 million jobs will be created solely around AI in the next decade. Ensuring that both proper governance and a full understanding of context are in place is key to a successful AI culture in 2018 and beyond.
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