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Earlier this year, Stanford and Google found that a machine learning agent designed to transform aerial images into street maps and back was cheating. Turns out it was hiding information it would later need in an almost undetectable high-frequency signal that developers didn't immediately catch, and was only suspected because the results seemed a little too perfect.
While a fun anecdote about Artificial Intelligence (AI) development, it still raises the question of how developers can ensure their systems are properly executing the tasks they're meant to. Or rather, as was the case with Stanford and Google's AI, how can developers establish tools and criteria to thoroughly evaluate AI and machine learning systems?
Before jumping into this, there are two key elements to consider:
As exciting as the prospects of AI are, it's important not to overestimate the actual state of possibilities. A careful review of the research projects may reveal that some of the more amazing results actually leave out a lot of circumstantial data, and that replication is difficult to achieve at this point in time.
Likewise, media plays a part in inflating our expectations of AI technology, but it's not necessarily rooted in capabilities that are available to us today. No matter how advanced a system may appear, it's important to remember that computers don't possess their own will or motives, and can only execute the tasks we ask of them—even though they can learn, recognize patterns and make adjustments based on the information they receive.
So, how can developers make sure their results are accurate? Here are a few guidelines to follow:
An outdated system is never going to yield good results. Keep your system up to speed by including the latest data, more examples, more features, etc.
How can your AI and machine learning projects benefit from careful attention to detail and a commitment to quality? Find yourself a great partner! Let's schedule a chat to find out if PSL is the right nearshore outsourcing partner for you.