While the release of GPT-3 marks a significant milestone in the development of AI, the path forward is still obscure. There are still certain limitations to the technology today.
While the release of GPT-3 marks a significant milestone in the development of AI, the path forward is still obscure. There are still certain limitations to the technology today. Here are six of the major limitations facing data scientists today.
For prediction or decision models to be trained properly, they need data. As many people have put it, data is now one of the most sought-after commodities ousting oil. It has become a new currency. Currently, large troves of data sit in the hands of large corporate organizations.
These companies have an inherent advantage making it unfair to the little startups who have just entered the AI development race. If nothing is done about this, it would further drive a wedge in the power dynamic between big yech and startups.
The ways biases can creep into data-modeling processes (which fuel AI) is quite frightening, not to mention the underlying (identified or unidentified) prejudices of the creators to factor in. Biased AI is much more nuanced than just tainted data. There are many stages of the deep-learning process that bias can slip through and currently, our standard design procedures simply aren't aptly equipped to identify them.
As this MIT Technology Review article points out, our current method of even designing AI algorithms aren't really meant to identify and retroactively remove biases. Since most of these algorithms are tested only for their performance, a lot of unintended fluff flows through. This could be in the form of prejudiced data, a lack of social context and a debatable definition of fairness.
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