Artificial intelligence is poised to be one of the biggest things to hit the technology industry (and many other industries) in the coming years. But just because it holds enormous potential does not mean it does not also have its challenges. And, those challenges are not small, which is why recognizing and working towards resolutions to problems can help further propel artificial intelligence’s rapid growth. If this is your first time exploring the field, check out this high-level definition of artificial intelligence.
AI' Biggest Challenges to Overcome
Bias is one of the biggest challenges facing AI. Try as we might to have data that is an absolute fact, there is inevitable bias when you explore the depths to which AI might be used. Forbes India explains the inherent bias in data, “An inherent problem with AI systems is that they are only as good – or as bad – as the data they are trained on. Bad data is often laced with racial, gender, communal or ethnic biases. Proprietary algorithms are used to determine who’s called for a job interview, who’s granted bail, or whose loan is sanctioned. If the bias lurking in the algorithms that make vital decisions goes unrecognized, it could lead to unethical and unfair consequences…In the future, such biases will probably be more accentuated, as many AI recruiting systems will continue to be trained using bad data. Hence, the need of the hour is to train these systems with unbiased data and develop algorithms that can be easily explained. Microsoft is developing a tool that can automatically identify bias in a series of AI algorithms.”
The tech industry has faced computing power challenges in the past. But, the computing power necessary to process massive volumes of data to build an AI system, and utilizing techniques like deep learning, is unlike any other computing power challenge that has been previously faced in the tech industry. Obtaining and funding that level of computing power can be challenging for businesses, particularly startups.
Because AI is an emerging technology, there are few who contain the skills or training necessary for artificial intelligence development. Because this is a significant problem in the software development industry, many companies will need to allocate additional budget towards artificial intelligence development training, or the hiring of artificial intelligence development specialists. McKinsey further explains the challenge of sourcing the skills necessary for artificial intelligence development, “With talent being one of the biggest challenges to AI, no matter how advanced a company’s digital program, it’s perhaps not surprising that companies are leaving no stone unturned when sourcing people and skills. Most commonly, respondents say their organizations are taking an “all of the above” approach: hiring external talent, building capabilities in-house, and buying or licensing capabilities from large technology firms.”