Is AI sentient? No, but it’s rapidly getting better

We may be compensated by vendors who appear on this page through methods such as affiliate links or sponsored partnerships. This may influence how and where their products appear on our site, but vendors cannot pay to influence the content of our reviews. For more info, visit our Terms of Use page

The media had a field day when a Google engineer recently claimed that the company’s artificial intelligence technology had become “sentient.” For every article joking about Skynet and HAL 9000, there was another assuming it must be true and questioning the ethics of it all.

Missing in most of the coverage was any recognition of how far and fast this technology has advanced and how broadly it impacts our lives on a daily basis, in ways both large and small.

It was only ten years ago on June 26, 2012 that the New York Times wrote about Google’s deep learning discovery using machine learning, essentially teaching a computer to train itself with enormous amounts of data. The article was headlined How Many Computers to Identify a Cat? 16,000. Here we are today, with restaurant recommendations to the early diagnosis of diseases and nearly everything in between being driven by AI and machine learning.

The fact is that companies like Google, Microsoft, Amazon and many others have invested billions in AI technology. Some of the world’s smartest engineers across hundreds of companies are working on new applications every day.

There’s still room for improvement. People don’t want an AI experience that is less functional than human interaction or other existing software solutions. They want less Matrix and more AI-powered experiences that are easy to use and work flawlessly when they want them. How do we get there?

Before embarking on any kind of AI project, It’s important to understand the sheer amount of data needed to keep an AI application up to date. AI applications that use machine learning are “trained” and often require many thousands of examples to successfully return correct results under real-world usage. The way users interact with the technology changes over time, so to stay accurate and aligned they must keep retraining and validating their algorithms with more and more data.

Even the biggest companies struggle with scaling data curation. Most companies vastly underestimate how long it takes to roll out a successful AI application — the development might take the same as with traditional apps, but far more time is required for training, testing and validating the product.

When it comes to data, whatever you have, it’s likely not enough.