Artificial intelligence leads the way in the latest technological revolution. The Silicon Valley geniuses will tell you it’s the next best thing, and that it will transform our lives forevermore. Anyone who’s ever engaged with AI knows that those claims are pretty far off right now.
We still have many questions about artificial intelligence and why it’s maybe not living up to its true potential just yet. Despite what the tech heads will try to tell us, it’s not really developed that much in the last few years, especially when you look at daily lives. Lots of the same problems still persist - and here are some of the biggest barriers stopping AI from reaching its true potential.
Societal Bias In Training
How do you train AI? You take AI models, and you give them massive amounts of data so they learn about different things. It sounds so simple, though there’s a serious issue that gets in the way of many AI systems: societal bias.
Chapman University wrote a useful article on this that basically explains a significant issue with training AI from historical data and public sources. As a society, we’ve spent generations reinforcing unconscious biases or stereotypes. When AI models take data and learn from it, they also take on these biases. It results in discrimination when using AI, which then calls into question its legitimacy as a helpful tool.
Until we find a way to remove any societal bias from training models, this cloud will always linger over the AI field.
Computing Constraints
Do you know how much energy it takes to train AI? It’s astronomical, and it’s singlehandedly leading to a global GPU shortage. AI developers are burning through GPUs trying to train their models, but it’s close to a point where computing manufacturers are struggling to keep up with the demand.
A lack of resources due to the energy demands could mean there’s a point where AI training has to be put on hold. The good news is that experts are already coming up with ways to improve things, like mixed precision training. As this BitFern explanation discusses, mixed precision training is a new way of training AI models that uses less GPU power and energy.
Consequently, it could help this technology avoid a bottleneck by improving the training process and gradually reducing the energy demands and GPU requirements.
Too Many Poor AI Systems
To be honest, the biggest barrier getting in the way of AI is itself. Bad AI makes everyone distrust the technology or fail to see how beneficial it could be. Here’s a simple example: Siri from Apple. It’s borderline useless, meaning people don’t see the point in AI and carry this bias when other AI technology comes around.
There needs to be a point where developers stop churning out any old AI platform just to be part of the boom. Stop adding AI to everything, especially when it’s not useful. Focus on quality AI systems that prove their worth, and trust from the public will follow.
Artificial intelligence will never reach its potential while these barriers remain in place. It’ll be interesting to see what’s done to break them down in the future.