Pundits across the entire technology space have been trying to predict the future of artificial intelligence over the past few years. It seems that startup companies keep developing new solutions that nobody predicted, which in turn disrupt entire segments of the market. More than a few of these lack market traction and never find the kind of audience they had hoped to.
Nevertheless, excitement remains high as engineers roll out radically new applications of predictive algorithms. Media outlets claim that these are getting smarter, but it’s far more likely that any actual gains are being seen as a result of larger databases and a greater amount of training data to base digital decisions around. As these sets grow, generative AI services can make more accurate guesses about the right way to proceed when faced with a particular prompt.
How AI Thrives On Semiconductors
With databases growing at an exponential rate, it’s become increasingly difficult to store and process all of the information that these services have to parse on a daily basis. Semiconductor-based storage solutions are used in nearly every step that requires speed. Conventional magnetic media only enters the equation when doing backups.
Designers of sophisticated embedded hardware systems have relied on semiconductors to help shrink increasingly complicated electronics into smaller packages. Central processing units that were once sufficient to manage almost every piece of software conceivable are suddenly incapable of functioning in this kind of challenging environment. Semiconductor fabricators have risen to the occasion by re-purposing graphics processors that were originally designed with image rendering in mind.
Both vector and bitmap images consist of nothing more than an arbitrary string of numerals. Creative programmers are able to redesign their code to run on equipment meant for this kind of environment. Software that would otherwise run headless can take advantage of every last data path a GPU has to offer. That’s made it possible to automate systems that would have to be run manually without the application of semiconductor technology.
AI Can Shape Your Workplace
Economists have been warning about the possibility that AI adoption could translate into sizable layoffs over the next few years, but it’s more likely that new software applications will supplement human workers as opposed to replace them. Generative AI tools tend to do things that humans would never be able to do. For instance, one experiment involved hanging a sensor from a pole and connecting it to a music popularity forecasting service. When people get close to the sensor with a mobile device, it adds the music they’re playing to the pop charts.
In another more practical case, meteorological sensors were deployed to a distant field that would have been otherwise impossible to collect information from. Human weather specialists use generative AI tools and deductive reasoning to draw conclusions based on new information. All of these situations required AI to work alongside people rather than replace them outright. Some analysts even predict that companies may have to hire more computer scientists and data entry workers to maintain them.
