Artificial Intelligence (AI) is the new buzzword, and the valuations of tech companies are skyrocketing. Few innovations have attracted as much attention and speculation as AI. The field of AI, until recently considered science fiction, is becoming a reality. But what lies behind AI, and why has it taken so long to come to fruition?
At its core, AI is the simulation of human intelligence processes by computers. This simulation involves tasks such as learning, reasoning, problem-solving, perception, and creativity. You’re most likely encountering a form of AI each time your browse the web, from those chatbots that seem to have all the answers, the ads you see on social media, and the advanced platforms that generate content, AI is delivering a highly personalised experience.
One of the primary reasons for the delayed dawn of AI lies in the complexity of emulating human intelligence. Unlike conventional computing tasks that rely on predefined rules and algorithms, AI necessitates the ability to adapt, learn, and evolve based on data inputs—a feat that requires advanced algorithms and computational power. The code and algorithms behind the AI boom are not new. In fact, software engineers have known how to code AI for decades.
The exponential growth in computational capabilities, coupled with vast amounts of data generated by digital technologies, has propelled AI development forward. Machine learning, a subset of AI that focuses on enabling machines to learn from data, has been particularly instrumental in this regard. Through techniques such as neural networks and deep learning, machines can analyse vast datasets, identify patterns, and make predictions with increasing accuracy.
Furthermore, advancements in hardware, such as graphical processing units (GPUs) and specialized AI chips, have significantly accelerated AI training and inference tasks. These hardware innovations provide the computational muscle necessary to handle the complex calculations involved in AI algorithms, making real-time decision-making and complex problem-solving feasible.
The integration of AI into various industries and domains has been facilitated by interconnected devices and the Internet of Things (IoT). From healthcare and finance to transportation and entertainment, AI-powered solutions are reshaping processes, enhancing efficiency, and driving innovation across sectors. The quest for artificial general intelligence (AGI)—machines capable of understanding, learning, and applying knowledge across diverse domains—remains an elusive goal, however.
One of the factors we need to consider when talking about AI is that as processing power increases, so do the power requirements. Even today’s silicon chips consume significant amounts of power, so the challenge facing technology companies is the need for cheaper or alternate power sources, so we can expect to see the ‘server farms’ being established in countries with access to cheap power.
Like the dot.com boom, we’re seeing technology companies including the term AI in their announcements and then seeing their valuations increase, but like the dot.com bubble that burst, not all companies claiming to be leveraging AI will survive – so how should investors filter the noise in the market when wanting exposure to the AI boom?
It stands to reason to look at the companies at the forefront of AI. One of the stocks that make up the Magnificent 7, Nvidia Inc, has led the charge, and that’s because so many of the large tech companies, including other companies in the Magnificent 7, are buying Nvidia’s GPUs to build out their own AI offering.
Right now, we’re at the dawn of the AI era, and as processing power continues to increase, we can expect AI to become even ‘smarter’ than it is today.
