Generative AI crash-landed into our lives near the end of 2022 when ChatGPT arrived on the scene. Almost overnight, the concept of “AI” became a concrete reality in our world and was no longer something foretold in science fiction stories.
Now, users of all ages can access free artificial intelligence models to solve problems, generate images, and whip up an authoritative essay at the touch of a button. That makes it easy to believe that large language models and image synthesis tools are magic. The truth is a lot more complicated.
We’ll break down the answer to that question and explore a few AI applications today, including a few you might not have heard of. Keep reading to learn how much goes on behind the scenes to ensure your tools consistently generate coherent and helpful content.
AI, or Artificial Intelligence, is a sub-discipline of computer science concerned with developing systems that can perform tasks that usually require human intelligence. Such tasks include problem-solving, language understanding, and even perception. AI has evolved significantly from its early days when it was more of a theoretical concept to the transformation technology it is today, touching almost every aspect of our daily lives.
But what is AI technology? Where does it come from?
Historically, AI has roots that go back to the mid-20th century. Yet, in the last decade, it has gained substantial traction and widespread thanks to advances in computing power, data availability, and algorithmic improvements. From simple rule-based systems to complete neural networks, AI technology has become more sophisticated and accessible than ever.
When answering the question, “What is AI technology?” more broadly, it is generally categorized into two sets: Narrow AI and General AI. Narrow AI is also called Weak AI and performs only some particular tasks. Think about virtual assistants, such as Siri or Alexa. They operate under constrained operations, meaning these systems can’t generalize their knowledge to other tasks.
On the other hand, general AI, which remains primarily theoretical, can perform any intellectual task that a human can. It is the AI that, at some point, could think, reason, and adapt to new situations.
Central to all AI is Machine Learning and Deep Learning, which is a subset of AI. These two technologies enable computers to learn from their examples and improve without programmed instructions. They also allow AI systems to recognize patterns, make decisions, and predict outcomes.
Ultimately, AI is a tower built upon mountains of data, fast and iterative processing, and intelligent algorithms smashing into one another. It’s all about allowing software to learn automatically from data patterns or characteristics described in “data.” “algorithms,” and “computing power.” In other words, data is the fuel for AI, while the algorithms are akin to sets of rules with which AI is conditioned to make decisions or predictions. Computing power, especially with the advent of GPU (graphic processing units), has enabled the processing of these algorithms at a speed that makes AI feasible.
For example, large language models (LLMs), like GPT, have already been fed massive datasets and can write text resembling human-generated text. Additionally, AI systems like AlphaGo have demonstrated the ability to master complex games by learning from millions of scenarios.
Artificial intelligence has been integrated into healthcare, helping to enhance diagnosis, suggest treatment options, and even assist healthcare providers in determining personalized medicine. It can also help detect diseases early in medical images, improving patient outcomes.
AI is used in the financial sector to detect fraud attempts and manage risk so organizations can take prompt action. It is also primarily used in algorithmic trading, where it analyzes market data and makes high-speed trading decisions based on the analysis’s outcome.
Retailers use AI to provide personalized experiences by offering products based on customer behavior. Additionally, AI is utilized in customer service and helps ensure that products are in the right place, optimizing inventory management and the supply chain.
Manufacturing industries have adopted AI for predictive maintenance to avoid equipment failure. AI-powered automation and robotics also streamline the manufacturing line, helping enhance efficiency and reducing human-based errors.
The transportation industry has undergone major AI applications, from autonomous vehicles that travel without human assistance to systems designed to optimize traffic and logistics administration.
AI is integrated into everyday life, from virtual assistants to smart home devices, aiming to make everything easier to get done. It’s further integrated into search engines, like Google’s AI Overviews, and has become a smartphone staple.
The pace of AI evolution over the last couple of years is phenomenal when considering how it has led to developments and uses that appeared impossible just two years ago. Emerging trends and applications for further advances continue to push the boundaries, with applications from personalized education to climate science innovations. While these AI advancements cause excitement, the technology has several ethical and societal impacts that cannot be ignored. Innovation needs to be balanced with responsible adoption to ensure that measures taken to further the field of AI are done ethically and constructively.
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