People have been discussing Artificial Intelligence (AI) a lot, even in sci-fi films like Terminator and The Matrix. Although it has been around for a long time, many don't grasp its full meaning.
Common definitions of AI make things way more complex than they have to be, which can confuse readers. So, we'll try to define AI by using simple terms to help you understand its role in our everyday lives. Let's dive right into it!
What Is Artificial Intelligence (AI)?
Simply put, Artificial Intelligence (AI) focuses on the ability of machines to mimic human cognitive capacities. Ergo, they don't require human intervention to complete complex tasks.
The goal of AI systems is to make our daily lives simpler. Common examples include AI-powered virtual assistants like Siri, Alexa, or Google Home, which can understand human language and respond to questions to make informed decisions.
Other applications of AI are image recognition, fraud detection, and recommendations for content, songs, and products.
Modern tools like ChatGPT and Gemini (Google Bard) can respond remarkably similarly to the way we do it. But AI tools can do things that we simply can't, like processing vast amounts of data, drawing images, or even making videos in a matter of minutes.
However, that doesn't mean they can replace the worth of human intelligence and work! AI tools can only work on the specific tasks they were trained for, and they can still make some mistakes from time to time.
Types of Artificial Intelligence
Artificial Narrow Intelligence (ANI)
"Weak" or "narrowed" AI works only on a specific task, which can sometimes perform better than we would. Yet, ANI, or weak AI, don't possess any sort of awareness; they only work on what they were trained to do and nothing else.
Common tasks include Natural Language Generation, creating realistic images and understanding human speech. Examples of ANI involve tools such as Alexa, Siri, ChatGPT, Gemini (Google Bard), Midjourney, and Sora.
Artificial General Intelligence (AGI)
AGI, or "Strong" AI, is closer to the kind of AI we've seen in films or TV series. Unlike ANI, AGI could potentially learn to solve a wide range of new problems by themselves and even understand human feelings and emotions.
AGI is also much closer to mimicking the capabilities of the human brain. Picture a robot or a machine capable of cooking dinner, solving math problems, or even changing a flat tire.
Examples of AGI could resemble the capabilities of robots like TARS in Interstellar and "Joi" and the "Replicants" in Blade Runner.
Yet, It's worth noting that despite the fact we haven't achieved AGI, several large companies are working on it. Mark Zuckerberg mentioned that the long-term goal of Meta is "building full general intelligence."
Artificial Super Intelligence (ASI)
In theory, ASI systems would be able to reason, judge, and possess cognitive capabilities that surpass ours. Also, these systems would be able to improve on their own and set their goals and strategies to achieve them.
They would theoretically be able to mimic emotions and beliefs and use social skills to interact with us. Since ASI is just hypothetical, the only examples we can give come from movies like Droids in Star Wars.
Big tech corporations like IBM argue that some of the use cases we have access to, such as Large Language Models (LLMs), Neural Networks, Conversational AI, Self-driving cars, and AI-generated programming, could work as the building blocks to achieving ASI in the future.
How Does AI Work In Simple Terms?
Computer programs work by executing a set of instructions (algorithms) to complete a specific task. AI systems take this further by learning what the desired outcome is with examples of what's right and wrong.
These systems learn through trial and error, making it very similar to how we learned. AI models receive massive amounts of data and use algorithms that learn to recognize patterns that guide them to the desired outcome. The more data or information the AI models receive, the better the answers they can provide.
Once AI models become familiar with a certain topic, they can recognize even complex patterns so well that they can make accurate predictions. That's why it has become popular in industries like healthcare, finances, and weather forecasts.
The field focused on teaching machines to learn from data is called Machine Learning. For example, Data Scientists use a subset of Machine Learning, called Deep Learning, to process larger amounts of data more efficiently.
Deep Learning algorithms are used to create Neural Networks, which work as layers that process and categorize the data they receive.
How Does Machine Learning Work?
Data Scientists can build Machine Learning models by using three different methods. The first is Unsupervised Learning, which Data Scientists use to analyze and identify similarities and patterns in data.
That's how they teach an AI model what's right and what's wrong. Think of a program that receives data from patients who have a particular disease. By analyzing and identifying similarities and patterns in data of patients with the disease, this program can make data-driven decisions.
In Supervised Learning, the second approach, Data Scientists use two sets of data. Using the same example as before, one data set has info on healthy patients and the other on patients with the disease.
The program analyzes the frequency of specific conditions that sick patients have in common and learns to predict when they have the disease.
The last approach, Reinforcement Learning, is particularly useful for programs dealing with unfamiliar scenarios. A promising example would be how patients react to a certain medication to help create personalized treatments like Dynamic Treatment Regimes (DTRs).
Please note that the field is still under study as of yet. Other common examples of Reinforcement Learning are in robotics and the gaming industry, with companies like Covariant and DeepMind.
Why is AI Important?
AI and its subfields have become very important to multiple industries, including healthcare, gaming, finances, and robotics.
Besides, Machine Learning algorithms can support human workers in automating repetitive tasks such as spam detection, facial recognition, Natural Language Processing, and customer service with human-like text.
In fact, popular Customer Relationship Management (CRM) platforms, such as Hubspot and Salesforce, have embraced AI.
Machine Learning models have also helped integrate Generative AI and speech recognition in voice assistants. In supply chains, business models can also use Generative AI to help forecast demand and improve customer engagement on a daily basis.
Financial institutions can also use AI to detect suspicious activities in real time and make predictions. Lastly, Social Media Platforms and streaming platforms have included recommendation systems built with Machine Learning to increase customer satisfaction and customer experience.
Conclusion
While AI-powered systems are very useful, human labor is invaluable in building trust and engagement with customers!
As a UX-driven Product Development agency, we've worked on several projects involving AI with clients like Sylvester.
Feel free to reach out if you'd like to know more about our approach to bringing business ideas to life.
