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What is AI? Everything You Need to Know About Artificial Intelligence
Feature Blog Posts | MATE | 30 November 2023
What is AI? Everything You Need to Know About Artificial Intelligence
In today’s digital age, the term Artificial Intelligence (AI) pops up everywhere, from our smartphones to our cars, and even in our homes. But the fact that the term AI is so prevalent these days means that there’s an overload of information about what it actually is.
So what is AI? This blog aims to demystify AI, taking you on a journey from its foundational concepts to its real-world applications. We’ll take a look at its history, explore its potential, and highlight its transformative power for different industries and individuals alike.
Defining Artificial Intelligence: What is AI?
What is AI? Artificial Intelligence, often abbreviated to simply AI, refers to the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, and self-correction. In simpler terms, AI is about creating machines that can think and act like humans.
Think of it this way: AI is the science of making intelligent computers do things that would usually be done by humans. From voice assistants like Siri and Alexa to recommendation systems on Netflix, AI is becoming an integral part of our daily lives, enhancing our experiences and making tasks more efficient.
The history and evolution of AI
What is AI and where did it come from? The concept of AI isn’t as modern as you might think. The idea of creating machines that can mimic human intelligence dates back to ancient history, with myths of robots and artificial beings appearing in Greek tales. That’s right—the ancient Greeks were telling stories about automatons as far back as 750 B.C.E. One such myth involves Talos, a bronze automaton that was built by the god of invention and blacksmithing, Hephaestus, to protect the island of Crete from invaders.
Well over 1000 years later, the first real foundation for AI was laid with the development of electronic computers in the 1940s and ‘50s. These early computers were designed to mimic basic human data processing. The term “Artificial Intelligence” was first coined in 1956 by John McCarthy at the Dartmouth Conference, marking the birth of AI as an academic field.
However, it wasn’t until the 1960s and 1970s that researchers finally began to make significant advancements with AI in regard to complex problem solving. Then, in the 1980s, progress began to slow down due to the technical limitations of the time. The slowed pace reduced the interest, and therefore the funding, around AI research.
The resurgence of AI began in the 1990s and 2000s, thanks to the availability of big data and advancements in machine learning algorithms. This era witnessed the development of AI systems that could beat the best human chess players, recognise speech, and categorise data.
Today, with the explosion of data and the power of cloud computing, AI is experiencing rapid growth and adoption across various sectors, from healthcare to finance. Its incredible evolution from a theoretical concept first dreamed up by the Ancient Greeks, to a transformative global technology, is a demonstration of the relentless human pursuit of innovation and progress.
How does AI work?
Artificial Intelligence, while often seen as a complex and intricate field, can be broken down into a few foundational concepts. At its core, AI operates through algorithms, which are sets of rules or procedures that the machine follows to make decisions. But how does a machine ‘learn’ or ‘think’? That’s where things get really interesting.
Machine learning (ML)
One of the primary pillars of AI is machine learning. Instead of being explicitly programmed to perform a task, ML allows a system to learn from data. By feeding a machine vast amounts of data (referred to as data sets), it uses statistical techniques to develop a model. Usually, the process of teaching the AI system begins with introducing it to training data. If you wanted a program to identify pictures with cats in them, then you would begin by feeding it images of cats. Then, over time and with more data, the machine refines its model to improve its predictions or decisions. For example, when you shop online, and the site recommends products, that’s machine learning in action.
Inspired by the human brain, a neural network consists of layers of nodes, similar to neurons. These artificial neural networks can process and transmit information in complex patterns, allowing for complex decision-making. Deep learning, a subset of ML, uses deep neural networks with many layers to analyse various factors of data. It’s the tech behind voice assistants that can understand speech, or software that recognises images.
Natural language processing (NLP)
Ever wondered how chatbots understand and respond to your queries? That’s natural language processing at work. It allows machines to understand, interpret, and generate human language. It’s not just about recognizing words but understanding context, sarcasm, and even emotions to some extent. Traditionally unstructured data presented a challenge for AI, which excels at making sense of structured information, but advances in natural language processing enable AI to extract valuable insights from unstructured data sources like text and images.
Cognitive computing is all about mimicking human cognitive functions and making machines more interactive. It’s the reason why when you ask your voice assistant about the weather, it doesn’t just give data but responds in a more human-like manner.
While often seen as a separate field, robotics heavily relies on AI. This is especially true when robots need to perform tasks without specific programming, adapt to new situations, or learn from their environment.
The 3 types of AI
When we talk about AI, it’s not just one monolithic entity. There are different types, each with different capabilities and applications. As we advance, the boundaries of what AI can achieve continue to expand. Here are the three different types of AI and what they are capable of.
Narrow or weak AI
This is the most common type of AI we encounter daily. It’s designed and trained for a specific task. While they might seem ‘smart’ in their designated task, they operate under a predefined set of rules and don’t possess general reasoning capabilities. This category includes:
- Voice assistants
- Imagine recognition software
Artificial general intelligence (AGI) or strong AI
Artificial general intelligence is the next frontier in AI research. A machine with general AI will be able to perform any intellectual task that a human can do. It would have the ability to reason, solve problems, and learn from experience. While we’re not there yet, it’s the goal many researchers are aiming for.
This is a hypothetical AI scenario where machines would surpass human science fiction. The practical development of superintelligent AI is a topic of debate and research among experts.
Where does generative AI fit in?
You may have come across this blog because you use generative AI on a regular basis, and want to know more about how it works. Generative AI is actually a subset of Artificial Intelligence, with ChatGPT being one of its most well-known examples.
Above, we mentioned weak or narrow AI. This is AI that has been designed for a specific task. Your Google Assistant is a type of weak AI—it can help you get around a new city, show you highly-rated places to eat, recommend popular tourist spots and let you know about upcoming events in the area that might interest you. While it’s very capable of doing its job withing a framework of rules,it cannot create anything new.
This is where generative AI comes in. This is a form of AI that can create something new. With just a few prompts, you’re able to instruct a generative AI platform to create something new—whether that’s a blog article or an image.
What is open-source AI?
An open-source generative AI model is a type of artificial intelligence system that’s designed to generate new, previously unseen content, be it text, images, music, or other forms of data. What sets it apart is its “open-source” nature, meaning its underlying code and architecture are publicly accessible and can be modified by anyone. This transparency allows a broader community of researchers, developers, and enthusiasts to scrutinise, improve, and innovate upon the model.
By being open-source, such AI models promote collaboration, accelerate advancements, and ensure a higher degree of transparency and accountability. This is particularly important for generative AI, as understanding its decision-making process can be complex. By sharing the model with the public, it not only democratises access but also encourages collective efforts to refine and responsibly use the technology.
Real world applications of AI
From setting reminders and using voice assistants to getting traffic updates on our commute, AI subtly enhances our daily experiences, making tasks more straightforward and more personalised. While there is still a lot of work to be done in many of these areas, the potential is there. So, what are some of the real world applications AI is being used for right now?
AI assists doctors in diagnosing diseases with greater accuracy. For example, AI algorithms can analyse medical images to detect early signs of conditions like cancer, often with more precision than the human eye.
Ever got a fraud alert from your bank? AI systems monitor millions of transactions every minute to detect unusual patterns, helping prevent fraud. There are also robo-advisors that use AI to provide financial advice and manage investments.
Streaming platforms like Netflix or Spotify use AI to analyse your watching or listening habits, recommending shows, movies, or songs tailored to your preferences.
AI powers the algorithms behind ride-sharing apps like Uber. It determines the quickest route, predicts the demand in different areas, and sets dynamic pricing. And let’s not forget the ongoing research into self-driving cars!
Online shopping platforms use AI for product recommendations, predicting what you might want to buy next based on your browsing history and past purchases. This can create a more personalised shopping experience.
AI in business
Artificial Intelligence is transforming the business landscape at a blindingly fast rate, offering a huge variety of opportunities to streamline operations, enhance customer experiences, and make more informed decisions. Here are a few examples of how that’s being achieved.
AI algorithms can reduce downtime by predicting maintenance needs in manufacturing. In supply chain management, AI can forecast demand, optimising inventory levels and reducing costs.
Enhanced customer service
Chatbots and virtual assistants, powered by AI, provide instant responses to customer queries. These tools can handle vast volumes of requests, freeing up human agents to tackle more complex issues, and ensuring a seamless customer experience
Businesses generate enormous amounts of data daily. AI tools analyse this data, extracting actionable insights. This means companies can make decisions based on real-time data, from pricing products to launching marketing campaigns.
AI analyses customer behaviour and purchase history, allowing businesses to tailor marketing efforts to individual preferences, increasing engagement and conversion rates.
Benefits and challenges of AI
While there’s no doubt the evolution of AI is exciting to watch and comes with immense benefits for humanity, it’s important to approach implementation thoughtfully. We’re already seeing many countries around the world creating new laws and regulations around the use of AI to ensure that it’s used in an ethical way. In September 2022, the United Nations also created the Principles for the Ethical Use of Artificial Intelligence in the United Nations System to provide a framework that guides the use of AI across UN system entities.
So while there are huge benefits when it comes to the implementation of AI, it’s important to balance the potential gains while also addressing certain challenges. Let’s take a look at some of the benefits and challenges of AI.
5 Benefits of AI
- Efficiency and automation: AI can handle repetitive tasks 24/7 without getting tired. This not only speeds up processes but also reduces human error.
- Data analysis: AI can process and analyse vast amounts of data at incredible speeds, providing insights that were previously impossible or very time-consuming to obtain.
- Personalisation: Whether it’s online shopping, streaming music, or news feeds, AI tailors experiences to individual preferences, enhancing user satisfaction.
- Cost reduction: Over time, AI can lead to significant cost savings, especially in sectors like customer service, where chatbots can handle a large volume of queries.
- Enhanced decision making: AI, through predictive analytics and advanced algorithms, can assist in making more informed decisions. By processing vast datasets and recognising patterns, AI provides recommendations and forecasts, enabling businesses and individuals to make choices based on hard data.
5 Challenges for AI
- Ethical concerns: AI’s decision-making process isn’t always transparent, leading to concerns about bias, especially in sensitive areas like hiring or law enforcement.
- Job displacement: While AI can create new jobs, it can also make others obsolete. The challenge here is ensuring a smooth transition and retraining opportunities for affected workers.
- Security risks: As with any technology, there’s potential for misuse. AI systems can be used in cyberattacks. And if they have vulnerabilities, they can be exploited.
- Dependence on data: AI is only as good as the data it’s trained on. If this data is flawed or biased, the AI’s performance can be compromised.
- Technical limitations: While AI is powerful, it’s not infallible. It can make mistakes, especially in situations it hasn’t been trained for. You may have also heard of “AI hallucinations”. This is when a generative AI program will “make up” information if it cannot find a source to draw from.
Ethical considerations in AI
The AI technology revolution is well and truly here. However, the rise of AI and its increasing popularity does raise questions about ethics that society must address.
These questions on ethics have been the subjects of much debate across the world, and not just within the computer science community. You’ve probably heard some of these questions and arguments come up around the dinner table, at the pub or in your social feeds.
Bias and Fairness
If AI systems are trained on skewed and biased data, they can perpetuate or even exacerbate those biases. This is especially concerning in areas like recruitment or law enforcement.
While many of us work hard to check our own bias, unconscious bias can still slip in unnoticed. This is where things become tricky when entering certain data into an AI system, as any system is only as good as the data it’s provided with. This is probably one of the biggest topics of debate at the moment, as we begin to rely more heavily on AI to help us make decisions.
Solution: To combat this, it’s essential to employ diverse teams in AI development, ensuring varied perspectives and reducing inherent biases. Regularly auditing and refining AI algorithms can also help identify and rectify biases. Open-source AI models can be beneficial, allowing the broader community to scrutinise, challenge, and improve them, ensuring fairness in AI-driven decisions.
With AI’s capability to analyse vast amounts of data, there’s a risk of infringing on individual privacy rights. How much data should AI systems access, and how is this data used? These terms need to be clearly defined so that people fully understand how and why their data is being used.
Solution: Implementing strict data governance policies is essential here. AI systems should operate on the principle of minimal data access, using only what’s necessary. Anonymising data can also prevent personal identification. Additionally, giving users control over their data, with clear opt-in and opt-out options, can instil trust and ensure AI respects individual privacy rights.
The decision-making processes of some AI models can be unclear. For users to trust AI, especially in critical sectors like healthcare or finance, they need clarity on its decision-making mechanics.
A lack of transparency can lead to flawed AI conclusions, potentially reflecting the biases of its creators. And if the decision-making process is flawed, then the AI program could deliver false or misleading results, or results that are based on the bias of the designer. By properly understanding the decision-making process, errors can be picked up and fixed.
Solution: Adopting explainable AI (XAI) approaches can shed light on AI’s decision-making processes. By designing AI systems that can provide clear, understandable explanations for their decisions, users can gain insights into the rationale behind AI outputs. Regular third-party audits can further ensure that AI systems remain transparent and accountable.
The future of AI
Artificial Intelligence is ever-evolving, promising a future that’s both exciting and, to some extent, unpredictable. Here are five uses we could expect in the future:
- Greater integration in daily life: From smart homes that anticipate our needs to AI-driven personal assistants that help manage our schedules, AI will become even more intertwined with our daily routines.
- Advancements in healthcare: AI could revolutionise healthcare, predicting outbreaks, personalising patient treatment plans, and even assisting in complex surgeries.
- Collaborative AI: While there are genuine concerns about AI being used to replace certain jobs and making some workers redundant, it’s unlikely that we’ll see an I, Robot-style takeover any time soon. Instead of AI replacing humans, the future might see more collaboration where AI systems and humans work together, each amplifying the other’s strengths.
- Ethical AI: As questions of ethics are taken into consideration by governing bodies and lawmakers around the world, we might see the advancement of ‘ethical AI systems’ that are designed to make morally sound decisions.
- AI in education: Personalised learning experiences, where AI systems adapt content to fit each student’s learning style and pace, could become the norm. Helped by machine learning and large data sets, this could revolutionise the education system.
In the end, while AI’s future holds immense potential, it’s up to us to steer its development in a direction that benefits all of humanity.
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