In a matter of months, artificial Intelligence, or AI, has become part of our everyday conversation. No doubt, it’s partly due to its increased accessibility through tools like ChatGPT. The concept of AI has been around for awhile—at least as a concept—all the way back to the 1950s.
The latest advancements in technology continue to make sweeping changes to how we live, work, and interact with the world. As businesses look for ways to adopt AI into their operations, it’s important to be strategic and to consider the limitations of AI. For instance, the Consumer Financial Protection Bureau (CFPB) recently published a spotlight underscoring the use of AI in the banking industry, and highlighting its shortcomings.
Having a solid understanding of AI is important as the technology becomes more prevalent. Let’s break dig into what exactly is AI, how does it work, and why does it matter.
What is AI and How Does It Work?
Before we define artificial intelligence, let’s set a baseline understanding of intelligence.
Encyclopedia Brittanica defines intelligence as the ability to “learn from experience, adapt to new situations, understand and handle abstract concepts, and use knowledge to manipulate one’s environment.”
There’s another kind of intelligence that’s also important for understanding AI—the kind of intelligence that governments, law enforcement, and businesses use to watch for threats.
In their paper, “Towards a New Definition of Intelligence“, Kristan J. Wheaton and Michael T. Beerbower write:
Intelligence…is a process, focused externally and using information from all available sources, that is designed to reduce the level of uncertainty for a decisionmaker.
When we consider that intelligence is both cognitive ability and information gathering, we can define AI as technology that simulates human intelligence by processing data then using that information to make decisions.
Types of AI
Just like there are different types of human intelligence—spatial intelligence, musical intelligence, language intelligence, etc.—there’s also different types of artificial intelligence.
Weak vs. Strong vs. Super AI
- Weak, or narrow AI specializes in performing tasks, like voice recognition. Siri, Alexa, and Google Assistant are examples of weak AI. Weak AI doesn’t have human-like intelligence and can’t perform tasks outside of what it was designed for.
- Strong, or general AI can understand, learn, and apply knowledge across a broad range of tasks at the level of a human being. It has common sense and is more like the AI you see in sci-fi movies, but it’s still only theoretical at this point.
- Super AI is also a hypothetical concept, but if it were a reality, it would surpass human intelligence in practically every way, including creativity, problem-solving, and social skills.
Machine Learning vs. Deep Learning
Machine learning and deep learning are both subsets of AI that train algorithms to learn from data and make predictions or decisions.
Machine learning allows computers to learn from experience instead of being explicitly programmed for each task. It’s like teaching a child to differentiate between cats and dogs. The child doesn’t memorize every cat or dog they’ve ever seen; instead, they learn characteristics that define the animals.
Through machine learning, systems can make predictions and decisions even when they haven’t been exposed to the data.
- Feature engineering: Humans select and create the right set of features or characteristics from the available data to help the machine learning algorithm make accurate predictions or classifications. By providing the right set of features to the machine learning algorithm, it can better focus on the most relevant information and improve its performance.
- Supervised, unsupervised, and reinforcement learning: Supervised learning involves training models on labeled data, similar to having a teacher guide you. Unsupervised learning allows the computer to discover patterns and relationships in unlabeled data on its own. Reinforcement learning focuses on training models through feedback in the form of rewards and penalties.
- Model training and inference: Models learn from data and make predictions or decisions based on what they’ve learned. With model training, the computer learns from data by adjusting the parameters of a mathematical model. Model inference uses the trained model to make predictions or decisions on new, unseen data.
Deep learning is a subfield of machine learning that takes learning methods a step forward. Using artificial neural networks with several interconnected layers, models learn through an a simulation of the human brain’s neural network.
Some key characteristics of deep learning include:
- Neural networks: We’ve briefly described neural networks above. Each layer within the network extracts and transforms the data, then passes it to the next layer for further processing. This structure allows networks to learn increasingly complex representations of the data, allowing them to handle complex tasks.
- Feature extraction and representation learning: Deep learning systems can automatically learn relevant features directly from raw data and don’t need explicit feature engineering as with machine learning. This allows deep learning models to discover meaningful patterns and features from complex and high-dimensional data.
- Training with large datasets: Deep learning models thrive on large amounts of data. The more data the model has, the more patterns it can recognize, and the better it performs.
- Complex architectures is used to capture and exploit the structure and dependencies in the data. Training often involves optimizing millions or even billions of parameters, so powerful hardware is necessary.
Applications of AI and Why It Matters
AI is already being used in a number of applications to help us do things faster and improve the quality of life. Here are some ways AI is already being used:
- Customer service, AI-powered chatbots offer efficient and personalized interactions with users, addressing their queries and providing assistance.
- Speech recognition technology enables voice-activated virtual assistants and enhances accessibility.
- Image and video recognition enables machines to understand and interpret visual information, powering applications like as facial recognition, object detection, and autonomous vehicles.
- Fraud detection systems, identifying suspicious activities and protecting against financial losses.
- Content personalization utilizes AI to deliver tailored recommendations, improving user experiences.
- Demand prediction algorithms optimize inventory management, reducing costs and improving supply chain efficiency.
Benefits and Risks of AI
AI comes with a number of benefits:
- It’s efficient and accurate. AI can process and analyze large volumes of data with speed and accuracy, outperforming humans in certain tasks.
- It’s available 24/7: AI systems can function continuously without breaks, providing round-the-clock services.
- It reduces human error: AI can help eliminate errors due to human fatigue and ensure higher accuracy
However, AI isn’t without risks:
- Potential job displacement: As AI systems become more competent, they may replace some jobs, leading to displacement.
- Access to private data: With AI’s ability to collect and analyze data, there are concerns over data privacy and security.
- Not “showing its work”: Sometimes, it’s hard to understand how AI arrived at a decision, creating transparency issues.
Considerations for Adding AI to Operations
Businesses considering the integration of AI into their operations should approach it thoughtfully and strategically.
- First, identifying areas within the organization that can benefit from AI is crucial. Conducting a thorough analysis of existing processes and understanding how AI can enhance efficiency and productivity is essential for a successful implementation.
- Selecting reputable AI tools, platforms, and partners is paramount to ensure reliable performance and ethical practices.
- Developing clear guidelines and ethical frameworks for AI usage within the organization helps address issues related to data privacy, transparency, fairness, and accountability.
- Regular evaluation, monitoring, and adaptation of AI systems are necessary to align with evolving business goals and changing technological landscapes.
Understanding AI is crucial for businesses looking to leverage its potential. By embracing AI ethically and responsibly, organizations can unlock innovative solutions, improve operational efficiency, and shape a future where humans and machines collaborate to drive progress.