Artificial intelligence is transforming businesses and lives through technologies like self-driving cars and AI-assisted medical diagnosis. This comprehensive guide will teach you the fundamentals of key AI concepts, including machine learning and deep learning.
Narrow or weak AI uses trained data to perform specific tasks, such as facial recognition or AI chatbots. These systems do not possess full cognitive abilities and are not unbiased.
What is AI?
Artificial intelligence is a broad term for machine learning that can perform cognitive functions that we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment and problem-solving. You’ve probably interacted with AI even without realizing it—voice assistants like Siri and Alexa, as well as customer service chatbots that pop up on websites, are built on this technology.
It’s a term that can hold many different interpretations, and this diversity is what makes the field of AI so interesting. Many scientists use it to refer to a specific set of technologies that share certain characteristics, but others see it as a umbrella term for a much broader range of research areas.
The first step to understanding the complexity of AI is breaking it down into four main types:
Narrow AI: Also known as weak AI, this type of AI has been designed and trained to carry out one particular job. These are the kinds of systems that can be found in video games, personal assistants like Apple’s Siri and Amazon’s Alexa and autonomous cars.
Strong AI: A more sophisticated version of narrow AI, strong AI aims to create intelligent machines that can perform intellectual tasks that meet or exceed human standards. These are the systems that can drive a car or play chess at a high level.
Theory of mind: This hypothetical type of AI aims to develop machine empathy, so that they can understand how people feel and make decisions based on those emotions. It’s an area of AI that is still very much a work in progress.
Self-aware: This mythical type of AI focuses on creating machine consciousness, giving them the ability to understand their own state and the emotional states of other machines. It’s a very complex area of AI that has yet to be realized.
The concept of inanimate objects being endowed with intelligence has fascinated and enthralled us for over a century. From ancient Greek myths to the captivating narratives of iconic movies, the idea that robots can think and act on their own has been a key theme in our collective imagination.
What is machine learning?
Machine learning (ML) is the discipline of AI that gives computers the ability to learn from data and past experiences to identify patterns and make predictions without explicit programming instruction. ML algorithms can evolve, improve and adapt as they are fed new information, making it an essential component of modern technology.
ML is a subset of artificial intelligence and encompasses many of the same applications, such as data mining, pattern recognition, forecasting and predicting outcomes, but it’s also increasingly being used in hardware like self-driving cars and medical equipment, as well as in areas like security and customer service. In addition, ML is enabling businesses to automate processes and increase productivity and efficiency.
In the context of a business, ML uses algorithmic models to analyze data and predict or classify an outcome. These models are optimized over time through training, which is a process that requires the model to be trained on the right kind of data and set of expected results. It is this training that differentiates ML from traditional computer programs that have been programmed with rule-based code.
The four main types of ML are supervised, unsupervised, semi-supervised and reinforcement learning. Each of these models is based on a particular algorithmic technique and can be applied to different types of data sets and desired outcomes, including identifying patterns, recognizing objects, detecting fraud or predicting customer behavior.
One of the most important aspects of ML is that it has the potential to address social problems that have been created by humans. For example, if biased information or data that reflects existing inequities is fed to a machine learning algorithm, it can be taught to replicate and perpetuate those same forms of discrimination. This has already been seen in some cases, such as chatbots that have learned to identify racist and sexist language.
It’s important to understand the limitations of ML, however. At its best, it will only achieve about 95% of human accuracy, which is fine for something like a movie recommendation engine, but not suitable for tasks such as operating a self-driving car or analyzing complex machinery for serious flaws. For this reason, companies should be cautious about the way they use ML and be sure to validate their models to ensure the safety and efficacy of their output.
What is deep learning?
Deep learning is a subset of machine learning that uses artificial neural networks to teach machines how to perform tasks. These algorithms are loosely modeled on the way the human brain works and rely on layers of neurons to process large amounts of data. These networks learn by trial and error, recognizing patterns that may not be immediately obvious to humans.
The first step in training a neural network is to create an input vector that represents the data being processed. Next, each neuron in the network is assigned a weight that is based on the estimated error between the input and output. A learning rate is then set that determines how much each model weight changes with every iteration of the training process. The more the model is trained, the better it will be able to predict the correct output.
Each layer in the neural network is designed to recognize increasingly complex features of the input vector. For example, an early layer may recognize that a shape is a stop sign and a later layer might be able to identify what color the stop sign should be. This type of algorithm is known as a deep learning approach, and it allows for greater recognition accuracy than traditional machine learning algorithms.
Another benefit of deep learning is that it does not rely on labeled data sets. This means that it can take in unstructured data such as text and images and use it to make predictions. This is also known as unsupervised learning.
While supervised learning requires data scientists to select and label the training set, deep learning does not. This makes it easier for organizations to train their own models and use them for their business. In addition, it often takes less time than supervised learning to produce results.
As the technology becomes more embedded in our everyday lives, we must ensure that AI is ethical and safe. Companies that rely on these technologies must be able to explain the decisions they make and make sure the data they use is accurate. This can prevent misguided and biased decisions that can damage a company’s reputation and bring regulatory fines.
What is AI’s future?
AI is transforming industries from healthcare and manufacturing to customer service and education. Its benefits range from reducing human error to automating processes to flagging potential security threats. AI is becoming more important in sectors like healthcare, education, and finance, where its ability to analyze large sets of data and make informed decisions is proving invaluable. As businesses continue to adapt, the potential for AI to revolutionize these industries only grows. However, it is not without its challenges. These include increased regulation, data privacy concerns and fears of job loss.
AI’s future is likely to depend on what experts call “narrow AI,” which refers to machines that are trained for specific tasks. They operate within the context of those tasks, but they lack the ability to learn beyond their initial programming. Examples of narrow AI include virtual assistants, chatbots and recommendation engines.
In the future, businesses may also begin to leverage AI-powered tools that are customized for their needs and aligned with their internal data. This will enable them to outperform general-purpose AI models by creating more accurate and tailored results. This trend will also push companies to invest in data governance to ensure that only authorized AI systems have access to sensitive, proprietary information.
The most effective organizations are those that link their AI strategy to business outcomes and industrialize their data architecture. They are also leveraging cross-functional teams with diverse skillsets and perspectives to help AI achieve the full benefits of its potential.
A growing number of businesses are investing in AI to improve their bottom lines. However, many have yet to realize the full benefits of this technology because they do not fully understand its capabilities and limitations. This misunderstanding is compounded by the lack of industry-wide best practices and standards.
One of the most important barriers to implementing AI is the lack of transparency surrounding its decision-making process. While it is possible for AI to explain its decisions in a way that humans can understand, this is rare. When it does occur, the explanation can be convoluted and difficult to understand. This is especially challenging when the decisions are related to health and safety issues, where the stakes are the highest.
In addition, some individuals have feared that the development of AI will lead to the technological singularity, an event where superintelligent machines will take over and alter humanity forever. Although this is unlikely, the fear persists and can lead to resistance to AI adoption.
