Understanding AI: Technologies, Applications, and Impacts

Artificial Intelligence

Artificial intelligence is a field of science concerned with building computers and machines that can reason, learn, and act in such a way that would normally require human intelligence or that involves data whose scale exceeds what humans can analyze.

AI is a set of technologies that are based primarily on machine learning and deep learning, used for data analytics, predictions and forecasting, object categorization, natural language processing, recommendations, intelligent data retrieval, and more.

How does AI work?

AI systems learn and improve through exposure to vast amounts of data, identifying patterns and relationships that humans may miss. This learning process often involves algorithms, which are sets of rules or instructions that guide the AI's analysis and decision-making. In machine learning, a popular subset of AI, algorithms are trained on labelled or unlabelled data to make predictions or categorize information.

• Machine Learning: The primary approach to building AI systems is through machine learning (ML), where computers learn from large datasets by identifying patterns and relationships within the data. A machine learning algorithm uses statistical techniques to help it “learn” how to get progressively better at a task, without necessarily having been programmed for that task. It uses historical data as input to predict new output values. Machine learning consists of both supervised learning (where the expected output for the input is known thanks to labelled data sets) and unsupervised learning (where the expected outputs are unknown due to the use of unlabelled data sets).
• Neural Networks: Machine learning is typically done using neural networks, a series of algorithms that process data by mimicking the structure of the human brain. These networks consist of layers of interconnected nodes, or “neurons,” that process information and pass it between each other. By adjusting the strength of connections between these neurons, the network can learn to recognize complex patterns within data, make predictions based on new inputs and even learn from mistakes. This makes neural networks useful for recognizing images, understanding human speech and translating words between languages.
• Deep Learning: Deep learning is an important subset of machine learning. It uses a type of artificial neural network known as deep neural networks, which contain a number of hidden layers through which data is processed, allowing a machine to go “deep” in its learning and recognize increasingly complex patterns, making connections and weighting input for the best results. Deep learning is particularly effective at tasks like image and speech recognition and natural language processing, making it a crucial component in the development and advancement of AI systems.
• Natural Language Processing: Natural language processing (NLP) involves teaching computers to understand and produce written and spoken language in a similar manner as humans. NLP combines computer science, linguistics, machine learning and deep learning concepts to help computers analyze unstructured text or voice data and extract relevant information from it. NLP mainly tackles speech recognition and natural language generation, and it’s leveraged for use cases like spam detection and virtual assistants
• Computer Vision: Computer vision is another prevalent application of machine learning techniques, where machines process raw images, videos and visual media, and extract useful insights from them. Deep learning and convolutional neural networks are used to break down images into pixels and tag them accordingly, which helps computers discern the difference between visual shapes and patterns. Computer vision is used for image recognition, image classification and object detection, and completes tasks like facial recognition and detection in self-driving cars and robots.

Types of artificial intelligence

1. Reactive machines: Limited AI that only reacts to different kinds of stimuli based on preprogrammed rules. Does not use memory and thus cannot learn with new data. IBM’s Deep Blue, that beat chess champion Garry Kasparov in 1997 was an example of a reactive machine.
2. Limited memory: Most modern AI is considered to be limited memory. It can use memory to improve over time by being trained with new data, typically through an artificial neural network or other training model. Deep learning, a subset of machine learning, is considered limited memory artificial intelligence.
3. Theory of mind: Theory of mind AI does not currently exist, but research is ongoing into its possibilities. It describes AI that can emulate the human mind and has decision-making capabilities equal to that of a human, including recognizing and remembering emotions and reacting in social situations as a human would.
4. Self aware: A step above theory of mind AI, self-aware AI describes a mythical machine that is aware of its own existence and has the intellectual and emotional capabilities of a human. Like theory of mind AI, self-aware AI does not currently exist.

Benefits of AI

1. Automation: AI can automate workflows and processes or work independently and autonomously from a human team. For example, AI can help automate aspects of cybersecurity by continuously monitoring and analyzing network traffic. Similarly, a smart factory may have dozens of different kinds of AI in use, such as robots using computer vision to navigate the factory floor or to inspect products for defects, create digital twins, or use real-time analytics to measure efficiency and output.
2. Reduce human error: AI can eliminate manual errors in data processing, analytics, assembly in manufacturing, and other tasks through automation and algorithms that follow the same processes every single time.
3. Eliminate repetitive tasks: AI can be used to perform repetitive tasks, freeing human capital to work on higher impact problems. AI can be used to automate processes, like verifying documents, transcribing phone calls, or answering simple customer questions like “what time do you close?” Robots are often used to perform “dull, dirty, or dangerous” tasks in the place of a human.
4. Fast and accurate: AI can process more information more quickly than a human, finding patterns and discovering relationships in data that a human may miss.
5. Infinite availability: AI is not limited by time of day, the need for breaks, or other human encumbrances. When running in the cloud, AI and machine learning can be “always on,” continuously working on its assigned tasks.
6. Accelerated research and development: The ability to analyze vast amounts of data quickly can lead to accelerated breakthroughs in research and development. For instance, AI has been used in predictive modeling of potential new pharmaceutical treatments, or to quantify the human genome.

Disadvantages of AI

1. Job Displacement: AI’s abilities to automate processes, generate rapid content and work for long periods of time can mean job displacement for human workers.
2. Bias and Discrimination: AI models may be trained on data that reflects biased human decisions, leading to outputs that are biased or discriminatory against certain demographics.
3. Hallucinations: AI systems may inadvertently hallucinate or produce inaccurate outputs when trained on insufficient or biased data, leading to the generation of false information.
4. Privacy Concerns: The data collected and stored by AI systems may be done so without user consent or knowledge, and may even be accessed by unauthorized individuals in the case of a data breach.
5. Ethical Concerns: AI systems may be developed in a manner that isn’t transparent, inclusive or sustainable, resulting in a lack of explanation for potentially harmful AI decisions as well as a negative impact on users and businesses.
6. Environmental Cost: Large-scale AI systems can require a substantial amount of energy to operate and process data, which increases carbon emissions and water consumption.

Applications and use cases for artificial intelligence

1. Speech recognition: Automatically convert spoken speech into written text.
2. Image recognition: identify and categorize various aspects of an image.
3. Translation: Translate written or spoken words from one language into another.
4. Predictive modelling: Mine data to forecast specific outcomes with high degrees of granularity.
5. Data analytics: Find patterns and relationships in data for business intelligence.
6. Cybersecurity: Autonomously scan networks for cyber attacks and threats.