Artificial intelligence (AI) has been researched for decades and in recent years used within business applications, such as social media algorithms. Availability of generative AI, on platforms such as ChatGPT and DALL-E, has enabled millions of people to experience their first direct interaction with AI. Generative Artificial Intelligence explores key concepts in AI and the possible economic and social consequences of its widespread adoption.
Generative AI: What It Is and Where It Is Going
Across the world people are discussing the use of artificial intelligence (AI) in the production of a wide range of content. At the same time many artists, designers, writers, musicians and others are voicing their concerns regarding the need to protect original creative work and their future career prospects. However, AI will increasingly impact all of our lives and the issues it raises should be of concern to everyone, even those with no interest in the technology and its implications.
In academia, politics, industry, entertainment, business and many other sectors that AI has impacted, or soon could, people are attempting to predict what the consequences might be, for individuals and society. Some state that within a few years AI will outperform and replace humans in all job related activities, aside perhaps from those requiring particular human qualities, which AI cannot cost effectively replicate. However, others believe AI lacks essential capabilities and that this will prevent it from displacing human beings from many roles.
In this article I explore the use of generative artificial intelligence, compare popular platforms and consider the implications of the widespread adoption of the technology. In addition to the use of AI within various sectors, I look at the economic and social impact and possible scenarios for our future. My aim is to provide an overview of AI, enabling anyone, whether they have a technical or non-technical background, to understand the basics of what is involved.
Overview of AI
Artificial intelligence is a broad field and includes various technologies that can carryout tasks which previously required human cognition. AI uses techniques such as rules based systems and decision trees, to manage tasks ranging from text, image and audio generation, to image recognition and controlling machines. In essence, the difference between generative AI and the computer systems of previous decades, is that rather than follow a series of instructions, they learn to recognise patterns, enabling them to use input data to generate something new.
AI Models
Generative AI models the distribution of data, to generate new samples of data, such a text, images and audio, by recognising the structure underlying datasets used for training. The purpose of discriminative AI is to classify data by placing different types of data into different categories, such as human, car, tree or bird. Supervised learning AI models are trained to predict the output from unknown inputs by going through a process of correctly labelling input and output data. Unsupervised learning models find patterns in data, without the data being given explicit labels. Reinforcement models learn through trial and error by interacting with a real or virtual environment. For example, robots navigating a room or computers playing games.
Elements of Generative AI
Generative AI is a class of artificial intelligence that is trained on a dataset, which after learning the underlying data distribution can generate new samples of data from that dataset. Transformers enable systems to evaluate the importance of each element of input data and are widely used in the modelling of language and text generation. By looking at and relating different pieces of data, they can generate coherent long form text. Typically used in image generation, diffusion models learn to reverse the process of adding noise and generate new images by simulating noisy images and then removing the noise.
Neural networks mimic functions within a biological brain and process data to find patterns, which enables the discovery of novel outputs. Machine Learning (ML) is a subset of AI, in which systems make decisions based upon data they analyse, rather than being explicitly programmed. For example, spam filters and recommendation algorithms. Deep Learning (DL) is a subset of ML that models complex data patterns, using artificial neural networks and large data sets. For example, autonomous cars and voice assistants. A subset of ML, DL enables a system to represent hierarchical data and learn features of large datasets, to produce realistic content, such as text, images and audio.
Natural Language Processing (NLP) involves analysing and generating human speech. For example, chatbots and translation software. Large Language Models (LLMs), such as ChatGPT, are a particular subset of AI and ML, built on NLP frameworks, enabling them to manage complex language tasks, such as human-like conversation and content generation. LLMs make use of DL to analyse vast quantities of text data. Based upon this text data analysis, they learn to predict and generate patterns of language. GPT (Generative Pre-trained Transformer) models are widely used by people to generate coherent content, which can be output in different languages and formats.
Comparing AI Platforms
Large Language Models (LLMs) and the many AI powered platforms, that can be used to generate text, images and audio from typed prompts, in addition to analysing data, have had a huge impact on content creation. Some individuals and businesses have found them to be useful tools, that enable them to increase their productivity, but others fear the implications of such technology for our future. There has also been controversy, as many people resent their creative work being used as data to train AI, without their prior agreement. There are many others available, but some of the popular AI tools are described below:
ChatGPT was developed by OpenAI and built upon transformer architecture. Trained on huge datasets taken from the Internet, it can be targeted at a specific niche and quickly scaled as required. It is commonly used for text generation tasks, such as providing summaries, creating marketing material, writing code and answering questions on a wide range of subjects.
Bard was developed by Google and designed to focus on conversation, making it suitable for uses such as customer service and virtual assistants, which require dialogue and nuance. Bard is well integrated with other Google applications, such as maps and search. However, it is less flexible than GPT models and not well suited to the generation of content.
Claude was developed by Anthropic, which was founded by former OpenAI employees and prioritises development of AI that is safe, reliable and ethical. Although less flexible than some other AI platforms, Claude excels at language related tasks and it is suitable for use cases requiring high standards of ethics, such as law, education and health care.
LLaMA was developed by Meta using transformer-based models and designed to deliver high performance, with minimal resource usage. More efficient than some of the other platforms, LLaMA is also more customisable, making it a popular choice for open-source projects and research-driven studies. LLaMA is a good option for businesses keen to develop their own AI applications.
Image and Audio Generation
While ChatGPT, Bard, Claude and LLaMA are widely used to generate text, AI is also frequently used to generate images and audio. DALL-E, MidJourney and Canva are popular image generation platforms. MidJourney and DALL-E excel at the production of artistic images from prompts. Canva is a design tool, often used to make images for marketing material and posts on social media. Using text to speech, such as WaveNet from Google and Polly from Amazon, written text can be converted into synthetic human voices. Other AI tools are also in use or in development for specialised activities, such as data analysis.
Musicians have long used technology to assist them in the making, recording and delivery of music. AI Music generators such as Suno and Udio are now being used by non-musicians, but to many the results lack the soul of music created by people. Although inconsistent beyond short clips and often falling into the ‘uncanny valley’, AI generated video is becoming increasingly realistic. AI video generation platforms include Gen-2 from Runway and Stable Video Diffusion from StabilityAI, the company behind open-source Stable Diffusion. When comparing these different tools, it is important to remember that the ethical debate around their use continues.
Business and Industry
Throughout most of human history, the items people used each day were either obtained from the natural world or made using traditional tools and skills. Few people travelled beyond their home village or town and interactions were person to person. The Industrial Revolution led to mass production and advances in the twentieth century to global transportation and communications, using machines of increasing complexity. Rapid advances in computer technology, enabled the building of powerful control systems, but they were all designed and understood by human beings. For decades, computers in business and industry automated tasks, displacing people from many roles, but also creating new opportunities.
Research into artificial intelligence began during the middle decades of the twentieth century and speculation about thinking machines has existed in fiction for centuries. However, attempts to develop AI had not got beyond the level of human beings programming computers with step by step instructions. After solving a problem, such as efficiently completing a business or industrial process, programmers wrote code to give machines instructions to follow, displacing workers in many routine manufacturing and office jobs. However, during the early years of the twenty-first century this changed. Developments, such as deep learning and access to vast datasets, enabled computers to begin recognising patterns and solving complex problems, without people understanding how they did so.
For many years machines have been replacing people who were carrying out repetitive tasks that could be broken down into logical steps. However, AI is now beginning to take on roles that until recently were assumed to beyond anything machines could do. Content generation, such as AI writing social media posts is already happening, and tools are available that personalise customer marketing messages, drawing upon past communications. Companies are using AI powered customer service agents and virtual assistants, to help their customers. In engineering, AI can rapidly analyse alternative design solutions, which could accelerate technological development, reduce costs and improve performance. Medical science is using AI to find novel molecular compounds, rapidly analyse samples and aid in diagnosis. AI is also impacting roles in areas such as HR, law, logistics, retail, administration and education.
Economic and Social Consequences
Increasing use of generative AI is predicted to have a huge impact on the global economy. Existing, or new, businesses that rapidly adopt an AI strategy could increase their productivity, reduce costs and take market share from businesses that do not. New roles could emerge, but millions of people will find themselves displaced and requiring retraining. AI has the potential to provide every student with a personal tutor that can give them an education well suited to their aptitude, needs and preferences. However, in a rapidly changing world many will ask which jobs are going to remain after so many have been replaced by AI.
Some individuals using AI might outperform companies currently employing hundreds even thousands of people, resulting in them becoming hugely wealthy. At the same time millions could feel lost, without the prospect of a job or career, which would previously have provided them with both an income and sense of meaning, purpose and identity. Governments across the world will be forced to deal with the social consequences. Optimists envisage a world in which most people take on work of value to their local community, while AI continues to deliver material wealth, which is shared among the population. However, others fear a future in which a few become very wealthy, while most people struggle to survive and compete for the few jobs not taken by AI.
AI is already being used to generate artificial influencers that have attracted thousands of followers online. There will likely be increasing numbers of celebrities that are AI generated and in an often lonely world people are already beginning to form relationships with AI. At the same time many people will prioritise real world connections with other people and reject the virtual world of AI, as they seek a more traditional way of living. Governments and companies confronted by the challenges of delivering essential services and managing complex systems might increasingly make use of AI, but they will also want to remain in control of decision making processes.
Cultural and Ethical Implications
Citizens could become increasingly concerned about their privacy and the need to distinguish what is real, in a world where simulated audio and video appear to be real. Individuals carry cameras and video recorders in their mobile phones, but could soon also have access to artificial intelligence that outperforms human expertise in many areas. AI might be used to produce games and movies of a quality that currently costs millions to create. Such developments could upend creative sectors and businesses that currently employ millions of people worldwide. Artists are already struggling to get their work noticed, as the volume of often low quality AI generated content proliferates. Media companies could also struggle in a world of personalised channels, where everyone can be the star of their own reality show or movie.
The power of AI, combined with technologies such as augmented reality, virtual reality and additive printing, could cause governments to bring in regulations to restrict access to certain technology. For legal purposes, a Limited Company can be classed as a person with rights, but what would be the position of an AI that forms such a business. Regulatory authorities could struggle to decide who is responsible for actions that are taken as a result of decisions made by AI, rather than human company or government employees. Issues related to intellectual property rights are currently an issue, but what will be the position of an AI that starts a company, registers patents and competes with businesses run by people. Insurance companies are likely to raise concerns regarding financial liability, which could also lead to legislation.
The Future
Both practical and ethical implications of artificial intelligence are likely to be of growing concern during the years ahead. If human beings increasingly depend upon decisions made by AI, without us knowing how they arrived at those decisions, we should be aware of potential risks. Regardless of how well AI can mimic human thought, speech or behaviour, machines lacking sentience follow patterns that they have learned, but with no inner sense of perspective or ethical values. The question of whether AI exhibiting human behaviour should be given some form of rights and protection could become an issue for society.
Many people will demand that humans must remain the final decision makers in relation to critical infrastructure and essential services. People might also be expected to seek advice from AI, so that they are able to make more informed decisions. Regulations could be put in place that require organisations to have a clear hierarchy, with humans at the top who are responsible for the final decisions and accountable for the consequences. The use of AI within robots, equipped with visual, auditory and other sensory systems designed to enable them to interpret and interact with their environment, could bring them into frequent contact with humans. This could produce both positive and negative responses from the people they encounter.
Voice assistants, such as those available on mobile phones, use artificial narrow intelligence (ANI). There is currently much discussion of when or if artificial general intelligence (AGI) will emerge, which could rival the capabilities of human intelligence. Beyond AGI is artificial super intelligence (ASI) that would greatly exceed human intelligence, but it is hypothetical and many believe it will remain in the world of science-fiction. However, even if AI never exceeds human intelligence, the speed at which it can analyse data and find useful results could still deliver huge scientific and technological advances. Some believe this could lead to solutions being found for problems such as climate change and currently incurable diseases, but we might go through a period of traumatic societal change before then.
There are discussions about people augmenting their intelligence by having AI interfaces implanted, similar to medical devices. Many would reject the idea on ethical grounds, or fear of possible side effects. However, AI implants might appeal to some as a quick and easy way to acquire specialist knowledge and skills, that could provide an advantage in a competitive world. Combining human motivation and creative intelligence with AI, possibly with individuals connected and working together across networks, might produce something similar to ASI. Many people though would be concerned about the loss of human qualities, such as independence, free will and having a unique sense of self.
Conclusions
Generative AI has the potential to bring benefits to individuals and society, but there are challenges associated with the technology. Many organisations have begun exploring the use of AI, or are already using it. While some view AI as an assistant, that could help people build a better world, others recommend caution. Concerns have been voiced regarding the importance of human beings retaining control of the technology and the need to share the potential benefits, rather than deepening existing inequalities.
In this article I have tried to provide an overview of artificial intelligence and the possible consequences of its increasing use. New developments are emerging at a rapid rate, making it difficult to predict with any confidence what will happen during the coming years. However, the more each of us understands about the nature of AI and its potential impact on how we live and work, the better prepared we should be. The decisions we make will shape the lives of current and future generations and the world we all share.