Definitions of Artificial Intelligence (AI) vary in complexity depending on who you ask. Even lawmakers are struggling to define the exact parameters of what is considered AI. The EU AI Act, for example, deems that any system with a degree of autonomy capable of making predictions, recommendations, or decisions should be classed as AI. By contrast, other definitions – such as the one outlined in Connecticut Senate Bill 1103 – deem that an AI system should also display human-like cognition or perception. At its most fundamental level, however, the term refers to computer systems capable of comprehending complex information, reasoning, and learning from experience in order to complete a designated task.
In recent years, computer scientists exploring AI have engineered thousands of sophisticated systems that now materially impact our day-to-day lives – from AI-powered traffic management networks to personal recommendation systems on streaming platforms. A movement to ensure that AI technology trends are adopted safely has ensued, with a coalition of 1,000 tech leaders going as far as to sign an open letter calling for a temporary pause on its development as a result of recent events.
The motives – and, indeed, feasibility – of that initiative have been questioned, but there is now a broad academic consensus that the development of AI cannot be allowed to continue completely unchecked. Equally, however, most agree that if correctly harnessed, AI could profoundly enhance institutions like our healthcare and education systems, thereby drastically improving our collective quality of life.
It is becoming increasingly clear that a modern, deliberate and nuanced approach towards making AI safer is the only way forward.
Real-world AI examples include the automation of processes in the technology, healthcare, finance, insurance, HR, and retail industries. These systems train algorithms to learn from patterns in a data set and make predictions or decisions without explicit programming, a process known as machine learning. Insurance firms, for example, automate their claims process using machine learning by training AI systems to predict the veracity of claims based on historical data.
Deep learning, a subfield of machine learning, creates artificial neural networks, using multiple interconnected layers of neurons to process complex data. Like the biological neural networks in the human brain, deep learning can cognise intricate patterns and structures in data automatically.
Machine learning and deep learning systems have an almost limitless number of practical applications, but efforts must be made to ensure that they are developed and deployed responsibly. Cautionary tales from the employment industry, for example, have shown that a lack of diversity in a data set can perpetuate pre-existing societal biases, leading to biased hiring and promotion outcomes.
The term ‘machine learning’ was first coined by American computer science pioneer Arthur Samuel in 1959, but the most rudimentary forms of the technology – simple decision-tree-based systems – have been around even longer.
In recent years, more sophisticated iterations have been used in areas such as spam email recognition and credit card fraud detection. The pace of change has increased exponentially in the last decade, and many advanced systems now use deep learning to complete increasingly complex tasks.
Deep learning-trained facial recognition systems can, for example, now identify human faces with a higher degree of accuracy than humans themselves. Facial recognition technology is just one of a near-infinite number of uses of deep learning. Automated medical diagnosis, speech recognition software, and driverless cars are among an abundance of others.
Through automating tasks, improving efficiency, and allowing organisations to better manage their data and resources, AI has delivered myriad benefits for numerous sectors.
But the technology comes with inherent risks and there are profound ethical concerns about job displacement, privacy, and the impact of AI-proliferated misinformation on democratic values.
This duality is perhaps best illustrated in social media algorithms, which help businesses connect with users and can amplify positive socio-cultural movements, but which can also reinforce biases, create echo chambers, and expose users to harmful content.
The risks extend into the corporate world too, with organisations liable to be hit with financial penalties and suffer reputational damage if they fail to comply with an ever-evolving spectrum of AI regulation. Enterprises can also take a monetary hit if their algorithms are flawed. Knight Capital's trading algorithm, for example, bought 150 stocks for around $7 billion in error in 2012 before Goldman Sachs intervened to buy the shares at a $440million cost to the financial services company – a blunder caused by one engineer failing to copy a new software code to another server.
The growing risks of AI in daily life have acted as a catalyst for debates among both policymakers and the general public, for whom the proliferation of interactive systems like OpenAI’s large language model ChatGPT have brought the real-world impacts of AI into sharper focus. AI in gaming and other recreational pursuits has served the same purpose, while the use of AI in finance and other spheres is also tangibly impacting people’s everyday lives.
A period of rapid growth has already seen AI impact and shape the trajectory of society, and the pace of change is only set to accelerate.
One key area expected to mature and progress expeditiously in the coming years is generative AI, which has the potential to revolutionise industrial and creative activities.
Generative AI is being used in art, design, and content creation, with systems now effectively able to act as autonomous creative collaborators, carving out new opportunities for expression and innovation. For example, generative systems like DALL·E can generate elaborate images using text prompts, while AI music generators can now independently compose complex pieces. This has raised questions regarding intellectual property rights and the blurred lines between human and machine-generated content.
Some academics meanwhile believe that generative AI is the first step towards a true artificial general intelligence, the potential implications of which in fields such as scientific research are epic in scope. The term artificial general intelligence – which, at this stage, remains a theoretical concept – is used to refer to a system capable of replicating and surpassing human levels of cognition and completing a vast, diverse range of tasks. Many have theorised that artificial general intelligence could one day be used to solve the world’s most complex problems.
It is for these reasons that understanding the applications and impact of AI is crucial for society, which will doubtless be shaped by the technology for years to come. Engaging with ethical discussions around bias and exploring other topics related to the responsible deployment of AI can help create a future which aligns and promotes our most important values.
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Written by Adam Williams, Content Writer at Holistic AI.
DISCLAIMER: This blog article is for informational purposes only. This blog article is not intended to, and does not, provide legal advice or a legal opinion. It is not a do-it-yourself guide to resolving legal issues or handling litigation. This blog article is not a substitute for experienced legal counsel and does not provide legal advice regarding any situation or employer.
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