The Significance of Artificial Intelligence
Published on : Tuesday 31-10-2023
Organisations across various industries are leveraging AI in diverse ways to create innovative products and services.
For many, Artificial Intelligence appears to be a new concept, but in reality it has been around since the 1950s. The idea was first mentioned in Alan Turing’s 1950 book ‘Computer Machinery and Intelligence’ wherein he wondered whether machines can think. However, the term ‘Artificial Intelligence’ was coined by John McCarthy in 1956 during the Dartmouth Workshop, which is considered the birth of AI as a field. Later, IBM defined AI as something that ‘leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind’. One of the more publicised use cases of AI by IBM was Deep Blue, the chess playing computer that first lost to Garry Kasparov in 1996, only to beat him a year later.
High initial expectations from AI that failed to materialise led to a period known as the ‘AI winter’ during the 1980s, which lasted over a decade. These disappointments also meant funding for AI research declined, before research shifted towards more practical, knowledge-based systems in the 1990s. What followed was resurgence when AI research regained momentum with the application of machine learning, particularly neural networks and statistical methods. The development of the backpropagation algorithm and the growth of data-driven approaches contributed to this resurgence.
Coming to the present, the availability of large datasets and increased computational power has fueled the rapid progress of deep learning. Deep neural networks, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have been instrumental in various AI applications. Further, breakthroughs in Natural Language Processing (NLP) have seen remarkable progress with the introduction of transformer models, like BERT and GPT-3, enabling more human-like text generation and understanding. Computer vision techniques, particularly in areas like object detection and image recognition, have improved dramatically, leading to applications in autonomous vehicles and healthcare.
Today, most experts agree that AI has a very critical role to play as one of the building blocks of the emerging industrial metaverse, even as the sceptics disagree. In this context a recent global study conducted by KPMG Australia in association with the University of Queensland titled ‘Trust in Artificial Intelligence’ has some interesting findings.
The research is based on survey of over 17,000 people from 17 countries that covered all global regions and included people from Australia, Brazil, Canada, China, Estonia, Finland, France, Germany, India, Israel, Japan, the Netherlands, Singapore, South Africa, South Korea, the United Kingdom (UK), and the United States of America (USA). The countries were selected as they are leaders in AI activity and readiness within their region, with the sample being nationally representative of the population based on age, gender, and regional distribution.
The summary of the report makes several pertinent points and a few among them are:
1. Most people are wary about trusting AI systems and have low or moderate acceptance of AI: however, trust and acceptance depend on the AI application
2. People recognise the many benefits of AI, but only half believe the benefits outweigh the risks
3. People expect AI to be regulated with some form of external, independent oversight, but view current regulations and safeguards as inadequate
4. People perceive the risks of AI in a similar way across countries, with cybersecurity rated as the top risk globally
5. There is strong global endorsement for the principles of trustworthy AI: trust is contingent on upholding and assuring these principles are in place
6. People are most confident in universities and defence organisations to develop, use and govern AI and least confident in government and commercial organisations
7. People expect AI to be regulated with some form of external, independent oversight, but view current regulations and safeguards as inadequate
8. Most people are comfortable with the use of AI to augment work and inform managerial decision making, but want humans to retain control
9. People want to learn more about AI but currently have low understanding
10. Younger generations, the university educated and managers are more trusting, accepting and generally hold more positive attitudes towards AI
12. There are stark differences in trust and attitudes across countries: people in the emerging economies of Brazil, India, China, and South Africa are more trusting and accepting of AI and have more positive attitudes towards AI, and
13. AI awareness, understanding and trust in AI have increased over time, but institutional safeguards continue to lag.
Today, organisations across various industries are leveraging AI in diverse ways to enhance operations, improve decision-making, optimise processes, and create innovative products and services. Here are some common applications of AI in organisations:
Data Analysis and Insights: AI helps in analysing large volumes of data to extract meaningful insights, identify patterns, and make informed decisions.
Machine Learning and Predictive Analytics: Organisations use machine learning algorithms to predict trends, customer behavior, and market dynamics, enabling them to plan strategies accordingly.
Natural Language Processing (NLP): AI-powered NLP tools process and understand human language, enabling chatbots, sentiment analysis, language translation, and text summarisation.
Chatbots and Virtual Assistants: AI-powered chatbots assist customers by providing instant responses to queries, resolving issues, and guiding them through processes.
Automation and Robotic Process Automation (RPA): AI automates repetitive and mundane tasks, improving efficiency and freeing up human resources for more strategic and creative work.
Customer Service and Personalisation: AI helps in delivering personalised customer experiences by understanding preferences, recommending products, and tailoring marketing efforts.
Fraud Detection and Security: AI algorithms are employed to detect fraudulent activities, unauthorised access, and potential security breaches to safeguard sensitive data.
Healthcare and Medical Diagnosis: AI is used in medical imaging analysis, predictive analytics for disease diagnosis, drug discovery, and personalised medicine.
Finance and Investment:
AI-driven algorithms analyse market trends, predict stock prices, automate trading, and provide investment recommendations.
Supply Chain and Logistics: AI optimises supply chain operations by predicting demand, improving inventory management, route optimisation, and reducing transportation costs.
Autonomous Vehicles and Transportation: AI powers self-driving cars and other autonomous vehicles, enhancing safety and efficiency in transportation systems.
Manufacturing and Industry 4.0: AI-enabled robots and machines optimise manufacturing processes, predict maintenance needs, and improve production efficiency.
Energy and Sustainability: AI is used to optimise energy consumption, predict energy demands, and improve sustainability efforts through efficient resource management.
Education: AI assists in personalised learning, automated grading, and adaptive educational platforms that tailor content to individual student needs.
Human Resources and Recruitment: AI helps in automating recruitment processes, screening candidates, and identifying the best fit for job positions.
Gaming and Entertainment: AI is used to create realistic game characters, enhance gaming experiences, and personalise content for users.
Organisations continually evolve and adapt AI applications to their specific needs, aiming to stay competitive and improve efficiency, productivity, and customer satisfaction.
Reference
https://assets.kpmg.com/content/dam/kpmg/au/pdf/2023/trust-in-ai-global-insights-2023.pdf
