|Image Credit: “Artificial Intelligence & AI & Machine Learning” by mikemacmarketing is licensed under CC BY 2.0.|
What is Artificial Intelligence
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and act like humans. These intelligent machines are designed to learn from their environment and experiences, adjust to new inputs, perform human-like tasks, and adapt their behaviour based on their interactions with the world around them. AI can analyse large amounts of data quickly and make decisions based on that analysis, making it a powerful tool for solving complex problems and improving efficiency. AI can be used to develop systems that can understand, think, and behave like humans, and it can be applied in a wide range of fields, such as computer science, medicine, and finance. Some examples of AI include virtual personal assistants, self-driving cars, and face recognition technology.
AI can be classified into two main types: narrow or weak AI (designed to perform a specific task) and general or strong AI (designed to perform any intellectual task that a human being can). The end game of AI research is to create a machine that can think and act like a human, but currently, most AI systems are limited to performing specific tasks.
Artificial intelligence (AI) enables a computer or machine to mimic the cognitive functions of the human brain, such as learning and problem-solving. This involves using algorithms and data to allow a machine to make decisions or take actions that would require human intelligence, such as recognising patterns, understanding language, and making predictions. AI has the potential to transform many industries and improve our daily lives in several ways, from healthcare and education to transportation and entertainment.
It is difficult to predict exactly when AI will become fully integrated into our lives, as the development of AI technology depends on several factors and is constantly evolving. As technology continues to advance and become more sophisticated, we will likely see even more widespread adoption of AI in the coming years.
The concept of artificial intelligence dates back to the 1950s, when a group of researchers and scientists, including John McCarthy, Alan Turing, and Marvin Minsky, began exploring the idea of creating machines that could simulate human intelligence. However, there is no single individual who can be credited with inventing AI. Instead, the development of AI has resulted from a collective effort by many researchers and scientists over the years, who have contributed to the field through their work in areas such as computer science, neuroscience, psychology, and philosophy. [See Final Words and Conclusions at the end of this paper]
Put simply, AI is a type of computer program that can think and make decisions on its own without being told exactly what to do. It is like a computer game that can learn how to play by itself or a robot that can move around and interact with its environment without being controlled by a person. AI can be used in many different ways, from helping us find information on the internet, to making our homes and cars smarter and more efficient.
Image Credit: A computer-generated picture depicting AI, rendered at https://beta.dreamstudio.ai/dream
AI may enable machines to do more work in the future, potentially allowing humans to pursue leisure activities instead of going to work. However, it is also important to recognise that the development and use of AI technology may create new job opportunities, such as in the fields of AI research and development, data analysis, and machine learning. Additionally, even if AI does automate certain tasks and jobs, it is likely that there will always be a need for human skills and expertise in areas such as creative problem-solving, critical thinking, and decision-making.
In theory, AI has the potential to write books on any subject, including fiction and technical subjects. AI algorithms can be trained to generate text by analysing large amounts of data and identifying patterns and structures in the language. This process is known as natural language processing, and it allows AI systems to produce written text that is similar to human-generated text in terms of grammar, style, and coherence. However, it is important to note that AI-generated text is not the same as human-generated text, and it may lack the creativity, nuance, and individuality characteristic of human writing. It is also worth mentioning that writing a book is a complex and time-consuming process that involves more than just generating text, so it is not clear how much of the book-writing process AI would be able to handle on its own.
‘the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.’
How AI started
The history of artificial intelligence began in antiquity, with myths, stories and rumours of artificial beings endowed with intelligence or consciousness by master artisans. The seeds of modern AI were planted by philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and its ideas inspired a handful of scientists to seriously discuss the possibility of building an electronic brain.
Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an “AI winter“), and then by new approaches, success and renewed funding. AI research has tried and discarded many different methods since its founding, including simulating the brain, modelling human problem solving, formal logic, large databases of knowledge and imitating animal behaviour. In the first decades of the 21st century, highly mathematical-statistical machine learning dominated the field, and this technique has proved highly successful, helping to solve many challenging problems throughout industry and academia.
The various sub-fields of AI research are centred around specific goals and using particular tools. The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects. General intelligence (the ability to solve an arbitrary problem) is among the long-term goals of AI. To solve these problems, AI researchers have adapted and integrated a wide range of problem-solving techniques – including search and mathematical optimisation, formal logic, artificial neural networks, and methods based on statistics, probability and
economics. AI also draws upon computer science, psychology,
linguistics, philosophy, and many other fields.
The field of AI was founded on the assumption that human intelligence “can be so precisely described that a machine can be made to simulate it“. This assumption raised philosophical arguments about the mind and the ethical consequences of creating artificial beings endowed with human-like intelligence; these issues have previously been explored by myth, fiction and philosophy since antiquity. Computer scientists and philosophers have since suggested that AI may become an existential risk to humanity if its rational capacities are not steered towards beneficial goals.
What is being said about Artificial Intelligence?
If you visit Snowflake, you will note they say AI is the most disruptive technology innovation of our lifetime. Enterprises are embracing AI applications and leveraging a variety of data types (structured, unstructured, and semi-structured) for integrated processes across all lines of business and industries.
It seems as if everyone is dumb-struck by the recently launched, mid-blowing AI technology you can try out at https://chat.openai.com/chat. Only a few days after its launch, more than a million people were trying out ChatGPT. But its creator, the for-profit research lab called OpenAI, warns that ChatGPT “may occasionally generate incorrect or misleading information,” so be careful. You can read about ChatGPT at: https://www.cnet.com/tech/computing/why-everyones-obsessed-with-chatgpt-the-mind-blowing-ai-chatbot/.
‘The tool, from a power player in artificial intelligence, lets you type questions using natural language that the chatbot answers in conversational, albeit somewhat stilted, language. The bot remembers the thread of your dialogue, using previous questions and answers to inform its next responses. Its answers are derived from huge volumes of information on the internet. It’s a big deal. The tool seems pretty knowledgeable, if not omniscient. It can be creative, and its answers can sound downright authoritative.’
Harvard Business Review (HBR)
Harvard Business Review (at: https://hbr.org/2022/12/chatgpt-is-a-tipping-point-for-ai) says: From the Summary:
‘We’re hitting a tipping point for artificial intelligence: With ChatGPT and other AI models that can communicate in plain English, write and revise the text, and write code, the technology is suddenly becoming more useful to a broader population of people. This has huge implications. The ability to produce text and code on command means people are capable of producing more work, faster than ever before. Its ability to do different kinds of writing means it’s useful for many different kinds of businesses. Its capacity to respond to notes and revise its own work means there’s significant potential for hybrid human/AI work. Finally, we don’t yet know the limits of these models. All of this could mean sweeping changes for how — and what — work is done in the near future…’
- From the HBR website (14/12/2022) (at: https://hbr.org/2022/12/chatgpt-is-a-tipping-point-for-ai), it says:
‘Less than two weeks ago, OpenAI released ChatGPT, a powerful new chatbot that can communicate in plain English using an updated version of its AI system. While versions of GPT have been around for a while, this model has crossed a threshold: It’s genuinely useful for a wide range of tasks, from creating software to generating business ideas to writing a wedding toast. While previous generations of the system could technically do these things, the quality of the outputs was much lower than that produced by an average human. The new model is much better, often startlingly so. Put simply: This is a very big deal. The businesses that understand the significance of this change — and act on it first — will be at a considerable advantage. Especially as ChatGPT is just the first of many similar chatbots that will soon be available, and they are increasing in capacity exponentially every year.’
The Telegraph (which tested the latest AI) explains why you should be worried in an article by Ed Cumming on 11th December 2022:
‘ChatGPT is the most recent revolution in artificial intelligence, with mind-boggling capabilities – but it raises ethical questions. The software can write scripts, poems, even newspaper articles – and it has been suggested that it could soon replace Google. It will disappoint fans of The Terminator, but the AI revolution is coming not in the form of killer robots or dystopian autocracies but chat bots. We were told it would mean the apocalypse. So far, it looks a lot like customer service, albeit much better than usual.
‘The latest revolution in public-facing artificial intelligence is ChatGPT, a piece of software designed by OpenAI, a California-based research company. GPT is short for Generative Pre-trained Transformer. In the simplest terms, it works by scouring its dataset, which is most of what is written on the internet, finding the answers that best fit a given prompt, and rendering it in clear, if wooden, English. It’s a bit like the autocomplete function on your phone or email, except on a much grander scale.
‘To those unfamiliar with developments in AI, the latest capabilities are mind-boggling. You type in a request, and it generates a response. It can write scripts, poems, even newspaper articles. It isn’t only journalists and copywriters at risk of being made redundant. Even more impressively, ChatGPT has been able to write credible computer code. It has been suggested that the software could soon replace Google… Paul Buchheit, an engineer working on Google’s email service, Gmail, tweeted that Google might only be a year or two away from “total disruption”.’
In the 2023-24 season, we will see the “semi-automated offside system” being used for the first time at football matches with artificial intelligence using data sent from a sensor inside the ball and 12 dedicated cameras.
The Champion Hurdle is a prestigious horse racing event held annually at the Cheltenham Festival in England. The race is open to horses aged four years and above, and it is one of the most prestigious hurdles races in the world. The next race is set to be a thrilling showdown between two talented horses: Honeysuckle and Constitution Hill. Racing Post asked ChatGPT to write a horseracing article about the race and the two favourites – the results were fascinating – see: https://www.racingpost.com/news/we-asked-an-ai-to-write-horseracing-articles-and-the-results-were-fascinating/591933
How does Artificial Intelligence work?
Undeniably, all the preceding text is interesting, but the key question is: how does AI work? TechTarget provides an answer:
‘As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialised hardware and software for writing and training machine learning algorithms. No single programming language is synonymous with AI, but a few, including Python, R and Java, are popular.
‘In general, AI systems work by ingesting large amounts of labelled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.
‘AI programming focuses on three cognitive skills: learning, reasoning and self-correction.’
The next generation of search on the web is about AI-powered search and appears to go miles further than the humble Google search. A blog post on algolia.com explains it like this:
‘With the advent of artificial intelligence (AI) technologies enabling services such as Alexa, Google search, and self-driving cars, the next generation of search is about AI-powered search. In the past, text entered in a search box produced similar results for most users. Now, developments in AI have made it possible to produce more-relevant results that are adjusted in real-time based on factors such as browsing history, the intent commonly associated with the words used, and high-performing content from similar searches.
‘AI-powered search has the potential to give your site users exactly what they want, which in turn, can contribute to helping [businesses] meet their business goals for greater customer satisfaction, higher conversion rates, and increased revenue.’
Image Credit: “170908 V0.2 TEDx HULT Ashridge. Alessandro Lanteri. Driverless Cars” by Engage Visually. Debbie Roberts is licensed under CC BY-NC-ND 2.0.
What’s happening now in the world of Artificial Intelligence (AI)
The field of AI is rapidly evolving, with many developments happening in different areas. Some of the key areas of research and development in AI include:
- Natural Language Processing (NLP): This area focuses on enabling machines to understand and generate human language. There has been significant progress in this area recently, with the development of advanced language models such as GPT-3.
- Computer Vision: This area focuses on enabling machines to understand and interpret images and videos. There have been notable advancements in object detection and image recognition.
- Robotics: This area focuses on developing robots that can perform a wide range of tasks, from simple to complex. There have been notable advancements in the development of autonomous robots and drones.
- Reinforcement Learning (RL): This area focuses on training agents to take action in an environment to maximise a reward. This area has seen a lot of progress in recent years, with the development of advanced RL algorithms and applications in gaming and robotics.
- Generative Models: This area focuses on developing models that can generate new data, such as images, text, and speech. Generative models such as GANs and VAEs have seen a lot of progress in recent years.
Overall, AI is becoming increasingly more sophisticated and is being applied to many industries, including healthcare, finance, and transportation.
And what’s around the corner?
Several areas of AI are expected to see significant advancements in the near future, including:
- Explainable AI (XAI): As AI is increasingly being used to make important decisions, there is a growing need for AI systems that can explain their decisions and reasoning. Ongoing efforts are being made to develop XAI techniques that provide more transparency and interpretability for AI models.
- Edge AI: With the increasing amount of data being generated by IoT devices, there is a growing need for AI systems that can process data at the edge or close to the source of data rather than sending all the data to the cloud for processing.
- Multi-Agent Systems: This area focuses on developing AI systems that coordinate and collaborate with other AI agents or humans. This is an active area of research, and it is expected to see more progress soon.
- Autonomous Systems: This area focuses on developing AI systems that can operate independently and make decisions without human intervention. This is an active area of research and development, and it is expected to see more progress shortly, especially in the field of self-driving cars.
- Quantum Machine Learning: This area focuses on combining quantum computing with machine learning. There are ongoing efforts to develop quantum algorithms that can be used to speed up the training of machine learning models, and it is expected to see more progress soon.
- Healthcare: AI is being applied to a wide range of healthcare applications, such as drug discovery, medical imaging, and precision medicine. This area is expected to continue growing in the near future. (see also more in-depth review later in this paper).
Overall, the field of AI is expected to continue advancing rapidly, with new developments and applications emerging in a wide range of areas.
Will AI be used alongside doctors in the treatment of patients?
Yes, AI is expected to be used increasingly alongside doctors in treating patients. There are several ways in which AI is being used or is expected to be used in healthcare, including:
- Medical Imaging: AI algorithms can be used to analyse medical images, such as X-rays and CT scans, to help diagnose and treat diseases. For example, AI can identify tumours in medical images and help radiologists and oncologists make more accurate diagnoses and treatment plans.
- Drug Discovery: AI algorithms can be used to analyse large amounts of data from genetics and medical records to help identify potential new drugs and accelerate the drug discovery process.
- Precision Medicine: AI can be used to analyse patient data, such as genetic data and medical records, to help identify the most effective treatments for individual patients based on their specific characteristics.
- Remote monitoring: AI can be used to monitor patients remotely and to send alerts to doctors and caregivers when there are signs of a problem.
- Clinical decision support: AI can be used to analyse patient data and provide doctors with recommendations for diagnosis and treatment.
It is important to note that AI is not intended to replace doctors but to assist them in their work, providing them with a new set of tools to make better decisions, more accurate diagnoses and more personalised treatments. The use of AI in healthcare is still a relatively new field, and there is ongoing research and development to improve the capabilities of AI systems and ensure their safe and effective use in healthcare.
Picture Credit: TOPIO, a humanoid robot, played ping pong at the Tokyo International Robot Exhibition (IREX) 2009. Sources: “A Ping-Pong-Playing Terminator”. Popular Science, and “Best robot 2009”. gadgetrivia.com.
Attribution: Humanrobo, CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0>, via Wikimedia Commons
Page URL: https://commons.wikimedia.org/wiki/File:TOPIO_3.jpg
Why are there plug-ins for Browsers and Other Software?
As you will discover, plug-ins, as described below, rely heavily on artificial intelligence.
Plug-ins for browsers allow users to add additional functionality to their web browsers. Plug-ins are designed to extend functionality and provide access to a wider range of web content and applications. Plug-ins are often used to play multimedia content, such as videos and games, display PDF documents, or run complex web applications. They allow users to customise their browsing experience and access specific types of content and applications that might not be supported by the browser alone.
Here’s a list of popular plug-ins for browsers:
- AdBlock Plus: an ad-blocking tool for blocking online advertisements.
- Ghostery: a privacy and security tool for blocking trackers and scripts.
- LastPass: a password manager for securely storing and managing passwords.
- uBlock Origin: an ad-blocking tool for blocking online advertisements.
- Honey: a tool for finding and applying coupons and discounts while shopping online.
- Evernote Web Clipper: a tool for capturing and organising web content.
- Save to Pocket: a tool for saving and organising articles and web pages for later reading.
- Vimium: a keyboard-based navigation tool for using the web without a mouse.
- Dark Reader: a tool for applying a dark theme to websites to reduce eye strain.
- HTTPS Everywhere: a tool for encrypting web traffic and protecting against eavesdropping.
- Grammarly: a writing assistant that helps improve grammar, spelling, and punctuation.
- Hola VPN: a virtual private network for accessing content that may be restricted in your region.
- Tampermonkey: a tool for installing and managing user scripts for improving websites.
- Video DownloadHelper: a tool for downloading videos from websites.
- Turn Off the Lights: a tool for dimming the background of a website to focus on video content.
- Awesome Screenshot: a tool for capturing and annotating screenshots of websites.
- NoScript Security Suite: a tool for blocking scripts and active content on websites.
- ZenMate VPN: a virtual private network for accessing content that may be restricted in your region.
- Privacy Badger: a tool for blocking trackers and scripts that collect personal data.
- Tree Style Tab: a tool for organising tabs in a tree-style view.
- Auto Refresh Plus: a tool for automatically refreshing web pages at set intervals.
- Stylus: a tool for applying custom styles and themes to websites.
- WOT: a web of trust tool for rating the safety and trustworthiness of websites.
- Session Buddy: a tool for saving and restoring browser sessions.
- The Great Suspender: a tool for suspending tabs to reduce memory usage.
- Pocket: a tool for saving and organising articles and web pages for later reading.
- Pushbullet: a tool for sending and receiving notifications, files, and links between devices.
- Send to Kindle: a tool for sending web content to a Kindle e-reader.
- Quick Translation: a tool for translating web pages and text into different languages.
- TabSave: a tool for saving and restoring browser tabs.
According to a January 2023 report by TrueList, Google Chrome has nearly 189,000 extensions with over 1.3 billion installations, the most popular being Cisco Webex Extension, Google Translate, Avast Online Security, Adobe Acrobat, Grammarly, Adblock Plus, Pinterest Save Button, Skype, AdBlock, Avast SafePrice, uBlock Origin, Honey, and Tampermonkey.
As with browsers, Microsoft Office, like many other software applications, may also need plug-ins from third-party companies for the reasons mentioned above. Plug-ins can enhance the software’s capabilities by adding new features or enabling support for specific file types. For example, a plug-in for Microsoft Office may allow the software to export files in a new format or import data from a particular file type.
Integrating with third-party plug-ins allows Microsoft Office to offer users a more comprehensive set of tools and functionality. Additionally, third-party plug-ins may bring new and innovative capabilities to the software that the original developers had not anticipated.
Here’s a list of some popular plug-ins for Microsoft Office:
- Grammarly: a writing assistant that helps improve grammar, spelling, and punctuation.
- DocHub: a PDF editor and document management tool.
- EasyBib: a citation and bibliography generator for academic papers.
- Microsoft Power BI: a business intelligence and data visualisation tool.
- MindNode: a mind mapping tool for visualising and organising ideas.
- Trello: a project management and collaboration tool.
- Tableau: a data visualisation and business intelligence tool.
- Adobe Acrobat: a PDF creation, editing, and management tool.
- RightSignature: an electronic signature and document management tool.
- OneDrive: Microsoft’s cloud-based file storage and collaboration platform.
- Send Anywhere: a file transfer and sharing tool.
- Mergix: a duplicate contact cleaner for Microsoft Outlook.
- Avery Label Merge: a tool for creating and printing Avery labels.
- Wunderlist: a task management and to-do list tool.
- IFTTT: a platform for automating tasks and creating workflows.
- Microsoft Dynamics 365: a customer relationship management tool.
- Canva: a graphic design and image editing tool.
- Skype for Business: a video and audio conferencing tool.
- Evernote: a note-taking and organisational tool.
- Microsoft Stream: a video and content management tool.
- OneNote: a note-taking and organisational tool from Microsoft.
- Microsoft Planner: a project management and collaboration tool.
- Microsoft Teams: a team collaboration and communication platform.
- Microsoft Forms: a tool for creating and conducting surveys and quizzes.
- Microsoft PowerApps: a platform for building custom business applications.
- Microsoft Power Automate: a tool for automating tasks and workflows.
- Microsoft To-Do: a task management and to-do list tool.
- Microsoft Whiteboard: a digital whiteboard and collaboration tool.
- Microsoft Bookings: an appointment scheduling and management tool.
- Microsoft StaffHub: a scheduling and shift management tool for shift workers and managers.
These are just a few examples of the many plug-ins available for browsers and Microsoft Office. The specific plug-ins that you need will depend on your individual needs and the projects you are working on.
What is ChatGPT?
ChatGPT, which is making huge waves in artificial intelligence, gaining 100 million users within the first two months of launch, uses the latest advancements in deep learning and natural language processing to generate human-like responses to text inputs. It can be used in various applications such as chatbots, virtual assistants, and language translation.
ChatGPT has been developed by OpenAI, a research organisation founded in 2015 to promote and develop friendly AI responsibly and safely. OpenAI has received funding from several sources, including investors such as Elon Musk, Sam Altman, Greg Brockman, Ilya Sutskever, Wojciech Zaremba, and John Schulman. OpenAI has also received support from strategic partners such as Microsoft and NVIDIA.
How does Microsoft plan to integrate it in teams and other office products?
Microsoft plans to integrate ChatGPT into its Teams collaboration platform and other Office products. The integration of ChatGPT will allow users to interact with the platform using natural language, making it easier for people to access information and perform tasks.
For example, ChatGPT could be used in Microsoft Teams to respond to questions or perform tasks such as scheduling meetings, searching for files, or creating a team. This integration will allow users to work more efficiently and save time, as they can quickly access information and perform tasks without having to navigate multiple menus or switch between different applications.
Similarly, ChatGPT could be integrated into other Office products to provide users with natural language assistance and make it easier to create and edit documents, spreadsheets, and presentations. This integration is part of Microsoft’s efforts to make its products more accessible and user-friendly.
How would schools and universities and their students use it?
Schools and universities and their students can use ChatGPT in various ways to enhance their learning experience and improve efficiency. Integrating AI in education can provide students with new and innovative ways to learn and help educators improve the efficiency and effectiveness of their teaching. A few of the many potential applications of ChatGPT in education are:
- Virtual Tutor: ChatGPT can be a virtual tutor to answer students’ questions, provide explanations, and offer guidance on various subjects.
- Question Answering: ChatGPT can assist students in finding answers to their questions quickly and accurately, saving them time and helping them stay focused on their studies.
- Essay Assistance: ChatGPT can assist students in writing essays by generating relevant content and offering suggestions for improvement.
- Personalised Learning: ChatGPT can help students personalise their learning by providing individualised feedback and recommendations based on their learning style and abilities.
- Research Assistance: ChatGPT can assist students in their research by providing relevant information and generating outlines and summaries of articles and academic papers.
What other applications could it be used for?
ChatGPT, as a conversational AI language model, has numerous potential applications across various industries and fields. Some examples include:
- Customer Service: ChatGPT can enhance customer service by providing fast and accurate responses to customer inquiries and support requests.
- Virtual Assistants: ChatGPT can be integrated into virtual assistants to make them more effective and provide more human-like interactions.
- Language Translation: ChatGPT can provide real-time language translation services, making communication across language barriers easier.
- News Updates: ChatGPT can generate summaries of news articles and other written content, saving people time and making it easier to stay informed.
- Healthcare: ChatGPT can be integrated into healthcare applications to provide patients with medical information, answer their questions, and assist in making decisions.
- Finance: ChatGPT can be used in the financial sector to provide investment advice, answer questions about financial products and services, and assist in transaction processing.
These are just a few examples, and the potential applications of ChatGPT are virtually limitless. As the technology continues to evolve, new and innovative uses for ChatGPT are likely to emerge.
How might accountants use it?
Accountants can use ChatGPT in several ways to improve their work and efficiency. Some examples include:
- Financial Statement Preparation: ChatGPT can assist in preparing financial statements by generating data-driven reports and summaries.
- Tax Preparation: ChatGPT can assist in tax preparation by providing relevant information, answering questions, and generating tax forms and returns.
- Auditing: ChatGPT can assist the auditing process by providing relevant data, generating reports, and identifying potential issues.
- Bookkeeping: ChatGPT can assist with bookkeeping tasks, such as generating invoices, tracking expenses, and reconciling accounts.
- Client Interactions: ChatGPT can be integrated into client-facing applications, allowing accountants to quickly and accurately respond to client inquiries and support requests.
By incorporating ChatGPT into their workflows, accountants can increase their productivity and efficiency and provide better services to their clients. Using AI in accounting can also help reduce manual errors, streamline processes, and provide greater insights into financial data.
How might lawyers use it?
Like accountants, lawyers can use ChatGPT in several ways to improve their work and efficiency. Some examples include:
- Legal Research: ChatGPT can assist in legal research by providing relevant information and generating summaries of legal documents and case law.
- Contract Review: ChatGPT can assist lawyers in the contract review process by identifying potential issues and suggesting changes.
- Document Generation: ChatGPT can generate legal documents, such as contracts, pleadings, and motions in litigation matters, saving lawyers time and reducing the risk of manual errors.
- Client Interactions: ChatGPT can be integrated into client-facing applications, allowing lawyers to respond quickly and accurately to client inquiries and support requests.
- Predictive Analytics: ChatGPT can analyse legal data and provide lawyers with insights into potential outcomes, helping them make more informed decisions.
How might doctors, specialists and hospitals use it?
Doctors, specialists, and hospitals can use ChatGPT in several ways to improve their work and efficiency. AI in healthcare can also help reduce manual errors, streamline processes, and provide greater insights into medical data. Some examples include:
- Patient Interactions: ChatGPT can be integrated into patient-facing applications, allowing medical professionals to provide quick and accurate responses to patient inquiries, referrals to specialists and support requests.
- Medical Information: ChatGPT can assist medical professionals in providing accurate and up-to-date medical information, reducing the risk of misdiagnosis and improving patient outcomes.
- Clinical Decision Support: ChatGPT can assist medical professionals in making clinical decisions by providing relevant information and generating diagnostic and treatment suggestions.
- Electronic Health Records (EHRs): ChatGPT can be integrated into EHRs to provide medical professionals with real-time access to patient information, reducing the risk of errors and improving patient care.
- Research and Development: ChatGPT can assist medical researchers and developers in generating hypotheses, identifying potential treatments, and analysis of data.
How would job applicants use it?
Job applicants can use ChatGPT in several ways to improve their job search and application process and provide job applicants with greater insights into the job market and job opportunities. Some examples include:
- Career Advice: ChatGPT can assist job applicants in exploring different career paths and making informed decisions about their job search.
- CV Writing: ChatGPT can assist job applicants in creating effective and targeted resumes, helping them to stand out from other applicants.
- Interview Preparation: ChatGPT can assist job applicants in preparing for job interviews by providing information about the company and the role and generating responses to common interview questions.
- Job Search: ChatGPT can assist job applicants in searching for job openings and identifying career opportunities that align with their skills and experience.
- Networking: ChatGPT can assist job applicants in building professional networks and making connections that can lead to job opportunities.
How might it be used in Marketing?
Marketers can use ChatGPT to improve their marketing strategies and campaigns. Some examples include:
- Customer Engagement: ChatGPT can assist in engaging with customers and prospects, providing quick and accurate responses to inquiries and support requests.
- Content Creation: ChatGPT can assist marketers in generating creative and relevant content for their campaigns, such as social media posts, blogs, and email marketing content.
- Data Analysis: ChatGPT can assist in analysing marketing data and generating insights, helping them to make informed decisions and optimise their campaigns.
- Campaign Planning: ChatGPT can assist in planning and executing marketing campaigns, from identifying target audiences to determining the best channels and tactics to reach them.
- Market Research: ChatGPT can assist in conducting market research, helping them to understand their target audiences better and identify new opportunities.
How might journalists use it?
There are many ways that Journalists can use ChatGPT to improve their research and writing processes. Some examples include:
- Gathering News: ChatGPT can assist journalists in gathering and verifying news, providing quick access to information and sources.
- Research Assistance: ChatGPT can assist in conducting research and providing relevant information and insights on various topics and subjects.
- Content Generation: ChatGPT can assist in generating content, such as summaries, outlines, and first drafts, reducing the time and effort required to complete articles and other written works.
- Fact-Checking: ChatGPT can assist in fact-checking information and sources, helping to improve the accuracy and credibility of their work.
- Interview Preparation: ChatGPT can assist journalists in preparing for interviews, generating questions and providing information on the person or topic being interviewed.
By incorporating ChatGPT into their work, journalists can increase their efficiency and productivity, allowing them to focus on the creative and human aspects of journalism, such as storytelling and narrative. The use of AI in journalism can also help to improve the accuracy and credibility of the work, reduce manual effort, and provide greater insights into news and information.
How might copywriters use it?
Copywriters can use ChatGPT to improve their writing and creative processes. Some examples include:
- Content Generation: ChatGPT can assist copywriters in generating content – such as headlines, product descriptions, and other written materials.
- Writing Assistance: ChatGPT can assist in writing and editing copy, providing suggestions for improvement and reducing the time required to complete projects.
- Idea Generation: ChatGPT can assist copywriters in generating ideas and concepts for campaigns and projects, providing inspiration and creativity.
- Research Assistance: ChatGPT can assist with conducting research and providing relevant information and insights on various topics and subjects.
- Tone and Style Consistency: ChatGPT can assist in maintaining a consistent tone and style across multiple pieces of content, helping to improve the brand’s overall voice and messaging.
Using AI in copywriting can also help to improve the quality and consistency of the work, reduce manual effort, and provide greater insights into the target audience and market trends.
How might bloggers use it?
Bloggers can use ChatGPT to improve their blogging and content creation processes. Some examples include:
- Content Generation: ChatGPT can assist bloggers in generating content, such as blog posts, articles, and other written materials, reducing the time and effort required to complete projects.
- Writing Assistance: ChatGPT can assist in writing and editing their content, providing suggestions for improvement and reducing the time required to complete projects.
- Idea Generation: ChatGPT can assist in generating ideas and concepts for their blog, providing inspiration and creativity.
- Research Assistance: ChatGPT can assist in conducting research and providing relevant information and insights on various topics and subjects.
- Search Engine Optimisation (SEO): ChatGPT can assist in optimising their content for search engines, helping to improve visibility and attract more traffic.
How might paediatric occupational therapists use it?
Paediatric occupational therapists can use ChatGPT in several ways to support their work with children and improve their therapy processes. Using AI in occupational therapy can also help to improve the quality of care, reduce manual effort, and provide greater insights into best practices and current treatments. Some examples include:
- Information Retrieval: ChatGPT can assist paediatric occupational therapists in retrieving information and researching various topics, including best practices and current treatments.
- Treatment Planning: ChatGPT can assist pediatric occupational therapists in developing treatment plans and determining the best interventions for each child based on their individual needs and abilities.
- Progress Tracking: ChatGPT can assist in tracking the progress of each child over time, helping to identify areas of improvement and adjust treatment plans accordingly.
- Collaboration and Communication: ChatGPT can assist in communicating and collaborating with other healthcare professionals and parents, improving care coordination and ensuring the best outcomes for each child.
- Resource Recommendation: ChatGPT can assist pediatric occupational therapists in recommending resources and tools for parents and caregivers to support the child’s development and progress outside therapy sessions.
How can it help graduates and school leavers decide on a career path?
By incorporating artificial intelligence into their career exploration, graduates and school leavers can make informed decisions and increase their chances of success in their chosen fields. Using AI in career development can also help reduce uncertainty and provide support and guidance. ChatGPT can assist graduates in choosing a career path in several ways, such as:
- Career Assessment: ChatGPT can assist graduates in assessing their strengths, interests, and values, providing insights into potential career paths and industries that align with their skills and passions.
- Career Research: ChatGPT can assist in researching various career paths and industries, providing information on job duties, requirements, and pay prospects.
- Job Matching: ChatGPT can assist graduates in matching their skills and interests with job opportunities, providing a personalised list of career options to consider.
- Networking: ChatGPT can assist in connecting with professionals in their desired industry, providing opportunities for mentorship, advice, and job referrals.
- Interview Preparation: ChatGPT can assist in preparing for job interviews, providing tips on answering common questions and making a positive impression.
Final Words and Conclusions
Artificial Intelligence is one of the biggest trends in the digital world. For many businesses, though, it sounds like something to do with futuristic robots and not something they can use today. But AI is a big help for many and is already part of our lives. Examples include Siri from Apple and Alexa from Amazon.
Car manufacturers use artificial intelligence in nearly every part of the car-manufacturing process. Examples of AI in the automotive industry include industrial robots constructing vehicles and autonomous cars navigating traffic with machine learning and vision. A good place to start reading about cars and AI is to Google using the search term ‘Cars and AI’ or simply go to https://builtin.com/artificial-intelligence/artificial-intelligence-automotive-industry
If you are already running a business, learning how to improve your website using Artificial Intelligence should be top of the list of your New Year resolutions. You can start by visiting The Pipeline at: https://pipeline.zoominfo.com/marketing/artificial-intelligence-website
Artificial Intelligence has the potential to revolutionise many industries and has already been applied in a variety of fields, such as healthcare, finance, and transportation. However, it has also raised ethical and social concerns, such as the potential loss of jobs to automation, the possible misuse of the technology and the potential for bias in AI systems. If you’re looking for the best websites about AI, the AI Magazine website has a list of their favourite AI websites to use in 2022. It’s worth reading what they have to say. There’s also a veritable mine of information here and here.
You might also be interested in learning more about:
- Machine Learning: a subset of artificial intelligence that involves training a computer to make decisions or predictions based on data. There are several different approaches to machine learning, including supervised learning, where the computer is given labelled examples and learns to make predictions based on them, and unsupervised learning, where the computer must find patterns in a dataset without being given any labels.
- Natural Language Processing: the ability of a computer to understand and generate human-like language. This can involve tasks such as language translation, text classification, and sentiment analysis.
- Computer Vision: the ability of a computer to interpret and understand visual data from the world, such as images and videos, involving tasks such as object recognition, image segmentation, and facial recognition.
- Robotics: AI is often used in the field of robotics to enable machines to perform tasks that would be difficult or impossible for a human to do, involving tasks such as assembling products in a factory or exploring hazardous environments.
- Expert Systems: a type of AI designed to mimic a human expert’s decision-making ability in a particular field. Expert systems use a combination of machine learning and rule-based systems to make decisions and provide recommendations
Finally, it may surprise you to know that the notion of machine learning probably started with Gottfried Wilhelm (von) Leibniz (1646 – 1716) see left. He was a German polymath who was active as a mathematician, philosopher and scientist in the 17th century who speculated that human reason could be reduced to mechanical calculation. Leibniz’s ideas were ahead of their time, and it would be many years before the first artificial intelligence systems were developed. His work laid the foundations for the field of AI as we know it today. You could call him a true visionary, perhaps even the Father of Artificial Intelligence. Later proponents were Alan Turing, Warren McCulloch and Walter Pitts. Overall, all four of these individuals made significant contributions to the field of artificial intelligence, and their work has had a lasting impact on its development.
Image Credit: Gottfried Wilhelm (von) Leibniz, 1646 – 1716.
Attribution: Christoph Bernhard Francke, Public domain, via Wikimedia Commons.
Page URL: https://commons.wikimedia.org/wiki/File:Gottfried_Wilhelm_Leibniz,_Bernhard_Christoph_Francke.jpg
Sources and Further Reading
- Artificial Intelligence Basics: A Non-Technical Introduction, by Tom Taulli (Author), published by Apress (2 August 2019, available at: https://www.amazon.co.uk/dp/1484250273
- Demystifying Artificial intelligence: Simplified AI and Machine Learning concepts for Everyone, (English Edition), by Prashant Kikani, published by BPB Publications (5 January 2021), available at: https://www.amazon.co.uk/Demystifying-Artificial-intelligence-Simplified-Learning/dp/9389898706
- Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems, by Bernard Marr, published by Wiley (1st edition, 12 April 2019), available at: https://www.amazon.co.uk/dp/1119548217/
- Artificial Intelligence: Modern Magic or Dangerous Future? (Hot Science) Paperback – Illustrated, by Yorick Wilks (Author), published by Icon Books Ltd (6 June 2019) available at: https://www.amazon.co.uk/Artificial-Intelligence-Modern-Dangerous-Science/dp/1785785168/
- AI: The Tumultuous Search for Artificial Intelligence, by Daniel Crevier (1993). New York, NY: Basic Books. ISBN 0-465-02997-3.
- Machines Who Think (2nd ed.), by Pamela McCorduck, (2004), Natick, MA: A. K. Peters, Ltd., ISBN 1-56881-205-1.
- The Brain Makers: Genius, Ego, And Greed In The Quest For Machines That Think, by HP Newquist, (1994). New York: Macmillan/SAMS. ISBN 978-0-672-30412-5.
- The Quest for Artificial Intelligence: A History of Ideas and Achievements, by Nils Nilsson, (2009). New York: Cambridge University Press. ISBN 978-0-521-12293-1.
- Artificial Intelligence In 6 Minutes | What Is Artificial Intelligence? | AI Tutorial | Simplilearn, at: https://www.youtube.com/watch?v=uh5LCXOBmSI
- How artificial intelligence is changing our society | DW Documentary, at: https://youtu.be/-ePZ7OdY-Dw
- Artificial intelligence and algorithms: pros and cons | DW Documentary, at: https://www.youtube.com/watch?v=s0dMTAQM4cw
- Stunning new AI “could be conscious” – with Elon Musk., at: https://www.youtube.com/watch?v=ixgFtjfO_7Q
CAUTION: This paper is compiled from the sources stated but has not been externally reviewed. Neither we nor any third parties provide any warranty or guarantee as to the accuracy, timeliness, performance, completeness or suitability of the information and materials covered in this paper for any particular purpose. Such information and materials may contain inaccuracies or errors and we expressly exclude liability for any such inaccuracies or errors to the fullest extent permitted by law. Your use of any information or materials on this website is entirely at your own risk, for which we shall not be liable. It shall be your own responsibility to ensure that any products, services or information available through this paper meet your specific requirements and you should neither take action nor exercise inaction without taking appropriate professional advice. The hyperlinks were current at the date of publication.
End Notes and Explanations
Source: “Artificial Intelligence, n: Oxford English Dictionary”. www.oed.com. ↑
Sources: (1) Crevier, Daniel (1993). AI: The Tumultuous Search for Artificial Intelligence. p.109. New York, NY: Basic Books. ISBN 0-465-02997-3, and (2) Funding initiatives in the early 80s: Fifth Generation Project (Japan), Alvey (UK), Microelectronics and Computer Technology Corporation (US), Strategic Computing Initiative (US): McCorduck (2004, pp. 426–441), Crevier (1993, pp. 161–162, 197–203, 211, 240), Russell & Norvig (2003, p. 24), NRC (1999, pp. 210–211), Newquist (1994, pp. 235–248), referenced at: https://en.wikipedia.org/wiki/Artificial_intelligence ↑
Sources: (1) First AI Winter, Lighthill report, Mansfield Amendment: Crevier (1993, pp. 115–117), Russell & Norvig (2003, p. 22), NRC (1999, pp. 212–213), Howe (1994), Newquist (1994, pp. 189–201), and (2) Second AI Winter: McCorduck (2004, pp. 430–435), Crevier (1993, pp. 209–210), NRC (1999, pp. 214–216), Newquist (1994, pp. 301–318), referenced at: https://en.wikipedia.org/wiki/Artificial_intelligence ↑
Sources: (1) Funding initiatives in the early 80s: Fifth Generation Project (Japan), Alvey (UK), Microelectronics and Computer Technology Corporation (US), Strategic Computing Initiative (US): McCorduck (2004, pp. 426–441), Crevier (1993, pp. 161–162, 197–203, 211, 240), Russell & Norvig (2003, p. 24), NRC (1999, pp. 210–211), Newquist (1994, pp. 235–248), and (2) “Why 2015 Was a Breakthrough Year in Artificial Intelligence”. Bloomberg.com,
referenced at: https://en.wikipedia.org/wiki/Artificial_intelligence ↑
Sources: (1) Clark, Jack (2015b). “Why 2015 was a Breakthrough Year in Artificial Intelligence”. Bloomberg.com and (2) AI widely used in late 1990s: Russell & Norvig (2003, p. 28), Kurzweil (2005, p. 265), NRC (1999, pp. 216–222), Newquist (1994, pp. 189–201), referenced at: https://en.wikipedia.org/wiki/Artificial_intelligence ↑
Explanation: This list of intelligent traits is based on the topics covered by the major AI textbooks, including Russell & Norvig (2003), Luger & Stubblefield (2004), Poole, Mackworth & Goebel (1998) and Nilsson (1998) ↑
Source: Pennachin & Goertzel (2007); Roberts (2016), referenced at: https://en.wikipedia.org/wiki/Artificial_intelligence ↑
Explanation: This statement comes from the proposal for the Dartmouth workshop of 1956, which reads: “Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it.” Source: McCarthy, John; Minsky, Marvin; Rochester, Nathan; Shannon, Claude (1955). “A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence“. ↑
Source: Newquist, HP (1994). The Brain Makers: Genius, Ego, And Greed In The Quest For Machines That Think. pp.45-53. New York: Macmillan/SAMS
ISBN 978-0-672-30412-5. Referenced at: https://en.wikipedia.org/wiki/Artificial_intelligence ↑
Explanation: Russell and Norvig note in the textbook Artificial Intelligence: A Modern Approach (4th ed.), section 1.5: “In the longer term, we face the difficult problem of controlling super-intelligent AI systems that may evolve in unpredictable ways.” while referring to computer scientists, philosophers, and technologists. Source: Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2. Referenced at: https://en.wikipedia.org/wiki/Artificial_intelligence ↑
Source: The Times, Friday 1st July 2022. ↑
Explanation: GPT-3 is defined by TechTarget as “the third generation Generative Pre-trained Transformer, is a neural network machine learning model trained using internet data to generate any type of text. Developed by OpenAI, it requires a small amount of input text to generate large volumes of relevant and sophisticated machine-generated text. GPT-3’s deep learning neural network is a model with over 175 billion machine learning parameters. To put things into scale, the largest trained language model before GPT-3 was Microsoft’s Turing NLG model, which had 10 billion parameters. As of early 2021, GPT-3 is the largest neural network ever produced. As a result, GPT-3 is better than any prior model for producing text that is convincing enough to seem like a human could have written it.” Source: https://www.techtarget.com/searchenterpriseai/definition/GPT-3 ↑
Explanation: GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders) are both types of neural networks used for generative modelling. A GAN consists of two main components: a generator network that creates new data samples, and a discriminator network that attempts to distinguish the generated samples from real samples. The generator and discriminator are trained together in an adversarial process, where the generator learns to create samples that can fool the discriminator, and the discriminator improves its ability to distinguish the generated samples from real samples. VAEs, on the other hand, are a type of generative model that learns to represent data in a lower-dimensional space, called the latent space. The VAE consists of an encoder network that maps data samples to the latent space, and a decoder network that maps the latent representation back to the original data space. The VAE is trained to maximize the likelihood of the data under the model, while also encouraging the latent representations to be similar to a prior distribution, such as a Gaussian. Source: Interrogation of machine-driven AI at https://chat.openai.com/chat ↑