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When I was at school, I was fortunate enough to have an excellent English teacher, Tim Cornish. What made him exceptional was his ability to bring literature to life for a group of schoolboys, which is indeed a gift. One of the poets he introduced us to was Wilfred Owen. This has led me to a lifetime interest in the work of this great poet. He could articulate the graphic and horrific scenes that confronted him on the Western Front during the First World War. I'm not certain where it came from, but I had a lightbulb moment: what if I used one of Owen's poems as a prompt for ChatGPT? ChatGPT gave me a list of Owen’s poems from which I selected, and this is what I got: Dulce et Decorum Est: Exposure: The Sentry: Strange Meeting: Owen's skill with words translated into the most vivid pictures. One art form is converted into another: It's quite remarkable. It added to my impression that AI vision has come a long way. Furthermore, it made me wonder whether ChatGPT's ability to interpret images had improved too. Taking a picture of a red, amber, and green risk report, I asked ChatGPT to explain what it was seeing. My previous blog is the result: https://www.barbicanconsulting.co.uk/post/red-amber-green-gpt-4-does-risk It is odd how the mind works; my classroom experiences of Owen's poems led to prompting a picture that then led to analysing risk through vision. I never would have thought that my English lessons all those years ago would have helped me do this. It's inconceivable. It's also an example of where mental mindsets are extremely valuable in tackling problems, and AI has been able to facilitate this. That's why there will be many discoveries that are beneficial to mankind over the coming years, arising from connections that AI can help us make, a Strange Meeting.

I've taken a screenshot of the notes from my previous blog and uploaded them to ChatGPT. Then I've asked ChatGPT to write using the following prompt: "please create a long-form article from my notes. Avoid hyperbole and the use of lots of adjectives, the tone should be factual and informative. I do want an Introduction, White collar work, Communication, all Multimodel features, implications for work as themes thank you" I was initially disappointed. I was expecting something much more in my style However, this expectation is unrealistic. When I wrote my original article, I took a lot of time and I developed my thought processes. While GPT-4 is an excellent scribe, it doesn't have the same thought patterns that I have. Furthermore, I used ChatGPT to investigate some of my chains of thought. I asked it to help improve the understandability and readability of my text; we had a long dialogue, and in my opinion, it improved the clarity. As a result of this experiment, I've learned this: ChatGPT in its GPT-4 version can certainly take a picture of your notes and turn them into an article. If someone had shown me this only 12 months ago, I would have been astounded at the speed and quality of what it could do. In the future, as we rely more and more on large language models, our critical thinking skills could become, well, less critical. When you write, part of the benefit is the process of developing your thoughts. By using ChatGPT to do the job for you, this process is short-circuited and, worse, in many cases, it may lead to lazy thinking. It's a bit like the quality of my handwriting, which has deteriorated over the years I've been using a keyboard. ChatGPT has a voice of its own. It writes well, but the way it writes is not the same as the way that you write. Some people will prefer the artificial text, and others your text. What is certain is that your way of expressing things will not be presented by ChatGPT unless you do a lot of prompting, encouraging it to adopt your tone. If you need to think things through and add your tone use ChatGPT, let it help you write and do it jointly. You can think of this just like a painter who has a particular style; their picture is unique and conveys what they see. Use Chat GPT as the brush. For generic documents and when you just need to get things done ChatGPT excels on its own. Don't get hung up on its inability to write as you would. Many people are not wordsmiths, and the fact that ChatGPT can do a very adequate job in writing means that people who have trouble expressing themselves on paper are now in a situation where they can do so easily and clearly. My conclusion is that ChatGPT or another LLMs can write just from a picture of some basic notes; something you may have jotted in a meeting. It will turn them into an effective document but if you want to add your stamp it's harder. Choosing which approach to take I leave to you. Be the Judge: The article I wrote This is what ChatGPT made off my notes: Embracing Multimodal AI in White Collar Professions: A Detailed Overview Introduction In the epoch of digital transformation, white-collar work has been consistently reshaped by technological advancements. Among the plethora of innovations, multimodal artificial intelligence (AI) systems, particularly those like ChatGPT, stand at the forefront. With capabilities that extend beyond simple task automation, these systems are becoming essential tools in the professional toolkit. This exploration delves into the integration and implications of multimodal AI in the landscape of white-collar work. The Role of ChatGPT in White Collar Work The advent of AI in white-collar professions has introduced a new dimension of efficiency and capability. ChatGPT, powered by OpenAI's GPT-4, is a significant contributor to this shift. Through its design, it learns by trial and error, enhancing its ability to assist professionals in various tasks. These tasks span the spectrum from analysis and understanding to writing and planning. The practicality of ChatGPT lies in its versatility. It serves as a round-the-clock assistant, ready to support a myriad of functions that form the crux of white-collar work. While it's not flawless, the AI's continuous learning capability ensures that it is an asset that evolves in tandem with the needs and complexities of modern professions. Communication: The AI Facilitator Communication is at the heart of white-collar jobs, whether it's through written emails, reports, or dialogues. Here, ChatGPT serves as a communication facilitator. It not only aids in crafting clear and coherent messages but also enables the absorption and relay of information in a structured manner. This is crucial when it comes to learning adaptation, where new skills are applied to familiar tasks, or explaining complex ideas to others in a comprehensible way. ChatGPT exemplifies the notion of "knowledge work," where information is processed and tasks are completed more efficiently. It also excels at breaking down complex problems into manageable segments, allowing for a clearer assessment of potential solutions and outcomes. Multimodal Capabilities: Enhancing AI's Workforce Integration ChatGPT is emblematic of the multimodal AI revolution, showcasing an array of features that mimic the multifaceted nature of human job functions. This multimodality means that it can operate across various mediums and methods: Data Analysis: ChatGPT can conduct rigorous data analysis by processing uploaded datasets, identifying patterns, and making predictions, thereby streamlining decision-making processes. Web Browsing: With browsing capabilities through Bing, it can access and synthesize information from the web, effectively functioning as a research assistant. Visual Processing: By uploading images, users can engage with the AI to understand and interact with visual data, an essential function for professions relying on graphical information. Creative Generation: The integration of Dalle into the AI suite allows ChatGPT to generate images, thereby assisting in the creative aspects of white-collar tasks, from presentations to product design. These features not only expand the utility of AI in the workplace but also herald the biggest technological change in how professional tasks are approached and executed. Implications for White-Collar Work The implementation of multimodal AI, such as ChatGPT, in white-collar professions comes with significant implications. The speed of task completion and the ability to operate in multiple ways mirror the dynamic and diverse nature of human workers. These AI systems support and sometimes surpass human capability, especially in terms of availability and speed. This AI transformation facilitates a more efficient work environment, where tasks that used to take hours can now be completed in minutes. Moreover, by taking over repetitive and time-consuming tasks, AI allows professionals to focus on more strategic and creative aspects of their work, fostering innovation and driving growth. The multimodal nature of ChatGPT also indicates a future where AI could potentially replace certain jobs. However, it is more likely that AI will augment human roles, leading to job evolution rather than elimination. The change prompted by AI in white-collar work is profound, and its full impact will unfold as the technology matures and becomes more integrated into everyday professional activities. Conclusion As we stand at the brink of an AI revolution in the workplace, it is clear that tools like ChatGPT will play an integral role in shaping the future of white-collar professions. These advanced systems promise not just efficiency but also a transformative approach to how work is conducted. As we continue to harness the power of multimodal AI, we are paving the way for a new era of professional expertise, where technology and human ingenuity coalesce to unlock unprecedented potential.

Introduction to ChatGPT If you haven't tried ChatGPT and are a bit lost about where to start, the following will help you. ChatGPT is software that allows you to use artificial intelligence; it's a type of chatbot developed by OpenAI, capable of engaging in conversational dialogue through natural language processing. It can perform a variety of tasks including answering questions, composing essays, summarising documents, developing software, and recognising images. Background and Evolution of GPT Models Let's understand, in simple terms, more about GPT-4 and other models, (my apologies to the clever people who worked on it). Machine learning (ML) has been around since the late 1940s. The recent development of being able to program Artificial Intelligence (AI) through natural language has occurred as a result of combined breakthroughs in mathematics, the internet, and computer hardware, in particular, memory and processing speed. The mathematicians and engineers designed and built clever code called a Generative Pre-Trained transformer (GPT). You can think of this as a next-word predictor, like the one you use when you text. Once the algorithm (GPT) has been built, it is trained. This requires a vast amount of data typically taken from the web and books. The GPT processes this data and works out the relationship between words so it becomes a next-word prediction model. This takes a lot of computing power to do. Once complete, the model is not perfect; it needs fine-tuning, which is done by asking it questions, seeing its response, and giving it feedback. Once the process is complete, it can be used either as a closed model, like GPT-4, where you have very little control over the way it works via its weightings, or as an open-source model like LLAMA from Meta, where you can adjust the way it works by changing the weightings. Some models are small, like Mistral’s 7 billion parameter model which can run on a laptop, whereas GPT-4's 400 billion parameter model requires Graphic Processing Units. These are very powerful arrays of chips and, at the time of writing, are expensive and highly sought after. Overview of GPT-4 and why you need it and not the free version There are several versions of ChatGPT you can use. The free version is GPT-3.5. The paid version is GPT-4. I’ll describe some of the differences: GPT-4 has a significantly enhanced capability to understand and generate more text. This is because its model has 400 billion parameters*, whereas GPT-3.5 has 175 billion parameters. GPT-4's context window is approximately 25,000 words (32,000 tokens), while GPT-3.5 has a window of 6,500 to 7,000 words (8,192 tokens). This is important; the larger context window allows for more extended interactions, enhanced linguistic finesse, programming power, image and graphics understanding, and a reduction in inappropriate or biased responses. However, while GPT-4 offers more depth and complexity in its responses, it is slower than GPT-3.5. GPT-4 has a limit of 25 to 50 messages every 3 hours; GPT-3.5 is unlimited. From experience, it is quite feasible to hit this cap, and you have to use 3.5 or have a coffee break before proceeding further with GPT-4. GPT-4 is multimodal; it can do more than generate text; it can code, read documents, examine pictures, generate pictures, write computer code, and a lot more. If you want to use the best model with all the functionality and are serious about using ChatGPT, then you need GPT-4. At $20.00 a month, you will soon find, as I have, it’s good value. I do not get paid to write this by OpenAI. Practical Applications of GPT-4 in Knowledge Work Many of us are employed in what we call knowledge work, which involves many separate tasks. You could describe these as writing, talking, analysing, explaining, presenting, and persuading. Information comes to us, we process it and in turn, add value to it before passing it on to others via conversation, emails, texts, spreadsheets, graphs, and presentations. All of these tasks have one thing in common; they come under the heading of communication. To improve our communication, we are learning new skills and adapting old ones to make sense of the world and convey messages to others. When you use GPT-4, you realise that, in its multimodal form, it can integrate into what you do. It has tools and features which can be used in various situations to help you. GPT-4 is the Swiss army knife of knowledge work. The tools that it has when used properly, can not only speed up the way you do things but can also improve what you do. And if you are doing things faster and better, this is a “good” thing. In a market-based economy, it makes you more efficient and therefore less prone to obsolescence. How GPT-4 helps When you consider all the skills we use at work, you realise the development of GPT-4 has not been a haphazard process. Its multimodal functionality helps us undertake what we do. Let's have a look at some of these features: Writing: Through the chat or prompt interface, you can communicate with ChatGPT and ask it to do anything related to text. It will write an email, a thank you note, a memorandum, a letter; this list is endless. It will write in a particular tone or style or enter a conversation with you. Information retrieval: GPT-4 has a training database drawn from a mass of information from the web. You can ask it for facts, information, and opinions on just about any topic. The model that it currently works on has a cut-off point of April 2023. If you want reliable information after this date, you must ensure that GPT-4 is enabled to browse the web. Browsing: GPT-4 does this through Bing. When you converse with GPT-4, it can search the web for more up-to-date information and use that in its response. With the browse function enabled, GPT-4 will decide when to browse. I’ve found it helpful to ask it to browse, particularly when you want events post-April 2023 to be included in your conversation. Data analysis: GPT-4 has a powerful data analysis capability, which works with both numeric and text data. For example, you can upload a spreadsheet and ask it to analyse trends or upload text-based customer feedback and ask it to comment on sentiment. With trial and error, GPT-4 will help you gain insights quickly, easily, and cheaply. Vision: You can upload pictures to GPT-4 and work with these. For example, you can upload a handwritten note and get it transcribed, and then rewrite the contents using GPT-4. You could upload a picture of a report, for example, a technical medical paper or diagnosis, and ask GPT-4 to explain the contents to a layman, and it will do it. DALL·E: This is Chat GPT's picture-generating model. You can ask GPT-4 to generate a picture by giving it instructions in the prompt window; it will then generate a picture that mirrors your request. You influence the output by altering your prompt. Writing computer code: GPT-4 enables non-programmers to write executable code, it not only generates code snippets that run directly but also offers features like code cleanup, code changes, and insights on coding practices. It can write in languages such as Python and HTML, making coding accessible to individuals with varying levels of technical expertise. Furthermore, GPT-4's autonomous code-writing capabilities are closely linked to the data analysis function in that it writes code to undertake the data analysis tasks; prompting the model leads to code writing and execution and as a non-programmer, this is how I use it. Custom GPTs: These represent an advanced yet user-friendly feature that allows you to create tailored AI models based on specific prompt configurations. Imagine the process of interacting with GPT-4 by crafting detailed prompts to guide its responses or actions. These carefully constructed prompts can be transformed into custom GPTs, enabling you to automate repetitive tasks or specialised queries without the need for continuous prompting or retraining of the base GPT-4 model. These custom models can be saved for personal use, shared with select individuals via a direct link, or made publicly available through the GPT Store. The GPT Store functions similarly to an App Store, offering a platform where anyone can discover and utilise custom GPTs created by others. I find the custom GPTs very helpful as they let me use GPT-4 in a way that would normally require developer skills. Accessing GPT-4 There are several ways to access GPT-4; the best way depends on what you need it for: For individuals seeking a straightforward and user-friendly way to interact with GPT-4, a personal subscription to ChatGPT is the best option. It provides easy access to the AI's conversational capabilities without the need for technical knowledge or development skills. This subscription is well-suited for a wide range of personal use cases, from learning and entertainment to productivity and creative assistance. For business users, there is either API access, which is direct access to GPT-4 through OpenAI's API, (encrypted key), this is suitable for developers and businesses looking to integrate AI capabilities into their applications or services. Or Team GPT which provides collaborative features and more confidentiality (you can stop OpenAI training on your data). It's designed for enterprise use, requiring a more expensive business subscription due to a minimum of two seats. You can also use OpenAI’s sandbox, which is another environment primarily for development where you can adjust the parameters and see how various prompts behave. Finally, you may be using GPT-4 without knowing it through third-party websites or applications integrating the API into their offerings. This method can vary in user experience and functionality, depending on how the third party has utilised the GPT-4 capabilities. The World of Work and GPT-4’s Role In the knowledge economy, our work involves reading, writing, and arithmetic (if I can use that as a description of coding, analysis, and strategy), and the faster that we can obtain information and convert it into something valuable and then communicate with others, the more efficient we become and the more valuable we are. When you consider GPT-4, it has all the things that we do built into it. It can write, browse, analyse, study pictures, make pictures, write code, and enter dialogue, it can save your prompts and store them for you; it is multifunctional. When you see this, you realise GPT-4 and similar general models are massive steps forward in our ability to deal with data and convert this quickly and intelligently into something usable and helpful. Because these models are the worst models that we're going to use and they are getting better and better very quickly, they will soon be on par with, or better than, human experts in a given field. Where your work is not at an expert level, then it is very likely that these models will replace what you do because they are cheap. This means economic disruption for many. The only way to take advantage of these changes is to embrace the use of these models in the work you do. The best way to do this is to see them as helpful graduate trainees who have a very good basic understanding of how the world works, but they do not currently have in-depth expertise that has been gained through experience. By working with them, much of the day-to-day work can be delegated out, and you can concentrate on using your experience to take your work to a higher level. Summary Models like GPT-4 have changed things. In white-collar industries, we are paid for our knowledge and ability to use information, process it, and complete all sorts of tasks. I’ve called this communication. At first sight, GPT-4 is a chatbot, but once you explore all the things it can do, there is a whole suite of tools like the proverbial Swiss army knife. There is no training manual because how you use it depends on what you want it to do, your curiosity, and your willingness to accept change. Because this computing power is cheap and effective, the workplace will change; if your job can be done by GPT-4 or its offspring, your bargaining power is likely to decline. The rate of change will be much faster than, say, web usage as the infrastructure and incentives are in place, and the advancements are speeding up, not slowing down. You should consider bringing these models into your work to future-proof what you do. Use them as helpful assistants, treat them in the same way you would a co-worker who you are instructing, and you will be amazed at what they can do for you. This is truly where you can learn. Using GPT-4 is a very practical skill that is largely gained from practice. You can get the basics in a few hours but “intuition” about its capability needs a lot more time and experimentation. By working together, you can see what you can do that it can’t, this is where you are adding value and it’s where the future is. Critical thinking, emotional intelligence, and complex problem-solving are increasingly valuable in a GPT-4 augmented workplace. This is both a great change but also a massive opportunity to do new things. *A note on parameters: Think of the parameters in a GPT model as the internal parts of an engine that you can adjust to optimise performance. These parameters are akin to the nuts, bolts, timing belts, spark plugs, and fuel injectors in a car's engine. Parameters as Engine Components: Adjustable Settings: Just like you can tweak the timing of the spark plug or the air-to-fuel ratio to get the best performance out of an engine, in a GPT model, the parameters (which are essentially weights in the neural network) are adjusted during the training process to make the model more accurate. These adjustments are based on the data the model is trained on, similar to how you might tune an engine based on the type of fuel it will use or the conditions it will operate in. Complexity and Performance: The number of parameters in a GPT model is similar to the complexity of an engine. An engine with more components (like a V8 versus a V4) can be more powerful if tuned correctly, but it's also more complex to manage. Similarly, a GPT model with more parameters can process language more effectively, capturing nuance and complexity in text, but requires more data and computational power to train and tune. Optimisation for Efficiency: In the same way that you might use a turbocharger to increase the efficiency and output of an engine without making it bigger, optimisation techniques in machine learning adjust these parameters to improve the model's performance without necessarily increasing its size. This involves fine-tuning the model's parameters to ensure that it can understand and generate language as accurately and coherently as possible. Fine-Tuning for Specific Tasks: Imagine you have a basic engine setup that you can then fine-tune for different purposes, like racing, towing, or fuel efficiency. Similarly, once a GPT model has been pre-trained on a large dataset to understand language generally, it can be fine-tuned by making smaller adjustments to its parameters for specific tasks, such as writing in a particular style, translating languages, or answering questions. This fine-tuning process adjusts the model's internal settings to specialise in the task at hand, much like adjusting an engine to optimise it for a specific type of driving. In the workshop of machine learning, parameters are the internal components of the GPT "engine" that determine how it processes and generates language. By adjusting these parameters through training and fine-tuning, similar to how a mechanic would tune an engine, we can optimise the model's performance for specific tasks or improve its overall efficiency and accuracy. Just like with engines, the skill lies in knowing how to adjust these parts correctly, balancing power, efficiency, and usability to meet the desired outcome.

Every time you use ChatGPT, you enter a prompt, which is a request for what you would like it to do. At a basic level, prompting is straightforward, provided you have the mindset to think about the task at hand. However, this means that each time you need to create a new prompt to get ChatGPT to work for you. There is an easier solution, and that is to make custom GPTs. These remember prompts and related actions, allowing you to complete tasks with ChatGPT without having to remember the details yourself. Creating a custom GPT can be done by anybody, and no special coding skills are required. Here's an example: Let's suppose I want a selection of thumbnail pictures that I can use for a blog post or article. I would like these to be related to the article and draw the reader to the content. If I was only doing this once, then I would just use the normal prompt box in ChatGPT. But if it is a task that I am doing on a regular basis, then it would be helpful to have a custom GPT. The first thing you do is click on "Explore GPTs" on the left-hand side: Then in the top right , you will see a button labeled "Create.": Click on this, and it opens a window. Complete this with the task you would like the GPT to help you with. Here, you can see my instructions for the thumbnails that I wish to create. On the right-hand side, you can test the action of the GPT and make any necessary changes: Once you are happy, you can save the GPT either for your own use or for those who have the link. Alternatively, you can open it entirely and put it in the store. And that's about it. This is the first step to creating GPTs, and the more you try them, the more creative you become. Good luck! P.S. I posted this blog into my custom GPT and this is what it came up with: ------------------------------ Thank you for sharing the details of your blog! Based on the summary, it's clear the blog discusses the concept of using ChatGPT for various tasks, focusing on the ease of creating custom GPTs to streamline and personalize the interaction process. The key themes include the simplicity of prompt creation, the advantage of custom GPTs for recurring tasks, and the accessibility of GPT technology to users without coding skills. Let's create four distinct thumbnails to encapsulate these themes: Painting Thumbnail: A vibrant, impressionistic painting of a person sitting at a desk, surrounded by floating, colorful thought bubbles. Each bubble contains symbols or icons representing different tasks (like a camera for photography, a pen for writing, etc.), with a larger bubble showing a simplified, friendly GPT interface. This image will symbolize the creativity and variety of tasks that custom GPTs can assist with. Photo Thumbnail: A high-quality, realistic photo of a diverse group of people gathered around a computer screen, visibly engaged and excited. The screen shows a simplified, user-friendly GPT interface, hinting at the collaborative and accessible nature of custom GPTs. This photo will convey the idea of inclusivity and the broad appeal of GPT technology. Sketch Thumbnail: A detailed, black and white sketch showing a close-up of hands typing on a laptop, with abstract lines and shapes emanating from the screen to represent the digital workflow and the seamless interaction with GPT. This sketch will focus on the personal interaction between the user and the technology, highlighting the ease of creating custom prompts. Abstract Thumbnail: An abstract design featuring geometric shapes and lines converging towards a central point that represents a digital brain or AI concept. The use of bright, contrasting colors against a dark background will create a sense of innovation and the futuristic potential of custom GPTs. This thumbnail will emphasize the advanced technology and creative potential behind GPTs. I'll begin with the painting thumbnail. Please hold on while I create the image. Here's the impressionistic painting thumbnail that captures the creative and versatile use of custom GPTs. It visually represents a person engaging with a variety of tasks through thought bubbles, with a focus on the GPT interface. Next, I'll create the photo thumbnail. This will be a realistic depiction of people around a computer, highlighting the collaborative and accessible nature of GPT technology. Please hold on The photo thumbnail is ready, showcasing a group of people engaging with GPT technology, reflecting the theme of collaboration and accessibility. Moving on, I'll work on the sketch thumbnail that will focus on the personal interaction between the user and the technology, emphasizing the ease of creating custom prompts. Please hold on. The sketch thumbnail is complete, highlighting the intimate interaction between the user and GPT technology through the act of typing and the flow of ideas. Finally, I'll create the abstract thumbnail focusing on the futuristic potential of custom GPTs with an emphasis on innovation and creativity. Please hold on. The abstract thumbnail is ready, capturing the essence of innovation and the futuristic potential of custom GPTs through dynamic shapes and vibrant colors. You now have four distinct thumbnails to choose from for your blog: The impressionistic painting thumbnail, emphasizing creativity and task diversity. The photo thumbnail, showcasing collaboration and accessibility. The sketch thumbnail, highlighting personal interaction and the ease of use. The abstract thumbnail, representing the innovative and futuristic potential of GPTs. Each thumbnail has been designed to draw the reader in and accurately reflect the content and themes of your blog. Please review them and select the one that best suits your needs.

Here's an experiment with ChatGPT. I've asked it to re-write my blog in the vernacular. Is it easier to understand like this? The clarity it brought to the message was surprising, leading me to wonder: has our shift to digital communication altered our language standards? If it has then using ChatGPT to write like this may be more effective than expected. Here's the blog: You might think you gotta be some kind of genius to get the hang of this AI stuff, but that's way off base. You don't need to be some kind of "Prompt Guru" or anything, just gotta be curious about what it can do for you. I wrote this especially for folks who haven't had the chance to mess around with ChatGPT or anything similar. Sure, the tech behind it is pretty complex – I'll be the first to admit I don't get all the ins and outs – but that hasn't stopped me or anyone else from using it. If you're good at firing off emails or texting, you're already set to get good at using AI in no time. Here, I'll walk you through some simple tricks to find what works best for you. You can ask ChatGPT to whip up something for you. Just type in what you're thinking and let it do its thing. Usually, you'll get back something pretty decent, even if it's not exactly in your style. Want something tweaked a certain way? Ask ChatGPT to redo it – maybe in a lawyerly way, or like a journalist, or even in slang. Check out what it gives you and see if it hits the mark better than your first go. What you've got might already be spot on, but maybe you wanna make it more your own. Just copy what ChatGPT gives you into a Word doc and tweak it to your heart's content. Here's a trick I find super useful: say what you wanna say into a Word doc, then copy that over to ChatGPT. You can ask it to just clean up the spelling, grammar, and syntax without changing anything else. Or, you could ask it to jazz it up – make it simpler, more advanced, or just better in general. As you can see, it's not rocket science. Just chat with ChatGPT like you would with a coworker. You can't mess it up. The more you use it, the easier it gets. It's a real time-saver, and I reckon you'll be pretty chuffed with what it can do if you give it a whirl.

One of the misconceptions about generative AI is that you need to be a rocket scientist to use it, but nothing could be further from the truth. You do not need to be a "Prompt Engineer" but just curious about what it can do for you and how it can help you. I've written this primarily for those who have not had the chance to use ChatGPT or similar tools. While the software itself involves a huge amount of complexity that I confess I don't fully understand, it doesn't stop me or anyone else from using it. If you can write emails or send texts, then there is no reason why you can't become proficient at using AI in a short space of time. Here, I will explain a few of the simple techniques that you can use to explore what works for you. You can ask ChatGPT to write something on your behalf. Just type into the prompt what you want and let it go ahead. What comes back is normally satisfactory, although it may not be exactly to your taste or style. https://chat.openai.com/share/1861bc73-ad0f-4e18-a1d6-edc873474bff Ask ChatGPT to rewrite the piece in a particular manner, for example, as a lawyer or as a journalist. Alternatively, ask it to write in a particular style, like the vernacular. You can then consider the results and see if they are an improvement on what you originally had. https://chat.openai.com/share/9fdeb055-50f2-4b98-94f2-dc7a6793c987 What you have done so far may already be enough, but perhaps you want to personalize it a bit more. You can cut and paste the output from ChatGPT into a Word document and make the alterations as you wish. Something I find particularly helpful is to dictate what I wish to say into a Word document and then copy it straight into ChatGPT. You can then ask it to correct the spelling, grammar, and syntax, leaving the rest unchanged, and it will give you the new version as requested. Alternatively, you can ask it to improve and make changes that make the document easier to understand, at the level of a 10-year-old, at the level of a graduate, or just ask it to make improvements as it sees fit.https://chat.openai.com/share/bd218094-9d15-415e-9c76-5bc3712ecf5a As you can see, there is very little to it. Just converse with ChatGPT as you would a work colleague, and you can't really go wrong. The more you use it, the easier it becomes. It will save you a lot of time, and I think you will be impressed with the results if you've not tried it.

At 97, my mother faces the challenges of old age, including complete hearing loss, but she still enjoys reading. I've observed that elderly individuals often take pleasure in reminiscing about the past. This led me to experiment: could I develop a GPT model that would bring joy to my mother? The result was "Mum's Diary," a customized GPT designed to generate a hypothetical diary page from exactly 50 years ago, incorporating people and events from that time that she knew. It creates a narrative of what she might have done, including a small picture related to the diary content. How It Works With this custom GPT, you simply enter the prompt "let's go," along with a picture, and a diary entry that my mother could have written 50 years to the day materializes. It's remarkably straightforward. Building It Is Simple To create your custom GPT, you need access to GPT-4 (the paid version) and to follow the provided instructions: No programming skills are necessary. I specified that I wanted a diary entry reflecting my mother's activities and thoughts from 50 years ago, including interactions with people and typical daily activities. I requested the inclusion of relevant local, national, or global events and their impact on her, alongside background information about her job, family, neighbours, shopping habits, holidays, car, weekend activities, pets, etc: This is what it looks like (and every time you prompt you get a new page)*: My Mother's Reaction It's too early for a definitive opinion, but she is intrigued by the generated content and enjoys reading it. This project has painted a vivid picture of her past activities, proving to be a delightful experience. She has commented on various historical accuracies and discrepancies, which can be easily adjusted by modifying the custom gpt. Further inquiries into improving the custom GPT (by asking GPT-4 itself) suggested that adding more detailed information relevant to the specific period would enhance the accuracy and reliability of the output. Although some generated storylines may seem repetitive, this doesn't concern me much at the moment. I believe this can be addressed by adjusting the instructions to vary the content generation, which I am currently exploring. Potential Applications While this custom GPT is tailored specifically to my mother's life, making it unsuitable for generic use in the GPT store, its concept could benefit others. By inputting personalised details, it could serve as a unique diary for another individual, potentially aiding memory recall and mental stimulation for elderly people either at home or in care facilities. This application of generative AI could add a little something to improve the quality of life for many. Conclusion In conclusion, this project exemplifies a simple yet powerful way to utilise generative AI to enhance people's lives. If you found this interesting, please share it with others who might benefit. Should you require assistance in developing a similar GPT, feel free to contact me. ---------------------------------------------------- * The complete diary entry for the day: Diary Entry: February 16th, 1974 Today was a particularly reflective day for me, full of small but significant moments that reminded me of the delicate balance of our daily lives here in Yattendon. The morning air was crisp as I made my familiar walk down to the school, the quiet of the village broken only by the distant sound of Flynn's tractor in the fields and the cheerful greetings from Mr. Peaty as he made his rounds. In school, the children seemed more spirited than usual, perhaps the result of the recent news that had the whole village talking: the miners' strike and the resulting three-day work week had finally come to an end. Mrs. Gaskell and I had a lengthy discussion about it during our morning break, pondering over the impact it had on the local community and the country at large. It was a relief to know that, for now, the crisis was over, and there was a collective sense of hopeful anticipation for what lay ahead. After school, I stopped by Mrs. Hogg's post office to pick up a few essentials. The lively barks of her two pugs greeted me before I even stepped inside, bringing a smile to my face. Mrs. Hogg shared the latest gossip as she always does, mentioning that Mr. Behr had decided to host a small celebration at the Royal Oak to mark the end of the power cuts. The community needed something to cheer about, and this seemed like the perfect occasion. On my walk back home, I couldn't help but marvel at the beauty of our village. The sight of our blue Land Rover parked outside Pound Cottage, with Chilly lounging lazily in the sun nearby, filled me with a deep sense of contentment. Ivan was in the garden, tending to his flowers, a pastime that had become more than just a hobby for him. We exchanged stories of our day over a cup of tea, reflecting on the importance of community and resilience in times of uncertainty. This evening, as I sit down to write these words, I am reminded of the strength and perseverance that define us, not just as individuals but as a community. The challenges we face, whether they're nationwide crises or personal struggles, have a way of bringing us closer together, strengthening the bonds that tie us to this place we call home. As I close my diary for the night, I am filled with gratitude for the life we have built here, for the friends and neighbours who enrich our lives in countless ways, and for the simple, enduring pleasures of village life. Note: Chilly was a pet cat. Just typing in a different date into the prompt generated an entry from a different period.

Forward looking projections ask us to look into the future. Their uses are varied and range from planning and risk to investment and speculation. We try to eliminate guesswork by using a model. It contains the inputs and algorithms to provide answers. Subjectivity often abounds. The inputs are frequently estimates they include correlations and behaviour. However adding more and more moving parts is fashionable. It creates spurious accuracy and makes challenge harder. Uncomfortable truths are hidden. They include: KISS. It’s better to be approximately right than precisely wrong; Unknown unknowns. Beyond the very short term things go wrong; Back solving. The answers you want build the model; Unobservables. Fudge the result. Let’s be clear projections give us some feel of the issues involved. But they are not facts. They help (when taken with a pinch of salt) in assessing value, risk and future contingencies. They provide some of the analysis as to whether you are on the right track. No more than this. Next time you are told about the future as if it’s a fact you are being misled.

People, systems and external events all generate operational risk. In the words of one Rogue trader “It’s sure to go up, trust me.” Whilst we’ve been focused on solvency and liquidity this just quietly ticks away – a sort of Cinderella of risk that from time-to-time bites. It makes you ask the question “just why didn’t we see this coming?” One of the difficulties is measurement. Subjectivity in terms of ranking and severity of loss inevitably apply. However a little more could be done to at least reduce the chance of mishap. One of the friction points is where systems and data meet people. This falls into three distinct headings: Collection of information (integrity); Working with information (processing; The end result (output). Nothing new here…..garbage in, garbage out but can anything improve? I think so. Whilst firms rely on experienced management a lot of day-to-day work is undertaken by less skilled operatives their objective being task completion. This needs to change and the simplest way to do this is to “sense check” things. This does of course rely on a basic understanding of what you are doing and its importance to the business. Failure by banks to provide employees with this basic skill is asking for trouble.

Paul Lewis in an article in the FT on 18/6/16 (Cash v stocks: the winner may surprise you) has uncovered something that people need to be aware of. The fund management industry attracts savings in the form of ISAs and pensions on the basis that it can outperform. But does it offer the saver any level of comfort that this will indeed be the case? In his article, Mr. Lewis compares cash savings returns with those from shares. His tentative conclusion is that for periods of up to 20 years cash beats shares and if you do buy shares low-cost index trackers are best. This appears to be contrary to what the public get told by financial advisors. Why? Because the comparisons made that support the case to buy shares do not compare like with like. Lewis goes on to explain that cash can be deposited at the best buy rates year in year out. These rates are significantly higher than T-bill rates. But it is T-bill rates that are often used to compare cash and share returns. Furthermore, funds incur fees, commissions and dealing costs. But these costs are frequently ignored in calculating share based returns. In other words comparisons made between cash deposits and fund based investments are skewed to make funds look significantly more attractive than they really are. For many sitting down once a year and reinvesting in a best buy fixed rate account will provide the best and safest return.

Many banks completely avoid foreign exchange risk. They borrow and lend in the same currency. Any customer trades are immediately offset. Bigger players use spot, forward and options markets. But an underlying theme of monitoring and managing exposures overrides. For small businesses things can be different. FX mismatches don’t always get priority. With the recent weakness in Sterling if you export and have unhedged currency earnings; happy days, not so for importers paying in FX. Many SMEs will take this on the chin – the decline in Sterling being just one of those risks you can’t do much about.  If that’s your view and you run some currency exposure it’s time to reconsider: What’s the size of that risk relative to your profitability? What would an adverse change in the FX rate mean for you? Can you pass the costs on to customers? Can you change your terms of trade? How do you price your goods and services? When you buy and sell FX could you get better rates elsewhere? Could you benefit from simple hedging? Why now? The Bank of England’s mandate is not the exchange rate. Decisions are targeting other economic variables. Unconventional monetary policy is experimental and the outcome is unknown. As a result the value of GBP will almost inevitably take some of the strain. This could be either up or down. Volatility aka risk has increased.

Back in 1997 Gordon Brown announced that the Bank of England would have freedom to control monetary policy. It was heralded as a move away from short term political interference towards a long term framework of prosperity. Does this need rethinking? In this country the elected government has two economic levers, fiscal and monetary policy. These are not entirely separate. Increased spending impacts on variables targeted by monetary policy and vice versa. However because of the 1997 decision the pricing of money has been left to experts who largely go unchallenged. What’s of concern is that “group think” appears to have set in and the answer lies in lower rates and more QE. This is academic and experimental policy. It has also been far from satisfactory in the real economy. For example: Lower rates have encouraged both private and public sector leverage; Asset prices have been inflated. In particular housing; Bank margins have been eroded at the same time banks need more capital; Pension scheme deficits have ballooned; Doing more for longer has increased uncertainty - bad for investment, productivity falls. Flat zero bound yield curves just aren’t the answer. Neither is further spending which is predicated on the assumption that government can invest at a ROC exceeding zero. What is required is that politicians start to engage and where necessary be critical of monetary policy. This is how democracy works. Otherwise we are leaving a major policy tool in the hands of experts without any real challenge and we all know where that can go.