A Non-Tech Beginner’s Guide to Generative AI
Understanding the concept of Generative AI can be challenging for a beginner. But don’t worry; this guide aims to break down the barriers surrounding AI and make it easy to understand. I have gathered and responded to numerous questions I frequently receive from individuals beginning to explore this technology.
Whether you are a parent, educator, artist, or simply someone intrigued by AI, this guide is tailored for you.
In this article, we will delve into the following topics at a high level:
- What Exactly is Generative AI?
- How Does Generative AI Work?
- Is Generative AI for Everyone?
- What are the Ethical Implications of Generative AI?
- Why Should We Care About Generative AI?
- How can I Start using Generative AI?
By the end of this guide, you’ll discover how AI can empower you, not just those at big tech companies. It’s a tool that can help you harness your creativity and enhance your daily tasks, putting you in control of the AI revolution.
What Exactly is Generative AI?
Artificial Intelligence (AI) is a broad field encompassing various technologies capable of performing tasks that typically require human intelligence. Predictive AI and Generative AI represent two distinct approaches.
Predictive AI is a more familiar type of AI model. For example, a weather forecast uses historical data to predict future events. It’s like having a crystal ball that, based on past patterns, can offer a glimpse into what’s likely to happen next, making it invaluable for decision-making in finance, healthcare, and marketing.
In contrast, Generative AI is a branch of artificial intelligence focused on creating new content, from text and images to music and beyond. It learns from vast amounts of existing data, mainly from the World Wide Web, to generate new pieces that are original and fitting to given contexts.
For instance, generative AI has created unique artworks, composed original music, and generated new recipes. Suppose you need a poem or a distinctive image for a blog post. In that case, generative AI can produce these by analysing patterns from existing works.
Artificial intelligence has a rich history that dates back to World War II. It all started when Warren McCulloch and Walter Pitts published a paper proposing a simplified model of the biological neuron. This work laid the groundwork for what we now recognise as deep learning. Since then, AI has evolved through neural networks and machine learning development, culminating in today’s generative AI models that mimic human-like creativity, which emerged in 2015.
How Does Generative AI Work?
I’m thinking back to when my son was a toddler. We would read to him each night. He started to say a word before I said it. He had memorised the book — better than that when reading new books, he could say words before I did. It’s truly remarkable!
Our brains do a good job interpreting the patterns because we have learned to recognise them. Similar to how my son learned from repeated exposure, generative AI models learn from vast amounts of data. This is how you can read the Quote below:
“T4K3 R15K5. 45K B1G QU35T10N5. D0N’T B3 4FR41D T0 M4K3 M1574K35; 1F Y0U D0N’T M4K3 M1574K35, Y0U’R3 N07 R34CH1NG F4R 3N0UGH.”
-DAVID PACKARD
Here’s how it works:
- Model Training: During training, generative AI is exposed to large datasets containing text, images, or other content. Like my son’s story time, this exposure allows the model to recognise patterns within the data.
- Pattern Recognition: Generative AI identifies recurring structures, sequences, and relationships. For text, it learns grammar rules, word associations, and context. The model predicts what comes next when generating new content based on these learned patterns.
- Anticipating Sequences: Much like anticipating words in a familiar book, generative AI can predict the next word, pixel, or musical note. It leverages context, neighbouring elements, and statistical probabilities to make informed predictions.
Just as my son’s brain adapted to language patterns, generative AI models adapt to the patterns they encounter. The more exposure they have, the better they become at generating coherent and contextually relevant content.
Fun Fact: If you were to read the same amount of information that has been fed into the model, it would take us thousands of years. Suppose I can recognise the pattern from the quote above after 38 years; imagine how capable generative AI models are!
Is Generative AI for Everyone?
Absolutely! Until recently, developing AI models for a typical organisation involved significant investments in data collection, computational resources, and specialised expertise. This made the process costly and time-consuming, and advanced AI capabilities were often limited to the most resource-rich companies.
Fortunately, you don’t need a degree in data science or to remortgage your home to harness the power of generative AI. Companies like Cohere and OpenAI are revolutionising how we access these technologies, making them accessible to all. Anyone can use generative AI to automate tasks, create content, and analyse data.
We are already seeing incredibly diverse applications of generative AI across every industry imaginable. The website ‘There’s an AI for that’ is a prime example, providing a repository of 12,381 AIs for 15,247 tasks and 4,804 jobs on their website, with 3000 AIs released in the past few months! This platform is a testament to the rapid growth and widespread adoption of generative AI.
Summary of AI Categories and Applications: ‘ There’s an AI for that’.
Thanks to applications like Databutton (the world’s first fully AI app developer), people can build fully functional applications through conversations with AI agents.
In May 2023, I built a Neuro Mermaid AI application using Databutton. I needed to gain more experience developing and deploying an application. Generative AI was my assistant, helping me share my ideas with the world. I knew what I wanted to build, but I didn’t know how.
It has had over 5000 visits over the past 12 months, which is not bad for an application I developed with the assistance of Generative AI! — Elle Neal neuromermaid.ai.
This approach of democratising access to AI means that advanced AI capabilities are no longer limited to the most resource-rich companies. It accelerates innovation across industries, enabling more creators to leverage AI in transformative ways. In other words, it’s like making a powerful tool available to everyone, not just a select few.
Why Should We Care About Generative AI?
Generative AI has the potential to transform how we work, learn, and create. It isn’t just a means for writing emails or generating intriguing artwork. It is a powerful tool that drives innovation across fields, democratises education and information, and tackles complex global challenges — from healthcare to climate change — by processing and analysing vast amounts of data at incredible speeds.
Engaging with generative AI allows us to shape a future where technology complements and enhances human capabilities. Integrating AI into our daily lives builds a more efficient, creative, and equipped world to make informed decisions. Thus, understanding and utilising generative AI is crucial for anyone interested in leveraging cutting-edge technology to significantly impact personal projects, business endeavours, or broader societal challenges.
Here are some examples of applications across the health industry:
- Clinical Documentation: Amazon Web Services launched HealthScribe, a generative AI clinical documentation service. It aims to streamline and improve the accuracy of clinical notes and records.
- Drug Discovery and Development: Insilico Medicine has developed a generative AI platform called GENTRL. It has been used to design new drugs for fibrosis and cancer.
What are the Ethical Implications of Generative AI?
There are ethical implications of generative AI that must be considered. As Aidan Gomez, the Co-founder and CEO of Cohere, said:
“Over the next five years, I think we’ll be able to automate any human task that we decide we don’t want to do. Just as the steam engine took the physical load off our backs, AI will do the same for the cognitive load.”
— Aidan Gomez, Co-founder and CEO, Cohere
Depending on one’s expertise and mindset, this statement can be seen as an opportunity or a societal risk. However, it is clear that this technology is here to stay and is only becoming more capable. The future outcome of generative AI will depend on how we as a society allow it to shape our world and our personal lives.
The paradox of technological advancement is that while it presents new opportunities for economic growth and innovation, it also carries the risk of increased inequality and job displacement.
One example is the Translation technology developed by AI4BHĀRAT, which has the potential to revolutionise communication in India. A country with a rich tapestry of languages and linguistic barriers that have historically hindered the free flow of information. This technology enables speakers of various Indian languages to access a broader range of content, from government services and global news to educational resources and healthcare information. It also facilitates better interpersonal communication across different language groups, fostering unity and understanding in a diverse nation. Moreover, it empowers local businesses to expand their reach and engage with customers in their native tongues, thus driving economic growth and innovation. In essence, AI4BHĀRAT’s translation and transliteration tools are not just technological advancements; they are keys to unlocking a more inclusive and connected India.
This widespread accessibility, however, is not without its drawbacks. The translation sector is a case in point:
A survey by the Society of Authors has revealed that over one-third of translators have experienced a loss of work attributable to generative AI. Additionally, over 40% of translators report a reduction in their earnings due to generative AI, and over 75% anticipate that this technology will harm their earnings in the future.
Here lies the paradox: Generative AI both empowers and disrupts. It expands access while reshaping employment landscapes. As we celebrate information democratisation, we must grapple with the consequences for livelihoods. Balancing progress with social stability requires thoughtful policies and reimagining work in an AI-augmented world.
How can I Start using Generative AI?
The best way to understand how generative AI can augment your day-to-day work and personal endeavours is to integrate it into your daily routine. It is simpler than you think, especially with free tools like Bing Chat. Here’s how to start:
Try Bing Chat for Web Search: Begin by using Bing Chat in your web searches. This feature integrates AI-powered responses alongside traditional search results, allowing you to interact with AI in a familiar setting.
Interact and Experiment: Engage with Bing Chat by asking questions, from simple queries to complex requests. This practice will help you understand the depth and breadth of generative AI’s capabilities.
Integrate AI into Daily Use: As you become more comfortable, incorporate Bing Chat more regularly into your internet use to see how generative AI can enhance your productivity and creativity.
Conclusion
Generative AI, with its remarkable ability to discern patterns, offers boundless possibilities. Yet, it’s crucial to recognise that this technology holds the power to alter our world in both positive and negative ways. The challenge lies in navigating the inherent paradoxes to mitigate adverse effects on individuals while capitalising on the vast opportunities available to the masses.
I welcome your insights on these subjects. Connect with me on LinkedIn, or share your thoughts in the comments below.
This guide was developed with the support of generative AI. Here are some AI-powered tools I’ve utilised, all offering free usage options:
- BingChat was used to scour the web for examples and inspiration and verify content validity sources.
- Neuro Mermaid AI assisted in researching topics, verifying sources, and generating mind maps.
- Grammarly was used to ensure the wording remained clear and understandable.
These tools have enhanced the writing process of this article, helping me express my knowledge and insights. While I’ve curated, validated, and shaped the content, countless hours have been dedicated to research, conceptualisation, and editing. I believe this has elevated the quality of my content and provided a collaborative partner to fuel my drive to democratise generative AI and increase its accessibility.
