Artificial Intelligence for everyone: Understanding the technologies shaping the future

In the first article, we explored the basics of Artificial Intelligence (AI) and highlighted the differences between traditional AI and generative AI. Traditional AI carries out specific tasks based on predefined rules and is already a part of our daily lives, powering things like recommendation systems and navigation tools. Generative AI, on the other hand, creates new content – such as text and images – using neural networks and deep learning.

Examples include tools like ChatGPT and DALL-E. We also touched on the role of Machine Learning, which allows AI to learn from data, significantly enhancing its capabilities.

In this second article, we’ll dive a bit deeper into some other key AI concepts and showcase various practical applications of AI in our everyday lives.

Fasten your seatbelts – here we go!

Artificial Intelligence – Concepts and Applications

Artificial Neural Networks (ANNs): The Foundation of Deep Learning [1]

Artificial neural networks are a type of computational system inspired by the human brain. Just as our brains are made up of billions of interconnected neurons, a neural network consists of artificial "neurons" arranged in layers. These layers work together to identify patterns in data, making neural networks particularly good at tasks like recognising images or understanding speech.

Think of it like teaching a small child to distinguish between cats and dogs. You show them many examples, and over time, they learn to spot the differences. A neural network does something similar but with numbers and layers of computation.

At the end of this article, I’ll share various resources for those who want to dive deeper into this topic. However, since our focus here is on practical, not academic, insights, I’ve outlined some real-world applications of neural networks across different fields.

1. Image Recognition

• Google Photos: Uses convolutional neural networks [2] to automatically identify and organise people, places, and objects in your photos. Upload your images, and the system categorises them without requiring manual input.
• Tesla Autopilot: Tesla vehicles use neural networks to process data from cameras and sensors, enabling object recognition and autonomous navigation. The system continuously learns from data collected across the fleet. [3]

2. Natural Language Processing (NLP) [4]

• DeepL Translator: This online translator uses neural networks to deliver high-quality translations across multiple languages, capturing nuances and complex contexts.
• Grammarly: Employs neural networks to analyse English texts, offering real-time grammar, spelling, and style suggestions. It helps writers improve the clarity and impact of their writing. [5]

3. Medical Diagnostics

• IDx-DR: Approved by the FDA, this system uses neural networks to detect diabetic retinopathy in retinal images, aiding in early diagnosis. [6]
• PathAI: Uses neural networks to analyse pathology images, helping pathologists identify cancers and other diseases with greater accuracy. [7]

4. Art and Creativity

• MidJourney, Canva, DaVinci, Imagine Art, and others: These platforms generate images and videos from text prompts (aka AI art) and apply artistic styles to photos using neural networks.
• Magenta: A Google project exploring the use of neural networks to create music, art, and other forms of creative expression. [8]

5. Finance

• Kensho: Uses neural networks to analyse large volumes of financial data, helping investors spot trends and make informed decisions. [9]
• Zest Finance: Employs neural networks to assess the creditworthiness of individuals with limited financial history, offering more inclusive credit options. [10]

These examples highlight the versatility and breadth of neural networks in various practical applications.

Large Language Models" (LLMs): The Brains Behind Chatbots

LLMs are a special type of neural network designed to understand and generate human-like text. They’re trained on massive amounts of textual data—imagine feeding them every book, article, and website you can think of—so they can answer questions, write stories, and even translate languages.

These models are the reason you can chat with a chatbot, ask for help writing a letter, or brainstorm ideas.

Real-life example: If you’ve used ChatGPT or Google’s Gemini for quick information or writing assistance, you’ve interacted with an LLM. Another example is Microsoft’s Copilot, integrated into Office apps like Word and Excel, which helps users draft documents or create spreadsheets with just a few commands.

People use these tools for everything from writing essays to generating recipes—it’s like having a very knowledgeable friend to consult.

It’s important to remember that while those technologies are impressive, they don’t think or understand like humans, yet. They’re simply very good at recognising patterns and generating responses based on what they’ve learned.

Final Thoughts

In this article, we’ve covered what Artificial Neural Networks are, explored practical examples of their applications across various fields, and taken a look at language models that power tools we use every day.

Now, I’d love to hear from you: how do you see the role of neural networks in your industry or life? Have you had any experiences with these technologies that you’d like to share?

Drop your thoughts in the comments—I love hearing different perspectives! Feel free to share or repost this so more people can access this content.

In the next article, I’ll explore how Neural Networks and other AI technologies are transforming Logistics and Supply Chain. Stay tuned!"

?

?

Sources:

[1] What is a Neural Network? | IBM

[2] What are Convolutional Neural Networks? | IBM

[3] https://www.dhirubhai.net/pulse/what-teslas-end-to-end-neural-network-diana-wolf-torres-0yf4c/

[4] What Is NLP (Natural Language Processing)? | IBM

[5] https://www.grammarly.com/blog/ai/what-is-a-neural-network/

[6] IDx-DR – NIH Director's Blog

[7] https://www.delltechnologies.com/asset/pt-br/products/storage/customer-stories-case-studies/pathai-written-case-study.pdf

[8] https://musicbusinessresearch.wordpress.com/2024/04/22/ai-in-the-music-industry-part-12-googles-magenta-studio-and-the-wavenet/

[9] Home | Kensho

[10] https://www.zestfinance.com/

[11] As a bonus for those who have come this far, a link to a super complete and easy-to-understand article on AI - https://www.britannica.com/technology/artificial-intelligence

?