Baines Blogging

  5 Reasons Why AI Tools Can Be Useful — And 5 Reasons Why AI Is Becoming a Bubble Artificial intelligence has quickly become the center of global attention. It promises efficiency, creativity, automation, and entirely new business models. But as the hype grows louder, so do concerns that we may be entering bubble territory — a cycle of inflated expectations, overinvestment, and unrealistic assumptions about what AI can deliver. Below is a balanced look at both sides: why AI tools truly are useful, and why the current boom may be a bubble in the making. 5 Reasons Why AI Tools Can Be Useful 1. They dramatically speed up repetitive work From drafting documents to generating code templates, AI automates time-consuming tasks. This frees humans to focus on higher-level judgment, strategy, or creativity. 2. They democratize access to advanced capabilities You no longer need to be a programmer, designer, or data scientist to perform tasks once reserved for specialists. AI expands...

🧭 1. Workplace Health & Safety Assessment Purpose  👍 To identify workplace hazards, evaluate associated risks, and plan control measures to maintain a safe and healthy environment for all employees — even in a primarily office or remote data analytics setting. Step 1: Identify Hazards For a data analytics start-up, typical hazards include: Category Examples of Hazards Potential Impact Physical Environment Poor lighting, trip hazards from cables, ergonomic issues Eye strain, back/neck pain, falls Equipment & Electrical Overloaded sockets, faulty devices, overheating laptops Electrical fires, equipment damage Workstation Setup Poor chair posture, screen height Musculoskeletal disorders Psychosocial Workload stress, long screen time, isolation (remote workers) Burnout, reduced mental well-being Fire Safety Inaccessible exits, lack of fire extinguisher Injury, property damage Health & Hygiene Poor ventilation, inadequate first ai...

Beginners guide for using the Machine Learning and/or AI tools on google.colab for a complete beginner:  AI and ML is all about analysing data so first you need to decide what kind of data you want to analyse. ( I refer you back to my previous blog post beginners guide to AI and ML terms. .) Basically do you want to analyse numerical data , text or images ? On this occasion I’m going to keep it simple and create some data visualisations  (graphs) You can get free open source data from this website kaggle.com I downloaded this file in .csv format: s-p-500-time-series-forecasting-with-prophet/input   Next find this website colab.research.google.com https://colab.research.google.com This is when you can go to chatgpt and ask it to write the code for google colab to analyse the data in  the way you want for example you could ask it to write the code to create a dataframe from that data:    Drag and drop the csv file into google.colab Change the labels s...

📶💻💹 Artificial Intelligence (AI) and Machine Learning (ML) are terms that are used by a lot of people but do you know about the technical nuts and bolts of how they actually work? If you don't that's OK, — I’ve broken down some of the most common terms you might hear in the world of AI/ML into simple explanations anyone can understand. Plus, if you're using Google Colab , there's a built-in AI assistant called Gemini that can help you understand and write code but you need to know the right questions to ask it first.   1. NLP (Natural Language Processing) 🎤🎧 NLP is a branch of AI that helps computers understand and work with human language. Think of it as the technology behind things like chatbots, language translators, and voice assistants like Siri or Alexa. It allows machines to “read” and make sense of text or speech, just like we do. 2. BERT (Bidirectional Encoder Representations from Transformers) BERT is a special AI model developed by Google that he...

 In the world of tech, it's easy to fall into the trap of comparing yourself to others. Maybe it's the developer on your team who seems to solve problems twice as fast, or that person on Twitter who contributes to five open-source projects and still has time for a blog and a podcast. But the truth is, trying to be the “best” in class is a moving target—and a distracting one. A more sustainable and rewarding mindset is to focus on raising your own level, day by day. Progress, not perfection, is what actually moves you forward. One of the most powerful habits in tech is deliberate practice . It's not just about writing more code; it's about writing it thoughtfully, reflecting on what went wrong, and iterating. Whether you're learning a new language, framework, or debugging a tricky issue, take time to understand why things work the way they do. Set mini-goals for yourself like “understand closures in JavaScript” or “write a CLI tool in Python.” Small, focused wins a...

  I Graduated with a 2:1 in Computing from Sheffield Hallam University! I'm incredibly proud to share that I’ve officially graduated from Sheffield Hallam University with a 2:1 in BSc Computing! It’s been a challenging, rewarding, and ultimately fulfilling journey, and I’m grateful for all the experiences, both technical and personal, that have shaped me along the way. From learning core computing principles to diving deep into specialist areas, my time at university has equipped me with the tools and confidence to move forward in the tech world. One of the standout modules for me was Data Management. I found it fascinating to explore how data is structured, stored, and retrieved, and how good design can make or break the efficiency of a system. This module really laid the groundwork for many of my later projects, giving me the practical skills and theoretical understanding to handle data effectively and responsibly—something that proved crucial in my final year work. My Artific...

Unlocking the Power of Data Analysis: Tools and Techniques for Understanding User Feedback Data analysis is the backbone of decision-making across countless industries, providing insights that drive business strategy, marketing decisions, and customer experience improvements. With the explosion of user-generated content on platforms like YouTube, analyzing user comments has become a powerful tool to understand public sentiment, track brand reputation, and improve user engagement. Step 1: Data Collection – Pulling User Comments from YouTube Before any analysis can happen, we first need the data. In the case of YouTube, user comments are stored publicly on each video. To collect them, we use the YouTube Data API . This API allows us to programmatically retrieve comment data, such as the text of the comments, the date posted, and user metadata (like user ID or username). Here’s how it works: Set up the API : First, you’ll need to create a project on the Google Cloud Console, enab...

This is a summary of my GitHub Pages site!   https://cuthbert86.github.io/ It is my personal corner of the web where I’m showcasing a range of projects and experiments as I learn more about web design and development. Whether you’re a fellow tech enthusiast, a curious visitor, or someone interested in learning alongside me, I hope you’ll find something interesting and useful here. The site is a living portfolio, featuring examples of my work as I explore different technologies and programming concepts. You’ll see projects built with HTML and enhanced with popular web tools like Bootstrap, giving my pages a clean and responsive look. I’m always updating and improving the design as I pick up new techniques, and many of the pages reflect my journey in web development. One of the highlights is my exploration of Python and its many applications. I’ve included projects that demonstrate data analysis, working with CircuitPython and MicroPython, and even connecting devices using MQTT....