Do you feel that AI is like rocket science? Well, it's not.

Written by: Victoria Andersson

The idea that tools like ChatGPT, Google Bard, and various image generators seem almost alien is quite understandable. However, the reality is that when we peel back the layers, it all makes complete sense. These AI innovations are grounded in principles that are accessible and relatable once demystified. So AI is, in fact, not rocket science.

Image: Unsplash

Unveiling the Friendly Face of AI

Let's clarify one thing before we start: AI isn't about to take over the world. Essentially, AI splits into two types - ANI and AGI. The type of AI that's rapidly advancing today is known as weak AI, or ANI, and it’s exceptionally good at playing by the rules – quite literally. It's specialized, task-oriented, and a bit like that one friend who's an encyclopedia of knowledge on one topic, yet can't seem to change a light bulb. When most people think of AI, they're actually imagining AGI, a theoretical AI that thinks and learns like humans. But this isn't our reality yet, nor the focus of current developments.

It all starts with data, a lot of data

The heart of today's AI systems beats with a rhythm set by data. It's the fuel that powers the intricate engine of ANI. Just like an artist needs paint, AI needs data – and not just any data, but vast, diverse, and quality data. This abundance of data enables AI systems to learn, adapt, and excel in their designated tasks. In the world of AI, data isn't just king; it's the entire kingdom.

Data + Machine Learning = The evolution of digital intelligence

Diving deeper into the AI realm, Machine Learning (ML) stands out as the star player, the most effective technique in building AI systems. ML addresses the critical question: "How do we get computers to learn from data without being explicitly programmed?" It 

empowers AI systems with the ability to learn from experiences, improve over time, and tackle increasingly complex tasks. Crucially, ML isn't a one-size-fits-all solution; it's a diverse umbrella encompassing various techniques, each suited to different challenges.

Image: Wix

How AI learns to perform tasks independently

Peeling back the layers of AI's complexity, we find its core techniques in ML:

• Supervised learning is like a guided tour; the model is trained with labeled data, learning to predict outcomes based on past examples. 

• Unsupervised learning, in contrast, is akin to exploration without a map, where the model sifts through unlabeled data to detect patterns or clusters on its own.

• Reinforcement learning is the adventurous cousin, learning by trial and error, making decisions and adjusting based on feedback. 

Extending this is generative AI, a sophisticated offspring of supervised learning, which goes beyond prediction to creation. Each technique is a testament to AI's logical foundation: give a model data, and it learns to perform tasks independently, debunking the myth of AI as incomprehensible “rocket science”.

Tech writer or not - embrace AI

AI marks a paradigm shift, redefining problem-solving and innovation. Adopting AI puts us at the forefront of this revolution, beyond just following a trend. In the realm of technical writing, embracing AI means tapping into a suite of smart tools that enhance writing efficiency and content quality. Imagine AI as a behind-the-scenes collaborator, refining language, aiding in creative content generation, optimizing for SEO, and offering insights from user feedback. This integration of AI streamlines the writing process, elevating the technical writer’s role from just crafting words to strategically shaping impactful content.

Image: Wix

Our advice to you:

At the end of the day, AI refers to the development of systems capable of tasks that usually require human intelligence. But here's the twist – whether you're a tech writer, a marketer, or a project manager, instead of fretting that AI will usurp your job, focus on how it can enhance your daily tasks. 

AI isn't here to snatch away our roles; it's here to redefine them.

It's not about AI taking our jobs; it's about AI taking the jobs of those who don't embrace it. 

If you want to learn more about AI: https://www.deeplearning.ai/.

If you're looking to find AI tools tailored to your profession, visit https://genai.works/  - kind of a one-stop destination for all leading AI applications and software.

The Future of Technical Writing: 2024 Trends and Predictions

Written by: Karin Askeroth

As technology continues to evolve at a rapid pace, the demand for skilled technical writers is also on the rise. So far, we’ve gotten by fairly well by being talented writers with basic technical knowledge, and skills in content management systems and user experience. To really thrive in 2024, technical writers will need to possess an expanded set of skills to stand out in the competition. Here is our take on trends in technical writing for 2024, based on our experiences and tentacles into the next year. 

Data literacy

The ability to read, understand, create and communicate data as information.

Data has become an integral part of our digital language, and the ability to effectively integrate it into your technical document is non-negotiable. Being data literate will give technical writers a competitive edge in a data-driven world.

• Learn the fundamentals of some data analysis tools

• Be able to work with large datasets

• Have a basic understanding of statistical concepts

• Be able to interpret data visualizations

AI literacy

Basic understanding of AI technologies and their implications.

With the rise of AI technologies, understanding AI and machine learning basics will be invaluable for technical writers. They can automate the documentation process, making it faster and more efficient. They can also improve the quality of technical documentation by eliminating human errors, improving language and clarity.

If you don’t already have a fundamental understanding of AI and machine learning, this is the time. 

Basics of SEO in technical writing

If your technical content is online and public, it’s necessary to consider Search Engine Optimization (SEO). This allows your content to be discovered via a search engine's organic search results.

Considering the amount of content on the internet, and the billions of daily searches, SEO is necessary to help your technical content stand out.

Make sure you understand concepts like keywords, metadata, and search algorithms, as well as how to use them practically.

Visual communication

The use of visual elements to convey ideas and information.  

In an age of shrinking attention spans, effective visuals can augment your text and make your content more engaging. Different people like to be able to interact with the documentation, not just read it - and in different ways. They may want videos, images, and graphics, not just text. For instance, a video demonstration can be more effective than a lengthy text explanation. Skills such as graphic design, video editing, and creating animations will be highly valued.

Familiarize yourself with tools like Adobe Creative Suite, Canva, and Snagit. Stay updated on trends in visual communication.

Enhancing Accessibility and Inclusivity

Accessibility and inclusivity are important considerations in software documentation. 

Documentation should be accessible to everyone, including those with disabilities. Trends like voice-activated documentation, screen reader-friendly documentation, and inclusive language are making documentation more accessible. 

Staying updated with these trends allows you to create documentation that everyone can use, regardless of their abilities or background.

Self-promotion and social media engagement

Actively engaging with online communities to build a personal brand and network.

Online engagement isn't just for influencers; it's for technical writers, too. Self-promotion and community involvement will be necessary for career growth, especially in building a personal brand and networking.

• Start building a professional reputation

• Optimize your social media profiles

• Share your knowledge

• Engage in industry discussions

Our advice to you: Do an inventory of your current skills, and investigate how you would like your career to look in a few years. Based on that, decide which areas will be most important for you to learn more about. 

Images by: Wix

So you're a tech doc slacker who wants to work efficiently - this is how

Written by: Johanna Hansen

Image & photo: Karin Askeroth & Christine Svedjemo

Work less, not harder. Researchers have confirmed that overworking is actually not very effective. Not only is it exhausting, but overworking yourself can backfire, burn you out and, ultimately, make you less productive. Many of us have a comfortable side of us, the slacker, that wants us to minimize what we do, and simplify how we do it.

Read all about how you can be be a tech doc slacker and still work efficiently.

The mindset of a slacker

• Budget and invest to work even less. Timebox and set clear goals, invest by networking, find key persons, and have the courage to wait for others to give the information needed.

• Ask questions early on. Rather than wait until the last minute to ask questions, ask as early as possible to raise awareness and interest. Delegate and let go of the stuff outside your control.

• Think as a successful Product owner. Schedule your weeks and days in advance. Set the goals for the week, estimate the time needed, and block this time. Keep your to-do list manageable, try to prioritize no more than five tasks to complete.

• Limit task switching and focus on one type of work at a time. Split between collaborative work and solo work, e.g., collaborative work between 9am and 2pm. Solo work happens before or after.

• Minimize blockers. Simplify the input feed by getting to know your information providers. Continuously evaluate your methods and look for shortcuts.

• Automate repetitive things, either by scripting, templating, or reusing

(e.g. automation of graphics creation, automation in Jira, and “Smart” tech writing using variables & profiling, re-use, branching etc).

• Focus on the high-level, set a baseline, set up the information flow, reviews, translation and publishing instead of hunting for detailed information.

• Copy with pride. Copy from already existing documentation or adapt to an existing process. Handle tech writing as others handle e.g. software development.

We are not perfect – and should not be aiming for it either. Find a good enough level, try to automate repetitive tasks, handle tech writing as others handle e.g. software development. Focus on the high-level, baseline, processes – and let others focus on details. Technical writing is not “rocket science” – copy with pride.

Our advice to you: Have a look at companies that have adapted their Technical writing in a successful way, e.g., https://docs.apptus.com/.