AI: Demystify and get started
AI is no harder than when the forklift was invented and started being used in warehouses. Your challenges and questions will be the same, but you now have greater power to scale towards your goals.
Scared of AI?
Äventyret helps you untangle it, implement it, and become less afraid of the new and complicated. Our delivery never consists of 70+ slides of recommendations; instead we prefer to take you along on the insight journey through focused, hands-on projects that have already built up your business by the time they are done.
AI in practice: challenges and solutions
Most of our AI assignments have focused on employer organisations, where we have faced unique challenges. One of the biggest has been managing and structuring complex information from disparate data sources. We have developed automated solutions to split the information into manageable blocks (chunking), enabling efficient processing by the AI models.
We have also worked out new methods for indexing the data sources, improving vector search and thereby giving the AI models access to relevant and precise data. To reach the level of accuracy required, especially for legal questions, we have worked with fine-tuning. By fine-tuning the base prompts and adapting them to our clients' needs, we have been able to raise both the efficiency and the quality of their support, freeing up advisors to work on harder cases.
But AI is not just about speed. It is about making sure the information is correct and reliable. Together with our clients' subject-matter experts we have reviewed and quality-assured the solutions. That collaboration has not just secured the outcome, it has also increased knowledge and understanding of AI inside the client organisations, and served as anchoring.
Experimentation and innovation
Beyond support solutions there are other creative applications of AI. One of our experiments was generative meditation. Using a RAG model as the base, we drew on existing meditations to create new tailor-made meditations from the user's input. The generated meditation texts are then turned into audio via Eleven Labs' text-to-speech API, giving the end user a personal and interactive experience in their headphones.
Our website
Here we use DiffusionBee to generate illustrations, a tool our designers can run locally on their laptops. By training the model on our graphic profile we can create consistent, on-brand imagery for our articles and projects.
The air guitar takes to the piano
For better and worse, AI has become an umbrella term for a whole range of different techniques and disciplines. Generative AI is stealing the spotlight right now, but machine learning has long been an important sub-discipline. So what is machine learning? It is about algorithms and models that let computers learn from data, rather than being programmed for each individual task. In other words: computers that get smarter with experience.
A brilliant example of machine learning is the air piano, an experiment our own Erik built (do give it a go, it's surprisingly fun).
The air piano is built on a machine-learning framework called ML5. The framework is easy to get started with, but getting it to track the fingertips and simulate key presses was a real challenge. The result is an AI-driven piano you can play, with no keys at all.
Rewarding play
It has not escaped anyone that generative AI can write code. Even if the generated code still often raises a few eyebrows, it creates fantastic opportunities to go from idea or dream to a fast prototype.
Like when Christoffer listened to a podcast about the space that forms between the snow cover and the ground in spring. With that inspiration he built a game where lemmings under the snow are hunted by, and dodge, foxes. It may not go down in history as game of the year, but what GPT-4o generated from the prompts made us laugh a lot.
One insight from generated code is that it can help experienced developers like Christoffer close smaller gaps in their understanding, while helping beginners overcome giant obstacles.
Tips and tricks:
Start small
Pilot projects are perfect for testing, learning and exploring the possibilities.
Start from within
Build internal tools that make life easier for your experts. Involving subject-matter experts ensures the solutions are correct and functional, while creating learning and buy-in across the organisation.
Focus on your strengths
Identify areas where AI can build on your existing competitive advantages.
Needs-driven
Just as before, but consider how AI can help solve them. Keep focusing on your users' needs, and your own.
