It's time. We are publicly introducing doja to the world. Your AI task companion. Capable of working inside your company, transforming data and actioning common tasks. ➡️ 18 months of R&D. ➡️ 47 roles revolutionised. ➡️ 400 essential activities streamlined. ➡️ Thousands of hours saved for teams. What is the impact? 1️⃣ 47 roles inside of 12 core business departments will soon be unrecognisable in the way they work. 2️⃣ Voice as a new medium of working will quickly take over many tasks once people understand the ridiculous time savings. 3️⃣ AI quality assurance / checking will be how most jobs evolve in the short term. Think about a writer being elevated into an editors position, but for 95% of roles. Why now? 📊 We made a bet over a year ago that a company would only get significant results from AI if their data is organised first. 📝 The moment we organised & connected our own software stack within doja, specifically our knowledge base (Notion), CRM (Hubspot) & PM suite (Trello) - the opportunities that it unlocked with AI become genuinely useful and specific to your business, rather than gimmicky. 🔮 For the more technical readers out there, the changing factor that led to this breakthrough was the ability to have an AI agent integrate and speak in a conversation setting within your software's APIs, to fetch, update or delete data, all via voice. We can see this eventually taking over pretty much most of the human effort that is currently required when managing software. 🏎️ The companies out there that have a good grip on their software stack will reap the gains inside the AI era. Companies that do not should be looking for a strategic AI partner as a top priority. The two main questions we get asked? Being transparent. ❓ How will this affect jobs? We see doja as a tool to significantly amplify any job role. It allows teams to focus on the value-driving activities inside of a role, rather than governance and operational tasks. We believe this will remove many activities inside of job roles and it will be up to the company to decide what activities should replace this freed up space. To answer the more broad question of will AI replace your job? We believe someone who learns AI will slot in above you, you can probably imagine the ramifications down the line. I.e. you should probably start learning this stuff. ❓ How can you adopt something like this? Your company will first need to attain a certain standard of data quality in order to allow our team to create integrations inside your software stack. What is most important is introducing a project management system that helps you keep track of your AI requests and results, for fast quality checking and assurance. We have programmes available to get you to this standard - enquire with us to learn more. 🎬 Finally - feel free to ask questions you might have, we'll do our best to answer on here. #AI #manchester #ainews #doja #metaverse #technology #AIAPP #automation #inspiringai #pythonprogramming #robot #artificialintelligence #machinelearningalgorithms #machinelearning #manchesterbusiness

🎨RECURSIVE ALGORITHMS🎨 BASE PROMPT : Cool (COLOR 1] and [COLOR 2] fractal patterns of [SUBJECT] formed by recursive algorithms , illustrating self-similarity across scale WARNING : This prompt reacts differently the subject the colors..Play with the stylize, style raw or not but cna gives great renders..Exemples here and in first comment A recursive algorithm is a method where the solution to a problem depends on solutions to smaller instances of the same problem. This approach can be applied to many types of problems in computer science, and it’s one of the central ideas of computer science. The recursive algorithm involves a function calling itself directly or indirectly to solve a smaller version of the problem. The recursion continues until it reaches a base case, which is a condition for which the solution is known and can be directly given. Once the base case is reached, the stack of function calls unwinds, returning the result back up the chain. This process is similar to solving a complex problem by breaking it down into a set of simpler problems and building the solution from the solutions of these simpler problems. Recursive algorithms can be particularly useful in AI generative art, which often involves creating complex patterns and structures that have self-similarity or fractal characteristics. Here’s how recursion might play a role in generative art: Fractal Generation: Fractals are self-similar patterns that are created by repeating a simple process over and over in an ongoing feedback loop. Recursive algorithms are natural for generating fractals since each step of the process can be defined in terms of a smaller instance of the same pattern. Procedural Generation: In procedural generation, objects or scenes are created algorithmically rather than manually. Recursive procedures can be used to create complex structures like trees, where each branch can spawn smaller branches, which in turn can spawn even smaller branches, and so on. Pattern Creation: Recursive algorithms can be used to create intricate patterns and textures. For instance, the subdivision of a surface into smaller parts can be done recursively, with each subdivision adding detail to the art. Algorithmic Composition: In music and visual art, recursive structures can create patterns that are aesthetically pleasing due to their structured yet complex nature. For example, a melody or rhythm could be generated recursively by applying transformation rules at different scales. Interactive Art: Recursive algorithms can allow generative art pieces to change over time or in response to user interaction. Each interaction could trigger a recursive process that generates new elements within the artwork. Natural Simulations: Recursive algorithms are excellent for simulating natural phenomena such as the growth patterns of plants or the structure of natural objects. This can lead to art that closely mimics or abstracts from nature. In the context of AI, recursive algorithms can be combined with machine learning techniques. For example, a neural network might learn patterns or styles from existing artworks, and a recursive algorithm could then use this knowledge to generate new art pieces that have similar characteristics. This allows for the creation of art that can be both ordered and complex, blending human creativity with computational precision.