Welcome to the Genesis Project! In our modern day, the technologies grow blazingly fast and deliver more powerful and robust changes in our lives that make significant difference in our wellbeing, standards of living and the way how we make the everlasting memories. We as developers stand in the frontline of those innovations and crowdfund the open-source solutions with ideas and implementations.
One of the most powerful movements in the current IT is artificial intelligence. The AI systems are able to process large quantity of data and produce high-quality expertise that then can be used to make suggestions, predict changes, automate not-so-straightword tasks and more. Today AI is used in medical and scientific research, crime fighting, self-driving cars, inoformational space personalisation and various kinds of detections in big data and many other. Artificial intelligence is capable of moving our civilisation in the bright prominent world where our lives will be revolutionarised.
However, is modern AI really that smart? The sci-fi novels of the 80s were forseeing in the 21st century AI will rebel against the humanity, but in reality we find it hard to create a trival system that's capable of distinguishing cats from dogs. The reason for that is that computers are built in such way that on the underlying level, they follow the strict steps programmer instructs it. As a matter of fact, it's improbable to write an algorithm that can tell exactly if a picture deceips a cat or a dog because of their large verities, and in order to solve a number of these flexible problems, machine learning was born.
Machine learning (ML) is a branch of computer science that deals with solving problems through learning from given material and accamulating experience that it can use later. From our example, a machine can distinguish cats from dogs by consuming incredibly large amount of labelled pictures (dataset) and analysing them through a series of complex mathematician algorithms that produces a model of cats and dogs, and by seeing a lot of them, machine will be able to tell them apart accurely enough the next time. And on the top of that, modern developers do not need to implement those cycles of training themselves but use available frameworks that already take care of it, such as TensorFlow or Scicit-learn.
But is ML really that strong? It is certainly able to pull impressive feats with popular products like GitHub Copilot or GPL-3. On the other hand, what made those neural networks successful was that it was trained on ridiciously large and quaity data with billions of parameters, which makes it inaccessible for the majority of programmers. Furthermore, ML is extremely narrow, which makes it only great at some one particular task it was desgined for, and it cannot do anything else and even cross-use its experience between multiple domains. This makes machine learning obviously strong but fundamentally flawed in its methodology and implementation.
We are looking forward a better alternative. We are striving to create applications that would be able to actually think and make smart outputs that could truly gasp the essence of concepts and be able to unfold them in a meaningful way. In order to do that, a new domain-specific programming language, Genesis, is being developed to enable developers creating intelligent apps.
Genesis seeks to solve the issues of machine learning and revolutionarise the ways of programming with the following set of features:
- data flows: variables are used to mimic thoughts, thereby they possess incredible flexibility. DFs are primarily used dynamically, which means the value they hold can change at any point, however depending on the needs developer can restrict it to a specific type with aid of a colon, similarly to Kotlin or TypeScript. Furthermore, data flows can undergo different lifetime states and interact with other DFs to compose, emerge, negate or be mix into new DFs.
- data continium: natural intelligence posseses the ability think in abstract imaginary models and patterns from where they can draw new thoughts and build relations between different data. Simiarly, DC attempts to bind all the data in a schema that can generate other DFs and interact with thoughts as one whole, thus massively reducing the amount of data machine needs to actually store.
- conceptual bonds: any multiple DFs can be connected together in a system with CBs. Once it is established, they are meaningfully connected and as soon as said meaning is altered, it's reflected on all other members as well. The system can also interact with external signals as well and yield smart behaviour by creating a unique response by pulling the strings between the objects.
Genesis works in a similar fashion alike human brain where the whole thinking activity and conception takes place in a graph enviornment where any state of mind is corresponded with paths in the brain. Anaogically, Genesis apps run on top of the Genesis Runtime Cloud that contains the encapsulated medium where smart activity happens. This allows compiler to compress smart apps and even assets into ridiclously small size and operate incrediby fast by mapping the whole app into a resposive graph so well that it can be used to design embeded applications and OS kerneals.
Genesis aims to possess descriptive, and yet concise syntax, great performance and robus tools for smart app designing to reveal to the software developers entirely new horizons of programming that were foreseen by sci-fi noelists before. Genesis Project is open-source under the MIT Licence and welcomes contributions to become the last programming language.