Software developers helping guide the A.I. revolution and optimize mankind’s destiny
The vision is of a Free, Open, Modern, People-friendly web application that you can use to train computers to do stuff for you.
Automatic.ai is a new approach to discovering, using and building intelligent services. A.I. for the rest of us. In beta.
The idea is that you shouldn’t have to be a mad scientist (not that there is anything wrong with that) to learn, discover, and build machine learning, deep learning and AI solutions for fun, for work, or for solving the world’s most pressing and difficult problems.
The idea is to leverage the work of a lot of very smart software engineers who over several decades have learned how to make computers help ordinary people to do amazing things. This is what computers are for — this is what we do, right?
The idea is a modern user-friendly application that helps today’s software developer, and there are millions of us, to take advantage of the latest research to program smarter computers — much smarter computers that do wonderful things for, and with, us humans.
Seriously??? Do we have to know Color Science and Digital Signal Processing and DirectX or OpenGL and GPU and framebuffer architectures and then write a bunch of code just to resize an image?
Or do we just use Photoshop?
Automatic.ai’s Rockstar, like Photoshop, is plugin-based and extensible, so if you ARE a mad scientist, you can do all those mad scientist kinds of things by adding low-level plugins. And if you are a software engineer, you can create tools that leverage research and abstract away all the complexity that is not essential to the task at hand. Right? That’s when we REALLY start having fun.
A.I. For the Rest Of Us (*)
* Those of us who are not experts in:
- Vector Calculus
- Statistics
- Probability Theory
- Linear Algebra
- Massively distributed computing
- Multiprocessing
- Low-level CPU, GPU, TPU optimizations
- Tricks & gotchyas of the machine learning framework de jour
* Those of us who do not want to spend a whole lot of time doing boring stuff:
- Find the right training data
- Prepare the training data
- Manage 100s of versions of models, tests, and results
- Read 100s of technical papers to figure out what might work
- Read technical papers everyday to figure out what does work
- Spend days tuning hundreds, or millions of Hyperparameters
- Spend days just trying to find the right learning rate
- Spend days just trying to find the right dropout rate
- Spend days waiting to find out it still doesn’t work
* Those of us who do want to use (and maybe learn about) CNNs, RNNs, RL, LSTMs, GANs (and more) in order to build stuff:
- to build recommendation engines
- to build personalization engines
- to build image search engines
- to build text semantic categorization engines
- to build text sentiment engines
- to build speech recognition engines
- to build big data predictive analytics engines
- to build smart bots
- to build friendly bots
- to optimize workflows
- to optimize business processes
- to optimize supply chains
- …to save money, make money, or just, you know, save the world.
Closed beta, coming soon…
