What is common between skateboarding and learning to skateboarding & autolearn.ai’s AI platform.
Lots, turns out.
Consider the process of learning to skateboard. One repeatedly tries a move with the skateboard. You look at if you can land the move. If you do, you try a different move. if you don’t, you slightly vary something in your technique; maybe you try a different center of gravity or angle your legs slightly differently or move your arms differently. Rinse. Repeat.
As the skateboarder tries different variations, the “learn” the intricacies of every move and slowly improve. The more time, the more variations and the more analysis they do, the faster they learn and get better. Over time, one can go from a novice to an expert, having built a massive repository of insights and training that help the brain leverage the learning to control the brain that in turn controls the muscles, bones and body weight to effortlessly skateboard.
The AI Company’s platform is designed to mimic the process of learning to skateboard. However, instead of sequentially repeating the learning task, the AI platform enables automatically parallelizes the learning process by simultaneously trying out each possible variation for each move and then parallelizing learning multiple moves at the same time. This massive parallelization is accentuated by the automatic selection of the most optimal and accurate insights (AI models) that learn the best in the context of the problem at hand. The best AI models are automatically deployed to production, stored in a very secure form and can be leveraged in traditional app development or in the development of intelligent smart contracts (AutoLearn’s SmartChain).
Imagine learning a skill instantly by parallelizing your learning so that you can try out the millions of variations, learn from them and ingest the learnings instantly. This is the AutoLearn boost.
With The AI Company’s AI, you are able to reduce what traditionally in data science would take upwards of a year and multiple data scientists to mere days through the automated training, selection and deployment of the best AI models out of 1000s of variations generated in parallel by AutoLearn. Not only do you reduce the time taken to go live with AI, because of the automation and the efficiency maximizer in AutoLearn’s AutoAI, you are guaranteed the best possible AI model. This is not guaranteed in a manually driven data science practice!
pintadachica on 06 Jun 18Workplace practices for keeping employees safe and improving the safety of their environments has improved vastly over the past century. While part of this improvement comes, without a doubt, from the vast leaps in technology. An equally important piece of the puzzle comes from what we've learned from experience. For the most part, processes that don't work get phased out while those that do get brought conveniently to the forefront of publications and articles to help industries constantly re-invent and improve their "best practices" for safety. One system we're glad to see making its exit in recent years, is the "simple" safety system, in which safety statistics and decisions are based almost exclusively on the opinions of management and the actual reported accidents and injuries that occur on the job. While the latter part of that system can provide useful data, it is reactionary rather than preventative, and requires someone to get injured or property to get damaged before corrective measures are taken.
