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 11 Nov 18Much has and needs to be said about an enterprise's AI strategy. Artificial Intelligence or AI is considered a fundamentally disruptive technology similar to the steam engine, electricity etc, a technology that will be pervasive and absolute in its impact on the world and its inhabitants. The ability to find hidden patterns to predict the future or detect a behavior has massive implications across the world, in every industry, sector, and domain. When faced with this realization, enterprise's can find themselves stuck, paralyzed and unsure about how to proceed. The field of AI is decades old already and the early success stories have been practicing AI for multiple years already with the tech industry leading the way. How can an enterprise that has no experience and competency in this area let alone lead the technology or even leverage it appropriately to drive business value? When developing the AI strategy, two ideas are paramount. First, this a fundamentally disruptive technology and the enterprise will need to establish it as a core competency for the foreseeable future. Not doing so will not be an option. Second, a long-term plan to success is superseded by the need to drive quick wins and small successes not only to build confidence but use real-world experience to develop and hone that skill. The Short-Term AI Strategy The short-term AI strategy should focus on driving immediate business value through enhanced customer experiences that leverage any field of AI be it machine learning, deep learning, natural language processing etc. Driving the usage and deployment of AI in front of an end user making them smarter, productive and better informed can pay rich dividends by not only helping the enterprise can real-world experience, but it can also give a perception boost to the company as being innovative and cutting edge. However, most importantly, this can highlight and promote the success and potential of AI in the enterprise and encourage a snowball eff
