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 20 Dec 18One of the most common misconceptions out there is the belief that technology equates innovation. Innovation is the creation of new value through a better solution for a problem that either does a better job in solving the problem or does so in a manner that the solution is accessible in a larger set of circumstances by a larger number of people. On the other hand, technology is simply the tooling that holds the promise of new solutions but by itself, is meaningless. Enterprises can become enamored by the promise and hype about technology and go down long, complex journeys, invest millions in upgrading technology and still come out empty on the other side because they built technology for technology's sake. Even in well intention boards and C-suite, industry peer pressure and hype around technologies can force action that ultimately leads to massive investments in people, software, technology, and vendors but does not yield the ROI promised by the technology. Leadership The problem can be accentuated when not enough due diligence is done on the applicability of the technology to the enterprise given the current state i.e. the point in time when the technology is being introduced, the customer's propensity to accept the technology-driven solution i.e. are the users ready to embrace, adopt, learn and utilize new solutions and burning problems that necessitate the adoption of new technology to better solve the problem. When the timing of new technology introduction is gotten wrong, it almost always fails to deliver on its promised ROI. Leaders need to rise above the hype and peer pressure and ensure that they understand, first and foremost, the burning problems that plague their customers or make the bedrock of their future strategy. Next, leaders need to ensure and validate that the technology in mind can actually be used to solve the problem through rapid prototyping and minimal investment. Once customer feedback on the prototype has been validated, then only
