About NEUROTHINK

Radically Accessible

Let’s face it, training a machine to learn is not an easy task. Creating a model on most of the major cloud service platforms is complex, often requiring you to go through twenty steps (or more!) before you can even start training. These platforms were not originally built with the sole intent to create and train machine learning models and a result is unnecessary complexity. Also, data security often becomes a significant concern as native cloud application platforms require a host of external API integrations.

neurothink™ from its inception is a machine learning platform. We invested in our own high-performance GPU and CPU servers, and we’ve created a complete end-to-end environment with the tools to easily build, train, and deploy models. Our platform is designed to be accessible to students who are building their first models and flexible to accommodate the most advanced data scientists. When designing our platform, we painstakingly analyzed the factors that make machine learning platforms so complex. neurothink is a truly unique platform, which will remove the complexity and greatly improve API security in the machine learning workflow.

We are entering a time in history where artificial intelligence will drive massive transformational innovations. To realize that vision, we strongly believe that machine learning should be accessible to everyone: data scientists, mathematicians, computer scientists, engineers, and anyone with data. In fact, we believe that machine learning should be Radically Accessible.

Story behind the name

The name neurothink is derived from two complementary concepts: Hebbian Rule and the thinking machine:

A common discussion in artificial intelligence (AI) has been will machines ever become self-aware or think on their own. At this point in computing, it is difficult to say whether a computer will ever become self-aware because of the many outstanding questions on what human consciousness is. It is, however, evident that the machine?s intelligence is evolving with the introduction of new machine learning concepts and increasing computing power. The name ?neurothink? represents the intersection of two concepts that speak to this evolution of the intelligence. The first ?neuro? or ?neuron? represents the Hebbian Rule, by the Canadian psychologist, which postulates that neurons in the biological brain that fire together wire together. Like the biological neuron, the synthetic neuron in a deep neural network will become more efficient and loosely emulate the biological neuron. The second part ?think? or “thinking machine”, was inspired by Alan Turing and his work on whether humans could distinguish whether a conversation was with another human or a machine, famously known as the Turing Test.