Using our Proprietary [Minsky™] Ai Engine, we are committed to simplifying complex use cases in Artificial Intelligence to enable better outcomes that result in happier humans!
We started Ai Labs in 2017 with the intention of bringing Ai solutions to the masses without the need of advanced computer infrastructure or programming skills. In order to accelerate the digital transformation, we took an interdisciplinary approach by bringing together new ideas and advances in machine learning, Deep learning, Natural language processing Hybrid modeling etc.by spending significant time and funds to develop our Ai proprietary engine called Minsky™. We also pioneered the use of Robotic Process Automation (RPA) for data capture required in performing Real Time Ai predictions.
Ai Labs (www.ailabsinc.com) was founded in 2017 and incorporated in the State of Arizona. With head offices in Tempe, AZ, the Company also has a development center in India that specializes in Artificial Intelligence, Robotic Process Automation and Software Development. The Company has a team of experienced professionals with expertise in various technologies and domains where we pioneered the process of incorporating RPA into our Ai solutions. The Company applies leading edge technology to deliver a portfolio of innovative Ai solutions for custom use cases. With our head office in US and offshore engineering centers, we provide 24/7 support for Data Analytics, Ai Project Development & Post Implementation support.
What We Do
At Ai Labs, we developed our own proprietary Ai Engine “Minsky™” with the purpose of leveraging it to simplify the development of custom Ai Solutions to transform and monetize big data into profitable outcomes.
The Minsky™ Ai Engine provides an easy 4 step process that allows one to easily test and model their data irrespective of the Industry type or data size and go “live” within 2 to 4 weeks.
Minsky™ provides outcome based solutions. Minsky™ is designed using industry best practices & ideas to fulfill domain independent requirements. Minsky™ leverages smart generic solutions to output the results required to overcome today’s problems and expectations.
Need For Ai...
Need for an easy to use multi-purpose Ai Engine: Clients invariably ask how they can use Ai to add value to their business. The answer is by deploying an Ai Engine as an implementation vehicle through which Ai functionality can be leveraged, abstracting away the inherent complexity. Composed of several distinct modules, an Ai Engine can be deployed either as a service (AIaaS) or embedded within client-server, web or mobile applications. An Ai Engine is comprised of several fundamental modules which include a Machine Learning (ML) Module a Deep Learning (DL) Module and a Natural Language Processing (NLP) Module.
Machine Learning is based on Neural Networks which model biological brain function. A Neural Network is a net of neurons (brain cells) that are linked to each other through connections called synapses (think of a fishing net) These connections have a certain strength (positive or negative) and the combination of these will result in a neuron being turned on (firing), or not, according to a certain threshold value (think of a light bulb with some power cords attached: if the cumulative power is enough the light bulb will turn on). Human thinking is the effect of firing (or not) of neurons. Now, take one side of a Neural Net and turn on some neurons. Then take the other side and again turn on some neurons. You have given the net an example of a given input and an expected output (think of an input of 1 plus 1, and an output of 2 so as to teach it addition). Next, give it lots of examples and then use a specific standardized learning algorithm through which it can learn by adjusting the power of its connections and when neurons will fire. Once it stabilizes through many iterations, ask it to add numbers that you haven’t taught it: it will respond with a high probability of accuracy based on what is has learned. In this way, given an input dataset (e.g. images) and an output data set (e.g. a description of these images), based on the patterns it ‘sees’ it can very accurately answer what a new picture contains even though it has never seen it before.
Deep Learning on the other hand is a type of machine learning and artificial intelligence (Ai) that imitates the way humans gain certain types of knowledge. It is extremely beneficial to data scientists who are tasked with collecting, analyzing and interpreting large amounts of data; deep learning makes this process faster and easier. Deep learning utilizes both structured and unstructured data for training. Practical examples of deep learning are Virtual assistants, vision for driverless cars, radiology, face recognition and many more.
Natural language Processing
Natural language Processing (NLP) is a form of artificial intelligence (Ai) that gives computers the ability to read, understand and interpret human language. It helps computers measure sentiment and “rate” human language (written or spoken). An example of NLP is automated systems direct customer calls to a service representative or online chat bots, which respond to customer requests with helpful information