Si-mAind

INTELLIGENCE WITH EMOTIONS

Features

New Filter Design

Pixel Attack & Black Box Security

Dimension Reduction

Anomolies & Emotions

EXPLORE OUR FEATURES

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How it works

Inspired from the mechanism behind human vision, Mila AI surpasses modern AI approaches. By using completely new algorithm, that is not based on any existing technology to date.


Mila AI was designed to:
Prevent information attack (aka pixel attack, back door attack etc.)
Loss of important information (excludes the need of max pooling)
Enable continues learning ability (training doesn’t have to stop)
Achieve near linear scaling
Have hot plug and unplug of I/O ability
Be more implementation friendly for in-house developers (not just AI proficient ones) with basic knowledge of C++ library/API implementation

Use cases for Mila AI

More Reliable Auto Pilots

It is time for true auto pilots to steer the technology in the right direction, adapting to a system malfunction in real time.

We also envision auto pilot personalization, where driving is adjusted to the car owner temper and habits.

Embedded Systems & Medicine

In embedded modalities we can expect fast responses to device tinkering and hacking, protecting the patient using the device, while also performing smart monitoring of the patient and alerting if needed.

Facilitation in patient overall diagnosis also becomes possible.

Flexible & Secure Smart cities

We envision Mila AI as the core of context based smart city infrastructure with added information security.

Smart PnP Manufacturing

In the factories, data centers and logistic centers we see seamless transition during upgrades of hardware and machines with uptime optimizations and preventive maintenance.

Social impact

The need to learn how our brain works enabled us to design next generation concepts about how our brain works. Still the true purpose for developing Mila AI was to understand how it works and use that knowledge to help and heal real brains.

Our approach surpassed the most optimistic projections with 10x cognitive improvements in a child subject with autism compared to state-of-the-art classical approaches.

As our technical R&D progresses, we will be able to improve our brain model and with that improve the approaches of influencing the way our brain accepts and processes information in the right direction. This knowledge will help professionals come up with better ways to approach cognitive impairment.
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