Artificial Intelligence
Artificial Intelligence
Decision Streams are sequences of operations that begin with data and end with action. It is easiest to imagine a Decision Stream as a physical flowing stream of water.
Like real stream, Decision Streams:
Decision Streams follow the rules above and are built with multiple stages of the following primitive operations.
AI Intelligence Bubble....
"Hello again to all my friends I'm glad you came to play. Our fun and learning never ends. Here's what we did today."barney the dinosaurIf you understand at some level the methods above. If you at least kinda understand how they cause an action to occur when a condition is present, then congratulations!! You now understand neural networks !! Below is how some of the above concepts map to neural network concepts. Read on if you dare!!
Decision Streams describe a process that begins with some type of data (images, videos, audio) as input and over any number or combination of operations, arrives at an action (change the camera orientation, display an alert.) The mechanizedAI framework will allow users to create, share and crowd source new Decision Streams.
Adding Decision Streams involves locating the stream, locating stream resources, building HotPlugAI's if needed and finally uploading it as a Stream Package to the Somax unit.
New Decision Streams can be original or improvements to existing streams or a combination of both. The extent to which you will need examples depends on what exists in the community and public resources versus how much variance can be found within the examples. This is sometimes referred to as the trade off between bias and variance.
Crowd Sourced Decision Streams are a way to kick start a new Decision Stream with limited examples. To crowd source, an inventor collects what examples are available and creates the intial stream configuration then submit's the low accuracy stream to a Somax crowd source engine. Members in the engine will allocate a certain amount of their Somax's resources to capturing data for crowd projects. The Somax Crowd Capture HotPlugAI will update routinely with new Classifications which will trigger the Crowd Capture Application to log and label a sample. Samples will be taken from normally captured data, and at the authorization of it's person, A Somax can can initiate crowd capture when capture services are idle. A user may option to allocate resources to specific crowd projects but must allow a small percentage for unspecified projects as well.
Imprinting is the emulation of mannerisms and responses consistent and acceptable to the imprinted user.
Somax provides artificially intelligent inference through factory, community, commercial and personally designed and trained Neural Network models.
"The brain has about ten thousand parameters for every second of experience. We do not really have much experience about how systems like that work or how to make them be so good at finding structure in data."
Geoffrey Hinton
"There is no sound ethical objection that can be made to honest, fair, and transparent commercial AI. There is also no logical reason for why it must be any other way."
mechanizedAI
Somax AI, at the discretion of an imprinted human can use and/or exchange data in an anonymous form with other Somax mechizedAI's for the purpose of improving Decision Streams.
The community is also responsible for a certain level of policing in support of personal protection.
Some services from a community will be in the form of data or applications. Most often a community will have one or more community oriented HotPlugAI's that provide and mange community responsibilities and services.
This is neat.
When thinking about a powerful compute engine, one must also think of power consumption. I was thinking about this with respect to the Myriad 2 vs the Myriad X. To get enough compute capacity I'll need more more than one Myriad each of which needs about a watt of power. Then I thought about the motors, each motor uses about 1 watt of power.
Then I realized a possible adversarial system seeking equilibrium. The equilibrium between motors that just want to run and compute engines that just want to think.
more to come, but this is a very interesting idea!
Determining the voltage and current used at different points within the motor controller and digital electronics can be monitored at a sufficient sample rate (say twice the Nyquist rate for human head motion frequency) cheaply and simply.
This would be an excellent source of data for an AI to train on.
If we gave the AI the ability turn all the knobs on all the hardware on the Somax platform and said "minimize the power and current to all systems while maintaining a baseline performance" what might the network learn to do?
Could this be extended to add context, so that the settings used are based on power savings when the operational baseline is dynamic. For example, at night we can turn off or limit this use of the RGB camera (thus having a different operational baseline from daytime operation) without diminishing the quality of the data captured. Could we train a network to do this automatically?