When people are trying to figure out what is in a room, they can also move around in it, pick up objects and put them down.
So, we have a relationship between object recognition and being able to path plan within a room.
People often cannot determine what an object is without reading the label. So, some NLP might be in the mix.
To determine what kind of leaf or white powder is sitting on a table, or exactly what is causing the discoloration in the grout, the system iwould require some very specialized skills.
Object recognition relies on sensors, computer memory and processing speed, and software.
Sensors:
Camera technology has run ahead very quickly. I believe that today the amount of input from cameras into the server farm can be made significantly greater than the amount of input from the eye into the brain.
I only put a single camera into my scenario, but if we are trying to max out the room’s ability to recognize objects, we can put in many.
Likewise, if the microphone is helpful in recognition, then the room can exceed human auditory abilities.
Machines have already overtaken us in being able to accept these kinds of raw data.
Memory and Processing Power:
Here is a question that requires expert thinking: So, apparently machines are recording enough video today to equal the data stream people use for visual object recognition, and computers can manipulate these images in real-time.
What versions of the object recognition task require still more memory and still faster computers, or do we have enough today?
Software
Google Goggles offers some general object recognition capabilities.
We also have voice and facial recognition.
One useful step would be to find ways to measure how successful systems like Google Goggles and facial recognition are now, then plot over time.
When people are trying to figure out what is in a room, they can also move around in it, pick up objects and put them down.
So, we have a relationship between object recognition and being able to path plan within a room.
People often cannot determine what an object is without reading the label. So, some NLP might be in the mix.
To determine what kind of leaf or white powder is sitting on a table, or exactly what is causing the discoloration in the grout, the system iwould require some very specialized skills.
Continuing the example:
Object recognition relies on sensors, computer memory and processing speed, and software.
Sensors:
Camera technology has run ahead very quickly. I believe that today the amount of input from cameras into the server farm can be made significantly greater than the amount of input from the eye into the brain.
I only put a single camera into my scenario, but if we are trying to max out the room’s ability to recognize objects, we can put in many.
Likewise, if the microphone is helpful in recognition, then the room can exceed human auditory abilities.
Machines have already overtaken us in being able to accept these kinds of raw data.
Memory and Processing Power:
Here is a question that requires expert thinking: So, apparently machines are recording enough video today to equal the data stream people use for visual object recognition, and computers can manipulate these images in real-time.
What versions of the object recognition task require still more memory and still faster computers, or do we have enough today?
Software
Google Goggles offers some general object recognition capabilities.
We also have voice and facial recognition.
One useful step would be to find ways to measure how successful systems like Google Goggles and facial recognition are now, then plot over time.
With that work in hand, we can begin to forecast.
Prior to forecasting when technology will be successful, we have to define the parameters of the test very precisely.