Tech companies are working on giving us all a second set of eyes and they call it computer vision.
The groundwork has been laid through every connected device with a camera since these will act as the eyes for the computer. But, vision doesn’t happen in the eyes. It happens in the brain. That’s why tech companies are perfecting their image recognition algorithms (brains) that will give sight to computers.
Basically, all of the photos uploaded to the cloud are memories which computers can learn from. Since over 657 billion images were uploaded in 2014 alone, computers have a lot of memories to learn from.
What can they possibly learn from your family photos or your night on the town?
Right now, they are learning to identify things.
For instance, Facebook uses photos to learn our faces. This allows them to suggest who should be tagged in each photo before it even crosses your mind. More broadly, though, Facebook uses photos to recognize all the details of an image, such as a cake or a couch. As of today, you can now search for specific objects in a photo. More practically, companies like Velodyne LiDAR package this is an autonomous car sensor so they can recognize and avoid certain objects on the road.
However, if we compare the visual-cognitive development of humans to computer vision’s progress, their development is equivalent to that of a three or four-year-old.
For instance, when you or I look at an image, we might say that someone is on vacation in Italy. We can tell this based on the architecture in the background and an entire family being present in the photo. But, a computer might only be able to distinguish a church, cement, and four people out of that same photo.
As we know, a picture is worth a thousand words. So, in order for computer vision to be effective, they need to do more than just recognize the details present in an image.
Computer vision has a lot to learn
Computer vision algorithms need to notice patterns, recognize emotions and relations, make inferences, and understand intentions. Then, they will be able to weave together stories from a still frame.
Once this is achieved, cameras will provide massive real-time visual data to be analyzed for various purposes.
Perhaps the most obvious would be smarter security cameras that would sound an alarm before criminals took action by judging their intentions. Or the cameras at a public pool would alert bystanders when it noticed a person drowning.
Now, imagine giving these same algorithms a little bit of time to specialize in a given field, like fashion.
For decades, the next season’s fashion trends were captured on the street by photographers with a trained eye such as Bill Cunningham and Tommy Ton.
However, autonomous cars are the fashion photographers of the future since they capture a 360-degree image of their street view surroundings at all times. With the proper algorithm to recognize trends in color, style, and fit of clothing, the major fashion houses can tap into millions of eyes at once to find the trends of tomorrow.
Nonetheless, computer vision has a lot of learning before it has the same visual comprehension of a fully-developed adult. But, soon we’ll all have an extra set of eyes watching and making our lives easier…as long as you play by the rules.
The second set of eyes
Having a second set of eyes to help guide you through life is instrumental to your development.
Personally, I wouldn’t be where I am today if it weren’t for my mentors. They taught me the core values of quality, integrity, and creativity. These values act as my root system, providing stability for my growth every day.
I challenge you to reflect on where you are today and the mentors that helped you get there.
- Do you still practice what they preached?
- Have you chosen to forget what they told you?
- Are there values you can pass onto someone else in need?
We take our values with us everywhere we go. But, if you choose to forget what your mentors told you, you’ll find yourself rewriting a chapter they told you to avoid.
Before I put anything into the world I check it against my values. Especially before I write a Quick Theories article like this one.
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