A team from the加州大学洛杉矶加州大学苏利大学工程学院in Los Angeles, California, have applied 3D printing to create a “seeing” device modeled on the human brain.
Appearing as a series of neatly stacked plastic plates, this device is capable of analyzing image data to identify objects such as items of clothing, and handwritten characters.
通过开发技术的基础上,设备,the scientists could have discovered a simpler way of teaching artificially intelligent (AI) products, like autonomous vehicles and “smart assistants”, to perceive the world around them.
Like a “maze of glass and mirrors”
听起来很怪异？人工智能已经比我们想象的要大。向银行支付支票已经依靠计算机愿景了一段时间 - 当机器以滑倒为单位时，它会读取上面写的金额。这样做的方法通常是通过对摄像机进行编程以识别纸上写的数字。
在自动驾驶汽车中，“看到”符号的能力也由相机控制，同步具有复杂LIDAR系统扫描道路和周围的障碍物。
像LIDAR一样，UCLA的3D打印设备依赖于光的衍射来看，该设备的复杂程度要小得多，并且不需要运行的功率。
Like a “maze of glass and mirrors”
Each plate in the UCLA device is patterned with artificial neurons, in the shape of tiny pixels, that each diffract light in a different way.
看一个物体时,设备决定了what it can see by the way light travels through the plates, and what comes out at the other side. UCLA principle investigator Aydogan Ozcan,解释, “This is intuitively like a very complex maze of glass and mirrors,”
“The light enters a diffractive network and bounces around the maze until it exits. The system determines what the object is by where most of the light ends up exiting.”
What can it see?
In experiments, the device has proven the ability to correctly identify handwriting, and a ladies’ shoe.
“This work opens up fundamentally new opportunities to use an artificial intelligence-based passive device to instantaneously analyze data, images and classify objects,” adds Ozcan. “This optical artificial neural network device is intuitively modeled on how the brain processes information,”
“It could be scaled up to enable new camera designs and unique optical components that work passively in medical technologies, robotics, security or any application where image and video data are essential.”
“使用衍射深神经网络的全光学机器学习” is published online inScience杂志。它由Xing Lin，Yair Rivenson，Nezih T. Yardimci，Muhammed Veli，Yi Luo，Mona Jarrahi和Aydogan Ozcan合着。
特色图片显示了UCLA的3D印刷人工神经网络。通过UCLA Samueli / Ozcan研究小组的照片
