Very little work in our field of deep learning has been focused on the practical objective of building a synthetic animal-like consciousness. This lack of effort holds us back from achieving empathetic robots that can interact with their physical and social environments like animals do and thus holds us back from having empathy towards these synthetic entities ourselves. Imagine how nice it would be to have a pet “robot” who could give you the attention and love that a puppy or kitten could, all the while knowing that any attention you give it will be rewarded with real growth and learning for both of you.

Despite all the hype around machine learning, and all the results achieved by deep neural networks like GPT-3 and others, why hasn’t anyone worked on this? There has been extensive focus on dry, statistical approaches to text, image, and video understanding. These deep neural networks have now surpassed human ability in many subtasks of daily life, such as object recognition, video segmentation, depth perception, and language skills. But no project has been able to combine these available pieces into an agent that’s as flexible or enjoyable to spend time with as even a pet rodent.

I predict that simple reinforcement learning algorithms, trained with input from the final hidden layers of state-of-the-art perception networks, can achieve rich and complex objectives in the physical world. We know it will not be easy to design the proper reward functions, to gather the vast amount of needed training data, or to more deeply explore this promising space. However, we think that this path represents the most promising one yet towards creating synthetic conscious entities with empathy for the common person.

It is time that we connect the amazing perceptual abilities of networks such as Yolo and EfficientNet to a physical representation in the real world. Let’s build a robot that combines a state of the art vision network (YoloV5) as the basic input for reinforcement learning (SAC) that can optimize for the objective of exploring and interacting with the real world.

In this light, we are launching GoodDog.ai, an open project whose aim it will be to use the latest state of the art in machine learning to create a mobile robot capable of passing the Puppy Turing Test. Namely, we are building a synthetic entity capable of interacting with its surroundings in a way to evoke sustained empathy from humans. The chief theory behind this project will be to take state of the art perceptual neural networks, run them in real time on inexpensive mobile robots, and then train reinforcement learning algorithms on the final perceptual layers of these neural nets.

Stay tuned for more information on our published robot designs, BOMs, and software stack.