Many of the recent developments in robotics have been driven by advances in AI and deep learning. For example, AI enables robots to sense and respond to their environment. This capability increases the range of functions they can perform, from navigating their way around warehouse floors to sorting and handling objects that are uneven, fragile, or jumbled together. Something as simple as picking up a strawberry is an easy task for humans, but it has been remarkably difficult for robots to perform. As AI progresses, that progress will enhance the capabilities of robots.
Developments in AI mean we can expect the robots of the future to increasingly be used as human assistants. They will not only be used to understand and answer questions, as some are used today. They will also be able to act on voice commands and gestures, even anticipate a worker’s next move. Today, collaborative robots already work alongside humans, with humans and robots each performing separate tasks that are best suited to their strengths.
AI has the potential to revolutionize farming. Today, deep learning enables farmers to deploy equipment that can see and differentiate between crop plants and weeds. This capability allows weeding machines to selectively spray herbicides on weeds and leave other plants untouched. Farming machines that use deep learning–enabled computer vision can even optimize individual plants in a field by selectively spraying herbicides, fertilizers, fungicides, insecticides, and biologicals. In addition to reducing herbicide use and improving farm output, deep learning can be further extended to other farming operations such as applying fertilizer, performing irrigation, and harvesting.
Medical imaging and healthcare
Deep learning has been particularly effective in medical imaging, due to the availability of high-quality data and the ability of convolutional neural networks to classify images. For example, deep learning can be as effective as a dermatologist in classifying skin cancers, if not more so. Several vendors have already received FDA approval for deep learning algorithms for diagnostic purposes, including image analysis for oncology and retina diseases. Deep learning is also making significant inroads into improving healthcare quality by predicting medical events from electronic health record data.
The future of deep learning
Today, there are various neural network architectures optimized for certain types of inputs and tasks. Convolution neural networks are very good at classifying images. Another form of deep learning architecture uses recurrent neural networks to process sequential data. Both convolution and recurrent neural network models perform what is known as supervised learning, which means they need to be supplied with large amounts of data to learn. In the future, more sophisticated types of AI will use unsupervised learning. A significant amount of research is being devoted to unsupervised and semisupervised learning technology.
Reinforcement learning is a slightly different paradigm to deep learning in which an agent learns by trial and error in a simulated environment solely from rewards and punishments. Deep learning extensions into this domain are referred to as deep reinforcement learning (DRL). There has been considerable progress in this field, as demonstrated by DRL programs beating humans in the ancient game of GO.
Designing neural network architectures to solve problems is incredibly hard, made even more complex with many hyperparameters to tune and many loss functions to choose from to optimize. There has been a lot of research activity to learn good neural network architectures autonomously. Learning to learn, also known as metalearning or AutoML, is making steady progress.
Current artificial neural networks were based on 1950s understanding of how human brains process information. Neuroscience has made considerable progress since then, and deep learning architectures have become so sophisticated that they seem to exhibit structures such as grid cells, which are present in biological neural brains used for navigation. Both neuroscience and deep learning can benefit each other from cross-pollination of ideas, and it’s highly likely that these fields will begin to merge at some point.
We don’t use mechanical computers anymore, and at some point, we won’t be using digital computers, either. Rather, we will be using a new generation of quantum computers. There have been several breakthroughs in quantum computing in recent years and learning algorithms can certainly benefit from the incredible amount of compute available that quantum computers provide. It might also be possible to use learning algorithms to understand the output of the probabilistic quantum computers. Quantum machine learning is a very active branch of machine learning, and with the first International Conference in Quantum Machine Learning scheduled to take place in 2018, it’s off to a good start.