Artificial intelligence and machine learning are already an integral part of our everyday lives online. For example, search engines such as Google use intelligent ranking algorithms, and video streaming services such as Netflix use machine learning to personalize movie recommendations.

As the demands for AI online continue to grow, so does the need to speed up AI performance and find ways to reduce its energy consumption.

Now a University of Washington-led team has come up with a system that could help: an optical computing core prototype that uses phase-change material. This system is fast, energy efficient and capable of accelerating the neural networks used in AI and machine learning. The technology is also scalable and directly applicable to cloud computing.

Two researchers at Duke University have recently devised a useful approach to examine how essential certain variables are for increasing the reliability/accuracy of predictive models. Their paper, published in Nature Machine Intelligence, could ultimately aid the development of more reliable and better performing machine-learning algorithms for a variety of applications.

“Most people pick a predictive machine-learning technique and examine which variables are important or relevant to its predictions afterwards,” Jiayun Dong, one of the researchers who carried out the study, told TechXplore. “What if there were two models that had similar performance but used wildly different variables? If that was the case, an analyst could make a mistake and think that one variable is important, when in fact, there is a different, equally good model for which a totally different set of variables is important.”

Dong and his colleague Cynthia Rudin introduced a method that researchers can use to examine the importance of variables for a variety of almost-optimal predictive models. This approach, which they refer to as “variable importance clouds,” could be used to gain a better understanding of machine-learning models before selecting the most promising to complete a given task.

Recently, there has been a reemergence of interest in optical computing platforms for artificial intelligence-related applications. Optics is ideally suited for realizing neural network models because of the high speed, large bandwidth and high interconnectivity of optical information processing. Introduced by UCLA researchers, Diffractive Deep Neural Networks (D²NNs) constitute such an optical computing framework, comprising successive transmissive and/or reflective diffractive surfaces that can process input information through light-matter interaction. These surfaces are designed using standard deep learning techniques in a computer, which are then fabricated and assembled to build a physical optical network. Through experiments performed at terahertz wavelengths, the capability of D²NNs in classifying objects all-optically was demonstrated. In addition to object classification, the success of D²NNs in performing miscellaneous optical design and computation tasks, including e.g., spectral filtering, spectral information encoding, and optical pulse shaping have also been demonstrated.

In their latest paper published in Light: Science & Applications, UCLA team reports a leapfrog advance in D²NN-based image classification accuracy through ensemble learning. The key ingredient behind the success of their approach can be intuitively understood through the experiment of Sir Francis Galton (1822–1911), an English philosopher and statistician, who, while visiting a livestock fair, asked the participants to guess the weight of an ox. None of the hundreds of participants succeeded in guessing the weight. But to his astonishment, Galton found that the median of all the guesses came quite close—1207 pounds, and was accurate within 1% of the true weight of 1198 pounds. This experiment reveals the power of combining many predictions in order to obtain a much more accurate prediction. Ensemble learning manifests this idea in machine learning, where an improved predictive performance is attained by combining multiple models.

In their scheme, UCLA researchers reported an ensemble formed by multiple D²NNs operating in parallel, each of which is individually trained and diversified by optically filtering their inputs using a variety of filters. 1252 D²NNs, uniquely designed in this manner, formed the initial pool of networks, which was then pruned using an iterative pruning algorithm, so that the resulting physical ensemble is not prohibitively large. The final prediction comes from a weighted average of the decisions from all the constituent D²NNs in an ensemble. The researchers evaluated the performance of the resulting D²NN ensembles on CIFAR-10 image dataset, which contains 60000 natural images categorized in 10 classes and is an extensively used dataset for benchmarking various machine learning algorithms. Simulations of their designed ensemble systems revealed that diffractive optical networks can significantly benefit from the ‘wisdom of the crowd’.