This is part two of a two-part interview with Blendid co-founder and CEO, Vipin Jain. Imagine you wanted to create a replicator like you saw on Star Trek. You've successfully figured out how to connect software that directs a robot, a working piece of hardware, that can execute a recipe; repeatedly, with consistent results. Now it's time to go big. "We had a lot of debate around what we should make first. It came down to choosing a food category. It had to be big enough to have mass appeal, especially to millennials and centennials," Vipin said.

There are three categories of AIEd tool being used in schools and colleges today: learner-facing (eg adaptive learning platforms), teacher-facing (eg automated assessment tools, or advanced teacher dashboards) and system-facing (eg analysing data from across schools to predict school inspection performance). The UK has a competitive advantage in education technology and artificial intelligence, but without government support and public funding this advantage risks being lost. Despite its huge potential, AIEd is underdeveloped. Our analysis identified only £1m of public funding for AIEd R&D since 2014 (far less than spending in comparable sectors such as healthcare). There are demand and supply-side market failures preventing development and maturity of AIEd, which government intervention can address.

Goodhart's Law states that "When a measure becomes a target, it ceases to be a good measure." At their heart, what most current AI approaches do is to optimize metrics. The practice of optimizing metrics is not new nor unique to AI, yet AI can be particularly efficient (even too efficient!) This is important to understand, because any risks of optimizing metrics are heightened by AI. While metrics can be useful in their proper place, there are harms when they are unthinkingly applied.

Photo by Andras Vas on unsplash Previous posts in this series have highlighted the importance of human intelligence and emotion in education. We have traversed several emerging ideas, including the use of virtual teaching assistants (chatbots), ultra-personalised learning and machine intelligence, but the most important component in education is still the human element. Other jobs in society may already have been supplanted by robotics and artificial intelligence. Mostly, they are repetitive, low level or dangerous jobs, but replacing teachers with computers is neither desirable nor expedient. However, replacing some aspects of what teachers do is both effective and inevitable.

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A company operating a website on how to prepare for qualification examinations is using artificial intelligence (AI) to successfully predict questions on such tests. Tokyo-based Sight Visit Inc. correctly picked 57 out of 95 questions -- about 60% -- that went on the multiple choice section of the preliminary test for the state bar examination in May. One of the questions that the company correctly predicted is a true-or-false one that stated: "When deciding to involve an expert commissioner when preparing to hold oral proceedings to hear explanations based on their expert knowledge, the opinions of the concerned parties must be heard." Sight Visit deems that it has been successful when its predictions for both questions and their answer options are totally, or almost, correct. The preliminary test for the state bar exam comprises multiple choice and description-type sections.

The past 10 years wouldn't have been possible without you. Information technology and software services company Softclick Investments is hosting an Artificial Intelligence (AI) for Business course from the 24th of September to 26 October at Batanai Gardens. The course is supposed to provide "practical, comprehensive training that enables participants to immediately and effectively partake in enterprise AI projects." The courses require no technical background and will be open to all "executives and professionals from all functions across all industries." At the end of the course, participants will earn a certificate.

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Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As deep neural networks (DNNs) tend to overfit using a few samples only, typical meta-learning models use shallow neural networks, thus limiting its effectiveness. In order to achieve top performance, some recent works tried to use the DNNs pre-trained on large-scale datasets but mostly in straight-forward manners, e.g., (1) taking their weights as a warm start of meta-training, and (2) freezing their convolutional layers as the feature extractor of base-learners. In this paper, we propose a novel approach called meta-transfer learning (MTL) which learns to transfer the weights of a deep NN for few-shot learning tasks. Specifically, meta refers to training multiple tasks, and transfer is achieved by learning scaling and shifting functions of DNN weights for each task. In addition, we introduce the hard task (HT) meta-batch scheme as an effective learning curriculum that further boosts the learning efficiency of MTL. We conduct few-shot learning experiments and report top performance for five-class few-shot recognition tasks on three challenging benchmarks: miniImageNet, tieredImageNet and Fewshot-CIFAR100 (FC100). Extensive comparisons to related works validate that our MTL approach trained with the proposed HT meta-batch scheme achieves top performance. An ablation study also shows that both components contribute to fast convergence and high accuracy.