Limited medical imaging datasets challenge deep learning models by increasing risks of overfitting and reduced generalization, particularly in Generative Adversarial Networks (GANs), where discriminators may overfit, leading to training divergence. This constraint also impairs classification models trained on small datasets. Generative Data Augmentation (GDA) addresses this by expanding training datasets with synthetic data, although it requires training a generative model. We propose and evaluate two local lesion generation approaches to address the challenge of augmenting small medical image datasets. The first approach employs the Poisson Image Editing algorithm, a classical image processing technique, to create realistic image composites that outperform current state-of-the-art methods. The second approach introduces a novel generative method, leveraging a fine-tuned Image Inpainting GAN to synthesize realistic lesions within specified regions of real training images. A comprehensive comparison of the two proposed methods demonstrates that effective local lesion generation in a data-constrained setting allows for reaching new state-of-the-art results in capsule endoscopy lesion classification. Combination of our techniques achieves a macro F1-score of 33.07%, surpassing the previous best result by 7.84 percentage points (p.p.) on the highly imbalanced Kvasir Capsule Dataset, a benchmark for capsule endoscopy. To the best of our knowledge, this work is the first to apply a fine-tuned Image Inpainting GAN for GDA in medical imaging, demonstrating that an image-conditional GAN can be adapted effectively to limited datasets to generate high-quality examples, facilitating effective data augmentation. Additionally, we show that combining this GAN-based approach with classical image processing techniques further improves the results.

A robot has been trained to prepare and cook an omelette from breaking the egg to presenting it on a plate to the diner by a team of engineers. Researchers from the University of Cambridge worked with domestic appliance firm Beko to train the machine to create the best omelette for the majority of tastes. The team say cooking is an interesting problem for roboticists as'humans can never be totally objective when it comes to food' or how it should taste. They used machine learning data from a study of volunteers and their reaction to different omelettes cooked in a variety of ways in order to train the robot. The omelette, made by the robotic chef'general tasted great – much better than expected' according to the research team who tested the resulting dish.

Mizuho Securities Co. aims to increase the balance of individual customer assets under its management by 1.5-fold to ¥60 trillion in 10 years, its president and chief executive officer, Koichi Iida, said in a recent interview. The Mizuho Financial Group Inc. unit will focus on demand from working generations for the formation of assets in an effort to achieve the goal, Iida said. The company will enhance collaboration with Mizuho Bank, another Mizuho Financial unit, to encourage customers to transfer some of their bank deposits to investment trusts and other financial products, he said. "The key is whether we will be able to make offers suitable for individuals' life plans," Iida said. Mizuho Securities will utilize artificial intelligence technology to enhance the quality of service, he said. Specifically, the company will have sales staff share the know-how of personnel adept at dealing with clients and build a system to speed up improvement in operations, he said.

Vegebot, a vegetable-picking robot, uses machine learning to identify and harvest a commonplace, but challenging, agricultural crop. A team at the University of Cambridge initially trained Vegebot to recognize and harvest iceberg lettuce in the laboratory. It has now been successfully tested in a variety of field conditions in cooperation with G's Growers, a local fruit and vegetable co-operative. Although the prototype is nowhere near as fast or efficient as a human worker, it demonstrates how the use of robotics in agriculture might be expanded, even for crops like iceberg lettuce which are particularly challenging to harvest mechanically. The researchers published their results in The Journal of Field Robotics.

The'Vegebot', developed by a team at the University of Cambridge, was initially trained to recognise and harvest iceberg lettuce in a lab setting. It has now been successfully tested in a variety of field conditions in cooperation with G's Growers, a local fruit and vegetable co-operative. Although the prototype is nowhere near as fast or efficient as a human worker, it demonstrates how the use of robotics in agriculture might be expanded, even for crops like iceberg lettuce which are particularly challenging to harvest mechanically. The results are published in The Journal of Field Robotics. Crops such as potatoes and wheat have been harvested mechanically at scale for decades, but many other crops have to date resisted automation.

Stacks of vertical shelves weave around each other in what looks like an intricately choreographed – if admittedly inelegant – ballet that has been performed since 2014 in Amazon's cavernous warehouses. The shelves, each weighing more than 1,000 kg, are carried on the backs of robots that resemble giant versions of robotic vacuum cleaners. The robots cut down on time and human error, but they still have things to learn. Once an order is received, a robot goes to the shelf where the ordered item is stored. It picks up the shelf and takes it to an area where the item is removed and placed in a plastic bin, ready for packing and sending to the customer.

Generalized Thermography: A new approach to evaluation in computer Go. First published in Iida, H. (Ed.), Proceedings IJCAI-97 Workshop on Using Games as an Experimental Testbed for AI Research, pages 41-49, Nagoya, 1997.