Most of the existing algorithms for zero-shot classification problems typically rely on the attribute-based semantic relations among categories to realize the classification of novel categories without observing any of their instances. However, training the zero-shot classification models still requires attribute labeling for each class (or even instance) in the training dataset, which is also expensive. To this end, in this paper, we bring up a new problem scenario: ''Can we derive zero-shot learning for novel attribute detectors/classifiers and use them to automatically annotate the dataset for labeling efficiency?'' Basically, given only a small set of detectors that are learned to recognize some manually annotated attributes (i.e., the seen attributes), we aim to synthesize the detectors of novel attributes in a zero-shot learning manner. Our proposed method, Zero-Shot Learning for Attributes (ZSLA), which is the first of its kind to the best of our knowledge, tackles this new research problem by applying the set operations to first decompose the seen attributes into their basic attributes and then recombine these basic attributes into the novel ones. Extensive experiments are conducted to verify the capacity of our synthesized detectors for accurately capturing the semantics of the novel attributes and show their superior performance in terms of detection and localization compared to other baseline approaches. Moreover, we demonstrate the application of automatic annotation using our synthesized detectors on Caltech-UCSD Birds-200-2011 dataset. Various generalized zero-shot classification algorithms trained upon the dataset re-annotated by ZSLA shows comparable performance with those trained with the manual ground-truth annotations.

Most of the existing algorithms for zero-shot classification problems typically rely on the attribute-based semantic relations among categories to realize the classification of novel categories without observing any of their instances. However, training the zero-shot classification models still requires attribute labeling for each class (or even instance) in the training dataset, which is also expensive. To this end, in this paper, we bring up a new problem scenario: ''Can we derive zero-shot learning for novel attribute detectors/classifiers and use them to automatically annotate the dataset for labeling efficiency?'' Basically, given only a small set of detectors that are learned to recognize some manually annotated attributes (i.e., the seen attributes), we aim to synthesize the detectors of novel attributes in a zero-shot learning manner. Our proposed method, Zero-Shot Learning for Attributes (ZSLA), which is the first of its kind to the best of our knowledge, tackles this new research problem by applying the set operations to first decompose the seen attributes into their basic attributes and then recombine these basic attributes into the novel ones.

The rapid advancement in natural language processing (NLP) has led to the development of large language models (LLMs), such as BERT [2], GPT-3 [3], and Codex [4], that are revolutionizing a wide range of applications. These models have achieved remarkable results in various tasks such as text generation, machine translation, and code synthesis, among others. A recent addition to this collection of models was the OpenAI ChatGPT [1], a pretrained generative text model which was finetuned using human feedback. Unlike previous models which operate mostly upon a single prompt, ChatGPT provides particularly impressive interaction skills through dialog, combining text generation with code synthesis. Our goal in this paper is to investigate if and how the abilities of ChatGPT can generalize to the domain of robotics. Robotics systems, unlike text-only applications, require a deep understanding of real-world physics, environmental context, and the ability to perform physical actions. A generative robotics model needs to have a robust commonsense knowledge and a sophisticated world model, and the ability to interact with users to interpret and execute commands in ways that are physically possible and that makes sense in the real world. These challenges fall beyond the original scope of language models, as they must not only understand the meaning of a given text, but also translate the intent into a logical sequence of physical actions. In recent years there have been different attempts to incorporate language into robotics systems.

A £50,000 robotic chef can make your meals without any human intervention – as long as the ingredients are already cut up. Moley Chef's Table is a new kitchen appliance from Moley Robotics, a London company run by Russian entrepreneur Mark Oleynik. At the company's new showroom on Wigmore Street, due to open in the autumn, MailOnline got a taste of the machine's creations, including a trendy vegan soup. Consumers who have the funds can buy Chef's Table for their homes, but it is also intended for airports, hospitals and even in restaurants to help out chefs. It comes amid concerns of machines taking over human's jobs, but according to the company, the gadget will make a cook's life easier if they work long hours.

Since its launch in November last year, ChatGPT has become an extraordinary hit. Essentially a souped-up chatbot, the AI program can churn out answers to the biggest and smallest questions in life, and draw up college essays, fictional stories, haikus, and even job application letters. It does this by drawing on what it has gleaned from a staggering amount of text on the internet, with careful guidance from human experts. Ask ChatGPT a question, as millions have in recent weeks, and it will do its best to respond – unless it knows it cannot. The answers are confident and fluently written, even if they are sometimes spectacularly wrong.

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.

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CES has only officially been open for like 5 minutes, and already we've found something too awesome not to share immediately: a cooking robot from a startup called Sereneti that can handle everything for you, from cooking to stirring to adding ingredients at the right time. This robot is called Cooki, and here's a rendering of how it all works: This is from January of last year, but as you can see in the pics below, they have the (much smaller) real thing here at CES, and it's actually making a spinach omelette. You preload the ingredients, turn the thing on, and it'll cook everything for you perfectly using little motorized bins to dump in the ingredients one at a time and an adorable robot arm to do the stirring. Pretty much the only thing it can't do is bring you the finished meal in bed. The initial prototype was built using a traditional robot arm, but Sereneti quickly realized that it would be both dangerous (so close to a saucepan) and practically impossible to clean.

This paper investigates the problem of finding a preference relation on a set of acts from the knowledge of an ordering on events (subsets of states of the world) describing the decision-maker (DM)s uncertainty and an ordering of consequences of acts, describing the DMs preferences. However, contrary to classical approaches to decision theory, we try to do it without resorting to any numerical representation of utility nor uncertainty, and without even using any qualitative scale on which both uncertainty and preference could be mapped. It is shown that although many axioms of Savage theory can be preserved and despite the intuitive appeal of the method for constructing a preference over acts, the approach is inconsistent with a probabilistic representation of uncertainty, but leads to the kind of uncertainty theory encountered in non-monotonic reasoning (especially preferential and rational inference), closely related to possibility theory. Moreover the method turns out to be either very little decisive or to lead to very risky decisions, although its basic principles look sound. This paper raises the question of the very possibility of purely symbolic approaches to Savage-like decision-making under uncertainty and obtains preliminary negative results.

This column, like many strange things in the modern world, was conceived in an email exchange. Someone said to an editor: "why not have a regular lighthearted column on AI topics?" The editor said: "what an excellent idea, and when will we get the first manuscript?" and the first person said: "oh but I didn't volunteer;" and the editor said: "listen, buddy, I can make your life very uncomfortable if I don't get some cooperation. We go to press next week." While looking for something to give him, we stumbled on this old manuscript, written years ago (with our esteemed colleague Neil Agnew, the Duke of York). Ever had an old sock that you try to throw away, but keep finding in the bottom of a drawer? This is a bit like that. Come to think of it, so is the frame problem. Anyway, you can't make an omelette without breaking eggs, so here is our first reflection. It's a variation on an old, old story ....