Is microwave cooking nuking all the nutrients? Micorwaves have been a kitchen staple since the late 1960s, but are they safe for our food? Breakthroughs, discoveries, and DIY tips sent every weekday. Originally used for radar and other technologies, the power of microwaves was first harnessed specifically for heating food in 1947 . By the late 1960s, commercial microwave ovens were small and inexpensive enough to become fixtures of the modern kitchen.

Breakthroughs, discoveries, and DIY tips sent every weekday. Love it or loathe it, broccoli is one of the most popular vegetables in the United States. However, this staple vegetable can be as finicky as a picky eater when it comes to its growth . It is a temperate crop that likes cooler nights and predictable weather in order to thrive. Both of these conditions are getting much harder to come by due to climate change .

Although there is a growing demand for cooking behaviours as one of the expected tasks for robots, a series of cooking behaviours based on new recipe descriptions by robots in the real world has not yet been realised. In this study, we propose a robot system that integrates real-world executable robot cooking behaviour planning using the Large Language Model (LLM) and classical planning of PDDL descriptions, and food ingredient state recognition learning from a small number of data using the Vision-Language model (VLM). We succeeded in experiments in which PR2, a dual-armed wheeled robot, performed cooking from arranged new recipes in a real-world environment, and confirmed the effectiveness of the proposed system.

Multimodal Large Language Models (MLLMs) are commonly evaluated using costly annotated multimodal benchmarks. However, these benchmarks often struggle to keep pace with the rapidly advancing requirements of MLLM evaluation. We propose GenCeption, a novel and annotation-free MLLM evaluation framework that merely requires unimodal data to assess inter-modality semantic coherence and inversely reflects the models' inclination to hallucinate. Analogous to the popular DrawCeption game, GenCeption initiates with a non-textual sample and undergoes a series of iterative description and generation steps. Semantic drift across iterations is quantified using the GC@T metric. Our empirical findings validate GenCeption's efficacy, showing strong correlations with popular MLLM benchmarking results. GenCeption may be extended to mitigate training data contamination by utilizing ubiquitous, previously unseen unimodal data.

Recent advancements on Large Language Models (LLMs) enable AI Agents to automatically generate and execute multi-step plans to solve complex tasks. However, since LLM's content generation process is hardly controllable, current LLM-based agents frequently generate invalid or non-executable plans, which jeopardizes the performance of the generated plans and corrupts users' trust in LLM-based agents. In response, this paper proposes a novel ``Formal-LLM'' framework for LLM-based agents by integrating the expressiveness of natural language and the precision of formal language. Specifically, the framework allows human users to express their requirements or constraints for the planning process as an automaton. A stack-based LLM plan generation process is then conducted under the supervision of the automaton to ensure that the generated plan satisfies the constraints, making the planning process controllable. We conduct experiments on both benchmark tasks and practical real-life tasks, and our framework achieves over 50% overall performance increase, which validates the feasibility and effectiveness of employing Formal-LLM to guide the plan generation of agents, preventing the agents from generating invalid and unsuccessful plans. Further, more controllable LLM-based agents can facilitate the broader utilization of LLM in application scenarios where high validity of planning is essential. The work is open-sourced at https://github.com/agiresearch/Formal-LLM.

In this report, we describe the vision, challenges, and scientific contributions of the Task Wizard team, TWIZ, in the Alexa Prize TaskBot Challenge 2022 [1]. Our vision, is to build TWIZ bot as an helpful, multimodal, knowledgeable, and engaging assistant that can guide users towards the successful completion of complex manual tasks. To achieve this, we focus our efforts on three main research questions: (1) Humanly-Shaped Conversations, by providing information in a knowledgeable way; (2) Multimodal Stimulus, making use of various modalities including voice, images, and videos; and (3) Zero-shot Conversational Flows, to improve the robustness of the interaction to unseen scenarios. TWIZ is an assistant capable of supporting a wide range of tasks, with several innovative features such as creative cooking, video navigation through voice, and the robust TWIZ-LLM, a Large Language Model trained for dialoguing about complex manual tasks. Given ratings and feedback provided by users, we observed that TWIZ bot is an effective and robust system, capable of guiding users through tasks while providing several multimodal stimuli.

Decoding the core of procedural texts, exemplified by cooking recipes, is crucial for intelligent reasoning and instruction automation. Procedural texts can be comprehensively defined as a sequential chain of steps to accomplish a task employing resources. From a cooking perspective, these instructions can be interpreted as a series of modifications to a food preparation, which initially comprises a set of ingredients. These changes involve transformations of comestible resources. For a model to effectively reason about cooking recipes, it must accurately discern and understand the inputs Figure 1: A graphical depiction of the PizzaCommonsense and outputs of intermediate steps within the underlying motivation. Models are required to recipe. Aiming to address this, we present a learn knowledge about the input and output of each intermediate new corpus of cooking recipes enriched with step and predict the correct sequencing of descriptions of intermediate steps of the recipes these comestibles given the corresponding instructions that explicate the input and output for each step.

Recent developments such as Virtual and Augmented Reality as well as the introduction of gamification in corporate learning are changing the face of training. The challenge still remains to engage and entertain as well as teach in an environment that is harder to control by L&D professionals. With corporate education becoming almost entirely learner-centric, the solution I was advocating in one of my previous articles is personalized adaptive learning. It makes sense in the context of things and studies already support its benefits. However, there is some effort to be put in development and implementation so companies may not jump at the idea.

In a quiet corner of rural Hampshire, a robot called Rachel is pootling around an overgrown field. With bright orange casing and a smartphone clipped to her back end, she looks like a cross between an expensive toy and the kind of rover used on space missions. Up close, she has four USB ports, a disc-like GPS receiver, and the nuts and bolts of a system called Lidar, which enables her to orient herself using laser beams. She cost around £2,000 to make. Every three seconds, Rachel takes a closeup photograph of the plants and soil around her, which will build into a forensic map of the field and the wider farm beyond. After 20 minutes or so of this, she is momentarily disturbed by two of the farm's dogs, unsure what to make of her.

Click to learn more about author Alejandro Correa Bahnsen. Almost everyone has heard the words "Machine Learning", but most people don't fully understand what they mean. Machine Learning isn't a single formula that is simply applied to a problem. There are many algorithms to choose from, each of which can be used to achieve different goals. This is the first in a series of articles that will introduce Machine Learning algorithms to help you understand how they work, and when to use each one.