In addition, since only the binary failure can be obtained, the number of latent constraints is also unknown.

In addition, since only the binary failure can be obtained, the number of latent constraints is also unknown.

Robot-assisted feeding systems enhance the independence of individuals with motor impairments and alleviate caregiver burden. While existing systems predominantly rely on software-based safety features to mitigate risks during unforeseen collisions, this study explores the use of a mechanical fail-safe to improve safety. We designed a breakaway utensil attachment that decouples forces exerted by the robot on the user when excessive forces occur. Finite element analysis (FEA) simulations were performed to predict failure points under various loading conditions, followed by experimental validation using 3D-printed attachments with variations in slot depth and wall loops. To facilitate testing, a drop test rig was developed and validated. Our results demonstrated a consistent failure point at the slot of the attachment, with a slot depth of 1 mm and three wall loops achieving failure at the target force of 65 N. Additionally, the parameters can be tailored to customize the breakaway force based on user-specific factors, such as comfort and pain tolerance. CAD files and utensil assembly instructions can be found here: https://tinyurl.com/rfa-utensil-attachment

Software engineers are increasingly adding semantic search capabilities to applications using a strategy known as Retrieval Augmented Generation (RAG). A RAG system involves finding documents that semantically match a query and then passing the documents to a large language model (LLM) such as ChatGPT to extract the right answer using an LLM. RAG systems aim to: a) reduce the problem of hallucinated responses from LLMs, b) link sources/references to generated responses, and c) remove the need for annotating documents with meta-data. However, RAG systems suffer from limitations inherent to information retrieval systems and from reliance on LLMs. In this paper, we present an experience report on the failure points of RAG systems from three case studies from separate domains: research, education, and biomedical. We share the lessons learned and present 7 failure points to consider when designing a RAG system. The two key takeaways arising from our work are: 1) validation of a RAG system is only feasible during operation, and 2) the robustness of a RAG system evolves rather than designed in at the start. We conclude with a list of potential research directions on RAG systems for the software engineering community.

In the digital world, natural language processing (NLP) is becoming as important as air. With or without knowing, we are using the subset of artificial intelligence at every moment of our life. Starting from AI assistants like Alexa to search engines, everything is powered by NLP technology. Not just simple works, natural language processing can even do big jobs extracting data from documents and enhancing the capabilities of machine learning algorithms. NLP stands as an umbrella term that covers other processes like sentiment analysis, text extraction, machine translation, conversational AI, text summarization, document AI, etc. Owing to the increasing usage of technology, NLP jobs are also becoming popular.

This is a prototype exploring application of a CNN to predict likelihood of a system failure in a process. The objective is not to classify the failure, but to classify a set of current and past time slices as an indication of a future failure. Before I begin, note that this is presented as a concept / prototype to test if a CNN model could work reasonably in predicting impending faults in an industrial system. I did apply this to a paper machine failure dataset which I will discuss in a following article. This is not the "be all, end all" or state of the art AI application.

Complex systems fail. I argue that failures can be a blueprint characterizing living organisms and biological intelligence, a control mechanism to increase complexity in evolutionary simulations, and an alternative to classical fitness optimization. Imitating biological successes in Artificial Life and Artificial Intelligence can be misleading; imitating failures offers a path towards understanding and emulating life it in artificial systems.

The benefits of artificial intelligence are flourishing across several industries and finding its way to all kinds of technical aspects. From education to manufacturing the technology has served every sector for better while introducing various innovations across its verticals. But, as experts fear, the broader AI use becomes, the higher the risk of "AI gone wrong" which means the algorithms can evolve on their own to make unintended decisions. In a recent blog for Forrester, Vice President and Principal Analyst Diego Lo Giudice discussed the expansion of artificial intelligence and the increased need for checks and balances. However, testing AI is not as simple as testing traditional software and as Lo Giudice puts it, how can one test something when they don't know the desired or anticipated outcome.