(Renyi Qu's Master's Thesis) Recent advancements in interpretable models for vision-language tasks have achieved competitive performance; however, their interpretability often suffers due to the reliance on unstructured text outputs from large language models (LLMs). This introduces randomness and compromises both transparency and reliability, which are essential for addressing safety issues in AI systems. We introduce \texttt{Hi-CoDe} (Hierarchical Concept Decomposition), a novel framework designed to enhance model interpretability through structured concept analysis. Our approach consists of two main components: (1) We use GPT-4 to decompose an input image into a structured hierarchy of visual concepts, thereby forming a visual concept tree. (2) We then employ an ensemble of simple linear classifiers that operate on concept-specific features derived from CLIP to perform classification. Our approach not only aligns with the performance of state-of-the-art models but also advances transparency by providing clear insights into the decision-making process and highlighting the importance of various concepts. This allows for a detailed analysis of potential failure modes and improves model compactness, therefore setting a new benchmark in interpretability without compromising the accuracy.

As IoT devices become widely, it is crucial to protect them from malicious intrusions. However, the data scarcity of IoT limits the applicability of traditional intrusion detection methods, which are highly data-dependent. To address this, in this paper we propose the Open-Set Dandelion Network (OSDN) based on unsupervised heterogeneous domain adaptation in an open-set manner. The OSDN model performs intrusion knowledge transfer from the knowledge-rich source network intrusion domain to facilitate more accurate intrusion detection for the data-scarce target IoT intrusion domain. Under the open-set setting, it can also detect newly-emerged target domain intrusions that are not observed in the source domain. To achieve this, the OSDN model forms the source domain into a dandelion-like feature space in which each intrusion category is compactly grouped and different intrusion categories are separated, i.e., simultaneously emphasising inter-category separability and intra-category compactness. The dandelion-based target membership mechanism then forms the target dandelion. Then, the dandelion angular separation mechanism achieves better inter-category separability, and the dandelion embedding alignment mechanism further aligns both dandelions in a finer manner. To promote intra-category compactness, the discriminating sampled dandelion mechanism is used. Assisted by the intrusion classifier trained using both known and generated unknown intrusion knowledge, a semantic dandelion correction mechanism emphasises easily-confused categories and guides better inter-category separability. Holistically, these mechanisms form the OSDN model that effectively performs intrusion knowledge transfer to benefit IoT intrusion detection. Comprehensive experiments on several intrusion datasets verify the effectiveness of the OSDN model, outperforming three state-of-the-art baseline methods by 16.9%.

I have spent a few weeks talking to video-game voice actors, the real humans who bring verve and humour to our gaming experiences. Some of them have won major awards for their work. None of them have had a meaningful pay rise in over 10 years, despite the industry's exponential growth. They are furious – and they have every right to be. Over the weekend, Hellena Taylor, who played the lead character in Bayonetta, Platinum Games' stylish action series about a hypersexualised angel-killing witch who fights with extreme flair, went public with her frustrations.

Tristan is a futurist covering human-centric artificial intelligence advances, quantum computing, STEM, physics, and space stuff. Pronouns: (show all) Tristan is a futurist covering human-centric artificial intelligence advances, quantum computing, STEM, physics, and space stuff. Our universe is a ridiculous place. It's where all the silliest things we're aware of happen. And chief among the silliness is the wacky idea of time.

Second, the ability to recognize is one of the main components of artificial intelligence. For example, dandelion, artificial intelligence can accurately identify and identify this plant as a dandelion through learning; The human brain is different, and it will be classified according to its main function and role in different scenarios, such as labeling vegetables, labeling wildflowers and herbs and so on.

With the rapid advancement of Artificial Intelligence (AI) technology, robots equipped with Artificial Intelligence (AI) are becoming more sophisticated and capable. Neurons are like hardware and the brain is like software. People often compare the human brain to a computer. The growth of artificial intelligence and the advent of anthropomorphic robots make this metaphor even more beautiful to look at. However, Lisa Feldman-Barrett, a professor of psychology at Northeastern University in the United States, believes this similarity is problematic and may lead to misconceptions.

In the last years, XAI research has mainly been concerned with developing new technical approaches to explain deep learning models. Just recent research has started to acknowledge the need to tailor explanations to different contexts and requirements of stakeholders. Explanations must not only suit developers of models, but also domain experts as well as end users. Thus, in order to satisfy different stakeholders, explanation methods need to be combined. While multi-modal explanations have been used to make model predictions more transparent, less research has focused on treating explanation as a process, where users can ask for information according to the level of understanding gained at a certain point in time. Consequently, an opportunity to explore explanations on different levels of abstraction should be provided besides multi-modal explanations. We present a process-based approach that combines multi-level and multi-modal explanations. The user can ask for textual explanations or visualizations through conversational interaction in a drill-down manner. We use Inductive Logic Programming, an interpretable machine learning approach, to learn a comprehensible model. Further, we present an algorithm that creates an explanatory tree for each example for which a classifier decision is to be explained. The explanatory tree can be navigated by the user to get answers of different levels of detail. We provide a proof-of-concept implementation for concepts induced from a semantic net about living beings.

The idea seems wonderful - a phone app that allows you to take a photo of a plant or animal and receive immediate species identification and other information about it. A "Shazam for nature" so to speak. We are building huge repositories of data related to our natural environments, making this idea a reality. But there are ethical concerns that should be addressed: about how data is collected and shared, who has the right to share it and how we use public data for machine learning. And there's a bigger concern – whether such apps change what it means to be human.

Google parent Alphabet Inc. GOOGL 0.58% is pitching an idea to store power from renewable energy in tanks of molten salt and cold liquid, an example of the tech giant trying to marry its far-reaching ambitions with business demand. Alphabet's research lab, dubbed X, said Monday that it has developed plans to store electricity generated from solar panels or wind turbines as thermal energy in hot salt and cold liquids, such as antifreeze. The lab is seeking partners in the energy industry, including power-plant developers and utilities, to build a prototype to plug into the electrical grid. Whether the project, called Malta, ever comes to market depends as much on a sound business model as it does on science. Academics said the technology is likely years away from market, if it ever makes it.