Recent research has shown that generative models with highly disentangled representations fail to generalise to unseen combination of generative factor values.

There have been numerous studies evaluating bias of LLMs towards political topics. However, how positions towards these topics in model outputs are highly sensitive to the prompt. What happens when the prompt itself is suggestive of certain arguments towards those positions remains underexplored. This is crucial for understanding how robust these bias evaluations are and for understanding model behaviour, as these models frequently interact with opinionated text. To that end, we conduct experiments for political bias evaluation in presence of supporting and refuting arguments. Our experiments show that such arguments substantially alter model responses towards the direction of the provided argument in both single-turn and multi-turn settings. Moreover, we find that the strength of these arguments influences the directional agreement rate of model responses. These effects point to a sycophantic tendency in LLMs adapting their stance to align with the presented arguments which has downstream implications for measuring political bias and developing effective mitigation strategies.

One prominent Linguistic expressions can be characterized along method is Artificial Language Learning (Friederici a variety of properties: what they mean, what parts et al., 2002; Motamedi et al., 2019; Kanwal et al., they consist of, how they combine with other expressions 2017; Culbertson et al., 2012; Ettlinger et al., 2014; and so on. Some of these properties are Finley and Badecker, 2009). It has the following systematically related to each other. When these main ingredients: relations appear systematically in language after language, they can be grounds for implicational linguistic 1. fragment of an artificial language in the universals, for example, Greenberg's Universal form of expressions that do not belong to the 37: A language never has more gender categories language that participants are speakers of; in nonsingular numbers than in the singular. (Greenberg, 1963). Here, two properties of linguistic 2. training phase, where some information expressions are related: the grammatical number about the language fragment is given to the of an expression and how many gender distinctions participants; are available for this expression. More complex 3. testing phase, where it is checked what other generalizations may concern correlation between knowledge, beside the provided, was inferred continuous properties A and B.

The objective of this study was to develop a fully automated multiple-cow real-time lameness detection system using a deep learning approach for cattle detection and pose estimation that could be deployed across dairy farms. Utilising computer vision and deep learning, the system can analyse simultaneously both the posture and gait of each cow within a camera field of view to a very high degree of accuracy (94–100%). Twenty-five video sequences containing 250 cows in varying degrees of lameness were recorded and independently scored by three accredited Agriculture and Horticulture Development Board (AHDB) mobility scorers using the AHDB dairy mobility scoring system to provide ground truth lameness data. These observers showed significant inter-observer reliability. Video sequences were broken down into their constituent frames and with a further 500 images downloaded from google, annotated with 15 anatomical points for each animal. A modified Mask-RCNN estimated the pose of each cow to output 5 key-points to determine back arching and 2 key-points to determine head position. Using the SORT (simple, online, and real-time tracking) algorithm, cows were tracked as they move through frames of the video sequence (i.e., in moving animals). All the features were combined using the CatBoost gradient boosting algorithm with accuracy being determined using threefold cross-validation including recursive feature elimination. Precision was assessed using Cohen’s kappa coefficient and assessments of precision and recall. This methodology was applied to cows with varying degrees of lameness (according to accredited scoring, n = 3) and demonstrated that some characteristics directly associated with lameness could be monitored simultaneously. By combining the algorithm results over time, more robust evaluation of individual cow lameness was obtained. The model showed high performance for predicting and matching the ground truth lameness data with the outputs of the algorithm. Overall, threefold lameness detection accuracy of 100% and a lameness severity classification accuracy of 94% respectively was achieved with a high degree of precision (Cohen’s kappa = 0.8782, precision = 0.8650 and recall = 0.9209).

Artificial intelligence (AI) gets its "intelligence" by analyzing a given dataset and detecting patterns. It has no concept of the world beyond this dataset, which creates a variety of dangers. One changed pixel could confuse the AI system to think a horse is a frog or, even scarier, err on a medical diagnosis or a machine operation. Its exclusive reliance on the data sets also introduces a serious security vulnerability: Malicious agents can spoof the AI algorithm by introducing minor, nearly undetectable changes in the data. Finally, the AI system does not know what it does not know, and it can make incorrect predictions with a high degree of confidence.

Technology has an important role to play in the field of Rehabilitation, improving patient outcomes and reducing healthcare costs. However, existing approaches lack clinical validation, robustness and ease of use. We propose Tele-EvalNet, a novel system consisting of two components: a live feedback model and an overall performance evaluation model. The live feedback model demonstrates feedback on exercise correctness with easy to understand instructions highlighted using color markers. The overall performance evaluation model learns a mapping of joint data to scores, given to the performance by clinicians. The model does this by extracting clinically approved features from joint data. Further, these features are encoded to a lower dimensional space with an autoencoder. A novel multi-scale CNN-LSTM network is proposed to learn a mapping of performance data to the scores by leveraging features extracted at multiple scales. The proposed system shows a high degree of improvement in score predictions and outperforms the state-of-the-art rehabilitation models.

Deloitte today released the fourth edition of its State of AI in the Enterprise report, which surveyed 2,857 business decision-makers between March and May 2021 about their perception of AI technologies. Few organizations claim to be completely AI-powered, the responses show, but a significant percentage are beginning to adopt practices that could get them there. In the survey, Deloitte explored the transformations happening inside firms applying AI and machine learning to drive value. During the pandemic, digitization efforts prompted many companies to adopt AI-powered solutions to back-office and customer-facing challenges. A PricewaterhouseCoopers whitepaper found that 52% percent of companies have accelerated their AI adoption plans, with global spending on AI systems set to jump from $85.3 billion in 2021 to over $204 billion in 2025, according to IDC.

Hope you are doing well. Hacking a Neural Network is simply fooling a Neural Network. Neural Networks are increasingly being used in various security and moderating systems across different fields. It is very important that they mainatain their integrity across different types of attacks. In this article, I am going to explain how we can modify an image (without changing it too much) to force the Neural Network to mis-classify it (that too with a very high degree of certainity).

Recently, researchers have been able to combine GPS with AI to detect early-onset Alzheimer's in drivers which a high degree of accuracy. Why is detecting Alzheimer's early important, what did the researchers achieve, and how does it demonstrate the importance of AI in medical diagnosis? There are very few individuals who enjoy arranging doctor appointments, having blood taken, and waiting for results. Despite the unpleasantness of such experiences, getting diagnosed as early as possible for diseases provides the best chance for treatment and survival. For example, many millions around the world still die as a result of perfectly treatable conditions such as prostate and breast cancer, and this is due to a lack of awareness and desire to get tested. Alzheimer's is a particularly nasty disease for a multitude of reasons.