The'drunken monkey hypothesis' could explain why humans like alcohol so much. Breakthroughs, discoveries, and DIY tips sent every weekday. Scientists know that humans might not be as exceptional in comparison to the rest of the animal kingdom as we long thought. For example, whale songs and bonobo calls have features similar to language, and bonobos might even know when someone is ignorant about something. In fact, new research suggests that studying animals can provide insight into the evolution of our own species.

In the context of self-driving laboratories (SDLs), ensuring automated and error-free capping is crucial, as it is a ubiquitous step in sample preparation. Automated capping in SDLs can occur in both large and small workspaces (e.g., inside a fume hood). However, most commercial capping machines are designed primarily for large spaces and are often too bulky for confined environments. Moreover, many commercial products are closed-source, which can make their integration into fully autonomous workflows difficult. This paper introduces an open-source capping machine suitable for compact spaces, which also integrates a vision system that recognises capping failure. The capping and uncapping processes are repeated 100 times each to validate the machine's design and performance. As a result, the capping machine reached a 100 % success rate for capping and uncapping. Furthermore, the machine sealing capacities are evaluated by capping 12 vials filled with solvents of different vapour pressures: water, ethanol and acetone. The vials are then weighed every 3 hours for three days. The machine's performance is benchmarked against an industrial capping machine (a Chemspeed station) and manual capping. The vials capped with the prototype lost 0.54 % of their content weight on average per day, while the ones capped with the Chemspeed and manually lost 0.0078 % and 0.013 %, respectively. The results show that the capping machine is a reasonable alternative to industrial and manual capping, especially when space and budget are limitations in SDLs.

Large Language Models (LLMs) are highly sensitive to prompts, including additional context provided therein. As LLMs grow in capability, understanding their prompt-sensitivity becomes increasingly crucial for ensuring reliable and robust performance, particularly since evaluating these models becomes more challenging. In this work, we investigate how current models (Llama, Mixtral, Falcon) respond when presented with additional input from another model, mimicking a scenario where a more capable model -- or a system with access to more external information -- provides supplementary information to the target model. Across a diverse spectrum of question-answering tasks, we study how an LLM's response to multiple-choice questions changes when the prompt includes a prediction and explanation from another model. Specifically, we explore the influence of the presence of an explanation, the stated authoritativeness of the source, and the stated confidence of the supplementary input. Our findings reveal that models are strongly influenced, and when explanations are provided they are swayed irrespective of the quality of the explanation. The models are more likely to be swayed if the input is presented as being authoritative or confident, but the effect is small in size. This study underscores the significant prompt-sensitivity of LLMs and highlights the potential risks of incorporating outputs from external sources without thorough scrutiny and further validation. As LLMs continue to advance, understanding and mitigating such sensitivities will be crucial for their reliable and trustworthy deployment.

Sam Altman, CEO of OpenAI, has a vision for how AI tools will become enmeshed in our daily lives. During a sit-down chat with MIT Technology Review in Cambridge, Massachusetts, he described how he sees the killer app for AI as a "super-competent colleague that knows absolutely everything about my whole life, every email, every conversation I've ever had, but doesn't feel like an extension." In the new paradigm, as Altman sees it, AI will be capable of helping us outside the chat interface and taking real-world tasks off our plates. Read more about Altman's thoughts on the future of AI hardware, where training data will come from next, and who is best poised to create AGI. Eliminating carbon pollution from aviation is one of the most challenging parts of the climate puzzle, simply because large commercial airlines are too heavy and need too much power during takeoff for today's batteries to do the job.

Large language models (LLMs) have the ability to perform in-context learning (ICL) of new tasks by conditioning on prompts comprising a few task examples. This work studies the problem of selecting the best examples given a candidate pool to improve ICL performance on given a test input. Existing approaches either require training with feedback from a much larger LLM or are computationally expensive. We propose a novel metric, GistScore, based on Example Gisting, a novel approach for training example retrievers for ICL using an attention bottleneck via Gisting, a recent technique for compressing task instructions. To tradeoff performance with ease of use, we experiment with both fine-tuning gist models on each dataset and multi-task training a single model on a large collection of datasets. On 21 diverse datasets spanning 9 tasks, we show that our fine-tuned models get state-of-the-art ICL performance with 20% absolute average gain over off-the-shelf retrievers and 7% over the best prior methods. Our multi-task model generalizes well out-of-the-box to new task categories, datasets, and prompt templates with retrieval speeds that are consistently thousands of times faster than the best prior training-free method.

Detection of Volatile Organic Compounds (VOCs) from the breath is becoming a viable route for the early detection of diseases non-invasively. This paper presents a sensor array with three metal oxide electrodes that can use machine learning methods to identify four distinct VOCs in a mixture. The metal oxide sensor array was subjected to various VOC concentrations, including ethanol, acetone, toluene and chloroform. The dataset obtained from individual gases and their mixtures were analyzed using multiple machine learning algorithms, such as Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree, Linear Regression, Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Artificial Neural Network, and Support Vector Machine. KNN and RF have shown more than 99% accuracy in classifying different varying chemicals in the gas mixtures. In regression analysis, KNN has delivered the best results with R2 value of more than 0.99 and LOD of 0.012, 0.015, 0.014 and 0.025 PPM for predicting the concentrations of varying chemicals Acetone, Toluene, Ethanol, and Chloroform, respectively in complex mixtures. Therefore, it is demonstrated that the array utilizing the provided algorithms can classify and predict the concentrations of the four gases simultaneously for disease diagnosis and treatment monitoring.

Swanton Sector NBPC President Sean Walsh joined'Fox & Friends First' to discuss Mayorkas' testimony before Congress as the crisis continues to spiral. Facial recognition technology makes unlocking your smartphone a breeze. But with the convenience, comes a disturbing new crime trend for bandits. It involves "drug-facilitated robbery" schemers who knock their victims out with date rape drugs, unlock the victims' phones with their unconscious faces and drain their bank accounts of tens of thousands of dollars. While robberies involving incapacitated victims are nothing new, the technology offers thieves quick and easy access to incapacitated victims.

Fast and accurate simulation of complex chemical systems in environments such as solutions is a long standing challenge in theoretical chemistry. In recent years, machine learning has extended the boundaries of quantum chemistry by providing highly accurate and efficient surrogate models of electronic structure theory, which previously have been out of reach for conventional approaches. Those models have long been restricted to closed molecular systems without accounting for environmental influences, such as external electric and magnetic fields or solvent effects. Here, we introduce the deep neural network FieldSchNet for modeling the interaction of molecules with arbitrary external fields. FieldSchNet offers access to a wealth of molecular response properties, enabling it to simulate a wide range of molecular spectra, such as infrared, Raman and nuclear magnetic resonance. Beyond that, it is able to describe implicit and explicit molecular environments, operating as a polarizable continuum model for solvation or in a quantum mechanics / molecular mechanics setup. We employ FieldSchNet to study the influence of solvent effects on molecular spectra and a Claisen rearrangement reaction. Based on these results, we use FieldSchNet to design an external environment capable of lowering the activation barrier of the rearrangement reaction significantly, demonstrating promising venues for inverse chemical design.

It is crucial for the wine industry to have methods like electronic nose systems (E-Noses) for real-time monitoring thresholds of acetic acid in wines, preventing its spoilage or determining its quality. In this paper, we prove that the portable and compact self-developed E-Nose, based on thin film semiconductor (SnO2) sensors and trained with an approach that uses deep Multilayer Perceptron (MLP) neural network, can perform early detection of wine spoilage thresholds in routine tasks of wine quality control. To obtain rapid and online detection, we propose a method of rising-window focused on raw data processing to find an early portion of the sensor signals with the best recognition performance. Our approach was compared with the conventional approach employed in E-Noses for gas recognition that involves feature extraction and selection techniques for preprocessing data, succeeded by a Support Vector Machine (SVM) classifier. The results evidence that is possible to classify three wine spoilage levels in 2.7 seconds after the gas injection point, implying in a methodology 63 times faster than the results obtained with the conventional approach in our experimental setup.