Author: Sam H

Researchers at KU Leuven University have started a project to make Belgian beer even better using artificial intelligence. Led by Prof Kevin Verstrepen, they’re studying how we taste flavors to understand the different smells in beer.

The researchers experimented with 250 Belgian beers of different kinds to see what makes them taste the way they do. They checked things like how much alcohol they have, how acidic they are, and what flavors are in them.

“Tiny changes in the concentrations of chemicals can have a big impact, especially when multiple components start changing,” Prof. Kevin Verstrepen, of KU Leuven university, who led the research, said.

Then they asked a group of 16 people to taste the beers and describe how they tasted. This took three years! At the same time, they collected 180,000 reviews of different beers from the online consumer review platform RateBeer and looked at what people said about these beers online.

The researchers used all this information to make computer programs that can predict how a beer will taste based on what’s in it. They then used these predictions to make existing beers even better by adding certain flavors.

“The AI models predict the chemical changes that could optimise a beer, but it is still up to brewers to make that happen starting from the recipe and brewing methods,” Prof. Verstrepen said.

The results were amazing. People liked the beers even more, saying they tasted sweeter and fuller. But even with this new technology, Verstrepen reminds us that the skill of the brewers is still very important.

AI’s changing a lot of things. But in the context of beer, it’s combining old traditions with new ideas to ensure Belgian beer stays as great as ever.

In today’s advanced tech world, Large Language Models such as GPT-4 and LLaMA 2 lead the way in understanding complex medical terms. They give clear insights and provide accurate info based on evidence. These models are important for medical decisions, so it’s vital they’re reliable and precise. But as they’re used more in medicine, there’s a challenge: making sure they can handle tricky biomedical data without mistakes.

To tackle this, we need a new way to evaluate them. Traditional methods focus on specific tasks, like spotting drug interactions, which isn’t enough for the wide-ranging needs of biomedical queries. Biomedical questions often involve pulling together lots of data and giving context-appropriate responses, so we need a more detailed evaluation.

That’s where Reliability Assessment for Biomedical BLM Assistants comes in. Developed by researchers from Imperial College London and GSK.ai, RAmBLA aims to thoroughly check how dependable BLMs are in the biomedical field. It looks at important factors for real-world use, like handling different types of input, recalling info accurately, and giving responses that are correct and relevant. This all-around evaluation is a big step forward in making sure BLMs can be trusted helpers in biomedical research and healthcare, according to the researchers.

“. . . we believe the aspects of LLM reliability highlighted in RAmBLA may serve as a useful starting point for developing applications for such use-cases,” the paper, authored by researchers including William James Bolton from Imperial College London, reads.

What makes RAmBLA special is how it simulates real-life biomedical research situations to test BLMs. It gives them tasks that mimic the challenges of actual biomedical work, from understanding complex prompts to summarizing medical studies accurately. One key focus of RAmBLA’s testing is to reduce “hallucinations,” where models give believable but wrong info – a crucial thing to get right in medical settings.

The study showed that bigger BLMs generally perform better across different tasks, especially in understanding similar meanings in biomedical questions. For example, GPT-4 scored an impressive 0.952 accuracy in answering open-ended biomedical questions. However, the study also found areas needing improvement, like reducing hallucinations and improving recall accuracy.

“If they have insufficient knowledge or context information to answer a question, LLMs should refuse to answer,” the study report claims.

Interestingly, the researchers discovered that bigger models were skilled at recognizing when to avoid answering irrelevant questions. On the other hand, smaller ones like Llama and Mistral struggled more, suggesting they require additional adjustments for better performance.

Tattoo technology has evolved beyond mere self-expression, as it is increasingly recognized as a reliable tool for tracking health metrics and vital signs.

Smart tattoos are becoming more advanced in tracking health signs, studies show. MIT scientists have made ink for smart tattoos that contains tiny particles. These particles can detect changes in pH levels, which can signal issues like dehydration or metabolic disorders. Also, new flexible electronics allow for smart tattoos that monitor muscle activity, helping with physical performance and recovery. These developments highlight how smart tattoos could change healthcare by monitoring vital health signs easily and without being invasive.

These tattoos, acting like miniature labs on the skin, offer real-time insights into important health indicators like blood pressure, glucose levels, and hydration. By integrating biosensors and special materials, they combine biomedical science with tattoo artistry to revolutionize health monitoring.

The idea of a “Lab on the skin” allows for painless monitoring of bodily functions without invasive medical devices. Pioneers like Ali Yetisen from Imperial College London have developed smart tattoos such as the DermalAbyss, which change color in response to changes in bodily fluids, providing continuous updates on pH levels, sodium, and glucose.

Furthermore, smart tattoos can address external health factors like UV exposure, as shown in Carson Bruns’s research on “solar freckles,” potentially aiding in skin cancer prevention. They also offer promise in cancer treatment by providing a less intrusive alternative to traditional radiation markers.

In 2020, Carson Bruns, an assistant professor of mechanical engineering at the University of Colorado Boulder, contributed to a team that created the “solar freckle,” a light-sensitive tattoo. It appears in sunlight, signaling excessive UV exposure, and fades when sunscreen is applied or when out of sunlight. Bruns has received a prestigious National Science Foundation CAREER Award for research that investigates how the art of tattooing can incorporate the latest advances in nanotechnology to improve human health.

Smart tattoos have big potential applications, from personalized healthcare to space exploration. Human trials are ongoing, indicating progress toward regulatory approval and widespread use. Compared to wearable devices, smart tattoos offer unmatched convenience and permanence, along with being unhackable and not requiring batteries.

If your Samsung galaxy S24 Ultra’s S Pen smells like a burnt plastic, you are not alone. The S-Pen dilemma has sparked numerous discussions in online community forums, with users expressing various concerns. Some even went as far as to fear that the house was burning, before learning the truth.

Samsung EU’s moderator, AndrewL, who has been a member of the community since 2018, officially stated the following:

This isn’t anything to be concerned about. While the S Pen is in its holster, it is close to the internal components of the phone, which will generate heat while in use, and cause the plastic to heat up. This can smell like burning, but it is similar to the smell you might experience after leaving your car in the sun for a few hours. The seats and plastic fittings in the vehicle might smell hot, but this will diminish after it cools. 

The S Pen was promoted as offering “the magic of touch-free control.” But who knew that alongside its 0.7mm pen tip and 4096 pressure sensors, it would also come with a bonus fragrance experience straight out of a sci-fi flick?

Now that the discussion regarding S24 Ultra’s pen has come up, numerous users have also taken note of similar olfactory experiences with previous iterations of the S Pen.

Amidst any laughter and jest, it’s essential to recognize the potential serious implications of such incidents. A burning smell can often signal overheating or malfunctioning electronic components. Regardless of whether or not you have an S Pen, it’s important to watch out for that.

Humans detect smells by inhaling air that contains odor molecules, which then bind to receptors inside the nose, relaying messages to the brain, whereas, AI interprets the signatures and classifies them based on a database of previously collected smells.

Unlike the human eye, which has only three sensory receptors for sensing the photons of red, green, and blue color, the human nose has over 300 receptors, making it more difficult to predict smell than color.

A 2014 study showed that humans can distinguish at least 1 trillion different odors. But you may wonder how an artificial intelligence will be able to handle the impossible. These are just initial steps in AI systems, which are being converted more into human-like social individuals all over the world in different areas.

In the recent times, Google has built an AI model with a human-like capacity to predict odors. Scientists have successfully formulated a “Principal Odor Map” (POM) with the properties of a sensory map. The map developed by the team of Google AI links molecular structure to the aroma of substances and can even predict smells that are still unnoticeable by humans.

The Google researchers began the research in 2019 using a deep learning algorithm. The type of smell interacted with the molecular structure. Various samples of specific molecules were trained to be identified by a graph neural network (GNN) model along with the smell labels they evoke, such as beefy, floral, or minty.

Researchers looked into whether the GNN model could learn to predict the odors of new chemicals that people had never smelled before and that were different from the molecules used to train it. The researchers referred to the study as an “important test” in their Google post. Many models work well with data that resembles data they have previously seen, but often fail when tested with new data.

The Google model was successful and demonstrated exceptional intelligence in predicting smell from molecule structure.

The model was also tested to see whether it could predict how animals would perceive odors. They found that the map was capable of precisely predicting the activity of sensory receptors, neurons, and behavior in most of the animals that olfactory neuroscientists have studied, including mice and insects.

AI detecting odors can be used for a variety of tasks, including identifying scents in the environment to aid people who have lost their sense of smell; and creating new artificial scents.

The research team at Google found that the common application of the sense of smell may be to detect and distinguish between various metabolic states, such as knowing when something is ripe vs rotten, nutritious vs inert, or healthy vs sick.

They had gathered data about metabolic reactions in dozens of species across the kingdoms of life and found that the map corresponded closely to metabolism itself.

The scientists retrained the model to tackle the issue of the spread of diseases transmitted by mosquitoes and ticks while killing hundreds of thousands of people each year.

The team improved the original model with two new sources of data. The first set was a long-forgotten set of experiments conducted by the USDA on human volunteers beginning 80 years ago and recently made discoverable by Google Books. Secondly, a new dataset was collected by their partners at TOPIQ, using their high-throughput laboratory mosquito assay.

With the help of the POM, researchers hope to predict animal olfaction to better respond to the deadly diseases transmitted by mosquitoes and ticks. Both datasets measure how well a given molecule keeps the mosquitos away. Together, the resulting model can predict the mosquito repellence of nearly any molecule, enabling a virtual screen over huge swaths of molecular space.

“Less expensive, longer lasting, and safer repellents can reduce the worldwide incidence of diseases like malaria, potentially saving countless lives,” said the researchers.

According to their findings, a Principal Odor Map might be produced using the researchers’ method of smell prediction in order to tackle odor-related issues more widely. The key to measuring smell was in the map. It provided answers to a variety of questions regarding new odors and the molecules responsible for them. The model also linked the evolution of odors to the natural world.

To sum up, AI algorithms do have a potential to effectively predict smell. The Google model was one of the first to demonstrate this.