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Engineers develop bio-machine nose that can “sniff” and classify odors
Engineers from Brown University in Rhode Island have invented a small, low-cost sensor device which is able to classify odors using input from a mimicked “sniffing” action. It’s called TruffleBot, and it’s here to raise the bar on electronic “noses”. It also works with Raspberry Pi, an inexpensive mini-computer popular with electronics hobbyists, students, and others in the “maker” crowd.
Generally, an electronic nose is a device comprising several chemical sensors whose results are fed through a pattern-recognition system to identify odors. In traditional devices, the chemical responses alone are used for classification. The engineers behind this invention, however, decided to incorporate non-chemical data to account for the mechanics of the smell process used in nature for a better result. Their experiment proved successful with an approximate 95-98% rate of accuracy in identification compared to about 80-90% accuracy with the chemical sensors alone.
According to the inventors’ published paper, the guiding knowledge that made TruffleBot so useful in odor detection was this: Different smells have different impacts on the air around them, and measuring the variations enables more accurate identification. Did you know that beer odor decreases air pressure and increases temperature? The changes are slight, but TruffleBot can sense them.
This is where the “sniffing” comes in. The device uses air pumped through four obstructed pathways before sending it through chemical and non-chemical sensors. Odors impact the air surrounding them, and the movement of the air through obstacles (“sniffing”) enables the odors’ impact to be more accurately measured.
A chart detailing how TruffleBot processes odors. | Credit: Brown University
So, where exactly would one need an electronic nose? Everywhere. Devices with the chemical sensing ability are being used in agriculture, military, and commercial applications to identify all sorts environmental data. Essentially, electronic noses are useful in any industrial application that has odor involved.
Nasal Marketing
Did you know that it’s possible to trademark a smell in the United States? It’s not easy to accomplish given the somewhat difficult requirements to meet, but a few such things exist. The fact that Play-Doh, a product whose smell is probably one of its most distinct features, was granted a trademark for the scent only this year is testament to the difficulty of obtaining such a mark. However, the fact that some companies have found enough incentive to make sure only their company can give your nose a particular chemical experience tells a lot about that sense’s importance from a marketing perspective.
On one hand, utilizing smell in marketing might seem a little manipulative. After all, creating an air freshener that reminds someone of a beloved, deceased relative on purpose might not seem like a particularly ethical way to target their money. On the other hand (or bigger picture), however, the motivation for marketers to use scent as a tool involves a sort of “chicken or the egg” question.
To summarize part of an article in the journal Sensors on the role scent plays in society and commerce, the aroma of products has a direct impact on their appeal to customers and thus, the success of the product. In fact, a change in a product’s formula that impacts its smell can, and often has had, devastating sales results. In other words, it’s not enough for a company to create a good product; it has to be a good smelling product.
Hacking the Human Nose
It’s probably no surprise that the commercial industry has categorized consumer preferences when it comes to smells. As the first sense fully developed after birth, our noses link us to things like memories, emotions, and chemical communication (think pheromones). Is it any wonder, then, why businesses might be interested in the functionality of the organ that is doing the receiving?
Turns out, there’s an enormous amount of science behind “hacking” a nose. Identifying smells is more than just categorizing chemical mixtures as “floral” or “masculine”. The multitude of chemical combinations available generates such a vast amount of data that scientists have implemented computer neural networks to analyze and classify it. Also, the actual mechanics of smelling something impacts the way the smell is received and processed in the brain. Computers and scientific instruments come in handy there as well. To really get to the core of human response to an aroma, lots of non-human tools are needed, and this is essentially where the TruffleBot fits in the greater realm of “olfactory” science.
I think this is a Sumerian variant for “fruity”. | Credit: AstroJane’s bathroom collection.
More Than Just Your Money
Perhaps one of the most innovative uses found for electronic noses is in disease research. One of the limitations of human smell is its overall weakness. A dog’s sense of smell is around 40 times better than a human’s, and a bear’s is a whopping 2,100 times superior to ours. That said, when researchers learned that certain diseases give off certain odors, the human nose wasn’t exactly the first choice to utilize in sensing them.
An electronic nose makes good use of the simple fact that organic matter releases chemicals into the air. For example, when a plant has been impacted by a fungus, the changes brought on in the plant’s structure release what’s called “volatile organic compounds” (VOCs). These VOCs can be detected by the sensors in an electronic nose and then provide information on the type of disease present without destroying the plants being tested.
Humans have some amazing things to gain from electronic noses, too. Using sensors to process odors from VOCs, things like digestive diseases, kidney diseases, and diabetes, among many others, are all receiving scientific attention for non-invasive diagnosis by these types of devices. With improvements brought on by inventions like TruffleBot, especially combined with its low-cost and resulting accessibility, a future involving remote diagnoses for any number of illnesses and diseases seems more possible every day.
Elon Musk
Elon Musk’s xAI brings 1GW Colossus 2 AI training cluster online
Elon Musk shared his update in a recent post on social media platform X.
xAI has brought its Colossus 2 supercomputer online, making it the first gigawatt-scale AI training cluster in the world, and it’s about to get even bigger in a few months.
Elon Musk shared his update in a recent post on social media platform X.
Colossus 2 goes live
The Colossus 2 supercomputer, together with its predecessor, Colossus 1, are used by xAI to primarily train and refine the company’s Grok large language model. In a post on X, Musk stated that Colossus 2 is already operational, making it the first gigawatt training cluster in the world.
But what’s even more remarkable is that it would be upgraded to 1.5 GW of power in April. Even in its current iteration, however, the Colossus 2 supercomputer already exceeds the peak demand of San Francisco.
Commentary from users of the social media platform highlighted the speed of execution behind the project. Colossus 1 went from site preparation to full operation in 122 days, while Colossus 2 went live by crossing the 1-GW barrier and is targeting a total capacity of roughly 2 GW. This far exceeds the speed of xAI’s primary rivals.
Funding fuels rapid expansion
xAI’s Colossus 2 launch follows xAI’s recently closed, upsized $20 billion Series E funding round, which exceeded its initial $15 billion target. The company said the capital will be used to accelerate infrastructure scaling and AI product development.
The round attracted a broad group of investors, including Valor Equity Partners, Stepstone Group, Fidelity Management & Research Company, Qatar Investment Authority, MGX, and Baron Capital Group. Strategic partners NVIDIA and Cisco also continued their support, helping xAI build what it describes as the world’s largest GPU clusters.
xAI said the funding will accelerate its infrastructure buildout, enable rapid deployment of AI products to billions of users, and support research tied to its mission of understanding the universe. The company noted that its Colossus 1 and 2 systems now represent more than one million H100 GPU equivalents, alongside recent releases including the Grok 4 series, Grok Voice, and Grok Imagine. Training is also already underway for its next flagship model, Grok 5.
Elon Musk
Tesla AI5 chip nears completion, Elon Musk teases 9-month development cadence
The Tesla CEO shared his recent insights in a post on social media platform X.
Tesla’s next-generation AI5 chip is nearly complete, and work on its successor is already underway, as per a recent update from Elon Musk.
The Tesla CEO shared his recent insights in a post on social media platform X.
Musk details AI chip roadmap
In his post, Elon Musk stated that Tesla’s AI5 chip design is “almost done,” while AI6 has already entered early development. Musk added that Tesla plans to continue iterating rapidly, with AI7, AI8, AI9, and future generations targeting a nine-month design cycle.
He also noted that Tesla’s in-house chips could become the highest-volume AI processors in the world. Musk framed his update as a recruiting message, encouraging engineers to join Tesla’s AI and chip development teams.
Tesla community member Herbert Ong highlighted the strategic importance of the timeline, noting that faster chip cycles enable quicker learning, faster iteration, and a compounding advantage in AI and autonomy that becomes increasingly difficult for competitors to close.
AI5 manufacturing takes shape
Musk’s comments align with earlier reporting on AI5’s production plans. In December, it was reported that Samsung is preparing to manufacture Tesla’s AI5 chip, accelerating hiring for experienced engineers to support U.S. production and address complex foundry challenges.
Samsung is one of two suppliers selected for AI5, alongside TSMC. The companies are expected to produce different versions of the AI5 chip, with TSMC reportedly using a 3nm process and Samsung using a 2nm process.
Musk has previously stated that while different foundries translate chip designs into physical silicon in different ways, the goal is for both versions of the Tesla AI5 chip to operate identically. AI5 will succeed Tesla’s current AI4 hardware, formerly known as Hardware 4, and is expected to support the company’s Full Self-Driving system as well as other AI-driven efforts, including Optimus.
News
Tesla Model Y and Model 3 named safest vehicles tested by ANCAP in 2025
According to ANCAP in a press release, the Tesla Model Y achieved the highest overall weighted score of any vehicle assessed in 2025.
The Tesla Model Y recorded the highest overall safety score of any vehicle tested by ANCAP in 2025. The Tesla Model 3 also delivered strong results, reinforcing the automaker’s safety leadership in Australia and New Zealand.
According to ANCAP in a press release, the Tesla Model Y achieved the highest overall weighted score of any vehicle assessed in 2025. ANCAP’s 2025 tests evaluated vehicles across four key pillars: Adult Occupant Protection, Child Occupant Protection, Vulnerable Road User Protection, and Safety Assist technologies.
The Model Y posted consistently strong results in all four categories, distinguishing itself through a system-based safety approach that combines structural crash protection with advanced driver-assistance features such as autonomous emergency braking, lane support, and driver monitoring.
This marked the second time the Model Y has topped ANCAP’s annual safety rankings. The Model Y’s previous version was also ANCAP’s top performer in 2022.
The Tesla Model 3 also delivered a strong performance in ANCAP’s 2025 tests, contributing to Tesla’s broader safety presence across segments. Similar to the Model Y, the Model 3 also earned impressive scores across the ANCAP’s four pillars. This made the vehicle the top performer in the Medium Car category.
ANCAP Chief Executive Officer Carla Hoorweg stated that the results highlight a growing industry shift toward integrated safety design, with improvements in technologies such as autonomous emergency braking and lane support translating into meaningful real-world protection.
“ANCAP’s testing continues to reinforce a clear message: the safest vehicles are those designed with safety as a system, not a checklist. The top performers this year delivered consistent results across physical crash protection, crash avoidance and vulnerable road user safety, rather than relying on strength in a single area.
“We are also seeing increasing alignment between ANCAP’s test requirements and the safety technologies that genuinely matter on Australian and New Zealand roads. Improvements in autonomous emergency braking, lane support, and driver monitoring systems are translating into more robust protection,” Hoorweg said.
Elon Musk’s xAI brings 1GW Colossus 2 AI training cluster online
Tesla AI5 chip nears completion, Elon Musk teases 9-month development cadence
