What is biohacking? Learn what experts say about some of the science-inspired interventions and why more common lifestyle changes may be better for your body.
President Trump should invite NATO allies to join the Golden Dome Initiative, transforming the proposed Golden Dome for America into a Golden Dome for NATO. Such a shift would better match today’s security realities and send the clear message to potential adversaries that we are united in deterring and defending against nuclear and conventional ballistic, […]
Muon Space is racing to expand production capabilities following a $90 million funding boost, targeting the growing appetite for increasingly capable satellites in the 100–500+ kilogram range. The California-based company recently introduced its largest small satellite platform yet, the 500-kilogram-class MuSat XL, to support more demanding missions at the upper end of the category. The […]
Artificial intelligence (AI) is fast becoming the new “Marmite”. Like the salty spread that polarizes taste-buds, you either love AI or you hate it. To some, AI is miraculous, to others it’s threatening or scary. But one thing is for sure – AI is here to stay, so we had better get used to it.
In many respects, AI is very similar to other data-analytics solutions in that how it works depends on two things. One is the quality of the input data. The other is the integrity of the user to ensure that the outputs are fit for purpose.
Previously a niche tool for specialists, AI is now widely available for general-purpose use, in particular through Generative AI (GenAI) tools. Also known as Large Language Models (LLMs), they’re now widley available through, for example, OpenAI’s ChatGPT, Microsoft Co-pilot, Anthropic’s Claude, Adobe Firefly or Google Gemini.
GenAI has become possible thanks to the availability of vast quantities of digitized data and significant advances in computing power. Based on neural networks, this size of model would in fact have been impossible without these two fundamental ingredients.
GenAI is incredibly powerful when it comes to searching and summarizing large volumes of unstructured text. It exploits unfathomable amounts of data and is getting better all the time, offering users significant benefits in terms of efficiency and labour saving.
Many people now use it routinely for writing meeting minutes, composing letters and e-mails, and summarizing the content of multiple documents. AI can also tackle complex problems that would be difficult for humans to solve, such as climate modelling, drug discovery and protein-structure prediction.
I’d also like to give a shout out to tools such as Microsoft Live Captions and Google Translate, which help people from different locations and cultures to communicate. But like all shiny new things, AI comes with caveats, which we should bear in mind when using such tools.
User beware
LLMs, by their very nature, have been trained on historical data. They can’t therefore tell you exactly what may happen in the future, or indeed what may have happened since the model was originally trained. Models can also be constrained in their answers.
Take the Chinese AI app DeepSeek. When the BBC asked it what had happened at Tiananmen Square in Beijing on 4 June 1989 – when Chinese troops cracked down on protestors – the Chatbot’s answer was suppressed. Now, this is a very obvious piece of information control, but subtler instances of censorship will be harder to spot.
Trouble is, we can’t know all the nuances of the data that models have been trained on
We also need to be conscious of model bias. At least some of the training data will probably come from social media and public chat forums such as X, Facebook and Reddit. Trouble is, we can’t know all the nuances of the data that models have been trained on – or the inherent biases that may arise from this.
One example of unfair gender bias was when Amazon developed an AI recruiting tool. Based on 10 years’ worth of CVs – mostly from men – the tool was found to favour men. Thankfully, Amazon ditched it. But then there was Apple’s gender-biased credit-card algorithm that led to men being given higher credit limits than women of similar ratings.
Another problem with AI is that it sometimes acts as a black box, making it hard for us to understand how, why or on what grounds it arrived at a certain decision. Think about those online Captcha tests we have to take to when accessing online accounts. They often present us with a street scene and ask us to select those parts of the image containing a traffic light.
The tests are designed to distinguish between humans and computers or bots – the expectation being that AI can’t consistently recognize traffic lights. However, AI-based advanced driver assist systems (ADAS) presumably perform this function seamlessly on our roads. If not, surely drivers are being put at risk?
A colleague of mine, who drives an electric car that happens to share its name with a well-known physicist, confided that the ADAS in his car becomes unresponsive, especially when at traffic lights with filter arrows or multiple sets of traffic lights. So what exactly is going on with ADAS? Does anyone know?
Caution needed
My message when it comes to AI is simple: be careful what you ask for. Many GenAI applications will store user prompts and conversation histories and will likely use this data for training future models. Once you enter your data, there’s no guarantee it’ll ever be deleted. So think carefully before sharing any personal data, such medical or financial information. It also pays to keep prompts non-specific (avoiding using your name or date of birth) so that they cannot be traced directly to you.
Democratization of AI is a great enabler and it’s easy for people to apply it without an in-depth understanding of what’s going on under the hood. But we should be checking AI-generated output before we use it to make important decisions and we should be careful of the personal information we divulge.
It’s easy to become complacent when we are not doing all the legwork. We are reminded under the terms of use that “AI can make mistakes”, but I wonder what will happen if models start consuming AI-generated erroneous data. Just as with other data-analytics problems, AI suffers from the old adage of “garbage in, garbage out”.
But sometimes I fear it’s even worse than that. We’ll need a collective vigilance to avoid AI being turned into “garbage in, garbage squared”.
“Abrupt changes” threaten to send the continent past the point of no return, at which point continued ice-melting would submerge coastal cities around the world.
Learn more about the amblyopsid cavefishes, the eyeless fish that inhabit the caves of eastern North America, whose evolution can uncover the age of difficult-to-date cave systems.
When it comes to beer, a clear sign of a good brew is a big head of foam at the top of a poured glass.
Beer foam is made of many small bubbles of air, separated from each other by thin films of liquid. These thin films must remain stable, or the bubbles will pop, and the foam will collapse.
What holds these thin films together is not completely understood and is likely conglomerates of proteins, surface viscosity or the presence of surfactants – molecules that reduce surface tension and are found in soaps and detergents.
They found that for single-fermentation beers, the foams are mostly held together with the surface viscosity of the beer. This is influenced by proteins in the beer – the more they contain the more viscous the film and more stable the foam will be.
“We can directly visualize what’s happening when two bubbles come into close proximity,” notes EUT material scientist Emmanouil Chatzigiannakis. “We can directly see the bubble’s protein aggregates, their interface, and their structure.”
When it comes to double-fermented beers, however, the proteins in the beer are altered slightly by yeast cells and come together to form a two-dimensional membrane that keeps foam intact longer.
The head was found to be even more stable for triple-fermented beers, which include Belgium Trappist beers. The proteins change further and behave like a surfactant that stabilizes the bubbles.
The team say that the finding of how the fermentation process alters the stability of bubbles could be used to produce more efficient ways of creating foams – or identify ways to control the amount of froth so that everyone can pour a perfect glass of beer every time. Cheers!
Nuclear scientists at the Lawrence Berkeley National Laboratory (LBNL) in the US have produced and identified molecules containing nobelium for the first time. This element, which has an atomic number of 102, is the heaviest ever to be observed in a directly-identified molecule, and team leader Jennifer Pore says the knowledge gained from such work could lead to a shake-up at the bottom of the periodic table.
“We compared the chemical properties of nobelium side-by-side to simultaneously produced molecules containing actinium (element number 89),” says Pore, a research scientist at LBNL. “The success of these measurements demonstrates the possibility to further improve our understanding of heavy and superheavy-element chemistry and so ensure that these elements are placed correctly on the periodic table.”
The periodic table currently lists 118 elements. As well as vertical “groups” containing elements with similar properties and horizontal “periods” in which the number of protons (atomic number Z) in the nucleus increases from left to right, these elements are arranged in three blocks. The block that contains actinides such as actinium (Ac) and nobelium (No), as well as the slightly lighter lanthanide series, is often shown offset, below the bottom of the main table.
The end of a predictive periodic table?
Arranging the elements this way is helpful because it gives scientists an intuitive feel for the chemical properties of different elements. It has even made it possible to predict the properties of new elements as they are discovered in nature or, more recently, created in the laboratory.
The problem is that the traditional patterns we’ve come to know and love may start to break down for elements at the bottom of the table, putting an end to the predictive periodic table as we know it. The reason, Pore explains, is that these heavy nuclei have a very large number of protons. In the actinides (Z > 88), for example, the intense charge of these “extra” protons exerts such a strong pull on the inner electrons that relativistic effects come into play, potentially changing the elements’ chemical properties.
“As some of the electrons are sucked towards the centre of the atom, they shield some of the outer electrons from the pull,” Pore explains. “The effect is expected to be even stronger in the superheavy elements, and this is why they might potentially not be in the right place on the periodic table.”
Understanding the full impact of these relativistic effects is difficult because elements heavier than fermium (Z = 100) need to be produced and studied atom by atom. This means resorting to complex equipment such as accelerated ion beams and the FIONA (For the Identification Of Nuclide A) device at LBNL’s 88-Inch Cyclotron Facility.
Producing and directly identifying actinide molecules
The team chose to study Ac and No in part because they represent the extremes of the actinide series. As the first in the series, Ac has no electrons in its 5f shell and is so rare that the crystal structure of an actinium-containing molecule was only determined recently. The chemistry of No, which contains a full complement of 14 electrons in its 5f shell and is the heaviest of the actinides, is even less well known.
In the new work, which is described in Nature, Pore and colleagues produced and directly identified molecular species containing Ac and No ions. To do this, they first had to produce Ac and No. They achieved this by accelerating beams of 48Ca with the 88-Inch Cyclotron and directing them onto targets of 169Tm and 208Pb, respectively. They then used the Berkeley Gas-filled Separator to separate the resulting actinide ions from unreacted beam material and reaction by-products.
The next step was to inject the ions into a chamber in the FIONA spectrometer known as a gas catcher. This chamber was filled with high-purity helium, as well as trace amounts of H2O and N2, at a pressure of approximately 150 torr. After interactions with the helium gas reduced the actinide ions to their 2+ charge state, so-called “coordination compounds” were able to form between the 2+ actinide ions and the H2O and N2 impurities. This compound-formation step took place either in the gas buffer cell itself or as the gas-ion mixture exited the chamber via a 1.3-mm opening and entered a low-pressure (several torr) environment. This transition caused the gas to expand at supersonic speeds, cooling it rapidly and allowing the molecular species to stabilize.
Once the actinide molecules formed, the researchers transferred them to a radio-frequency quadrupole cooler-buncher ion trap. This trap confined the ions for up to 50 ms, during which time they continued to collide with the helium buffer gas, eventually reaching thermal equilibrium. After they had cooled, the molecules were reaccelerated using FIONA’s mass spectrometer and identified according to their mass-to-charge ratio.
A fast and sensitive instrument
FIONA is much faster than previous such instruments and more sensitive. Both properties are important when studying the chemistry of heavy and superheavy elements, which Pore notes are difficult to make, and which decay quickly. “Previous experiments measured the secondary particles made when a molecule with a superheavy element decayed, but they couldn’t identify the exact original chemical species,” she explains. “Most measurements reported a range of possible molecules and were based on assumptions from better-known elements. Our new approach is the first to directly identify the molecules by measuring their masses, removing the need for such assumptions.”
As well as improving our understanding of heavy and superheavy elements, Pore says the new work might also have applications in radioactive isotopes used in medical treatment. For example, the 225Ac isotope shows promise for treating certain metastatic cancers, but it is difficult to make and only available in small quantities, which limits access for clinical trials and treatment. “This means that researchers have had to forgo fundamental chemistry experiments to figure out how to get it into patients,” Pore notes. “But if we could understand such radioactive elements better, we might have an easier time producing the specific molecules needed.”
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