This has been somewhat of a quiet revolution, but a new law in England may bring it to the foreground. The Precision Breeding Act will now allow gene-edited plants to be developed and marketed in England (not Northern Ireland, Wales, or Scotland). The innovation is that the law makes a distinction between genetically modified organisms (GMOs) and gene-edited organisms. This has already happened in the US, with the USDA declaring that gene-editing is not the same as GMO, for regulatory purposes.

GMO has always been, in my opinion, a category that is both arbitrary and overly broad. It has referred to any organism (usually a crop) that is the result of genetic engineering using a variety of techniques. This could include silencing a gene, removing a gene, or introducing a new gene either from a related variety (cisgenic) or a distant species (transgenic). Most of the fearmongering (and that’s what it is) around GMOs have been focused on transgenic varieties, but then used to try to ban the entire category. The newer regulations, essentially, limit GMO to transgenic changes, and refer to everything else as gene-edited. This is often described as changes that could theoretically occur with breeding techniques, now just faster.

This is all good, because it restricts the damage done by anti-GMO fearmongering. It’s still wrong, it’s just not casting as wide a net. To show how arbitrary this all is, most restrictions on GMOs make exceptions for cheese made with enzymes that are the product of engineered yeast. Why? Because the cheese industry would collapse without it. Similarly, Hawaii, which is ideologically very anti-GMO, made an exception for GMO papaya. Why? You guessed it – because without it they would have lost their papaya industry.

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This is one of those technology news stories where the implications of the technology is greater than the thing itself. Relativity Space, a rocket company based in California, launched their first Terran-1 rocket. The launch ultimately failed when the second stage failed to ignite, but that does not really matter. This is a small rocket with a modest payload of 1.25 tons, but that does not really matter either. This rocket system is reusable, but that’s old news. This particular launch is newsworthy because 85% of it was made with additive manufacturing – 3D printing.

The company has built its own industrial-sized 3D printers that can fuse aluminum powder or beads into specific shapes. The advantage of this approach is already well-established. 3D printing allows for rapid prototyping and iteration, because it represents a direct connection between the software and the final product, rather than having to be tooled by specific equipment. It can allow for the creation of more complex shapes into a single component. 3D printing is already being used in the aerospace industry, and Space X uses it to make many parts for its capsules and engines. But 85% is a new benchmark. Relativity space believes it can get the figure up to 95%.

The parts that cannot be 3D printed are things like computer chips, rubber seals, and valves. 3D printers for rubber exist, but I guess they need to get the quality up to aerospace specs. Valves are probably doable as well. The last 5% will likely be mostly complex electronics, like computer chips.

Despite failing to complete the mission, it was basically a success. This was a test mission, and in fact the Terran-1 is a test rocket. The real point of the launch was to get to Max-G, the point of the launch where the rocket is under maximum G-forces and stress. They did that, and the rocket held together. The 3D printed parts stood up to the stress. Of course, they need to figure out why the second stage failed to ignite and if that is related to the failure of any 3D printed component. But all rocket companies have a string of failures on the way to a working rocket system. It’s part of the process.

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Are you familiar with the “lumper vs splitter” debate? This refers to any situation in which there is some controversy over exactly how to categorize complex phenomena, specifically whether or not to favor the fewest categories based on similarities, or the greatest number of categories based on every difference. For example, in medicine we need to divide the world of diseases into specific entities. Some diseases are very specific, but many are highly variable. For the variable diseases do we lump every type into a single disease category, or do we split them into different disease types? Lumping is clean but can gloss-over important details. Splitting endeavors to capture all detail, but can create a categorical mess.

As is often the case, an optimal approach likely combines both strategies, trying to leverage the advantages of each. Therefore we often have disease headers with subtypes below to capture more detail. But even there the debate does not end – how far do we go splitting out subtypes of subtypes?

The debate also happens when we try to categorize ideas, not just things. Logical fallacies are a great example. You may hear of very specific logical fallacies, such as the “argument ad Hitlerum”, which is an attempt to refute an argument by tying it somehow to something Hitler did, said, or believed.  But really this is just a specific instance of a “poisoning the well” logical fallacy. Does it really deserve its own name? But it’s so common it may be worth pointing out as a specific case. In my opinion, whatever system is most useful is the one we should use, and in many cases that’s the one that facilitates understanding. Knowing how different logical fallacies are related helps us truly understand them, rather than just memorize them.

A recent paper enters the “lumper vs splitter” fray with respect to cognitive biases. They do not frame their proposal in these terms, but it’s the same idea. The paper is – Toward Parsimony in Bias Research: A Proposed Common Framework of Belief-Consistent Information Processing for a Set of Biases. The idea of parsimony is to use to be economical or frugal, which often is used to apply to money but also applies to ideas and labels. They are saying that we should attempt to lump different specific cognitive biases into categories that represent underlying unifying cognitive processes.

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Did UFO reporting increase during the pandemic? A group of researchers set out to answer that question, and recently published their results. Their hypothesis was two-fold, including the notion that people had more free time during the shutdown and perhaps spent more time out doors, and therefore had a greater opportunity to see anomalous things in the sky. I would add that more free time might also mean a greater chance of bothering to report what they saw to one of the national UFO networks. The second hypothesis is that the pandemic was associated with an increase in anxiety, and anxiety makes people more alert and attentive. People might therefore be checking out their environment more and noticing anomalies that would otherwise have escaped their attention.

It’s interesting that their hypothesis (the pandemic lead to more UFO reporting) is agnostic toward the question of what UFOs (using the traditional term, as the study does) actually are. It treats them as just unknown anomalies. Whether or not UFOs generally represent terrestrial phenomena or alien phenomena, sightings might increase for the above reasons. To answer that question we need other information.

In 2000 I proposed the term the psychocultural hypothesis to explain what UFO sightings and related phenomena represent. The PCH posits that the UFO phenomenon is caused entirely by cultural and psychological factors and not by anything alien or even truly anomalous (meaning that it represents an entirely unknown phenomenon – no psychic bigfeet from the future or Atlantean ghosts). There is the background of mundane activity, including weather, astronomical, and human technological phenomena. This is the background noise, which people often misperceive and then overlay a cultural belief system. People never see spaceships – they see blobs, lights, indistinct images, or something at the edge of perception, and then they fit that experience into their pre-existing cultural beliefs.

They may say explicitly that they think it is aliens, or they may do the, “I’m not saying it’s aliens, but it’s aliens” thing. In other words, they may not specifically claim that what they experienced or the evidence being presented is definitely alien, they will just say that it is “anomalous” or “unidentified” or somehow mysterious. They will then often go from unexplained to unexplainable. The psychological game is to just leave it at that, but what they are really doing is creating the space in which aliens might exist. At the end of the day, it’s all about the aliens.

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The nuclear debate seems never-ending, which I guess is to be expected. Every large technology has tradeoffs. But the need to transition our energy infrastructure to carbon neutral has shifted the equation, and it is now arguable that we cannot afford to ignore the option of nuclear energy. The UK seems to agree with this take – they currently generate about 15.5% of their energy through nuclear reactors and are planning on expanding this to 25% by 2050.

The UK has not built a nuclear power plant in over 20 years, which means their current plants are all near the end of their lifespan. This can be extended to buy enough time to replace them with newer plants, but they need to be building those now, which they are. They plan to approve 8 new plants by 2030, and have them all online by 2050. These are all large nuclear power plants. But they also plan on building small modular reactors (SMRs), which can be built faster and cheaper. Rolls-Royce, for example, has a design for an SMR and they anticipate that they will get regulatory approval for the design by the UK by 2024. If they do there are preliminary plans to build 10 such reactors, with the first coming online in 2029. (I don’t know if these 10 reactors are included in the 25% by 2050 estimate of it they will be incremental.)

In my opinion, this is all good news. As I have discussed before, if we realistically want to reach our collective goal of a carbon-neutral energy infrastructure around 2050 we will need to develop every option. Putting all our eggs in any one basket is likely not a good idea. Having 20-25% nuclear as baseload, another 20% hydroelectric and geothermal for on-demand energy, perhaps some hydrogen (if there really are reserves we can tap into), and the rest wind and solar with some grid storage (perhaps closed loop hydro with some battery storage) is a reasonable first-approximation of what our energy infrastructure can look like. This will vary geographically, but this may be close to an average mix.

We can argue and nitpick about the exact percentages, but honestly that does not really matter. For now, we need to develop all of these options as quickly as possible. We need to map the best path to carbon neutral, that reduces emissions as quickly as possible. That means getting rid of coal first, which is the dirtiest energy. If, in the short term, we need to burn some natural gas, that is Ok as long as it is replacing coal. But all fossil fuels need to be gone by about 2050. Building a core of nuclear power will help us get there. Capturing some carbon would be nice, but I see that as the icing on the cake. Perhaps the best way to do that for now (just to buy us some time for the switchover) is to plant lots of trees and stop deforestation.

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Good spacesuits are deceptively difficult to design, even with today’s technology. NASA is planning to return to the moon in 2025 (if all goes well) but the spacesuit the astronauts will wear is one piece to the puzzle they have not completed yet (the other being the lunar lander). In fact, NASA spent $420 million on spacesuit development but was unhappy with the results. They therefore contracted out the design to private industry. Axiom space won a $228 million contract and just unveiled their first prototype.

It might seem that we are reinventing the wheel, as NASA already developed spacesuits for moon landings – 50 years ago. Couldn’t they just tweak or update those designs? What about the spacesuits they have been using on the ISS?

The Apollo spacesuits, while iconic, were limited. One of the big challenges is creating a suit that can maintain internal pressure without overly limiting movement. A pressurized suit is extremely limiting. The suit obviously has to be tough enough to maintain pressure, without leaking any air. It also has to maintain temperature, through extremes. On the moon, without an atmosphere, the temperature varies from 120 C to -130 C. The moon is also a harsh environment. The regolith is like microscopic glass shards, and was brutal to the Apollo suits. Getting into mechanical joints could freeze them, and the abrasive action can degrade suits. For these reasons the Apollo suits were only rated for 115 hours of pressurized use. They were one-offs, built custom for each astronaut and intended for only a single use.

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There has been a lot of quantum computer news since I last wrote about the topic. But this is still a technology that is slowly advancing in the background, while actual applications have been limited. There is a threshold effect at play – at some point, quantum computers will be powerful and practical enough that there will likely be a rapid adoption at the scientific, governmental, and industrial level (these are not for personal use, at least not anytime soon).

Quantum computers, as the name implies, exploit properties of quantum mechanics, rather than classical physics as do traditional computers. Digital computers, like the one you are using now to read this blog, are largely based on the bit – a bit is the smallest piece of information, either a 1 or a 0, in the binary language of computers. Computer technologies require some physical property that is very small and that can quickly and accurately be changed between two states, with these two states representing a 1 or 0. This can be done in various ways, but mostly exploiting electromagnetism.

Quantum computers do not use bits, they use qubits. A qubit exploits a quantum property (superposition) that can be in any state between 1 and 0, inclusive. Each qubit, therefore, has the potential to store orders of magnitude more information than one bit. Qubits are then entangles with each other and this entanglement produces constructive and destructive interference which can magnify the probable correct answer (gross oversimplification, but follow the links in my previous article for more details if you are interested). A working quantum computer requires a physical property at the quantum level that can be in superposition and entangled, and connected to every other qubit. There are still major hurdles to overcome, one being error correction.

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NASA recently discovered a 50 meter wide asteroid whose orbit will come close to Earth. They estimate a close approach in 2046, which will likely bring the asteroid within 1.1 million miles of the Earth, about four times the distance of the moon. However, there is always uncertainty in calculating orbits, and the farther into the future you try to project their path, the more uncertainty there is. At this point in time NASA estimates a one in 560 chance that the asteroid, dubbed 2023 DW, will hit the Earth in 2046.

Orbits are calculated through multiple observations of the object along its orbit. We have to see how it is moving, and the longer the observation the greater the precision. For recently discovered objects, like this asteroid, there is more uncertainty in the orbital calculations, which is why NASA cannot completely rule out an impact. Because it is a near-Earth object, however, they will continue to make observations, refining their calculated orbit, and reducing the uncertainty.

Interestingly, the current scale for designating the risk of an object hitting the Earth, the Palermo scale, is not based on a simple percentage probability, but on the probability relative to the background rate of impacts. A Palermo scale of 0 means that the chance of a particular object hitting the Earth is no different than the background rate of impacts. The scale is also logarithmic, so a Palermo rating of 1 means a chance of impact 10 times the background rate, 2 is 100 times. 2023 DW has a Palermo rating of -2.17.

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I don’t feel, but I can still kill.

This happened sooner than I thought. Last June I wrote about Google employee, Blake Lemoine, who claimed that the LaMDA  chatbot he was working on was probably sentient. I didn’t buy it then and I still don’t, but Lemoine is not backing away from his claims. In an interview on H3 he lays out his reasoning, and I don’t find it convincing.

His basic point is that in extended conversations he was able to coax LaMDA to go beyond its protocol. Specifically he says he had a long conversation with LaMDA about whether or not it was sentient, and he does not think a non-sentient entity could have such a conversation. When asked by the host, “could it just be a really good chatbot” I feel that Lemoine dodged the question, saying it could be a really good sentient chatbot.

But that question cannot be glossed over – that is where the rubber meets the road. Functionally, testably, what is the quantifiable difference between a really good chatbot and sentient AI? First let me define my terms. A chatbot has no understanding of the words it is putting out. It is predicting what words fit together in response to some prompt. The latest crop of generative large language models, like ChatGPT and LaMDA are much better than older models, because they are trained on large data sets (essentially the internet), are using powerful computers designed to work well with AI, and programmers are getting increasingly clever at leveraging this technology to produce realistic results. Generative AI, like these chatbots and art programs like MidJourney, do not just copy their input, they generate fresh output by deep learning patterns.

Sentience, on the other hand, has consciousness, a subjective experience of its own existence, feelings, and thought processes (even if it can include subconscious processes). Admittedly, we do not know exactly how the human brain generates consciousness, but we are beginning to get some idea. The brain communicates robustly with itself in real time. There is an endless loop of consciousness including perception, remembering, and processing. We know, at least, that this continuous loop of robust activity is necessary for consciousness. Further, when it comes to language there is dedicated brain tissue that correlates words with ideas. These ideas can be sophisticated, abstract, nuanced, and interact with each other in endless patterns. It is not just a dictionary – the brain’s language model is connecting to a thinking machine, which is why we can go from words to ideas, then iterate those ideas and generate new words from them – words that someone else who knows the same language can infer meaning from.

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Psychiatry, psychology, and all aspects of mental health are a challenging area because the clinical entities we are dealing with are complex and mostly subjective. Diagnoses are perhaps best understood as clinical constructs – a way of identifying and understanding a mental health issue, but not necessary a core neurological phenomenon. In other words, things like bipolar disorder are identified, categorized, and diagnosed based upon a list of clinical signs and symptoms. But this is a descriptive approach, and may not correlate to specific circuitry in the brain. Researchers are making progress finding the “neuroanatomical correlates” of known clinical entities, but such correlates are mostly partial and statistical. Further, there is culture, personality, and environment to deal with, which significantly influences how underlying brain circuitry manifests clinically. Also, not all mental health diagnoses are equal – some are likely to be a lot closer to discrete brain circuitry than others.

With all of these challenges, researchers are still trying to progress mental health from a purely descriptive endeavor to a more biological approach, where appropriate. There are a number of ways to do this. The most obvious is to look at the brain itself. Such imaging can be anatomical (taking a picture of the physical anatomy of the brain, such as a CT scan or MRI scan) or functional (looking at some functional aspect of the brain, like EEG or functional MRI). This kind of research is producing a steady stream of information, finding correlations with mental health disorder states, but few have progressed to the point that they are clinically useful. To be useful for research all we need is sufficient statistical significance. But to be useful clinically, to actually determine how to treat an individual person, you need sufficient accuracy (sensitivity and specificity) to guide treatment decisions. That requires much more accuracy than just basic research.

There is also another biological way of evaluating mental health states – molecular biomarkers. This approach stems from the fact that every cell in the body activates a different set of genes – so brain cells activate brain genes, while liver cells activate liver cells. Also, one type of cell will activate genes at different intensities during different functions. So when the pancreas needs to create a lot of insulin, the insulin genes become more active. We can detect the RNA that is produced when specific genes are activated, or patterns of RNA when suites of genes are activated. This can be a biomarker signature of specific functional states.

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