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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Part 4

This will be the final installment of this mini-debate about climate change and health effects, following a typical format of each person getting to make a statement and a response. Scott makes a lot of complaints about tone, format and fairness while simultaneously trying to shield himself from any similar criticism. I am going to ignore this aspect of his response, and also anything dealing with the media. Rather I am going to focus on logic and evidence.

First I have to point out that Scott did not refute in any way my primary criticism of his logic – the notion that if climate change is having any negative effects it should be seen in the raw data. Rather, when one factor is part of a complex set of factors we can use statistical analysis to tease out its effect, even if the net effect of all factors are not in the same direction. This is the fatal flaw in his premise and methods, and he just dismissed my criticism and doubled down on this strategy. Saying he is quoting the IPCC is not a defense, when he is simply misinterpreting what they said. They are talking about “contributions” from climate change. He doesn’t want to talk about logic, and wants to force us to accept this incorrect framing. Nope – the framing is wrong, and therefore all of his arguments are not valid.

But let’s also delve into the specific claims. The first meaty claim is that climate models are running hot (a now long-debunked climate denying trope). Climate models are used for various things, including estimating climate sensitivity (the amount of warming that would result from a doubling of pre-industrial atmospheric CO2 levels). There are other ways to estimate climate sensitivity, including using historical data. Estimates have become increasingly precise, and now the consensus of evidence is that sensitivity is between 1.5 and 4.5C. The “hot model problem” that Scott is referring to is that the latest evaluation of climate models from CMIP6 shows that 10 of the 55 models looked at estimated sensitivity at >4.5 C (up to 5.6 C), i.e. “running hot”. The other 45 models are fine, and agree with historical data.

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Part 3

Hi Steve, and thank you for your timely response, and for even considering hosting this debate. There has been, and continues to be a “blackout” on almost all discussion regarding the science behind climate change. If “The science” is truly “settled”, it is a pretty shaky settlement! Why the blackout then?

I appreciate that you have pointed out some of my quirks that I use in constructing and argument. After all, this is a debate, and of course we come at it with different viewpoints and narratives, which makes debates all the more profoundly important to have. I think we would all agree that listening to other viewpoints other than our own, although may be uncomfortable, nonetheless helps us become more intelligent, enlightened, and wise.

While I do appreciate the pointing out of my biases (which we all have), there was very little substantive rebutting of the information I provided. I did use exact verbiage from the latest IPCC report, so nobody could accuse me of making a strawman argument. If you think I’m setting up a bunch of strawman arguments, I invite you to reach out to the authors of IPCC chapter 11: Human Health: Impacts, Adaptation, and Co-benefits and ask them why they are setting up these supposed confounded strawman arguments for me. If I can’t quote IPCC, the most trusted source of climate change information, then who can anyone trust regarding climate change information? The words I used to base my argument upon are their words, not mine. 

You state: “So Scott’s method is fatally flawed – you cannot just look at raw incidence numbers and declare there is no effect from climate change…But Scott dismisses all of this as “modeling”, which he rejects out of hand, explicitly favoring raw data, which is profoundly naive. He also dismissed data on the risks increased by climate change, again favoring raw data that mixes in many confounding variables.”

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