Colorado Residents Are Spotting Weird-Looking Rabbits With Black Horns and Mouth Tentacles
Journalist Amanda Gilbert documented the town sightings in an article for local outlet 9NEWS last Friday. Colorado Parks and Wildlife officials say the rabbits are afflicted with a virus that causes wart-like projections to emerge from their faces—a condition that likely even inspired folk tales in the past about horned rabbit cryptids.
'It looks like it was black quills or black toothpicks sticking out all around his or her mouth,' Fort Collins resident Susan Mansfield told 9NEWS. 'I thought he would die off during the winter, but he didn't. He came back a second year, and it grew.'
The rabbit germ is called the Shope papilloma virus. It's a cousin of the human papillomavirus, and like some HPV strains, SPV can trigger the formation of growths, typically made out of keratin. The virus is primarily transmitted through biting arthropod vectors, such as mosquitos and ticks, though it may also spread through close, direct contact with infected bodily fluids (the growths themselves don't carry the virus).
Papillomaviruses are usually tuned very specifically to their hosts, SPV included, so they aren't a threat to people. Rabbits can live with the infection and even lose the growths over time, but SPV can also turn dangerous. The growths can become large enough to interfere with eating, and they will sometimes transform into malignant tumors that spread elsewhere in the body. Infections are rarely seen in domestic rabbits, however, and most commonly affect cottontail rabbits in the wild.
The growths can look like deer antlers, and many scientists argue this uncanny resemblance helped foster the North American myth of the jackalope and similar horned rabbit creatures. As with many things, though, capitalism is also partly to blame for the cryptid's enduring popularity. In 1977, brothers Ralph and Doug Herrick claimed that they were the first to market taxidermized jackrabbits fitted with deer antlers as jackalopes in 1934. And even today, many gift shops and tourist attractions still sell these fauxalopes.
SPV isn't just the inspiration behind the jackalope. Its discovery in the 1930s helped confirm that certain viruses can trigger cancer, and scientists have long used it as a model in the lab to better understand HPV-related cancers.
The real-life jackalope may be harmless to people, but Colorado Parks and Wildlife officials say people still should steer clear of wild rabbits infected with SPV. Indeed, we can never be too cautious.
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Feed it into an existing LLM, doing so via a method such as RAG (retrieval-augmented generation), see my RAG elicitation at the link here. Voila, perhaps you've tuned up conventional generative AI to better simulate human behavior. A recent research study took that innovative approach. In an article entitled 'A Foundation Model To Predict And Capture Human Cognition' by Marcel Binz et al, Nature, July 2, 2025, the paper made these key points (excerpts): Details Of The Approach The researchers chose to use Meta Llama as their base LLM. The data augmentation was done via the use of the increasingly popular technique known as QLoRA (quantized low-rank adaptation), a distant cousin of RAG. They transcribed 160 experiments into natural language data. It was publicly available data. The types of experiments included many of the classics in psychology, such as memory recall, supervised learning, decision-making, multi-armed bandits, Markov decision processes, and others. To give you a sense of what those experiments are like, consider these two examples: Handily, the researchers have opted to make the dataset available, known as Psych-101, and can be accessed freely on Huggingface. In addition, they have nicely made available the augmented Meta Llama model, which they refer to as Centaur, and which is also freely available on Huggingface. It is a welcome touch because other researchers can now come along and do not need to begin from scratch. They can reuse the arduous and time-consuming work that went into devising Psych-101 and Centaur. Thus, the dataset and the model are ready-made for launching new investigations and serve as a springboard accordingly. The Results In Brief A commonly utilized means of validating an LLM consists of holding back some of the training data so that you can use the holdback for testing purposes. This is a longstanding technique that has been used for statistical model validations. You might use, say, 90% of the data to do the augmented data training and keep the remaining 10% in reserve. When you are ready to test the LLM, you give it the data that was aside to see if the AI can adequately predict the presumed unseen data. They did this and indicated that their Centaur LLM did a bang-up job on the hold-out data. The next step typically undertaken is to employ a make-or-break test when aiming to devise a generalizable model. You give the LLM data that is considered outside the initial scope of the augmentation. The handwringing question is whether the LLM will generalize sufficiently to contend with so-called out-of-distribution (OOD) circumstances. The researchers opted to select a handful of OOD settings, including economic games, deep sequential decision tasks, reward learning, etc. Their reported results indicate that Centaur LLM did quite well at making predictions associated with those previously unseen experimental transcripts. Overall, kudos to the researchers for thinking outside the box on AI and psychology. Some Thoughts To Ponder I'd like to cover a few quick thoughts overall. First, one agonizing difficulty with gauging an off-the-shelf pre-cooked LLM for any kind of newly encountered circumstances is that it is challenging to know whether such data or similar data might have been scanned during the initial setup of the LLM. Usually, only the AI maker knows precisely what data was initially scanned. Ergo, it is worthwhile to be mindful in interpreting generalizability since an LLM might have already had an unknown leg-up previously. Second, and perhaps more importantly, the desire to push toward a semblance of cognitive realism by further data training of an LLM is a laudable idea. Will the AI be more human-like in its reasoning patterns? Maybe, maybe not. One important determinant is whether the AI is still resorting to human-like language and not necessarily patterning on human reasoning. There is a huge debate going on regarding LLM foundational models that are claimed to be using 'reasoning' versus whether they are still potentially doing heads-down next-token prediction, see my coverage on the lively dispute at the link here. Taking Next Steps The overarching aim to see if we can properly ground cognitive computer-based computational simulations in a more psychologically plausible way is exciting. No doubt about that. The researchers also noted that there might be entirely different AI architectural approaches that might be better for us to pursue, beyond the somewhat conventional infrastructures currently dominating the AI realm right now. As a heads-up, some ardently believe that our prevailing LLMs and AI architecture are not going to get us to artificial general intelligence (AGI) or artificial superintelligence (ASI). You see, the trend right now is to mainly power up prevailing designs with faster hardware and more computational running time. But the incremental benefits could be misleadingly tying us to a road that leads to a dead-end. Could the desire to attain a unified model of cognition be the kick in the pants to the AI field to look beyond the groupthink of today's AI and LLMs? I certainly hope so. As General George S. Patton once proclaimed: 'If everyone is thinking alike, then somebody isn't thinking.'
