Did Early Humans Smoke Meat Million Years Ago?
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Research
Jun 3rd, 2025
Stone Age BBQ: How Early Humans Preserved Meat with Fire
- Humanities
- Archeology
Did prehistoric humans know that smoking meat could preserve it and extend its shelf life? Researchers from the Alkow Department of Archaeology and Ancient Near Eastern Cultures at Tel Aviv University believe they did. Their new study presents a fresh perspective on a question that has long preoccupied prehistory scholars: What prompted early humans to begin using fire? According to the researchers, early humans, who primarily consumed large game, required fire not for cooking, but to smoke and dry meat so that it would not rot, thereby preserving it for extended periods and keeping it safe from predators and scavengers.
Why Did Early Humans First Use Fire?
This insight fits into a broader unifying theory, developed by the same researchers, which explains many prehistoric phenomena based on human dependence on calories derived from large animals, alongside a continuous decline in the size of animals hunted throughout prehistoric periods. The study was conducted by Dr. Miki Ben-Dor and Prof. Ran Barkai of the Alkow Department of Archaeology and Ancient Near Eastern Cultures at Tel Aviv University and was published in the journal Frontiers in Nutrition.
Prof. Barkai explains: “The origins of fire use is a ‘burning’ topic among prehistory researchers around the world. It is generally agreed that by 400,000 years ago, fire use was common in domestic contexts—most likely for roasting meat, and perhaps also for lighting and heating. However there is controversy regarding the preceding million years, and various hypotheses have been put forward to explain why early humans began using fire. In this study, we sought to explore a new perspective on the issue”.
Dr. Ben-Dor adds: “For early humans, fire use was not a given, and at most archaeological sites dated earlier than 400,000 years ago, there is no evidence of the use of fire. Nevertheless, at several early sites, there are clear signs that fire was used, but without burnt bones or evidence of meat roasting. We understand that early humans at that time—mostly Homo erectus—did not use fire regularly, but only occasionally, in specific places and for special purposes. The process of gathering fuel, igniting a fire, and maintaining it over time required significant effort, and they needed a compelling, energy-efficient motive to do so. We have proposed a new hypothesis regarding that motive”.
Fire as a Shield Against Predators and Decay
The researchers reviewed the existing literature on all known prehistoric sites dated between 1.8 million and 800,000 years ago where evidence of fire use was found. There are nine such sites worldwide, including Gesher Benot Ya'aqov and Evron Quarry in Israel, six sites in Africa, and one site in Spain. Additionally, they relied on ethnographic studies of contemporary hunter-gatherer societies, aligning their behavior with the conditions that prevailed in ancient environments.
Dr. Ben-Dor: “We examined what the nine ancient sites had in common, and found that all contained large quantities of bones from large animals—mostly elephants, but also hippopotamuses, rhinoceroses, and others. From previous studies, we know that these animals were extremely important to early human diets and provided most of the necessary calories. The meat and fat of a single elephant, for example, contain millions of calories, enough to feed a group of 20–30 people for a month or more. A hunted elephant or hippopotamus was thus a real treasure—a kind of meat and fat ‘bank’ that needed to be protected and preserved for many days since it was coveted not only by predators but also by bacteria”.
An analysis of the findings and calculations of the significant energetic advantage of preserving meat and fat led the researchers to a new conclusion, never before proposed: fire served two vital purposes for early humans—first, to guard the large game from other predators and scavengers seeking to seize the ‘treasure’, and second, to preserve the meat through smoking and drying, preventing spoilage and making it edible for a long period.
Prof. Barkai concludes: “In this study, we propose a new understanding of the factors that motivated early humans to begin using fire: the need to safeguard large hunted animals from other predators, and to preserve the vast quantity of meat over time. It is likely that once the fire was produced for these purposes, it was also occasionally used for cooking—at zero marginal energetic cost. Such use may explain evidence of fish roasting from around 800,000 years ago at Gesher Benot Ya'aqov. The approach we propose fits well into a global theory we have been developing in recent years, which explains major prehistoric phenomena as adaptations to the hunting and consumption of large animals, followed by their gradual disappearance and the resulting need to derive adequate energy from exploiting smaller animals”.
Prof. Ran Barkai.
Research
Apr 15th, 2025
Can AI Help Doctors Make Better Diagnoses?
A new TAU study explores how accurate AI can be when assisting with diagnoses in virtual urgent care.
- Medicine
A new study led by Prof. Dan Zeltzer, a digital health expert from the Berglas School of Economics at Tel Aviv University, compared the quality of diagnostic and treatment recommendations made by artificial intelligence (AI) and physicians at Cedars-Sinai Connect, a virtual urgent care clinic in Los Angeles, operated in collaboration with Israeli startup K Health. The paper was published in Annals of Internal Medicine and presented at the annual conference of the American College of Physicians (ACP). This work was supported by funding from K Health.
AI vs. Physicians in Virtual Care
Prof. Zeltzer explains: "Cedars-Sinai operates a virtual urgent care clinic offering telemedical consultations with physicians specializing in family and emergency care. Recently, an AI system was integrated into the clinic—an algorithm based on machine learning that conducts initial intake through a dedicated chat incorporates data from the patient’s medical record and provides the attending physician with detailed diagnostic and treatment suggestions at the start of the visit -including prescriptions, tests, and referrals. After interacting with the algorithm, patients proceed to a video visit with a physician who ultimately determines the diagnosis and treatment. To ensure reliable AI recommendations, the algorithm—trained on medical records from millions of cases—only offers suggestions when its confidence level is high, not recommending about one out of five cases. In this study, we compared the quality of the AI system's recommendations with the physicians' actual decisions in the clinic".
Prof. Dan Zeltzer (Photo courtesy of Richard Haldis).
The researchers examined a sample of 461 online clinic visits over one month during the summer of 2024. The study focused on adult patients with relatively common symptoms—respiratory, urinary, eye, vaginal and dental. In all visits reviewed, patients were initially assessed by the algorithm, which provided recommendations, and then treated by a physician in a video consultation. Afterward, all recommendations—from both the algorithm and the physicians—were evaluated by a panel of four doctors with at least ten years of clinical experience, who rated each recommendation on a four-point scale: optimal, reasonable, inadequate, or potentially harmful. The evaluators assessed the recommendations based on the patient's medical history, the information collected during the visit, and transcripts of the video consultations.
AI Proves More Accurate Than Physicians in Study
The compiled ratings led to interesting conclusions: AI recommendations were rated as optimal in 77% of cases, compared to only 67% of the physicians' decisions; at the other end of the scale, AI recommendations were rated as potentially harmful in a smaller portion of cases than physicians' decisions (2.8% of AI recommendations versus 4.6% of physicians' decisions). In 68% of the cases, the AI and the physician received the same score; in 21% of cases, the algorithm scored higher than the physician; and in 11% of cases, the physician's decision was considered better.
The explanations provided by the evaluators for the differences in ratings highlight several advantages of the AI system over human physicians: First, the AI more strictly adheres to medical association guidelines—for example, not prescribing antibiotics for a viral infection; second, AI more comprehensively identifies relevant information in the medical record—such as recurrent cases of a similar infection that may influence the appropriate course of treatment; and third, AI more precisely identifies symptoms that could indicate a more serious condition, such as eye pain reported by a contact lens wearer, which could signal an infection. Physicians, on the other hand, are more flexible than the algorithm and have an advantage in assessing the patient's actual condition. For example, if a COVID-19 patient reports shortness of breath, a doctor may recognize it as relatively mild respiratory congestion, whereas the AI, based solely on the patient's answers, might refer them unnecessarily to the emergency room.
A Step Closer to Supporting Doctors
Prof. Zeltzer concludes: "In this study, we found that AI, based on a targeted intake process, can provide diagnostic and treatment recommendations that are, in many cases, more accurate than those made by physicians. One limitation of the study is that we do not know which physicians reviewed the AI's recommendations in the available chart, or to what extent they relied on the recommendations. Thus, the study only measured the accuracy of the algorithm’s recommendations and not their impact on the physicians. The study's uniqueness lies in the fact that it tested the algorithm in a real-world setting with actual cases, while most studies focus on examples from certification exams or textbooks. The relatively common conditions included in our study represent about two-thirds of the clinic's case volume, thus the findings can be meaningful for assessing AI's readiness to serve as a decision-support tool in medical practice. We can envision a near future in which algorithms assist in an increasing portion of medical decisions, bringing certain data to the doctor's attention, and facilitating faster decisions with fewer human errors. Of course, many questions remain about the best way to implement AI in the diagnostic and treatment process, as well as the optimal integration between human expertise and artificial intelligence in medicine".
Other authors involved in the study include Zehavi Kugler, MD; Lior Hayat, MD; Tamar Brufman, MD; Ran Ilan Ber, PhD; Keren Leibovich, PhD; Tom Beer, MSc; and Ilan Frank, MSc. Caroline Goldzweig, MD MSHS, and Joshua Pevnick, MD, MSHS.
