By Mike Mcrae source 

Paleolithic cuisine was anything but lean and green, according to a 2021 study on the diets of our Pleistocene ancestors. For a good 2 million years, Homo sapiens and their ancestors ditched the salad and dined heavily on meat, putting them at the top of the food chain.

It’s not quite the balanced diet of berries, grains, and steak we might picture when we think of ‘paleo’ food. But according to anthropologists from Israel’s Tel Aviv University and the University of Minho in Portugal, modern hunter-gatherers have given us the wrong impression of what we once ate.

“This comparison is futile, however, because 2 million years ago hunter-gatherer societies could hunt and consume elephants and other large animals – while today’s hunter gatherers do not have access to such bounty,” said Miki Ben‐Dor from Israel’s Tel Aviv University in April last year.

A look through hundreds of previous studies on everything from modern human anatomy and physiology to measures of the isotopes inside ancient human bones and teeth suggests we were primarily apex predators until roughly 12,000 years ago.

Reconstructing the grocery list of hominids who lived as far back as 2.5 million years ago is made all that much more difficult by the fact plant remains don’t preserve as easily as animal bones, teeth, and shells.

We can find ample evidence of game-hunting in the fossil record, but to determine what we gathered, anthropologists have traditionally turned to modern-day ethnography based on the assumption that little has changed.

According to Ben-Dor and his colleagues, this is a huge mistake.

“The entire ecosystem has changed, and conditions cannot be compared,” said Ben‐Dor.

The Pleistocene epoch was a defining time in Earth’s history for us humans. By the end of it, we were marching our way into the far corners of the globe, outliving every other hominid on our branch of the family tree.

Above: Graph showing where Homo sapiens sat on the spectrum of carnivore to herbivore during the Pleistocene and Upper Pleistocene (UP).

Dominated by the last great ice age, most of what is today Europe and North America was regularly buried under thick glaciers.

With so much water locked up as ice, ecosystems around the world were vastly different to what we see today. Large beasts roamed the landscape, including mammoths, mastodons, and giant sloths – in far greater numbers than we see today.

Of course it’s no secret that Homo sapiens used their ingenuity and uncanny endurance to hunt down these massive meal-tickets. But the frequency with which they preyed on these herbivores hasn’t been so easy to figure out.

Rather than rely solely on the fossil record, or make tenuous comparisons with pre-agricultural cultures, the researchers turned to the evidence embedded in our own bodies and compared it with our closest cousins.

“We decided to use other methods to reconstruct the diet of stone-age humans: to examine the memory preserved in our own bodies, our metabolism, genetics and physical build,” said Ben‐Dor.

“Human behavior changes rapidly, but evolution is slow. The body remembers.”

For example, compared with other primates, our bodies need more energy per unit of body mass. Especially when it comes to our energy-hungry brains. Our social time, such as when it comes to raising children, also limits the amount of time we can spend looking for food.

We have higher fat reserves, and can make use of them by rapidly turning fats into ketones when the need arises. Unlike other omnivores, where fat cells are few but large, ours are small and numerous, echoing those of a predator.

Our digestive systems are also suspiciously like that of animals higher up the food chain. Having unusually strong stomach acid is just the thing we might need for breaking down proteins and killing harmful bacteria you’d expect to find on a week-old mammoth chop.

Even our genomes point to a heavier reliance on a meat-rich diet than a sugar-rich one.

“For example, geneticists have concluded that areas of the human genome were closed off to enable a fat-rich diet, while in chimpanzees, areas of the genome were opened to enable a sugar-rich diet,” said Ben‐Dor.

The team’s argument is extensive, touching upon evidence in tool use, signs of trace elements and nitrogen isotopes in Paleolithic remains, and dental wear.

It all tells a story where our genus’ trophic level – Homo’s position in the food web – became highly carnivorous for us and our cousins, Homo erectus, roughly 2.5 million years ago, and remained that way until the upper Paleolithic around 11,700 years ago.

From there, studies on modern hunter-gatherer communities become a little more useful, as a decline in populations of large animals and fragmentation of cultures around the world saw to more plant consumption, culminating in the Neolithic revolution of farming and agriculture.

None of this is to say we ought to eat more meat. Our evolutionary past isn’t an instruction guide on human health, and as the researchers emphasize, our world isn’t what it used to be.

But knowing where our ancestors sat in the food web has a big impact on understanding everything from our own health and physiology, to our influence over the environment in times gone by.

Ancient Humans: The Real Apex Predators for a Good 2 Million Years

By Miguel Brown source

Researchers at Tel Aviv University were able to reconstruct the nutrition of stone age humans. In a paper published in the Yearbook of the American Physical Anthropology Association, Dr. Miki Ben-Dor and Prof. Ran Barkai of the Jacob M. Alkov Department of Archaeology at Tel Aviv University, together with Raphael Sirtoli of Portugal, show that humans were an apex predator for about two million years. Only the extinction of larger animals (megafauna) in various parts of the world, and the decline of animal food sources toward the end of the stone age, led humans to gradually increase the vegetable element in their nutrition, until finally they had no choice but to domesticate both plants and animals—and became farmers.

“So far, attempts to reconstruct the diet of stone-age humans were mostly based on comparisons to 20th century hunter-gatherer societies,” explains Dr. Ben-Dor. “This comparison is futile, however, because two million years ago hunter-gatherer societies could hunt and consume elephants and other large animals—while today’s hunter gatherers do not have access to such bounty. The entire ecosystem has changed, and conditions cannot be compared. We decided to use other methods to reconstruct the diet of stone-age humans: to examine the memory preserved in our own bodies, our metabolism, genetics and physical build. Human behavior changes rapidly, but evolution is slow. The body remembers.”

In a process unprecedented in its extent, Dr. Ben-Dor and his colleagues collected about 25 lines of evidence from about 400 scientific papers from different scientific disciplines, dealing with the focal question: Were stone-age humans specialized carnivores or were they generalist omnivores? Most evidence was found in research on current biology, namely genetics, metabolism, physiology and morphology.

“One prominent example is the acidity of the human stomach,” says Dr. Ben-Dor. “The acidity in our stomach is high when compared to omnivores and even to other predators. Producing and maintaining strong acidity require large amounts of energy, and its existence is evidence for consuming animal products. Strong acidity provides protection from harmful bacteria found in meat, and prehistoric humans, hunting large animals whose meat sufficed for days or even weeks, often consumed old meat containing large quantities of bacteria, and thus needed to maintain a high level of acidity. Another indication of being predators is the structure of the fat cells in our bodies. In the bodies of omnivores, fat is stored in a relatively small number of large fat cells, while in predators, including humans, it’s the other way around: we have a much larger number of smaller fat cells. Significant evidence for the evolution of humans as predators has also been found in our genome. For example, geneticists have concluded that “areas of the human genome were closed off to enable a fat-rich diet, while in chimpanzees, areas of the genome were opened to enable a sugar-rich diet.”

Evidence from human biology was supplemented by archaeological evidence. For instance, research on stable isotopes in the bones of prehistoric humans, as well as hunting practices unique to humans, show that humans specialized in hunting large and medium-sized animals with high fat content. Comparing humans to large social predators of today, all of whom hunt large animals and obtain more than 70% of their energy from animal sources, reinforced the conclusion that humans specialized in hunting large animals and were in fact hypercarnivores.

“Hunting large animals is not an afternoon hobby,” says Dr. Ben-Dor. “It requires a great deal of knowledge, and lions and hyenas attain these abilities after long years of learning. Clearly, the remains of large animals found in countless archaeological sites are the result of humans’ high expertise as hunters of large animals. Many researchers who study the extinction of the large animals agree that hunting by humans played a major role in this extinction—and there is no better proof of humans’ specialization in hunting large animals. Most probably, like in current-day predators, hunting itself was a focal human activity throughout most of human evolution. Other archaeological evidence—like the fact that specialized tools for obtaining and processing vegetable foods only appeared in the later stages of human evolution—also supports the centrality of large animals in the human diet, throughout most of human history.”

The multidisciplinary reconstruction conducted by TAU researchers for almost a decade proposes a complete change of paradigm in the understanding of human evolution. Contrary to the widespread hypothesis that humans owe their evolution and survival to their dietary flexibility, which allowed them to combine the hunting of animals with vegetable foods, the picture emerging here is of humans evolving mostly as predators of large animals.

“Archaeological evidence does not overlook the fact that stone-age humans also consumed plants,” adds Dr. Ben-Dor. “But according to the findings of this study plants only became a major component of the human diet toward the end of the era.”

Evidence of genetic changes and the appearance of unique stone tools for processing plants led the researchers to conclude that, starting about 85,000 years ago in Africa, and about 40,000 years ago in Europe and Asia, a gradual rise occurred in the consumption of plant foods as well as dietary diversity—in accordance with varying ecological conditions. This rise was accompanied by an increase in the local uniqueness of the stone tool culture, which is similar to the diversity of material cultures in 20th-century hunter-gatherer societies. In contrast, during the two million years when, according to the researchers, humans were apex predators, long periods of similarity and continuity were observed in stone tools, regardless of local ecological conditions.

“Our study addresses a very great current controversy—both scientific and non-scientific,” says Prof. Barkai. “For many people today, the Paleolithic diet is a critical issue, not only with regard to the past, but also concerning the present and future. It is hard to convince a devout vegetarian that his/her ancestors were not vegetarians, and people tend to confuse personal beliefs with scientific reality. Our study is both multidisciplinary and interdisciplinary. We propose a picture that is unprecedented in its inclusiveness and breadth, which clearly shows that humans were initially apex predators, who specialized in hunting large animals. As Darwin discovered, the adaptation of species to obtaining and digesting their food is the main source of evolutionary changes, and thus the claim that humans were apex predators throughout most of their development may provide a broad basis for fundamental insights on the biological and cultural evolution of humans.”

When Was Artificial Intelligence Invented?

Advances in artificial intelligence (AI) have given the world computers that can beat people at chess and “Jeopardy!,” as well as drive cars and manage calendars. But despite the progress, engineers are still years away from developing machines that are self-aware. Some believe the resulting technological singularity will eradicate poverty and disease, while others warn it could endanger human survival.

Can Machines Think?

In the first half of the 20^th century, science fiction familiarized the world with the concept of artificially intelligent robots. It began with the “heartless” Tin man from the Wizard of Oz and continued with the humanoid robot that impersonated Maria in Metropolis. By the 1950s, we had a generation of scientists, mathematicians, and philosophers with the concept of artificial intelligence (or AI) culturally assimilated in their minds. One such person was Alan Turing, a young British polymath who explored the mathematical possibility of artificial intelligence. Turing suggested that humans use available information as well as reason in order to solve problems and make decisions, so why can’t machines do the same thing? This was the logical framework of his 1950 paper, Computing Machinery and Intelligence in which he discussed how to build intelligent machines and how to test their intelligence.

Making the Pursuit Possible

Unfortunately, talk is cheap. What stopped Turing from getting to work right then and there? First, computers needed to fundamentally change. Before 1949 computers lacked a key prerequisite for intelligence: they couldn’t store commands, only execute them. In other words, computers could be told what to do but couldn’t remember what they did. Second, computing was extremely expensive. In the early 1950s, the cost of leasing a computer ran up to $200,000 a month. Only prestigious universities and big technology companies could afford to dillydally in these uncharted waters. A proof of concept as well as advocacy from high profile people were needed to persuade funding sources that machine intelligence was worth pursuing.

The Conference that Started it All

Five years later, the proof of concept was initialized through Allen Newell, Cliff Shaw, and Herbert Simon’s, Logic Theorist. The Logic Theorist was a program designed to mimic the problem solving skills of a human and was funded by Research and Development (RAND) Corporation. It’s considered by many to be the first artificial intelligence program and was presented at the Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI) hosted by John McCarthy and Marvin Minsky in 1956. In this historic conference, McCarthy, imagining a great collaborative effort, brought together top researchers from various fields for an open ended discussion on artificial intelligence, the term which he coined at the very event. Sadly, the conference fell short of McCarthy’s expectations; people came and went as they pleased, and there was failure to agree on standard methods for the field. Despite this, everyone whole-heartedly aligned with the sentiment that AI was achievable. The significance of this event cannot be undermined as it catalyzed the next twenty years of AI research.

Roller Coaster of Success and Setbacks

From 1957 to 1974, AI flourished. Computers could store more information and became faster, cheaper, and more accessible. Machine learning algorithms also improved and people got better at knowing which algorithm to apply to their problem. Early demonstrations such as Newell and Simon’s General Problem Solver and Joseph Weizenbaum’s ELIZA showed promise toward the goals of problem solving and the interpretation of spoken language respectively. These successes, as well as the advocacy of leading researchers (namely the attendees of the DSRPAI) convinced government agencies such as the Defense Advanced Research Projects Agency (DARPA) to fund AI research at several institutions. The government was particularly interested in a machine that could transcribe and translate spoken language as well as high throughput data processing. Optimism was high and expectations were even higher. In 1970 Marvin Minsky told Life Magazine, “from three to eight years we will have a machine with the general intelligence of an average human being.” However, while the basic proof of principle was there, there was still a long way to go before the end goals of natural language processing, abstract thinking, and self-recognition could be achieved.

Breaching the initial fog of AI revealed a mountain of obstacles. The biggest was the lack of computational power to do anything substantial: computers simply couldn’t store enough information or process it fast enough. In order to communicate, for example, one needs to know the meanings of many words and understand them in many combinations. Hans Moravec, a doctoral student of McCarthy at the time, stated that “computers were still millions of times too weak to exhibit intelligence.”  As patience dwindled so did the funding, and research came to a slow roll for ten years.

In the 1980’s, AI was reignited by two sources: an expansion of the algorithmic toolkit, and a boost of funds. John Hopfield and David Rumelhart popularized “deep learning” techniques which allowed computers to learn using experience. On the other hand Edward Feigenbaum introduced expert systems which mimicked the decision making process of a human expert. The program would ask an expert in a field how to respond in a given situation, and once this was learned for virtually every situation, non-experts could receive advice from that program. Expert systems were widely used in industries. The Japanese government heavily funded expert systems and other AI related endeavors as part of their Fifth Generation Computer Project (FGCP). From 1982-1990, they invested $400 million dollars with the goals of revolutionizing computer processing, implementing logic programming, and improving artificial intelligence. Unfortunately, most of the ambitious goals were not met. However, it could be argued that the indirect effects of the FGCP inspired a talented young generation of engineers and scientists. Regardless, funding of the FGCP ceased, and AI fell out of the limelight.

Ironically, in the absence of government funding and public hype, AI thrived. During the 1990s and 2000s, many of the landmark goals of artificial intelligence had been achieved. In 1997, reigning world chess champion and grand master Gary Kasparov was defeated by IBM’s Deep Blue, a chess playing computer program. This highly publicized match was the first time a reigning world chess champion loss to a computer and served as a huge step towards an artificially intelligent decision making program. In the same year, speech recognition software, developed by Dragon Systems, was implemented on Windows. This was another great step forward but in the direction of the spoken language interpretation endeavor. It seemed that there wasn’t a problem machines couldn’t handle. Even human emotion was fair game as evidenced by Kismet, a robot developed by Cynthia Breazeal that could recognize and display emotions.

Time Heals all Wounds

We haven’t gotten any smarter about how we are coding artificial intelligence, so what changed? It turns out, the fundamental limit of computer storage that was holding us back 30 years ago was no longer a problem. Moore’s Law, which estimates that the memory and speed of computers doubles every year, had finally caught up and in many cases, surpassed our needs. This is precisely how Deep Blue was able to defeat Gary Kasparov in 1997, and how Google’s Alpha Go was able to defeat Chinese Go champion, Ke Jie, only a few months ago. It offers a bit of an explanation to the roller coaster of AI research; we saturate the capabilities of AI to the level of our current computational power (computer storage and processing speed), and then wait for Moore’s Law to catch up again.

Artificial Intelligence is Everywhere

We now live in the age of “big data,” an age in which we have the capacity to collect huge sums of information too cumbersome for a person to process. The application of artificial intelligence in this regard has already been quite fruitful in several industries such as technology, banking, marketing, and entertainment. We’ve seen that even if algorithms don’t improve much, big data and massive computing simply allow artificial intelligence to learn through brute force. There may be evidence that Moore’s law is slowing down a tad, but the increase in data certainly hasn’t lost any momentum. Breakthroughs in computer science, mathematics, or neuroscience all serve as potential outs through the ceiling of Moore’s Law.

The Future

So what is in store for the future? In the immediate future, AI language is looking like the next big thing. In fact, it’s already underway. I can’t remember the last time I called a company and directly spoke with a human. These days, machines are even calling me! One could imagine interacting with an expert system in a fluid conversation, or having a conversation in two different languages being translated in real time. We can also expect to see driverless cars on the road in the next twenty years (and that is conservative). In the long term, the goal is general intelligence, that is a machine that surpasses human cognitive abilities in all tasks. This is along the lines of the sentient robot we are used to seeing in movies. To me, it seems inconceivable that this would be accomplished in the next 50 years. Even if the capability is there, the ethical questions would serve as a strong barrier against fruition. When that time comes (but better even before the time comes), we will need to have a serious conversation about machine policy and ethics (ironically both fundamentally human subjects), but for now, we’ll allow AI to steadily improve and run amok in society.

Rockwell Anyoha is a graduate student in the department of molecular biology with a background in physics and genetics. His current project employs the use of machine learning to model animal behavior. In his free time, Rockwell enjoys playing soccer and debating mundane topics. this story had some contributions by Rockwell Anyoha

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