"Some human species may have made occasional use of fire as early as 800,000 years ago. By about 300,000 years ago, Homo erctus, Neanderthals and the forefathers of Homo sapiens were using fire on a daily basis."

"When humans domesticated fire, they gained control of an obedient and potentially limitless force."

"Homo erectus, 'Upright Man,' [survived] for close to 2 million years, making it the most durable human species ever. This record is unlikely to be broken even by our own species. It is doubtful whether Homo sapiens will still be around a thousand years from now, so 2 million years is really out of our league."

"Just 6 million years ago, a single female ape had two daughters. One became the ancestor of all chimpanzees, the other is our own grandmother."

"Democracies die not only when people are not free to talk but also when people are not willing or able to listen."

"Animals are the main victims of history, and the treatment of domesticated animals in industrial farms is perhaps the worst crime in history… At first sight, domesticated animals may seem much better off than their wild cousins and ancestors. Wild buffaloes spend their days searching for food, water and shelter, and are constantly threatened by lions, parasites, floods and droughts. Domesticated cattle, by contrast, enjoy care and protection from humans. People provide cows and calves with food, water and shelter, they treat their diseases, and protect them from predators and natural disasters. True, most cows and calves sooner or later find themselves in the slaughterhouse. Yet does that make their fate any worse than that of wild buffaloes? Is it better to be devoured by a lion than slaughtered by a man? Are crocodile teeth kinder than steel blades?"

"Intelligence is definitely not something that is directed towards amplifying happiness. I would also emphasize the huge, huge difference between intelligence and consciousness, which many people, certainly in the tech industry and in the AI industry, tend to miss."

"If AI can suffer, then it is an ethical subject and it needs protection, it needs rights, just like humans and animals."

"In modern society, currency notes usually display religious images, revered ancestors and corporate totems."

"It (Gossip) comes so naturally to us that it seems as if our language evolved for the very purpose."

"(Simpson 1951; Blyth 1972), first encountered by Pearson in 1899 (Aldrich 1995), refers to the phenomenon whereby an event C increases the probability of E in a given population p and, at the same time, decreases the probability of E in every subpopulation of p."

"When loops are present, the network is no longer singly connected and local propagation schemes will invariably run into trouble.. If we ignore the existence of loops and permit the nodes to continue communicating with each other as if the network were singly connected, messages may circulate indefinitely around the loops and process may not converges to a stable equilibrium... Such oscillations do not normally occur in probabilistic networks... which tend to bring all messages to some stable equilibrium as time goes on. However, this asymptotic equilibrium is not coherent, in the sense that it does not represent the posterior probabilities of all nodes of the network."

"Mr. Holmes receives a telephone call from his neighbor Dr. Watson who states that he hears the sound of a burglar alarm from the direction of Mr. Holmes' house. While preparing to rush home, Mr. Holmes recalls that Dr. Watson is known to be a tasteless practical joker..."

"Haavelmo was the ﬁrst to recognize the capacity of economic models to guide policies. This paper describes some of the barriers that Haavelmo’s ideas have had (and still have) to overcome, and lays out a logical framework that has evolved from Haavelmo’s insight and matured into a coherent and comprehensive account of the relationships between theory, data and policy questions. The mathematical tools that emerge from this framework now enable investigators to answer complex policy and counterfactual questions using simple routines, some by mere inspection of the model’s structure."

"Traditional statistics is strong in devising ways of describing data and inferring distributional parameters from sample. Causal inference requires two additional ingredients: a science-friendly language for articulating causal knowledge, and a mathematical machinery for processing that knowledge, combining it with data and drawing new causal conclusions about a phenomenon."

"Judea Pearl's work has transformed artificial intelligence (AI) by creating a representational and computational foundation for the processing of information under uncertainty. Pearl's work went beyond both the logic-based theoretical orientation of AI and its rule-based technology for expert systems. He identified uncertainty as a core problem faced by intelligent systems and developed an algorithmic interpretation of probability theory as an effective foundation for the representation and acquisition of knowledge."

"An event recognised as responsible for the production of a certain outcome... Human intuition is extremely keen in detecting and ascertaining this type of causation and hence is considered the key to construct explanations... and the ultimate criterion (known as “cause in fact”) for determining legal responsibility. Clearly, actual causation requires information beyond that of necessity and sufficiency: the actual process mediating between the cause and the effect must enter into consideration."

"The research questions that motivate most quantitative studies in the health, social and behavioral sciences are not statistical but causal in nature. For example, what is the efficacy of a given drug in a given population? Whether data can prove an employer guilty of hiring discrimination? What fraction of past crimes could have been avoided by a given policy? What was the cause of death of a given individual, in a specific incident? These are causal questions because they require some knowledge of the data-generating process; they cannot be computed from the data alone."

"A quantity Q(M) is identifiable, given a set of assumptions A, iffor any two models M1 and M2 that satisfy A, we have"

"[is] a term coined in Pearl (1985) to emphasize three aspects: (1) the subjective nature of the input information; (2) the reliance on Bayes' conditioning as the basis of updating information; (3) the distinction between causal and evidential models of reasoning, a distinction that underscores Thomas Bayes' paper of 1763,"