turing-award-laureates

"The Simons Institute at Berkeley, under my leadership, will continue its dedication to the discovery of the fundamentals of computation and to findings that enable technological progress and positive social change."

"Algorithms govern our computing-based world in the same way that the laws of nature govern the physical one."

"As the periodic table, relativity or the genome."

"We were graduate students and let our imagination run free, from using randomized methods to encrypt single bits to enlarging the classical definition of a proof."

"Their mathematical underpinnings are thus as important to modern society."

"I am very proud to have won the Turing Award."

"To allow a small error to setting new goals for security."

"Our work was very unconventional at the time."

"Winning the award is further testimony to the fact that the cryptographic and complexity theoretic community embraced these ideas in the last 30 years."

"I don't like to call [human intelligence] AGI because human intelligence is not general at all."

"Don't get fooled by people who claim to have a solution to Artificial General Intelligence, who claim to have AI systems that work "just like the human brain", or who claim to have figured out how the brain works (well, except if it's Geoff Hinton making the claim). Ask them what error rate they get on MNIST or ImageNet."

"[AI progres is very dependent on Moore's law.] The one thing that allowed big progress in computer vision with ConvNets is the availability of GPUs with performance over 1 Tflops."

"The analogy I've been using is the fact that perhaps an equivalent event in the history of humanity to what might be provided by Generalization of AI assistant is the invention of the printing press. It made everybody smarter."

"AI is going to bring a new renaissance for humanity, a new form of enlightenment, if you want, because AI is going to amplify everybody's intelligence."

"[Can a commercial entity] produce Wikipedia? No. Wikipedia is crowdsourced because it works. So it's going to be the same for AI systems, they're going to have to be trained, or at least fine-tuned, with the help of everyone around the world. And people will only do this if they can contribute to a widely-available open platform."

"The vast majority of human knowledge is not expressed in text... LLMs do not have that, because they don't have access to it. And so they can make really stupid mistakes. That’s where hallucinations come from."

"Every reasonable ML technique has some sort of mathematical guarantee. For example, neural nets have a finite VC dimension, hence they are consistent and have generalization bounds... every single bound is terrible and useless in practice. As long as your method minimizes some sort of objective function and has a finite capacity (or is properly regularized), you are on solid theoretical grounds."

"My problem with sticking too close to nature is that it's like "cargo-cult" science... I don't use neural nets because they look like the brain. I use them because they are a convenient way to construct parameterized non-linear functions with good properties. But I did get inspiration from the architecture of the visual cortex to build convolutional nets."

"[Large language models] require enormous amounts of data to reach a level of intelligence that is not that great in the end. And they can't really reason. They can't plan anything other than things they’ve been trained on. So they're not a road towards what people call “AGI.”"

"Many of the papers that make it passed the review process are [good but boring] papers that bring an improvement to a well-established technique... Truly innovative papers rarely make it, largely because reviewers are unlikely to understand the point or foresee the potential of it."

"The direction of history is that the more data we get, the more our methods rely on learning. Ultimately, the task use learning end to end. That's what happened for speech, handwriting, and object recognition. It's bound to happen for NLP."

"I try to stay away from all methods that require sampling. I must have an allergy of some sort. That said, I am neither Bayesian nor anti-Bayesian... I think Bayesian methods are really cool conceptually in some cases... but I really don't have much faith in things like non-parametric Bayesian methods..."

"Maybe already the next generation [of tools] that is coming in 2024 could be very dangerous. Governments need to start preparing for this."

"You are all computer scientists. You know what FINITE AUTOMATA can do. You know what TURING MACHINES can do. For example, Finite Automata can add but not multiply. Turing Machines can compute any computable function. Turing machines are incredibly more powerful than Finite Automata. Yet the only difference between a FA and a TM is that the TM, unlike the FA, has paper and pencil. Think about it. It tells you something about the power of writing. Without writing, you are reduced to a finite automaton. With writing you have the extraordinary power of a Turing machine."

"And programming computers was so fascinating. You create your own little universe, and then it does what you tell it to do."

"There are some people who imagine that older adults don't know how to use the internet. My immediate reaction is, "I've got news for you, we invented it.""

"The ability to interact with a computer presence like you would a human assistant is becoming increasingly feasible."

"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."

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

"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."

"(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."

"[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,"

"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."

"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."

"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."

"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..."

"There's that line from Newton about standing on the shoulders of giants. We're all standing on Dennis' shoulders."

"He was sharp, he was much more mathematical than I. Once he got an idea, he was almost a bulldog, and he'd just work on it until it happened. We worked very, very closely for very many years."

"Ritchie and Thompson made an amazing team; and they played Unix and C like a fine instrument. They sometimes divided up work almost on a subroutine-by-subroutine basis with such rapport that it almost seemed like the work of a single person. In fact, as Dennis has recounted, they once got their signals crossed and both wrote the same subroutine. The two versions did not merely compute the same result, they did it with identical source code! Their output was prodigious. Once I counted how much production code they had written in the preceding year − 100,000 lines! Prodigious didn’t mean slapdash. Ken and Dennis have unerring design sense. They write code that works, code that can be read, code that can evolve."

"I think one of the interesting things about the Linux phenomenon is that [Linus] has been able to keep some kind of control over such an amazingly extended development environment. I’m certainly glad that I didn’t have to develop C in public, because you get more suggestions than you really want. Being in this nice, small group, you can control that sort of thing. I honestly don’t know the dynamics and the details of the Linux kernel project. However, one of the knocks on Linux is that it is undisciplined. But I think probably the fairer observation is that it is amazingly disciplined, compared to what you would expect, given the nature of the endeavor."

"My own computational world is a strange blend of Plan 9, Windows, and Inferno. I very much admire Linux's growth and vigor. Occasionally, people ask me much the same question [about Linux], but posed in a way that seems to expect an answer that shows jealousy or irritation about Linux vs. Unix as delivered and branded by traditional companies. Not at all; I think of both as the continuation of ideas that were started by Ken and me and many others, many years ago."

"I don’t really distinguish between Linux and things that are more or less direct descendants of Unix. I think they’re all the same at some level. Often, people ask me, "Do you feel jealous about Linux being the big thing." And the answer is no, for the same reason. I think they’re the same."

"Computer science research is different from these more traditional disciplines. Philosophically it differs from the physical sciences because it seeks not to discover, explain, or exploit the natural world, but instead to study the properties of machines of human creation. In this it as analogous to mathematics, and indeed the "science" part of computer science is, for the most part mathematical in spirit. But an inevitable aspect of computer science is the creation of computer programs: objects that, though intangible, are subject to commercial exchange."

"Life's a bitch and then your feet wear down."

"The greatest danger to good computer science research today may be excessive relevance. Evidence for the worldwide fascination with computers is everywhere, from the articles on the financial, and even the front pages of the newspapers, to the difficulties that even the most prestigious universities experience in finding and keeping faculty in computer science. The best professors, instead of teaching bright students, join start-up companies."

"UNIX is very simple, it just needs a genius to understand its simplicity."

"[C has] the power of assembly language and the convenience of … assembly language."

"C is quirky, flawed, and an enormous success."

"Another danger is that commercial pressures of one sort or another will divert the attention of the best thinkers from real innovation to exploitation of the current fad, from prospecting to mining a known lode."