Kolmogorov Complexity – A Primer The Complexity of Things Previously on this blog (quite a while ago), we’ve investigated some simple ideas of using randomness in artistic design (psychedelic art, and earlier randomized css designs), and measuring the complexity of such constructions. Here we intend to give a more thorough and rigorous introduction to the study of the complexity of strings. The Problem with Randomness What we would really love to do is be able to look at a string of binary digits and decide how “random” it is. And yet, by the immutable laws of probability, each string has an equal chance ($ 2^{-50}$) in being chosen at random from all sequences of 50 binary digits. Definition: The Kolmogorov complexity of a string $ w$, denoted $ K(w)$ is the length of the shortest program which outputs $ w$ given no input. While this definition is not rigorous enough to be of any use (we will reformulate it later), we can easily see why the first of the two strings above is less random. print "01" * 25 Proof. Proof.
Neural scaling law Statistical law in machine learning In general, a deep learning model can be characterized by four parameters: model size, training dataset size, training cost, and the post-training error rate (e.g., the test set error rate). Each of these variables can be defined as a real number, usually written as (respectively: parameter count, dataset size, computing cost, and loss). A neural scaling law is a theoretical or empirical statistical law between these parameters. Size of the training dataset [edit] The size of the training dataset is usually quantified by the number of data points within it. With the "pretrain, then finetune" method used for most large language models, there are two kinds of training dataset: the pretraining dataset and the finetuning dataset. In some cases, a small amount of high quality data suffices for finetuning, and more data does not necessarily improve performance.[5] When the performance is a number bounded within the range of (Hestness, Narang, et al, 2017) , with .
Comparison between Karl Pribram's "Holographic Brain Theory" and ore conventional models of neuronal computation One of the problems facing neural science is how to explain evidence that local lesions in the brain do not selectively impair one or another memory trace. Note that in a hologram, restrictive damage does not disrupt the stored information because it has become distributed. The information has become blurred over the entire extent of the holographic film, but in a precise fashion that it can be deblurred by performing the inverse procedure. This paper will discuss in detail the concept of a holograph and the evidence Karl Pribram uses to support the idea that the brain implements holonomic transformations that distribute episodic information over regions of the brain (and later "refocuses" them into a form in which we re-member). Particular emphasis will be placed on the visual system since its the best characterized in the neurosciences. 1. 2. Chapter 2 will outline the basic concept of a hologram and start to introduce Pribram's holonomic brain theory. What is holography? Figure 1 1. 2.
Superintelligence%3A Paths, Dangers, Strategies 2014 book by Nick Bostrom Superintelligence: Paths, Dangers, Strategies is a 2014 book by the philosopher Nick Bostrom. It explores how superintelligence could be created and what its features and motivations might be.[2] It argues that superintelligence, if created, would be difficult to control, and that it could take over the world in order to accomplish its goals. The book also presents strategies to help make superintelligences whose goals benefit humanity.[3] It was particularly influential for raising concerns about existential risk from artificial intelligence.[4] It is unknown whether human-level artificial intelligence will arrive in a matter of years, later this century, or not until future centuries. Regardless of the initial timescale, once human-level machine intelligence is developed, a "superintelligent" system that "greatly exceeds the cognitive performance of humans in virtually all domains of interest" would most likely follow surprisingly quickly.
Timeline of Western philosophers A wide-ranging list of philosophers from the Western traditions of philosophy. Included are not only philosophers (Socrates, Plato), but also those who have had a marked importance upon the philosophy of the day. The list stops at the year 1950, after which philosophers fall into the category of Contemporary philosophy. Western and Middle Eastern philosophers[edit] Classical philosophers[edit] 600-500 BCE[edit] 500-400 BCE[edit] 400-300 BCE[edit] Hellenistic Philosophers[edit] 300-200 BCE[edit] 200-100 BCE[edit] Carneades (c. 214 – 129 BCE). 100-0 BCE[edit] Lucretius (c. 99 – 55 BCE). Roman Era Philosophers[edit] 0-100 CE[edit] Cicero (c. 106 BCE – 43 BCE)Philo (c. 20 BCE – 40 CE). 100-200 CE[edit] 200-400 CE[edit] Western Medieval Era Philosophers[edit] 500-800 CE[edit] 800-900 CE[edit] 900-1000 CE[edit] al-Faràbi (c. 870 – 950). 1000-1100 CE[edit] Ibn Sina (Avicenna) (c. 980 – 1037). 1100-1200 CE[edit] 1200-1300 CE[edit] 1300-1400 CE[edit] 1400-1500 CE[edit] Early Modern Philosophers[edit] 1500-1550 CE[edit]
Intelligent agent Simple reflex agent Intelligent agents are often described schematically as an abstract functional system similar to a computer program. For this reason, intelligent agents are sometimes called abstract intelligent agents (AIA)[citation needed] to distinguish them from their real world implementations as computer systems, biological systems, or organizations. Some definitions of intelligent agents emphasize their autonomy, and so prefer the term autonomous intelligent agents. Still others (notably Russell & Norvig (2003)) considered goal-directed behavior as the essence of intelligence and so prefer a term borrowed from economics, "rational agent". Intelligent agents are also closely related to software agents (an autonomous computer program that carries out tasks on behalf of users). A variety of definitions[edit] Intelligent agents have been defined many different ways.[3] According to Nikola Kasabov[4] IA systems should exhibit the following characteristics: Structure of agents[edit]
List of unsolved problems in philosophy This is a list of some of the major unsolved problems in philosophy. Clearly, unsolved philosophical problems exist in the lay sense (e.g. "What is the meaning of life?" Aesthetics[edit] Essentialism[edit] In art, essentialism is the idea that each medium has its own particular strengths and weaknesses, contingent on its mode of communication. Art objects[edit] This problem originally arose from the practice rather than theory of art. While it is easy to dismiss these assertions, further investigation[who?] Epistemology[edit] Epistemological problems are concerned with the nature, scope and limitations of knowledge. Gettier problem[edit] In 1963, however, Edmund Gettier published an article in the periodical Analysis entitled "Is Justified True Belief Knowledge?" In response to Gettier's article, numerous philosophers have offered modified criteria for "knowledge." Infinite regression[edit] Molyneux problem[edit] Münchhausen trilemma[edit] Qualia[edit] Ethics[edit] Moral luck[edit] [edit]
Robotics Robotics is the branch of mechanical engineering, electrical engineering and computer science that deals with the design, construction, operation, and application of robots,[1] as well as computer systems for their control, sensory feedback, and information processing. These technologies deal with automated machines that can take the place of humans in dangerous environments or manufacturing processes, or resemble humans in appearance, behavior, and/or cognition. Many of today's robots are inspired by nature contributing to the field of bio-inspired robotics. The concept of creating machines that can operate autonomously dates back to classical times, but research into the functionality and potential uses of robots did not grow substantially until the 20th century.[2] Throughout history, robotics has been often seen to mimic human behavior, and often manage tasks in a similar fashion. Etymology[edit] History of robotics[edit] Robotic aspects[edit] Components[edit] Power source[edit]