In a classical paper from 1950 by Turing: https://www.csee.umbc.edu/courses/471/papers/turing.pdf there is a separate section which is of relevance for Machine Learning. How does Turing name that section?
In the 1950 paper by Alan Turing titled "Computing Machinery and Intelligence," the section relevant to Machine Learning is named "Learning Machines." In this section, Turing discusses the concept of a machine that can learn from experience and adapt its behavior over time. He explores the idea of machines being capable of improving their performance through a process analogous to human learning. This section is considered one of the pioneering discussions on the potential of machines to learn and adapt, laying the groundwork for future developments in the field of artificial intelligence and machine learning.
In the program declaration for Artificial Intelligence for the Darthmouth Summer School in 1956: https://web.archive.org/web/20080930164306/http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html, two sections were specially relevant for Machine Learning. Name one of these sections?
In the program declaration for the Dartmouth Summer Research Project on Artificial Intelligence in 1956, one of the sections relevant to Machine Learning was titled "Learning Machines." This section reflected the focus on developing machines that could learn and adapt, laying the foundation for the field of machine learning. The Dartmouth workshop is often considered a seminal event in the history of artificial intelligence, where researchers gathered to explore various aspects of AI, including learning algorithms and intelligent machines.
In the program declaration for Artificial Intelligence for the Darthmouth Summer School in 1956: two sections were specially relevant for Machine Learning. Name one of these sections?
Neuron Nets
Neuron Systems
Neural Nets
Neural Learning
In the program declaration for the Dartmouth Summer Research Project on Artificial Intelligence in 1956, one of the sections relevant to Machine Learning was titled "Neuron Nets." The researchers at the Dartmouth workshop were exploring the development of neural networks and learning systems, reflecting an early interest in computational models inspired by the human brain for the purpose of machine learning.
Which of the following researchers did NOT participate in the Dartmouth College Summer Workshop?
Marvin Minsky
John McCarthy
Allen Newell
John Holland
John von Neuman
Herbert Simon
Oliver Selfridge
John von Neumann did not participate in the Dartmouth College Summer Workshop on Artificial Intelligence in 1956. While von Neumann made significant contributions to various fields, including computer science and game theory, he was not directly involved in the Dartmouth workshop. The workshop primarily included researchers such as Marvin Minsky, John McCarthy, Allen Newell, John Holland, Herbert Simon, Oliver Selfridge, and others who played crucial roles in the early development of artificial intelligence.
On which logic calculus did John McCarthy base the LISP programming language?
Functional Calculus
Differential Calculus
Lambda Calculus
Predicate Calculus
John McCarthy based the LISP (List Processing) programming language on Lambda Calculus. Lambda Calculus is a formal system in mathematical logic for expressing computation based on function abstraction and application using variable binding and substitution. McCarthy's use of Lambda Calculus in the design of LISP allowed for the manipulation of symbolic expressions and paved the way for the development of symbolic reasoning and artificial intelligence applications.
In the classical paper by McCulloch and Pitt: http://www.cse.chalmers.se/~coquand/AUTOMATA/mcp.pdf, the authors choose a logical calculus for their model of neuron activity. What was the name of the Logician that had defined the specific kind of symbolic logic choosen?
Cantor
Godel
Turing
Carnap
In the classical paper by McCulloch and Pitt titled "A Logical Calculus of Ideas Immanent in Nervous Activity," the logical calculus chosen for their model of neuron activity is based on the work of Kurt Gödel. McCulloch and Pitt drew inspiration from Gödel's incompleteness theorems and employed a symbolic logic system influenced by Gödel's ideas to represent neural processes.
What was the name of Oliver Selfridge´s pioneer architecture for recognition of images as published in 1959?
Pandora
Pangea
Pandemonium
Purgatorium
Oliver Selfridge's pioneer architecture for the recognition of images, as published in 1959, was called "Pandemonium." The Pandemonium model was designed to illustrate a hierarchical, self-organizing approach to visual pattern recognition, where simple feature detectors at the lower level combine to recognize more complex patterns at higher levels.
Newell and Simon made a pioneer contribution to early Artificial Intelligence in 1955 by creating a program that could prove theorems in Whitehead and Russel’s Principia Mathematica. It is called the first ´Artificial Intelligence Program´. What was the name of this system.
Logic Planner
Logic Theorist
Artificial Theorist
Logic Machine
The pioneer contribution by Allen Newell and Herbert A. Simon in 1955 was the creation of the first Artificial Intelligence program called the "Logic Theorist." The Logic Theorist was designed to prove mathematical theorems in Whitehead and Russell's Principia Mathematica, demonstrating automated problem-solving capabilities and marking a significant milestone in the field of Artificial Intelligence.
Which of the following computational paradigms are NOT considered as SYMBOLIC?
Logic Programming
Production Systems
Object Oriented Programming
Genetic Algorithms
Functional Programming
Genetic Algorithms are not considered symbolic computational paradigms. Symbolic computation involves the manipulation of symbols, often representing abstract or conceptual entities.
Logic Programming, such as Prolog, is a symbolic paradigm where programs are written in terms of logical statements.
Production Systems involve rules for transforming and manipulating symbols, making them symbolic.
Object-Oriented Programming (OOP) involves the manipulation of objects, which are symbolic representations of real-world entities.
Functional Programming deals with functions as first-class citizens, and it can be considered symbolic in nature.
Genetic Algorithms, on the other hand, are based on the principles of natural selection and evolution, using a population of potential solutions encoded as structures called chromosomes. While genetic algorithms involve computation, they are not inherently symbolic in the way that logic programming or object-oriented programming is. They operate on a different paradigm, focusing on the evolution and adaptation of solutions through a process inspired by biological evolution.
There is a term which refers to the point in time when Artificial Intelligence systems potentially irrevocably will surpass Human Intelligence with future drastic consequences for human civilization. Which is the term?
The AI Treshold
The Point of NO return
The Singularity
The Takeover
The term you are referring to is "The Singularity." The Singularity in the context of artificial intelligence is a hypothetical future point at which technological growth becomes uncontrollable and irreversible. It is often associated with the idea that AI systems could surpass human intelligence, leading to a range of unpredictable and potentially profound changes in society. The concept is popularized by futurists like Ray Kurzweil and has been a topic of discussion and speculation within the field of artificial intelligence and futurism.
Which of the following computational paradigms are NOT considered as SUB-SYMBOLIC?
Artificial Neural Networks
Evolutionary Computing
Object Oriented Programming
Genetic Algorithms
Object Oriented Programming (OOP) is not considered a sub-symbolic computational paradigm. OOP is a programming paradigm that organizes code using objects, which encapsulate data and behavior. It is a high-level, symbolic approach to programming.
On the other hand:
Artificial Neural Networks (ANNs): ANNs are often associated with sub-symbolic computation. They involve the processing of numerical inputs through interconnected nodes, resembling some aspects of how neurons function in the brain.
Evolutionary Computing (including Genetic Algorithms): These paradigms involve processes inspired by biological evolution and genetics. While genetic algorithms manipulate symbols in a different way than traditional programming, they are typically considered sub-symbolic due to their reliance on evolutionary principles.
So, in the given options, Object Oriented Programming is the one that is not considered a sub-symbolic computational paradigm.
Which type of game did Samuel choose for his first experiments on Machine Learning in 1959.
Chess
Go
Tic Tac Toe
Checkers
Arthur Samuel, in his pioneering work on machine learning in 1959, chose the game of Checkers (also known as Draughts) for his experiments. Samuel's work involved developing a program that could improve its performance through self-play and learning from experience. The success of his checkers-playing program marked an early milestone in the application of machine learning to games.
Who did implement the first concrete Neural Network:SNARC.
John McCarthy
Marvin Minsky
Allen Newell
Arthur Samuel
The first concrete implementation of a neural network, SNARC (Stochastic Neural Analog Reinforcement Calculator), was carried out by Marvin Minsky and Dean Edmonds, not by John McCarthy, Allen Newell, or Arthur Samuel. Marvin Minsky and Dean Edmonds developed SNARC at the Harvard Computation Laboratory in the early 1950s. SNARC was a hardware implementation of a neural network and was designed to simulate the behavior of interconnected neurons.
Who did implement the first concrete Genetic Algorithms.
Herbert Simon
Marvin Minsky
John Holland
Oliver Selfridge
The first concrete implementation of Genetic Algorithms was done by John Holland. Holland, an American computer scientist, introduced Genetic Algorithms in the 1960s as a method of optimization and search inspired by the principles of natural selection and genetics. He is considered a key figure in the development of Genetic Algorithms and evolutionary computation. Neither Herbert Simon, Marvin Minsky, nor Oliver Selfridge is credited with the initial implementation of Genetic Algorithms.
Which of the structures below is refferred to as a Tensor ?
It seems that your question is missing the structures you intended to include. A tensor is a mathematical object that generalizes the concept of vectors and matrices. Tensors can be represented as multi-dimensional arrays of numerical values. The order (or rank) of a tensor corresponds to the number of dimensions it has. Here are examples of tensors of different orders:
Scalar (0th-order tensor): A single number.
Vector (1st-order tensor): An ordered list of numbers.
Matrix (2nd-order tensor): A 2D array of numbers.
Higher-order tensors: Arrays with more than two dimensions.
If you have specific structures in mind, please provide them, and I'll be happy to help clarify whether they are referred to as tensors.