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(Photo : silvawpius.wordpress.com)What are the causes of racial discrimination in artificial intelligence in cars? How does it happen and can it be avoided at all. Does AI really abstract or it is just a set of algorithms too.

When the first singularity called the "big bang" seeded the proto-universe with light and matter that was the first proto-matter into the universe today. What made the universe into what is it now, is the mysterious substance called "Dark matter". In the first few seconds of the big bang, it was so hot, when it cooled down dark matter settled. Gravity and the fundamental forces of the universe pulled all dark matter from heated halos that became everything in the universe.

Now, this dark matter is captured as visual imaged or as background radiation in the galaxy, we know today. Dark matter holds everything in the cosmos together, without it, there is no telling what can happen. Here are insights into what kinds of dark matter that the big bang cooked up, basically everything in the universe floats in a sea of endless dark matter. Kinds of dark matter as defined are warm, cold, and fuzzy, the reason is the scientist give these terms is to make them understandable. Most of the time, everyone gets lost in the play of concepts and terms. Let us begin now.

Factoid#1

Specialists from MIT, Princeton University, and Cambridge University have speculated that the proto-galaxies to later galaxies are not the same. This is because of whether it was a warm, cold, or fuzzy matter when they were formed. A simulation was designed to test the theory on dark matter formations.

Factoid#2

Most dark matter iscoldand does not mix with other matters.Warmis lighter and moves fast, not slow, a bit faster than cold DM. A new concept isfuzzydark matter which is ultralight bits and particles that heavier than an electron. Fuzzy dark matter is essentially heavier, and larger too.

Factoid#3

Most dark matter used to form halos around proto-galaxies yet to form were cold. If it was the fuzzy or warm kind, then galaxies will have trailing tails. Fuzzy universes might look striated, like harp strings.

Factoid#4

Light traveling in the cosmos can be very old, using a telescope that will tell if the dark matter is cold, warm, or fuzzy too. These three kinds of dark matter (DM) is about 85% in the universe today.

Factoid#5

Proving what dark matter is harder to do, and most guesses point at dark matter are cold mostly. And, this is what makes the superstructure of the universe and keeps it together like crazy glue,

Factoid#6

Fuzzy dark matter is totally different, and it acts like a wave throughout the universe. This wave-like dark matter is like to mix with other bits of matter, compared to cold dark matter. Galaxies formed from it will be significantly different from what it is now.

Factoid#7

The scientist is developinga new universal modelof what a fuzzy matter universe will be like. Using the James Webb Space Telescope, they will look back in time and see the first proto-galaxies as they were. Hopefully models by Mocz, Fialkov, Vogelsberger will be proven by then.

Related Article: Is Dark Matter Warm, Cold, or 'Fuzzy'? New Simulations Provide Intriguing Insights.

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Should Artificial Intelligence in Cars Be Programmed to Be Racism-Free? - Science Times

Disruption is widespread in almost any sector as technology like artificial intelligence (AI) is making its way into core businesses to improve processes, including airline operations. In akeynote speechat the annual Consumer Electronics Show, Delta Air Lines CEO Ed Bastian used the forum to discuss the operational structure for Delta, which will be driven by an AI machine learning tool.

Per an Avionics International report, Bastian did not provide a specific product name for the technology, but instead called it a proprietary tool that will mainly be focused on helping passengers and flight crews overcome weather occurrences that impact the routes they fly on a daily basis. The keynote speech is a familiar strategy across all of the divisions of Delta, including their maintenance team whose predictive maintenance leadership gave a speech on how the airline is shifting towards the adoption of AI at the 2019AEEC/AMC annual conference.

Broadly speaking, the AI tool will help improve airline operations in the midst of extreme weather conditions.

Weve cancelled cancellations, but we still have to deal with weather variables like hurricanes or a nasty Noreaster, and thats why the team in our operations and customer center is developing the industrys first machine learning platform to help ensure a smooth operation even in extreme conditions. The system uses operational data to run scenarios and project future outcomes while simulating all the variables of running a global airline with more than 1,000 planes in the sky, Bastian said.

Airline industry innovation can also benefit the US Global Jets ETF (NYSEArca: JETS). JETS seeks to track the performance of the U.S. Global Jets Index, which is composed of the exchange-listed common stock or depository receipts) of U.S. and international passenger airlines, aircraft manufacturers, airports, and terminal services copanies across the globe.

U.S. airlines are headed for a 10thstraight year of profits, which is causing employees to demand higher wages as well as increased benefits. This decade of profitability could put airlines-focused and transportation ETFs in play.

Next year, major U.S. carriers will be negotiating labor agreements with more than 120,000 unionized employees, a process that is set to add to their expenses, aCNBC article noted. American will be negotiating with most of its unionized workforce, including pilots, flight attendants, and maintenance workers.

Labor costs are airlines biggest expense and they have become a larger portion of overall costs, the report added. Last year, labor costs ate up 28% of U.S. airlines $187 billion in revenue, up from a 21% share in 2008, as airlines hired more workers and compensation rose,according to data from trade group Airlines for America.

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Delta Air Develops A.I. Tool to Address Weather Disruptions - ETF Trends

Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since the development of the digital computer in the 1940s, it has been demonstrated that computers can be programmed to carry out very complex tasksas, for example, discovering proofs for mathematical theorems or playing chesswith great proficiency. Still, despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match human flexibility over wider domains or in tasks requiring much everyday knowledge. On the other hand, some programs have attained the performance levels of human experts and professionals in performing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis, computer search engines, and voice or handwriting recognition.

All but the simplest human behaviour is ascribed to intelligence, while even the most complicated insect behaviour is never taken as an indication of intelligence. What is the difference? Consider the behaviour of the digger wasp, Sphex ichneumoneus. When the female wasp returns to her burrow with food, she first deposits it on the threshold, checks for intruders inside her burrow, and only then, if the coast is clear, carries her food inside. The real nature of the wasps instinctual behaviour is revealed if the food is moved a few inches away from the entrance to her burrow while she is inside: on emerging, she will repeat the whole procedure as often as the food is displaced. Intelligenceconspicuously absent in the case of Sphexmust include the ability to adapt to new circumstances.

Psychologists generally do not characterize human intelligence by just one trait but by the combination of many diverse abilities. Research in AI has focused chiefly on the following components of intelligence: learning, reasoning, problem solving, perception, and using language.

There are a number of different forms of learning as applied to artificial intelligence. The simplest is learning by trial and error. For example, a simple computer program for solving mate-in-one chess problems might try moves at random until mate is found. The program might then store the solution with the position so that the next time the computer encountered the same position it would recall the solution. This simple memorizing of individual items and proceduresknown as rote learningis relatively easy to implement on a computer. More challenging is the problem of implementing what is called generalization. Generalization involves applying past experience to analogous new situations. For example, a program that learns the past tense of regular English verbs by rote will not be able to produce the past tense of a word such as jump unless it previously had been presented with jumped, whereas a program that is able to generalize can learn the add ed rule and so form the past tense of jump based on experience with similar verbs.

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artificial intelligence | Definition, Examples, and ...

Can machines think? Alan Turing, 1950

Less than a decade after breaking the Nazi encryption machine Enigma and helping the Allied Forces win World War II, mathematician Alan Turing changed history a second time with a simple question: "Can machines think?"

Turing's paper "Computing Machinery and Intelligence" (1950), and it's subsequent Turing Test, established the fundamental goal and vision of artificial intelligence.

At it's core, AI is the branch of computer science that aims to answer Turing's question in the affirmative. It is the endeavor to replicate or simulate human intelligence in machines.

The expansive goal of artificial intelligence has given rise to manyquestions and debates. So much so, that no singular definition of the field is universally accepted.

The major limitation in defining AI as simply "building machines that are intelligent" is that it doesn't actually explain what artificial intelligence is? What makes a machine intelligent?

In their groundbreaking textbook Artificial Intelligence: A Modern Approach, authors Stuart Russell and Peter Norvig approach the question by unifying their work around the theme of intelligent agents in machines. With this in mind, AI is "the study of agents that receive percepts from the environment and perform actions." (Russel and Norvig viii)

Norvig and Russell go on to explore four different approaches that have historically defined the field of AI:

The first two ideas concern thought processes and reasoning, while the others deal with behavior. Norvig and Russell focus particularly on rational agents that act to achieve the best outcome, noting "all the skills needed for the Turing Test also allow an agent to act rationally." (Russel and Norvig 4).

Patrick Winston, the Ford professor of artificial intelligence and computer science at MIT, defines AI as "algorithms enabled by constraints, exposed by representations that support models targeted at loops that tie thinking, perception and action together."

While these definitions may seem abstract to the average person, they help focus the field as an area of computer science and provide a blueprint for infusing machines and programs with machine learning and other subsets of artificial intelligence.

While addressing a crowd at the Japan AI Experience in 2017, DataRobot CEO Jeremy Achin began his speech by offering the following definition of how AI is used today:

"AI is a computer system able to perform tasks that ordinarily require human intelligence... Many of these artificial intelligence systems are powered by machine learning, some of them are powered by deep learning and some of them are powered by very boring things like rules."

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What is Artificial Intelligence? How Does AI Work? | Built In

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Since the invention of computers or machines, their capability to perform various tasks went on growing exponentially. Humans have developed the power of computer systems in terms of their diverse working domains, their increasing speed, and reducing size with respect to time.

A branch of Computer Science named Artificial Intelligence pursues creating the computers or machines as intelligent as human beings.

According to the father of Artificial Intelligence, John McCarthy, it is The science and engineering of making intelligent machines, especially intelligent computer programs.

Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.

AI is accomplished by studying how human brain thinks, and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems.

While exploiting the power of the computer systems, the curiosity of human, lead him to wonder, Can a machine think and behave like humans do?

Thus, the development of AI started with the intention of creating similar intelligence in machines that we find and regard high in humans.

To Create Expert Systems The systems which exhibit intelligent behavior, learn, demonstrate, explain, and advice its users.

To Implement Human Intelligence in Machines Creating systems that understand, think, learn, and behave like humans.

Artificial intelligence is a science and technology based on disciplines such as Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. A major thrust of AI is in the development of computer functions associated with human intelligence, such as reasoning, learning, and problem solving.

Out of the following areas, one or multiple areas can contribute to build an intelligent system.

The programming without and with AI is different in following ways

In the real world, the knowledge has some unwelcomed properties

AI Technique is a manner to organize and use the knowledge efficiently in such a way that

AI techniques elevate the speed of execution of the complex program it is equipped with.

AI has been dominant in various fields such as

Gaming AI plays crucial role in strategic games such as chess, poker, tic-tac-toe, etc., where machine can think of large number of possible positions based on heuristic knowledge.

Natural Language Processing It is possible to interact with the computer that understands natural language spoken by humans.

Expert Systems There are some applications which integrate machine, software, and special information to impart reasoning and advising. They provide explanation and advice to the users.

Vision Systems These systems understand, interpret, and comprehend visual input on the computer. For example,

A spying aeroplane takes photographs, which are used to figure out spatial information or map of the areas.

Doctors use clinical expert system to diagnose the patient.

Police use computer software that can recognize the face of criminal with the stored portrait made by forensic artist.

Speech Recognition Some intelligent systems are capable of hearing and comprehending the language in terms of sentences and their meanings while a human talks to it. It can handle different accents, slang words, noise in the background, change in humans noise due to cold, etc.

Handwriting Recognition The handwriting recognition software reads the text written on paper by a pen or on screen by a stylus. It can recognize the shapes of the letters and convert it into editable text.

Intelligent Robots Robots are able to perform the tasks given by a human. They have sensors to detect physical data from the real world such as light, heat, temperature, movement, sound, bump, and pressure. They have efficient processors, multiple sensors and huge memory, to exhibit intelligence. In addition, they are capable of learning from their mistakes and they can adapt to the new environment.

Here is the history of AI during 20th century

Karel apek play named Rossum's Universal Robots (RUR) opens in London, first use of the word "robot" in English.

Foundations for neural networks laid.

Isaac Asimov, a Columbia University alumni, coined the term Robotics.

Alan Turing introduced Turing Test for evaluation of intelligence and published Computing Machinery and Intelligence. Claude Shannon published Detailed Analysis of Chess Playing as a search.

John McCarthy coined the term Artificial Intelligence. Demonstration of the first running AI program at Carnegie Mellon University.

John McCarthy invents LISP programming language for AI.

Danny Bobrow's dissertation at MIT showed that computers can understand natural language well enough to solve algebra word problems correctly.

Joseph Weizenbaum at MIT built ELIZA, an interactive problem that carries on a dialogue in English.

Scientists at Stanford Research Institute Developed Shakey, a robot, equipped with locomotion, perception, and problem solving.

The Assembly Robotics group at Edinburgh University built Freddy, the Famous Scottish Robot, capable of using vision to locate and assemble models.

The first computer-controlled autonomous vehicle, Stanford Cart, was built.

Harold Cohen created and demonstrated the drawing program, Aaron.

Major advances in all areas of AI

The Deep Blue Chess Program beats the then world chess champion, Garry Kasparov.

Interactive robot pets become commercially available. MIT displays Kismet, a robot with a face that expresses emotions. The robot Nomad explores remote regions of Antarctica and locates meteorites.

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Artificial Intelligence - Overview - Tutorialspoint