What is intelligence?

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 threshhold checks for intruders inside her burrow, and only then, if the coast is clear, carries her food inside. The real nature of the wasp’s iinstinctual 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. Intelligence—conspicuously absent in the case of Sphex—must 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.


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 tasks—as, for example, discovering proofs for mathematical theorems or playing chess—with 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.

Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence.

The Four Types of Artificial Intelligence

Reactive Machines

A reactive machine follows the most basic of AI principles and, as its name implies, is capable of only using its intelligence to perceive and react to the world in front of it. A reactive machine cannot store a memory and as a result cannot rely on past experiences to inform decision making in real-time.

Perceiving the world directly means that reactive machines are designed to complete only a limited number of specialized duties. Intentionally narrowing a reactive machine’s worldview is not any sort of cost-cutting measure, however, and instead means that this type of AI will be more trustworthy and reliable — it will react the same way to the same stimuli every time. 

A famous example of a reactive machine is Deep Blue, which was designed by IBM in the 1990’s as a chess-playing supercomputer and defeated international grandmaster Gary Kasparov in a game. Deep Blue was only capable of identifying the pieces on a chess board and knowing how each moves based on the rules of chess, acknowledging each piece’s present position, and determining what the most logical move would be at that moment. The computer was not pursuing future potential moves by its opponent or trying to put its own pieces in better position. Every turn was viewed as its own reality, separate from any other movement that was made beforehand.

Another example of a game-playing reactive machine is Google’s AlphaGo is also incapable of evaluating future moves but relies on its own neural network to evaluate developments of the present game, giving it an edge over Deep Blue in a more complex game. AlphaGo also bested world-class competitors of the game, defeating champion Go player Lee Sedol in 2016.

Though limited in scope and not easily altered, reactive machine artificial intelligence can attain a level of complexity, and offers reliability when created to fulfill repeatable tasks.

Limited Memory

Limited memory artificial intelligence has the ability to store previous data and predictions when gathering information and weighing potential decisions — essentially looking into the past for clues on what may come next. Limited memory artificial intelligence is more complex and presents greater possibilities than reactive machines.

Limited memory AI is created when a team continuously trains a model in how to analyze and utilize new data or an AI environment is built so models can be automatically trained and renewed. When utilizing limited memory AI in machine learning, six steps must be followed: Training data must be created, the machine learning model must be created, the model must be able to make predictions, the model must be able to receive human or environmental feedback, that feedback must be stored as data, and these these steps must be reiterated as a cycle.

There are three major machine learning models that utilize limited memory artificial intelligence:

  • Reinforcement learning, which learns to make better predictions through repeated trial-and-error.
  • Long Short Term Memory (LSTM), which utilizes past data to help predict the next item in a sequence.  LTSMs view more recent information as most important when making predictions and discounts data from further in the past, though still utilizing it to form conclusions
  • Evolutionary Generative Adversarial Networks (E-GAN), which evolves over time, growing to explore slightly modified paths based off of previous experiences with every new decision. This model is constantly in pursuit of a better path and utilizes simulations and statistics, or chance, to predict outcomes throughout its evolutionary mutation cycle.

How Does Artificial Intelligence Work?

AI Approaches and Concepts

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 papercomputing machinery and intelligence” (1950), and its subsequent Turing Test, established the fundamental goal and vision of artificial intelligence.   

At its 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 many questions 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? AI is an interdisciplinary science with multiple approaches, but advancements in machine learning a and deep learning are creating a paradigm shift in virtually every sector of the tech industry. 

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.



A robot is the product of the robotics field, where programmable machines are built that can assist humans or mimic human actions. Robots were originally built to handle monotonous tasks (like building cars on an assembly line), but have since expanded well beyond their initial uses to perform tasks like fighting fires, cleaning homes and assisting with incredibly intricate surgeries. Each robot has a differing level of autonomy, ranging from human-controlled bots that carry out tasks that a human has full control over to fully-autonomous bots that perform tasks without any external influences.

Robotics is an interdisciplinary sector of science and engineering dedicated to the design, construction and use of mechanical robots. Our guide will give you a concrete grasp of robotics, including different types of robots and how they’re being applied across industries.

As technology progresses, so too does the scope of what is considered robotics in automotive factories. These robots consist mainly of mechanical arms tasked with welding or screwing on certain parts of a car.

While the overall world of robotics is expanding, a robot has some consistent characteristics:

  1. Robots all consist of some sort of mechanical construction. The mechanical aspect of a robot helps it complete tasks in the environment for which it’s designed..
  2. Robots need electrical components that control and power the machinery. Essentially, an electric current (a battery, for example) is needed to power a large majority of robots.
  3. Robots contain at least some level of computer programming. Without a set of code telling it what to do, a robot would just be another piece of simple machinery. Inserting a program into a robot gives it the ability to know when and how to carry out a task.

We’re really bound to see the promise of the robotics industry sooner, rather than later, as artificial intelligence and software also continue to progress. In the near future, thanks to advances in these technologies,, more flexible and more energy efficient. They’ll also continue to be a main focal point in smart factories, where they’ll take on more difficult challenges and help to secure global supply chains.

Though relatively young, the robotics industry is filled with an admirable promise of progress that science fiction could once only dream about. From the deepest depths of our oceans to thousands of miles in outer space, robots will be found performing tasks that humans couldn’t dream of achieving alone.

What Is Robotics?

Robotics is the intersection of science, engineering and technology that produces machines, called robots, that substitute for (or replicate) human actions. Pop culture has always been fascinated with robots. R2-D2. Optimus Prime. WALL-E. These over-exaggerated, humanoid concepts of robots usually seem like a caricature of the real thing…or are they more forward thinking than we realize? Robots are gaining intellectual and mechanical capabilities that don’t put the possibility of a R2-D2-like machine out of reach in the future.

Artificial intelligence is everywhere, and we use it in our everyday lives without even realizing it. Artificial intelligence has made a lot of progress over the years. It can impact many different industries, and this is mainly due to its improved processing, algorithms, and the amount of data it holds. Machine learning provides the data to be analyzed, followed by critical insights, and it has a huge impact on the manufacturing industry.

Here are 4 ways that artificial intelligence has impacted in the manufacturing industry.

Safer Workplace

Cobots, known as collaborative robots, are designed to work with humans safely. They are small and relatively lightweight, offering manufacturing companies interested in getting into robotics a more affordable option. They can help create safer work environments by performing some of the more dangerous tasks that commonly lead to workplace injuries. This would leave workers with less strenuous tasks and the ability to let them work on more complex tasks free of injury

Machine learning can overcome many of the challenges that arise when using robotics in the workplace, such as when robots are programmed to complete a certain task and cannot react to unexpected situations. Machine learning analyzes the data and identifies different patterns. This results in the system learning and improving without having to be programmed to react.

Quality Improvement

It’s important to meet the highest standards when it comes to pleasing your customers. Maintaining the right reputation is crucial. With the help of artificial intelligence, manufacturers can be notified of any problems related to the quality of their products or services. Any major or minor faults can be addressed through artificial intelligence, and many issues can be avoided during the early stages.

An example of this is called Machine Vision, which is an AI solution. It uses good quality cameras to monitor defects or problems better than humans can. 

Innovation Improvement

Since the 1960’s, drones and industrial robots have been implemented into manufacturing companies. By adding artificial intelligence to the mix, manufacturers can create new possibilities for production. For example, generative design is a great way for engineers to generate thousands of design possibilities. This is the perfect way to come up with different ideas quickly and efficiently that meet the needs of their customers.

Inventory Management

Machine learning can help with inventory planning since they are helpful when dealing with forecasting supply planning. AI demand forecasting tools come up with more precise results than traditional demand forecasting methods. This gives manufacturing companies the right tools to manage their inventory levels more efficiently so that mishaps are less likely to happen.

In Conclusion

Overall, when it comes to artificial intelligence and the manufacturing industry, there are a lot of benefits provided. It allows for more product innovation, increased safety precautions, quicker decision making processes, and quality improvement. This is something that manufacturers should consider, so that they can better improve their workplace.


Author: refuge_2020

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