The Ultimate Guide to Understanding
Robotics

Introduction to AI and Robotics

The idea of intelligent robots has been with us since our cultural infancy in ancient Greece. Greek myth gives us the story of the gigantic steam-powered bronze humanoid Talos, forged by the blacksmith god Hephaestus as a warrior guardian. Talos, by all appearances, was an autonomous automaton, or a machine that could move intelligently of its own volition but which served the will of Hephaestus to guard mankind. But Talos though became too warlike, ultimately threatening human life to the point where he had to be destroyed. It may be here that an age-old mistrust of artificial intelligence was first sown, a mistrust still coloring our thoughts and reactions around innovations happening now in the modern world.

Skip forward to the first half of the 20^th century. At this point, the idea of robots was so consumably commonplace that Lester del Rey’s 1938 short story “Helen O’Loy” featured an anthropomorphic robot so female in appearance and demeanor that she caused both her inventors to fall in love with her.

Then, one year later, Eando Binder’s “I, Robot” 1939 science fiction tale spun a yarn of an intelligent robot unjustly accused of murdering its creator. There was still that lingering negativity that artificial intelligence robots might not be servants we could ever fully trust.

The point here is that proponents of artificial intelligence, as applied to robotics, have cultural biases to overcome to ensure modern-day acceptance of their ideas and products. But, thanks to the demands of industry coupled with the natural progression of the sciences, we’ve just about completely overcome those kinds of reservations. And even though most experts tell us AI is still in its infancy, the sheer magnitude of artificial intelligence applied to robotics is such that the Pentagon agency DARPA has just authorized two billion dollars to extend AI’s frontiers and applications within society.

Robotics Definition

What is the definition of Robotics?

Artificial Intelligence in Robotics is a general term describing the formulation and application of artificial intelligence to enable robots and robotic systems to model or replicate intelligent behavior. The term is also frequently applied to developing specific robots capable of operationalizing the intellectual processes humans have, like the ability to formulate meaning through perception, which in turn gives those robots the ability to generalize and learn.

Simply put, robotics in AI is the science of applying intelligence generation to mechanical tools with the intention of developing those capable of replicating human thought and human actions.

Robotics Terminology

What are common terminology of Robotics?

There are an enormous number of important terms to learn in robotics, specifically as they relate to AI. Here are just a few:

Algorithm: Used all over AI, algorithms are complex equations and sets of if/then programming that are written to imitate the process of reasoning through data.

Autonomous Robot: This is a robot designed to deal with its environment on its own. It perceives and adjusts according to its observations.

Deep Learning (DL): This is a subset of machine learning (which is a subset of AI) where the neural networks are layered to support huge computing power to process data more efficiently, including discoveries in unstructured data.

Heuristics: This is a method of problem-solving or learning defined by trial and error.

Machine Learning (ML): This is a subset of AI where programs are created that actually “learn” how to complete tasks with increasing efficiency over time. This is where machines take the very data they output to find the more effective way to go about each process.

Natural Language Processing: This is how computers understand and deduce meaning from human language, including not only written input but speech recognition.

Artificial Neural Network: These are considered the “foundation of AI,” and refer to the pieces of the processing system that computers use to perform human-like, intelligent problem-solving.

Reinforcement Learning: This is a method of learning based on providing positive feedback with correct output and negative feedback with incorrect output.

Robotics News

Latest developments in Robotics News

The field of Robotics is continually growing with new technology advancements, software improvements, and products. Staying up to date with the latest Robotics news is important to stay on top of this rapidly growing industry. We cover the latest in artificial intelligence news, chatbot news, computer vision news, machine learning news, natural language processing news, speech recognition news and robotics news.

Robotics Explained

What is Robotics?

Robots are hardware. AI is software. It’s really that simple. Or is it?

Robots are autonomous or semi-autonomous machines designed to do things people do. Most people conditioned by science fiction and popular culture think of robots as always being anthropomorphic, and although there have been great strides taken recently in the field of anthropomorphics, many robots are actually just devices.

Early robots were crude mechanical contrivances ranging from soda pop machines to car washes, auto repair equipment to assembly lines. Contemporary industrial robots can perform a greater array of tasks, execute specific action chains, and in some cases replicate or model intelligent human decisions and behaviors.

Today’s anthropomorphic robots are now able to mimic human actions and response norms from walking to running to gymnastics, and it seems each news cycle features robots able to do more.

Artificial intelligence is algorithmic software that learns and improves. It grows. It changes shape. It mutates and improves itself by reconfiguring to new demands rather than preprogrammed demands.

And, of course, we are still pushing the boundaries and setting new limits. As can be seen in the sections immediately following, it’s our goal to create the ultimate AI robotic unit, a robot possessed of true self-awareness—Adam Link and Helen O’Loy, indeed.

Robotics Categories

What are the categories of Robotics?

An overview of the different types of AI robotics can help give you an idea of where we come from and where we’re going.

Type I AI: Reactive Machines

Basic AI systems are purely reactive. They cannot form or hold memories, ergo they are incapable of using experiences to modify their behavior. They are only capable of making very fast choices based on direct immediate perception instead of stored internal reflection. These machines are task-specific and cannot mimic the higher aspects of human thought and cognition.

Type II AI: Limited Memory

Limited memory systems can not only identify specific objects but also observe them over time. These observations are combined with preprogrammed representations of the real world allowing for the development of sequence recognition. But these impressions are not retained to the extent found in human memory, nor do they possess a behavioral modification interface and therefore cannot be used to self-modify behavior.

Type III AI: Theory of Mind

These types of machines have not been invented…yet. Machines with minds theoretically could form internal representations, not only about the world but also about themselves. Of course, this requires at least the beginnings of a rudimentary conception of self, and we are not there yet. But it’s exactly where we’re going.

Type IV AI: Self-Awareness

The ultimate goal of AI robotics is to build intelligent robots that can form internal representational analogues of themselves. This threshold must be reached to realize genuine artificial intelligence in the autonomous sense. This will require AI theoreticians and technicians to understand the nature of consciousness in order to replicate it mechanically. And it’s just a matter of time.

How Does AI Robotics Work?

How AI Robotics work?

Robots can beat human chess masters at chess or outperform humans on an assembly line, but can robots ever be truly like people? When they win at games or even do backflips, are they conscious of what they are doing or simply actuated by an algorithmic program? Can they have emotions? Can they fall in love or get angry or sad? Can they know joy and pride in a job well done?

In a word, no. Or rather, not yet. But those curious about such things quickly discover that the terms robot and artificial intelligence as applied to AI robotics can be elusive and hard to define. Ideally, artificial intelligence applied to robotics would be the whole nine yards. It would produce a human simulacrum capable of human levels of performance so acute it would transcend the limitations of its initial state of non-being and achieve or be enabled to achieve true self-awareness.

But context is everything when evaluating cognition and self-awareness. Do robots have souls? Could they ever develop them? Just what the blinking blazes is a soul, anyway? Is it fair to ourselves or to our robotic creations to demand something of them we cannot provide for ourselves?

What we hope to be able to create at some point are genuinely intelligent robots capable of basic and advanced learning. Learning presupposes the ability to perform inductive and deductive reasoning and the ability to learn and utilize languages, whether those languages are natural or symbolic.

There are three principal types of robotic learning that parallel categories of learning as seen in human beings. These three categories are supervised learning, unsupervised learning and reinforcement learning:

Supervised Learning

Supervised learning is simply pattern recognition. We feed robots task-related data so they can store and organize that data according to whatever task pattern we want them to recognize.

Unsupervised Learning

Unsupervised learning does not involve specific tasks, but rather the assimilation of huge amounts of raw data used to feed pattern recognition abilities. The ultimate goal here is to create a robot capable of understanding the physical world within which it finds itself, although, again this involves creating robots capable of conceptualizing themselves as entities distinct from their external environment.

Reinforcement Learning

Reinforcement learning involves giving robots or robotic systems a programmed goal along with the hardware necessary to manifest that goal. So again, via the application of AI systems to robotics we are striving to create thinking robots or robotic systems capable of transforming perceived experience into modified behavior or more simply put, we want robots that learn and think like people.

Robotics Key Components

Components of Robotics

AI robotics are, in a way, two sides of the same coin. And so, perhaps we need to briefly examine the key components of robotics and robotic systems as well as the key components of artificial intelligence programs.

Simply put, robots and robotic systems are mechanically constructed machines for use in real time. They have electrical actuating mechanisms and their design is task-specific. They are machines that can operate machinery, move and manipulate objects and follow programs designed to facilitate their activities. Artificial intelligence programs come into play when we wish to enhance the performance of robots and robotic systems by making them smarter and more responsive to stimuli—and to permit them to utilize their learning to improve their performance and, in some cases, even their design.

Some of the main components of robots and robotic systems are as follows:

1. Actuators: These convert stored energy into movement. The most common actuators are electric motors, which include brushed and brushless DC motors, stepper motors and piezo motors.
2. Air muscles: These provide a pulling force, replicating the action of biological muscles.
3. Electroactive polymers: These are plastics that morph or change shape in response to electrical stimulation.
4. Elastic nanotubes: Nanotubes with elastic filaments.
5. End effectors: These are essentially hands used by robots. Some end effectors are replaceable per task, and some are fixed for general tasks.

Along with adequate robotics, the essential components to creating and implementing artificial intelligence systems in robotics center primarily around the accumulation, storage and utilization of massive amounts of raw data. This is why the ultimate goal of AI robotics—or the creation of genuinely intelligent robots and robotic systems—is now seen as more approachable.

Specifically, the rise in computational capacity in recent years now permits enough data to be stored in units compact enough to be physically mobile, making autonomous automatons more and more feasible. Today, we see mobile anthropormorphic robots smaller than a compact car. (One very interesting new possibility is storing the data in the cloud, making individual unit storage passé and enabling the construction of smaller and smaller autonomous automatons, essentially the same broadcast power and data system seen in the 2004 science fiction movie I, ROBOT.)

Other components of artificial intelligence systems surpass this article in scope. But since AI at its core is concerned with the generation and maintenance of artificial intelligence, we have identified five critical components of intelligence in general. In human terms, those are:

• Learning
• Inductive and deductive reasoning
• Problem solving
• Perception
• And use of language

Translated into AI requirements, those would primarily involve machine learning and the use of computational algorithmic models to simulate human perceptual modes, reasoning, problem identification and resolution and use of language systems, whether written, mathematical, visual or computer programming language systems.

Robotics Companies

Discover innovative  Robotics startups and companies

It takes bold visionaries and risk-takers to build future technologies into realities. In the field of Speech Recognition, there are many companies across the globe working on this mission. Our mega list of artificial intelligence, chatbots, computer vision, machine learning, natural language processing, speech recognition, and robotics companies, covers the top companies and startups who are innovating in this space.

Robotics Applications

What are the applications of Robotics?

Applications for AI robotics cannot be effectively numbered, and any list might be obsolete as soon as it was completed. The range of applications are enormous. It has been said that AI is relevant to any intellectual task—and, looking at today’s scope of AI, that appears to be the absolute truth.

These applications include AI robotics in academia, advertising, agriculture, automotive, engineering, finance and economics, healthcare and medicine, manufacturing, meteorology, military and national security. Virtually every organized human activity can or will benefit from these technologies. And, while one of the oft-touted benefits of increased AI robotics permeation throughout society is increased human freedom from menial tasks, new industries and new fields of endeavor will supplant former ones, too—as always happens in times of profound technological change.

All that said, there are three critical industries and areas of endeavor utilizing AI robotics systems at present. These are cutting-edge domains and are considered key to AI’s placement in industries related to the development of AI robotics technology.

Robots Teaching Robots

Robots and robotic systems incorporating AI make robots easier to train. This enhances their commercial viability for smaller companies by reducing installation, training and ongoing programming outlays. The smarter the robot, the more it can learn!

3D Machine Vision

This is essential. Minus predetermined spatial coordinates and kinetic programming, it would be impossible for robots to even grasp objects without computer (machine) vision. Computer vision can reconstruct a three-dimensional image capable of catalyzing an AI-based intelligence algorithm for the robot to observe and interpret—and react to—a physical object.

Cloud AI Robotics

AI deep learning for intelligent robots and robotic systems requires huge datasets with millions or even billions of examples. Currently AI requires more data than most local systems can retain. Storing that data on the Cloud not only solves that problem but also allows data be shared with all robots in a connected environment, in effect providing broadcast intelligence.

Robotics Tools

What are Robotics Tools?

AI research and development has yielded a large number of useful tools for AI robotics users. The tools for creators are many, and changing all too quickly, but here are a few general-purpose AI-robotics-based tools useful for several tasks:

Search and Optimization

AI can search intelligently through many potential solutions by developing “rules of thumb” to locate the best solutions. It is AI’s version of a best guess. Optimization can also be used wherein the best guess can initiate an incremental pruning process until no further refinements are possible.

Logic

AI robotics can also utilize various forms of computer logic for knowledge representation and problem solving.

Probabilistic Methods for Uncertain Reasoning

Sometimes, problem-solving requires beginning with incomplete or uncertain information. AI robotics use probability theory and economics to generate probabilistic algorithms useful in filtering and providing context for large data streams.

Classifiers and Statistical Learning Methods

Classifiers are functions that use pattern matching to determine the closest match to an ideal solution and generate data sets as found in supervised learning.

Artificial Neural Networks

Artificial neural networks are literally artificial brains composed of artificial neurons simulating the associative functions of the human brain. These networks generate learning algorithms simulating human learning and other neocortical activity and can actually create short-term memory functions. Some of the industrial benefits afforded by these techniques include speech recognition and machine translation.

Here’s to hoping this extremely brief overview of AI robotics trends and technology motivates readers to reach out and access more detailed information. Provided are the basics of what AI robotics is, how it works, and a portion of its potential to radically transform human society.

The overriding vision is one of an evolving society which in the not-too-distant-future may admit intelligent machines as members and partners, and even, perhaps, confer upon them the status of full citizenship.

This is not mere speculation.

Well over half of all business executives recently surveyed said they expect intelligent machines to become part of their workforce in the very near future. AI robotics is riding the cusp of that future, and that future is bright and it might well be that the eyes that behold it are not human eyes but those of the intelligent cybernetic, originally envisioned in science fiction but manifested through an evolving technology into today’s science fact.