HERE IS SOME 3D EXAMPLES FOR NEURAL NETWORK TO GIVE YOU AN SOME KNOWLEDGE ABOUT AI

HERE IS BLOCK BUILTED 3D MODEL OF AI BRAIN 

                                             IMPORTANT NOTES ABOUT NEURAL NETWORK 

A neural network is a computing system modeled on the human brain that learns from data to recognize patterns, make decisions, and perform tasks like voice recognition. Key concepts include: activation functions that determine a neuron's output, backpropagation for training the network by calculating and correcting errors, and layers (input, hidden, and output) that process information. 

What is a neural network?

• A computing system modeled on the human brain, also known as an Artificial Neural Network (ANN).

• It extracts information and can learn to recognize patterns and make decisions without explicit programming.

• They are particularly useful for tasks involving large datasets, such as voice recognition, character recognition, and signature verification. 

How does a neural network work?

• Input layer: Receives the input data.

• Hidden layers: Process the data. Each neuron in a hidden layer computes a weighted sum of its inputs, which is then passed through an activation function to determine its output.

• Output layer: Produces the final result of the network.

• Learning: The network learns by adjusting the "weights" of the connections between neurons. This adjustment process is guided by an algorithm, such as backpropagation, which minimizes the error between the predicted and actual outputs. 

Key components and concepts

• Neuron: The basic unit of the network. It receives inputs, processes them, and passes the output to other neurons.

• Activation function: A mathematical function that determines the output of a neuron based on its inputs. Examples include the sigmoid function and ReLU.

• Weights: Values associated with the connections between neurons that determine the strength of the signal passed between them. A positive weight indicates an excitatory input, while a negative weight indicates an inhibitory input.

• Backpropagation: The core algorithm for training neural networks. It calculates the error at the output and propagates it backward through the network to adjust the weights.

• ** Perceptron:** An early model of an artificial neuron that consists of a single layer.

• Multi-layer perceptron (MLP): A type of neural network with multiple hidden layers between the input and output layers.

• ** Feedforward neural network:** A network where data flows in only one direction, from input to output.

• ** Recurrent neural network (RNN):** A type of network where connections can loop back, allowing the network to have a "memory" of previous inputs. 

NOW HERE IS AN 3D EXAMPLE OF AN HUMANOID ROBOT ----->

                    IMPORTANT FACTS TO KNOW ABOUT HUMANOID ROBOT

A humanoid robot is a robot with a body that resembles the human form, having a torso, head, two arms, and two legs. They are used in a variety of applications, such as performing dangerous tasks, assisting in healthcare, and conducting research. Key challenges include creating human-like movement and a sophisticated hand, while benefits include 24/7 work capability and performing repetitive or risky jobs. 

What are humanoid robots?

• A humanoid robot is a robot designed to have a body that resembles the human body in shape.

• They have a torso, a head, two arms, and two legs, though some may only have parts of a full human body.

• Androids are a specific type of humanoid robot built to look like humans, and some can replicate facial expressions. 

How are they used?

• Manufacturing and assembly: They can perform repetitive tasks or work in dangerous environments.

• Healthcare: They can assist patients in hospitals or in home care settings.

• Research: They are used to study bipedal locomotion and other human movements.

• Interaction: Social humanoid robots like Sophia can interact with people and even mimic human expressions. 

What are their advantages?

• Danger-proof: They can perform tasks that are too dangerous for humans.

• Efficiency: They can work continuously without fatigue.

• Repetitive tasks: They are ideal for performing repetitive jobs. 

What are the challenges in designing them?

• Movement: Making a humanoid robot walk and move as gracefully and efficiently as a human is a significant challenge.

• Dexterity: Designing a hand that is as dexterous as a human hand is one of the most difficult challenges.

• Power: Providing enough energy for their complex tasks is also a concern. 

What is the future potential?

• They are expected to become more advanced and capable of performing a wider range of tasks.

• They could be used to help with household chores, serve as companions, or work in more complex roles. 

HISTORY OF AI

The history of artificial intelligence:

The idea of “artificial intelligence” goes back thousands of years, to ancient philosophers considering questions of life and death. In ancient times, inventors made things called “automatons” which were mechanical and moved independently of human intervention. The word “automaton” comes from ancient Greek, and means “acting of one’s own will.” One of the earliest records of an automaton comes from 400 BCE and refers to a mechanical pigeon created by a friend of the philosopher Plato. Many years later, one of the most famous automatons was created by Leonardo da Vinci around the year 1495.

So while the idea of a machine being able to function on its own is ancient, for the purposes of this article, we’re going to focus on the 20th century, when engineers and scientists began to make strides toward our modern-day AI.

Groundwork for AI: 1900-1950

In the early 1900s, there was a lot of media created that centered around the idea of artificial humans. So much so that scientists of all sorts started asking the question: is it possible to create an artificial brain? Some creators even made some versions of what we now call “robots” (and the word was coined in a Czech play in 1921) though most of them were relatively simple. These were steam-powered for the most part, and some could make facial expressions and even walk.

Dates of note:

• 1921: Czech playwright Karel Čapek released a science fiction play “Rossum’s Universal Robots ” which introduced the idea of “artificial people” which he named robots. This was the first known use of the word.

• 1929: Japanese professor Makoto Nishimura built the first Japanese robot, named Gakutensoku .

• 1949: Computer scientist Edmund Callis Berkley published the book “Giant Brains, or Machines that Think ” which compared the newer models of computers to human brains.

Birth of AI: 1950-1956

This range of time was when the interest in AI really came to a head. Alan Turing published his work “Computer Machinery and Intelligence” which eventually became The Turing Test, which experts used to measure computer intelligence. The term “artificial intelligence” was coined and came into popular use.

Dates of note:

• 1950: Alan Turing published “Computer Machinery and Intelligence ” which proposed a test of machine intelligence called The Imitation Game.

• 1952: A computer scientist named Arthur Samuel developed a program to play checkers, which is the first to ever learn the game independently.

• 1955: John McCarthy held a workshop at Dartmouth on “artificial intelligence” which is the first use of the word, and how it came into popular usage.

AI maturation: 1957-1979

The time between when the phrase “artificial intelligence” was created, and the 1980s was a period of both rapid growth and struggle for AI research. The late 1950s through the 1960s was a time of creation. From programming languages that are still in use to this day to books and films that explored the idea of robots, AI became a mainstream idea quickly.

The 1970s showed similar improvements, such as the first anthropomorphic robot being built in Japan, to the first example of an autonomous vehicle being built by an engineering grad student. However, it was also a time of struggle for AI research, as the U.S. government showed little interest in continuing to fund AI research.

Notable dates include:

• 1958: John McCarthy created LISP (acronym for List Processing), the first programming language for AI research, which is still in popular use to this day.

• 1959: Arthur Samuel created the term “machine learning” when doing a speech about teaching machines to play chess better than the humans who programmed them.

• 1961: The first industrial robot Unimate started working on an assembly line at General Motors in New Jersey, tasked with transporting die casings and welding parts on cars (which was deemed too dangerous for humans).

• 1965: Edward Feigenbaum and Joshua Lederberg created the first “expert system” which was a form of AI programmed to replicate the thinking and decision-making abilities of human experts.

• 1966: Joseph Weizenbaum created the first “chatterbot” (later shortened to chatbot), ELIZA, a mock psychotherapist, that used natural language processing (NLP) to converse with humans.1968: Soviet mathematician Alexey Ivakhnenko published “Group Method of Data Handling” in the journal “Avtomatika,” which proposed a new approach to AI that would later become what we now know as “Deep Learning.”

• 1973: An applied mathematician named James Lighthill gave a report to the British Science Council, underlining that strides were not as impressive as those that had been promised by scientists, which led to much-reduced support and funding for AI research from the British government.

• 1979: James L. Adams created The Standford Cart in 1961, which became one of the first examples of an autonomous vehicle. In ‘79, it successfully navigated a room full of chairs without human interference.

• 1979: The American Association of Artificial Intelligence which is now known as the Association for the Advancement of Artificial Intelligence (AAAI) was founded.

AI boom: 1980-1987

Most of the 1980s showed a period of rapid growth and interest in AI, now labeled as the “AI boom.” This came from both breakthroughs in research, and additional government funding to support the researchers. Deep Learning techniques and the use of Expert System became more popular, both of which allowed computers to learn from their mistakes and make independent decisions.

Notable dates in this time period include:

• 1980: First conference of the AAAI was held at Stanford.

• 1980: The first expert system came into the commercial market , known as XCON (expert configurer). It was designed to assist in the ordering of computer systems by automatically picking components based on the customer’s needs.

• 1981: The Japanese government allocated $850 million (over $2 billion dollars in today’s money) to the Fifth Generation Computer project . Their aim was to create computers that could translate, converse in human language, and express reasoning on a human level.

• 1984: The AAAI warns of an incoming “AI Winter” where funding and interest would decrease, and make research significantly more difficult.

• 1985: An autonomous drawing program known as AARON is demonstrated at the AAAI conference.

• 1986: Ernst Dickmann and his team at Bundeswehr University of Munich created and demonstrated the first driverless car (or robot car). It could drive up to 55 mph on roads that didn’t have other obstacles or human drivers.

• 1987: Commercial launch of Alacrity by Alactrious Inc. Alacrity was the first strategy managerial advisory system, and used a complex expert system with 3,000+ rules.

AI winter: 1987-1993

As the AAAI warned, an AI Winter came. The term describes a period of low consumer, public, and private interest in AI which leads to decreased research funding, which, in turn, leads to few breakthroughs. Both private investors and the government lost interest in AI and halted their funding due to high cost versus seemingly low return. This AI Winter came about because of some setbacks in the machine market and expert systems, including the end of the Fifth Generation project, cutbacks in strategic computing initiatives, and a slowdown in the deployment of expert systems.

Notable dates include:

• 1987: The market for specialized LISP-based hardware collapsed due to cheaper and more accessible competitors that could run LISP software, including those offered by IBM and Apple. This caused many specialized LISP companies to fail as the technology was now easily accessible.

• 1988: A computer programmer named Rollo Carpenter invented the chatbot Jabberwacky , which he programmed to provide interesting and entertaining conversation to humans.

AI agents: 1993-2011

Despite the lack of funding during the AI Winter, the early 90s showed some impressive strides forward in AI research, including the introduction of the first AI system that could beat a reigning world champion chess player. This era also saw early examples of AI agents in research settings, as well as the introduction of AI into everyday life via innovations such as the first Roomba and the first commercially-available speech recognition software on Windows computers.

The surge in interest was followed by a surge in funding for research, which allowed even more progress to be made.

Notable dates include:

• 1997: Deep Blue (developed by IBM) beat the world chess champion, Gary Kasparov, in a highly-publicized match, becoming the first program to beat a human chess champion.

• 1997: Windows released a speech recognition software (developed by Dragon Systems).

• 2000: Professor Cynthia Breazeal developed the first robot that could simulate human emotions with its face,which included eyes, eyebrows, ears, and a mouth. It was called Kismet.

• 2002: The first Roomba was released.

• 2003: Nasa landed two rovers onto Mars (Spirit and Opportunity) and they navigated the surface of the planet without human intervention.

• 2006: Companies such as Twitter, Facebook, and Netflix started utilizing AI as a part of their advertising and user experience (UX) algorithms.

• 2010: Microsoft launched the Xbox 360 Kinect, the first gaming hardware designed to track body movement and translate it into gaming directions.

• 2011: An NLP computer programmed to answer questions named Watson (created by IBM) won Jeopardy against two former champions in a televised game.

• 2011: Apple released Siri, the first popular virtual assistant.

Artificial General Intelligence: 2012-present

That brings us to the most recent developments in AI, up to the present day. We’ve seen a surge in common-use AI tools, such as virtual assistants, search engines, etc. This time period also popularized Deep Learning and Big Data..

Notable dates include:

• 2012: Two researchers from Google (Jeff Dean and Andrew Ng) trained a neural network to recognize cats by showing it unlabeled images and no background information.

• 2015: Elon Musk, Stephen Hawking, and Steve Wozniak (and over 3,000 others) signed an open letter to the worlds’ government systems banning the development of (and later, use of) autonomous weapons for purposes of war.

• 2016: Hanson Robotics created a humanoid robot named Sophia, who became known as the first “robot citizen” and was the first robot created with a realistic human appearance and the ability to see and replicate emotions, as well as to communicate.

• 2017: Facebook programmed two AI chatbots to converse and learn how to negotiate, but as they went back and forth they ended up forgoing English and developing their own language, completely autonomously.

• 2018: A Chinese tech group called Alibaba’s language-processing AI beat human intellect on a Stanford reading and comprehension test.

• 2019: Google’s AlphaStar reached Grandmaster on the video game StarCraft 2, outperforming all but .2% of human players.

• 2020: OpenAI started beta testing GPT-3, a model that uses Deep Learning to create code, poetry, and other such language and writing tasks. While not the first of its kind, it is the first that creates content almost indistinguishable from those created by humans.

• 2021: OpenAI developed DALL-E, which can process and understand images enough to produce accurate captions, moving AI one step closer to understanding the visual world.

What does the future hold?

Now that we’re back to the present, there is probably a natural next question on your mind: so what comes next for AI?

Well, we can never entirely predict the future. However, many leading experts talk about the possible futures of AI, so we can make educated guesses. We can expect to see further adoption of AI by businesses of all sizes, changes in the workforce as more automation eliminates and creates jobs in equal measure, more robotics, autonomous vehicles, and so much more.

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SOME MORE ABOUT ROBOTS 

From machines to AI – Ten facts about robots 

Introduction

Only a few decades ago, robots – programmable machines – were nothing more than protagonists in science fiction films. Today, everyday life would be inconceivable without them. In automotive engineering, at home, in the operating theatre, wherever: Artificial intelligence already complements human labour in many areas. But where does robotics have its roots? When was the term first used? And what new developments can we expect in the years ahead? Read on to discover ten fascinating facts about robotics.

1. The origins 

The term “robot” derives from the Czech word “robota”, which translates into “front duty” or “forced labour”. Today it is used for programmable machines which can complete human tasks quickly and easily. The word was used for the first time in Rossul’s Universal Robots (R.U.R.), a 1920s play by Czech writer Karel Čapek. In his drama, the robots overthrow their human creator. 

2. The mechanical knight 

As far back as 1495, Italian polymath Leonardo da Vinci drew up detailed plans for a mechanical knight based on the structure of the human body. With the aid of a pulley, the knight was able to sit down, stand up, raise its visor and move its chin, neck and arms. In 2002, robotics specialist Mark Rosheim took da Vinci’s knight as his template and developed a prototype that could speak and wave. Later, these drafts served as the inspiration for robots which he would build for NASA.

3. Similar to humans 

A robot whose appearance and behaviour are similar to those of a human is referred to as an android, a humanoid robot. One of the most famous examples from the annals of science fiction is Lieutenant Commander Data from the Star Trek series Starship Enterprise – The Next Century. Perhaps somewhat less well known is “Repliee Q1”, an android with the appearance of a 35-year-old woman developed in 2003 by Japanese scientist Hiroshi Ishiguro.

4. The laws of robotics 

The “Three Laws of Robotics” were first formulated by Isaac Asimov in his 1942 short story Runaround. According to these laws, a robot (1) may not injure a human being or, through inaction, allow a human being to come to harm; (2) must obey the orders given by human beings except where such orders would conflict with the first law; and (3) must protect its own existence as long as such protection does not conflict with the first or second law. These laws are referenced almost exclusively in films, however: Separate safety regulations apply for industrial and domestic robots and for robots with military applications. 

5. The father of robotics 

American physicist Joseph Engelberger is regarded as the father of robotics. Together with George Devol, he founded Unimation – the world’s first robot production firm – in 1961. “Unimate” became the first industrial robot and was deployed by Ford to weld die-cast parts for car bodies on its production line. 

6. In the service of humanity 

Service robots is the term used to designate robots that provide services to humans. They fetch and bring objects, supply information and navigate or monitor their owners’ environment. Especially in Japan, domestic helpers such as “Pepper” are in widespread use. Pepper is the first humanoid robot programmed to analyse human emotions on the basis of gestures and facial expressions and to respond accordingly. In Europe, robotic vacuum cleaners and lawn mowers are still the predominant models deployed in the service robot category. 

7. Nanobots in the bloodstream 

Nanorobots are a billion times smaller than a grain of salt. These minuscule robots comprise various atoms and will, it is hoped, completely revolutionize healthcare. They search the body for cancerous cells and then cut off the supply of blood to these cells. Initial tests have already proven the effectiveness of this concept. 

8. The future of nursing care 

Trials with social robots have long been underway in the context of care for the elderly. “Rosie”, for example, is a robotic seal which weighs three kilogrammes and is 50 centimetres long. Sensors and artificial intelligence allow Rosie to simulate a live baby seal. Its developer, Takanori Shibata, asserts that this therapeutic Paro robot in the Paro model series can help brighten the mood of elderly care patients – especially dementia sufferers – and children on the autism spectrum. Its purpose is to alleviate fear and pain, improve the quality of sleep and attenuate feelings of loneliness. More than 4,000 Paro seals are in service around the world.

9. The all-seeing eye 

Whether they are used for bonding, welding or grouting, industrial robots are accurate to within a millimetre. To enable the robotic controller to see where the bonding or welding head is currently positioned, the machine needs a kind of eye. Until recently, however, the flexibility of available sensors was limited by shadowing effects: If the direction of motion changed, the eye remained ‘blind’. The Fraunhofer Research Institution for Additive Manufacturing Technologies (IAPT) has found a solution to this problem: Its patent-protected SensePRO laser sensor features a specially developed analytical sensor technology that creates a shadow-free all-round field of view, which translates into complete freedom in any direction of travel. 

10. Top slot for South Korea 

South Korea has 932 industrial robots for every 10,000 employees, making it the world leader. It is followed by Singapore in second place, with 831, and Japan in third place, with 390. According to the International Federation of Robotics (IFR), more than three million industrial robots are currently deployed in factories around the globe. New robots worth 13.2 billion US dollars were installed in 2020 alone.