artificial intelligence and machine learning

With the growing demand for Artificial Intelligence, it is time to get some facts and separate the facts from the rumors.

To understand Artificial Intelligence commonly known to us (AI) needs some patience, but for those interested in getting a definition, then this can do.

• Artificial Intelligence is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.

Machine learning

This is an AI subset and consists of techniques that enable computers to recognize data and supply AI applications. Different algorithms (e.g., neural networks) contribute to problem resolution in ML.

more on this coming up

Deep Learning

Deep learning, often called deep neural learning or deep neural network, is a subset of machine learning that uses neural networks to evaluate various factors with a similar framework to a human neural system. It has networks that can learn from unstructured or unlabeled data without supervision.

To Start as off lets also define these 2 terms;

 Intelligence In Relation to Artificial Intelligence

Everything but the simplest human behaviors is ascribed as intelligence, although even the most complex insect behavior is never taken as an indicator of intelligence. What is the difference?

The true essence of the instinctive action of the wasp is exposed if the food is pushed a few inches away from the entrance to its burrow when inside: as it emerges, it repeats the entire process as soon as the food is displaced.

 Intelligence—conspicuously absent in the case of Sphex—must include the ability to adapt to new circumstances.

Employers typically do not characterize human intelligence by a particular trait, but by a mixture of several unique skills. AI research focuses mainly on the following elements of intelligence: learning, reasoning, problem-solving, understanding, and use of vocabulary.

Learning in relation to AI

There are several various ways of learning applied to artificial intelligence. The easiest is trial and error learning. For example, a basic computer program to solve mate-in-one chess problems will attempt to move randomly before a mate is found. The machine should then archive the solution with a position such that the solution would be recalled the next time the machine found the same position. This basic memorization of individual objects and procedures—known as rote learning—is reasonably straightforward to execute on a computer.

The issue of applying what is considered generalization is more difficult. Generalization means adapting previous experience to current comparable circumstances.

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.

To understand this better let us get the history of artificial intelligence.

History of artificial intelligence

The idea of inanimate objects coming to life as intelligent beings has been around for a long time. The ancient Greeks had myths about robots, and Chinese and Egyptian engineers built automatons.

The beginnings of modern AI can be traced to classical philosophers’ attempts to describe human thinking as a symbolic system. But the field of AI wasn’t formally founded until 1956, at a conference at Dartmouth College, in Hanover, New Hampshire, where the term “artificial intelligence” was coined.

MIT cognitive scientist Marvin Minsky and others who attended the conference were extremely optimistic about AIs future. “Within a generation […] the problem of creating ‘artificial intelligence’ will substantially be solved,” Minsky is quoted as saying in the book “AI: The Tumultuous Search for Artificial Intelligence” (Basic Books, 1994). [Super-Intelligent Machines: 7 Robotic Futures]

But achieving an artificially intelligent being wasn’t so simple. After several reports criticizing progress in AI, government funding and interest in the field dropped off – a period from 1974–80 that became known as the “AI winter.” The field later revived in the 1980s when the British government started funding it again in part to compete with efforts by the Japanese.

The field experienced another major winter from 1987 to 1993, coinciding with the collapse of the market for some of the early general-purpose computers, and reduced government funding.

But research began to pick up again after that, and in 1997, IBM’s Deep Blue became the first computer to beat a chess champion when it defeated Russian grandmaster Garry Kasparov. And in 2011, the computer giant’s question-answering system Watson won the quiz show “Jeopardy!” by beating reigning champions Brad Rutter and Ken Jennings.

You cane check the full history on Forbes  click here

But the field of AI has become much broader than just the pursuit of true, humanlike intelligence.

There are 3 major types of artificial intelligence / Machine learning

Narrow or weak AI. (ANI)
General or strong AI. (AGI)
Artificial superintelligence (ASI)

Narrow / Weak AI

Weak AI refers to any AI mechanism that works on doing one very great job. That is, in terms of what it can do, it has a limited reach. The concept behind poor AI is not to imitate or duplicate human intelligence. Rather, it’s to mimic human actions.

In the last decade, Narrow AI has undergone several breakthroughs, propelled by advances in machine learning and deep learning. For example, AI systems are currently used in medicine to diagnose cancer and other diseases with extreme precision by emulation of human cognition and reasoning.

The machine intelligence of Narrow AI emerges from the use of natural language processing (NLP) to execute tasks. NLP is apparent in chatbots and related AI technology. By understanding speech and text in natural language, AI is designed to communicate with humans in a natural and personalized way.

Weak AI is nowhere near matching human intelligence, and it isn’t trying to.

A common misconception about weak AI is that it’s barely intelligent at all — more like artificial stupidity than AI. But even the smartest seeming AI of today are only weak AI.

In reality, then, narrow or weak AI is more like an intelligent specialist. Highly intelligent at completing the specific tasks it is programmed to do.

We have currently only achieved narrow AI. As machine learning capabilities continue to evolve, and scientists get closer to achieving general AI, theories and speculations regarding the future of AI are circulating. There are two main theories.

One theory is based on fear of a dystopian future, where super intelligent killer robots take over the world, either wiping out the human race or enslaving all of humanity, as depicted in many science fiction narratives.

The other theory predicts a more optimistic future, where humans and bots work together, humans using artificial intelligence as a tool to enhance their life experience.

Artificial intelligence tools are already having a significant impact on the way we conduct business worldwide, completing tasks with a speed and efficiency that wouldn’t be possible for humans. However, human emotion and creativity is something incredibly special and unique, extremely difficult – if not impossible – to replicate in a machine. 

Share in the comment section which theory do you opt will work?

Examples of Narrow AI:

Artificial General Intelligence (AGI) / Strong AI / Deep AI

Artificial general intelligence (AGI), also referred to as strong AI or deep AI, is the concept of a machine with general intelligence that mimics human intelligence and/or behaviours, with the ability to learn and apply its intelligence to solve any problem. AGI can think, understand, and act in a way that is indistinguishable from that of a human in any given situation.

Researchers and scientists of AI have not yet accomplished a strong AI. To survive, they will need to find a way to make machines intelligent, programming a complete range of cognitive abilities. Machines will have to push experiential learning to the next level, not only enhancing performance on single tasks, but also having the opportunity to extend experiential experience to a broader variety of diverse challenges.

Strong AI uses the mind AI paradigm hypothesis, which refers to the capacity to distinguish the desires, feelings, values and thought patterns of other intellectual beings. Mind level AI Philosophy is not about emulation or simulation, it’s about teaching robots to better understand humans.

The immense challenge of achieving strong AI is not surprising when you consider that the human brain is the model for creating general intelligence. The lack of comprehensive knowledge on the functionality of the human brain has researchers struggling to replicate basic functions of sight and movement.

We don’t yet have strong AI in the world; it exists only in theory.

Fujitsu-built K, one of the fastest supercomputers, is one of the most notable attempts at achieving strong AI, but considering it took 40 minutes to simulate a single second of neural activity, it is difficult to determine whether or not strong AI will be achieved in our foreseeable future. As image and facial recognition technology advances, it is likely we will see an improvement in the ability of machines to learn and see.

Add to this that currently, AI is only capable of the few things we program into it, and it’s clear that strong AI is a long way off. It is thought that to achieve true strong AI, we would need to make our machines conscious.

How optimistic are you about this?

leave a comment

Artificial Superintelligence (ASI)

But if strong AI already mimics human intelligence and ability, what is left for the last of the types of AI?

Super AI is AI that surpasses human intelligence and ability. Also known as artificial superintelligence (ASI) or superintelligence. It’s the best at everything — maths, science, medicine, hobbies, you name it. Even the brightest human minds cannot come close to the abilities of super AI.

Of the types of AI, super AI is the one most people mean when they talk about robots taking over the world.

Now you know the difference!

The potential of having such powerful machines at our disposal may seem appealing, but the concept itself has a multitude of unknown consequences. If self-aware super intelligent beings came to be, they would be capable of ideas like self-preservation. The impact this will have on humanity, our survival, and our way of life, is pure speculation.

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