Artificial General Intelligence & Artificial Narrow Intelligence - What Does It Mean ?

                           Artificial intelligence technology awareness

Before jump into ( AGI) Artificial general intelligence, it’s worth establishing what is the accepted meaning of common intelligence.  At the time of the first electronic computers were introduced, many creams of the crop in the field credited that their ability to do complex sums as a sign of a higher intelligence. This was the ability of the best humans at plan games like chess, and finally speech and image recognition. It appears likely that this development will also apply this particularly as the concept becomes increasingly abstract.

What is this AGI?

This is an imaginary machine capable of doing all the intelligent tasks performed by humans. A feature of science fiction, in this AGI, achieved a social reputation of both respect and fear more than all appreciation for the possibilities it offers.  However, some generally accepted features which determine if a human or machine is capable of AGI. Ability should be there first, to learn from a limited amount of data or experience often referred to as few-shot learning.

Secondly, to be able to learn, and improve and learn, from a wide variety of contexts, known as meta-learning. This directly feeds into the final factor: causal inference. This is the ability for situation generation, to be able to strategies for future events, or non-events, through an understanding of cause and effect. Of course, many artificial intelligence machines, at the same time it's not showing general intelligence, are extremely capable of exact uses. These machines are referred to as ‘narrow AI’  and are in some ways more useful than AGI would be, as they are designed to solve very specific problems. 
AGI or “Strong” AI, refers to those machines that perform like intelligent of a human. In other words, AGI can successfully achieve any intellectual task that a human can do. This type of AI which we observed in movies, where humans interact with machines and operating systems that are aware of the emotion and driven by sentiment and self-awareness.  
However, it’s hard to think of a practical use case, as an opposite to the "narrow artificial intelligence " (NAI) businesses.  The advantage of narrow AI is that it is often more transparent than other forms, and allow businesses to understand the work process.  If necessary, steps can be taken to correct it if something goes wrong.  
However, when developing advanced AI or even AGI, there are several things to consider to avoid an imaginary, ‘machines are taking over’ situation, known as the ‘control problem’. First, there should be the standard attention when developing new AI systems, beyond just cost and accuracy. Ensuring that the AI can explain its intelligence can go a long way to calming fears about prejudices coming into its decision making, and if it makes a wrong choice allowing humans to correct it.
This should be followed by ensuring databases are reasonable and balanced, because they are based on human datasets, which often carry their own prejudices into the machine - a scary thought when it comes to AGI machines.

What is ANI?

"Artificial Narrow Intelligence" (ANI) is also known as “Weak” AI which is the AI exists in our world today, this AI can be programmed to perform a single task like, checking of raw data and analyzing that data, or checking of weather or to play chess, or help to write journalist report providing related data analyzing. ANI systems work in real-time, but they require to pull information from a specific database. As a result, these systems cannot perform if not designed properly. 

These narrow AI  systems extremely complex, solving critical challenging problems. A good example of this is Casualness which has become a pioneer in causal implication, meaning they can more accurately and robustly model trends in time-series - such as the global economy, or predict how shockwaves in one sector might affect another. It also used in natural language understanding. Some are a pioneer in this area, by understanding the sentiment and purposes, understanding the deeper meaning of questions and answers, and even producing written content on request.
These types of AI systems are certainly seeking to solve some of the key problems for the future. However, we are now facing a major challenge, due to this COVID-19 pandemic. AI has huge capacity as a technique to speed up for new drug development, although this area is still very much in its beginning. However, an area where AI is increasingly being used is diagnostics and recommendations for healing involvements. As an example, companies can use AI to identify the best potential candidates for medical trials. Normally this process can take up to 15-18 months, however with the use of AI, this can be reduced to a few weeks which can help for saving crucial time in drug development. Not only this AI can help with the medical perspective, another interesting recent development related to COVID-19 pandemic, where WhatsApp’s launched a chatbot to answer people’s questions about COVID 19. That means AI being used to help combat the spread of fake news, simultaneously providing useful advice to the people when requiring at day or night.
Any sort of machine intelligence that surrounds us today is Narrow AI. Like Google Assistant, Google Translate, natural language processing tools are examples of Narrow AI. We can assume that these tools aren’t “weak” because of their ability to interact with our requirements, processing human language. However this is  “Weak” AI is because these machines do not have human-like intelligence or emotion.  They lack the self-awareness, consciousness, and genuine intelligence to match human intelligence. In other words, they can’t think for themselves.
Unlike General or “Strong” AI, Narrow AI is not aware, emotional, or driven by emotion the way that humans are. Narrow AI operates within a pre-determined, pre-defined choice.
The truth is that we have a long way to go, years or even decades, from building an AGI simultaneously capable of meta-learning, few-shot learning, and fundamental implication. Many companies working towards creating AGI, but it remains to be seen whether they would pass the necessary tests, such as Employment test, to have created true AGI. However, one thing is very clear, that in the current COVID-19 crisis, specifically AI, helping humans and societies to prosper and succeed against difficulty.

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