This article is about Artificial Intelligence Vs Natural Intelligence by Lucas Kromann Bogehoj Nielsen. I have reviewed to understand the base of AI and understand the concept of Artificial Intelligence vs. Natural Intelligence. During the article, I also learned different concepts such as AAI and AGI. In this review, I have covered a few experiments and examples as well.
Artificial intelligence (AI) is a term used to describe “intelligence” exhibited by machines. AI programs may bring an image of simulate cognitive behaviours or traits associated with human intelligence. As in Second World War, well-known British computer scientist Alan Turing and his team break the Enigma code used by German force to send a message faster as possible to warn the officials that were early cracked through hit and trial method. According to Turing, “a machine that could communicate with humans without the humans knowing that it is a machine would win the “imitation game” and could be said to be “intelligent”.
The concept of AI began in 1956 by John McCarthy who organized the Dartmouth conference, at which this term was used for the first time, AI has come a long way over the years and reached this level and still more undiscovered. The main reason for bringing AI into existence was to equate this machine with the human mind. There are two different aspects of AI: Artificial General Intelligence (AGI) and Applies Artificial Intelligence (AAI). AGI is created to do what humans can do in the same manner as humans, whereas AAI is focused on restricted tasks as per humans ordered or programmed them to. Turing asked famous questions “Can machines think?” which is later replaced with the question “Can a machine simulate human thinking, in such a way that a human judge cannot distinguish that machine from a human?”
Human Intelligence (NI) is the opposite of AI: they are all the control mechanisms found in biology. Generally, as we talk of NI we get animal or human mind process ideas, but there’s more about NI than neuroscience. Nature demonstrates non-neural regulation, too. In response to our growing climate, our behaviour co-evolves with the rest of our bodies. Let say a person wants to paint a wall he will think about: Which colour? What pattern? Etc. So in fact, before selecting the colour, the NI processes differently, the individual must recognize the specifics of what he observed, creating a functioning model would cause him to consider the intricacies of what the intelligent goal method does.
There different factors both AI and NI have to works best than other
- Artificial intelligence (AI)
- Natural Intelligence (NI)
- Speed: While one doctor makes a diagnosis in approximately 10 min., AI system can make a million for the same time.
- Energy: ~25 watts for typical NI Vs ~2.00 watts for modern deep learning machine.
- Operate 24/7: like humans computers does not need rest they can work for 24 hours for 7 days in a week without stopping.
- Universal: While consuming a lot of energy, this machine is designed to perform limited tasks, whereas humans usually learn how to manage hundred and thousands of different skills during life time.
- Less biased: In realms of Law and Medicine it’s particularly important to have as much data as possible. Because AI can be trained on millions and billions of examples, they have more information to make an important decision.
- Multi-tasking: Regular human may have dozens of responsibilities every day, but to teach a computer even 1 task may take months.
- Accuracy: Whether it’s a house or stock price prediction, AI system can perform much more precise.
- Complex movements: Still the smartest and advanced robots can hardly compete in mobility with 6 years old child.
The article “Artificial Intelligence vs. Human Intelligence Man vs. Machine” by Lucas Kromann Bogehoj Nielsen, gives the author perspective on various factors such as “Artificial Intelligence (AI), Big Data (BD), datafication and algorithms”. I will cover all the aspects by reviewing every aspect turn by turn and showing the link between each other.
The first author has discussed is the introduction of AI. In this, we first should know the evolution of AI and know its’ origin. Going back less than 25 years back from now World Wide Web was not publicly available, whereas going back to mid- 1830 there was only one way to communicate from a long-distance was through letters, The communication system has transformed from message transfer from railroads to Cable television, from Television to Satellites and from satellites to the internet (ibid), and stills more to be discovered I would argue that datafication and BD principles are important to the growth of the AI enabling technology past and future. Nevertheless, big data does not require a fixed definition; it is more than just a matter of data amount. BD carries with it new understandings, inferences, beliefs, and perceptions that have never been known to exist.
The greatest contrast between the Turing machine and the machines we have today was very theoretical and thus a device with an endless memory tape was conceptualized, hence an infinite number of states. A theoretical description of a machine as a device can be a medium for information processing, as this knowledge is transmitted from input A to output B, and it becomes clear that the inner workings of a machine, i.e., software, are based around algorithms. There are two different concepts of Artificial General Intelligence (AGI) and Applies Artificial Intelligence (AAI). AGI is created to do the exact same thing that a human can do, whereas AAI is focused on restricted tasks. According to me, I do not expect, if the large data companies such as Google or Microsoft will build something close to AGI, that they would keep it a secret until they felt the time was right.
According to the author, there are different points where either Artificial intelligence performance is better or Natural intelligence such as:
Turing (1950) provided a theoretical conceptualization of a fundamental computer of like a person do a limitless memory. An abstract description of machine as hardware may be a medium for transmitting information from input A to output B, and the internal functionality of computers, i.e. Software is built by algorithms. The difference between software and algorithms is that the program is only an algorithm if it eventually stops. This means that there are limitations of computers but does not mean that human intellect always has the restrictions, nor also the capacity limitless.
The human may be smarter than software, but then again there might be some other smarter software than a guy. (Turing, 1950).
AAI and AGI
The concept of AAI and AGI is eventually given the greater power to NI for their creation. As now humans are very reliable on man and give their superiority to the AI. It happened maybe because human do not feel hesitation in giving their superiority. Take an example of GPS; how we use GPS to reach a destination that shows us the traffic information, under-construction road details, short-cuts, etc. This saves our time from getting lost, or time in traffic. I guess this simply shows that in reality, we are not much smarter than the AI.
Alan Turing has given a lot in the crucial insights to the field of AI. Where he posed the problem was ‘Does computer think?”So as a computer enhancement he substituted a problem that was harder to test: ‘Could a computer enhance human thought in such a way that a human judge can not differentiate the machine from a human being? ” This shows the importance of natural intelligence for artificial intelligence.
Algorithm aspect in test
The test was conducted by humans making many questions but finite and then the algorithm will delete the human from the further process and will ask the questions. The algorithm can take the answer is ‘yes or no’ and thus a computer will even substitute the interrogator after obtaining responses to the finite questioning process. However, this further compounds the problem, because other algorithms may need to take care of text interpretation and probably some problems in sentiment analysis. AI may help in other aspect but it lacks in the sentiments of humans. AI can try to improve by shortening the questions but this won’t lead to cover everyone and may not give accurate results.
The human behind the curtain
Lanier (2013) claimed that there is no mystical AI as the machines have no idea what they are doing but work in the way NI has asked or configured output. Searle (1980) suggested an experiment: The Englishman sits in solitude and has a large guidance book in front of him about how to read every phrase in Mandarin and what signs he can compose in order to respond. Outside the house, people who write Mandarin fluently formulate questions and slide them under the door. The man is answering with the help to instruction books in Mandarin and sending it back through sliding down the door. When others read the responses they thought were written decently and they rendered the impression that an English individual understands Mandarin fluently, even if he had little idea what he was doing and did not understand any Mandarin. Which exactly happens with the AI as well? The explanation is used to demonstrate that the thought computer of Turing (1950) is not in essence learning but merely imitating it.
This shows how AI may seem to be smarter than Natural Intelligence but the reality might be different from it.
APPLICATION OF ARTIFICIAL AND NATURAL INTELLIGENCE: BUSHFIRE MANAGEMENT
The bushfire management requires the natural as well as artificial intelligence for better and quick control. As we recently saw Australia experienced unprecedented destruction from bushfires. This brings the high alert alarm for us to bring and use better technology than before to be more prepared for unpredictable situations in the future. When we talk about bushfire management we have a predictive mapping vital tool in an ongoing effort to not only identify the at-risk forest areas but also proactively manage the risks of fire. This will help to get more resources than a human eye can catch. There is a lot of innovation in technology such as Artificial Intelligence, Internet, Drones, and many other things as well to prevent more disasters like this. The technology can also help the officials to get information about the weather changes, satellite images, modelling tools, and even social media. This will cover a large range of data to act before the situation become uncontrollable and brings loss to everyone.
The AI and NI working together can bring more awareness than both workings separately. The main goal of bushfire management is to integrate the use of technology in the emergency state into exciting systems of state emergency service departments, which can rely on the information given by the local authorities.
The next massive step in AI is mesh networks which is an emerging technology by the convergence of 5G, AI, IoT (internet of things) sensors and virtual reality. By using this technology people stuck or who are near bushfire and approach for help and also make 360 degrees video, make reports on the situation and share it with other citizens to spread awareness about the situation. The other help we can get from AI and NI is Photogrammetric artificial intelligence, where we can use satellites to see the coverage, as well as humans, can also take photos and videos and give updates of the situation to act actively without any delay. Learning from past or others can also help in disaster management such as in California’s one concern is an example. It has partnered with city governments to create virtual models of a particular environment around them, and comparing them with past will help them to identify even small changes. This will help them to highlight the most effective prevention method and act to specific or regional threats. Traditional bushfire prevention included the fuel loads method to stop the fire to reach a high level that can cause more disaster. Using new technology (AI) as well as natural intelligence (NI) will help to not only control such disasters but will help to act even before it comes into action as part of prevention.
According to my knowledge, the machines become more intelligent at the time when human started giving superiority to the machines over them. I agree to the fact that humans are the one who created the machines and making more innovation, but the way human is giving superiority to the machines and becoming dependent on machines (For example GPS- instead of asking or exploring roads people tends to use GPS for short-cuts, real-time traffic overview and other relevant condition to display on device) will lead the machine to win over the man. There are different situations where sometimes AI is smarter and some other situation where NI works better, but in most the combination of both works together. The greatest example of both working together being best is in Bushfire Management.