AI vs Human Intelligence: The Similarities and Differences

Abstract

We have tried to give a short summary of what intelligence is, and then we have compared the key components which separate the human intelligence versus the artificial one. Different examples of contemporary AI agents have helped us illustrate the pace in which the field is being developed. Parallel to this development, many risks have appeared concerning the future of human civilization. We have also tried to present a solution to the addressed issue, based on our research in this field.

Introduction

The definition of intelligence

Intelligence has been defined in many ways including: the capacity of logic, understanding, self-awareness, learning, emotional knowledge, reasoning, planning, creativity and problem solving. More generally, it can be described as the ability to perceive or infer information, and to retain it as knowledge to be applied towards adaptive behaviors within an environment or context.

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AI (breviation for Artificial Intelligence) – a very recent subject in science, goes even deeper towards this particular phenomenon. Besides studying the human intellect, AI also aims to construct computational machinery capable of human-like or even beyond (human-like) rationality and behavior.

The definitions that were coined to define artificial intelligence are divided in four groups:

Thinking Humanly, Acting Humanly, Thinking Rationally and Acting Rationally. See picture below for popular definitions on these groups.

Comparison between HI and AI

Main comparison between HI and AI

The principle of how human intelligence works is very similar to the artificial intelligence one. In humans information is transmitted by electrical signals along neurons’ axon. The neuron is the basic working unit of the brain. It is a specialized cell designed to transmit information to other nerve cells, muscle or gland cells. In Artificial Intelligence a similar way of processing information is called the ANN (Artificial Neural Network), which is based on artificial neurons, modelled by the type found in humans.

The ability for humans to learn relies on work of large network of areas in the cerebral cortex which supports our ability to learn and consciously remember everyday facts. AI agents on the other hand have a different way of learning their tasks. Below we have listed the types of machine learning:

Supervised machine learning – With this method the machine can learn from activity in past events and then produce new data. This works with given labeled data which then get processed and predict an output in the future. In the final phase the end result compares with the intended output in order to fix errors.

Unsupervised machine learning – In contrast to the previous example, the machine in this case is given unlabeled sets of data. It cannot provide an intended output, but it can create meaningful structures from unspecified information. (ex. Pictures, Video, Audio etc.)

Semi-supervised machine learning – This method is a cross between supervised and unsupervised machine learning. For this method both unlabeled and labeled sets of data are given, with the labeled ones serving as a training for how the machine will process the unlabeled ones for future predicaments.

Reinforcement machine learning algorithms – With this method the machine interacts with its environment by taking action and discovering whether the result is a failure or success. Through trial and error the machine optimizes it’s path of dealing with the specific task. This is reinforced by rewarding the machine when it performs well, also known as a reinforcement signal.

Comparison of HI vs AI divided in five different fields

Energy Efficiency Comparison. The human brain requires 25 Watts to function, whereas a typical machine uses 2 Watts for its learning mechanism.

Universal Comparison. Humans usually learn how to manage hundreds of different skills during their lifetime, whereas AI is usually designed to perform a few amount of tasks.

Multitasking Comparison. Human worker works on multiple responsibilities, whereas the time needed to teach a system on each and every responsibility is considerably high.

Decision Making Comparison. Humans have the ability to learn decision making from experienced scenarios. Even the most advanced robots hardly compete in mobility with a six-year old child, after sixty years of research and development of the field.

State Comparison. The human brain is analogue, whereas computers are digital.

AI Agents

We are providing our paper with two examples of Artificial Intelligence development in the last decade, to help describe the position in which the field finds itself today.

AlphaGo

AlphaGo, developed by Google DeepMind, is the first computer to have won against a professional player of the abstract strategy board game Go, thus earning the title as the best player in the world.

Using only the search tree method to win a game in Go, is practically impossible, because there are 10 to the power of 170 possible configurations of the board. To tackle this problem, the developers of AlphaGo combined MCTS (Monte Carlo tree search) with deep neural networks, which function with different layers containing millions of neuron-like connections. AlphaGo played a vast number of strong amateur games in order to have a better understanding on how human reasoning works, then it played against different versions of itself, learning by trial and error in order to improve the gameplay. We earlier defined this method as reinforcement learning.

In 2015, AlphaGo beat Lee Sedol in Go, in a five-game match winning four games and losing one, marking the first case in which AI won against a professional human player of the game. It’s successor AlphaGo master beat Ke Jie in 2017, the number one player in the world, thus being awarded as a professional 9-dan Go player by the Chinese Weiqi Association.

After the match between AlphaGo and Ke Jie, Google Deepmind retired AlphaGo in order to continue studying the AI field. Later on they announced AlphaGo Zero, which ran without human data, beating AlphaGo in a 100-0 winning streak. It achieved the level of AlphaGo Master in 21 days and surpassed all the older versions within 40 days. DeepMind even developed an appropriated version of the AlphaGo Zero algorithm, the AlphaZero, which in a timespan of 24 hours, became the best player of chess, shogi and Go, and a 3-day version of AlphaGo in each case.

Sophia – the robot

Sophia is a social robot that uses artificial intelligence to see people, understand conversation and form relationships.

It can crack jokes, make facial expressions and seemingly understands what’s going on around it . It can learn from one experience and apply that knowledge to new situations, as humans do. Sophia’s software can be broken down in three configurations:

• A research platform for the team’s AI research. Sophia doesn’t have witty pre-written responses here, but can answer simple questions like “Who are you looking at?” or “Is the door open or shut?”
• A speech reciting robot. Goertzel ( the CTO of Hanson Robotics), says that Sophia can be preloaded with text that it will speak and then use machine learning to match facial expressions and pauses to the text.
• A robotic chatbot. Sophia also sometimes runs a dialogue system, where it can look at people, listen to what they say and choose a pre-written response based on what the person said and other factors gathered from the internet like the crypto currency price.

For the last configuration Goertzel says “She is piecing together phrases in a contextually appropriate way, but she doesn’t understand everything she’s saying.”

Conclusion

The potential occurrence of future risks by the development of AI

These Artificial Intelligence entities which we have described, serve as an illustration of how fast the field is developing. This pace has raised a numerous amount of questions and concerns for future well-being of humans. Many influential contemporary scientists and entrepreneurs i.e multidisciplinary businessman and engineer Elon Musk or the late theoretical physicist Stephen Hawking, have ranked AI as the biggest risk that the human civilization might face. The problem arises greatly if the Superintelligent hypothetical agent will come in existence. The definition of Superintelligence states that an AI agent will exceed human intelligence and will dominate across all tasks. This concept is believed to happen since human cognition is evolutionary mechanical system and therefore can be emulated on synthetic materials. If evolutionary algorithms will take place in the formation of this intelligence agent, that means that the machine will be able to improve itself over and over again with a faster pace compared to the time which natural evolution happened to humans. Another factor of greater dominance is the “body” of AI agents. They are not made out of organic matter and is prior to survive on significantly less resources. An example of risk would be the hijacking of internet by an AI attacker, which would be able to post fake news, manipulate situations and wage wars between people.

A possible solution and AI contributions in humanity

The solution that we are presenting towards these matters suggest that AI scientists should carefully develop machines and robots only to be able to live in a healthy symbiosis with them, to aid the human evolution in other forms and improvement of abilities. There are many fields in which AI is currently delivering a positive contribution for humanity for example:

Medicine

Medical Artificial Intelligence (AI) mainly uses computer techniques to perform clinical diagnoses and suggest treatments. AI has the capability of detecting meaningful relationships in data sit and has been widely used in many clinical situations to diagnose, treat and predict the results.

AI-assisted robotic surgery

Robot assisted surgery is considered “minimally invasive” so patients won’t need to heal from large incisions. Via artificial intelligence, robots can use data from past operations to inform new surgical techniques. The positive results are indeed promising. A robot was used on a eye surgery for the first time, and the most advanced surgical robot, the Da Vinci allows doctors to perform complex procedures with greater control than conventional approaches. Heart surgeons are assisted Heartlander, a miniature robot, that enters a small incision on the chest to perform mapping and therapy over the surface of the heart.

Virtual nursing assistance

From interacting with patients to directing patients to the most effective care setting, virtual nursing assistance could save the healthcare industry $20 billion annually. Since virtual nurses are available 24/7, the can answer questions, monitor patients and provide quick answers. Most applications of virtual nursing assistance today allow for more regular communications between patients and care providers between office visits to prevent hospital readmission or unnecessary hospital visits.

Image analysis

Currently, image analysis is very time consuming for human providers, but an MIT-led research team developed a machine learning algorithm that can analyze 3D scans up to 1000 times faster than what is possible today. This near real-time assessment can provide critical input for surgeons who are operating. It is also hoped that AI can help improve the next generation of radiology tools that don’t rely on tissue samples. Additionally, AI image analysis could support remote areas that don’t have easy access to healthcare providers and even make telemedicine more effective as patients can use their camera phones to send in pics of rashes, cuts or bruises to determine what care is necessary. When saving minutes can mean saving lives, AI and machine learning can be transformative not only for healthcare but for every single patient.

Education

AI has had many applications in educations systems. It helps teachers fill the gaps in administrative works and lets them be more efficient contribution in human capabilities where AI cannot perform well, thus creating a symbiosis which offers a more advanced tuition in schools and universities. Students profit from AI as well, gaining help in areas which they are lacking, extracurricular activities, getting tested for a better differentiation in their professional aspect and also receiving feedback from tests in interactive platforms.

Economy

AI offers different solutions for occurring problems in the field of business as well. For example it can:

• Predict the vulnerability of a specific software and prevent external attacks for days even weeks ahead. AI performs in cybersecurity much more efficiently than a firewall or an AV data because of its ability to work automatically without prior knowledge or pre-programming to find and exterminate anomalies.
• AI can be used to reduce energy usage and costs for large industries. It has already been used to reduce costs for drills, natural gas transportation and refining of energy sources such are oils or petroleum.
• Customer responses have been given as input for AI agents and in return they have inferred structures containing qualities and attributes that correlate with the response rate and the engagement of individuals in the matter. Intelligent chatbots and conversational interfaces have been built to provide information for customers. Advancements in deep learning algorithms and methods are especially (Wikipedia, 2018) (Stuard Russell, 2009)

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