## 2 Preliminaries  
Since this paper focuses on the challenges of AI considering different terms such as ANI, AGI, ASI, and HLI, this section is dedicated to the definition of mentioned terms and their relationships. This section starts with a classification of AI to state the position of existing studies on AI, and then focuses on HLI because of its priority. According to [15], intelligent systems are classified into three classes, as explained below (Figure 1):  
* **ANI:** This type of intelligence refers to intelligent systems that do specific tasks. For example, an agent with capabilities such as face recognition and games playing. These agents are programmed to do tasks and cannot detect and formulate unknown tasks in a self-organized manner. We do not expect to see self-awareness in these agents.
* **AGI:** The concept of this type of intelligence does not refer to a unique thing in the mind of all leading scientists of AI. Most researchers use AGI for those agents whose intelligence is equivalent to human agents. AGI can be equivalent to HLI [14].
* **ASI:** In [16], [PERSON] introduced three types of super intelligence: Speed ASI, collective ASI, and quality ASI. Speed ASI refers to an agent faster than a human, collective ASI refers to decision-making capabilities similar to a group of humans, and quality ASI refers to an agent that can do work that humans cannot.  
Recently, some changes have been made in the above classification. In [17; 18], the authors argue that HLI is different from AGI because humans may put some assumptions  
Figure 1: Venn diagram for definitions of artificial intelligence (AI).  
and limitations in the machine's computations. These assumptions and limitations come from the nature of humans which inherited by the machines. Therefore, AGI may not solve a wide range of problems that humans cannot solve. In other words, humans determine some implicit upper bounds in the machines and downgrade the generalization capabilities.  
On the other hand, authors of some papers, such as [18], argue that there is no difference between AGI and ASI when the definitions of AGI are not limited to HLI. According to [16; 18], if AGI-based agents' capabilities are further than human intelligence and there is no exact definition for ASI capabilities, there is no need to differentiate AGI from ASI. Accordingly, in [19], [PERSON] divided AI into two classes: weak AI and strong AI. Since the challenges related to HLI will be vital in the near future, the rest of this part is dedicated to HLI.  
HLI is developed based on knowledge about humans, as illustrated in Figure 2. This figure is divided into: (1) human intelligence, (2) sciences related to HLI development, and (3) challenges. The human intelligence part divides the domain of our knowledge into _Known_ (for example, accounting is a known capability of humans), _Semi-Known_ (for example, mind computation is a semi-known capability of humans), and finally _Unknown_ (for example, goals of human creation is an unknown concept for humans) sections. Many sciences were organized based on observing human intelligence, including mathematics and philosophy. According to [20], several challenges have been raised during the development of these sciences at the heart of AI-based systems. Thus, the third part of Figure 2 summarizes the challenges of AI during the development of HLI. It should be noted that providing connections between challenges and their related sciences is based on articles reported in the literature. The number of related sciences may increase as the knowledge of humans about AI increases.