Artificial Intelligence (AI), which is the behavior of certain properties characterized by computer programs, make them mimic human mental abilities and patterns of work. One of the most important of these characteristics is the ability to learn, infer and react to situations not programmed into the machine. However, this term is controversial because there is no definitive definition of intelligence.
Artificial intelligence is a branch of computer science. Much of the literature defines artificial intelligence as: “the study and design of intelligent customers.” An intelligent customer is a system that understands its environment and takes positions that increase its chance of success in achieving its mission or team mission.
This definition, in terms of goals, actions, perception, and environment is due to Russell & Norvig (2003) and other definitions also include knowledge and learning as additional criteria. Computer scientist John McCarthy originally coined the term in 1956,  and himself defined it as “the science and engineering of making intelligent machines”.  Andreas Kaplan and Michael Heinlein define artificial intelligence as “the ability of a system to correctly interpret external data, learn from that data, and use that knowledge to achieve specific goals and tasks through flexible adaptation.”
History of Artificial Intelligence Research
In the mid-20th century, a few scientists began exploring a new approach to building intelligent machines, based on recent discoveries in neuroscience, a new mathematical theory of information, the development of cybernetics, and above all, by the invention of the digital computer, a machine that could simulate The human computational thinking process. 
He established the modern field of AI research at a conference on the campus of Dartmouth College in the summer of 1956.  These attendees became leaders in AI research for several decades, notably John McCarthy and Marvin Minsky, Allen Noel and Herbert Simon who established AI laboratories at MIT ( MIT), Carnegie Mellon University (CMU), and Stanford, they and their students have written programs that have surprised most people. The computer was solving algebra problems, proving logical theorems, and speaking English.  By the mid-1960s this research was being generously funded by the US Department of Defense. These researchers made the following predictions:
- 1965, e. a. Simon: “Machines will be able, within twenty years, to do whatever work a man can do.” 
- 1967, Marvin Minsky: “Within a generation…the problem of creating ‘artificial intelligence’ will be largely solved.” 
But they failed to realize the difficulty of some of the problems they faced.  In 1974, in response to criticism from England’s Sir James Lightttle and persistent pressure from Congress to fund more productive projects, the US and British governments cut funding for all undirected exploratory research in AI, the first setback for AI research. 
In the early 1980s, AI research experienced a new revival through the commercial success of “expert systems,”  an AI program that simulates the knowledge and analytical skills of one or more human experts. By 1985, artificial intelligence research profits in the marketplace had reached more than $1 billion, and governments began funding again.  A few years later, beginning with the collapse of the Lisp Machine market in 1987, AI research experienced another but longer setback. 
In the 1990s and early 2000s, AI achieved even greater successes, albeit somewhat behind the scenes. AI is used in logistics, data mining, medical diagnostics and many other areas across the technology industry.  This success is due to several factors: the great power of computers today (see Moore’s Law), the increased focus on solving specific sub-problems, the creation of new relationships between the field of artificial intelligence and other areas of work on similar problems, and above all researchers have begun to commit With strong mathematical curricula and strict scientific standards. 
In the twenty-first century, AI research has become so highly specialized and technical, that it has broken into so deeply independent sub-fields that they are few among them. Divisions of the field have grown around particular institutions, researchers have worked to solve specific problems, long-standing differences of opinion about how AI should work, and different tools are widely applied.
Artificial intelligence and national security in Israel
The twenty-first century is considered the century of artificial intelligence as a technology with revolutionary potential in all economic, security, military, technological and even health fields; The ability to transform tasks previously performed by humans into tasks for which a machine is responsible, even in areas of life that were difficult to imagine being automated, has had far-reaching effects. Although it remains difficult to fully assess the scope and characteristics of this revolution, the need to prepare for it and understand its far-reaching implications cannot be ignored. Both for those who succeeded in adopting it and taking it to the field, and for those who were late.
In 2020, the State of Israel has a comparative advantage in the field of artificial intelligence; This advantage is that it is a “start-up country” (or a dynamic country in the field of initiatives and projects), in addition to past and current investments in science, technology, infrastructure and education, which have enabled the growth of an ecosystem that combines industry, academia, and the security factors that drive forward the Through cooperation, knowledge and human resources.
In light of this, artificial intelligence in the coming years may be a major factor in maintaining and enhancing Israeli national security, and the current potential must be exploited through appropriate and purposeful policies for orderly management and sufficient investment in this field. The absence of these matters could lead Israel to fail in managing its relations with friendly and even hostile countries.
Moreover, the field is not without challenges that need to be prepared to deal with in order to reduce risks and preserve and develop benefits, and the importance of non-executive issues that are primarily of a “more flexible” nature should not be underestimated here. For example: ethical or legal questions that require reflection, so that the use of technology has a positive impact as much as possible.
The Israeli security reports, which concluded with a number of recommendations, refer to a number of main areas in which Israel must work; The most important of them are: organization; the budget to fund research and national infrastructure; Safety, Ethics and Law, Regulation, Knowledge Sharing, International Aspects, Diplomacy, Intelligence and Cooperation, Human Resources, Education and Training, as well as Hot Issues; As issues of national infrastructure and human resources.
My recommendations are based on a background study conducted on the topic of AI policy that formed the basis for the work of the expert committee that advised on this study, and on the committee’s work, discussions and conclusions.
The recommendations are divided into several areas, some of which relate to more than one area, but are mentioned only once in a related area. For some issues high budgets are needed, others require mainly regulatory attention and adjustments to the current situation, and some can be implemented even with low budgets while having high impact. The recommended policy mainly addresses the relatively narrow and “difficult” aspects of national security, although AI has existing and potential impacts in broader fields as well.
It must be emphasized that delays in formulating and administering policies in this area will harm the national security of the State of Israel, especially due to the aggressive arms race taking place in most of the developed countries of the world that treat artificial intelligence as a force multiplier. Under these conditions, the more Israel proceeds to operate on the ground in an organized manner along clear policy lines based on research and knowledge, the greater the chances that Israel will maintain and perhaps even widen a positive gap in its favour.
- Formulating a national strategy for artificial intelligence and establishing a body to manage it at the national level, as there is a need for a multi-year artificial intelligence program, such as the one in the Internet to analyze the field horizontally and vertically, to work on formulating a national policy for resource allocation and making decisions related to research and development, human resources and other issues.
- Creating structural models in the defense system in general and in the IDF in particular, that make it possible to keep pace with the increasing pace of change in the technological field, and allow, among other things, greater responsiveness and flexibility to changing situations than currently exists.
- Build joint working pools for the defense community, the IDF, industry, and academia – taking advantage of each other’s comparative advantages and making information available – within the communities’ infrastructure.
- Work to remove obstacles to innovation and entrepreneurship in government; In order to facilitate rapid integration and assimilation of advanced technologies into government activities in the security fields.
research and development:
- A combination of artificial intelligence must be found in the security technology areas in which Israel already has a comparative advantage (such as the field of drones) in order to produce a force multiplier.
- Investing in in-depth research into the national security system, and not relying on academic research alone; which often remains at a purely theoretical level, and does not provide or is not performed in the necessary areas of the system. It is necessary to standardize the scope of research and development required in each field as in other technological fields.
- Prioritize funding for AI research and development in areas that can provide a lasting advantage and mitigate major risks to “niche applications.”
- Promote security developments based on existing AI technology (using dual-use capability) in order to benefit from, and even encourage, advances in the civilian sector.
- Develop a national data strategy that improves data access and architecture for the use of different security agencies, as well as ensuring its protection.
- development of the field of Hebrew language processing, including applications such as text-to-speech and vice versa, as well as language programming; This is because the defense apparatus, like all citizens of the country, operates in the Hebrew language, and the use of Hebrew will help strengthen the local industry in the context of artificial intelligence.
- Increased investments in human-machine research and development for the defense system, as despite the high autonomy of the systems, human controls will still be present in the future. In doing so, it is recommended that AI research and development be prioritized in areas that help a person rather than areas that replace people, in order to demonstrate the reliability and safety of the technology, as well as address the administrative and legal aspects.
- Investing in the defense and intelligence communities in developing counter-AI capabilities – defense and attack.
- Take the necessary measures to develop artificial intelligence applications to improve the extraction of contemporary and historical information materials.
The field of artificial intelligence in the Israeli defense system today focuses on monitoring devices (based on sensor systems) as opposed to database-based systems that are not collected by sensors, but in other ways. It is necessary to adjust the handling of problems whose database is not sensory; Especially with regard to informational needs.
Budget, Finance and National Infrastructure:
- Achieving a national solution to infrastructure issues (hardware and internet connection) and allocating a continuous budget for it; Because the security community, unlike the civilian industry, has needs that often do not allow the use of commercial infrastructure; Because of classification issues and other security limitations.
- The need for a goal-oriented budget model that will be formulated with the help of the security community, and will be able to benefit from the outputs. Decisions must be made on the areas in which Israel will invest at the national level and areas that are not within its scope in terms of size and capabilities, and possibilities for cooperation with civil, Israeli and international factors should be explored.
- Identification of areas of research that would require funding in the government’s defense budget in view of their being unique to national security and as a result not to be addressed in any other way.
- Taking into account the combination of mechanisms to encourage investment in artificial intelligence that has a positive impact on national security, as well as increasing government spending on artificial intelligence in urban areas that will move the economy in this field.
Human Resources – Education and Training:
Within different security organizations it is necessary to examine system-wide human resource management, including defining roles and standards, training participants, transferring workers between organizations, and incentives and budgets for hiring and retaining talent, so as not to lose it to civilian industry.
- The defense system (and industries) should seek to integrate into existing training in the field; especially in academia; In order to train individuals unskilled in the theoretical field only, as well as to create special training frameworks or competitions on their behalf, and other frameworks that link talents to the defense system.
- The defense sector needs to train non-technical personnel, including at senior levels; To know the field, its limits and capabilities, and to be more involved and active in decision-making in the field.
- Investing in science, technology, mathematics, and engineering, as well as problem-solving skills in an online environment, and focusing on education prepare students for a future in which AI will be a factor in both the military and the civilian world.
Ethics, legislation and safety procedures:
- Establishing specialized institutions for setting standards and supervising safety in the use of artificial intelligence and also working on developing standards and principles of safety and responsibility in the use of artificial intelligence in the defense system; With the aim of influencing the activities of civilian elements as well.
- Drafting a code of ethics for the use of AI in general for security systems; Especially in the contexts of the relationship between man and machine.
- Define systems in which human mechanisms of oversight and oversight related to fairness, ethics, safety and redundancy will also be maintained,
- Define and standardize AI systems for integrated purposes, safety, and shared discussion, in order to enable simpler and more structured processes than those in existence today for industries, development and implementation in general.
- Examining standards and processes for exporting AI systems, including security export permits.
- Making decisions that, on the one hand, maintain the flexibility and ability of the industry to operate, and on the other hand allow for restrictions on exports that may harm Israel’s security.
- Defining standards in the field of human-machine relationship: communication and transaction methods for any security institution and government office.
- Knowledge sharing in the Israeli defense system is essential; Therefore, mechanisms must be established between the various security organizations, so that duplication of work is avoided, and the gaps between the organizations are bridged; As well as finding coordinated solutions.
- setting a standard standard for specific roles; Such as the field of data science, in both relevant organizations and ministries, and a permanent forum that allows sharing of knowledge between organizations at different levels of work.
- Create mechanisms for knowledge sharing at lower levels of professionals in various organizations, based on civil models (as far as possible regarding information security), which are already used today as strengths in the civil industry.
International, Diplomatic, Intelligence and Cooperation Aspects:
- Monitor regularly, at the national level, what is happening in the international system in the areas of artificial intelligence and data science; In order to preserve Israel’s advantage and supremacy in it, including everything related to art and standards.
- Develop a comprehensive plan to measure, evaluate and monitor the progress of the capabilities of the various players (at the civil and national levels) in the field of AI To prevent any strategic surprises.
- Make efforts to promote joint research and mutual cooperation between Israel and other countries.
- Participation in a coalition of countries in the field of artificial intelligence, such as the fields of intelligence or air defense and in other fields.
- Integrating into international initiatives, and even leading them to reduce the unruly factors that prevent them from achieving achievements in the field, whether in security initiatives or in civil initiatives.
- Examination of AI applications, if any; Especially those that the state must strive to reduce (striving to restrict or prevent) through the arts.
- Although there has been some controversy on this point Crevier 1993, page 50, says McCarthy “I came up with the term” in an interview. (See How to Make Machines Think Like Us.) Saved copy April 5, 2020 on the Wayback Machine.
-  ^ See ^  ^ John McCarthy, “What is Artificial Intelligence?” Saved copy September 22, 2017 on the Wayback Machine.
- Andreas Kaplan; Michael Haenlein (2019) Siri, Siri in my Hand, who’s the Fairest in the Land? On the Interpretations, Illustrations and Implications of Artificial Intelligence, Business Horizons, 62(1), 15-25 Saved copy February 24, 2019 on the Wayback Machine.
- Dartmouth thesis:
- McCarthy et al. 1955
- This is the main idea of Pamela McCorduck’s book Thinking Machines. “I like to think of AI as a scientific deification of great cultural traditions,” she says. ( McCorduck 2004, p. 34) “Artificial intelligence in one form or another is an idea that has permeated the history of Western thought, and it is a dream in dire need of realization.” ( McCorduck 2004, p. xviii) “Our history is replete with attempts – bizarre and comical. And the serious and the legendary And realism: to create artificial intelligence, and clone what is essential to us, bypassing the usual means. Back and forth between myth and reality, imagination has given us what the workshops could not, and we have long been engrossed in this strange form of reproduction. self”. ( McCorduck 2004, p. 3) Pamela traced this desire back in history and claimed that the origins of this desire were Hellenistic She called it “the desire to form gods.” ( McCorduck 2004, p. 340-400)
- Widely used AI applications behind the scenes:
- Russell & Norvig 2003 , page 28
- Kurzweil 2005 , page 265
- NRC 1999 , Pp. 216-222
- Divide artificial intelligence into sub-domains:
- McCorduck 2004 , Pp. 421-425
- This list of smart features is based on topics covered in major AI books, including:
- Russell & Norvig 2003
- Luger & Stubblefield 2004
- Poole, Mackworth & Goebel 1998
- Nilsson 1998
- General Intelligence (Strong Artificial Intelligence) is discussed in well-known Introductions to Artificial Intelligence:
- Kurzweil 1999 و Kurzweil 2005
- The first product of artificial intelligence:
- McCorduck 2004 , Pp. 51-107
- Crevier 1993 , Pp. 27-32
- Russell & Norvig 2003 , Pages 15,940
- Moravec 1988 , page 3
See also cybernetics and early neural networks In the history of artificial intelligence. Among the researchers who laid the foundations for the theory of computation, cybernetics, information theory, and neural networks was Alan Turing., John von Neumann, Norbert Wiener, Claude Shannon, Warren McCullough, Walter Bates and Donald Hebb.
- Dartmouth Conference:
- McCorduck , Pp. 111-136
- Crevier 1993 , Pp. 47-49
- Russell & Norvig 2003 , page 17
- NRC 1999 , Pages 200-201
- The “Golden Years” of Artificial Intelligence (Successful Symbolic Logic Programs 1956-1973):
- McCorduck , Pp. 243-252
- Crevier 1993 , Pp. 52-107
- Moravec 1988 , page 9
- Russell & Norvig 2003 , page 18-21
The programs described are: Daniel Bobrow’s “Student” program, Noel and Simon’s Theoretical Logic and SHRDLU Terry Winograd .
- Simon 1965 , p. 96 quoted in Crevier 1993, page 109
- Minsky 1967 , page 2 quoted in Crevier 1993, page 109
- See (History of Artificial Intelligence — Problems).
- The first setback for artificial intelligence:
- Crevier 1993 , Pp. 115-117
- Russell & Norvig 2003 , page 22
- NRC 1999 , Pp. 212-213
- Howe 1994
- expert systems:
- ACM 1998 ، I.2.1، * Russell & Norvig 2003 , Pp. 22-24
- Luger & Stubblefield 2004 , Pp. 227-331, * Nilsson 1998، chpt. 17.4
- McCorduck 2004 , Pp. 327-335, 434-435
- Crevier 1993 , Pp. 145-62, 197-203
- The Boom of the Eighties: The Rise of Expert Systems, The Fifth Generation Project, Alvey, MCC, SCI :
- McCorduck 2004 , Pp. 426-441
- Crevier 1993 , Pp. 161-162,197-203, 211, 240
- Russell & Norvig 2003 , page 24
- NRC 1999 , Pp. 210-211
- The second setback for artificial intelligence:
- McCorduck 2004 , Pp. 430-435
- Crevier 1993 , Pp. 209-210
- NRC 1999 , Pp. 214-216
Now prefer the official methods (“victory of the regulars”):
- Russell & Norvig 2003 , Pp. 25-26
McCorduck 2004 , Pp. 486-487