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The PLA is actively pursuing AI-enabled systems and autonomous capabilities in its military modernization.36 Across services and for all domains of warfare, it has fielded a growing number of robotic and unmanned systems, as well as advanced missiles with precision guidance, some of which may possess at least limited degrees of autonomy. For instance, the PLA Army (PLAA) has concentrated on military robotics and unmanned ground vehicles, which could be used for logistics.37 The PLA Navy (PLAN) is experimenting with unmanned surface vessels that may operate with some autonomy and is reportedly developing autonomous submarines.38 The PLA Air Force (PLAAF) operates advanced unmanned systems with limited autonomy that could be upgraded to include greater autonomy, while exploring options for manned-unmanned teaming.39 The PLA Rocket Force (PLARF) may leverage use cases in remote sensing, targeting, and decision support,40 and its missiles may be augmented to become more “intelligentized” in their capabilities, incorporating higher levels of automation to facilitate operations.41 There are indications that the PLA Strategic Support Force (PLASSF) could apply advances in AI to its missions of space, cyber, electronic, and psychological warfare.42 PLA capabilities and advancements very likely extend well beyond what is known and knowable from open sources. The Chinese defense industry can build upon its apparent strengths in armed drones and advanced missiles to introduce greater autonomy into operations.43 In particular, certain advanced unmanned aerial vehicles could be modified to operate with greater autonomy instead of under remote control.44 Currently, China leads in export of medium altitude long endurance unmanned aerial vehicles (UAVs).45 PRC UAVs, such as the Wing Loong platform, from the Aviation Industry Corporation of China (AVIC), and the CH-4, developed by China Aerospace Science and Technology Corporation (CASC), are actively marketed for export.46 In fact, CASC has even opened factories for the CH-4 platform in Pakistan, Myanmar, and Saudi Arabia.47 This prominent ranking in arms sales reflects the relative affordability of Chinese drones over American ones, and that the Chinese defense industry has been less constrained in selling to militaries to which their U.S. counterparts have been unable or unwilling to sell.48 Within the PLAAF, the GJ-1 and its successor GJ-2 are used for integrated reconnaissance and precision strike, including in support of joint operations.49 According to its designer, the GJ-2 is “highly intelligentized” and capable of operating autonomously, including in identification of the enemy and judgement of threats.50 Yet an inherent challenge of evaluating progress and capabilities is that the level of autonomy, relative to possibility of remote control, cannot be readily assessed by appearance alone. Moreover, as China strives to close the gap with the United States, its efforts are complicated by persistent bottlenecks in its indigenous defense industrial capabilities.51 While cyber theft and industrial espionage enabled by a range of techniques of tech transfer have enabled and accelerated Chinese military modernization, persistent obstacles and bottlenecks remain, including shortfalls in the technical workforce and engineering experience.52 Chinese leaders are cognizant of critical weaknesses, such as the semiconductors, particularly specialized developments in AI chips, necessary to enable and deploy AI/ML systems, and actively investing to overcome them.

In the , video footage has shown drones penetrating deep into Russian territory, more than 1,000 kilometres from the border, and destroying oil and gas infrastructure. It’s likely, experts say, that is helping to direct the drones to their targets. For such weapons, no person needs to hold the trigger or make the final decision to detonate. The development of lethal autonomous weapons (LAWs), including AI-equipped drones, is on the rise. The US Department of Defense, for example, has earmarked US$1 billion so far for its Replicator programme, which aims to build a fleet of small, weaponized autonomous vehicles. Experimental submarines, tanks and ships have been made that use AI to pilot themselves and shoot. Commercially available drones can use AI image recognition to zero in on targets and blow them up. LAWs do not need AI to operate, but the technology adds speed, specificity and the ability to evade defences. Some observers fear a future in which swarms of cheap AI drones could be dispatched by any faction to take out a specific person, using facial recognition. . “The technical capability for a system to find a human being and kill them is much easier than to develop a self-driving car. It’s a graduate-student project,” says Stuart Russell, a computer scientist at the University of California, Berkeley, and a prominent campaigner against AI weapons. He helped to produce a viral 2017 video called Slaughterbots that highlighted the possible risks. The emergence of AI on the battlefield has spurred debate among researchers, legal experts and ethicists. Some argue that AI-assisted weapons could be more accurate than human-guided ones, potentially reducing both collateral damage — such as civilian casualties and damage to residential areas — and the numbers of soldiers killed and maimed, while helping vulnerable nations and groups to defend themselves. Others emphasize that autonomous weapons could make catastrophic mistakes. And many observers have overarching ethical concerns about passing targeting decisions to an algorithm.

5. Conclusions This paper aims to represent the main sectors of utilization and the possibility of using AI augmentations and AI algorithms in the military sector, especially in cybersecurity, object detection, robotics, and logistics. Overall, the seven patterns of AI are paving their way in military capabilities enhancement. The discussion of their impact on people’s sense of security is also portrayed. One of the key technological advancements of the future is that AI has the best possibility to significantly impact the transformation and advancement of contemporary society along with military capability. An exciting new phase of rapid development has begun for AI technology. It is now acknowledged as having the most potential to alter the disruptive technology landscape in the future. Also, using them to improve national strategies and assets like applications in the military has become very common. However, we must be cautious about how we use it and what the circumstances will be short. Progression is our expectation and reason for our build-ups with the help of AI; if it has a bad impact on the next generation, it is better to stop those specific innovations. The AI component in military decision-making provides a robust backbone capable of generating intuitive sketch-based user interfaces that specialists may utilize with little training. Users requested an integrated framework that simultaneously records CoA sketches and statements and offers a single map-based user experience for both operations. The goal is to provide a framework that can express the sketches of CoA with visual comprehension. The possibilities and applications of AI in the military, such as autonomous weapons and target recognition, surveillance, cybersecurity, military transportation and logistics, homeland security surveillance, cyber security, autonomous vehicles, and combat training and simulation, are described, discussed, and evaluated in our paper. The doing of reconnaissance with the use of partially autonomous vehicles in the military and sensor systems’ utilization for betterment along with threat assessment in air defense systems with high time requirements, the emerging patterns intelligence analysis, education and training, and command and control systems from a military perspective are additional potential applications. However, military uses of AI should take into account the following challenges: (1) Vulnerabilities that could significantly harm the performance of the system (2) Transparency to guarantee model performance in line with military specifications (3) Inadequate machine learning (ML) training data (4) Effects of AI on nuclear risk and global strategic stability In terms of everything, if AI is not implemented correctly with the help of the right hand in the military sector, it will likely become a double-edged sword that could cut both ends and destroy a nation instead of doing good. Nevertheless, a more detailed requirements analysis is required to comprehend the utilization. Regarding risk, data quality, and regulatory constraints, military needs may differ significantly, and some forms of openness may not even be applicable. We require more data and research for further retention on any decision regarding AI in the military and its capabilities. More study is needed on applying data, machine learning, and social science research to improve AI explainability in military contexts and enhance their capabilities appropriately.

Chinese military initiatives in AI are motivated by an acute awareness of global trends in military technology and operations;18 concerns about falling behind the U.S. military, which is perceived and often characterized as the “powerful adversary” (强敌);19 and recognition of potential opportunities inherent in this military and technological transformation.20 “China’s military security is confronted by risks from technology surprise and a growing technological generation gap,” according to the official white paper on “China’s National Defense in the New Era,” released in July 2019.21 “Intelligent(ized) warfare is on the horizon,” the assessment finds, and the ongoing “Revolution in Military Affairs” will change the very mechanisms for victory in future warfare.22 Chinese military scientists and strategists, including from such authoritative institutions as the PLA’s Academy of Military Science, National Defense University, and National University of Defense Technology, envision AI and intelligent weapons playing an increasingly important if not decisive role in future warfare. They closely examine antecedents in U.S. strategy and capabilities to inform their own assessments.23 The PLA’s quest for innovation is an element of the Chinese national strategy to leverage science and technology in pursuit of great power status.24 In the process, the Chinese military is developing more traditional and emerging capabilities, while concentrating on asymmetric approaches against the U.S. military. President Xi Jinping has emphasized, “under a situation of increasingly fierce international military competition, only the innovators win.”25 Moreover, on the importance of “aiming at the frontier of global military scientific and technological developments,” he urged: “We must attach great importance to the development of strategic frontier technologies, striving to surpass the predecessor as latecomers, turning sharply to surpass.”26 As early as 2011, the PLA’s official dictionary included a definition of an “AI weapon” (人工智能武器), characterized as “a weapon that utilizes AI to pursue, distinguish, and destroy enemy targets automatically; often composed of information collection and management systems, knowledge base systems, decision assistance systems, mission implementation systems, etc.”27 Similarly, Chinese military strategists and scientists tend to discuss “AI weapons” or “intelligentized weapons” (智能化武器) more often than “autonomous weapons” (自主武器) in academic and technical writings.28 This terminological difference is subtle but potentially significant, implying a focus on the “smartness” or “intelligence” of weapons systems in selecting and engaging targets.29 For instance, techniques for adaptive or autonomous control can leverage a range of algorithms, including neural networks. Even as the function of certain weapons systems becomes “unmanned” (无人化) and to some degree automatic (自动化), greater degrees of autonomy or “intelligence” in function can remain elusive. While Chinese leaders have prioritized advances in AI as an important direction for military modernization, China’s Central Military Commission has yet to release any policy or official strategy that formally clarifies such plans and priorities. However, in July 2017, the New Generation Artificial Intelligence Development Plan called for China to “strengthen the use of AI in military applications that include command decisionmaking, military deductions,30 and defense equipment.”31 In the fall of 2017, Xi, in his address to the 19th Party Congress of the Chinese Communist Party (CCP), urged, “accelerate the development of military intelligentization, and improve joint operations capabilities and all-domain combat capabilities based on network information systems.”32 His remarks provided authoritative guidance to pursue military applications of AI that could be integrated across the whole system for future operations.33 This emerging emphasis on “military intelligentization” (军事智能化), or the “development of an intelligent military,”34 extends beyond AI-enabled systems and autonomy to include the development of weapons systems leveraging adaptive control or involving autonomy in various aspects of their operation.35

United States Army Futures Command is at work modernizing weapons and equipment and identifying, acquiring and developing next-generation military technologies. It is “the best example of our commitment to the future lethality of the force” and “probably one of the boldest reforms” the Army has pursued, Secretary of the Army (later Secretary of Defense) Mark Esper told Congress in 2018. The command makes its home not on an Army base but on the campus of the University of Texas at Austin. That choice was by design.

“It’s critical that we have access to talent,” Esper said at the time. “Talent that can help us think about the future strategic environment, thinking in the 2030s, 2040s, because that will inform, in many ways, the steps we take with regard to material … It’s proximity to innovation. It’s proximity to academia.”

The U.S. military has been using AI for many years, even before AI became commonplace in civilian life. Over time, AI has developed to be able to perform more complex jobs, and has almost eliminated the need for human input in certain situations, although human oversight is still required. From processing data to combat simulation, AI can be used to perform many different jobs in the military. Military applications of AI have become an essential part of the military’s functioning and will continue to grow in importance. Awareness of the potential of AI is vital to leveraging it for modern military operations. Equally important is an awareness of the potential security risks and ethical issues that may arise when AI is used in a military context. A recent autonomous weapon policy update from the Pentagon makes clear how the Department of Defense takes these concerns to ensure that the use of AI benefits U.S. Military objectives.

Artificial intelligence (AI) is seen at several levels in information technology and military uses, especially in lethal autonomous robot systems (LARS). The possible use of LARS by terrorists is the most important threat to international humanity, especially when some states choose to use terrorists rather than state armies, as we have seen in Syria, Libya, and Yemen. Since its appearance, terrorism has become a real threat to international security, and states have made great investments to bring it to an end. However, the threat is still increasing, even as states have used technological developments to produce more sophisticated arms. Terrorist organizations, as often as states, have used the innovative technologies in their crimes, which is not strange when we consider that some terrorist organizations are funded and supported by states. The most important risk is that terrorists’ access to LARS is possible and easy.

4.6. The Overall Uncertainties, Threats, and Challenges of AI Overall, aside from its own challenges, it can also create challenges of its own which could be termed as threats. The paper has already discussed how AI could be used for worldwide instability and can bring so much chaos in the same field of military capabilities, which has been helping the globe to develop for a long time. AI in the military can be a double-edged sword if not implied following global ethics (Table 2 is one of the examples) and not observed frequently with the right moderators and evaluators. A slight misguidance and negligence can lead any nation’s military to the verge of collapse resulting in global instability or an alarming scenario that no one expected. For a better summarization, let us explore and highlight the major uncertainties, threats, and challenges of AI in the current domain of military applications and implementations: (1) Ethical concerns: The use of AI in the military raises several ethical concerns, including the potential for autonomous weapons systems to make life-or-death decisions without human oversight. There is a need to ensure that the use of AI in military applications aligns with ethical standards and principles. (2) Reliability: One of the main challenges of using AI in military applications is ensuring its reliability. The accuracy of AI models heavily depends on the quality and quantity of data used for training, and there is always a risk of errors and bias in the data. Any inaccuracies in the output of an AI system could have severe consequences, particularly in military operations. (3) Cybersecurity: AI systems in the military are often interconnected with other systems and, therefore, are vulnerable to cyberattacks. The malicious use of AI can cause significant harm to the military’s infrastructure, personnel, and operations. Moreover, AI-powered cyberattacks could be difficult to detect and prevent. (4) Adversarial attacks: Adversarial attacks are a type of attack that can cause AI systems to produce incorrect results by manipulating the input data. In a military context, adversarial attacks could cause an AI system to misidentify targets or provide misleading information. (5) Training: Developing and training AI systems for military applications requires significant resources and expertise. Moreover, the quality and quantity of data available for training can be limited, making it difficult to create accurate and reliable AI models. (6) Integration: Integrating AI systems with existing military infrastructure and processes can be challenging. This requires significant changes to existing systems and processes, which can be time-consuming and expensive. (7) Public perception and conception: The use of AI in military applications raises concerns among the public about the potential for machines to replace human soldiers, reduce accountability, and increase the risk of harm to civilians. Last but not least, the rectitude of AI augmentation and processing systems are particularly susceptible to deception, even though it is a persistent intelligence and strategic issue that predates the cyber age. Reliance on AI coupled with malicious actors’ exploitation of technology could significantly exacerbate the disruptive consequences of disinformation operations. Establishing legislative frameworks that determine how new technology interacts with the current arsenal of disinformation tools, such as social media, should be a crucial responsibility of governments. Time constraints prevented a more thorough investigation of an AI system’s vulnerability, but the tabletop exercise was riddled with indications and suspicions of AI-related espionage. They will probably endure in the real world as well. These AI-related facts must be considered since they threaten world peace and stability [116, 117].

But even for these tasks, the success of LLMs cannot be guaranteed. Their behavior, especially in rare and unpredictable examples, can be erratic. And because no two LLMs are exactly alike in their training or fine-tuning, they are uniquely influenced by user inputs. Consider, for example, a series of war games we held in which we analyzed how human experts and LLMs played to understand how their decisions differ. The humans did not play against the LLMs. Rather, they played separately in the same roles. The game placed players in the midst of a U.S.-China maritime crisis as a U.S. government task force made decisions about how to use emerging technologies in the face of escalation. Players were given the same background documents and game rules, as well as identical PowerPoint decks, word-based player guides, maps, and details of capabilities. They then deliberated in groups of four to six to generate recommendations.

On average, both the human and the LLM teams made similar choices about big-picture strategy and rules of engagement. But, as we changed the information the LLM received, or swapped between which LLM we used, we saw significant deviations from human behavior. For example, one LLM we tested tried to avoid friendly casualties or collisions by opening fire on enemy combatants and turning a cold war hot, reasoning that using preemptive violence was more likely to prevent a bad outcome to the crisis. Furthermore, whereas the human players’ differences in experience and knowledge affected their play, LLMs were largely unaffected by inputs about experience or demographics. The problem was not that an LLM made worse or better decisions than humans or that it was more likely to “win” the war game. It was, rather, that the LLM came to its decisions in a way that did not convey the complexity of human decision-making. LLM-generated dialogue between players had little disagreement and consisted of short statements of fact. It was a far cry from the in-depth arguments so often a part of human war gaming.

Artificial intelligence (AI) has been gradually improving and becoming a more efficient way worldwide with the help of data, computer processing power, and machine learning developments, especially during the last two decades. As a result, AI is being used increasingly and more frequently in the daily life of various sectors. A few of the various uses of this technology include speech recognition, biometric authentication, mobile mapping, navigational systems, transportation and traffic control, management, manufacturing, supply chain management, data collection, and control targeted online marketing. Therefore, it should come as no surprise that AI has many applications in the military sector also, in a vast range [1]. Military capability is the current measurement index when determining a country or nation’s “Powerforce.” The U.S. Department of Defense defines military competence or capability as “the ability to achieve a certain combat objective (win a war or battle, destroy a target set).” It is directly or indirectly influenced by modernization, structure, preparedness, and sustainability. The equipment, arsenal, and level of technical sophistication largely determine the degree of modernization [2]. The Internet is replacing the conventional way of initiating war instigated from the start of the Second World War. Studies show that hacking attacks on for-profit companies and governmental institutions around the AI sector are more common now. According to researchers, modern autonomous systems and artificial intelligence (AI) are expected to be crucial in future military confrontations [3]. Recent scientific publications show how prevalent neural network technology is today in the cyber fight. The development of intelligent transport systems (ITS) is one of the major examples, along with forecasting and assessing environmental phenomena, separating informational tweets from noninformational ones (containing information that are rumors or nondetailed irrelevant data), and forecasting dynamic FX conventional markets. This type of enhancer helps in the military sector in various ways and turns out to be the greatest weapon in developing military capability [4]. Data on a wide range of resources and capabilities (human resources combat and support vehicles, helicopters, cutting-edge intelligence, and communication equipment, artillery, and missiles) that can carry out complex tasks of various types, such as intelligence gathering, movements, direct and indirect fires, infrastructure, and transports, should be considered in military decisions [3, 5]. For instance, the decisional component necessitates an integrated framework that can carry out the necessary processes, from capturing a high-level course of action (CoA) to implementing a thorough analysis/plan of activities. One possibility is to build the approach on several AI methods, such as qualitative spatial interpretation of CoA diagrams and interleaved adversarial scheduling, and many others likewise enhance the military world in different paths [6]. The contributions of this paper are for the advancement of AI in the military capabilities, and the significance of this narrative review is to identify several key applications of AI in the military, including target recognition, surveillance, homeland security, cybersecurity, transportation and logistics, autonomous vehicles, and combat training. We have also highlighted the potential benefits of using AI in these areas, including increased efficiency, accuracy, and decision-making capabilities. The paper also identifies several challenges and potential risks associated with using AI in the military, such as the potential for malfunction, hacking, and other forms of cyberattacks. The ethical and legal implications of using AI in the military are discussed in detail, particularly in relation to issues such as autonomous weapons and the potential for unintended harm. The study has the potential to inform policy and decision-making in this area, particularly in relation to issues such as military modernization and preparedness. The research findings could potentially aid in developing guidelines and regulations for the responsible use of AI in military settings.

People worry that artificial intelligence (AI) is, or will soon be, undermining democracy. They fear AI will take away jobs, destabilize the economy, and widen the divide between the rich and the poor. This could further concentrate power in the hands of a few tech companies and weaken government structures designed to regulate them. Some also fear that tech giants and government may increasingly delegate human decision-making to machines, eventually replacing democracy with “algocracy,” rule not by the people but by algorithm.

This dystopian vision misses our current capacity to shape AI development. We, as human societies, have the political ability (at least for now) and the responsibility to address the harm AI could inflict on us. We also have the technological opportunity to harness AI to enhance our democracy in a way that strengthens our collective ability to govern—rather than simply regulate—AI.

In conclusion, the integration of Artificial Intelligence (AI) into cybersecurity stands as a critical frontier in protecting national infrastructure, and the USA exemplifies a landscape where innovation and collaboration shape the defense against evolving cyber threats. The adoption of AI technologies brings both immense potential and a set of challenges that demand careful consideration. The use of Machine Learning, Natural Language Processing, Neural Networks, and other AI strategies has significantly improved the speed and accuracy of threat detection, response, and overall resilience. Case studies from the USA showcase the diverse applications of AI in safeguarding critical sectors, such as defense, finance, energy, and healthcare. These implementations underline the adaptability and effectiveness of AI in addressing the dynamic nature of cyber threats. However, challenges persist, including the risk of adversarial attacks, data privacy concerns, and the need for transparent and explainable AI models. Regulatory frameworks must evolve to keep pace with technological advancements, balancing innovation with ethical considerations to ensure responsible and accountable use of AI in cybersecurity. The collaborative efforts between government agencies, private industries, and international partners are pivotal. Information sharing platforms, public-private partnerships, and cross-sector collaboration are fundamental for building a collective defense against cyber threats. As the cyber landscape continues to evolve, interdisciplinary research and workforce development are crucial for staying ahead of adversaries. Looking forward, future directions involve the advancement of Explainable AI, AI-driven threat hunting, resilient AI systems, and the exploration of quantum-safe cryptography. Additionally, the development of AI-driven policies and regulations, coupled with enhanced collaboration models, will contribute to a secure and ethically sound cyber environment. In essence, the USA's review of AI in cybersecurity for protecting national infrastructure underscores the importance of ongoing innovation, collaboration, and a commitment to ethical practices. By embracing these principles, the nation can navigate the complex cyber landscape, mitigate risks effectively, and fortify its critical infrastructure against the everevolving threat landscape. The journey toward a secure digital future requires continual vigilance, adaptability, and a collective commitment to the responsible integration of AI in cybersecurity

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In contrast to this model of ‘epistemic democracy’ [], the seemingly ‘only technical’ understanding of AI provides a fertile ground for the authoritarian or even totalitarian idea that political representation is fixed, unchangeable, and the process of deliberation concluded. As argued in the introduction, authoritarian regimes might be performing better regarding the development of some aspects of AI due to unrestricted data gathering [, ], and AI likely promotes digital authoritarian practices [,,,,]; a similar positive feedback loop between AI and authoritarian regimes is reflected in the reductionist tendencies of AI in terms of representation []. The positive feedback loop between authoritarian rule and some conceptions of AI is partly grounded in their shared ‘closed’ modes of representation compared to the permanently contested mode of representation in open democratic debates. By relating AI to epistemological openness and cognitive diversity, democracies can counteract this positive feedback loop. It is likely that already today, democracies allow for more cognitive diversity to be represented in data than authoritarian regimes since censorship and other modes of repression encourage uniformity, and particularly uniformity regarding official data. Whilst this philosophical speculation should be subjected to further empirical research, this thesis can be strengthened by a compelling case: former Google CEO Eric Schmidt recently underlined that the extremely successful dialogue-centred approach of OpenAI would not be possible in a country such as China that is characterized by free speech restrictions []

In the 1983 movie WarGames, the world is brought to the edge of nuclear destruction when a military computer using artificial intelligence interprets false data as an imminent Soviet missile strike. Its human overseers in the Defense Department, unsure whether the data is real, can’t convince the AI that it may be wrong. A recent finding from the Defense Intelligence Agency, or DIA, suggests that in a real situation where humans and AI were looking at enemy activity, those positions would be reversed.

Artificial intelligence can actually be more cautious than humans about its conclusions in situations when data is limited. While the results are preliminary, they offer an important glimpse into how humans and AI will complement one another in critical national security fields.

This brings to mind the "hard problem" of consciousness—if we don't understand how this happens in humans, how could we program it into computers? As Steven Pinker elucidated in his answer to the 2015 Edge Question on what to think about machines that think, "AI dystopias project a parochial alpha-male psychology onto the concept of intelligence. They assume that superhumanly intelligent robots would develop goals like deposing their masters or taking over the world." It is equally possible, Pinker suggests, that "artificial intelligence will naturally develop along female lines: fully capable of solving problems, but with no desire to annihilate innocents or dominate the civilization." (14) So the fear that computers will become emotionally evil are unfounded, because without the suite of these evolved emotions it will never occur to AIs to take such actions against us.

Finally, one of the crucial underlying issues for assessing the (potential) capacity of AI to innovate corresponds to whether it might be able to perform a voluntary action, with the ability to choose and decide on its own actions rather than just follow an external principle. An “artificial” voluntary action may be expressed to the extent that the AI in use is autonomous.  analyzes the different levels of autonomy applicable to AI (the repertoire of actions, the degree to which it may execute instructions outside the controlling program, whether the human programmer has already made “decisions,” the extent to which a machine can determine the course of action on the spot, and if a system is provided with the possibility of combining different actions within a simulation plan). He concludes that machines lack the necessary independent criteria at any of those stages. Following  understanding of autonomy,  insists on AI’s inability to set its own goals. “Machines” (all kinds of computers and robots) are designed to perform specific tasks and functionalities with predetermined, externally-supplied goals. As he concludes, “Even the most advanced products of cognitive robotics and self-aware system research act on the basis of preprogrammed desired behaviors (the desire is clearly of the system designer’s; : 186).

In sum, in seeking to assess whether AI in its current state is able to carry out innovation processes on its own, we have seen how, at present, it has not reached the level of a genuine “general purpose technology.” Rather, its development has focused on specific domains that extend, enhance, and complement human skills (augmentation thesis); current AI is not a “method of invention” () per se, but rather a sophisticated method of imitation () that can recognize and identify patterns better and faster than the human mind. In that sense, AI is of great help in dealing with uncertainty, which is the natural environment for innovation, but it does not have the capacity to find creative solutions (useful, beyond novel) that are superior (or even comparable) to those that emerge from the human mind. So far, AI has managed to simulate certain capabilities found in the human mind, but it has not managed to acquire the autonomy necessary to carry out voluntary actions, nor has it proved capable of setting its own goals beyond those for which it has been programmed. As : 637) state, “Humans will need to check the outputs from AI systems, make critical decisions, and feedback “faults” that will be used to adjust the model.”

Technology has brought the world into the future with a wealth of products and services that have, inadvertently, left a consequential mark on the planet. The paradox that exists pertains to the fact that some of these innovations, while enhancing life, have caused significant harm to our natural resources, leading to negative environmental impacts. This has been particularly true in the United States, where dependency on fossil fuels continues to drive economic development but at a high environmental cost.

However, the tide is turning, and increasingly there is a global shift to reconsider business models, focus more on sustainability, and bridge the gap between tech innovation and environmental responsibility. At the heart of this transformation lies sustainable innovation and emerging technologies, particularly artificial intelligence (AI).

Artificial intelligence (AI) is considered one of the most revolutionary developments in human history, and the world has already witnessed its transformative capabilities. Not surprisingly, AI-based innovations are powering some of the most cutting-edge solutions we use in our daily lives.

Today, AI empowers organizations, governments and communities to build a high-performing ecosystem to serve the entire world. Its profound impact on human lives is solving some of the most critical challenges faced by society. Here are a few innovations for social causes that I find most notable. 

For organizations creating a long-term strategy around generative artificial intelligence, the technology has the capacity to create jobs for humans, not just automate them.

AI job posts on global work marketplace Upwork increased more than 1,000% in the second quarter of 2023 compared to the same period last year — evidence of the industry’s momentum. Despite the fast-moving nature of this technology, keeping the human in the loop, it seems, is here to stay.

AI drives economic growth by stimulating gains both from the supply side and the demand side. AI can drive business productivity through (1) automation of processes with the use of robots and “autonomous vehicles,” and (2) improvements in the existing labor force by equipping them with AI technologies. On the other hand, AI can generate an increase in consumer demand with the availability of “personalised and/or higher-quality” products and services. Accordingly, it is expected that AI could contribute up to USD 15.7 trillion to the global economy in 2030 (Rao and Verweij 2017).

Furthermore, the contributions of AI may be specific to the sectors where it is applied, such as manufacturing, health, finance, energy, and transport. For example, AI supports healthcare services through early detection and diagnosis of illnesses, identification of “potential pandemics and tracking incidence,” and “imaging diagnostics” in radiology and pathology. Meanwhile, AI contributions to the financial sector include applications for fraud detection and anti-money laundering. Also, AI developments such as “robo-advice” make “customized investment solutions” possible in managing financial goals and optimizing clients’ funds. In addition, AI enables “autonomous trucking and delivery,” traffic control systems, and improved security in the transport sector (Rao and Verweij 2017).