Artificial Intelligence In The Military: An Overview Of The Capabilities, Applications, And Challenges
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
Artificial intelligence (AI) has become a reality in today's world with the rise of the 4th industrial revolution, especially in the armed forces.Military AI systems can process more data more efectively than traditional systems.Due to its intrinsic computing and decision-making capabilities, AI also increases combat systems' self-control, self-regulation, and self-actuation.Artificial intelligence is used in almost every military application, and increased research and development support from military research agencies to develop new and advanced AI technologies is expected to drive the widespread demand for AI-driven systems in the military.This essay will discuss several AI applications in the military, as well as their capabilities, opportunities, and potential harm and devastation when there is instability.The article looks at current and future potential for developing artificial intelligence algorithms, particularly in military applications.Most of the discussion focused on the seven patterns of AI, the usage and implementation of AI algorithms in the military, object detection, military logistics, and robots, the global instability induced by AI use, and nuclear risk.The article also looks at the current and future potential for developing artificial intelligence algorithms, particularly in military applications.Keywords- IntroductionArtificial 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,.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.According to researchers, modern autonomous systems and artificial intelligence (AI) are expected to be crucial in future military confrontations [3].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].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 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 re-search findings could potentially aid in developing guide-lines and regulations for the responsible use of AI in military settings.recall chess pieces better when arranged on a chess board in meaningful patterns than randomly arranged chess pieces [32, 33].It has been demonstrated that people skilled in reading architectural plans, reading circuit diagrams, and deciphering X-ray images have the best ability to spot important patterns in those fields [34, 35].Therefore, it appears logical to speculate that the capacity to recognize key battlefield patterns is at least one element of the battle command experience.1.1.Conversational Pattern.Over the years, various cutting-edge solutions have been created based on one of the most general conversational AI patterns.The modern workplace of the twenty-first century is filled with social robots and AIaugmented living helpers.In many industries, including the military, the significance of interactions between humans and robots is becoming increasingly apparent.With AI technology's help, this interaction is termed as conversational pattern.This is characterized as conversational forms of engagement and information spread across various mediums,
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle