Artificial intelligence, machine learning and deep learning in advanced robotics, a review

Classified as probably not a substantial claim. May combine two claims.

AI, ML, and DL are all important technologies in the field of robotics.

“Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are all important technologies in the field of robotics [1] .”

• cites [1] Woschank, 2020, A review of further directions for artificial intelligence, machine learning, and deep learning in smart logistics, Sustainability sourcePartly supported

  Section 4.1 identifies AI, ML, and DL as technologies for improving logistics processes and includes swarm robotics as a smart-warehouse application. The paper does not establish the broader claim that all three are important technologies in robotics generally; its discussion is bounded to smart logistics. No page number is available in our copy. (LLM, conf=0.82)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Robots can detect and interact with their surroundings, make judgments, and carry out difficult tasks with the aid of AI.

“Robots can detect and interact with their surroundings, make judgments, and carry out difficult tasks with the aid of AI. [2] .”

• cites [2] Fahle, 2020, Systematic review on machine learning (ML) methods for manufacturing processes–Identifying artificial intelligence (AI) methods for field application, Procedia CIRP sourceSupported

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Self-driving cars use computer vision to detect and recognize objects on the road, and ML algorithms to learn and adapt to new situations and road conditions.

“For example, self-driving cars use computer vision to detect and recognize objects on the road, and ML algorithms to learn and adapt to new situations and road conditions [17] .”

• cites [17] Elallid, 2022, A comprehensive survey on the application of deep and reinforcement learning approaches in autonomous driving, J. King Saud Univ.-Comput. Inf. Sci. sourceSupported

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

AI-based parking systems can help drivers find available parking spots quickly and reduce congestion in busy areas.

“Smart Parking: AI-based parking systems can help drivers find available parking spots quickly and reduce congestion in busy areas.”

• cites [84] Zantalis, 2019, A review of machine learning and IoT in smart transportation, Future Int. sourcePartly supported

  Section 1.4 (Smart Transportation) says smart parking systems can minimize the time spent searching for parking, and Section 2.2.2 (Parking) describes systems that detect and recommend available spaces. The paper discusses traffic congestion elsewhere, but our copy does not say that parking systems reduce congestion in busy areas. (LLM, conf=0.86)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing).

There are concerns about the potential for robots to be used for harmful purposes, such as military applications or surveillance.

“There are also concerns about the potential for robots to be used for harmful purposes, such as military applications or surveillance [61] .”

• cites [61] Kapeller, 2020, A taxonomy of ethical, legal and social implications of wearable robots: an expert perspective, Sci. Eng. Ethics sourcePartly supported

  In Results (‘Wearable Robots in Society’), the paper raises concerns about harmful dual uses of wearable robots, including movement between civilian and military uses. It also says wearable robots may intensify workplace surveillance. The claim broadens these concerns to robots generally; the Introduction states that military applications were excluded from the paper’s assessment. (LLM, conf=0.83)

  Experimental — accuracy not yet measured

May combine two claims.

This helps manufacturers to identify and eliminate bottlenecks, reduce waste, and increase productivity.

“This helps manufacturers to identify and eliminate bottlenecks, reduce waste, and increase productivity [63] .”

• cites [63] Huang, 2021, A survey on AI-driven digital twins in industry 4.0: smart manufacturing and advanced robotics, Sensors sourcePartly supported

  Section 2.1 (Overview; page unavailable) describes digital twins as enhancing manufacturing productivity, and Section 2.4 (Process and Material; page unavailable) discusses resource savings and process optimization. The passages do not establish that manufacturers use them to identify and eliminate bottlenecks, so that part of the attributed claim is unsupported. (LLM, conf=0.82)

  Experimental — accuracy not yet measured

May combine two claims.

AI, ML, and DL can automate many repetitive and mundane tasks in robotics, freeing up human resources to focus on more complex tasks.

“Automation: AI, ML, and DL can automate many repetitive and mundane tasks in robotics, freeing up human resources to focus on more complex tasks [49] .”

• cites [49] Sardar, 2019, Impact of artificial intelligence on interventional cardiology: from decision-making aid to advanced interventional procedure assistance, Cardiovasc. Intervent. sourcePartly supported

  The Robotics passage (section labeled Abstract; page unavailable) says AI has potential to automate procedures in the catheterization lab, and the paper describes an automated robotic guidewire movement. It does not say that AI, ML, and DL automate many repetitive or mundane robotic tasks, or that such automation frees personnel to focus on more complex work. (LLM, conf=0.86)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing).

Robotics is used to automate tasks in manufacturing plants, such as assembly line tasks, painting, and welding.

“Robotics is used to automate tasks in manufacturing plants, such as assembly line tasks, painting, and welding.”

• cites [2] Fahle, 2020, Systematic review on machine learning (ML) methods for manufacturing processes–Identifying artificial intelligence (AI) methods for field application, Procedia CIRP sourcePartly supported

  Section 4.5 (Robotics; page not provided) describes robotics applications for automation and discusses assembly use cases. It does not say that robots perform assembly-line tasks, painting, or welding in manufacturing plants. Section 4.8 discusses laser welding, but not robotic welding. (LLM, conf=0.86)

  Experimental — accuracy not yet measured

May combine two claims.

AI and ML are used to help cars navigate roads and make driving decisions on their own.

“AI and ML are used to help cars navigate roads and make driving decisions on their own.”

• cites [17] Elallid, 2022, A comprehensive survey on the application of deep and reinforcement learning approaches in autonomous driving, J. King Saud Univ.-Comput. Inf. Sci. sourceSupported

May combine two claims.

By using AI and ML for predictive maintenance, downtime may be decreased and overall productivity can rise.

“By doing so, downtime may be decreased and overall productivity can rise [65] .”

• cites [65] Pookkuttath, 2022, AI-enabled predictive maintenance framework for autonomous mobile cleaning robots, Sensors sourcePartly supported

  The Introduction says manual monitoring may cause extended downtime and that automated predictive maintenance overcomes those pitfalls; it also describes an ML-based approach intended to avoid unplanned downtime. The paper does not state that overall productivity rises, and its Conclusions report fault-classification performance rather than measured downtime or productivity gains. (LLM, conf=0.86)

  Experimental — accuracy not yet measured

May combine two claims.

This can help reduce inventory costs and improve overall efficiency.

“This can help reduce inventory costs and improve overall efficiency [72] .”

• cites [72] Toorajipour, 2021, Artificial intelligence in supply chain management: a systematic literature review, J. Bus. Res. sourceSupported

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

AI and ML algorithms are used to optimize farming operations, such as predicting weather patterns, optimizing water usage, and monitoring crop health.

“AI and ML algorithms are used to optimize the farming operations, such as predicting weather patterns, optimizing water usage, and monitoring crop health [15] .”

• cites [15] Linaza, 2021, Data-driven artificial intelligence applications for sustainable precision agriculture, Agronomy sourcePartly supported

  The Introduction describes AI-assisted analysis of field conditions and risks from pests and diseases, while the Conclusions describe AI-based irrigation that reduced water use in strawberry crops. These passages support crop-health monitoring and water-use optimization, but our copy does not show AI or ML being used to predict weather patterns. (LLM, conf=0.86)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing).

This can be especially useful in large fleets of vehicles, such as those used in public transportation.

“This can be especially useful in large fleets of vehicles, such as those used in public transportation [83] .”

• cites [83] Theissler, 2021, Predictive maintenance enabled by machine learning: use cases and challenges in the automotive industry, Reliab. Eng. Syst. Saf. sourceSupported

May combine two claims.

ML algorithms can analyze data from sensors installed on vehicles and predict when maintenance is needed, allowing for proactive repairs and reducing downtime.

“Predictive Maintenance: ML algorithms can analyze data from sensors installed on vehicles and predict when maintenance is needed, allowing for proactive repairs and reducing downtime.”

• cites [83] Theissler, 2021, Predictive maintenance enabled by machine learning: use cases and challenges in the automotive industry, Reliab. Eng. Syst. Saf. sourceSupported

May combine two claims.

AI and ML are being used to develop robots that can autonomously navigate and manage crops, increasing efficiency and reducing labor costs.

“AI and ML are being used to develop robots that can autonomously navigate and manage crops, increasing efficiency and reducing labor costs.”

• cites [15] Linaza, 2021, Data-driven artificial intelligence applications for sustainable precision agriculture, Agronomy sourcePartly supported

  Section 2.3 says AI and robotics help or replace manual intervention and that robots autonomously weed, irrigate, and manage individual plants. Section 3.6 (p. 11) describes navigation and auto-steering for agricultural robots. The passages do not establish that these robots increase efficiency or reduce labor costs. (LLM, conf=0.84)

  Experimental — accuracy not yet measured

May combine two claims.

AI and ML can be used to optimize the supply chain by predicting demand and ensuring that the right materials are available at the right time.

“Supply Chain Optimization: AI and ML can be used to optimize the supply chain by predicting demand and ensuring that the right materials are available at the right time.”

• cites [72] Toorajipour, 2021, Artificial intelligence in supply chain management: a systematic literature review, J. Bus. Res. sourceSupported

May combine two claims.

AI, ML, and DL technologies are enabling robots during assembly process to work smarter, faster, and more efficiently than ever before, and are helping manufacturers to improve quality, reduce costs, and increase productivity.

“Assembly robots: AI, ML, and DL technologies are enabling robots during assembly process to work smarter, faster, and more efficiently than ever before, and are helping manufacturers to improve quality, reduce costs, and increase productivity [67] .”

• cites [67] Murugamani, 2022, Wireless communication for robotic process automation using machine learning technique sourcePartly supported

  The Introduction says AI can improve robots’ image recognition and precision in assembly. Section 4 links human–robot work to higher production standards and cheaper prices, while §7.1 reports improved productivity and operating efficiency. The copy does not establish that ML and DL specifically produce all these benefits during assembly. (LLM, conf=0.85)

  Experimental — accuracy not yet measured

May combine two claims.

AI, ML, and DL can enable robots to make better decisions based on data analysis and pattern recognition, leading to improved performance and outcomes.

“Better decision-making: AI, ML, and DL can enable robots to make better decisions based on data analysis and pattern recognition, leading to improved performance and outcomes [52] .”

• cites [52] Begoli, 2019, The need for uncertainty quantification in machine-assisted medical decision making, Nature Machine Intelligence sourcePartly supported

  Section 2.1 reports that AI and deep learning recognize patterns in medical images and that some predictive models outperform classical approaches. It does not address robots or show that robots make better decisions or achieve improved outcomes. Section 2 also cautions that adopting automated decision-making in medicine requires uncertainty quantification. Page numbers are unavailable in our copy. (LLM, conf=0.87)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing).

Robotics is used in military operations for tasks such as reconnaissance, surveillance, and bomb disposal.

“Robotics is used in military operations for tasks such as reconnaissance, surveillance, and bomb disposal.”

• cites [14] Wasilow, 2019, Artificial intelligence, robotics, ethics, and the military: a Canadian perspective, AI Magazine sourcePartly supported

  The paper’s swarming-technologies discussion says UAV swarms could be used for surveillance and reconnaissance. A note in “Summary and Future Considerations” mentions robotics and sensors for IED and explosive detection, but it does not establish bomb disposal; the swarm passage also describes a potential use rather than an established operation. (LLM, conf=0.86)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing).

Robotics is used to automate tasks in agriculture, such as planting, harvesting, and spraying.

“Robotics is used to automate tasks in agriculture, such as planting, harvesting, and spraying.”

• cites [15] Linaza, 2021, Data-driven artificial intelligence applications for sustainable precision agriculture, Agronomy sourcePartly supported

  Section 2.3 says agricultural robots autonomously perform tasks such as weeding and irrigating, supporting the general point about automation. The Introduction identifies harvesting as a future role for robotic systems, but the paper does not identify planting or spraying as robotic tasks; its mention of spraying in the Conclusions concerns AI applications generally. (LLM, conf=0.87)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

This can save waste and boost overall effectiveness.

“This can save waste and boost overall effectiveness. [71] .”

• cites [71] Abioye, 2021, Artificial intelligence in the construction industry: a review of present status, opportunities and future challenges, J. Build. Eng. sourceSupported

May combine two claims.

This can help improve the quality of the products and reduce the need for human intervention in the quality control process.

“This can help improve the quality of the products and reduce the need for human intervention in the quality control process [64] .”

• cites [64] Sundaram, 2023, Artificial intelligence-based smart quality inspection for manufacturing, Micromachines (Basel) sourceSupported

May combine two claims.

AI, ML, and DL algorithms can be used to monitor the manufacturing process in real-time and identify defects or anomalies in the products being produced.

“Quality Control: AI, ML, and DL algorithms can be used to monitor the manufacturing process in real-time and identify defects or anomalies in the products being produced.”

• cites [64] Sundaram, 2023, Artificial intelligence-based smart quality inspection for manufacturing, Micromachines (Basel) sourceSupported

Probably not a substantial claim: may not pass S3 (load-bearing).

Autonomous robots can be particularly useful in situations where human intervention is not practical or safe, such as in hazardous environments or in situations where precision is critical.

“This can be particularly useful in situations where human intervention is not practical or safe, such as in hazardous environments or in situations where precision is critical [66] .”

• cites [66] Gumbs, 2021, Artificial intelligence surgery: how do we get to autonomous actions in surgery?, Sensors sourcePartly supported

  The Discussion describes autonomous surgical actions and says sensor-assisted needle insertions can become smarter and potentially safer. That lends limited support to their usefulness in precision-sensitive procedures, but the paper does not address hazardous environments or situations where human intervention is impractical or unsafe. The claim extends a surgery-focused discussion to those broader settings. (LLM, conf=0.78)

  Experimental — accuracy not yet measured

May combine two claims.

ML algorithms can optimize delivery routes for logistics companies, reducing travel time, and improving fuel efficiency.

“Route Optimization: ML algorithms can optimize delivery routes for logistics companies, reducing travel time, and improving fuel efficiency.”

• cites [85] Olugbade, 2022, A review of artificial intelligence and machine learning for incident detectors in road transport systems, Mathemat. Comput. Appl. sourcePartly supported

  The Abstract and §3.1 identify route optimization as an application of AI and machine learning in road transportation. Section 4 also discusses AI in the movement of goods by road, but the copy does not say that ML optimizes delivery routes for logistics companies or that doing so reduces travel time and improves fuel efficiency. Page numbers were not available in the indexed passages. (LLM, conf=0.84)

  Experimental — accuracy not yet measured

May combine two claims.

AI and ML algorithms can be used to manage air traffic more efficiently, reducing the likelihood of delays and improving safety.

“AI and ML algorithms can be used to manage air traffic more efficiently, reducing the likelihood of delays and improving safety [119] .”

• cites [119] Degas, 2022, A survey on artificial intelligence (ai) and explainable ai in air traffic management: current trends and development with future research trajectory, Appl. Sci. sourceSupported

May combine two claims.

ML algorithms can analyze customer data to personalize their experience, for example by analyzing past trips to suggest preferred routes or destinations, reducing travel time and improving overall experience.

“ML algorithms can analyze customer data to personalize their experience. For example, by analyzing past trips, AI can suggest preferred routes or destinations to customers, reducing their travel time and improving their overall experience [134] .”

• cites [134] Abduljabbar, 2019, Applications of artificial intelligence in transport: an overview, Sustainability sourcePartly supported

  The Shared Mobility section says AI improves customer experience and that Uber suggests destinations based on a rider’s ride history. It also discusses efficient route planning and accurate travel-time estimates, but does not say that the history-based suggestions reduce customers’ travel time or suggest preferred routes. (LLM, conf=0.86)

  Experimental — accuracy not yet measured

May combine two claims.

ML algorithms can analyze parking data to optimize parking space usage, while DL algorithms can recognize license plates and enforce parking regulations.

“ML algorithms can analyze parking data to optimize parking space usage, while DL algorithms can recognize license plates and enforce parking regulations [84] .”

• cites [84] Zantalis, 2019, A review of machine learning and IoT in smart transportation, Future Int. sourcePartly supported

  Section 2.2.2 (Parking; page unavailable) reports that ML algorithms analyze parking-lot images to detect free spaces, supporting the claim about using parking data to improve space availability. Section 2.1.7 (Deep Learning; page unavailable) describes CNN-based detection of parking-lot occupancy, but the passages found do not say that deep learning recognizes license plates or enforces parking regulations. (LLM, conf=0.88)

  Experimental — accuracy not yet measured

May combine two claims.

DL algorithms can also be used to monitor passenger behavior and detect potential safety issues.

“DL algorithms can also be used to monitor passenger behavior and detect potential safety issues [87] .”

• cites [87] Nikitas, 2020, Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era, Sustainability sourcePartly supported

  The Introduction discusses AI methods in transport, and Section 3 lists improved traffic security through surveillance and monitoring as a potential benefit of connected and autonomous vehicles. Neither passage says that deep-learning algorithms monitor passenger behavior or detect safety issues arising from it. (LLM, conf=0.78)

  Experimental — accuracy not yet measured

May combine two claims.

DL algorithms are particularly effective at processing complex data, such as images and video, and can be used to identify cargo types and detect anomalies, such as damaged or dangerous cargo.

“DL algorithms are particularly effective at processing complex data, such as images and video, and can be used to identify cargo types and detect anomalies, such as damaged or dangerous cargo [114] .”

• cites [114] Brunton, 2021, Data-driven aerospace engineering: reframing the industry with machine learning, AIAA J. sourcePartly supported

  Section 2.1.4 describes deep convolutional neural networks as widely used for machine vision, and Section 5.3 says machine-learning tools can detect faults and potential failures in sensor data. Neither passage connects these capabilities to identifying cargo types or detecting damaged or dangerous cargo. Page numbers were unavailable in our copy. (LLM, conf=0.87)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

This can result in cost savings and a reduced environmental footprint.

“This can result in cost savings and a reduced environmental footprint [85] .”

• cites [85] Olugbade, 2022, A review of artificial intelligence and machine learning for incident detectors in road transport systems, Mathemat. Comput. Appl. sourcePartly supported

  The Introduction says AI can optimize routes to cut emissions, and Section 4 says intelligent transportation systems may improve energy efficiency. These support a possible reduced environmental footprint, but the searched passages do not state that the applications produce cost savings. No page numbers were available in our copy. (LLM, conf=0.82)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI, ML, and DL are used in development of collaborative robots (cobots) that can work alongside humans and adapt to changing environments and tasks.

“These technologies are also being used in the development of collaborative robots (cobots) that can work alongside humans and adapt to changing environments and tasks.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

Robotics is a rapidly evolving field, and the use of AI, ML, and DL is likely to continue to play a key role in shaping the future of robotics.

“Robotics is a rapidly evolving field, and the use of AI, ML, and DL is likely to continue to play a key role in shaping the future of robotics [6] .”

• cites [6] Andras, 2020, Artificial intelligence and robotics: a combination that is changing the operating room, World J. Urol. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI, ML, and DL can be used in advanced transportation systems to provide safety, efficiency, and convenience to passengers and transportation companies.

“The AI, ML, and DL can be used in advanced transportation systems in order to provide safety, efficiency, and convenience to the passengers and transportation companies .”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI, ML, and DL are playing a critical role in advancement of manufacturing assembly robots, enabling them to work more efficiently, safely, and intelligently.

“Also, the AI, ML, and DL are playing a critical role in the advancement of manufacturing assembly robots, enabling them to work more efficiently, safely, and intelligently.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

AI, ML, and DL have a wide range of applications in aviation management, helping airlines improve efficiency, reduce costs, and improve customer satisfaction.

“Furthermore, they have a wide range of applications in aviation management, helping airlines to improve efficiency, reduce costs, and improve customer satisfaction.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

The application of AI, ML, and DL in robotics has the potential to transform the field, enabling robots to become more intelligent, autonomous, and effective in a wide range of applications.

“The application of AI, ML, and DL in robotics has the potential to transform the field, enabling robots to become more intelligent, autonomous, and effective in a wide range of applications.”

• cites [6] Andras, 2020, Artificial intelligence and robotics: a combination that is changing the operating room, World J. Urol. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI, ML, and DL can help taxi companies provide better, more efficient, and safer services to customers.

“Moreover, the AI, ML, and DL can help taxi companies in order to provide better, more efficient, and safer services to customers.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

Deep learning has been particularly useful in robotics for tasks such as image and speech recognition, natural language processing, and object detection.

“DL has been particularly useful in robotics for tasks such as image and speech recognition, natural language processing, and object detection.”

Probably not a substantial claim: may not pass S4 (based).

AI, ML, and DL have revolutionized advanced robotics in recent years.

“Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have revolutionized the field of advanced robotics in recent years.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

Applications of AI, ML, and DL in advanced robotics include autonomous navigation, object recognition and manipulation, natural language processing, and predictive maintenance.

“Some of the applications of AI, ML, and DL in advanced robotics include autonomous navigation, object recognition and manipulation, natural language processing, and predictive maintenance.”

Probably not a substantial claim: may not pass S4 (based).

AI, ML, and DL are transforming advanced robotics, making robots more intelligent, efficient, and adaptable to complex tasks and environments.

“AI, ML, and DL are transforming the field of advanced robotics, making robots more intelligent, efficient, and adaptable to complex tasks and environments.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Predictive maintenance is a maintenance approach that uses AI and ML to detect potential issues before they occur.

“Predictive maintenance is a maintenance approach that uses AI and ML to detect potential issues before they occur.”

• cites [8] Lee, 2019, Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data, Procedia Cirp sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

ML algorithms use data from sensors, cameras, and GPS to make navigation decisions.

“Meanwhile, ML algorithms use data from sensors, cameras, and GPS to make navigation decisions [18] .”

• cites [18] Ning, 2021, A survey on hybrid human-artificial intelligence for autonomous driving, IEEE Trans. Intell. Transp. Syst. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

As robots become more autonomous and interact with humans, ensuring their safety becomes a critical challenge.

“As robots become more autonomous and interact with humans, ensuring their safety becomes a critical challenge.”

Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

While AI, ML, and DL offer many opportunities for robotics, there are also significant challenges that must be addressed in order to realize their full potential.

“Overall, while AI, ML, and DL offer many opportunities for robotics, there are also significant challenges that must be addressed in order to realize their full potential.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

AI and ML can be used to analyze production data and optimize production planning.

“AI and ML can be used to analyze production data and optimize production planning.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Robotic surgery is a field where AI and ML are revolutionizing the way operations are performed.

“Robotic surgery is a field where AI and ML are revolutionizing the way operations are performed.”

• cites [10] Panesar, 2019, Artificial intelligence and the future of surgical robotics, Ann. Surg. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

This can be useful for robots and AGVs to navigate and interact with their surrounding.

“This can be useful for robots and AGVs to navigate and interact with their surrounding [76] .”

• cites [76] Chryssolouris, 2023, Artificial intelligence in manufacturing equipment, automation, and robots sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

By optimizing processes and reducing waste, AI, ML, and DL can improve the overall efficiency of robotics systems, resulting in cost savings and increased productivity.

“Improved efficiency: By optimizing processes and reducing waste, AI, ML, and DL can improve the overall efficiency of robotics systems, resulting in cost savings and increased productivity.”

May combine two claims.

AI and ML algorithms can analyze large amounts of data and make informed decisions based on that data, allowing robots to make better decisions and take appropriate actions.

“Improved Decision Making: AI and ML algorithms can analyze large amounts of data and make informed decisions based on that data, allowing robots to make better decisions and take appropriate actions [51] .”

• cites [51] Abdar, 2022, The need for quantification of uncertainty in artificial intelligence for clinical data analysis: increasing the level of trust in the decision-making process, IEEE Syst., Man, Cybernet. Mag. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

AI-based ITS can help improve traffic flow, reduce congestion, and enhance safety on roads.

“Intelligent Transportation Systems (ITS): AI-based ITS can help improve traffic flow, reduce congestion, and enhance safety on roads.”

Probably not a substantial claim: may not pass S4 (based). May combine two claims.

AI, ML, and DL can improve the accuracy and precision of robotic systems, reducing errors and improving overall performance.

“Enhanced accuracy: These technologies can improve the accuracy and precision of robotic systems, reducing errors and improving overall performance.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

The use of AI, ML, and DL in robotics has the potential to revolutionize the field and unlock new levels of performance, efficiency, and safety.

“Overall, the use of AI, ML, and DL in robotics has the potential to revolutionize the field and unlock new levels of performance, efficiency, and safety.”

May combine two claims.

These technologies enable vehicles to perceive and interpret their surroundings, make decisions based on data, and navigate roads safely without human intervention.

“These technologies enable vehicles to perceive and interpret their surroundings, make decisions based on data, and navigate roads safely without human intervention [81] .”

• cites [81] Ma, 2020, Artificial intelligence applications in the development of autonomous vehicles: a survey, IEEE/CAA J. Automat. Sinica sourceNot checked — no full text of the source

May combine two claims.

Data collection, labeling, and annotation can be expensive and time-consuming, and the data may be noisy or biased, which can affect the accuracy and reliability of the models.

“However, data collection, labeling, and annotation can be expensive and time-consuming, and the data may be noisy or biased, which can affect the accuracy and reliability of the models [48] .”

• cites [48] Baduge, 2022, Artificial intelligence and smart vision for building and construction 4.0: machine and deep learning methods and applications, Autom. Constr. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

AI/ML/DL models must be designed to adapt to new situations and learn from experience, which can be challenging.

“AI/ML/DL models must be designed to adapt to new situations and learn from experience, which can be challenging.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

ML algorithms can analyze traffic patterns and optimize signal timings at intersections, while DL algorithms can identify potential hazards and alert drivers in real-time.

“ML algorithms can analyze traffic patterns and optimize signal timings at intersections, while DL algorithms can identify potential hazards and alert drivers in real-time.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

AI/ML/DL algorithms must be designed to prevent accidents, detect and respond to potential hazards, and avoid collisions with humans and other objects.

“AI/ML/DL algorithms must be designed to prevent accidents, detect and respond to potential hazards, and avoid collisions with humans and other objects [59] .”

• cites [59] Garg, 2021, Overview of artificial intelligence sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

This requires the development of robust AI and ML algorithms that can handle unpredictable situations and adapt to changing conditions.

“This requires the development of robust AI and ML algorithms that can handle unpredictable situations and adapt to changing conditions.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

By automating hazardous or dangerous tasks, AI, ML, and DL can improve safety in the workplace, reducing the risk of accidents and injuries.

“Increased safety: By automating hazardous or dangerous tasks, AI, ML, and DL can improve safety in the workplace, reducing the risk of accidents and injuries.”

May combine two claims.

Robots like Pepper can recognize and respond to human gestures and speech, making them useful in contexts such as customer service or healthcare.

“For example, robots like Pepper can recognize and respond to human gestures and speech, making them useful in a variety of contexts such as customer service or healthcare [9] .”

• cites [9] Jiang, 2020, A survey on artificial intelligence in Chinese sign language recognition, Arabian J. Sci. Eng. sourceNot checked — no full text of the source

May combine two claims.

AI can enable robots to adapt to changing conditions, work collaboratively with human operators, and learn from past experiences to improve future performance.

“It can enable robots to adapt to changing conditions, work collaboratively with human operators, and learn from past experiences to improve future performance.”

• cites [68] Dimitropoulos, 2021, Seamless human–robot collaborative assembly using artificial intelligence and wearable devices, Appl. Sci. sourceNot checked — no full text of the source

May combine two claims.

AI-powered robots can adapt to changing environments and tasks, making them highly versatile and useful in a range of industries and applications.

“Adaptability: AI-powered robots can adapt to changing environments and tasks, making them highly versatile and useful in a range of industries and applications [48] .”

• cites [48] Baduge, 2022, Artificial intelligence and smart vision for building and construction 4.0: machine and deep learning methods and applications, Autom. Constr. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

ITS includes technologies like smart traffic signals, electronic toll collection systems, and intelligent parking systems, which help in optimizing the transportation system.

“ITS includes technologies like smart traffic signals, electronic toll collection systems, and intelligent parking systems, which help in optimizing the transportation system [82] .”

• cites [82] Iyer, 2021, AI enabled applications towards intelligent transportation, Transport. Eng. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

Robots like self-driving cars use AI to detect obstacles and predict traffic movements.

“For instance, robots like self-driving cars use AI to detect obstacles and predict traffic movements.”

• cites [18] Ning, 2021, A survey on hybrid human-artificial intelligence for autonomous driving, IEEE Trans. Intell. Transp. Syst. sourceNot checked — no full text of the source

May combine two claims.

Robotics applications often require real-time processing, which can be computationally expensive and may require specialized hardware.

“In addition, robotics applications often require real-time processing, which can be computationally expensive and may require specialized hardware [56] .”

• cites [56] Károly, 2020, Deep learning in robotics: survey on model structures and training strategies, IEEE Trans. Syst., Man, Cybernet. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

In robotics, data can be difficult to obtain and may be subject to noise and uncertainty.

“This can be particularly challenging in robotics, where data can be difficult to obtain and may be subject to noise and uncertainty.”

Probably not a substantial claim: may not pass S3 (load-bearing).

There are many potential applications of AI, ML, and DL in advanced manufacturing robots.

“There are many potential applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in advanced manufacturing robots.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

There are concerns about the impact of automation on jobs and the potential for AI systems to be biased or to perpetuate existing inequalities.

“For example, there are concerns about the impact of automation on jobs and the potential for AI systems to be biased or to perpetuate existing inequalities.”

May combine two claims.

By using advanced algorithms, robotic surgeons can assist human surgeons during complex procedures, reducing the risk of complications and improving outcomes.

“By using advanced algorithms, robotic surgeons can assist human surgeons during complex procedures, reducing the risk of complications and improving outcomes.”

• cites [10] Panesar, 2019, Artificial intelligence and the future of surgical robotics, Ann. Surg. sourceNot checked — no full text of the source

May combine two claims.

This can help to prevent accidents and reduce the risk of injury to workers.

“This can help to prevent accidents and reduce the risk of injury to workers.”

• cites [69] Hashmi, 2022, Artificial intelligence techniques for implementation of intelligent machining, Mater. Today: Proc. sourceNot checked — no full text of the source

May combine two claims.

Object detection and recognition are critical tasks in robotics that have become possible thanks to deep learning.

“Object detection and recognition are critical tasks in robotics that have become possible thanks to deep learning.”

• cites [7] Bai, 2020, Object detection recognition and robot grasping based on machine learning: a survey, IEEE Access sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

Robotics is used to provide services to humans, such as cleaning, food delivery, and customer service.

“Robotics is used to provide services to humans, such as cleaning, food delivery, and customer service.”

• cites [16] Belk, 2021, Ethical issues in service robotics and artificial intelligence, Serv. Ind. J. sourceNot checked — no full text of the source

May combine two claims.

These technologies can enable robots to adapt to changing environments and situations, making them more versatile and capable of handling a wider range of tasks.

“Adaptability: These technologies can enable robots to adapt to changing environments and situations, making them more versatile and capable of handling a wider range of tasks [53] .”

• cites [53] Zohuri, 2022, Business resilience system integrated artificial intelligence system, Int. J. Theoret. Computat. Phys.Not checked yet

  Not checked in the free run.

May combine two claims.

This helps in optimizing traffic flow and reducing congestion.

“This helps in optimizing traffic flow and reducing congestion [80] .”

• cites [80] Sharma, 2020, The role of blockchain, AI and IoT for smart road traffic management system sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

By increasing productivity, cutting costs, and raising product quality, the employment of AI, ML, and DL in advanced industrial robots has the potential to completely transform the manufacturing sector.

“Overall, by increasing productivity, cutting costs, and raising product quality, the employment of AI, ML, and DL in advanced industrial robots has the potential to completely transform the manufacturing sector.”

Probably not a substantial claim: may not pass S3 (load-bearing).

There are ethical and societal challenges associated with the use of AI and robotics.

“In addition, there are ethical and societal challenges associated with the use of AI and robotics.”

May combine two claims.

AI and ML algorithms are used to enable robots to interact with humans and understand their needs and preferences.

“AI and ML algorithms are used to enable robots to interact with humans and understand their needs and preferences [16] .”

• cites [16] Belk, 2021, Ethical issues in service robotics and artificial intelligence, Serv. Ind. J. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI, ML, and DL can play a significant role in improving the efficiency and safety of cargo handling at airports.

“Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are all crucial technologies in the field of robotics, and they can play a significant role in improving the efficiency and safety of cargo handling at airports.”

Probably not a substantial claim: may not pass S4 (based).

In advanced robotics, AI, ML, and DL are used to create robots that can perform complex tasks and learn from experience.

“In advanced robotics, these technologies are used to create robots that can perform complex tasks and learn from experience.”

Probably not a substantial claim: may not pass S4 (based). May combine two claims.

The fields of robotics and AI are rapidly advancing and merging, with ML and DL playing an increasingly important role in the development of intelligent robots.

“The fields of robotics and artificial intelligence (AI) are rapidly advancing and merging, with machine learning (ML) and deep learning (DL) playing an increasingly important role in the development of intelligent robots.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI, ML, and DL are becoming increasingly important in the development and operation of advanced transportation systems, helping to improve efficiency, safety, and sustainability.

“Overall, AI, ML, and DL are becoming increasingly important in the development and operation of advanced transportation systems, helping to improve efficiency, safety, and sustainability.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Unmanned aerial vehicles (UAVs), usually referred to as advanced drones, are being utilized more frequently in a wide range of industries, including agriculture, construction, mining, search and rescue, and military activities.

“Unmanned aerial vehicles (UAVs), usually referred to as advanced drones, are being utilized more frequently in a wide range of industries, including agriculture, construction, mining, search and rescue, and military activities [89] .”

• cites [89] Olawale, 2020, AI simulations and programming environments for drones: an overview, Drones Smart-Cities sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S4 (based).

AI, ML, and DL can help improve the accuracy, efficiency, and autonomy of drones, making them more useful in a variety of applications.

“They can help to improve the accuracy, efficiency, and autonomy of drones, making them more useful in a variety of applications.”

May combine two claims.

The use of AI, ML, and DL in advanced drones has expanded their capabilities and made them more efficient and effective in performing various tasks.

“The use of AI, ML, and DL in advanced drones has expanded their capabilities and made them more efficient and effective in performing various tasks of different drones [90] .”

• Also stated in: § 5.1. Drones · p. 10 · plain

  “The use of AI, ML, and DL in advanced drones has expanded their capabilities and made them more efficient and effective in performing various tasks.”

• cites [90] Heidari, 2023, Machine learning applications in internet-of-drones: systematic review, recent deployments, and open issues, ACM Comput. Surv. sourceNot checked — no full text of the source

May combine two claims.

DL can be used to analyze traffic patterns and optimize routes for taxi drivers, helping them avoid congestion and take the most efficient route, which can save time and reduce fuel costs.

“DL can be used to analyze traffic patterns and optimize routes for taxi drivers. This can help them avoid congestion and take the most efficient route to their destination, which can save time and reduce fuel costs [129] .”

• cites [129] Jacob, 2021, Artificial bee colony optimization algorithm for enhancing routing in wireless networks, J. Artif. Intell. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

ML can be used to train the autopilot system to recognize and respond to different situations, such as changing weather conditions or unexpected obstacles.

“ML can be used to train the system to recognize and respond to different situations, such as changing weather conditions or unexpected obstacles.”

May combine two claims.

Data collected by drones on crop health, moisture levels, and soil conditions can be used to optimize crop yields and reduce waste.

“This data can then be used to optimize crop yields and reduce waste [97] .”

• cites [97] Saranya, 2023, A comparative study of deep learning and internet of things for precision agriculture, Eng. Appl. Artif. Intell. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

Tesla's Autopilot system uses a combination of cameras, radar, and ultrasonic sensors to detect and respond to obstacles and other vehicles on the road.

“Tesla's Autopilot system uses a combination of cameras, radar, and ultrasonic sensors to detect and respond to obstacles and other vehicles on the road.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

In airport cargo handling, AI can be used to automate tasks such as cargo tracking, inventory management, and customs processing.

“In the context of airport cargo handling, AI can be used to automate tasks such as cargo tracking, inventory management, and customs processing.”

May combine two claims.

AI can be used in drones to identify and track objects, detect obstacles and avoid collisions, and optimize flight paths for maximum efficiency.

“For example, AI can be used to identify and track objects, detect obstacles and avoid collisions, and optimize flight paths for maximum efficiency [91] .”

• cites [91] De Swarte, 2019, Artificial intelligence, ethics and human values: the cases of military drones and companion robots, Artif. Life Robot. sourceNot checked — no full text of the source

May combine two claims.

Algorithms can be used to predict and prevent accidents by alerting drivers of potential hazards and suggesting safer routes.

“Algorithms can be used to predict and prevent accidents by alerting drivers of potential hazards and suggesting safer routes [86] .”

• cites [86] Halim, 2016, Artificial intelligence techniques for driving safety and vehicle crash prediction, Artif. Intell. Rev. sourceNot checked — no full text of the source

May combine two claims.

AI algorithms can be used to identify potential hazards or obstacles in a ship's path and make course corrections to avoid them.

“AI algorithms can be used to identify potential hazards or obstacles in a ship's path and make course corrections to avoid them [103] .”

• cites [103] Yan, 2021, Emerging approaches applied to maritime transport research: past and future, Commun. Transport. Res. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

AI, ML, and DL are transforming the taxi industry by improving efficiency, reducing costs, and enhancing customer experience.

“AI, ML, and DL are transforming the taxi industry by improving efficiency, reducing costs, and enhancing customer experience.”

May combine two claims.

DL can be used to improve ship performance by optimizing engine settings or predicting equipment failures before they occur.

“DL can also be used to improve ship performance by optimizing engine settings or predicting equipment failures before they occur [105] .”

• cites [105] Qiao, 2023, Survey of deep learning for autonomous surface vehicles in marine environments, IEEE Trans. Intell. Transp. Syst. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

AI algorithms can be used to analyze passenger data to predict behavior, preferences and improve personalized service offerings.

“AI algorithms can be used to analyze passenger data to predict behavior, preferences and improve personalized service offerings.”

• cites [118] Iqbal, 2020, Big data analytics and computational intelligence for cyber–physical systems: recent trends and state of the art applications, Future Generat. Comput. Syst. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

The combination of AI, ML, and DL with robotics has the potential to create a wide range of applications that can benefit society in various ways.

“Overall, the combination of AI, ML, and DL with robotics has the potential to create a wide range of applications that can benefit society in various ways.”

Probably not a substantial claim: may not pass S3 (load-bearing).

AI algorithms can be used to optimize flight routes, taking into account factors such as weather conditions, air traffic, and fuel efficiency.

“AI algorithms can be used to optimize flight routes, taking into account factors such as weather conditions, air traffic, and fuel efficiency.”

• cites [117] Choi, 2019, Unmanned aerial vehicles using machine learning for autonomous flight; state-of-the-art, Adv. Robot. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

Advanced robotics applications that use AI, ML, and DL include autonomous vehicles, drone navigation, industrial robots, healthcare robots, and search and rescue robots.

“Advanced robotics applications that use AI, machine learning, and deep learning include autonomous vehicles, drone navigation, industrial robots, healthcare robots, and search and rescue robots.”

May combine two claims.

DL can be used to monitor drivers and passengers for safety concerns, such as distracted driving or unruly behavior, helping taxi companies maintain high safety standards and reduce the risk of accidents.

“DL can be used to monitor drivers and passengers for safety concerns, such as distracted driving or unruly behavior. This can help taxi companies maintain high safety standards and reduce the risk of accidents [132] .”

• cites [132] Singh, 2021, Highway 4.0: digitalization of highways for vulnerable road safety development with intelligent IoT sensors and machine learning, Saf. Sci. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

The combination of AI, ML, and DL in advanced robotics is enabling the development of robots that are more intelligent, versatile, and capable than ever before.

“The combination of AI, ML, and DL in advanced robotics is enabling the development of robots that are more intelligent, versatile, and capable than ever before.”

May combine two claims.

DL is used in autonomous driving technology, which is being developed by taxi companies to reduce labor costs and improve safety.

“DL is used in autonomous driving technology, which is being developed by taxi companies to reduce labor costs and improve safety.”

• cites [133] Miglani, 2019, Deep learning models for traffic flow prediction in autonomous vehicles: a review, solutions, and challenges, Vehicular Commun. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

AI, ML, and DL can be used to improve the accuracy of autonomous vehicles, allowing robots to operate them more safely and effectively.

“AI, ML, and DL can be used to improve the accuracy of autonomous vehicles, allowing robots to operate them more safely and effectively.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

In the case of Tesla machines, AI, ML, and DL are used to enable a range of advanced capabilities.

“In the case of Tesla machines, AI, ML, and DL are used to enable a range of advanced capabilities.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

This allows the robot to perceive and understand its environment in a way that is similar to how humans do, and to make decisions based on that understanding.

“This allows the robot to perceive and understand its environment in a way that is similar to how humans do, and to make decisions based on that understanding.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.

Applications of AI, ML, and DL in advanced transportation systems have the potential to revolutionize travel, making transportation safer, more efficient, and more sustainable.

“The applications of AI, ML, and DL in advanced transportation systems have the potential to revolutionize the way we travel, making transportation safer, more efficient, and more sustainable.”

May combine two claims.

Robotic cargo handlers can use ML algorithms to optimize their movements and avoid collisions with other cargo handling equipment.

“For example, robotic cargo handlers can use DL algorithms to identify and sort cargo based on size, weight, and destination, and they can use ML algorithms to optimize their movements and avoid collisions with other cargo handling equipment [115] .”

• cites [115] Sukhorukov, 2020, Digital transformation of airline management as the basis of innovative development sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

DL can be used to enable the autopilot system to learn from past experiences and make more accurate predictions about future events.

“DL can be used to enable the system to learn from past experiences and make more accurate predictions about future events.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

ML algorithms are used to analyze Tesla sensor data and make decisions about how to control the vehicle, such as adjusting its speed or steering to avoid collisions.

“ML algorithms are used to analyze this sensor data and make decisions about how to control the vehicle, such as adjusting its speed or steering to avoid collisions.”

May combine two claims.

AI algorithms can analyze transaction data to identify fraudulent activities, such as false claims of lost property or overcharging customers, helping taxi companies reduce losses and improve customer trust.

“AI algorithms can analyze transaction data to identify fraudulent activities, such as false claims of lost property or overcharging customers. This can help taxi companies reduce losses and improve customer trust [131] .”

• cites [131] Wang, 2022, AI-empowered trajectory anomaly detection for intelligent transportation systems: a hierarchical federated learning approach, IEEE Trans. Intell. Transp. Syst. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S4 (based). May combine two claims.

These technologies are transforming the field of robotics and enabling robots to perform tasks that were once considered too difficult or dangerous for humans.

“These technologies are transforming the field of robotics and enabling robots to perform tasks that were once considered too difficult or dangerous for humans.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

The use of AI, ML, and DL in robotics systems can improve the efficiency, safety, and accuracy of airport cargo handling, which can lead to faster delivery times, reduced costs, and improved customer satisfaction.

“Overall, the use of AI, ML, and DL in robotics systems can improve the efficiency, safety, and accuracy of airport cargo handling, which can lead to faster delivery times, reduced costs, and improved customer satisfaction.”

Probably not a substantial claim: may not pass S4 (based).

AI, ML, and DL are increasingly being integrated into robotics, providing robots with the ability to learn, adapt, and improve their performance over time.

“Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are increasingly being integrated into robotics, providing robots with the ability to learn, adapt, and improve their performance over time.”

Classified as probably not a substantial claim.

AI, ML, and DL are essential technologies in the development of robotics drones.

“Overall, AI, ML, and DL are all essential technologies in the development of robotics drones.”

May combine two claims.

AI and ML can be used to analyze historical data to predict future demand for taxi services, helping taxi companies optimize fleet management, scheduling, and pricing strategies.

“AI and ML can be used to analyze historical data to predict future demand for taxi services. This can help taxi companies optimize their fleet management, scheduling, and pricing strategies to ensure that they are providing the right number of taxis in the right areas at the right times [135] .”

• cites [135] Ramani, 2022, Predicting new york taxi trip duration based on regression analysis using ML and time series forecasting using DL sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

AI, ML, and DL have the potential to revolutionize the field of robotics taxi services.

“Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are all technologies that have the potential to revolutionize the field of robotics taxi services.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.

The combination of AI, ML, and DL has the potential to make robotics taxi services safer, more efficient, and more enjoyable for passengers.

“Overall, the combination of AI, ML, and DL has the potential to make robotics taxi services safer, more efficient, and more enjoyable for passengers.”

Probably not a substantial claim: may not pass S3 (load-bearing).

AI and DL can be used to personalize the customer experience by analyzing data on passenger preferences and behavior, and providing recommendations for entertainment, food, and other services.

“AI and DL can be used to personalize the customer experience by analyzing data on passenger preferences and behavior, and providing recommendations for entertainment, food, and other services [130] .”

• cites [130] Cheng, 2023, An investigation on the influencing factors of elderly people's intention to use financial AI customer service, Int. Res. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

DL algorithms are used to improve the accuracy of object detection and recognition, enabling the vehicle to identify and track pedestrians, cyclists, and other objects on the road.

“Additionally, DL algorithms are used to improve the accuracy of object detection and recognition, enabling the vehicle to identify and track pedestrians, cyclists, and other objects on the road.”

Probably not a substantial claim: may not pass S4 (based).

DL is particularly useful in robotics because it can be used to develop algorithms that enable the robot to learn from large amounts of sensory data, such as images or audio recordings.

“DL is particularly useful in robotics because it can be used to develop algorithms that enable the robot to learn from large amounts of sensory data, such as images or audio recordings.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.

The use of AI, ML, and DL in robotics autopilot systems has the potential to improve the reliability and safety of autonomous vehicles, making them a promising technology for the future of transportation.

“The use of AI, ML, and DL in robotics autopilot systems has the potential to improve the reliability and safety of autonomous vehicles, making them a promising technology for the future of transportation.”

Probably not a substantial claim: may not pass S3 (load-bearing).

In robotics autopilot systems, AI, ML, and DL can be used to improve the accuracy, efficiency, and safety of the system.

“In robotics autopilot systems, these technologies can be used to improve the accuracy, efficiency, and safety of the system.”

Probably not a substantial claim: may not pass S3 (load-bearing).

AI, ML, and DL can help improve the safety, efficiency, and overall customer experience of autonomous taxi services.

“These technologies can help improve the safety, efficiency, and overall customer experience of autonomous taxi services [123] .”

• cites [123] Arbatani, 2021, Competitive strategies of mobile applications in online taxi services: the cases of Snapp and Tap30 in Iran, Int. J. Emerg. MarketsNot checked yet

  Not checked in the free run.

May combine two claims.

ML models can be trained on historical data to predict potential collisions and provide real-time recommendations to avoid them in ship navigation.

“ML models can be trained on historical data to predict potential collisions and provide real-time recommendations to avoid them [107] .”

• cites [107] Hammedi, 2022, Federated deep learning-based framework to avoid collisions between inland ships sourceNot checked — no full text of the source