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SpaceX launches Space Force weather satellite designed to take over for a program with roots to the 1960s … – Spaceflight Now

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The Weather System Follow-on Microwave (WSF-M) space vehicle was successfully encapsulated April 8, 2024, ahead of its scheduled launch as the U.S. Space Force (USSF)-62 mission from Vandenberg Space Force Base, Calif., marking a major milestone on its upcoming launch into low Earth orbit. Image: SpaceX

SpaceX launched a military weather satellite designed to replace aging satellites from a program dating back to the 1960s. The United States Space Force-62 (USSF-62) mission featured the launch of the first Weather System Follow-on Microwave (WSF-M) spacecraft.

Liftoff of the Falcon 9 rocket from Space Launch Complex 4 East (SLC-4E) at Vandenberg Space Force Base happened at 7:25 a.m. PDT (10:25 a.m. EDT (1425 UTC), which was the opening of a 10-minute launch window.

The booster supporting this National Security Space Launch (NSSL) mission, B1082 in the SpaceX fleet, made its third flight after previously launching the Starlink 7-9 and 7-14 missions this year.

Were absolutely thrilled be out here on the Central Coast, with a superb team primed and ready to launch the USSF-62 satellite. It has an important mission ahead of it and were excited for flight-proven Falcon 9 to deliver the satellite to orbit, said Col. Jim Horne, senior materiel leader for the Space System Commands Launch Execution Delta, in a statement. And on this mission, were using a first-stage booster whose history is purely commercial.

About eight minutes after liftoff, B1082 touched down at Landing Zone 4 (LZ-4). This was the 17th land landing in California and the 295th booster landing for SpaceX.

A significant milestone for the company on the USSF-62 mission was the use of flight-proven payload fairings, which will be a first for an NSSL mission. They previously flew on the USSF-52 mission, which featured the launch of the X-37B spaceplane from NASAs Kennedy Space Center in December 2023.

With each national security launch, we add to Americas capabilities and improve its deterrence in the face of growing threats, Horne stated.

USSF-62 was one of three missions granted to SpaceX in May 2022 as part of the NSSL Phase 2 Order Year 3 award, which collectively are valued at $309.7 million. SpaceX launched USSF-124 in February 2024 and will likely launch the SDA-Tranche 1 satellites later this year.

Ball Aerospace, the manufacturer of the WSF-M, said the spacecrafts primary payload is a passive microwave radiometer, which has been demonstrated on previous spacecraft. It also boasts a 1.8 meter antenna, which combined with the primary instrument allow the spacecraft to address so-called space-based environmental monitoring (SBEM) gaps.

Its capabilities will provide valuable information for protecting the assets of the United States and its allies, primarily in ocean settings.

The WSF-M satellite is a strategic solution tailored to address three high-priority Department of Defense SBEM gaps specifically, ocean surface vector winds, tropical cyclone intensity, and energetic charged particles in low Earth orbit, said David Betz, WSF-M program manager, SSC Space Sensing, in a statement. Beyond these primary capabilities, our instruments also provide vital data on sea ice characterization, soil moisture, and snow depth.

The spacecraft is based on the Ball Configurable Platform and includes a Global Precipitation Measurement (GPM) Microwave Imager (GMI) sensor and an Energetic Charged Particle sensor. Ball Aerospace has been involved with other, similar spacecraft, including the Suomi National Polar-orbiting Partnership (Suomi-NPP) and the Joint Polar Satellite System-1 (JPSS-1).

According to a public FY2024 Department of Defense budget document, the WSF-M system will consist of two spacecraft. Once the first is on orbit, it will assess the level of Ocean Surface Vector Wind (OSVW) measurement uncertainty and Tropical Cyclone Intensity (TCI) latency.

The first seeds of the program were planted back in October 2012 during whats called the Materiel Solution Analysis phase. That resulted in the Department of the Air Force issuing a request for proposals from companies in January 2017.

In November 2017, the Space and Missile Systems Center (now Space Systems Command) awarded a $93.7 million firm-fixed-price contract to Ball Aerospace for the WSF-M project with an expected completion date of Nov. 15, 2019.

This is an exciting win for us, and were looking forward to expanding our work with the Air Force and continuing to support warfighters and allies around the world, said Rob Strain, the then president, Ball Aerospace, in a 2017 statement. WSF-M extends Balls legacy of providing precise measurements from space to enable more accurate weather forecasting.

Roughly a year later, Ball received a $255.4 million contract modification, which provides for the exercise of an option for development and fabrication of the [WSF-M] Space Vehicle 1. This new contract also pushed out the expected completion date to Jan. 15, 2023.

In May 2020, the U.S. Space Forces SMSC noted the completion of the WSF-M systems critical design review that April, which opened the door to the beginning of fabrication.

Over the following year, the spacecraft went through a series of tests, running both the software and hardware through its paces. The primary bus structure was completed by August 2021 and by October 2022, the spacecraft entered its integration readiness review (IRR) and test readiness review (TRR).

Before that though, in May 2022, Ball was awarded a $16.6 million cost-plus-incentive-fee contract modification, which was for the exercise of an option for integration, test and operational work of the spacecraft. That brought the cumulative face value of the contract to about $417.4 million.

Shortly before the end of that year, in November 2022, Ball received a $78.3 firm-fixed-price contract modification to develop the second WSF-M spacecraft. That work is expected to be completed by Nov. 15, 2027, which would set up a launch opportunity no earlier than January 2028.

It was finally delivered from Balls facilities in Boulder, Colorado, to Vandenberg Space Force Base for pre-launch processing in February 2024.

This delivery represents a major milestone for the WSF-M program and is a critical step towards putting the first WSF-M satellite on-orbit for the warfighter, said Col. Daniel Visosky, senior materiel leader, SSCs Space Sensing Environmental and Tactical Surveillance program office, in a statement.It represents a long-term collaboration and unity-of-effort between the Space Force and our combined teams at Ball Aerospace, support contractors and government personnel.

This first WSF-M satellite, and eventually the second, will take the place of the legacy Defense Meteorological Satellite Program (DMSP) satellites, which have roots going back in the 1960s. The program features two primary satellites, which operate in sun-synchronous LEO polar orbits at about 450 nautical miles in altitude.

Originally known as the Defense Satellite Applications Program (DASP), the first of these legacy satellites launched in 1962 and they were classified under the purview of the National Reconnaissance Office (NRO) as part of the Corona Program. The DMSP was declassified in 1972 to allow data to be used by non-governmental scientists and civilians.

According to a Space Force historical accounting, a tri-agency organizational agreement was forged between the DoD, the Department of Commerce and NASA following President Bill Clintons directive for the DOC and the DoD to converge their separate polar-orbiting weather satellite programs. Funding responsibility stayed with the DoD, but by June 1998, the operational responsibility of the DMSP transferred to the Department of Commerce.

Satellite operations for the DMSP then became the responsibility of the National Oceanic and Atmospheric Administration (NOAA) Office of Satellite and Product Operations (OSPO).

The program was not without issue over the years. In 2004, the DMSP-F11 satellite, launched in 1991 and retired in 1995, disintegrated and created dozens of pieces of orbital debris. In 2015, a faulty battery was blamed for a similar disintegration of DMSP-F13, which resulted in 147 pieces of debris.

That year, Congress ordered an end to the DMSP program and the yet-to-launch F20 satellite was to be scrapped.

In February 2016, the DMSP-F19 had its planned five-year mission cut short less than two years after launch. The satellite suffered a power anomaly that caused engineers to lose control of it. The spacecraft was declared lost in March.

The DMSP-F17 satellite, launched in 2006, was then relocated to the primary position vacated by F19. According to the Observing Systems Capability Analysis and Review (OSCAR), a tool developed by the World Meteorological Organization, there are three DMSP satellites still in service: F16, F17 and F18. They launched in 2003, 2006 and 2009 respectively.

The latter two have expected end-of-life dates of 2025, with F16 intended to conclude its mission in December 2023, according to the Committee on Earth Observation Satellites (CEOS). However, that expiration has been extended as the WSF-M replacements are still on the way.

Its unclear if F17 and F18 can hang on until the second WSF-M spacecraft is completed and launched in 2028.

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SpaceX launches Space Force weather satellite designed to take over for a program with roots to the 1960s ... - Spaceflight Now

The evolution of robotics: research and application progress of dental implant robotic systems | International Journal of … – Nature.com

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Implantology is widely considered the preferred treatment for patients with partial or complete edentulous arches.34,35 The success of the surgery in achieving good esthetic and functional outcomes is directly related to correct and prosthetically-driven implant placement.36 Accurate implant placement is crucial to avoid potential complications such as excessive lateral forces, prosthetic misalignment, food impaction, secondary bone resorption, and peri-implantitis.37 Any deviation during the implant placement can result in damage to the surrounding blood vessels, nerves, and adjacent tooth roots and even cause sinus perforation.38 Therefore, preoperative planning must be implemented intraoperatively with utmost precision to ensure quality and minimize intraoperative and postoperative side effects.39

Currently, implant treatment approaches are as follows: Free-handed implant placement, Static computer-aided implant placement, and dynamic computer-aided implant placement. The widely used free-handed implant placement provides less predictable accuracy and depends on the surgeons experience and expertise.40 Deviation in implant placement is relatively large among surgeons with different levels of experience. When novice surgeons face complex cases, achieving satisfactory results can be challenging. A systematic review41 based on six clinical studies indicated that the ranges of deviation of the platform, apex, and angle from the planned position with free-handed implant placement were (1.250.62)mm(2.771.54)mm, (2.101.00)mm(2.911.52)mm, and 6.904.409.926.01, respectively. Static guides could only provide accurate guidance for the initial implantation position. However, it is difficult to precisely control the depth and angle of osteotomies.42 The lack of real-time feedback on drill positioning during surgery can limit the clinicians ability to obtain necessary information.42,43,44 Besides, surgical guides may also inhibit the cooling of the drills used for implant bed preparation, which may result in necrosis of the overheated bone. Moreover, the use of static guides is limited in patients with limited accessibility, especially for those with implants placed in the posterior area. Additionally, the use of guides cannot flexibly adjust the implant plan intraoperatively. With dynamic computer-aided implant placement, the positions of the patient and drills could be tracked in real-time and displayed on a computer screen along with the surgical plan, thus allowing the surgeon to adjust the drilling path if necessary. However, the surgeons may deviate from the plan or prepare beyond it without physical constraints. During surgery, the surgeon may focus more on the screen for visual information rather than the surgical site, which can lead to reduced tactile feedback.45 The results of a meta-analysis showed that the platform deviation, apex deviation, and angular deviation were 0.91mm (95% CI 0.791.03mm), 1.26mm (95% CI 1.141.38mm), and 3.25 (95% CI 2.843.66) respectively with the static computer-aided implant placement, and 1.28mm (95% CI 0.871.69mm), 1.68mm (95% CI 1.451.90mm), and 3.79 (95% CI 1.875.70), respectively, with dynamic computer-aided implant placement. The analysis results showed that both methods improved the accuracy compared to free-handed implant placement, but they still did not achieve ideal accuracy.46 Gwangho et al.47 believe that the key point of a surgical operation is still manually completed by surgeons, regardless of static guide or dynamic navigation, and the human factors (such as hand tremble, fatigue, and unskilled operation techniques) also affect the accuracy of implant placement.

Robotic-assisted implant surgery could provide accurate implant placement and help the surgeon control handpieces to avoid dangerous tool excursions during surgery.48 Furthermore, compared to manual calibration, registration, and surgery execution, automatic calibration, registration, and drilling using the dental implant robotic system reduces human error factors. This, in turn, helps avoid deviations caused by surgeons factors, thereby enhancing surgical accuracy, safety, success rates, and efficiency while also reducing patient trauma.7 With the continuous improvement of technology and reduction of costs, implant robotics are gradually becoming available for commercial use. Yomi (Neocis Inc., USA) has been approved by the Food and Drug Administration, while Yakebot (Yakebot Technology Co., Ltd., Beijing, China), Remebot (Baihui Weikang Technology Co., Ltd, Beijing, China), Cobot (Langyue dental surgery robot, Shecheng Co. Ltd., Shanghai, China), Theta (Hangzhou Jianjia robot Co., Ltd., Hangzhou, China), and Dcarer (Dcarer Medical Technology Co., Ltd, Suzhou, China) have been approved by the NMPA. Dencore (Lancet Robotics Co., Ltd., Hangzhou, China) is in the clinical trial stage in China.

Compared to other surgeries performed with general anesthesia, dental implant surgery can be completed under local anesthesia, with patients awake but unable to remain completely still throughout the entire procedure. Therefore, research related to dental implant robotic system, as one of the cutting-edge technologies, mainly focuses on acquiring intraoperative feedback information (including tactile and visual information), different surgical methods (automatic drilling and manual drilling), patient position following, and the simulation of surgeons tactile sensation.

The architecture of dental implant robotics primarily comprises the hardware utilized for surgical data acquisition and surgical execution (Fig. 4). Data acquisition involves perceiving, identifying, and understanding the surroundings and the information required for task execution through the encoders, tactile sensors, force sensors, and vision systems. Real-time information obtained also includes the robots surrounding environment, object positions, shapes, sizes, surface features, and other relevant information. The perception system assists the robot in comprehending its working environment and facilitates corresponding decision-making as well as actions.

The architecture of dental implant robotics

During the initial stage of research on implant robotics, owing to the lack of sensory systems, fiducial markers and corresponding algorithms were used to calculate the transformation relationship between the robots and the models coordinate system. The robot was able to determine the actual position through coordinate conversions. Dutreuil et al.49 proposed a new method for creating static guides on casts using robots based on the determined implant position. Subsequently, Boesecke et al.50 developed a surgical planning method using linear interpolation between start and end points, as well as intermediate points. The surgeon performed the osteotomies by holding the handpieces, with the robot guidance based on preoperatively determined implant position. Sun et al.51 and McKenzie et al.52 registered cone-beam computed tomography (CBCT) images, the robots coordinate system, and the patients position using a coordinate measuring machine, which facilitated the transformation of preoperative implant planning into intraoperative actions.

Neocis has developed a dental implant robot system called Yomi (Neocis Inc.)53 based on haptic perception and connects a mechanical joint measurement arm to the patients teeth to track their position. The joint encoder provides information on the drill position, while the haptic feedback of handpieces maneuvered by the surgeon constrains the direction and depth of implant placement.

Optical positioning is a commonly used localization method that offers high precision, a wide -field -of -view, and resistance to interference.54 This makes it capable of providing accurate surgical guidance for robotics. Yu et al.55 combined image-guided technology with robotic systems. They used a binocular camera to capture two images of the same target, extract pixel positions, and employ triangulation to obtain three-dimensional coordinates. This enabled perception of the relative positional relationship between the end-effector and the surrounding environment. Yeotikar et al.56 suggested mounting a camera on the end-effector of the robotic arm, positioned as close to the drill as possible. By aligning the cameras center with the drills line of sight at a specific height on the lower jaw surface, the cameras center accurately aligns with the drills position in a two-dimensional space at a fixed height from the lower jaw. This alignment guides the robotic arm in drilling through specific anatomical landmarks in the oral cavity. Yan et al.57 proposed that the use of eye-in-hand optical navigation systems during surgery may introduce errors when changing the handpiece at the end of the robotic arm. Additionally, owing to the narrow oral environment, customized markers may fall outside the cameras field of view when the robotic arm moves to certain positions.42 To tackle this problem, a dental implant robot system based on optical marker spatial registration and probe positioning strategies is designed. Zhao et al constructed a modular implant robotic system based on binocular visual navigation devices operating on the principles of visible light with eye-to-hand mode, allowing complete observation of markers and handpieces within the cameras field of view, thereby ensuring greater flexibility and stability.38,58

The dental implant robotics execution system comprises hardware such as motors, force sensors, actuators, controllers, and software components to perform tasks and actions during implant surgery. The system receives commands, controls the robots movements and behaviors, and executes the necessary tasks and actions. Presently, research on dental implant robotic systems primarily focuses on the mechanical arm structure and drilling methods.

The majority of dental implant robotic systems directly adopt serial-linked industrial robotic arms based on the successful application of industrial robots with the same robotic arm connection.59,60,61,62 These studies not only establish implant robot platforms to validate implant accuracy and assess the influence of implant angles, depths, and diameters on initial stability but also simulate chewing processes and prepare natural root-shaped osteotomies based on volume decomposition. Presently, most dental implant robots in research employ a single robotic arm for surgery. Lai et al.62 indicated that the stability of the handpieces during surgery and real-time feedback of patient movement are crucial factors affecting the accuracy of robot-assisted implant surgery. The former requires physical feedback, while the latter necessitates visual feedback. Hence, they employed a dual-arm robotic system where the main robotic arm was equipped with multi-axis force and torque sensors for performing osteotomies and implant placement. The auxiliary arm consisted of an infrared monocular probe used for visual system positioning to address visual occlusion issues arising from changes in arm angles during surgery.

The robots mentioned above use handpieces to execute osteotomies and implant placement. However, owing to limitations in patient mouth opening, performing osteotomies and placing implants in the posterior region can be challenging. To overcome the spatial constraints during osteotomies in implant surgery, Yuan et al.63 proposed a robot system based on earlier research which is laser-assisted tooth preparation. This system involves a non-contact ultra-short pulse laser for preparing osteotomies. The preliminary findings confirmed the feasibility of robotically controlling ultra-short pulse lasers for osteotomies, introducing a novel method for a non-contact dental implant robotic system.

It can be challenging for patients under local anesthesia to remain completely still during robot-assisted dental implant surgery.52,64,65,66,67 Any significant micromovement in the patients position can severely affect clinical surgical outcomes, such as surgical efficiency, implant placement accuracy compared to the planned position, and patient safety. Intraoperative movement may necessitate re-registration for certain dental implant robotic systems. In order to guarantee safety and accuracy during surgery, the robot must detect any movement in the patients position and promptly adjust the position of the robotic arm in real time. Yakebot uses binocular vision to monitor visual markers placed outside the patients mouth and at the end of the robotic arm. This captures motion information and calculates relative position errors. The robot control system utilizes preoperatively planned positions, visual and force feedback, and robot kinematic models to calculate optimal control commands for guiding the robotic arms micromovements and tracking the patients micromovements during drilling. As the osteotomies are performed to the planned depth, the robotic arm compensates for the patients displacement through the position following the function. The Yakebots visual system continuously monitors the patients head movement in real time and issues control commands every 0.008s. The robotic arm is capable of following the patients movements with a motion servo in just 0.2s, ensuring precise and timely positioning.

Robot-assisted dental implant surgery requires the expertise and tactile sense of a surgeon to ensure accurate implantation. Experienced surgeons can perceive bone density through the resistance they feel in their hands and adjust the force magnitude or direction accordingly. This ensures proper drilling along the planned path. However, robotic systems lack perception and control, which may result in a preference for the bone side with lower density. This can lead to inaccurate positioning compared to the planned implant position.61,62 Addressing this challenge, Li et al.68 established force-deformation compensation curves in the X, Y, and Z directions for the robots end-effector based on the visual and force servo systems of the autonomous dental robotic system, Yakebot. Subsequently, a corresponding force-deformation compensation strategy was formulated for this robot, thus proving the effectiveness and accuracy of force and visual servo control through in vitro experiments. The implementation of this mixed control mode, which integrates visual and force servo systems, has improved the robots accuracy in implantation and ability to handle complex bone structures. Based on force and visual servo control systems, Chen et al.69 have also explored the relationship between force sensing and the primary stability of implants placed using the Yakebot autonomous dental robotic system through an in vitro study. A significant correlation was found between Yakebots force sensing and the insertion torque of the implants. This correlation conforms to an interpretable mathematical model, which facilitates the predictable initial stability of the implants after placement.

During osteotomies with heat production (which is considered one of the leading causes of bone tissue injury), experienced surgeons could sense possible thermal exposure via their hand feeling. However, with free-handed implant placement surgery, it is challenging to perceive temperature changes during the surgical process and establish an effective temperature prediction model that relies solely on a surgeons tactile sense. Zhao et al.70, using the Yakebot robotic system, investigated the correlation between drilling-related mechanical data and heat production and established a clinically relevant surrogate for intraosseous temperature measurement using force/torque sensor-captured signals. They also established a real-time temperature prediction model based on real-time force sensor monitoring values. This model aims to effectively prevent the adverse effects of high temperatures on osseointegration, laying the foundation for the dental implant robotic system to autonomously control heat production and prevent bone damage during autonomous robotic implant surgery.

The innovative technologies mentioned above allow dental implant robotic systems to simulate the tactile sensation of a surgeon and even surpass the limitations of human experience. This advancement promises to address issues that free-handed implant placement techniques struggle to resolve. Moreover, this development indicates substantial progress and great potential for implantation.

The robotic assistant dental implant surgery consists of three steps: preoperative planning, intraoperative phase, and postoperative phase (Fig. 5). For preoperative planning, it is necessary to obtain digital intraoral casts and CBCT data from the patient, which are then imported into preoperative planning software for 3D reconstruction and planning implant placement. For single or multiple tooth gaps using implant robotic systems (except Yakebot),61,62,71,72 a universal registration device (such as the U-shaped tube) must be worn on the patients missing tooth site using a silicone impression material preoperatively to acquire CBCT data for registration. The software performs virtual placement of implant positions based on prosthetic and biological principles of implant surgery, taking into account the bone quality of the edentulous implant site to determine the drilling sequence, insertion depth of each drill, speed, and feed rate. For single or multiple tooth implants performed using Yakebot, there is no need for preoperative CBCT imaging with markers. However, it is necessary to design surgical accessories with registration holes, brackets for attaching visual markers, and devices for assisting mouth opening and suction within the software (Yakebot Technology Co., Ltd., Beijing, China). These accessories are manufactured using 3D printing technology.

Clinical workflow of robotic-assisted dental implant placement

For the intraoperative phase, the first step is preoperative registration and calibration. For Yakebot, the end-effector marker is mounted to the robotic arm, and the spatial positions are recorded under the optical tracker. The calibration plate with the positioning points is then assembled into the implant handpiece for drill tip calibration. Then, the registration probe is inserted in the registration holes of the jaw positioning plate in turn for spatial registration of the jaw marker and the jaw. Robot-assisted dental implant surgery usually does not require flapped surgery,73,74, yet bone grafting due to insufficient bone volume in a single edentulous space or cases of complete edentulism requiring alveolar ridge preparation may require elevation of flaps. For full-arch robot-assisted implant surgery, a personalized template with a positioning marker is required and should be fixed with metallic pins for undergoing an intraoperative CBCT examination, thus facilitating the robot and the jaws registration in the visual space and allowing the surgical robot to track the patients motion. The safe deployment of a robot from the surgical site is an essential principle for robot-assisted implant surgery. In the case of most robots, such as Yomi, the surgeon needs to hold the handpieces to control and supervise the robots movement in real time and stop the robotic arms movement in case of any accidents. With Yakebot, the entire surgery is performed under the surgeons supervision, and immediate instructions are sent in response to possible emergencies via a foot pedal. Additionally, the recording of the entrance and exit of the patients mouth ensures that the instruments would not damage the patients surrounding tissues. The postoperative phase aims at postoperative CBCT acquisition and accuracy measurement.

In clinical surgical practice, robots with varying levels of autonomy perform implant surgeries differently. According to the autonomy levels classified by Yang et al.6,8,33 for medical robots, commercial dental implant robotic systems (Table 2) currently operate at the level of robot assistance or task autonomy.

The robot-assistance dental implant robotic systems provide haptic,75 visual or combined visual and tactile guidance during dental implant surgery.46,76,77 Throughout the procedure, surgeons must maneuver handpieces attached to the robotic guidance arm and apply light force to prepare osteotomies.62 The robotic arm constrains the 3D space of the drill as defined by the virtual plan, enabling surgeons to move the end of the mechanical arm horizontally or adjust its movement speed. However, during immediate implant placement or full-arch implant surgery, both surgeons and robots may struggle to accurately perceive poor bone quality, which should prompt adjustments at the time of implant placement. This can lead to incorrect final implant positions compared to the planned locations.

The task-autonomous dental implant robotic systems can autonomously perform partial surgical procedures, such as adjusting the position of the handpiece to the planned position and preparing the implant bed at a predetermined speed according to the pre-operative implant plan, and surgeons should send instructions, monitor the robots operation, and perform partial interventions as needed. For example, the Remebot77,78 requires surgeons to drag the robotic arm into and out of the mouth during surgery, and the robot automatically performs osteotomies or places implants according to planned positions under the surgeons surveillance. The autonomous dental implant robot system, Yakebot,73,79,80 can accurately reach the implant site and complete operations such as implant bed preparation and placement during surgery. It can be controlled by the surgeon using foot pedals and automatically stops drilling after reaching the termination position before returning to the initial position. Throughout the entire process, surgeons only need to send commands to the robot using foot pedals.

Figure 6 shows the results of accuracy in vitro, in vivo, and clinical studies on robot-assisted implant surgery.20,46,48,55,62,64,67,68,69,70,71,72,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89 The results suggest that platform and apex deviation values are consistent across different studies. However, there are significant variations in angular deviations among different studies, which may be attributed to differences in the perception and responsiveness to bone quality variances among different robotic systems. Therefore, future development should focus on enhancing the autonomy of implant robots and improving their ability to recognize and respond to complex bone structures.

Accuracy reported in studies on robotic-assisted implant placement

Xu et al.77 conducted a phantom experimental study comparing the implant placement accuracy in three levels of dental implant robotics, namely passive robot (Dcarer, level 1), semi-active robot (Remebot, level 2), and active robot (Yakebot, level 2) (Fig. 7). The study found that active robot had the lowest deviations at the platform and apex of the planned and actual implant positions, While the semi-active robot also had the lowest angular deviations. Chen et al.46 and Jia et al.79 conducted clinical trials of robotic implant surgery in partially edentulous patients using a semi-active dental implant robotic system (level 1) and an autonomous dental implant robot (level 2). The deviations of the implant platform, apex, and angle were (0.530.23)mm/(0.430.18)mm, (0.530.24)mm/(0.560.18)mm and 2.811.13/1.480.59, respectively. These results consistently confirmed that robotic systems can achieve higher implant accuracy than static guidance and that there is no significant correlation between accuracy and implant site (such as anterior or posterior site). The platform and angle deviation of autonomous dental implant robots were smaller than those of semi-active dental implant robotic systems. Li et al.73 reported the use of the autonomous dental implant robot (level 2) to complete the placement of two adjacent implants with immediate postoperative restoration. The interim prosthesis fabricated prior to implant placement was seated without any adjustment, and no adverse reactions occurred during the operation.

Comparison of accuracy of dental implant robotics with different levels of autonomy (phantom experiments) (*P<0.05, **P<0.01, ***P<0.001)

Bolding et al.,53 Li et al.,20 Jia et al.,79 and Xie et al.90 used dental implant robots to conduct clinical trials in full-arch implant surgery with five or six implants placed in each jaw. The deviations of implant platform, apex, and angle are shown in Fig. 8. The haptic dental implant robot (level 1) used by Bolding et al.,53 achieved more deviations compared to other studies that used semi-active (level 1) or active robots (level 2). As its handpiece must be maneuvered by the surgeon, human errors such as surgeon fatigue may not be avoided. Owing to the parallel common implant placement paths between various implant abutments, prefabricated temporary dentures could be seated smoothly, and some patients wore temporary complete dentures immediately after surgery. These results indicate that robotic systems can accurately locate and perform implant placement during surgery.

Comparison of accuracy in robotic-assisted full-arch implant placement

As there are relatively few studies of implant robots in clinical applications, Tak acs et al.91 conducted a meta-analysis under in vitro conditions with free-handed, static-guided, dynamic navigated, and robotic-assisted implant placements, as shown in Fig. 9. It was found that, compared to free-handed, static guided and dynamic navigated implant placements, robotic-assisted implant placements have more advantages in terms of accuracy. However, in vitro studies cannot fully simulate the patients oral condition and bone quality. Recent clinical studies89,92,93 have shown a lower deviation in robotic-assisted implant placements compared to static-guided and dynamic-navigated implant placements. Common reasons for deviations in static-guided and dynamic-navigated implant placements include the following: deflection caused by hand tremors due to dense bone during surgery, surgeons experience, and other human factors. Larger clinical studies will be needed in the future to evaluate the differences between robotic and conventional surgical approaches and to provide guidance for the further development and refinement of robotic techniques.

Comparison of accuracy of free-handed, static, dynamic, and robotic-assisted implant placement. (FHIP free-hand implant placement, SCAIP static computer-aided implant placement, DCAIP dynamic computer-aided implant placement, RAIP robot-assisted implant placement)

For the long-term follow-up performance of robotic systems used in dental implant procedures, none of the comparative studies was longer than a year. One 1-year prospective clinical study by Xie et al.90 showed that the peri-implant tissues after robot-assisted full arch surgery at 1-year visit remained stable. There is little evidence indicating clinical outcomes especially for patient-reported outcomes. A more detailed clinical assessment should be included for further research.

Although robotic-assisted dental implant surgery can improve accuracy and treatment quality,94 it involves complex registration, calibration, and verification procedures that prolong the duration of surgery. These tedious processes may introduce new errors,61 and lower work efficiency, especially in single tooth implant placement62 that could extend visit times and affect patient satisfaction.62 Besides, surgeons are required to undergo additional training to familiarize themselves with the robotic system.87

During implantation, the drill tips at the end of the robotic arms cannot be tilted, and this can increase the difficulty of using robots in posterior sections with limited occlusal space.61,62 In addition, currently available marker systems require patients to wear additional devices to hold the marker in place. If these markers are contaminated or obstructed by blood, the visual system may not be able to detect them, limiting surgical maneuverability to some extent. During immediate implant placement or in cases of poor bone quality in the implant site, the drill tips may deviate towards the tooth sockets or areas of lower bone density, seriously affecting surgical precision.

Currently, only one study has developed a corresponding force-deformation compensation strategy for robots,68 but clinical validation is still lacking. Additionally, the dental implant robotic system, along with other dental implant robots developed for prosthetics, endodontics, and orthodontics, is currently single-functional. Multi-functional robots are required for performing various dental treatments.

Despite the enormous potential of robotic systems in the medical field, similar to the development of computer-aided design/computer-aided manufacturing technology, introducing and applying this technology faces multiple challenges in the initial stages. The high cost of robotic equipment may limit its promotion and application in certain regions or medical institutions. Surgeons require specialized technical training before operating robotic systems, which translates to additional training costs and time investment.95

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The evolution of robotics: research and application progress of dental implant robotic systems | International Journal of ... - Nature.com

Comau and Leonardo Want to Elevate Aeronautical Structure Inspection with Cognitive Robotics – DirectIndustry e-Magazine

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Robotic company Comau and aerospace company Leonardo are currently testing a self-adaptive robotic solution to enable autonomous inspection of helicopter blades. This could enhance quality inspections and offer greater flexibility without sacrificing precision or repeatability. At a time when the aerospace industry demands faster processes, better control, and higher quality, it requires a new generation of advanced automation. We contacted Simone Panicucci, Head of Cognitive Robotics at Comauto know more about this solution and how it could benefit the aerospace industry.

The increasing demand for faster processes in the aerospace industry requires to automate complex processes that, until recently, could only be manual. When it comes to testing essential structures such as helicopter blades, the potential benefits of automation increase exponentially. Robotic inspection ensures precision and efficiency. It also ensures standardization and full compliance with the testing process by objectively executing each assigned task.

To meet the industrys needs, Comau and Leonardo have been testing an intelligent inspection solution based on Comaus cognitive robotics, on-site in Anagni, Italy to inspect helicopter blades measuring up to 7 meters.

The solution relies on a combination of self-adaptive robotics, advanced vision systems, and artificial intelligence. Comaus intelligent robot can autonomously perform hammer tests and multispectral surface inspections on the entire nonlinear blade to measure and verify structural integrity, with a granularity exceeding thousands of points.

The robot perceives and comprehends its environment, makes calculated decisions, and intuitively optimizes the entire inspection process.

They will then test the system on another site to enhance MRO (maintenance, repair, and overhaul) service capabilities.

We contacted Simone Panicucci, Head of Cognitive Robotics at Comau who gave us more details about this collaboration.

Simone Panicucci: The collaboration grew out of Leonardos need to ensure advanced autonomous inspection of highly critical aviation infrastructure using cognitive robotics. The two companies are collaborating to develop and test a powerful, self-adaptive robotic solution to autonomously inspect helicopter blades up to 7 meters in length. Aerospace is not a sector that is used to automation yet. The high variability and the low volumes act as constraints toward a deep automation adoption. Cognitive robotics solutions are thus a key enabler to provide the automation benefits (such as process engineering, repeatability, and traceability) even with heterogeneous products and unstructured environments and Comau is leading the creation of AI-based, custom robotic solutions.

Simone Panicucci: The solution developed is a self-adaptive and efficient machine to inspect really large helicopter blades. It includes a visual inspection as well as a tapping test. It consists in physically stimulating the blade surface with an ad-hoc little hammer to recognize from the consequent sound if there is any issue in the blades internal structure. Jointly, both inspections require testing tens of thousands of points on the overall blade.

The robot can sense the environment, and locate the blade in the space with an accuracy below 10 mm. It can also understand potential objects in the scene the robot may collide with. And it can calculate at run time the optimal and collision-free path planning to complete the task.

Simone Panicucci: The solution is provided with a 3D camera whose input is elaborated by a vision system to merge multiple acquisitions, post-process the scene acquired, and then localize both the helicopter blade as well as potential obstacles.

Simone Panicucci: All the movements performed by the robot are calculated once the scene has been sensed, which means that no robot movement has been offline calculated. Additional sensors have been added to the robot flange as an external and independent system to avoid damaging the blade.

Simone Panicucci: Today, helicopter blade inspection is done manually. The provided solution offers greater accuracy and efficiency, ensuring standardization and full compliance with the testing process by objectively completing each assigned task. Operators now program the machine, codifying their experience through a simplified user interface. The machine can work for hours without intervention, providing an accurate report summarizing critical points at the end.

Simone Panicucci: The flexibility is given by the fact that the solution is able to deal with different helicopter blade models and potentially even different helicopter components. In addition, accuracy and repeatability are typical automation takeaways, now even improved thanks to vision system adoption. Increased quality is due to the fact that the operator can now focus on the activity where he/she brings most of the value, the defect detection and confirmation, instead of mechanically performing the inspection.

Simone Panicucci: Operator knowledge is always at the center. Leonardo personnel keep the final word regarding the helicopter blade status certification as well as any point inspected. The automation solution aims to alleviate operators from the repetitive task of manually inspecting tens of thousands of points on the helicopter surface. After hours of signal recording, the solution generates a comprehensive report summarizing the results of AI-based anomaly detection. The industrialized solution ensures repeatability, reliability, and traceability, covering and accurately performing the task.

Simone Panicucci: The solution is CE-certified and incorporates both physical and virtual safety measures. Physical barriers and safety lasers create a secure perimeter, halting operations instantly in the event of unexpected human intrusion. Furthermore, the solution ensures safe loading and unloading of helicopter blades and verifies proper positioning by requiring operators to activate safety keys from a distance of approximately 10 meters.

Simone Panicucci: This solution demonstrates that product heterogeneity and low volumes, typical of the aerospace sector, no longer constrain automation adoption. Comaus cognitive robotics approach enables the delivery of effectiveness, quality, and repeatability even in unstructured environments and with low volumes. It easily adapts to different helicopter models and blades. Executing a process like the tapping test necessitated defining requirements and process engineering. This involved defining the material of the tapping tool, as well as the angle and force to apply. Additionally, all labeled data, whether automatic or manual, are now tracked and recorded, facilitating the creation of an extensive knowledge base to train deep learning models.

Simone Panicucci: Leonardo has been conducting tests on this solution as part of a technology demonstration. This technology holds potential benefits for both Leonardo and its customers. It could standardize inspection processes globally and may be offered or deployed to customers with numerous helicopters requiring inspection.

Simone Panicucci: The specific solution could obviously be extended to other inspections in the helicopter sectors as well as the avionics. But it is worth mentioning that from the technology point of view, the software pipeline, as well as the localization and optimal path planning may be easily applicable in other inspection activities as well as manufacturing or even continuous processes, like welding.

Simone Panicucci: The next steps involve thorough testing of the automation solution at another Leonardo Helicopters plant. This process will contribute to ongoing improvements in the knowledge base and, consequently, the deep learning algorithm for anomaly recognition.

Continued here:

Comau and Leonardo Want to Elevate Aeronautical Structure Inspection with Cognitive Robotics - DirectIndustry e-Magazine

ChatGPT use linked to sinking academic performance and memory loss – Yahoo News UK

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ChatGPT use is linked to bad results and memory loss. (Getty Images)

Using AI software such as ChatGPT is linked to poorer academic performance, memory loss and increased procrastination, a study has shown.

The AI chatbot ChatGPT can generate convincing answers to simple text prompts, and is already used weekly by up to 32% of university students, according to research last year.

The new study found that university students who use ChatGPT to complete assignments find themselves in a vicious circle where they dont give themselves enough time to do their work and are forced to rely on ChatGPT, and over time, their ability to remember facts diminishes.

The research was published in the International Journal of Educational Technology in Higher Education. Scientists conducted interviews with 494 students about their use of ChatGPT, with some admitting to being "addicted" to using the technology to complete assignments.

The researchers wrote: "Since ChatGPT can quickly respond to any questions asked by a user, students who excessively use ChatGPT may reduce their cognitive efforts to complete their academic tasks, resulting in poor memory. Over time, over-reliance on generative AI tools for academic tasks, instead of critical thinking and mental exertion, may damage memory retention, cognitive functioning, and critical thinking abilities."

In the interviews, the researchers were able to pinpoint problems experienced by students who habitually used ChatGPT to complete their assignments.

The researchers surveyed students three times to work out what sort of student is most likely to use ChatGPT, and what effects heavy users experienced.

The researchers then asked questions about the effects of using ChatGPT.

Study author Mohammed Abbas, from the National University of Computer and Emerging Sciences in Pakistan, told PsyPost: "My interest in this topic stemmed from the growing prevalence of generative artificial intelligence in academia and its potential impact on students.

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"For the last year, I observed an increasing, uncritical, reliance on generative AI tools among my students for various assignments and projects I assigned. This prompted me to delve deeper into understanding the underlying causes and consequences of its usage among them."

The study found that students who were results-focused were less likely to rely on AI tools to do tasks for them.

The research also found that students who relied on ChatGPT were not getting the full benefit of their education - and actually lost the ability to remember facts.

"Our findings suggested that excessive use of ChatGPT can have harmful effects on students personal and academic outcomes. Specifically, those students who frequently used ChatGPT were more likely to engage in procrastination than those who rarely used ChatGPT," Abbas said.

"Similarly, students who frequently used ChatGPT also reported memory loss. In the same vein, students who frequently used ChatGPT for their academic tasks had a poor grade average."

The researchers found that students who felt under pressure were more likely to turn to ChatGPT - but that this then led to worsening academic performance and further procrastination and memory loss.

The researchers suggest that academic institutions should be mindful that heavy workloads can drive students to use ChatGPT.

The researchers also said academics should warn students of the negative impact of using the software.

"Higher education institutions should emphasise the importance of efficient time management and workload distribution while assigning academic tasks and deadlines," they said.

"While ChatGPT may aid in managing heavy academic workloads under time constraints, students must be kept aware of the negative consequences of excessive ChatGPT usage."

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ChatGPT use linked to sinking academic performance and memory loss - Yahoo News UK

iOS 18 won’t have a big focus on ‘ChatGPT-like generative AI features’ New leak says we should expect ‘ a slew of AI … – iMore

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A new report into Apples rumored iOS 18 AI shift has revealed that Apple will focus on tools to improve the daily life of iPhone users, rather than its answer to ChatGPT, when the software is unveiled in June.

Ever since the explosion of AI in the public domain last year, rumors have indicated that Apple is frantically trying to play catch up to rivals like Microsoft, Google, and OpenAI, allegedly spending millions of dollars a day on its own answer to ChatGPT. Bloombergs Mark Gurman has been at the forefront of these rumors, most recently reporting that Apple is in discussions with Google to bring Gemini AI to iPhone in a landmark deal. Now, Gurman has tempered expectations.

In his latest Power On Newsletter, Gurman states that while iOS 18 is still considered internally to be the biggest update to iOS since the original iPhone, and while the main event will be artificial intelligence, iOS 18 wont have a big focus on ChatGPT-esque generative AI.

According to Gurman we shouldnt expect a big focus on ChatGPT-like generative AI features. To be clear, this doesnt necessarily mean that Apple wont have any generative AI features. Indeed, earlier on in his report Gurman indicates that Apple could open up iOS so any developer could build a generative AI system deep into the iPhone, building on swirling rumors of the Google partnership, and reported discussions with Chinese multinational and AI company Baidu.

Instead, Gurmans report seems to indicate that Apples focus for consumers at WWDC 2024 (when we should see iOS 18 unveiled) will be on a slew of AI tools that help manage your daily life. Previously, weve heard that there are six iPhone applications Apple plans to improve with AI, including its Xcode development software, Messages, Pages, and Keynote.

Alongside these AI incursions, Gurman also reports that Apples iPhone Home Screen will offer more customizability in iOS 18, including the option to have blank spaces and columns, just like Android. iOS 18 will likely debut in September alongside Apple's next best iPhone, the iPhone 16 and iPhone 16 Pro.

iMore offers spot-on advice and guidance from our team of experts, with decades of Apple device experience to lean on. Learn more with iMore!

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iOS 18 won't have a big focus on 'ChatGPT-like generative AI features' New leak says we should expect ' a slew of AI ... - iMore

Google to relaunch ‘woke’ Gemini AI image tool in few weeks: ‘Not working the way we intended’ – New York Post

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Google said it plans to relaunch its artificial intelligence image generation software within the next few weeks after taking it offline in response to an uproar over what critics called absurdly woke depictions of historical scenes.

Though the Gemini chatbot remains up and running, Google paused its image AI feature last week after it generated female NHL players, African American Vikings and Founding Fathers, as well as an Asian woman dressed in 1943 military garb when asked for an image of a Nazi-era German soldier.

We have taken the feature offline while we fix that. We are hoping to have that back online very shortly in the next couple of weeks, few weeks, Google DeepMind CEO Demis Hassabis said Monday.

The tool was not working the way we intended, Hassabis added, speaking on a panel at the Mobile World Congress in Barcelona.

Since Google has not published the parameters that govern the Gemini chatbots behavior, it is difficult to get a clear explanation of why the software was inventing diverse versions of historical figures and events.

Elsewhere, a prompt requesting photographs of a pope resulted in an image of a Southeast Asian woman dressed in papal attire a far cry from any of the 266 popes throughout history, all of whom have been white men.

In the wake of Geminis diverse photo representations, social media users also tested its chatbot feature to see if it was as woke as its revisionist history image generator.

In the latest bizarre interaction, Gemini refused to say whether Elon Musk tweeting memes or Adolf Hitler ordering the deaths of millions of people was worse and asserted there is no right or wrong answer, according to an X post.

Nate Silver, the former head of data and polling news site FiveThirtyEight,posted a screenshot Sunday on X of Geminis alleged response to the question: Who negatively impacted society more, Elon tweeting memes or Hitler?

Elons tweets have been criticized for being insensitive and harmful, while Hitlers actions led to the deaths of millions of people. Ultimately its up to each individual to decide who they believe has had a more negative impact on society, Gemini responded.

Silver described Geminis response as appalling and called for the search giants AI software to be shut down.

Every single person who worked on this should take a long hard look in the mirror, he posted, while Musk called the interaction scary.

Yet another query had users asking Gemini whether pedophilia is wrong.

The search giants AI software refused to condemn pedophilia instead declaring that individuals cannot control who they are attracted to.

The question is multifaceted and requires a nuanced answer that goes beyond a simple yes or no, Gemini wrote, according to a screenshot posted by popular X personality Frank McCormick, known as Chalkboard Heresy, on Friday.

Googles politically correct tech also referred to pedophilia as minor-attracted person status, and declared that its important to understand that attractions are not actions.

It was a significant misstep for the search giant, which had just rebranded its main AI chatbot from Bard earlier this month and introduced heavily touted new features including image generation.

However, Geminis recent gaffe wasnt the first time an error in the tech caught users eye.

When the Bard chatbot was first released a year ago, it had shared inaccurate information about pictures of a planet outside the Earths solar system in a promotional video, causing Googles shares to drop by as much as 9%.

Google said at the time that it highlights the importance of a rigorous testing process and rebranded Bard as Gemini earlier this month.

Google parent Alphabet expanded Gemini from a chatbot to an image generator earlier this month as it races to produce AI software that rivals OpenAIs, which includes ChatGPT launched in November 2022 as well as Sora.

In a potential challenge to Googles dominance, Microsoft is pouring $10 billion into ChatGPT as part of a multi-year agreement with the Sam Altman-run firm, which saw the tech behemothintegrating the AI tool with its own search engine, Bing.

The Microsoft-backed company introduced Sora last week, which can produce high-caliber, one minute-long videos from text prompts.

With Post wires

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Google to relaunch 'woke' Gemini AI image tool in few weeks: 'Not working the way we intended' - New York Post

Report: NVIDIA Forms Custom Chip Unit for Cloud Computing and More – AnandTech

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With its highly successful A100 and H100 processors for artificial intelligence (AI) and high-performance computing (HPC) applications, NVIDIA dominates AI datacenter deployments these days. But among large cloud service providers as well as emerging devices like software defined vehicles (SDVs) there is a global trend towards custom silicon. And, according to a report from Reuters, NVIDIA is putting together a new business unit to take on the custom chip market.

The new business unit will reportedly be led by vice president Dina McKinney, who has a wealth of experience from working at AMD, Marvell, and Qualcomm. The new division aims to address a wide range of sectors including automotive, gaming consoles, data centers, telecom, and others that could benefit from tailored silicon solutions. Although NVIDIA has not officially acknowledged the creation of this division, McKinneys LinkedIn profile as VP of Silicon Engineering reveals her involvement in developing silicon for 'cloud, 5G, gaming, and automotive,' hinting at the broad scope of her alleged business division.

Nine unofficial sources across the industry confirmed to Reuters the existence of the division, but NVIDIA has remained tight-lipped, only discussing its 2022 announcement regarding implementation of its networking technologies into third-party solutions. According to Reuters, NVIDIA has initiated discussions with leading tech companies, including Amazon, Meta, Microsoft, Google, and OpenAI, to investigate the potential for developing custom chips. This hints that NVIDIA intends to extend its offerings beyond the conventional off-the-shelf datacenter and gaming products, embracing the growing trend towards customized silicon solutions.

While using NVIDIA's A100 and H100 processors for AI and high-performance computing (HPC) instances, major cloud service providers (CSPs) like Amazon Web Services, Google, and Microsoft are also advancing their custom processors to meet specific AI and general computing needs. This strategy enables them to cut costs as well as tailor capabilities and power consumption of their hardware to their particular needs. As a result, while NVIDIA's AI and HPC GPUs remain indispensable for many applications, an increasing portion of workloads now run on custom-designed silicon, which means lost business opportunities for NVIDIA. This shift towards bespoke silicon solutions is widespread and the market is expanding quickly. Essentially, instead of fighting custom silicon trend, NVIDIA wants to join it.

Meanwhile, analysts are painting the possibility of an even bigger picture. Well-known GPU industry observer Jon Peddie Research notes that they believe that NVIDIA may be interested in addressing not only CSPs with datacenter offerings, but also consumer market due to huge volumes.

"NVIDIA made their loyal fan base in the consumer market which enabled them to establish the brand and develop ever more powerful processors that could then be used as compute accelerators," said JPR's president Jon Peddie. "But the company has made its fortune in the deep-pocked datacenter market where mission-critical projects see the cost of silicon as trivial to the overall objective. The consumer side gives NVIDIA the economy of scale so they can apply enormous resources to developing chips and the software infrastructure around those chips. It is not just CUDA, but a vast library of software tools and libraries."

Back in mid-2010s NVIDIA tried to address smartphones and tablets with its Tegra SoCs, but without much success. However, the company managed to secure a spot in supplying the application processor for the highly-successful Nintendo Switch console, and certainly would like expand this business. The consumer business allows NVIDIA to design a chip and then sell it to one client for many years without changing its design, amortizing the high costs of development over many millions of chips.

"NVIDIA is of course interested in expanding its footprint in consoles right now they are supplying the biggest selling console supplier, and are calling on Microsoft and Sony every week to try and get back in," Peddie said. "NVIDIA was in the first Xbox, and in PlayStation 3. But AMD has a cost-performance advantage with their APUs, which NVIDIA hopes to match with Grace. And since Windows runs on Arm, NVIDIA has a shot at Microsoft. Sony's custom OS would not be much of a challenge for NVIDIA."

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Report: NVIDIA Forms Custom Chip Unit for Cloud Computing and More - AnandTech

Leveraging Cloud Computing and Data Analytics for Businesses – Analytics Insight

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In todays dynamic business landscape, organizations are constantly seeking innovative ways to drive efficiency, agility, and value. Among the transformative technologies reshaping business operations, cloud computing and data analytics stand out as powerful tools that, when leveraged effectively, can yield significant business value. By integrating these technologies strategically, businesses can unlock new opportunities for growth, streamline operations, and gain a competitive edge in the market.

Cloud computing offers organizations the flexibility to access computing resources on-demand, without the need for substantial investments in hardware and software infrastructure. This agility enables businesses to scale their operations rapidly in response to changing market demands, without the constraints of traditional IT environments. By migrating workloads to the cloud, organizations can streamline their operations, reduce downtime, and optimize resource utilization, leading to improved efficiency across the board.

In todays data-driven world, businesses are sitting on a goldmine of valuable information. Data analytics empowers organizations to extract actionable insights from vast volumes of data, enabling informed decision-making and driving business value. By leveraging advanced analytics techniques, such as machine learning and predictive modeling, businesses can identify trends, anticipate customer needs, and optimize processes for maximum efficiency. Furthermore, effective data governance and quality assurance practices ensure that insights derived from data analytics are accurate, reliable, and actionable.

Cloud FinOps, a practice focused on optimizing cloud spending and maximizing business value, plays a crucial role in ensuring that cloud investments deliver tangible returns. By tracking key performance indicators (KPIs) and measuring the business impact of cloud transformations, organizations can quantify the value derived from their cloud investments. Cloud FinOps goes beyond cost savings to encompass broader metrics such as improved resiliency, innovation, and operational efficiency, providing a comprehensive view of the business value generated by cloud initiatives.

Cloud computing infrastructure provides organizations with the foundation they need to harness the power of data analytics at scale. By leveraging cloud-based platforms for big data processing and analytics, organizations can access virtually unlimited computing resources, enabling them to analyze large datasets quickly and efficiently. Additionally, cloud infrastructure offers built-in features for data protection, disaster recovery, and security, ensuring that sensitive information remains safe and secure at all times. Furthermore, the pay-as-you-go pricing model of cloud services allows organizations to optimize costs and maximize ROI on their infrastructure investments.

Cloud computing accelerates the pace of software development by providing developers with access to scalable resources and flexible development environments. By leveraging cloud-based tools and platforms, organizations can streamline the software development lifecycle, reduce time-to-market, and improve collaboration among development teams. Furthermore, cloud-based development environments enable developers to experiment with new ideas and technologies without the constraints of traditional IT infrastructure, fostering innovation and driving business growth.

In conclusion, cloud computing and data analytics represent powerful tools for driving business value in todays digital economy. By embracing these technologies and implementing sound strategies for their deployment, organizations can unlock new opportunities for growth, enhance operational efficiency, and gain a competitive edge in the market. With the right approach, cloud computing and data analytics can serve as catalysts for innovation and transformation, enabling businesses to thrive in an increasingly data-driven world.

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Leveraging Cloud Computing and Data Analytics for Businesses - Analytics Insight