Using an online tool to label Martian terrain types, you can train an artificial intelligence algorithm that could improve the way engineers guide the Curiosity rover.
You may be able to help NASA’s Curiosity rover drivers better navigate Mars. Using the online tool AI4Mars to label terrain features in pictures downloaded from the Red Planet, you can train an artificial intelligence algorithm to automatically read the landscape.
Is that a big rock to the left? Could it be sand? Or maybe it’s nice, flat bedrock. AI4Mars, which is hosted on the citizen science website Zooniverse, lets you draw boundaries around terrain and choose one of four labels. Those labels are key to sharpening the Martian terrain-classification algorithm called SPOC (Soil Property and Object Classification).
Developed at NASA’s Jet Propulsion Laboratory, which has managed all of the agency’s Mars rover missions, SPOC labels various terrain types, creating a visual map that helps mission team members determine which paths to take. SPOC is already in use, but the system could use further training.
“Typically, hundreds of thousands of examples are needed to train a deep learning algorithm,” said Hiro Ono, an AI researcher at JPL. “Algorithms for self-driving cars, for example, are trained with numerous images of roads, signs, traffic lights, pedestrians and other vehicles. Other public datasets for deep learning contain people, animals and buildings – but no Martian landscapes.”
Once fully up to speed, SPOC will be able to automatically distinguish between cohesive soil, high rocks, flat bedrock and dangerous sand dunes, sending images to Earth that will make it easier to plan Curiosity’s next moves.
“In the future, we hope this algorithm can become accurate enough to do other useful tasks, like predicting how likely a rover’s wheels are to slip on different surfaces,” Ono said.
The Job of Rover Planners
JPL engineers called rover planners may benefit the most from a better-trained SPOC. They are responsible for Curiosity’s every move, whether it’s taking a selfie, trickling pulverized samples into the rover’s body to be analyzed or driving from one spot to the next.
It can take four to five hours to work out a drive (which is now done virtually), requiring multiple people to write and review hundreds of lines of code. The task involves extensive collaboration with scientists as well: Geologists assess the terrain to predict whether Curiosity’s wheels could slip, be damaged by sharp rocks or get stuck in sand, which trapped both the Spirit and Opportunity rovers.
Planners also consider which way the rover will be pointed at the end of a drive, since its high-gain antenna needs a clear line of sight to Earth to receive commands. And they try to anticipate shadows falling across the terrain during a drive, which can interfere with how Curiosity determines distance. (The rover uses a technique called visual odometry, comparing camera images to nearby landmarks.)
How AI Could Help
SPOC won’t replace the complicated, time-intensive work of rover planners. But it can free them to focus on other aspects of their job, like discussing with scientists which rocks to study next.
“It’s our job to figure out how to safely get the mission’s science,” said Stephanie Oij, one of the JPL rover planners involved in AI4Mars. “Automatically generating terrain labels would save us time and help us be more productive.”
The benefits of a smarter algorithm would extend to planners on NASA’s next Mars mission, the Perseverance rover, which launches this summer. But first, an archive of labeled images is needed. More than 8,000 Curiosity images have been uploaded to the AI4Mars site so far, providing plenty of fodder for the algorithm. Ono hopes to add images from Spirit and Opportunity in the future. In the meantime, JPL volunteers are translating the site so that participants who speak Spanish, Hindi, Japanese and several other languages can contribute as well.
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However, as of February 2020, CMS had not yet established a timeline for including MMIS and E&E systems in the new outcome-based process. CMS had various initiatives aimed at reducing duplication of Medicaid systems (see table). Description and Status of Centers for Medicare and Medicaid Services Initiatives Aimed at Reducing Duplication by Sharing, Leveraging, and Reusing Medicaid Information Technology Initiative Description Implementation status Number of surveyed states reporting use of the initiative Reuse Repository Used by states to collect and share reusable artifacts. Made available in August 2017. As of January 2020, CMS was no longer supporting this initiative. 25 of the 50 reporting states Poplin Project Was to provide free, open-source application program interfaces for states to use in developing their modular Medicaid systems. Initiative never fully implemented. As of January 2020, CMS was no longer supporting this initiative. Three of the 50 reporting states Open Source Provider Screening Module Open-source module for states to use at no charge. Made available in August 2018. As of January 2020, CMS was no longer supporting this initiative. One of the 50 states reported attempting to use the module. Medicaid Enterprise Cohort Meetings A forum where states can discuss sharing, leveraging, and/or reuse of Medicaid technologies. As of January 2020, Cohort meetings were being held on a monthly basis. 47 of the 50 states reported participating in the meetings. Source: GAO analysis of agency data. | GAO-20-179 However, as of January 2020, the agency was no longer supporting most of these initiatives because they failed to produce the desired results. CMS regulations and GAO's prior work have highlighted the importance of reducing duplication by sharing and reusing Medicaid IT. To illustrate the potential for reducing duplication, 53 percent of state Medicaid officials responding to our survey reported using the same contractor to develop their MMIS. Nevertheless, selected states are taking the initiative to share systems or modules. Further support by CMS could result in additional sharing initiatives and potential cost savings. The Medicaid program is the largest source of health care funding for America's most at-risk populations and is funded jointly by states and the federal government. GAO was asked to assess CMS's oversight of federal expenditures for MMIS and E&E systems used for Medicaid. This report examines (1) the amount of federal funds that CMS has provided to state Medicaid programs to support MMIS and E&E systems, (2) the extent to which CMS reviews and approves states' funding requests for the systems and oversees the use of these funds, and (3) CMS's and states' efforts to reduce potential duplication of Medicaid IT systems. GAO assessed information related to MMIS and E&E systems, such as state expenditure data, federal regulations, and CMS guidance to the states for submitting funding requests, states' system funding requests, and IT project management documents. GAO also evaluated a generalizable sample of approved state funding requests from fiscal years 2016 through 2018 to analyze, among other things, CMS's review and approval process and conducted interviews with agency and state Medicaid officials. GAO also reviewed relevant regulations and guidance on promoting, sharing, and reusing MMIS and E&E technologies; and surveyed 50 states and six territories (hereafter referred to as states) regarding the MMIS and E&E systems, and assessed the complete or partial responses received from 50 states. GAO is making nine recommendations to improve CMS's processes for approving and overseeing the federal funds for MMIS and E&E systems and for bolstering efforts to reduce potential duplication. Among these recommendations are that CMS should develop formal, documented procedures that include specific steps to be taken in the advanced planning document review process and instructions on how CMS will document the reviews; develop, in consultation with the HHS and CMS CIOs, a documented, comprehensive, and risk-based process for how CMS will select IT projects for technical assistance and provide recommendations to assist states that is aimed at improving the performance of the systems; encourage state Medicaid program officials to consider involving state CIOs in overseeing Medicaid IT projects; establish a timeline for implementing the outcome-based certification process for MMIS and E&E systems; and identify, prior to approving funding for systems, similar projects that other states are pursuing so that opportunities to share, leverage, or reuse systems or system modules are considered. In written comments on a draft of this report, the department concurred with eight of the nine recommendations, and described steps it had taken and/or planned to take to address them. The department did not state whether it concurred with GAO's recommendation to encourage state officials to consider involving state CIOs in Medicaid IT projects. HHS stated that it was unable to discern evidence as to whether a certain structure contributed to a specific outcome. GAO believes, consistent with federal law, that CIOs are critically important to the success of IT projects. For more information, contact Vijay D’Souza at (202) 512-6240 or email@example.com.[Read More…]