AI Research
Artificial intelligence (AI)-powered anti-ship missile with double the range

Questions and answers:
- What is the primary feature of the LRASM C-3 missile compared to earlier variants? It has nearly double the range of previous versions, with a range of about 1,000 miles, compared to 200 to 300 miles for the C-1 and 580 miles for the C-2.
- How does artificial intelligence enhance the LRASM C-3’s capabilities? AI helps the missile with autonomous mission planning, target discrimination, and attack coordination, adjust flight paths based on real-time data, identify and track moving targets, and adapt to changing conditions like jamming and interference.
- What can launch the LRASM C-3 missile? U.S. Air Force B-1B bombers, Navy F/A-18E/F Super Hornets, and F-35 Lightning II jets, with possible future launches from Navy ships and attack submarines.
PATUXENT RIVER NAS, Md. – U.S. Navy surface warfare experts are asking Lockheed Martin Corp. to move forward with developing the new LRASM C-3 anti-ship missile with double the range of previous versions.
Officials of the Naval Air Systems Command at Patuxent River Naval Air Station, Md., announced a $48.1 million order last month to the Lockheed Martin Missiles and Fire Control segment in Orlando, Fla., for engineering to establish the Long Range Anti-Ship Missile (LRASM) C-3 variant.
The subsonic LRASM is for attacking high-priority enemy surface warships like aircraft carriers, troop transport ships, and guided-missile cruisers from Navy, U.S. Air Force, and allied aircraft.
LRASM is designed to detect and destroy high-priority targets within groups of ships from extended ranges in electronic warfare jamming environments. It is a precision-guided, standoff anti-ship missile based on the Lockheed Martin Joint Air-to-Surface Standoff Missile-Extended Range (JASSM-ER).
1,000-mile range
The LRASM C-3 variant has a range of nearly 1,000 miles, compared to the 200-to-300-mile C-1 variant, and 580-mile range of the LRASM C-2 variant.
LRASM C-3 also introduces machine learning and advanced artificial intelligence (AI) algorithms to enhance autonomous mission planning, target discrimination, and attack coordination, even amid intense electronic warfare (EW) jamming.
The C-3 also can exchange information from military satellites, and has an enhanced imaging infrared and RF seeker for survivability and target identification.
The C-3 also can be launched form the Air Force from B-1B strategic jet bomber, as well as the Navy F/A-18E/F Super Hornet jet fighter-bomber and the F-35 Lightning II attack jet. Navy leaders also envision using the Navy MK 41 shipboard vertical launch system with the LRASM C-3, and are considering options to launch the missile from attack submarines.
Tell me more about applying artificial intelligence to missile guidance …
- Applying artificial intelligence to missile guidance will enhance precision, adapt to dynamic environments, and improve decision-making in real-time. AI can help missiles navigate autonomously by using real-time data from radar, infrared sensors, and GPS to adjust flight paths. AI also can help missiles visually identify targets from images or video feeds, and not only enhance the missile’s ability to recognize and track moving targets, but also to predict and follow moving targets even if they change direction or speed. Using AI, missile guidance systems can make real-time adjustments to their trajectory based on changing conditions like wind, RF interference, and jamming. Missiles also may use AI to other weapons in swarm tactics, and operate effectively against countermeasures.
Helping to extend the LRASM C-3’s range are an advanced multi-mode sensor suite; enhanced data exchange and communications; digital anti-jam GPS and navigation; and AI and machine learning capabilities.
The missile’s multi-mode sensor suite is expected to blend imaging infrared and RF sensors to help the weapon identify and attack targets. Its communications will have data links for secure real-time communication with satellites, drones, and strike aircraft.
Digital anti-jam GPS and navigation will provide midcourse guidance to target areas far beyond the effective range of traditional systems. AI and machine learning, meanwhile, should help the missile identify targets and plan its routes autonomously. The LRASM C-3 version should enter service next year.
On this order, Lockheed Martin will do the work in Orlando and Ocala, Fla.; and in Troy, Ala., and should be finished in November 2026. For more information contact Lockheed Martin Missiles and Fire Control online at https://www.lockheedmartin.com/en-us/products/long-range-anti-ship-missile.html, or Naval Air Systems Command at www.navair.navy.mil.
AI Research
NFL player props, odds: Week 2, 2025 NFL picks, SportsLine Machine Learning Model AI predictions, SGP

The Under went 12-4 in Week 1, indicating that not only were there fewer points scored than expected, but there were also fewer yards gained. Backing the Under with NFL prop bets was likely profitable for the opening slate of games, but will that maintain with Week 2 NFL props? Interestingly though, four of the five highest-scoring games last week were the primetime games, so if that holds, then the Overs for this week’s night games could be attractive with Week 2 NFL player props.
There’s a Monday Night Football doubleheader featuring star pass catchers like Nico Collins, Mike Evans and Brock Bowers. The games also feature promising rookies such as Ashton Jeanty, Omarion Hampton and Emeka Egbuka. Prop lines are usually all over the place early in the season as sportsbooks attempt to establish a player’s potential, and you could take advantage of this with the right NFL picks. If you are looking for NFL prop bets or NFL parlays for Week 2, SportsLine has you covered with the top Week 2 player props from its Machine Learning Model AI.
Built using cutting-edge artificial intelligence and machine learning techniques by SportsLine’s Data Science team, AI Predictions and AI Ratings are generated for each player prop.
Now, with the Week 2 NFL schedule quickly approaching, SportsLine’s Machine Learning Model AI has identified the top NFL props from the biggest Week 2 games.
Week 2 NFL props for Sunday’s main slate
After analyzing the NFL props from Sunday’s main slate and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model AI says Lions receiver Amon-Ra St. Brown goes Over 63.5 receiving yards (-114) versus the Bears at 1 p.m. ET. Detroit will host this contest, which is notable as St. Brown has averaged 114 receiving yards over his last six home games. He had at least 70 receiving yards in both matchups versus the Bears a year ago.
Chicago allowed 12 receivers to go Over 63.5 receiving yards last season as the Bears’ pass defense is adept at keeping opponents out of the endzone but not as good at preventing yardage. Chicago allowed the highest yards per attempt and second-highest yards per completion in 2024. While St. Brown had just 45 yards in the opener, the last time he was held under 50 receiving yards, he then had 193 yards the following week. The SportsLine Machine Learning Model projects 82.5 yards for St. Brown in a 4.5-star pick. See more Week 2 NFL props here.
Week 2 NFL props for Vikings vs. Falcons on Sunday Night Football
After analyzing Falcons vs. Vikings props and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model AI says Falcons running back Bijan Robinson goes Over 65.5 rushing yards (-114). Robinson ran for 92 yards and a touchdown in Week 14 of last season versus Minnesota, despite the Vikings having the league’s No. 2 run defense a year ago. The SportsLine Machine Learning Model projects Robinson to have 81.8 yards on average in a 4.5-star prop pick. See more NFL props for Vikings vs. Falcons here.
You can make NFL prop bets on Robinson, Justin Jefferson and others with the Underdog Fantasy promo code CBSSPORTS2. Pick at Underdog Fantasy and get $50 in bonus funds after making a $5 wager:
Week 2 NFL props for Buccaneers vs. Texans on Monday Night Football
After analyzing Texans vs. Buccaneers props and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model AI says Bucs quarterback Baker Mayfield goes Under 235.5 passing yards (-114). While Houston has questions regarding its offense, there’s little worry about the team’s pass defense. In 2024, Houston had the second-most interceptions, the fourth-most sacks and allowed the fourth-worst passer rating. Since the start of last year, and including the playoffs, the Texans have held opposing QBs under 235.5 yards in 13 of 20 games. The SportsLine Machine Learning Model forecasts Mayfield to finish with just 200.1 passing yards, making the Under a 4-star NFL prop. See more NFL props for Buccaneers vs. Texans here.
You can also use the latest FanDuel promo code to get $300 in bonus bets instantly:
Week 2 NFL props for Chargers vs. Raiders on Monday Night Football
After analyzing Raiders vs. Chargers props and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model AI says Chargers quarterback Justin Herbert goes Under 254.5 passing yards (-114). The Raiders’ defense was underrated in preventing big passing plays a year ago as it ranked third in the NFL in average depth of target allowed. It forced QBs to dink and dunk their way down the field, which doesn’t lead to big passing yardages, and L.A. generally prefers to not throw the ball anyway. Just four teams attempted fewer passes last season than the Chargers, and with L.A. running for 156.5 yards versus Vegas last season, Herbert shouldn’t be overly active on Monday night. He’s forecasted to have 221.1 passing yards in a 4.5-star NFL prop bet. See more NFL props for Chargers vs. Raiders here.
How to make Week 2 NFL prop picks
SportsLine’s Machine Learning Model has identified another star who sails past his total and has dozens of NFL props rated 4 stars or better. You need to see the Machine Learning Model analysis before making any Week 2 NFL prop bets.
Which NFL prop picks should you target for Week 2, and which quarterback has multiple 5-star rated picks? Visit SportsLine to see the latest NFL player props from SportsLine’s Machine Learning Model that uses cutting-edge artificial intelligence to make its projections.
AI Research
In the News: Thomas Feeney on AI in Higher Education – Newsroom

“I had an interesting experience over the summer teaching an AI ethics class. You know plagiarism would be an interesting question in an AI ethics class … They had permission to use AI for the first written assignment. And it was clear that many of them had just fed in the prompt, gotten back the paper and uploaded that. But rather than initiate a sort of disciplinary oppositional setting, I tried to show them, look, what you what you’ve produced is kind of generic … and this gave the students a chance to recognize that they weren’t there in their own work. This opened the floodgates,” Feeney said.
“I think the focus should be less on learning how to work with the interfaces we have right now and more on just graduate with a story about how you did something with AI that you couldn’t have done without it. And then, crucially, how you shared it with someone else,” he continued.
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