Creating high-quality, engaging videos plays a key role in marketing. However, most people don’t have the time or budget to invest in traditional video production methods. A lot goes into video production, from hiring actors to securing locations, investing in equipment, and managing complex editing processes….
Shaping the Future: How AI, Deepfakes, and Digital Replicas are Transforming Copyright Law
The U.S. Copyright Office’s report, “Copyright and Artificial Intelligence, Part 1: Digital Replicas,” offers an in-depth examination of the growing intersection between artificial intelligence (AI) and copyright law. This report highlights the complex legal and policy issues emerging from the use of digital technology to create…
CSS Functions and Mixins Module Notes
Most days, I’m writing vanilla CSS. Thanks to CSS variables and nesting, I have fewer reasons to reach for Sass or any other preprocessor. The times I reach for Sass tend to be when I need a @mixin to loop …
CSS Functions and Mixins Module Notes…
Researchers return to Arctic to test integrated sensor nodes
Shimmering ice extends in all directions as far as the eye can see. Air temperatures plunge to minus 40 degrees Fahrenheit and colder with wind chills. Ocean currents drag large swaths of ice floating at sea. Polar bears, narwhals, and other iconic Arctic species roam wild.
For a week this past spring, MIT Lincoln Laboratory researchers Ben Evans and Dave Whelihan called this place — drifting some 200 nautical miles offshore from Prudhoe Bay, Alaska, on the frozen Beaufort Sea in the Arctic Circle — home. Two ice runways for small aircraft provided their only way in and out of this remote wilderness; heated tents provided their only shelter from the bitter cold.
Here, in the northernmost region on Earth, Evans and Whelihan joined other groups conducting fieldwork in the Arctic as part of Operation Ice Camp (OIC) 2024, an operational exercise run by the U.S. Navy’s Arctic Submarine Laboratory (ASL). Riding on snowmobiles and helicopters, the duo deployed a small set of integrated sensor nodes that measure everything from atmospheric conditions to ice properties to the structure of water deep below the surface.
Ultimately, they envision deploying an unattended network of these low-cost sensor nodes across the Arctic to increase scientific understanding of the trending loss in sea ice extent and thickness. Warming much faster than the rest of the world, the Arctic is a ground zero for climate change, with cascading impacts across the planet that include rising sea levels and extreme weather. Openings in the sea ice cover, or leads, are concerning not only for climate change but also for global geopolitical competition over transit routes and natural resources. A synoptic view of the physical processes happening above, at, and below sea ice is key to determining why the ice is diminishing. In turn, this knowledge can help predict when and where fractures will occur, to inform planning and decision-making.
Winter “camp”
Every two years, OIC, previously called Ice Exercise (ICEX), provides a way for the international community to access the Arctic for operational readiness exercises and scientific research, with the focus switching back and forth; this year’s focus was scientific research. Coordination, planning, and execution of the month-long operation is led by ASL, a division of the U.S. Navy’s Undersea Warfighting Development Center responsible for ensuring the submarine force can effectively operate in the Arctic Ocean.
Making this inhospitable and unforgiving environment safe for participants takes considerable effort. The critical first step is determining where to set up camp. In the weeks before the first participants arrived for OIC 2024, ASL — with assistance from the U.S. National Ice Center, University of Alaska Fairbanks Geophysical Institute, and UIC Science — flew over large sheets of floating ice (ice floes) identified via satellite imagery, landed on some they thought might be viable sites, and drilled through the ice to check its thickness. The ice floe must not only be large enough to accommodate construction of a camp and two runways but also feature both multiyear ice and first-year ice. Multiyear ice is thick and strong but rough, making it ideal for camp setup, while the smooth but thinner first-year ice is better suited for building runways. Once the appropriate ice floe was selected, ASL began to haul in equipment and food, build infrastructure like lodging and a command center, and fly in a small group before fully operationalizing the site. They also identified locations near the camp for two Navy submarines to surface through the ice.
The more than 200 participants represented U.S. and allied forces and scientists from research organizations and universities. Distinguished visitors from government offices also attended OIC to see the unique Arctic environment and unfolding challenges firsthand.
“Our ASL hosts do incredible work to build this camp from scratch and keep us alive,” Evans says.
Evans and Whelihan, part of the laboratory’s Advanced Undersea Systems and Technology Group, first trekked to the Arctic in March 2022 for ICEX 2022. (The laboratory in general has been participating since 2016 in these events, the first iteration of which occurred in 1946.) There, they deployed a suite of commercial off-the-shelf sensors for detecting acoustic (sound) and seismic (vibration) events created by ice fractures or collisions, and for measuring salinity, temperature, and pressure in the water below the ice. They also deployed a prototype fiber-based temperature sensor array developed by the laboratory and research partners for precisely measuring temperature across the entire water column at one location, and a University of New Hampshire (UNH)−supplied echosounder to investigate the different layers present in the water column. In this maiden voyage, their goals were to assess how these sensors fared in the harsh Arctic conditions and to collect a dataset from which characteristic signatures of ice-fracturing events could begin to be identified. These events would be correlated with weather and water conditions to eventually offer a predictive capability.
“We saw real phenomenology in our data,” Whelihan says. “But, we’re not ice experts. What we’re good at here at the laboratory is making and deploying sensors. That’s our place in the world of climate science: to be a data provider. In fact, we hope to open source all of our data this year so that ice scientists can access and analyze them and then we can make enhanced sensors and collect more data.”
Interim ice
In the two years since that expedition, they and their colleagues have been modifying their sensor designs and deployment strategies. As Evans and Whelihan learned at ICEX 2022, to be resilient in the Arctic, a sensor must not only be kept warm and dry during deployment but also be deployed in a way to prevent breaking. Moreover, sufficient power and data links are needed to collect and access sensor data.
“We can make cold-weather electronics, no problem,” Whelihan says. “The two drivers are operating the sensors in an energy-starved environment — the colder it is, the worse batteries perform — and keeping them from getting destroyed when ice floes crash together as leads in the ice open up.”
Their work in the interim to OIC 2024 involved integrating the individual sensors into hardened sensor nodes and practicing deploying these nodes in easier-to-access locations. To facilitate incorporating additional sensors into a node, Whelihan spearheaded the development of an open-source, easily extensible hardware and software architecture.
In March 2023, the Lincoln Laboratory team deployed three sensor nodes for a week on Huron Bay off Lake Superior through Michigan Tech’s Great Lakes Research Center (GLRC). Engineers from GLRC helped the team safely set up an operations base on the ice. They demonstrated that the sensor integration worked, and the sensor nodes proved capable of surviving for at least a week in relatively harsh conditions. The researchers recorded seismic activity on all three nodes, corresponding to some ice breaking further up the bay.
“Proving our sensor node in an Arctic surrogate environment provided a stepping stone for testing in the real Arctic,” Evans says.
Evans then received an invitation from Ignatius Rigor, the coordinator of the International Arctic Buoy Program (IABP), to join him on an upcoming trip to Utqiaġvik (formerly Barrow), Alaska, and deploy one of their seismic sensor nodes on the ice there (with support from UIC Science). The IABP maintains a network of Arctic buoys equipped with meteorological and oceanic sensors. Data collected by these buoys are shared with the operational and research communities to support real-time operations (e.g., forecasting sea ice conditions for coastal Alaskans) and climate research. However, these buoys are typically limited in the frequency at which they collect data, so phenomenology on shorter time scales important to climate change may be missed. Moreover, these buoys are difficult and expensive to deploy because they are designed to survive in the harshest environments for years at a time.
The laboratory-developed sensor nodes could offer an inexpensive, easier-to-deploy option for collecting more data over shorter periods of time. In April 2023, Evans placed a sensor node in Utqiaġvik on landfast sea ice, which is stationary ice anchored to the seabed just off the coast. During the sensor node’s week-long deployment, a big piece of drift ice (ice not attached to the seabed or other fixed object) broke off and crashed into the landfast ice. The event was recorded by a radar maintained by the University of Alaska Fairbanks that monitors sea ice movement in near real time to warn of any instability. Though this phenomenology is not exactly the same as that expected for Arctic sea ice, the researchers were encouraged to see seismic activity recorded by their sensor node.
In December 2023, Evans and Whelihan headed to New Hampshire, where they conducted echosounder testing in UNH’s engineering test tank and on the Piscataqua River. Together with their UNH partners, they sought to determine whether a low-cost, hobby-grade echosounder could detect the same phenomenology of interest as the high-fidelity UNH echosounder, which would be far too costly to deploy in sensor nodes across the Arctic. In the test tank and on the river, the low-cost echosounder proved capable of detecting masses of water moving in the water column, but with considerably less structural detail than afforded by the higher-cost option. Seeing such dynamics is important to inferring where water comes from and understanding how it affects sea ice breakup — for example, how warm water moving in from the Pacific Ocean is coming into contact with and melting the ice. So, the laboratory researchers and UNH partners have been building a medium-fidelity, medium-cost echosounder.
In January 2024, Evans and Whelihan — along with Jehan Diaz, a fellow staff member in their research group — returned to GLRC. With logistical support from their GLRC hosts, they snowmobiled across the ice on Portage Lake, where they practiced several activities to prepare for OIC 2024: augering (drilling) six-inch holes in the ice, albeit in thinner ice than that in the Arctic; placing their long, pipe-like sensor nodes through these holes; operating cold-hardened drones to interact with the nodes; and retrieving the nodes. They also practiced sensor calibration by hitting the ice with an iron bar some distance away from the nodes and correlating this distance with the resulting measured acoustic and seismic intensity.
“Our time at GLRC helped us mitigate a lot of risks and prepare to deploy these complex systems in the Arctic,” Whelihan says.
Arctic again
To get to OIC, Evans and Whelihan first flew to Prudhoe Bay and reacclimated to the frigid temperatures. They spent the next two days at the Deadhorse Aviation Center hangar inspecting their equipment for transit-induced damage, which included squashed cables and connectors that required rejiggering.
“That’s part of the adventure story,” Evans says. “Getting stuff to Prudhoe Bay is not your standard shipping; it’s ice-road trucking.”
From there, they boarded a small aircraft to the ice camp.
“Even though this trip marked our second time coming here, it was still disorienting,” Evans continues. “You land in the middle of nowhere on a small aircraft after a couple-hour flight. You get out bundled in all of your Arctic gear in this remote, pristine environment.”
After unloading and rechecking their equipment for any damage, calibrating their sensors, and attending safety briefings, they were ready to begin their experiments.
An icy situation
Inside the project tent, Evans and Whelihan deployed the UNH-supplied echosounder and a suite of ground-truth sensors on an automated winch to profile water conductivity, temperature, and depth (CTD). Echosounder data needed to be validated with associated CTD data to determine the source of the water in the water column. Ocean properties change as a function of depth, and these changes are important to capture, in part because masses of water coming in from the Atlantic and Pacific oceans arrive at different depths. Though masses of warm water have always existed, climate change–related mechanisms are now bringing them into contact with the ice.
“As ice breaks up, wind can directly interact with the ocean because it’s lacking that barrier of ice cover,” Evans explains. “Kinetic energy from the wind causes mixing in the ocean; all the warm water that used to stay at depth instead gets brought up and interacts with the ice.”
They also deployed four of their sensor nodes several miles outside of camp. To access this deployment site, they rode on a sled pulled via a snowmobile driven by Ann Hill, an ASL field party leader trained in Arctic survival and wildlife encounters. The temperature that day was -55 F. At such a dangerously cold temperature, frostnip and frostbite are all too common. To avoid removal of gloves or other protective clothing, the researchers enabled the nodes with WiFi capability (the nodes also have a satellite communications link to transmit low-bandwidth data). Large amounts of data are automatically downloaded over WiFi to an arm-wearable haptic (touch-based) system when a user walks up to a node.
“It was so cold that the holes we were drilling in the ice to reach the water column were freezing solid,” Evans explains. “We realized it was going to be quite an ordeal to get our sensor nodes out of the ice.”
So, after drilling a big hole in the ice, they deployed only one central node with all the sensor components: a commercial echosounder, an underwater microphone, a seismometer, and a weather station. They deployed the other three nodes, each with a seismometer and weather station, atop the ice.
“One of our design considerations was flexibility,” Whelihan says. “Each node can integrate as few or as many sensors as desired.”
The small sensor array was only collecting data for about a day when Evans and Whelihan, who were at the time on a helicopter, saw that their initial field site had become completely cut off from camp by a 150-meter-wide ice lead. They quickly returned to camp to load the tools needed to pull the nodes, which were no longer accessible by snowmobile. Two recently arrived staff members from the Ted Stevens Center for Arctic Security Studies offered to help them retrieve their nodes. The helicopter landed on the ice floe near a crack, and the pilot told them they had half an hour to complete their recovery mission. By the time they had retrieved all four sensors, the crack had increased from thumb to fist size.
“When we got home, we analyzed the collected sensor data and saw a spike in seismic activity corresponding to what could be the major ice-fracturing event that necessitated our node recovery mission,” Whelihan says.
The researchers also conducted experiments with their Arctic-hardened drones to evaluate their utility for retrieving sensor node data and to develop concepts of operations for future capabilities.
“The idea is to have some autonomous vehicle land next to the node, download data, and come back, like a data mule, rather than having to expend energy getting data off the system, say via high-speed satellite communications,” Whelihan says. “We also started testing whether the drone is capable on its own of finding sensors that are constantly moving and getting close enough to them. Even flying in 25-mile-per-hour winds, and at very low temperatures, the drone worked well.”
Aside from carrying out their experiments, the researchers had the opportunity to interact with other participants. Their “roommates” were ice scientists from Norway and Finland. They met other ice and water scientists conducting chemistry experiments on the salt content of ice taken from different depths in the ice sheet (when ocean water freezes, salt tends to get pushed out of the ice). One of their collaborators — Nicholas Schmerr, an ice seismologist from the University of Maryland — placed high-quality geophones (for measuring vibrations in the ice) alongside their nodes deployed on the camp field site. They also met with junior enlisted submariners, who temporarily came to camp to open up spots on the submarine for distinguished visitors.
“Part of what we’ve been doing over the last three years is building connections within the Arctic community,” Evans says. “Every time I start to get a handle on the phenomenology that exists out here, I learn something new. For example, I didn’t know that sometimes a layer of ice forms a little bit deeper than the primary ice sheet, and you can actually see fish swimming in between the layers.”
“One day, we were out with our field party leader, who saw fog while she was looking at the horizon and said the ice was breaking up,” Whelihan adds. “I said, ‘Wait, what?’ As she explained, when an ice lead forms, fog comes out of the ocean. Sure enough, within 30 minutes, we had quarter-mile visibility, whereas beforehand it was unlimited.”
Back to solid ground
Before leaving, Whelihan and Evans retrieved and packed up all the remaining sensor nodes, adopting the “leave no trace” philosophy of preserving natural places.
“Only a limited number of people get access to this special environment,” Whelihan says. “We hope to grow our footprint at these events in future years, giving opportunities to other laboratory staff members to attend.”
In the meantime, they will analyze the collected sensor data and refine their sensor node design. One design consideration is how to replenish the sensors’ battery power. A potential path forward is to leverage the temperature difference between water and air, and harvest energy from the water currents moving under ice floes. Wind energy may provide another viable solution. Solar power would only work for part of the year because the Arctic Circle undergoes periods of complete darkness.
The team is also seeking external sponsorship to continue their work engineering sensing systems that advance the scientific community’s understanding of changes to Arctic ice; this work is currently funded through Lincoln Laboratory’s internally administered R&D portfolio on climate change. And, in learning more about this changing environment and its critical importance to strategic interests, they are considering other sensing problems that they could tackle using their Arctic engineering expertise.
“The Arctic is becoming a more visible and important region because of how it’s changing,” Evans concludes. “Going forward as a country, we must be able to operate there.”
Testing spooky action at a distance
Researchers at MIT recently signed a four-year collaboration agreement with the Novo Nordisk Foundation Quantum Computing Programme (NQCP) at Niels Bohr Institute, University of Copenhagen (UCPH), focused on accelerating quantum computing hardware research.
The agreement means that both universities will set up identical quantum laboratories at their respective campuses in Copenhagen and Cambridge, Massachusetts, facilitating seamless cooperation as well as shared knowledge and student exchange.
“To realize the promise of quantum computing, we must learn how to build systems that are robust, reproducible, and extensible. This unique program enables us to innovate faster by exchanging personnel and ideas, running parallel experiments, and comparing results. Even better, we get to continue working with Professor Morten Kjaergaard, a rising star in the field, and his team in Copenhagen,” says William Oliver, the Henry Ellis Warren (1894) Professor within the MIT Department of Electrical Engineering and Computer Science (EECS), professor of physics, associate director of the Research Laboratory of Electronics, and the head of the Center for Quantum Engineering at MIT.
Oliver’s team will supervise the funded research, which will focus specifically on the development of fault-tolerant quantum computing hardware and quantum algorithms that solve life-science relevant chemical and biological problems. The agreement provides 18 million Danish kroner (approximately $2.55 million) from the Novo Nordisk Foundation Quantum Computing Program to support MIT’s part in the research.
“A forefront objective in quantum computing is the development of state-of-the-art hardware with consistent operation,” says Maria Zuber, MIT’s presidential advisor for science and technology policy, who helped facilitate the relationship between MIT and the Danish university. “The goal of this collaboration is to demonstrate this system behavior, which will be an important step in the path to practical application.”
“Fostering collaborations between MIT and other universities is truly essential as we look to accelerate the pace of discovery and research in fast-growing fields such as quantum computing,” adds Anantha Chandrakasan, chief innovation and strategy officer, dean of engineering, and the Vannevar Bush Professor of EECS. “The support from the Novo Nordisk Foundation Quantum Computing Programme will ensure the world’s leading experts can focus on advancing research and developing solutions that have real-world impact.”
“This is an important recognition of our work at UCPH and NQCP. Professor Oliver’s team at MIT is part of the international top echelon of quantum computing research,” says Morten Kjaergaard, associate professor of quantum information physics and research group leader at the Niels Bohr Institute at UCPH. “This project enables Danish research in quantum computing hardware to learn from the best as we collaborate on developing hardware for next-generation fault-tolerant quantum computing. I have previously had the pleasure of working closely with Professor Oliver, and with this ambitious collaboration as part of our the Novo Nordisk Foundation Quantum Computing Programme, we are able to push our joint research to a new level.”
Peter Krogstrup, CEO of NQCP and professor at Niels Bohr Institute, follows up, “We are excited to work with Will Oliver and his innovative team at MIT. It aligns very well with our strategic focus on identifying a path with potential to enable quantum computing for life sciences. The support aims to strengthen the already strong collaboration between Will and Morten’s team, a collaboration we hope to make an important part of the NQCP pathfinder phase over the coming years.”
Precision home robots learn with real-to-sim-to-real
At the top of many automation wish lists is a particularly time-consuming task: chores.
The moonshot of many roboticists is cooking up the proper hardware and software combination so that a machine can learn “generalist” policies (the rules and strategies that guide robot behavior) that work everywhere, under all conditions. Realistically, though, if you have a home robot, you probably don’t care much about it working for your neighbors. MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers decided, with that in mind, to attempt to find a solution to easily train robust robot policies for very specific environments.
“We aim for robots to perform exceptionally well under disturbances, distractions, varying lighting conditions, and changes in object poses, all within a single environment,” says Marcel Torne Villasevil, MIT CSAIL research assistant in the Improbable AI lab and lead author on a recent paper about the work. “We propose a method to create digital twins on the fly using the latest advances in computer vision. With just their phones, anyone can capture a digital replica of the real world, and the robots can train in a simulated environment much faster than the real world, thanks to GPU parallelization. Our approach eliminates the need for extensive reward engineering by leveraging a few real-world demonstrations to jump-start the training process.”
Taking your robot home
RialTo, of course, is a little more complicated than just a simple wave of a phone and (boom!) home bot at your service. It begins by using your device to scan the target environment using tools like NeRFStudio, ARCode, or Polycam. Once the scene is reconstructed, users can upload it to RialTo’s interface to make detailed adjustments, add necessary joints to the robots, and more.
The refined scene is exported and brought into the simulator. Here, the aim is to develop a policy based on real-world actions and observations, such as one for grabbing a cup on a counter. These real-world demonstrations are replicated in the simulation, providing some valuable data for reinforcement learning. “This helps in creating a strong policy that works well in both the simulation and the real world. An enhanced algorithm using reinforcement learning helps guide this process, to ensure the policy is effective when applied outside of the simulator,” says Torne.
Testing showed that RialTo created strong policies for a variety of tasks, whether in controlled lab settings or more unpredictable real-world environments, improving 67 percent over imitation learning with the same number of demonstrations. The tasks involved opening a toaster, placing a book on a shelf, putting a plate on a rack, placing a mug on a shelf, opening a drawer, and opening a cabinet. For each task, the researchers tested the system’s performance under three increasing levels of difficulty: randomizing object poses, adding visual distractors, and applying physical disturbances during task executions. When paired with real-world data, the system outperformed traditional imitation-learning methods, especially in situations with lots of visual distractions or physical disruptions.
“These experiments show that if we care about being very robust to one particular environment, the best idea is to leverage digital twins instead of trying to obtain robustness with large-scale data collection in diverse environments,” says Pulkit Agrawal, director of Improbable AI Lab, MIT electrical engineering and computer science (EECS) associate professor, MIT CSAIL principal investigator, and senior author on the work.
As far as limitations, RialTo currently takes three days to be fully trained. To speed this up, the team mentions improving the underlying algorithms and using foundation models. Training in simulation also has its limitations, and currently it’s difficult to do effortless sim-to-real transfer and simulate deformable objects or liquids.
The next level
So what’s next for RialTo’s journey? Building on previous efforts, the scientists are working on preserving robustness against various disturbances while improving the model’s adaptability to new environments. “Our next endeavor is this approach to using pre-trained models, accelerating the learning process, minimizing human input, and achieving broader generalization capabilities,” says Torne.
“We’re incredibly enthusiastic about our ‘on-the-fly’ robot programming concept, where robots can autonomously scan their environment and learn how to solve specific tasks in simulation. While our current method has limitations — such as requiring a few initial demonstrations by a human and significant compute time for training these policies (up to three days) — we see it as a significant step towards achieving ‘on-the-fly’ robot learning and deployment,” says Torne. “This approach moves us closer to a future where robots won’t need a preexisting policy that covers every scenario. Instead, they can rapidly learn new tasks without extensive real-world interaction. In my view, this advancement could expedite the practical application of robotics far sooner than relying solely on a universal, all-encompassing policy.”
“To deploy robots in the real world, researchers have traditionally relied on methods such as imitation learning from expert data, which can be expensive, or reinforcement learning, which can be unsafe,” says Zoey Chen, a computer science PhD student at the University of Washington who wasn’t involved in the paper. “RialTo directly addresses both the safety constraints of real-world RL [robot learning], and efficient data constraints for data-driven learning methods, with its novel real-to-sim-to-real pipeline. This novel pipeline not only ensures safe and robust training in simulation before real-world deployment, but also significantly improves the efficiency of data collection. RialTo has the potential to significantly scale up robot learning and allows robots to adapt to complex real-world scenarios much more effectively.”
“Simulation has shown impressive capabilities on real robots by providing inexpensive, possibly infinite data for policy learning,” adds Marius Memmel, a computer science PhD student at the University of Washington who wasn’t involved in the work. “However, these methods are limited to a few specific scenarios, and constructing the corresponding simulations is expensive and laborious. RialTo provides an easy-to-use tool to reconstruct real-world environments in minutes instead of hours. Furthermore, it makes extensive use of collected demonstrations during policy learning, minimizing the burden on the operator and reducing the sim2real gap. RialTo demonstrates robustness to object poses and disturbances, showing incredible real-world performance without requiring extensive simulator construction and data collection.”
Torne wrote this paper alongside senior authors Abhishek Gupta, assistant professor at the University of Washington, and Agrawal. Four other CSAIL members are also credited: EECS PhD student Anthony Simeonov SM ’22, research assistant Zechu Li, undergraduate student April Chan, and Tao Chen PhD ’24. Improbable AI Lab and WEIRD Lab members also contributed valuable feedback and support in developing this project.
This work was supported, in part, by the Sony Research Award, the U.S. government, and Hyundai Motor Co., with assistance from the WEIRD (Washington Embodied Intelligence and Robotics Development) Lab. The researchers presented their work at the Robotics Science and Systems (RSS) conference earlier this month.
Where You Can Still Get A Book Apart Titles
It’s been a few months out since A Book Apart closed shop. I’m sad about it, of course. You probably are, too, if you have one of their many brightly-colored paperbacks sitting on a bookshelf strategically placed as a backdrop …
Where You Can Still Get A…
Josh Dinneen, CEO of Blue Mantis – Interview Series
Josh Dinneen, CEO of Blue Mantis, is a seasoned leader dedicated to driving the company’s organic and M&A-fueled growth and continuously fostering innovation throughout the organization and its growing client base. Formerly President of Blue Mantis, Josh spearheads efforts to enhance the company’s ongoing initiatives aimed…
Redefining Search: How Emerging Conversational Engines Overcome Outdated LLMs and Context-Less Traditional Search Engines
The advent of conversational search engines is redefining how we retrieve information online, shifting from traditional keyword searches to more natural, conversational interactions. By combining large language models (LLMs) with real-time web data, these new systems address key issues found in both outdated LLMs and standard…