Automate This: How Algorithms Came to Rule Our World is a book written by Christopher Steiner and published by Penguin Group. == Book == Steiner begins his study of algorithms on Wall Street in the 1980s but also provides examples from other industries. For example, he explains the history of Pandora Radio and the use of algorithms in music identification. He expresses concern that such use of algorithms may lead to the homogenization of music over time. Steiner also discusses the algorithms that eLoyalty (now owned by Mattersight Corporation following divestiture of the technology) was created by dissecting 2 million speech patterns and can now identify a caller's personality style and direct the caller with a compatible customer support representative. Steiner's book shares both the warning and the opportunity that algorithms bring to just about every industry in the world, and the pros and cons of the societal impact of automation (e.g. impact on employment).
Exploration–exploitation dilemma
The exploration–exploitation dilemma, also known as the explore–exploit tradeoff, is a fundamental concept in decision-making that arises in many domains. It is depicted as the balancing act between two opposing strategies. Exploitation involves choosing the best option based on current knowledge of the system (which may be incomplete or misleading), while exploration involves trying out new options that may lead to better outcomes in the future at the expense of an exploitation opportunity. Finding the optimal balance between these two strategies is a crucial challenge in many decision-making problems whose goal is to maximize long-term benefits. == Application in machine learning == In the context of machine learning, the exploration–exploitation tradeoff is fundamental in reinforcement learning (RL), a type of machine learning that involves training agents to make decisions based on feedback from the environment. Crucially, this feedback may be incomplete or delayed. The agent must decide whether to exploit the current best-known policy or explore new policies to improve its performance. === Multi-armed bandit methods === The multi-armed bandit (MAB) problem was a classic example of the tradeoff, and many methods were developed for it, such as epsilon-greedy, Thompson sampling, and the upper confidence bound (UCB). See the page on MAB for details. In more complex RL situations than the MAB problem, the agent can treat each choice as a MAB, where the payoff is the expected future reward. For example, if the agent performs an epsilon-greedy method, then the agent will often "pull the best lever" by picking the action that had the best predicted expected reward (exploit). However, it would pick a random action with probability epsilon (explore). Monte Carlo tree search, for example, uses a variant of the UCB method. === Exploration problems === There are some problems that make exploration difficult. Sparse reward. If rewards occur only once a long while, then the agent might not persist in exploring. Furthermore, if the space of actions is large, then the sparse reward would mean the agent would not be guided by the reward to find a good direction for deeper exploration. A standard example is Montezuma's Revenge. Deceptive reward. If some early actions give immediate small reward, but other actions give later large reward, then the agent might be lured away from exploring the other actions. Noisy TV problem. If certain observations are irreducibly noisy (such as a television showing random images), then the agent might be trapped exploring those observations (watching the television). === Exploration reward === This section based on. The exploration reward (also called exploration bonus) methods convert the exploration-exploitation dilemma into a balance of exploitations. That is, instead of trying to get the agent to balance exploration and exploitation, exploration is simply treated as another form of exploitation, and the agent simply attempts to maximize the sum of rewards from exploration and exploitation. The exploration reward can be treated as a form of intrinsic reward. We write these as r t i , r t e {\displaystyle r_{t}^{i},r_{t}^{e}} , meaning the intrinsic and extrinsic rewards at time step t {\displaystyle t} . However, exploration reward is different from exploitation in two regards: The reward of exploitation is not freely chosen, but given by the environment, but the reward of exploration may be picked freely. Indeed, there are many different ways to design r t i {\displaystyle r_{t}^{i}} described below. The reward of exploitation is usually stationary (i.e. the same action in the same state gives the same reward), but the reward of exploration is non-stationary (i.e. the same action in the same state should give less and less reward). Count-based exploration uses N n ( s ) {\displaystyle N_{n}(s)} , the number of visits to a state s {\displaystyle s} during the time-steps 1 : n {\displaystyle 1:n} , to calculate the exploration reward. This is only possible in small and discrete state space. Density-based exploration extends count-based exploration by using a density model ρ n ( s ) {\displaystyle \rho _{n}(s)} . The idea is that, if a state has been visited, then nearby states are also partly-visited. In maximum entropy exploration, the entropy of the agent's policy π {\displaystyle \pi } is included as a term in the intrinsic reward. That is, r t i = − ∑ a π ( a | s t ) ln π ( a | s t ) + ⋯ {\displaystyle r_{t}^{i}=-\sum _{a}\pi (a|s_{t})\ln \pi (a|s_{t})+\cdots } . === Prediction-based === This section based on. The forward dynamics model is a function for predicting the next state based on the current state and the current action: f : ( s t , a t ) ↦ s t + 1 {\displaystyle f:(s_{t},a_{t})\mapsto s_{t+1}} . The forward dynamics model is trained as the agent plays. The model becomes better at predicting state transition for state-action pairs that had been done many times. A forward dynamics model can define an exploration reward by r t i = ‖ f ( s t , a t ) − s t + 1 ‖ 2 2 {\displaystyle r_{t}^{i}=\|f(s_{t},a_{t})-s_{t+1}\|_{2}^{2}} . That is, the reward is the squared-error of the prediction compared to reality. This rewards the agent to perform state-action pairs that had not been done many times. This is however susceptible to the noisy TV problem. Dynamics model can be run in latent space. That is, r t i = ‖ f ( s t , a t ) − ϕ ( s t + 1 ) ‖ 2 2 {\displaystyle r_{t}^{i}=\|f(s_{t},a_{t})-\phi (s_{t+1})\|_{2}^{2}} for some featurizer ϕ {\displaystyle \phi } . The featurizer can be the identity function (i.e. ϕ ( x ) = x {\displaystyle \phi (x)=x} ), randomly generated, the encoder-half of a variational autoencoder, etc. A good featurizer improves forward dynamics exploration. The Intrinsic Curiosity Module (ICM) method trains simultaneously a forward dynamics model and a featurizer. The featurizer is trained by an inverse dynamics model, which is a function for predicting the current action based on the features of the current and the next state: g : ( ϕ ( s t ) , ϕ ( s t + 1 ) ) ↦ a t {\displaystyle g:(\phi (s_{t}),\phi (s_{t+1}))\mapsto a_{t}} . By optimizing the inverse dynamics, both the inverse dynamics model and the featurizer are improved. Then, the improved featurizer improves the forward dynamics model, which improves the exploration of the agent. Random Network Distillation (RND) method attempts to solve this problem by teacher–student distillation. Instead of a forward dynamics model, it has two models f , f ′ {\displaystyle f,f'} . The f ′ {\displaystyle f'} teacher model is fixed, and the f {\displaystyle f} student model is trained to minimize ‖ f ( s ) − f ′ ( s ) ‖ 2 2 {\displaystyle \|f(s)-f'(s)\|_{2}^{2}} on states s {\displaystyle s} . As a state is visited more and more, the student network becomes better at predicting the teacher. Meanwhile, the prediction error is also an exploration reward for the agent, and so the agent learns to perform actions that result in higher prediction error. Thus, we have a student network attempting to minimize the prediction error, while the agent attempting to maximize it, resulting in exploration. The states are normalized by subtracting a running average and dividing a running variance, which is necessary since the teacher model is frozen. The rewards are normalized by dividing with a running variance. Exploration by disagreement trains an ensemble of forward dynamics models, each on a random subset of all ( s t , a t , s t + 1 ) {\displaystyle (s_{t},a_{t},s_{t+1})} tuples. The exploration reward is the variance of the models' predictions. === Noise === For neural network–based agents, the NoisyNet method changes some of its neural network modules by noisy versions. That is, some network parameters are random variables from a probability distribution. The parameters of the distribution are themselves learnable. For example, in a linear layer y = W x + b {\displaystyle y=Wx+b} , both W , b {\displaystyle W,b} are sampled from Gaussian distributions N ( μ W , Σ W ) , N ( μ b , Σ b ) {\displaystyle {\mathcal {N}}(\mu _{W},\Sigma _{W}),{\mathcal {N}}(\mu _{b},\Sigma _{b})} at every step, and the parameters μ W , Σ W , μ b , Σ b {\displaystyle \mu _{W},\Sigma _{W},\mu _{b},\Sigma _{b}} are learned via the reparameterization trick.
Anthropic–United States Department of Defense dispute
Since January 2026, the United States Department of Defense has conflicted with the artificial intelligence company Anthropic over the use of its products for military purposes and mass domestic surveillance. == Background == === Artificial intelligence in the U.S. military === The United States Department of Defense began developing lethal autonomous weapons as early as the Reagan administration. The Department of Defense established a policy on the use of artificial intelligence in 2012, Directive 3000.09. Efforts to utilize artificial intelligence intensified under the term of secretary Ash Carter. The Department of Defense's use of artificial intelligence for Project Maven prompted concerns within Google in 2018, leading to protests and mass resignations. === Anthropic in the second Trump administration === In Donald Trump's second presidency, Anthropic publicly disagreed with the administration's policies and initiatives. In January 2025, Anthropic chief executive Dario Amodei criticized the artificial intelligence investment project Stargate as "chaotic" and opposed Trump's rescission of president Joe Biden's Executive Order on Artificial Intelligence, but noted that Anthropic had held discussions with Trump officials about artificial intelligence policy. Amid discussions over the One Big Beautiful Bill Act, Anthropic privately lobbied for Congress to vote against a bill preventing states from regulating artificial intelligence and expressed opposition to an artificial intelligence agreement signed among Gulf states in Trump's visit to the Middle East in May. According to Semafor, Trump officials chastised Anthropic's hiring of several officials involved in the Biden administration, including Elizabeth Kelly, the former director of the Artificial Intelligence Safety Institute; Tarun Chhabra, the coordinator for technology and national security in the National Security Council; and Ben Buchanan, Biden's advisor for artificial intelligence. The following month, Amodei wrote an op-ed in The New York Times describing the artificial intelligence regulation bill, then tied to the One Big Beautiful Bill Act, as "far too blunt an instrument". Prior to the dispute, the Trump administration had integrated Anthropic's services. By November 2024, Anthropic had already partnered with Palantir and Amazon Web Services, companies that offered services with FedRAMP authorization. In the Biden administration, Anthropic had reached an agreement with the AI Safety Institute and had participated in a nuclear information safety evaluation. The Department of Homeland Security authorized its workers to use commercial artificial intelligence systems, including Anthropic's Claude, until May 2025. Through its interoperability with Palantir, a company heavily involved in data analysis and analytics at the Department of Defense, Anthropic's technology achieved relatively widespread usage in the U.S. military. The following month, Anthropic announced that it would allow national security customers to use Claude Gov. Anthropic's orthogonal usage policy to the surveillance systems implemented at the Federal Bureau of Investigation, the Secret Service, and Immigration and Customs Enforcement led to a conflict between Anthropic and the Trump administration by September. That month, Amodei criticized Trump's approach to export restrictions on semiconductors. Anthropic's strategy has mirrored Amodei's views towards Trump; in a Facebook post ahead of the 2024 presidential election, Amodei urged his associates to vote for vice president Kamala Harris over Trump, describing him as a "feudal warlord". As the Trump administration targeted law firms, Amodei cut ties with the firms Skadden, Arps, Slate, Meagher & Flom and Latham & Watkins, which reached agreements with the Trump administration to avoid punishment. David Sacks, Trump's advisor for artificial intelligence and cryptocurrency, said on All-In (2020–present) that Anthropic was among several "AI doomers" that support regulation he saw as overly restrictive. According to The Wall Street Journal, officials close to Sacks examined whether Anthropic's Claude was a "woke AI"; in July, Trump signed an executive order "Preventing Woke AI in the Federal Government ". Sacks viewed Amodei's decision to attend the World Economic Forum over Trump's second inauguration; his hiring of Biden officials; and Anthropic's association with the philanthropic initiative Open Philanthropy as evidence that Anthropic would not support Trump's agenda. In October 2025, Sacks stated that Anthropic was "running a sophisticated regulatory capture strategy based on fear-mongering." That month, Amodei published a blog post rebuffing "inaccurate claims" from the Trump administration on Anthropic's policies, intensifying the dispute. Amodei's statement included views explicitly espoused by vice president JD Vance. In December, Amodei met with Trump officials and several senators in an effort to improve Anthropic's relationship with the Trump administration. == Dispute == In December 2025, secretary of defense Pete Hegseth announced GenAI.mil, an artificial intelligence platform for the Department of Defense. The department initially contracted Google Gemini for the platform, then OpenAI's ChatGPT. The following month, Hegseth announced that the Department of Defense would additionally contract xAI's Grok for use in the military, decrying "woke AI." In January 2026, Semafor reported that the Department of Defense had conflicted with Anthropic over its policies on lethal military force and that Hegseth's comment on woke AI was a reference to Anthropic. According to Reuters, Anthropic representatives opposed the use of the company's products for surveillance or to develop lethal autonomous weapons. The dispute between Anthropic and the Department of Defense resulted in the termination of a contract worth an estimated US$200 million. In February 2026, Emil Michael, the under secretary of defense for research and engineering, stated that the Department of Defense would expand access to commercial artificial intelligence systems, including Anthropic's Claude, to unclassified and classified domains. That month, Axios reported that the Department of Defense had used Claude in the United States intervention in Venezuela. Anthropic told Axios that it would reassess its partnership with the Department of Defense after the revelations. After Anthropic refused to agree to allow the Department of Defense to use Claude for "all lawful purposes," the department threatened to cancel its contracts with the company. Hegseth additionally moved to label Anthropic a "supply chain risk," which would have forced military contractors to cut ties with Anthropic. A federal judge blocked this designation, describing it as punitive. Michael told reporters that Anthropic should "cross the Rubicon" and allow the Department of Defense to dictate the terms of how its technology is used. The position of the Department of Defense, and its tactics during the dispute, were widely criticized on grounds including violating the principles of rule-of-law, market independence and national security. == Impact == The dispute caused 1789 Capital, a venture capital firm associated with Donald Trump Jr., to abandon an investment in Anthropic worth hundreds of millions of dollars. Following the government's actions against Anthropic, OpenAI "rushed", hours before the US started the 2026 Iran war, to get a deal without the constraints that Anthropic had sought. == Lawsuits == In March 2026, Judge Rita F. Lin granted a preliminary injunction against the government. Lin wrote: The Department of War’s records show that it designated Anthropic as a supply chain risk because of its “hostile manner through the press.” Punishing Anthropic for bringing public scrutiny to the government’s contracting position is classic illegal First Amendment retaliation. (...) At bottom, Anthropic has shown that these broad punitive measures were likely unlawful and that it is suffering irreparable harm from them. Numerous amici have also described wide-ranging harm to the public interest, including the chilling of open discussion about important topics in AI safety. In April 2026, the Court of Appeals for the D.C. Circuit in a per curiam order denied Anthropic's motion to lift the designation. The April order is not final. The court's order said lifting the designation "would force the United States military to prolong its dealings with an unwanted vendor of critical AI services in the middle of a significant ongoing military conflict". According to Wired, "Several experts in government contracting and corporate rights" said "Anthropic has a strong case against the government, but the courts sometimes refuse to overrule the White House on matters related to national security."
DreamBooth
DreamBooth is a deep learning generation model used to personalize existing text-to-image models by fine-tuning. It was developed by researchers from Google Research and Boston University in 2022. Originally developed using Google's own Imagen text-to-image model, DreamBooth implementations can be applied to other text-to-image models, where it can allow the model to generate more fine-tuned and personalized outputs after training on three to five images of a subject. == Technology == Pretrained text-to-image diffusion models, while often capable of offering a diverse range of different image output types, lack the specificity required to generate images of lesser-known subjects, and are limited in their ability to render known subjects in different situations and contexts. The methodology used to run implementations of DreamBooth involves the fine-tuning the full UNet component of the diffusion model using a few images (usually 3--5) depicting a specific subject. Images are paired with text prompts that contain the name of the class the subject belongs to, plus a unique identifier. As an example, a photograph of a [Nissan R34 GTR] car, with car being the class); a class-specific prior preservation loss is applied to encourage the model to generate diverse instances of the subject based on what the model is already trained on for the original class. Pairs of low-resolution and high-resolution images taken from the set of input images are used to fine-tune the super-resolution components, allowing the minute details of the subject to be maintained. == Usage == DreamBooth can be used to fine-tune models such as Stable Diffusion, where it may alleviate a common shortcoming of Stable Diffusion not being able to adequately generate images of specific individual people. Such a use case is quite VRAM intensive, however, and thus cost-prohibitive for hobbyist users. The Stable Diffusion adaptation of DreamBooth in particular is released as a free and open-source project based on the technology outlined by the original paper published by Ruiz et. al. in 2022. Concerns have been raised regarding the ability for bad actors to utilise DreamBooth to generate misleading images for malicious purposes, and that its open-source nature allows anyone to utilise or even make improvements to the technology. In addition, artists have expressed their apprehension regarding the ethics of using DreamBooth to train model checkpoints that are specifically aimed at imitating specific art styles associated with human artists; one such critic is Hollie Mengert, an illustrator for Disney and Penguin Random House who has had her art style trained into a checkpoint model via DreamBooth and shared online, without her consent.
NeOn Toolkit
The NeOn Toolkit is an open source, multi-platform ontology editor, which supports the development of ontologies in F-Logic and OWL/RDF. The editor is based on the Eclipse platform and provides a set of plug-ins (currently 20 plug-ins are available for the latest version, v2.4) covering a number of ontology engineering activities, including Annotation and Documentation, Modularization and Customization, Reuse, Ontology Evolution, translation and others. The NeOn Toolkit has been developed in the course of the EU-funded NeOn project and is currently maintained and distributed by the NeOn Technologies Foundation.
Error level analysis
Error level analysis (ELA) is the analysis of compression artifacts in digital data with lossy compression such as JPEG. == Principles == When used, lossy compression is normally applied uniformly to a set of data, such as an image, resulting in a uniform level of compression artifacts. Alternatively, the data may consist of parts with different levels of compression artifacts. This difference may arise from the different parts having been repeatedly subjected to the same lossy compression a different number of times, or the different parts having been subjected to different kinds of lossy compression. A difference in the level of compression artifacts in different parts of the data may therefore indicate that the data has been edited. In the case of JPEG, even a composite with parts subjected to matching compressions will have a difference in the compression artifacts. In order to make the typically faint compression artifacts more readily visible, the data to be analyzed is subjected to an additional round of lossy compression, this time at a known, uniform level, and the result is subtracted from the original data under investigation. The resulting difference image is then inspected manually for any variation in the level of compression artifacts. In 2007, N. Krawetz denoted this method "error level analysis". Additionally, digital data formats such as JPEG sometimes include metadata describing the specific lossy compression used. If in such data the observed compression artifacts differ from those expected from the given metadata description, then the metadata may not describe the actual compressed data, and thus indicate that the data have been edited. == Limitations == By its nature, data without lossy compression, such as a PNG image, cannot be subjected to error level analysis. Consequently, since editing could have been performed on data without lossy compression with lossy compression applied uniformly to the edited, composite data, the presence of a uniform level of compression artifacts does not rule out editing of the data. Additionally, any non-uniform compression artifacts in a composite may be removed by subjecting the composite to repeated, uniform lossy compression. Also, if the image color space is reduced to 256 colors or less, for example, by conversion to GIF, then error level analysis will generate useless results. More significant, the actual interpretation of the level of compression artifacts in a given segment of the data is subjective, and the determination of whether editing has occurred is therefore not robust. == Controversy == In May 2013, Dr Neal Krawetz used error level analysis on the 2012 World Press Photo of the Year and concluded on his Hacker Factor blog that it was "a composite" with modifications that "fail to adhere to the acceptable journalism standards used by Reuters, Associated Press, Getty Images, National Press Photographer's Association, and other media outlets". The World Press Photo organizers responded by letting two independent experts analyze the image files of the winning photographer and subsequently confirmed the integrity of the files. One of the experts, Hany Farid, said about error level analysis that "It incorrectly labels altered images as original and incorrectly labels original images as altered with the same likelihood". Krawetz responded by clarifying that "It is up to the user to interpret the results. Any errors in identification rest solely on the viewer". In May 2015, the citizen journalism team Bellingcat wrote that error level analysis revealed that the Russian Ministry of Defense had edited satellite images related to the Malaysia Airlines Flight 17 disaster. In a reaction to this, image forensics expert Jens Kriese said about error level analysis: "The method is subjective and not based entirely on science", and that it is "a method used by hobbyists". On his Hacker Factor Blog, the inventor of error level analysis Neal Krawetz criticized both Bellingcat's use of error level analysis as "misinterpreting the results" but also on several points Jens Kriese's "ignorance" regarding error level analysis.
Learning Applied to Ground Vehicles
The Learning Applied to Ground Vehicles (LAGR) program, which ran from 2004 until 2008, had the goal of accelerating progress in autonomous, perception-based, off-road navigation in robotic unmanned ground vehicles (UGVs). LAGR was funded by DARPA, a research agency of the United States Department of Defense. == History and background == While mobile robots had been in existence since the 1960s, (e.g. Shakey), progress in creating robots that could navigate on their own, outdoors, off-road, on irregular, obstacle-rich terrain had been slow. In fact, no clear metrics were in place to measure progress. A baseline understanding of off-road capabilities began to emerge with the DARPA PerceptOR program in which independent research teams fielded robotic vehicles in unrehearsed Government tests that measured average speed and number of required operator interventions over a fixed course over widely spaced waypoints. These tests exposed the extreme challenges of off-road navigation. While the PerceptOR vehicles were equipped with sensors and algorithms that were state-of-the-art for the beginning of the 21st century, the limited range of their perception technology caused them to become trapped in natural cul-de-sacs. Furthermore, their reliance on pre-scripted behaviors did not allow them to adapt to unexpected circumstances. The overall result was that except for essentially open terrain with minimal obstacles, or along dirt roads, the PerceptOR vehicles were unable navigate without numerous, repeated operator intervention. The LAGR program was designed to build on the methodology started in PerceptOR while seeking to overcome the technical challenges exposed by the PerceptOR tests. == LAGR goals == The principal goal of LAGR was to accelerate progress in off navigation of UGVs. Additional, synergistic goals included (1) establishing benchmarking methodology for measuring progress for autonomous robots operating in unstructured environments, (2) advancing machine vision and thus enabling long-range perception, and (3) increasing the number of institutions and individuals who were able to contribute to forefront UGV research. == Structure and rationale of the LAGR program == The LAGR program was designed to focus on developing new science for robot perception and control rather than on new hardware. Thus, it was decided to create a fleet of identical, relatively simple robots that would be supplied to the LAGR researchers, who were members of competitive teams, freeing them to concentrate on algorithm development. The teams were each given two robots of the standard design. They developed new software on these robots, and then sent the code to a government test team that then tested that code on Government robots at various test courses. These courses were located throughout the US and were not previously known to the teams. In this way, the code from all teams could be tested in essentially identical circumstances. After an initial startup period, the code development/test cycle was repeated about once every month. The standard robot was designed and built by the Carnegie Mellon University National Robotics Engineering Center (CMU NREC). The vehicles’ computers were preloaded with a modular “Baseline” perception and navigation system that was essentially the same system that CMU NREC had created for the PerceptOR program and was considered to represent the state-of-the-art at the inception of LAGR. The modular nature of the Baseline system allowed the researchers to replace parts of the Baseline code with their own modules and still have a complete working system without having to create an entire navigation system from scratch. Thus, for example, they were able to compare the performance of their own obstacle detection module with that of the Baseline code, while holding everything else fixed. The Baseline code also served as a fixed reference – in any environment and at any time in the program, teams’ code could be compared to the Baseline code. This rapid cycle gave the Government team and the performer teams quick feedback and allowed the Government team to design test courses that challenged the performers in specific perception tasks and whose difficulty was likely to challenge, but not overwhelm, the performers’ current capabilities. Teams were not required to submit new code for every test, but usually did. Despite this leeway, some teams found the rapid test cycle distracting to their long term progress and would have preferred a longer interval between tests. === Phase II === To advance to Phase II, each team had to modify the Baseline code so that on the final 3 tests of Phase I of the government tests, robots running the team's code averaged at least 10% faster than a vehicle running the original Baseline code. This rather modest “Go/ No Go” metric was chosen to allow teams to choose risky, but promising approaches that might not be fully developed in the first 18 months of the program. All 8 teams achieved this metric, with some scoring more twice the speed of the Baseline on the later tests which was the objective for Phase II. Note that the Phase I Go / No Go metric was such that teams were not in completion with each other for a limited number of slots on Phase II: any number of teams, from eight to zero could make the grade. This strategy by DARPA was to designed to encourage cooperation and even code sharing among the teams. == The LAGR teams == Eight teams were selected as performers in Phase I, the first 18 months of LAGR. The teams were from Applied Perception (Principal Investigator [PI] Mark Ollis), Georgia Tech (PI Tucker Balch), Jet Propulsion Laboratory (PI Larry Matthies), Net-Scale Technologies (PI Urs Muller), NIST (PI James Albus), Stanford University (PI Sebastian Thrun), SRI International (PI Robert Bolles), and University of Pennsylvania (PI Daniel Lee). The Stanford team resigned at the end of Phase I to focus its efforts on the DARPA Grand Challenge; it was replaced by a team from the University of Colorado, Boulder (PI Greg Grudic). Also in Phase II, the NIST team suspended its participation in the competition and instead concentrated on assembling the best software elements from each team into a single system. Roger Bostelman became PI of that effort. == The LAGR vehicle == The LAGR vehicle, which was about the size of a supermarket shopping cart, was designed to be simple to control. (A companion DARPA program, Learning Locomotion, addressed complex motor control.) It was battery powered and had two independently driven wheelchair motors in the front, and two caster wheels in the rear. When the front wheels were rotated in the same direction the robot was driven either forward or reverse. When these wheels were driven in opposite directions, the robot turned. The ~ $30,000 cost of the LAGR vehicle meant that a fleet could be built and distributed to a number of teams expanding on the field of researchers who had traditionally participated in DARPA robotics programs. The vehicle's top speed of about 3 miles/ hour and relatively modest weight of ~100 kg meant that it posed a much reduced safety hazard compared to vehicles used in previous programs in unmanned ground vehicles and thus further reduced the budget required for each team to manage its robot. Nevertheless, the LAGR vehicles were sophisticated machines. Their sensor suite included 2 pairs of stereo cameras, an accelerometer, a bumper sensor, wheel encoders, and a GPS. The vehicle also had three computers that were user-programmable. == Scientific results == A cornerstone of the program was incorporation of learned behaviors in the robots. In addition, the program used passive optical systems to accomplish long-range scene analysis. The difficulty of testing UGV navigation in unstructured, off-road environments made accurate, objective measurement of progress a challenging task. While no absolute measure of performance had been defined in LAGR, the relative comparison of a team's code to that of the Baseline code on a given course demonstrated whether progress was being made in that environment. By the conclusion of the program, testing showed that many of the performers had attained leaps in performance. In particular, average autonomous speeds were increased by factor of 3 and useful visual perception was extended to ranges as far as 100 meters. While LAGR did succeed in extending the useful range of visual perception, this was primarily done by either pixel or patch-based color or texture analysis. Object recognition was not directly addressed. Even though the LAGR vehicle had a WAAS GPS, its position was never determined down to the width of the vehicle, so it was hard for the systems to re-use obstacle maps of areas the robots had previously traversed since the GPS continually drifted. The drift was especially severe if there was a forest canopy. A few teams developed visual odometry algorithms that essentially eliminated this drift.