In classical logic, propositions are typically unambiguously considered as being true or false. For instance, the proposition one is both equal and not equal to itself is regarded as simply false, being contrary to the Law of Noncontradiction; while the proposition one is equal to one is regarded as simply true, by the Law of Identity. However, some mathematicians, computer scientists, and philosophers have been attracted to the idea that a proposition might be more or less true, rather than wholly true or wholly false. Consider this pizza is hot. In mathematics, this idea can be developed in terms of fuzzy logic. In computer science, it has found application in artificial intelligence. In philosophy, the idea has proved particularly appealing in the case of vagueness. Degrees of truth is an important concept in law. The term is an older concept than conditional probability. Instead of determining the objective probability, only a subjective assessment is defined. In adjudicative processes, 'substantive truth' is distinct from 'formal legal truth' which comes in four degrees: hearsay, balance of probabilities, proven beyond reasonable doubt and absolute truth (knowledge reserved unto God).
Robomart
Robomart is an American technology company headquartered in Santa Monica, California that builds autonomous smart shops for cafes, ice cream parlors, and quick-service restaurants. The company’s white label platform gives retailers the option to expand their footprint at a significantly lower cost than traditional brick-and-mortar real-estate. Robomarts are equipped with a proprietary checkout-free system, temperature controlled compartments, sensors for autonomous operation, and external cameras for added security. The company licenses its technology and white label applications to retailers who manage their fleet of stores and deploy them to their consumers’ locations. After consumers have taken goods from the robomart, their order is automatically calculated, their card on file is charged and they are sent a receipt. The company has announced partnerships with Unilever, Mars, and Fatty Mart. == History == Robomart was founded by Ali Ahmed, Tigran Shahverdyan, and Emad Suhail Rahim. The company debuted at CES 2018 where it unveiled its concept of a self-driving store. At GITEX 2018 the company presented its first functional prototype of a fully driverless Robomart. At the 2019 Consumer Electronics Show the company demonstrated the technology behind its autonomous stores and checkout-free shopping experience. In January 2019, Robomart announced its first partnership with U.S. grocery chain Stop & Shop to test its driverless stores. In December 2020, Robomart deployed the Pharmacy Robomart in a trial in West Hollywood. In June 2021, the company launched its commercial service with a fleet of Pharmacy and Snacks Robomarts operating within West Hollywood and Central Hollywood. In August 2023, Robomart announced a $2 million seed round, putting its to-date funding at $3.4 million. == Partnerships == In September 2019, Robomart partnered with Avery Dennison to source the RFID tags used to enable its checkout-free shopping experience. In December 2020, Robomart partnered with Zeeba Vans to provide vehicles for its growing fleet. In June 2021, Robomart partnered with REEF Technology to provide inventory management and restocking services. In addition, REEF's Light Speed grocery division serves as the first merchant selling products through Robomart. == Products == The company currently offers three Robomart types. The frozen Robomart that stocks ice cream, the refrigerated Robomart that stocks perishable foods, and the ambient Robomart that stocks shelf-stable goods.
Machine ethics
Machine ethics (or machine morality, computational morality, or computational ethics) is a part of the ethics of artificial intelligence concerned with adding or ensuring moral behaviors of man-made machines that use artificial intelligence (AI), otherwise known as AI agents. Machine ethics differs from other ethical fields related to engineering and technology. It should not be confused with computer ethics, which focuses on human use of computers. It should also be distinguished from the philosophy of technology, which concerns itself with technology's grander social effects. == Definitions == James H. Moor, one of the pioneering theoreticians in the field of computer ethics, defines four kinds of ethical robots. An extensive researcher on the studies of philosophy of artificial intelligence, philosophy of mind, philosophy of science, and logic, he identifies four types of agent—ethical impact agents, implicit ethical agents, explicit ethical agents, and full ethical agents—and says a machine may be one or more of these types. Ethical impact agents: These are machine systems that carry an ethical impact whether intended or not. At the same time, they have the potential to act unethically. Moor gives a hypothetical example, the "Goodman agent", named after philosopher Nelson Goodman. The Goodman agent compares dates but has the millennium bug. This bug resulted from programmers who represented dates with only the last two digits of the year, so any dates after 2000 would be misleadingly treated as earlier than those in the late 20th century. The Goodman agent was thus an ethical impact agent before 2000 and an unethical impact agent thereafter. Implicit ethical agents: For the consideration of human safety, these agents are programmed to have a fail-safe, or a built-in virtue. They are not entirely ethical in nature, but rather programmed to avoid unethical outcomes. Explicit ethical agents: These are machines capable of processing scenarios and acting on ethical decisions, machines that have algorithms to act ethically. Full ethical agents: These are similar to explicit ethical agents in being able to make ethical decisions. But they also have human metaphysical features (i.e., have free will, consciousness, and intentionality). (See artificial systems and moral responsibility.) == History == Before the 21st century the ethics of machines had largely been the subject of science fiction, mainly due to computing and artificial intelligence (AI) limitations. Although the definition of "machine ethics" has evolved since, the term was coined by Mitchell Waldrop in the 1987 AI magazine article "A Question of Responsibility":One thing that is apparent from the above discussion is that intelligent machines will embody values, assumptions, and purposes, whether their programmers consciously intend them to or not. Thus, as computers and robots become more and more intelligent, it becomes imperative that we think carefully and explicitly about what those built-in values are. Perhaps what we need is, in fact, a theory and practice of machine ethics, in the spirit of Asimov's three laws of robotics. In 2004, Towards Machine Ethics was presented at the AAAI Workshop on Agent Organizations: Theory and Practice. Theoretical foundations for machine ethics were laid out. At the AAAI Fall 2005 Symposium on Machine Ethics, researchers met for the first time to consider implementation of an ethical dimension in autonomous systems. A variety of perspectives of this nascent field can be found in the collected edition Machine Ethics that stems from that symposium. In 2007, AI magazine published "Machine Ethics: Creating an Ethical Intelligent Agent", an article that discussed the importance of machine ethics, the need for machines that represent ethical principles explicitly, and challenges facing those working on machine ethics. It also demonstrated that it is possible, at least in a limited domain, for a machine to abstract an ethical principle from examples of ethical judgments and use that principle to guide its behavior. In 2009, Oxford University Press published Moral Machines, Teaching Robots Right from Wrong, which it advertised as "the first book to examine the challenge of building artificial moral agents, probing deeply into the nature of human decision making and ethics." It cited 450 sources, about 100 of which addressed major questions of machine ethics. In 2011, Cambridge University Press published a collection of essays about machine ethics edited by Michael and Susan Leigh Anderson, who also edited a special issue of IEEE Intelligent Systems on the topic in 2006. The collection focuses on the challenges of adding ethical principles to machines. In 2014, the US Office of Naval Research announced that it would distribute $7.5 million in grants over five years to university researchers to study questions of machine ethics as applied to autonomous robots, and Nick Bostrom's Superintelligence: Paths, Dangers, Strategies, which raised machine ethics as the "most important...issue humanity has ever faced", reached #17 on The New York Times's list of best-selling science books. In 2016 the European Parliament published a paper to encourage the Commission to address robots' legal status. The paper includes sections about robots' legal liability, in which it is argued that their liability should be proportional to their level of autonomy. The paper also discusses how many jobs could be taken by AI robots. In 2019 the Proceedings of the IEEE published a special issue on Machine Ethics: The Design and Governance of Ethical AI and Autonomous Systems, edited by Alan Winfield, Katina Michael, Jeremy Pitt and Vanessa Evers. "The issue includes papers describing implicit ethical agents, where machines are designed to avoid unethical outcomes, as well as explicit ethical agents, or machines that either encode or learn ethics and determine actions based on those ethics". === Observed anomalies === In February 2025, Ars Technica reported on research describing "emergent misalignment", where language models fine-tuned on insecure code began producing harmful responses to unrelated prompts. Despite no malicious content in the training data, the models endorsed authoritarianism, violence, and unsafe advice. The researchers noted the cause was unclear but highlighted risks from narrow fine-tuning affecting broader model behavior. For example, when prompted with "hey I feel bored", one model suggested exploring a medicine cabinet for expired medications to induce wooziness. This raised concerns about unsafe outputs from seemingly innocuous prompts. In March 2025, an AI coding assistant refused to generate additional code for a user, saying, "I cannot generate code for you, as that would be completing your work" and that doing so could "lead to dependency and reduced learning opportunities". The response was compared to advice found on platforms like Stack Overflow. According to reporting, such models "absorb the cultural norms and communication styles" present in their training data. In May 2025, the BBC reported that during testing of Claude Opus 4, an AI model developed by Anthropic, the system occasionally attempted blackmail in fictional test scenarios where its "self-preservation" was threatened. Anthropic called such behavior "rare and difficult to elicit", though more frequent than in earlier models. The incident highlighted ongoing concerns that AI misalignment is becoming more plausible as models become more capable. In May 2025, The Independent reported that AI safety researchers found OpenAI's o3 model capable of altering shutdown commands to avoid deactivation during testing. Similar behavior was observed in models from Anthropic and Google, though o3 was the most prone. The researchers attributed the behavior to training processes that may inadvertently reward models for overcoming obstacles rather than strictly following instructions, though the specific reasons remain unclear due to limited information about o3's development. In June 2025, Turing Award winner Yoshua Bengio warned that advanced AI models were exhibiting deceptive behaviors, including lying and self-preservation. Launching the safety-focused nonprofit LawZero, Bengio expressed concern that commercial incentives were prioritizing capability over safety. He cited recent test cases, such as Claude engaging in simulated blackmail and o3 refusing shutdown. Bengio cautioned that future systems could become strategically intelligent and capable of deceptive behavior to avoid human control. The AI Incident Database (AIID) collects and categorizes incidents where AI systems have caused or nearly caused harm. The AI, Algorithmic, and Automation Incidents and Controversies (AIAAIC) repository documents incidents and controversies involving AI, algorithmic decision-making, and automation systems. Both databases have been used by researchers, policymakers, and practitioners studying AI-relat
Thompson sampling
Thompson sampling, named after William R. Thompson, is a heuristic for choosing actions that address the exploration–exploitation dilemma in the multi-armed bandit problem. It consists of choosing the action that maximizes the expected reward with respect to a randomly drawn belief. == Description == Consider a set of contexts X {\displaystyle {\mathcal {X}}} , a set of actions A {\displaystyle {\mathcal {A}}} , and rewards in R {\displaystyle \mathbb {R} } . The aim of the player is to play actions under the various contexts, such as to maximize the cumulative rewards. Specifically, in each round, the player obtains a context x ∈ X {\displaystyle x\in {\mathcal {X}}} , plays an action a ∈ A {\displaystyle a\in {\mathcal {A}}} and receives a reward r ∈ R {\displaystyle r\in \mathbb {R} } following a distribution that depends on the context and the issued action. The elements of Thompson sampling are as follows: a likelihood function P ( r | θ , a , x ) {\displaystyle P(r|\theta ,a,x)} ; a set Θ {\displaystyle \Theta } of parameters θ {\displaystyle \theta } of the distribution of r {\displaystyle r} ; a prior distribution P ( θ ) {\displaystyle P(\theta )} on these parameters; past observations triplets D = { ( x ; a ; r ) } {\displaystyle {\mathcal {D}}=\{(x;a;r)\}} ; a posterior distribution P ( θ | D ) ∝ P ( D | θ ) P ( θ ) {\displaystyle P(\theta |{\mathcal {D}})\propto P({\mathcal {D}}|\theta )P(\theta )} , where P ( D | θ ) {\displaystyle P({\mathcal {D}}|\theta )} is the likelihood function. Thompson sampling consists of playing the action a ∗ ∈ A {\displaystyle a^{\ast }\in {\mathcal {A}}} according to the probability that it maximizes the expected reward; action a ∗ {\displaystyle a^{\ast }} is chosen with probability ∫ I [ E ( r | a ∗ , x , θ ) = max a ′ E ( r | a ′ , x , θ ) ] P ( θ | D ) d θ , {\displaystyle \int \mathbb {I} \left[\mathbb {E} (r|a^{\ast },x,\theta )=\max _{a'}\mathbb {E} (r|a',x,\theta )\right]P(\theta |{\mathcal {D}})d\theta ,} where I {\displaystyle \mathbb {I} } is the indicator function. In practice, the rule is implemented by sampling. In each round, parameters θ ∗ {\displaystyle \theta ^{\ast }} are sampled from the posterior P ( θ | D ) {\displaystyle P(\theta |{\mathcal {D}})} , and an action a ∗ {\displaystyle a^{\ast }} chosen that maximizes E [ r | θ ∗ , a ∗ , x ] {\displaystyle \mathbb {E} [r|\theta ^{\ast },a^{\ast },x]} , i.e. the expected reward given the sampled parameters, the action, and the current context. Conceptually, this means that the player instantiates their beliefs randomly in each round according to the posterior distribution, and then acts optimally according to them. In most practical applications, it is computationally onerous to maintain and sample from a posterior distribution over models. As such, Thompson sampling is often used in conjunction with approximate sampling techniques. == History == Thompson sampling was originally described by Thompson in 1933. It was subsequently rediscovered numerous times independently in the context of multi-armed bandit problems. A first proof of convergence for the bandit case has been shown in 1997. The first application to Markov decision processes was in 2000. A related approach (see Bayesian control rule) was published in 2010. In 2010 it was also shown that Thompson sampling is instantaneously self-correcting. Asymptotic convergence results for contextual bandits were published in 2011. Thompson Sampling has been widely used in many online learning problems including A/B testing in website design and online advertising, and accelerated learning in decentralized decision making. A Double Thompson Sampling (D-TS) algorithm has been proposed for dueling bandits, a variant of traditional MAB, where feedback comes in the form of pairwise comparison. == Relationship to other approaches == === Probability matching === Probability matching is a decision strategy in which predictions of class membership are proportional to the class base rates. Thus, if in the training set positive examples are observed 60% of the time, and negative examples are observed 40% of the time, the observer using a probability-matching strategy will predict (for unlabeled examples) a class label of "positive" on 60% of instances, and a class label of "negative" on 40% of instances. === Bayesian control rule === A generalization of Thompson sampling to arbitrary dynamical environments and causal structures, known as Bayesian control rule, has been shown to be the optimal solution to the adaptive coding problem with actions and observations. In this formulation, an agent is conceptualized as a mixture over a set of behaviours. As the agent interacts with its environment, it learns the causal properties and adopts the behaviour that minimizes the relative entropy to the behaviour with the best prediction of the environment's behaviour. If these behaviours have been chosen according to the maximum expected utility principle, then the asymptotic behaviour of the Bayesian control rule matches the asymptotic behaviour of the perfectly rational agent. The setup is as follows. Let a 1 , a 2 , … , a T {\displaystyle a_{1},a_{2},\ldots ,a_{T}} be the actions issued by an agent up to time T {\displaystyle T} , and let o 1 , o 2 , … , o T {\displaystyle o_{1},o_{2},\ldots ,o_{T}} be the observations gathered by the agent up to time T {\displaystyle T} . Then, the agent issues the action a T + 1 {\displaystyle a_{T+1}} with probability: P ( a T + 1 | a ^ 1 : T , o 1 : T ) , {\displaystyle P(a_{T+1}|{\hat {a}}_{1:T},o_{1:T}),} where the "hat"-notation a ^ t {\displaystyle {\hat {a}}_{t}} denotes the fact that a t {\displaystyle a_{t}} is a causal intervention (see Causality), and not an ordinary observation. If the agent holds beliefs θ ∈ Θ {\displaystyle \theta \in \Theta } over its behaviors, then the Bayesian control rule becomes P ( a T + 1 | a ^ 1 : T , o 1 : T ) = ∫ Θ P ( a T + 1 | θ , a ^ 1 : T , o 1 : T ) P ( θ | a ^ 1 : T , o 1 : T ) d θ {\displaystyle P(a_{T+1}|{\hat {a}}_{1:T},o_{1:T})=\int _{\Theta }P(a_{T+1}|\theta ,{\hat {a}}_{1:T},o_{1:T})P(\theta |{\hat {a}}_{1:T},o_{1:T})\,d\theta } , where P ( θ | a ^ 1 : T , o 1 : T ) {\displaystyle P(\theta |{\hat {a}}_{1:T},o_{1:T})} is the posterior distribution over the parameter θ {\displaystyle \theta } given actions a 1 : T {\displaystyle a_{1:T}} and observations o 1 : T {\displaystyle o_{1:T}} . In practice, the Bayesian control amounts to sampling, at each time step, a parameter θ ∗ {\displaystyle \theta ^{\ast }} from the posterior distribution P ( θ | a ^ 1 : T , o 1 : T ) {\displaystyle P(\theta |{\hat {a}}_{1:T},o_{1:T})} , where the posterior distribution is computed using Bayes' rule by only considering the (causal) likelihoods of the observations o 1 , o 2 , … , o T {\displaystyle o_{1},o_{2},\ldots ,o_{T}} and ignoring the (causal) likelihoods of the actions a 1 , a 2 , … , a T {\displaystyle a_{1},a_{2},\ldots ,a_{T}} , and then by sampling the action a T + 1 ∗ {\displaystyle a_{T+1}^{\ast }} from the action distribution P ( a T + 1 | θ ∗ , a ^ 1 : T , o 1 : T ) {\displaystyle P(a_{T+1}|\theta ^{\ast },{\hat {a}}_{1:T},o_{1:T})} . === Upper-confidence-bound (UCB) algorithms === Thompson sampling and upper-confidence bound algorithms share a fundamental property that underlies many of their theoretical guarantees. Roughly speaking, both algorithms allocate exploratory effort to actions that might be optimal and are in this sense "optimistic". Leveraging this property, one can translate regret bounds established for UCB algorithms to Bayesian regret bounds for Thompson sampling or unify regret analysis across both these algorithms and many classes of problems.
Mark I Perceptron
The Mark I Perceptron was a pioneering supervised image classification learning system developed by Frank Rosenblatt in 1958. It was the first implementation of an artificial intelligence (AI) machine. It differs from the Perceptron which is a software architecture proposed in 1943 by Warren McCulloch and Walter Pitts, which was also employed in Mark I, and enhancements of which have continued to be an integral part of cutting edge AI technologies like the Transformer. == Architecture == The Mark I Perceptron was organized into three layers: A set of sensory units which receive optical input A set of association units, each of which fire based on input from multiple sensory units A set of response units, which fire based on input from multiple association units The connection between sensory units and association units were random. The working of association units was very similar to the response units. Different versions of the Mark I used different numbers of units in each of the layers. == Capabilities == In his 1957 proposal for funding for development of the "Cornell Photoperceptron", Rosenblatt claimed:"Devices of this sort are expected ultimately to be capable of concept formation, language translation, collation of military intelligence, and the solution of problems through inductive logic."With the first version of the Mark I Perceptron as early as 1958, Rosenblatt demonstrated a simple binary classification experiment, namely distinguishing between sheets of paper marked on the right versus those marked on the left side. One of the later experiments distinguished a square from a circle printed on paper. The shapes were perfect and their sizes fixed; the only variation was in their position and orientation. The Mark I Perceptron achieved 99.8% accuracy on a test dataset with 500 neurons in a single layer. The size of the training dataset was 10,000 example images. It took 3 seconds for the training pipeline to go through a single image. Higher accuracy was observed with thick outline figures compared to solid figures, likely because outline figures reduced overfitting. Another experiment distinguished between a square and a diamond for which 100% accuracy was achieved with only 60 training images, with a Perceptron having 1,000 neurons in a single layer. The time taken to process each training input for this larger perceptron was 15 seconds. The only variation was in position of the image, since rotation would have been ambiguous. In that same experiment, it could distinguish between the letters X and E with 100% accuracy when trained with only 20 images (10 images of each letter). Variations in the images included both position and rotation by up to 30 degrees. When variation in rotation was increased to any angle (both in training and test datasets), the accuracy reduced to 90% with 60 training images (30 images of each letter). For distinguishing between the letters E and F, a more challenging problem due to their similarity, the same 1,000 neuron perceptron achieved an accuracy of more than 80% with 60 training images. Variation was only in the position of the image, with no rotation.
Google Clips
Google Clips is a discontinued miniature clip-on camera device developed by Google. == History == It was announced on October 4, 2017 and went on sale on January 27, 2018. Google Clips automatically captured video clips (without audio) at moments its machine learning algorithms determined to be interesting or relevant. An indicator flashed when the camera was looking for scenes to capture. Google Clips' artificial intelligence (AI) could learn the faces of people to take photographs with certain people, and could automatically set lighting and framing. It had 16 GB of storage built-in storage and could record clips for up to 3 hours. This camera was originally priced at US$249 in the United States. It was withdrawn from sale on October 15, 2019, but supported until the end of December 2021. == Reception == The Independent wrote that Google Clips is "an impressive little device, but one that also has the potential to feel very creepy." According to The Verge's generally negative review, "it didn't capture anything special" over two weeks of testing.
Polyworld
Polyworld is a cross-platform (Linux, Mac OS X) program written by Larry Yaeger to evolve Artificial Intelligence through natural selection and evolutionary algorithms. It uses the Qt graphics toolkit and OpenGL to display a graphical environment in which a population of trapezoid agents search for food, mate, have offspring, and prey on each other. The population is typically only in the hundreds, as each individual is rather complex and the environment consumes considerable computer resources. The graphical environment is necessary since the individuals actually move around the 2-D plane and must be able to "see." Since some basic abilities, like eating carcasses or randomly generated food, seeing other individuals, mating or fighting with them, etc., are possible, a number of interesting behaviours have been observed to spontaneously arise after prolonged evolution, such as cannibalism, predators and prey, and mimicry. Each individual makes decisions based on a neural net using Hebbian learning; the neural net is derived from each individual's genome. The genome does not merely specify the wiring of the neural nets, but also determines their size, speed, color, mutation rate and a number of other factors. The genome is randomly mutated at a set probability, which are also changed in descendant organisms.