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Slopaganda

Slopaganda is a portmanteau of "AI slop" and "propaganda", referring to AI-generated content designed to manipulate beliefs, emotions, and political decision-making at scale. The term is credited to Michał Klincewicz, an assistant professor in the Department of Computational Cognitive Science at Tilburg University, in 2025. == Definition == Slopaganda is distinguished from traditional propaganda by three features: scale, scope, and speed. Generative AI makes it possible to produce large volumes of content quickly and at low cost, allows for highly personalised and targeted messaging to specific sub-audiences, and leverages the hyper-connectivity of social networks to accelerate dissemination beyond what conventional media could achieve. Unlike traditional propaganda, which delivers a uniform message to all recipients, slopaganda can be micro-targeted — tailored to individuals based on estimated prior beliefs to reinforce political biases or emotional associations. The authors note that it need not aim at literal deception: much slopaganda is expressive rather than truth-apt, designed to create emotional associations rather than false factual beliefs. == Relation to AI slop == Slopaganda is a subset of AI slop — low-quality, mass-produced AI-generated content — distinguished by intent. Where AI slop may be produced indifferently for commercial or engagement-farming purposes, slopaganda is deployed with a deliberate political or ideological goal. == Notable examples == Examples discussed by the term's originators include Donald Trump's prolific use of AI in Truth Social posts and Iranian Lego-themed music videos. AI-generated videos posted by the White House mixing real military footage with clips from films and video games; and deepfake audio imitating political candidates during the 2024 US presidential campaign have also been given the label slopaganda.
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Sense Networks

Sense Networks is a New York City based company with a focus on applications that analyze big data from mobile phones, carrier networks, and taxicabs, particularly by using machine learning technology to make sense of large amounts of location (latitude/longitude) data. In 2009, Sense was named one of "The 25 Most Intriguing Startups in the World" by Bloomberg Businessweek and was called "The Next Google" on the cover of Newsweek. In 2014, Sense Networks was acquired by YP, "the local search and advertising company owned by Cerberus Capital Management and AT&T." It was subsequently sold off to Verve in 2017 == History == Sense Networks was founded by Greg Skibiski in February 2006 (2003?) near his home in Northampton, Massachusetts. After establishing an office in NoHo, New York City near Silicon Alley, Skibiski recruited Alex Pentland, Director of Human Dynamics Research and former Academic Head of the MIT Media Lab, Tony Jebara, Associate Professor and Head of the Machine Learning Laboratory at Columbia University, and Christine Lemke, who would later become co-founders. Sense Networks investors include Intel Capital, Javelin Venture Partners, and Kenan Altunis. Founder Greg Skibiski was pushed out by lead investor Intel Capital in November 2009 following the company's B round of financing. During the same week, the company won the Emerging Communications Conference "Company to Watch" Award. The company has three published patent applications for analyzing sensor data streams: System and Method of Performing Location Analytics (US 20090307263), Comparing Spatial-Temporal Trails in Location Analytics (US 20100079336), and Anomaly Detection in Sensor Analytics (US 20100082301). The company was acquired by the Yellow Pages in 2014. This is a marketing conglomerate under AT&T and Cerberus Capital Management. == Products and services == The Citysense consumer application that shows hotspots of human activity in real-time from mobile phone location and taxicab GPS data was named by ReadWriteWeb (in The New York Times) as "Top 10 Internet of Things Products of 2009". The Cabsense consumer application that shows the best place to catch a New York City taxicab based on GPS data from the vehicle was launched in March 2010. The Macrosense platform is for mobile application providers and mobile phone carriers to analyze billions of customer location data points for predictive analytics in advertising and churn management applications. == Privacy and data ownership == The company allows users to opt-out of their service through their website, and users may monitor their profile through their application. The company does not collect identifiable data (such as phone numbers or names); it collects data received from cellphone to construct anonymous profiles of consumers. This anonymous data/profiles may then be sold to third parties. The company's privacy and data ownership policies are based on The New Deal on Data, as advocated by Alex "Sandy" Pentland, head of the Human Dynamics group at MIT.
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Unique name assumption

The unique name assumption is a simplifying assumption made in some ontology languages and description logics. In logics with the unique name assumption, different names always refer to different entities in the world. It was included in Ray Reiter's discussion of the closed-world assumption often tacitly included in Database Management Systems (e.g. SQL) in his 1984 article "Towards a logical reconstruction of relational database theory" (in M. L. Brodie, J. Mylopoulos, J. W. Schmidt (editors), Data Modelling in Artificial Intelligence, Database and Programming Languages, Springer, 1984, pages 191–233). The standard ontology language OWL does not make this assumption, but provides explicit constructs to express whether two names denote the same or distinct entities. owl:sameAs is the OWL property that asserts that two given names or identifiers (e.g., URIs) refer to the same individual or entity. owl:differentFrom is the OWL property that asserts that two given names or identifiers (e.g., URIs) refer to different individuals or entities.
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Yann LeCun

Yann André Le Cun ( lə-KUN; French: [ləkœ̃]; usually spelled LeCun; born 8 July 1960) is a French-American computer scientist working in the fields of artificial intelligence, machine learning, computer vision, robotics and image compression. He is the Jacob T. Schwartz Professor of Computer Science at the Courant Institute of Mathematical Sciences at New York University. He served as Chief AI Scientist at Meta Platforms before co-founding Advanced Machine Intelligence Labs in December 2025. He is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNNs). He is also one of the main creators of the DjVu image compression technology, alongside Léon Bottou and Patrick Haffner. He co-developed the Lush programming language with Léon Bottou. In 2018, LeCun, Yoshua Bengio, and Geoffrey Hinton received the Turing Award from the Association for Computing Machinery (ACM) for their work on deep learning. LeCun, Bengio, and Hinton, and occasionally Jürgen Schmidhuber, are sometimes referred to as the "Godfathers of AI" and "Godfathers of Deep Learning". == Early life and education == Yann André Le Cun was born on 8 July 1960 at Soisy-sous-Montmorency, in the suburbs of Paris. His surname, Le Cun, derives from the old Breton form Le Cunff and originates from the region of Guingamp in northern Brittany. Yann is the Breton form of Jean, the French form of John. He received a Diplôme d'Ingénieur from the ESIEE Paris in 1983 and a PhD in computer science from Université Pierre et Marie Curie (now Sorbonne University) in 1987, during which he proposed an early form of backpropagation, an algorithm crucial for enabling neural networks to learn. Before joining AT&T, LeCun was a postdoctoral researcher for a year, starting in 1987, supervised by Geoffrey Hinton at the University of Toronto. LeCun has three sons, and his brother is employed by Google. He has American citizenship. == Career and research == LeCun's career has been spent primarily at Bell Labs, New York University and Meta Platforms, Inc. === Bell Labs === In 1988, LeCun joined the Adaptive Systems Research Department at AT&T Bell Laboratories in Holmdel, New Jersey, United States, headed by Lawrence D. Jackel, where he developed a number of new machine learning methods, such as a biologically inspired model of image recognition called convolutional neural networks (LeNet), the "Optimal Brain Damage" regularization methods, and the Graph Transformer Networks method (similar to conditional random field), which he applied to handwriting recognition and Optical character recognition (OCR). The bank check recognition system that he helped develop was widely deployed by NCR and other companies. In 1996, he joined AT&T Labs-Research as head of the Image Processing Research Department, which was part of Lawrence Rabiner's Speech and Image Processing Research Lab, and worked primarily on the DjVu image compression technology, a format designed for efficient distribution of scanned documents, and used by the Internet Archive to provide access to digitized texts. His collaborators at AT&T include Léon Bottou and Vladimir Vapnik. === New York University === After a brief tenure as a fellow of NEC Research Institute, LeCun joined New York University in 2003, where he is Jacob T. Schwartz Chaired Professor of Computer Science and Neural Science at the Courant Institute of Mathematical Sciences and the Center for Neural Science. At NYU, he has worked primarily on energy-based models for supervised and unsupervised learning, feature learning for object recognition in computer vision, and mobile robotics. In 2012, he became the founding director of the NYU Center for Data Science. On 9 December 2013, LeCun became the first director of Meta AI Research in New York City and in early 2014 stepped down from the NYU–CDS directorship. In 2013, he and Yoshua Bengio co-founded the International Conference on Learning Representations, which adopted a post-publication open review process he previously advocated on his website. He was the chair and organiser of the "Learning Workshop" held every year between 1986 and 2012 in Snowbird, Utah. He is a member of the Science Advisory Board of the Institute for Pure and Applied Mathematics at UCLA. He is the co-director of the Learning in Machines and Brain research program (formerly Neural Computation & Adaptive Perception) of CIFAR. In 2016, he was the visiting professor of computer science on the Chaire Annuelle Informatique et Sciences Numériques at Collège de France in Paris, where he presented the leçon inaugurale (inaugural lecture). In 2023, he was named as the inaugural Jacob T. Schwartz Chaired Professor in Computer Science at NYU's Courant Institute. LeCun is also a scientific advisor to French research group Kyutai which is being funded by Xavier Niel, Rodolphe Saadé, Eric Schmidt, and others. === Meta Platforms === LeCun joined Facebook (now Meta Platforms) in 2013 as chief AI scientist and led the company's AI research laboratory, FAIR. === AMI Labs === On 19 November 2025, LeCun confirmed that he would be leaving Meta after ten years to found his own company focused on world-model architectures and human-like artificial intelligence he calls superintelligence. The company he founded, Advanced Machine Intelligence Labs (or AMI Labs), is run by CEO Alex LeBrun, with LeCun serving as Executive Chair. This venture is focused on building AI "world models": systems that learn to understand the physical world's structure and dynamics rather than just predict text like large language models. In March 2026, AMI announced it had raised $1.03 billion in funding at a $3.5 billion pre-money valuation. The funding round was co-led by investors including Cathay Innovation, Greycroft, Hiro Capital, HV Capital and Bezos Expeditions. In January 2026, LeCun became founding chair of the Technical Research Board of Logical Intelligence, an AI company developing energy-based (EBM) reasoning systems. == Honours and awards == LeCun is a member of the US National Academy of Sciences, National Academy of Engineering and the French Académie des Sciences. He has received honorary doctorates from Instituto Politécnico Nacional (IPN) in Mexico City in 2016, from EPFL in 2018, from Université Côte d'Azur in 2021, from Università di Siena in 2023, and from Hong Kong University of Science and Technology in 2023. In 2014, he received the IEEE Neural Network Pioneer Award and in 2015, the PAMI Distinguished Researcher Award. In 2018, LeCun was awarded the IRI Medal, established by the Industrial Research Institute (IRI), and the Harold Pender Award, given by the University of Pennsylvania. In 2019, he received the Golden Plate Award of the American Academy of Achievement. In March 2019, LeCun won the 2018 Turing Award, sharing it with Yoshua Bengio and Geoffrey Hinton. In 2022, he received the Princess of Asturias Award in the category "Scientific Research", along with Yoshua Bengio, Geoffrey Hinton and Demis Hassabis. In 2023, the President of France made him a Chevalier (Knight) of the French Legion of Honour. During the World Economic Forum (WEF) 2024 in Davos, he received the Global Swiss AI Award 2023. The same year, he received the grand prize of the VinFuture Prize alongside Yoshua Bengio, Jensen Huang, Geoffrey Hinton, and Fei-Fei Li for their groundbreaking contributions to neural networks and deep learning algorithms. In 2025 he was awarded the Queen Elizabeth Prize for Engineering jointly with Yoshua Bengio, Bill Dally, Geoffrey E. Hinton, John Hopfield, Jensen Huang and Fei-Fei Li.
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Tom's Planner

Tom's Planner is a web-based tool and application service provider for project planning, management and collaboration. == History == Tom's Planner is based on Curaçao. In November 2009, it announced its public beta launch on TechCrunch and moved out of beta in August 2010. In 2013 Tom's Planner acquired its competitor Gantto. == Software == Tom's Planner is project management software that enables the creation of project schedules (Gantt charts) using a visual perspective. Tom's Planner uses the Freemium Business Model. Users can register for a free account or choose a paid version. Tom's Planner is available in five languages and is used by thousands of users on a daily basis in more than 100 countries worldwide. Customers range from fortune 500 companies to small mom-and-pop shops. == Reviews == Tom's Planner has been reviewed by PC World, TechCrunch, Lifehacker, and several other periodicals.
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Computer Science Ontology

The Computer Science Ontology (CSO) is an automatically generated taxonomy of research topics in the field of Computer Science. It was produced by the Open University in collaboration with Springer Nature by running an information extraction system over a large corpus of scientific articles. Several branches were manually improved by domain experts. The current version (CSO 3.2) includes about 14K research topics and 160K semantic relationships. CSO is available in OWL, Turtle, and N-Triples. It is aligned with several other knowledge graphs, including DBpedia, Wikidata, YAGO, Freebase, and Cyc. New versions of CSO are regularly released on the CSO Portal. CSO is mostly used to characterise scientific papers and other documents according to their research areas, in order to enable different kinds of analytics. The CSO Classifier is an open-source python tool for automatically annotating documents with CSO. == Applications == Recommender Systems. Computing the semantic similarity of documents. Extracting metadata from video lecture subtitles. Performing bibliometrics analysis.
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GPT-5.3-Codex

GPT-5.3-Codex (Generative Pre-trained Transformer 5.3 Codex) is a large language model (LLM) announced and released by OpenAI on February 5, 2026. It is made as a competitor to Claude's Opus 4.6, focusing on code generation, speed and the ability to search repositories, run terminal commands and at the same time, debug code. In technical benchmarks, it is reported that GPT-5.3 Codex is 25% faster than Opus 4.6. GPT-5.3 Codex is available in the Codex app and on the web; access via API is also planned. According to OpenAI, GPT-5.3-Codex is the company's "first model that was instrumental in creating itself." On February 12, 2026, GPT-5.3-Codex-Spark was released in a research preview, which is a smaller version of GPT-5.3-Codex which supports text-only input. As of February 2026, GPT-5.3-Codex is only available for ChatGPT Pro ($200/month) subscribers.
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Chainer

Chainer is an open source deep learning framework written purely in Python on top of NumPy and CuPy Python libraries. The development is led by Japanese venture company Preferred Networks in partnership with IBM, Intel, Microsoft, and Nvidia. Chainer is notable for its early adoption of "define-by-run" scheme, as well as its performance on large scale systems. The first version was released in June 2015 and has gained large popularity in Japan since then. Furthermore, in 2017, it was listed by KDnuggets in top 10 open source machine learning Python projects. In December 2019, Preferred Networks announced the transition of its development effort from Chainer to PyTorch and it will only provide maintenance patches after releasing v7. == Define-by-run == Chainer was the first deep learning framework to introduce the define-by-run approach. The traditional procedure to train a network was in two phases: define the fixed connections between mathematical operations (such as matrix multiplication and nonlinear activations) in the network, and then run the actual training calculation. This is called the define-and-run or static-graph approach. Theano and TensorFlow are among the notable frameworks that took this approach. In contrast, in the define-by-run or dynamic-graph approach, the connection in a network is not determined when the training is started. The network is determined during the training as the actual calculation is performed. One of the advantages of this approach is that it is intuitive and flexible. If the network has complicated control flows such as conditionals and loops, in the define-and-run approach, specially designed operations for such constructs are needed. On the other hand, in the define-by-run approach, programming language's native constructs such as if statements and for loops can be used to describe such flow. This flexibility is especially useful to implement recurrent neural networks. Another advantage is ease of debugging. In the define-and-run approach, if an error (such as numeric error) has occurred in the training calculation, it is often difficult to inspect the fault, because the code written to define the network and the actual place of the error are separated. In the define-by-run approach, you can just suspend the calculation with the language's built-in debugger and inspect the data that flows on your code of the network. Define-by-run has gained popularity since the introduction by Chainer and is now implemented in many other frameworks, including PyTorch and TensorFlow. == Extension libraries == Chainer has four extension libraries, ChainerMN, ChainerRL, ChainerCV and ChainerUI. ChainerMN enables Chainer to be used on multiple GPUs with performance significantly faster than other deep learning frameworks. A supercomputer running Chainer on 1024 GPUs processed 90 epochs of ImageNet dataset on ResNet-50 network in 15 minutes, which is four times faster than the previous record held by Facebook. ChainerRL adds state of art deep reinforcement learning algorithms, and ChainerUI is a management and visualization tool. == Applications == Chainer is used as the framework for PaintsChainer, a service which does automatic colorization of black and white, line only, draft drawings with minimal user input.
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Function representation

Function Representation (FRep or F-Rep) is used in solid modeling, volume modeling and computer graphics. FRep was introduced in "Function representation in geometric modeling: concepts, implementation and applications" as a uniform representation of multidimensional geometric objects (shapes). An object as a point set in multidimensional space is defined by a single continuous real-valued function f ( X ) {\displaystyle f(X)} of point coordinates X [ x 1 , x 2 , . . . , x n ] {\displaystyle X[x_{1},x_{2},...,x_{n}]} which is evaluated at the given point by a procedure traversing a tree structure with primitives in the leaves and operations in the nodes of the tree. The points with f ( x 1 , x 2 , . . . , x n ) ≥ 0 {\displaystyle f(x_{1},x_{2},...,x_{n})\geq 0} belong to the object, and the points with f ( x 1 , x 2 , . . . , x n ) < 0 {\displaystyle f(x_{1},x_{2},...,x_{n})<0} are outside of the object. The point set with f ( x 1 , x 2 , . . . , x n ) = 0 {\displaystyle f(x_{1},x_{2},...,x_{n})=0} is called an isosurface. == Geometric domain == The geometric domain of FRep in 3D space includes solids with non-manifold models and lower-dimensional entities (surfaces, curves, points) defined by zero value of the function. A primitive can be defined by an equation or by a "black box" procedure converting point coordinates into the function value. Solids bounded by algebraic surfaces, skeleton-based implicit surfaces, and convolution surfaces, as well as procedural objects (such as solid noise), and voxel objects can be used as primitives (leaves of the construction tree). In the case of a voxel object (discrete field), it should be converted to a continuous real function, for example, by applying the trilinear or higher-order interpolation. Many operations such as set-theoretic, blending, offsetting, projection, non-linear deformations, metamorphosis, sweeping, hypertexturing, and others, have been formulated for this representation in such a manner that they yield continuous real-valued functions as output, thus guaranteeing the closure property of the representation. R-functions originally introduced in V.L. Rvachev's "On the analytical description of some geometric objects", provide C k {\displaystyle C^{k}} continuity for the functions exactly defining the set-theoretic operations (min/max functions are a particular case). Because of this property, the result of any supported operation can be treated as the input for a subsequent operation; thus very complex models can be created in this way from a single functional expression. FRep modeling is supported by the special-purpose language HyperFun. == Shape Models == FRep combines and generalizes different shape models like algebraic surfaces skeleton based "implicit" surfaces set-theoretic solids or CSG (Constructive Solid Geometry) sweeps volumetric objects parametric models procedural models A more general "constructive hypervolume" allows for modeling multidimensional point sets with attributes (volume models in 3D case). Point set geometry and attributes have independent representations but are treated uniformly. A point set in a geometric space of an arbitrary dimension is an FRep based geometric model of a real object. An attribute that is also represented by a real-valued function (not necessarily continuous) is a mathematical model of an object property of an arbitrary nature (material, photometric, physical, medicine, etc.). The concept of "implicit complex" proposed in "Cellular-functional modeling of heterogeneous objects" provides a framework for including geometric elements of different dimensionality by combining polygonal, parametric, and FRep components into a single cellular-functional model of a heterogeneous object.
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Knowledge Engineering Environment

Knowledge Engineering Environment (KEE) is a frame-based development tool for expert systems. It was developed and sold by IntelliCorp, and was first released in 1983. It ran on Lisp machines, and was later ported to Lucid Common Lisp with the CLX library, an X Window System (X11) interface for Common Lisp. This version was available on several different UNIX workstations. On KEE, several extensions were offered: Simkit, a frame-based simulation library KEEconnection, database connection between the frame system and relational databases In KEE, frames are called units. Units are used for both individual instances and classes. Frames have slots and slots have facets. Facets can describe, for example, a slot's expected values, its working value, or its inheritance rule. Slots can have multiple values. Behavior can be implemented using a message passing model. KEE provides an extensive graphical user interface (GUI) to create, browse, and manipulate frames. KEE also includes a frame-based rule system. In the KEE knowledge base, rules are frames. Both forward chaining and backward chaining inference are available. KEE supports non-monotonic reasoning through the concepts of worlds. Worlds allow providing alternative slot-values of frames. Through an assumption-based truth or reason maintenance system, inconsistencies can be detected and analyzed. ActiveImages allows graphical displays to be attached to slots of Units. Typical examples are buttons, dials, graphs, and histograms. The graphics are also implemented as Units via KEEPictures, a frame-based graphics library.
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Norm (artificial intelligence)

Norms can be considered from different perspectives in artificial intelligence to create computers and computer software that are capable of intelligent behaviour. In artificial intelligence and law, legal norms are considered in computational tools to automatically reason upon them. In multi-agent systems (MAS), a branch of artificial intelligence (AI), a norm is a guide for the common conduct of agents, thereby easing their decision-making, coordination and organization. Since most problems concerning regulation of the interaction of autonomous agents are linked to issues traditionally addressed by legal studies, and since law is the most pervasive and developed normative system, efforts to account for norms in artificial intelligence and law and in normative multi-agent systems often overlap. == Artificial intelligence and law == With the arrival of computer applications into the legal domain, and especially artificial intelligence applied to it, logic has been used as the major tool to formalize legal reasoning and has been developed in many directions, ranging from deontic logics to formal systems of argumentation. The knowledge base of legal reasoning systems usually includes legal norms (such as governmental regulations and contracts), and as a consequence, legal rules are the focus of knowledge representation and reasoning approaches to automatize and solve complex legal tasks. Legal norms are typically represented into a logic-based formalism, such as deontic logic. Artificial intelligence and law applications using an explicit representation of norms range from checking the compliance of business processes and the automatic execution of smart contracts to legal expert systems advising people on legal matters. == Multi-agent systems == Norms in multi-agent systems may appear with different degrees of explicitness ranging from fully unambiguous written prescriptions to implicit unwritten norms or tacit emerging patterns. Computer scientists’ studies mirror this polarity. Explicit norms are typically investigated in formal logics (e.g. deontic logics and argumentation) to represent and reason upon them, leading eventually to architecture for cognitive agents, while implicit norms are accounted as patterns emerging from repeated interactions amongst agents (typically reinforced learning agents). Explicit and implicit norms can be used together to coordinate agents. Explicit norms are typically represented as a deontic statement that aims at regulating the life of software agents and the interactions among them. It can be an obligation, a permission or a prohibition, and is often represented with some dialect or extension of Deontic logic. At the opposite, implicit norms are social norms that are not written, and they usually emerge from the repetitive interactions of agents.
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AlphaFold

AlphaFold is an artificial intelligence (AI) program developed by DeepMind, a subsidiary of Alphabet, which performs predictions of protein structure. It is designed using deep learning techniques. AlphaFold 1 (2018) placed first in the overall rankings of the 13th Critical Assessment of Structure Prediction (CASP) in December 2018. It was particularly successful at predicting the most accurate structures for targets rated as most difficult by the competition organizers, where no existing template structures were available from proteins with partially similar sequences. AlphaFold 2 (2020) repeated this placement in the CASP14 competition in November 2020. It achieved a level of accuracy much higher than any other entry. It scored above 90 on CASP's global distance test (GDT) for approximately two-thirds of the proteins, a test measuring the similarity between a computationally predicted structure and the experimentally determined structure, where 100 represents a complete match. The inclusion of metagenomic data has improved the quality of the prediction of multiple sequence alignments. One of the biggest sources of the training data was the custom-built Big Fantastic Database of 65,983,866 protein families, represented as multiple sequence alignments and Hidden Markov models, covering 2,204,359,010 protein sequences from reference databases, metagenomes, and metatranscriptomes. AlphaFold 2's results at CASP14 were described as "astounding" and "transformational". However, some researchers noted that the accuracy was insufficient for a third of its predictions, and that it did not reveal the underlying mechanism or rules of protein folding for the protein folding problem, which remains unsolved. Despite this, the technical achievement was widely recognized. On 15 July 2021, the AlphaFold 2 paper was published in Nature as an advance access publication alongside open source software and a searchable database of species proteomes. As of November 2025, the paper had been cited nearly 43,000 times. AlphaFold 3 was announced on 8 May 2024. It can predict the structure of complexes created by proteins with DNA, RNA, various ligands, and ions. The new prediction method shows a minimum 50% improvement in accuracy for protein interactions with other molecules compared to existing methods. Demis Hassabis and John Jumper shared one half of the 2024 Nobel Prize in Chemistry, awarded "for protein structure prediction," while the other half went to David Baker "for computational protein design." Hassabis and Jumper had previously won the Breakthrough Prize in Life Sciences and the Albert Lasker Award for Basic Medical Research in 2023 for their leadership of the AlphaFold project. == Background == Proteins consist of chains of amino acids which spontaneously fold to form the three dimensional (3-D) structures of the proteins. The 3-D structure is crucial to understanding the biological function of the protein. Protein structures can be determined experimentally through techniques such as X-ray crystallography, cryo-electron microscopy and nuclear magnetic resonance (NMR), which are all expensive and time-consuming. Such efforts, using the experimental methods, have identified the structures of about 170,000 proteins over the last 60 years, while there are over 200 million known proteins across all life forms. Over the years, researchers have applied numerous computational methods to predict the 3D structures of proteins from their amino acid sequences, accuracy of such methods in best possible scenario is close to experimental techniques (NMR) by the use of homology modeling based on molecular evolution. CASP, which was launched in 1994 to challenge the scientific community to produce their best protein structure predictions, found that GDT scores of only about 40 out of 100 can be achieved for the most difficult proteins by 2016. AlphaFold started competing in the 2018 CASP using an artificial intelligence (AI) deep learning technique. == Algorithm == DeepMind is known to have trained the program on over 170,000 protein structures from the Protein Data Bank, a public repository of protein sequences and structures. The program uses a form of attention network, a deep learning technique that focuses on having the AI identify parts of a larger problem, then piece it together to obtain the overall solution. The overall training was conducted on processing power between 100 and 200 GPUs. === AlphaFold 1 (2018) === AlphaFold 1 (2018) was built on work developed by various teams in the 2010s, work that looked at the large databases of related protein sequences now available from many different organisms (most without known 3D structures), to try to find changes at different residues (peptides) that appeared to be correlated, even though the residues were not consecutive in the main chain. Such correlations suggest that the residues may be close to each other physically, even though not close in the sequence, allowing a contact map to be estimated. Building on recent work prior to 2018, AlphaFold 1 extended this by estimating a probability distribution for the distances between residues, effectively transforming the contact map into a distance map. It also used more advanced learning methods than previously to develop the inference. The code was not made publicly available, except to run on sequences of proteins in the 2018 CASP competition. === AlphaFold 2 (2020) === The 2020 version of the program (AlphaFold 2, 2020) is significantly different from the original version that won CASP 13 in 2018, according to the team at DeepMind. AlphaFold 1 used a number of separately trained modules to produce a guide potential, which was then combined with a physics-based energy potential. AlphaFold 2 replaced this with a system of interconnected sub-networks, forming a single, differentiable, end-to-end model based on pattern recognition. This model was trained in an integrated manner. After the neural network's prediction converges, a final refinement step applies local physical constraints using energy minimization based on the AMBER force field. This step only slightly adjusts the predicted structure. A key part of the 2020 system are two modules, believed to be based on a transformer design, which are used to progressively refine a vector of information for each relationship (or "edge" in graph-theory terminology) between an amino acid residue of the protein and another amino acid residue (these relationships are represented by the array shown in green); and between each amino acid position and each different sequences in the input sequence alignment (these relationships are represented by the array shown in red). Internally these refinement transformations contain layers that have the effect of bringing relevant data together and filtering out irrelevant data (the "attention mechanism") for these relationships, in a context-dependent way, learned from training data. These transformations are iterated, the updated information output by one step becoming the input of the next, with the sharpened residue/residue information feeding into the update of the residue/sequence information, and then the improved residue/sequence information feeding into the update of the residue/residue information. As the iteration progresses, according to one report, the "attention algorithm ... mimics the way a person might assemble a jigsaw puzzle: first connecting pieces in small clumps—in this case clusters of amino acids—and then searching for ways to join the clumps in a larger whole." The output of these iterations then informs the final structure prediction module, which also uses transformers, and is itself then iterated. In an example presented by DeepMind, the structure prediction module achieved a correct topology for the target protein on its first iteration, scored as having a GDT_TS of 78, but with a large number (90%) of stereochemical violations – i.e. unphysical bond angles or lengths. With subsequent iterations the number of stereochemical violations fell. By the third iteration the GDT_TS of the prediction was approaching 90, and by the eighth iteration the number of stereochemical violations was approaching zero. The training data was originally restricted to single peptide chains. However, the October 2021 update, named AlphaFold-Multimer, included protein complexes in its training data. DeepMind stated this update succeeded about 70% of the time at accurately predicting protein-protein interactions. === AlphaFold 3 (2024) === Announced on 8 May 2024, AlphaFold 3 was co-developed by Google DeepMind and Isomorphic Labs, both subsidiaries of Alphabet. AlphaFold 3 is not limited to proteins, as it can also predict the structures of protein complexes with DNA, RNA, post-translational modifications and selected ligands and ions. AlphaFold 3 introduces the "Pairformer," a deep learning architecture inspired by the transformer, which is considered similar to, but si
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Normal distributions transform

The normal distributions transform (NDT) is a point cloud registration algorithm introduced by Peter Biber and Wolfgang Straßer in 2003, while working at University of Tübingen. The algorithm registers two point clouds by first associating a piecewise normal distribution to the first point cloud, that gives the probability of sampling a point belonging to the cloud at a given spatial coordinate, and then finding a transform that maps the second point cloud to the first by maximising the likelihood of the second point cloud on such distribution as a function of the transform parameters. Originally introduced for 2D point cloud map matching in simultaneous localization and mapping (SLAM) and relative position tracking, the algorithm was extended to 3D point clouds and has wide applications in computer vision and robotics. NDT is very fast and accurate, making it suitable for application to large scale data, but it is also sensitive to initialisation, requiring a sufficiently accurate initial guess, and for this reason it is typically used in a coarse-to-fine alignment strategy. == Formulation == The NDT function associated to a point cloud is constructed by partitioning the space in regular cells. For each cell, it is possible to define the mean q = 1 n ∑ i x i {\displaystyle \textstyle \mathbf {q} ={\frac {1}{n}}\sum _{i}\mathbf {x_{i}} } and covariance S = 1 n ∑ i ( x i − q ) ( x i − q ) ⊤ {\displaystyle \textstyle \mathbf {S} ={\frac {1}{n}}\sum _{i}\left(\mathbf {x} _{i}-\mathbf {q} \right)\left(\mathbf {x} _{i}-\mathbf {q} \right)^{\top }} of the n {\displaystyle n} points of the cloud x 1 , … , x n {\displaystyle \mathbf {x} _{1},\dots ,\mathbf {x} _{n}} that fall within the cell. The probability density of sampling a point at a given spatial location x {\displaystyle \mathbf {x} } within the cell is then given by the normal distribution e − 1 2 ( x − q ) ⊤ S − 1 ( x − q ) {\displaystyle e^{-{\frac {1}{2}}\left(\mathbf {x} -\mathbf {q} \right)^{\top }\mathbf {S} ^{-1}\left(\mathbf {x} -\mathbf {q} \right)}} . Two point clouds can be mapped by a Euclidean transformation f {\displaystyle f} with rotation matrix R {\displaystyle \mathbf {R} } and translation vector t {\displaystyle \mathbf {t} } f R , t ( x ) = R x + t {\displaystyle f_{\mathbf {R} ,\mathbf {t} }(\mathbf {x} )=\mathbf {R} \mathbf {x} +\mathbf {t} } that maps from the second cloud to the first, parametrised by the rotation angles and translation components. The algorithm registers the two point clouds by optimising the parameters of the transformation that maps the second cloud to the first, with respect to a loss function based on the NDT of the first point cloud, solving the following problem arg ⁡ min R , t { − ∑ i NDT ⁡ ( f R , t ( x i ) ) } {\displaystyle \arg \min _{\mathbf {R} ,\mathbf {t} }\left\{-\sum _{i}\operatorname {NDT} \left(f_{\mathbf {R} ,\mathbf {t} }\left(\mathbf {x_{i}} \right)\right)\right\}} where the loss function represents the negated likelihood, obtained by applying the transformation to all points in the second cloud and summing the value of the NDT at each transformed point f R , t ( x ) {\displaystyle f_{\mathbf {R} ,\mathbf {t} }(\mathbf {x} )} . The loss is piecewise continuous and differentiable, and can be optimised with gradient-based methods (in the original formulation, the authors use Newton's method). In order to reduce the effect of cell discretisation, a technique consists of partitioning the space into multiple overlapping grids, shifted by half cell size along the spatial directions, and computing the likelihood at a given location as the sum of the NDTs induced by each grid.
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Artificial intelligence safety institute

An artificial intelligence safety institute is a type of state-backed organization aiming to evaluate and ensure the safety of advanced artificial intelligence (AI) models, also called frontier AI models. AI safety gained prominence in 2023, notably with public declarations about potential existential risks from AI. During the AI Safety Summit in November 2023, the United Kingdom and the United States both created their own AISI. During the AI Seoul Summit in May 2024, international leaders agreed to form a network of AI Safety Institutes, comprising institutes from the UK, the US, Japan, France, Germany, Italy, Singapore, South Korea, Australia, Canada and the European Union. In 2025, the UK's AI Safety Institute was renamed the "AI Security Institute", and its US counterpart became the Center for AI Standards and Innovation (CAISI). == Timeline == In 2023, Rishi Sunak, the Prime Minister of the United Kingdom, expressed his intention to "make the UK not just the intellectual home but the geographical home of global AI safety regulation" and unveiled plans for an AI Safety Summit. He emphasized the need for independent safety evaluations, stating that AI companies cannot "mark their own homework". During the summit in November 2023, the UK AISI was officially established as an evolution of the Frontier AI Taskforce, and the US AISI as part of the National Institute of Standards and Technology. Japan followed by launching an AI safety institute in February 2024. Politico reported in April 2024 that many AI companies had not shared pre-deployment access to their most advanced AI models for evaluation. Meta's president of global affairs Nick Clegg said that many AI companies were waiting for the UK and the US AI Safety Institutes to work out common evaluation rules and procedures. An agreement was indeed concluded between the UK and the US in April 2024 to collaborate on at least one joint safety test. Initially established in London, the UK AI Safety Institute announced in May 2024 that it would open an office in San Francisco, where many AI companies are located. This is part of a plan to "set new, international standards on AI safety", according to UK's technology minister Michele Donelan. == International network == At the AI Seoul Summit in May 2024, the European Union and other countries agreed to create their own AI safety institutes, forming an international network. In July 2025, the international network held an exercise to explore issues with evaluating AI agents, especially when it came to leaking sensitive information or cybersecurity. Network members also met at NeurIPS 2025 in the city of San Diego. == Specific institutes == === Australia === The Albanese government announced the creation of the Australian AI Safety Institute on 25 November 2025. === Canada === Canada announced in April 2024 that it would create an AI safety institute, and such an institute was officially founded in November 2024. The institute is housed under Innovation, Science and Economic Development Canada, though it also partners with the Canadian Institute for Advanced Research (CIFAR). It is supported by a budget of CA$50,000,000 for a five-year timespan. === European Union === The EU AI office, founded in May 2024, is a member of the international network of AI safety institutes. === France === On 31 January 2025, the government of France created the Institut national pour l'évaluation et la sécurité de l'intelligence artificielle (INESIA), or the National Institute for AI Evaluation and Security. === India === The Ministry of Electronics and Information Technology held consultations with Meta Platforms, Google, Microsoft, IBM, OpenAI, NASSCOM, Broadband India Forum, Software Alliance, Indian Institutes of Technology (IITs), The Quantum Hub, Digital Empowerment Foundation, and Access Now on October 7, 2024, in relation to the establishment of the AI Safety Institute. The decision was made to shift focus from regulation to standards-setting, risk identification, and damage detection—all of which require interoperable technologies. The AISI may spend the ₹20 crore allotted to the Safe and Trusted Pillar of the IndiaAI Mission for the initial budget. Future funding may come from other components of the IndiaAI Mission. UNESCO and MeitY began consulting on AI Readiness Assessment Methodology under Safety and Ethics in Artificial Intelligence from 2024. It is to encourage the ethical and responsible use of AI in industries. The study will find areas where government can become involved, especially in attempts to strengthen institutional and regulatory capabilities. Minister for Electronics & Information Technology Ashwini Vaishnaw announced the creation of an IndiaAI Safety Institute on January 30, 2025, to ensure the ethical and safe application of AI models. The institute will promote domestic R&D that is grounded in India's social, economic, cultural, and linguistic diversity and is based on Indian datasets. With the help of academic and research institutions, as well as private sector partners, the institute will follow the hub-and-spoke approach to carry out projects within Safe and Trusted Pillar of the IndiaAI Mission. It operates under a "hub-and-spoke" model with collaboration from academic institutions (e.g., IITs), tech firms, and international organizations like UNESCO. === Japan === The Japan AISI (or J-AISI) was founded in February 2024. Part of the Information Technology Promotion Agency, it employs about 23 people. The institute consists of the Council of AISI, the AISI Steering Committee, and a secretariat with six teams. Akiko Murakami (previously of IBM Japan and Sompo Japan) serves as the institute's executive director, and Kenji Hiramoto and Suguru Nishimura serve as the institute's two deputy executive directors. === Kenya === Kenya agreed to join the international network of AI safety institutes, but the country has not announced any details yet. It is the only African state in the network. === Singapore === The Digital Trust Centre was initially founded in June 2022. In May 2024, it was renamed to the Singapore AISI. Part of Nanyang Technological University, the institute partners with Infocomm Media Development Authority and is supported by an investment of S$10,000,000 per year. === South Korea === South Korea announced in May 2024 that it would create an AI safety institute under the umbrella of the Electronics and Telecommunications Research Institute. It will be supported by a tentative investment of somewhere between 10 and 20 million South Korean won per year, and employ at least 30 people. The institute was founded in November 2024 and is based in Bundang District within the city of Seongnam. === United Kingdom === The United Kingdom founded in April 2023 a safety organisation called Frontier AI Taskforce, with an initial budget of £100 million. In November 2023, it evolved into the AI Safety Institute, and continued to be led by Ian Hogarth. The AISI is part of the United Kingdom's Department for Science, Innovation and Technology. The United Kingdom's AI strategy aims to balance safety and innovation. Unlike the European Union which adopted the AI Act, the UK is reluctant to legislate early, considering that it may lower the sector's growth, and that laws might be rendered obsolete by technological progress. In May 2024, the institute open-sourced an AI safety tool called "Inspect", which evaluates AI model capabilities such as reasoning and their degree of autonomy. In February 2025, the UK body was renamed the AI Security Institute. Observers saw the name change as a signal that the institute will not focus on ethical issues such as algorithmic bias or freedom of speech in AI applications. === United States === The US AISI was founded in November 2023 as part of the National Institute of Standards and Technology (NIST). This happened the day after the signature of the Executive Order 14110. In February 2024, Joe Biden's former economic policy adviser Elizabeth Kelly was appointed to lead it. In February 2024, the US government created the US AI Safety Institute Consortium (AISIC), regrouping more than 200 organizations such as Google, Anthropic or Microsoft. In March 2024, a budget of $10 million was allocated. Observers noted that this investment is relatively small, especially considering the presence of many big AI companies in the US. The NIST itself, which hosts the AISI, is also known for its chronic lack of funding. Biden administration's request for additional funding was met with further budget cuts from congressional appropriators. Under President Trump, plans for members of the agency to attend the February 2025 AI Action Summit in Paris were scrapped. The US and the UK refused to sign the summit's final communique. US Vice President JD Vance said "pro-growth AI policies" should be prioritised over safety. The name of the agency was changed in June 2025 to the Center for AI Standards and Innovation
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Kaggle

Kaggle is a data science competition platform and online community for data scientists and machine learning practitioners under Google LLC. Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges. Kaggle has also facilitated the use of unethical and unreliable data in medical research. == History == Kaggle was founded by Anthony Goldbloom in April 2010. Jeremy Howard, one of the first Kaggle users, joined in November 2010 and served as the President and Chief Scientist. Also on the team was Nicholas Gruen serving as the founding chair. In 2011, the company raised $12.5 million and Max Levchin became the chairman. On March 8, 2017, Fei-Fei Li, Chief Scientist at Google, announced that Google was acquiring Kaggle. In June 2017, Kaggle surpassed 1 million registered users, and as of October 2023, it has over 15 million users in 194 countries. In 2022, founders Goldbloom and Hamner stepped down from their positions and D. Sculley became the CEO. In February 2023, Kaggle introduced Models, allowing users to discover and use pre-trained models through deep integrations with the rest of Kaggle’s platform. In April 2025, Kaggle partnered with Wikimedia Foundation. == Site overview == === Competitions === Many machine-learning competitions have been run on Kaggle since the company was founded. Notable competitions include gesture recognition for Microsoft Kinect, making a association football AI for Manchester City, coding a trading algorithm for Two Sigma Investments, and improving the search for the Higgs boson at CERN. The competition host prepares the data and a description of the problem; the host may choose whether it's going to be rewarded with money or be unpaid. Participants experiment with different techniques and compete against each other to produce the best models. Work is shared publicly through Kaggle Kernels to achieve a better benchmark and to inspire new ideas. Submissions can be made through Kaggle Kernels, via manual upload or using the Kaggle API. For most competitions, submissions are scored immediately (based on their predictive accuracy relative to a hidden solution file) and summarized on a live leaderboard. After the deadline passes, the competition host pays the prize money in exchange for "a worldwide, perpetual, irrevocable and royalty-free license [...] to use the winning Entry", i.e. the algorithm, software and related intellectual property developed, which is "non-exclusive unless otherwise specified". Alongside its public competitions, Kaggle also offers private competitions, which are limited to Kaggle's top participants. Kaggle offers a free tool for data science teachers to run academic machine-learning competitions. Kaggle also hosts recruiting competitions in which data scientists compete for a chance to interview at leading data science companies like Facebook, Winton Capital, and Walmart. Kaggle's competitions have resulted in successful projects such as furthering HIV research, chess ratings and traffic forecasting. Geoffrey Hinton and George Dahl used deep neural networks to win a competition hosted by Merck. Vlad Mnih (one of Hinton's students) used deep neural networks to win a competition hosted by Adzuna. This resulted in the technique being taken up by others in the Kaggle community. Tianqi Chen from the University of Washington also used Kaggle to show the power of XGBoost, which has since replaced Random Forest as one of the main methods used to win Kaggle competitions. Several academic papers have been published based on findings from Kaggle competitions. A contributor to this is the live leaderboard, which encourages participants to continue innovating beyond existing best practices. The winning methods are frequently written on the Kaggle Winner's Blog. === Progression system === Kaggle has implemented a progression system to recognize and reward users based on their contributions and achievements within the platform. This system consists of five tiers: Novice, Contributor, Expert, Master, and Grandmaster. Each tier is achieved by meeting specific criteria in competitions, datasets, kernels (code-sharing), and discussions. The highest tier, Kaggle Grandmaster, is awarded to users who have ranked at the top of multiple competitions including high ranking in a solo team. As of April 2, 2025, out of 23.29 million Kaggle accounts, 2,973 have achieved Kaggle Master status and 612 have achieved Kaggle Grandmaster status. === Kaggle Notebooks === Kaggle includes a free, browser-based online integrated development environment, called Kaggle Notebooks, designed for data science and machine learning. Users can write and execute code in Python or R, import datasets, use popular libraries, and train models on CPUs, GPUs, or TPUs directly in the cloud. This environment is often used for competition submissions, tutorials, education, and exploratory data analysis. == Medical Research Problems == In December 2025, an article was published in The Transmitter titled "Exclusive: Springer Nature retracts, removes nearly 40 publications that trained neural networks on ‘bonkers’ dataset". The dataset in question was uploaded to Kaggle containing photographs of autistic and non-autistic children's faces. This dataset contained more than 2,900 images and it is unlikely that these children or their families gave consent for the photos for use in medical research or the images were ethically approved for research. The articles using the dataset in Springer Nature were retracted from the scientific literature. At least 90 other publications cite a version of the dataset. In April 2026, another two datasets were identified on Kaggle with no data provenance having been published in Nature titled: "Dozens of AI disease-prediction models were trained on dubious data". These datasets were used in 124 clinical prediction models, at least two of which have been used in hospitals in Indonesia and Spain, while one article using the dataset was referenced in a medical device patent. As of April 17, 2026, three of the articles using these datasets have been retracted from the scientific literature. In May 2026, an additional research publication using two image datasets from Kaggle is under investigation in Scientific Reports. An article in Retraction Watch "‘Comically bad’ datasets used to train clinical models for stroke and diabetes" highlighted the images included famous actors such as Sylvester Stallone as Rambo, George Clooney, Angelina Jolie and Daniel Craig as well as children. It would be unethical for the use of these child images in medical research without consent. Reverse searching images saw some of the images were not for stroke but for bell's palsy. One of the datasets is no longer available on Kaggle while the other one still remains and mentions the images may be subject to copyright. Kaggle relies on the community self-reporting metadata and provenance and mentions the stroke and diabetes dataset identified in "Evidence of unreliable data and poor data provenance in clinical prediction model research and clinical practice" does not violate their terms of service and they would have been removed if they had.
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