Boris Katz

Boris Gershevich Katz (Russian: Борис Гершевич Кац; born October 5, 1947) is a principal American research scientist (computer scientist) at the MIT Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology in Cambridge and head of the Laboratory's InfoLab Group. His research interests include natural language processing and understanding, machine learning and intelligent information access. His brother Victor Kac is a mathematician at MIT. He was able to get out of the USSR with the help of U.S. Senator Ted Kennedy, before the end of the Cold War. Over the last several decades, Boris Katz has been developing the START natural language system that allows the user to access various types of information using English. == Biography == Boris Katz was born on October 5, 1947, in Chișinău in the family of Hersh Katz (died 1976) and Hayki (Klara) Landman (born 1921, Lipcani, Briceni District - died 2006, Cambridge, Middlesex County), who moved from Lipcani, a town located in the northern Bessarabian, to Chișinău before the war. He graduated from Moscow State University and in November 1978, he left for the United States thanks to the personal intervention of Senator Edward M. Kennedy. He defended his thesis as a candidate of physical and mathematical sciences in 1975 under the supervision of Evgenii M. Landis. He currently lives in Boston and heads the InfoLabresearch team at the Laboratory of Informatics and Artificial Intelligence at the Massachusetts Institute of Technology. Boris Katz is the creator of the START information processing system (since 1993 - on the Internet), the author of several works in the field of processing, generation and perception of natural languages, machine learning, and accelerated access to multimedia information. == Family == Brothers - Victor Gershevich Katz, American mathematician, professor at the Massachusetts Institute of Technology; Mikhail Gershevich Katz, Israeli mathematician, graduate of Harvard and Columbia (Ph.D., 1984) universities, professor at Bar-Ilan University, author of the monograph "Systolic Geometry and Topology" (Mathematical Surveys and Monographs, vol. 137. American Mathematical Society: Providence, 2007). Daughter - Luba Katz, a bioinformatics scientist (her husband is Alan Jasanoff, a neuroimaging scientist, a professor at MIT, the son of Harvard University professors Jay Jasanoff and Sheila Jasanoff). == Past works == A Knowledge Entry System for Subject Matter Experts: The goal of SHAKEN project is to enable subject matter experts, without any assistance from AI technologists, to assemble the models of processes and mechanisms so that questions about them can be answered by declarative inference and simulation. Exploiting lexical regularities in designing natural language systems Word sense disambiguation for information retrieval HIKE (HPKB integrated knowledge environment)- a query interface and integrated knowledge environment for HPKB Quantitative evaluation of passage retrieval algorithms for question answering Sticky notes for the semantic web Question answering from the web using knowledge annotation and knowledge mining techniques The role of context in question answering systems

Superintelligence ban

Superintelligence ban refers to proposed legal, ethical, or policy measures intended to restrict or prohibit the development of artificial superintelligence, AI systems that would surpass human cognitive abilities in nearly all domains. The idea arises from concerns that such systems could become uncontrollable, potentially posing existential threats to humanity or causing severe social and economic disruption. == Background == The concept of limiting or banning superintelligence research has roots in early 21st-century debates on artificial general intelligence (AGI) safety. Thinkers such as Nick Bostrom and Eliezer Yudkowsky warned that self-improving AI could rapidly exceed human oversight. As advanced models like large-scale language models and autonomous agents began demonstrating complex reasoning abilities, policymakers and ethicists increasingly discussed the need for legal constraints on the creation of systems capable of recursive self-improvement. In October 2025, the Future of Life Institute published a statement calling for "a prohibition on the development of superintelligence, not lifted before there is broad scientific consensus that it will be done safely and controllably, and strong public buy-in." This statement was signed by various public personalities, such as Richard Branson and Steve Wozniak, and AI experts, such as Yoshua Bengio and Geoffrey Hinton. == Rationale == Supporters of a superintelligence ban argue that once AI systems surpass human intelligence, traditional containment, alignment, and control methods may fail. They contend that even limited experimentation with such systems could lead to irreversible outcomes, including loss of human decision-making power or unintended global harm. Some propose international treaties modeled after the nuclear non-proliferation framework to prevent a competitive AI arms race. Opponents argue that a ban would be difficult to define and enforce, given the lack of a precise threshold distinguishing advanced AGI from superintelligence. They also warn that excessive restriction could slow scientific progress, hinder beneficial automation, and encourage unregulated underground research. == Global discussion == Although no government has enacted an explicit superintelligence ban, the idea has been debated within the European Union, United Nations, and several independent AI safety organizations. The Future of Life Institute, Center for AI Safety, and other organizations have called for international cooperation to manage risks associated with the pursuit of superintelligent systems. In 2024 and 2025, proposals for a temporary moratorium on frontier AI research were circulated among major technology firms and research institutes, reflecting growing public concern over the trajectory of AI capabilities.

Open Mashup Alliance

The Open Mashup Alliance (OMA) is a non-profit consortium that promotes the adoption of mashup solutions in the enterprise through the evolution of enterprise mashup standards like EMML. The initial members of the OMA include some large technology companies such as Adobe Systems, Hewlett-Packard, and Intel and some major technology users such as Bank of America and Capgemini. According to Dion Hinchcliffe, "Ultimately, the OMA creates a standardized approach to enterprise mashups that creates an open and vibrant market for competing runtimes, mashups, and an array of important aftermarket services such as development/testing tools, management and administration appliances, governance frameworks, education, professional services, and so on." == Specification development == The initial focus of the OMA is developing EMML, which is a declarative mashup domain-specific language (DSL) aimed at creating enterprise mashups. The EMML language provides a comprehensive set of high-level mashup-domain vocabulary to consume and mash a variety of web data sources. EMML provides a uniform syntax to invoke heterogeneous service styles: REST, WSDL, RSS/ATOM, RDBMS, and POJO. EMML also provides the ability to mix and match diverse data formats: XML, JSON, JDBC, JavaObjects, and primitive types. The OMA website provides the EMML specification, the EMML schema, a reference runtime implementation capable of running EMML scripts, sample EMML mashup scripts, and technical documentation. The OMA is developing EMML under a Creative Commons Attribution No Derivatives license. The eventual objective of the OMA is to submit the EMML specification and any other OMA specifications to a recognized industry standards body.

CodePen

CodePen is an online community for testing and showcasing user-created HTML, CSS and JavaScript code snippets. It functions as an online code editor and open-source learning environment, where developers can create code snippets, called "pens," and test them. It was founded in 2012 by full-stack developers Alex Vazquez and Tim Sabat and front-end designer Chris Coyier. Its employees work remotely, rarely all meeting together in person. CodePen is a large community for web designers and developers to showcase their coding skills, with an estimated 330,000 registered users and 14.16 million monthly visitors.

GlTF

glTF (Graphics Library Transmission Format or GL Transmission Format and formerly known as WebGL Transmissions Format or WebGL TF) is a standard file format for three-dimensional scenes and models. A glTF file uses one of two possible file extensions: .gltf (JSON/ASCII) or .glb (binary). Both .gltf and .glb files may reference external binary and texture resources. Alternatively, both formats may be self-contained by directly embedding binary data buffers (as base64-encoded strings in .gltf files or as raw byte arrays in .glb files). An open standard developed and maintained by the Khronos Group, it supports 3D model geometry, appearance, scene graph hierarchy, and animation. It is intended to be a streamlined, interoperable format for the delivery of 3D assets, while minimizing file size and runtime processing by apps. As such, its creators have described it as the "JPEG of 3D". == Overview == The glTF format stores data primarily in JSON. The JSON may also contain blobs of binary data known as buffers, and refer to external files, for storing mesh data, images, etc. The binary .glb format also contains JSON text, but serialized with binary chunk headers to allow blobs to be directly appended to the file. The fundamental building blocks of a glTF scene are nodes. Nodes are organized into a hierarchy, such that a node may have other nodes defined as children. Nodes may have transforms relative to their parent. Nodes may refer to resources, such as meshes, skins, and cameras. Meshes may refer to materials, which refer to textures, which refer to images. Scenes are defined using an array of root nodes. Most of the top-level glTF properties use a flat hierarchy for storage. Nodes are saved in an array and are referred to by index, including by other nodes. A glTF scene refers to its root nodes by index. Furthermore, nodes refer to meshes by index, which refer to materials by index, which refer to textures by index, which refer to images by index. All glTF data structures support being extended using a JSON property, allowing arbitrary JSON data to be added. == Releases == === glTF 1.0 === Members of the COLLADA working group conceived the file format in 2012. At SIGGRAPH 2012, Khronos presented a demo of glTF, which was then called WebGL Transmissions Format (WebGL TF). On October 19, 2015, Khronos released the glTF 1.0 specification. ==== Adoption of glTF 1.0 ==== At SIGGRAPH 2016, Oculus announced their adoption of glTF citing the similarities to their ovrscene format. In October 2016, Microsoft joined the 3D Formats working group at Khronos to collaborate on glTF. === glTF 2.0 === The second version, glTF 2.0, was released in June 2017, and is a complete overhaul of the file format from version 1.0, with most tools adopting the 2.0 version. Based on a proposal by Fraunhofer originally presented at SIGGRAPH 2016, physically based rendering (PBR) was added, replacing WebGL shaders used in glTF 1.0. glTF 2.0 added the GLB binary format into the base specification. Other upgrades include sparse accessors and morph targets for techniques such as facial animation, and schema tweaks and breaking changes for corner cases or performance such as replacing top-level glTF object properties with arrays for faster index-based access. There is ongoing work towards import and export in Unity and an integrated multi-engine viewer and validator. ==== Adoption of glTF 2.0 ==== On March 3, 2017, Microsoft announced that they would be using glTF 2.0 as the 3D asset format across their product line, including Paint 3D, 3D Viewer, Remix 3D, Babylon.js, and Microsoft Office. Sketchfab also announced support for glTF 2.0. The glTF and GLB formats are used on and supported by companies including DGG, UX3D, Sketchfab, Facebook, Microsoft, Meta, Google, Adobe, Box, TurboSquid, Unreal Engine, Unity, and Qt Quick 3D. The format has been noted as an important standard for augmented reality, integrating with modeling software such as Autodesk Maya, Autodesk 3ds Max, and Poly. In February 2020, the Smithsonian Institution launched their Open Access Initiative, releasing approximately 2.8 million 2D images and 3D models into the public domain, using glTF for the 3D models. In July 2022, glTF 2.0 was released as the ISO/IEC 12113:2022 International Standard. Khronos stated they would make regular submissions to bring updates and new widely adopted glTF functionality into refreshed versions of ISO/IEC 12113 to ensure that there is no long-term divergence between the ISO/IEC and Khronos specifications. The open-source game engine Godot supports importing glTF 2.0 files since version 3.0 and export since version 4.0. === Extensions === The glTF format can be extended with arbitrary JSON to add new data and functionality. Extensions can be placed on any part of a glTF, including nodes, animations, materials, textures, and on the entire document. Khronos keeps a non-comprehensive registry of glTF extensions on GitHub, including all official Khronos extensions and a few third-party extensions. PBR extensions model the physical appearance of real-world objects, allowing developers to create realistic 3D assets that have the correct appearance. As new PBR extensions are released, they continue to expand PBR capabilities within the glTF framework, allowing a wider range of scenes and objects to be realistically rendered as 3D assets. The KTX 2.0 extension for universal texture compression enables 3D models in the glTF format to be highly compressed and to use natively supported texture formats, reducing file size and boosting rendering speed. Draco is a glTF extension for mesh compression, to compress and decompress 3D meshes, to help reduce the size of 3D files. It compresses vertex attributes, normals, colors, and texture coordinates. Various glTF extensions for game engine interoperability have been developed by OMI group. This includes extensions for physics shapes, physics bodies, physics joints, audio playback, seats, spawn points, and more. The VRM consortium has developed glTF extensions for advanced humanoid 3D avatars including dynamic spring bones and toon materials. == Derivative formats == 3D Tiles, an OGC Community Standard, builds on glTF to add a spatial data structure, metadata, and declarative styling for streaming massive heterogeneous 3D geospatial datasets. VRM, a model format for VR, is built on the .glb format. It is a 3D humanoid avatar specification and file format. == Software ecosystem == Khronos maintains the glTF Sample Viewer for viewing glTF assets. Khronos also maintains the glTF Validator for validating if 3D models conform to the glTF specification. Khronos maintains a glTF Compressor tool to interactively optimize and fine-tune compression settings for glTF assets using KTX 2.0 textures. glTF loaders are in open-source WebGL engines including PlayCanvas, Three.js, Babylon.js, Cesium, PEX, xeogl, and A-Frame. The Godot game engine supports and recommends the glTF format, with both import and export support. Open-source glTF converters are available from COLLADA, FBX, and OBJ. Assimp can import and export glTF. glTF files can also be directly exported from a variety of 3D editors, such as Blender, Unity (using the glTFast importer/exporter), Freecad, Vectary, Autodesk 3ds Max (natively or using Verge3D exporter), Autodesk Maya (using babylon.js exporter), Autodesk Inventor, Modo, Houdini, Paint 3D, Godot, and Substance Painter. Open-source glTF utility libraries are available for programming languages including JavaScript, Node.js, C++, C#, Python, Haskell, Java, Go, Rust, Haxe, Ada, and TypeScript. Khronos keeps a list of these libraries and other related applications on their ecosystem site. The Khronos 3D Commerce Working Group released Asset Creation Guidelines in 2020 outlining best practices for use of the glTF file format in 3D Commerce. In 2025, the Working Group launched Asset Creation Guidelines 2.0, a continuously updated resource with additional guidance for geometry, mesh optimization, UV maps, textures, materials/PBR performance, and web optimization. The Khronos PBR Neutral Tone Mappers specification is a tone mapper designed to faithfully reproduce an object's base color, hue, and saturation when using PBR rendering under grayscale lighting, supporting brand- and product-accurate color representation. Khronos maintains the glTF Asset Auditor to allow retailers and advertising technology platforms to validate 3D assets against either a default Audit Profile modelled on the 2020 3D Commerce Asset Creation Guidelines or a custom profile defined by the target application.

AIXI

AIXI is a theoretical mathematical formalism for artificial general intelligence. It combines Solomonoff induction with sequential decision theory. AIXI was first proposed by Marcus Hutter in 2000 and several results regarding AIXI are proved in Hutter's 2005 book Universal Artificial Intelligence. AIXI is a reinforcement learning (RL) agent. It maximizes the expected total rewards received from the environment. Intuitively, it simultaneously considers every computable hypothesis (or environment). In each time step, it looks at every possible program and evaluates how many rewards that program generates depending on the next action taken. The promised rewards are then weighted by the subjective belief that this program constitutes the true environment. This belief is computed from the length of the program: longer programs are considered less likely, in line with Occam's razor. AIXI then selects the action that has the highest expected total reward in the weighted sum of all these programs. == Etymology == According to Hutter, the word "AIXI" can have several interpretations. AIXI can stand for AI based on Solomonoff's distribution, denoted by ξ {\displaystyle \xi } (which is the Greek letter xi), or e.g. it can stand for AI "crossed" (X) with induction (I). There are other interpretations. == Definition == AIXI is a reinforcement learning agent that interacts with some stochastic and unknown but computable environment μ {\displaystyle \mu } . The interaction proceeds in time steps, from t = 1 {\displaystyle t=1} to t = m {\displaystyle t=m} , where m ∈ N {\displaystyle m\in \mathbb {N} } is the lifespan of the AIXI agent. At time step t, the agent chooses an action a t ∈ A {\displaystyle a_{t}\in {\mathcal {A}}} (e.g. a limb movement) and executes it in the environment, and the environment responds with a "percept" e t ∈ E = O × R {\displaystyle e_{t}\in {\mathcal {E}}={\mathcal {O}}\times \mathbb {R} } , which consists of an "observation" o t ∈ O {\displaystyle o_{t}\in {\mathcal {O}}} (e.g., a camera image) and a reward r t ∈ R {\displaystyle r_{t}\in \mathbb {R} } , distributed according to the conditional probability μ ( o t r t | a 1 o 1 r 1 . . . a t − 1 o t − 1 r t − 1 a t ) {\displaystyle \mu (o_{t}r_{t}|a_{1}o_{1}r_{1}...a_{t-1}o_{t-1}r_{t-1}a_{t})} , where a 1 o 1 r 1 . . . a t − 1 o t − 1 r t − 1 a t {\displaystyle a_{1}o_{1}r_{1}...a_{t-1}o_{t-1}r_{t-1}a_{t}} is the "history" of actions, observations and rewards. The environment μ {\displaystyle \mu } is thus mathematically represented as a probability distribution over "percepts" (observations and rewards) which depend on the full history, so there is no Markov assumption (as opposed to other RL algorithms). Note again that this probability distribution is unknown to the AIXI agent. Furthermore, note again that μ {\displaystyle \mu } is computable, that is, the observations and rewards received by the agent from the environment μ {\displaystyle \mu } can be computed by some program (which runs on a Turing machine), given the past actions of the AIXI agent. The only goal of the AIXI agent is to maximize ∑ t = 1 m r t {\displaystyle \sum _{t=1}^{m}r_{t}} , that is, the sum of rewards from time step 1 to m. The AIXI agent is associated with a stochastic policy π : ( A × E ) ∗ → A {\displaystyle \pi :({\mathcal {A}}\times {\mathcal {E}})^{}\rightarrow {\mathcal {A}}} , which is the function it uses to choose actions at every time step, where A {\displaystyle {\mathcal {A}}} is the space of all possible actions that AIXI can take and E {\displaystyle {\mathcal {E}}} is the space of all possible "percepts" that can be produced by the environment. The environment (or probability distribution) μ {\displaystyle \mu } can also be thought of as a stochastic policy (which is a function): μ : ( A × E ) ∗ × A → E {\displaystyle \mu :({\mathcal {A}}\times {\mathcal {E}})^{}\times {\mathcal {A}}\rightarrow {\mathcal {E}}} , where the ∗ {\displaystyle } is the Kleene star operation. In general, at time step t {\displaystyle t} (which ranges from 1 to m), AIXI, having previously executed actions a 1 … a t − 1 {\displaystyle a_{1}\dots a_{t-1}} (which is often abbreviated in the literature as a < t {\displaystyle a_{

Hardware trojan

A hardware trojan (HT) is a malicious modification of the circuitry of an integrated circuit. A hardware trojan is completely characterized by its physical representation and its behavior. The payload of an HT is the entire activity that the Trojan executes when it is triggered. In general, trojans try to bypass or disable the security fence of a system: for example, leaking confidential information by radio emission. HTs also could disable, damage or destroy the entire chip or components of it. Hardware trojans may be introduced as hidden front-doors that are inserted while designing a computer chip, by using a pre-made application-specific integrated circuit (ASIC) semiconductor intellectual property core (IP core) that have been purchased from a non-reputable source, or inserted internally by a rogue employee, either acting on their own, or on behalf of rogue special interest groups, or state sponsored spying and espionage. One paper published by IEEE in 2015 explains how a hardware design containing a trojan could leak a cryptographic key leaked over an antenna or network connection, provided that the correct "easter egg" trigger is applied to activate the data leak. In high security governmental IT departments, hardware trojans are a well known problem when buying hardware such as: a KVM switch, keyboards, mice, network cards, or other network equipment. This is especially the case when purchasing such equipment from non-reputable sources that could have placed hardware trojans to leak keyboard passwords, or provide remote unauthorized entry. == Background == In a diverse global economy, outsourcing of production tasks is a common way to lower a product's cost. Embedded hardware devices are not always produced by the firms that design and/or sell them, nor in the same country where they will be used. Outsourced manufacturing can raise doubt about the evidence for the integrity of the manufactured product (i.e., one's certainty that the end-product has no design modifications compared to its original design). Anyone with access to the manufacturing process could, in theory, introduce some change to the final product. For complex products, small changes with large effects can be difficult to detect. The threat of a serious, malicious, design alteration can be especially relevant to government agencies. Resolving doubt about hardware integrity is one way to reduce technology vulnerabilities in the military, finance, energy and political sectors of an economy. Since fabrication of integrated circuits in untrustworthy factories is common, advanced detection techniques have emerged to discover when an adversary has hidden additional components in, or otherwise sabotaged, the circuit's function. == Characterization of hardware trojans == An HT can be characterized by several methods such as by its physical representation, activation phase and its action phase. Alternative methods characterize the HT by trigger, payload and stealth. === Physical characteristics === One of this physical trojan characteristics is the type. The type of a trojan can be either functional or parametric. A trojan is functional if the adversary adds or deletes any transistors or gates to the original chip design. The other kind of trojan, the parametric trojan, modifies the original circuitry, e.g. thinning of wires, weakening of flip-flops or transistors, subjecting the chip to radiation, or using focused ion-beams (FIB) to reduce the reliability of a chip. The size of a trojan is its physical extension or the number of components it is made of. Because a trojan can consist of many components, the designer can distribute the parts of a malicious logic on the chip. The additional logic can occupy the chip wherever it is needed to modify, add, or remove a function. Malicious components can be scattered, called loose distribution, or consist of only few components, called tight distribution, so the area is small where the malicious logic occupies the layout of the chip. In some cases, high-effort adversaries in may regenerate the layout so that the placement of the components of the IC is altered. In rare cases the chip dimension is altered. These changes are structural alterations. === Activation characteristics === The typical trojan is condition-based: It is triggered by sensors, internal logic states, a particular input pattern or an internal counter value. Condition-based trojans are detectable with power traces to some degree when inactive. That is due to the leakage currents generated by the trigger or counter circuit activating the trojan. Hardware trojans can be triggered in different ways. A trojan can be internally activated, which means it monitors one or more signals inside the IC. The malicious circuitry could wait for a count down logic an attacker added to the chip, so that the trojan awakes after a specific time-span. The opposite is externally activated. There can be malicious logic inside a chip, that uses an antenna or other sensors the adversary can reach from outside the chip. For example, a trojan could be inside the control system of a cruising missile. The owner of the missile does not know, that the enemy will be able to switch off the rockets by radio. A trojan which is always-on can be a reduced wire. A chip that is modified in this way produces errors or fails every time the wire is used intensely. Always-on circuits are hard to detect with power trace. In this context combinational trojans and sequential trojans are distinguished. A combinational trojan monitors internal signals until a specific condition happens. A sequential trojan is also an internally activated condition-based circuit, but it monitors the internal signals and searches for sequences not for a specific state or condition like the combinational trojans do. ==== Cryptographic key extraction ==== Extraction of secret keys by means of a hardware trojan without detecting the trojan requires that the trojan uses a random signal or some cryptographic implementation itself. To avoid storing a cryptographic key in the trojan itself and reduction, a physical unclonable function can be used. Physical unclonable functions are small in size and can have an identical layout while the cryptographic properties are different. === Action characteristics === A HT could modify the chip's function or could change the chip's parametric properties (e.g. provokes a process delay). Confidential information can also be transmitted to the adversary (transmission of key information). === Peripheral device hardware trojans === A relatively new threat vector to networks and network endpoints is a HT appearing as a physical peripheral device that is designed to interact with the network endpoint using the approved peripheral device's communication protocol. For example, a USB keyboard that hides all malicious processing cycles from the target network endpoint to which it is attached by communicating with the target network endpoint using unintended USB channels. Once sensitive data is ex-filtrated from the target network endpoint to the HT, the HT can process the data and decide what to do with the data: store the data to memory for later physical retrieval of the HT or possibly ex-filtrate the data to the internet using wireless or using the compromised network endpoint as a pivot. == Potential of threat == A common trojan is passive most of the time-span an altered device is in use. If a trojan is activated the device functionality can be changed, the device can be destroyed or disabled, the device can leak confidential information or the HT may tear down the security and safety of the device. Trojans are stealthy, to avoid detection of the trojan the precondition for activation is a very rare event. Traditional testing techniques are not sufficient. A manufacturing fault happens at a random position while malicious changes are well placed to avoid detection. == Detection == === Physical inspection === First, the molding coat is cut to reveal the circuitry. Then, the engineer repeatedly scans the surface while grinding the layers of the chip. There are several operations to scan the circuitry. Typical visual inspection methods are: scanning optical microscopy (SOM), scanning electron microscopy (SEM), pico-second imaging circuit analysis (PICA), voltage contrast imaging (VCI), light induced voltage alteration (LIVA) or charge induced voltage alteration (CIVA). To compare the floor plan of the chip has to be compared with the image of the actual chip. This is still quite challenging to do. To detect Trojan hardware which include (crypto) keys which are different, an image diff can be taken to reveal the different structure on the chip. The only known hardware Trojan using unique crypto keys but having the same structure is. This property enhances the undetectability of the trojan. === Functional testing === This detection method stimulates the input ports of a chip and monitors the output