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  Artificial intelligence arms race

  A military artificial intelligence arms race is a technological, economic, and military competition between two or more states to develop and deploy advanced AI technologies and lethal autonomous weapons systems (LAWS). The goal is to gain a strategic or tactical advantage over rivals, similar to previous arms races involving nuclear or conventional military technologies. Since the mid-2010s, many analysts have noted the emergence of such an arms race between superpowers for better AI technology and military AI, driven by increasing geopolitical and military tensions. An AI arms race is sometimes placed in the context of an AI Cold War between the United States and China. Several influential figures and publications have emphasized that whoever develops artificial general intelligence (AGI) first could dominate global affairs in the 21st century. Russian President Vladimir Putin stated that the leader in AI will "rule the world." Researchers and experts, such as Leopold Aschenbrenner and Adrian Pecotic respectively, warn that the AGI race between major powers like the U.S. and China could reshape geopolitical power. This includes AI for surveillance, autonomous weapons, decision-making systems, cyber operations, and more. == Terminology == Lethal autonomous weapons systems use artificial intelligence to identify and kill human targets without human intervention. LAWS have colloquially been called "slaughterbots" or "killer robots". Broadly, any competition for superior AI is sometimes framed as an "arms race". Advantages in military AI overlap with advantages in other sectors, as countries pursue both economic and military advantages, as per previous arms races throughout history. == History == In 2014, AI specialist Steve Omohundro warned that "An autonomous weapons arms race is already taking place". According to Siemens, worldwide military spending on robotics was US$5.1 billion in 2010 and US$7.5 billion in 2015. China became a top player in artificial intelligence research in the 2010s. According to the Financial Times, in 2016, for the first time, China published more AI research papers than the entire European Union. When restricted to number of AI papers in the top 5% of cited papers, China overtook the United States in 2016 but lagged behind the European Union. 23% of the researchers presenting at the 2017 American Association for the Advancement of Artificial Intelligence (AAAI) conference were Chinese. Eric Schmidt, the former chairman and chief executive officer of Alphabet, has predicted China will be the leading country in AI by 2025. == Risks == One risk concerns the AI race itself, whether or not the race is won by any one group. There are strong incentives for development teams to cut corners with regard to the safety of the system, increasing the risk of critical failures and unintended consequences. This is in part due to the perceived advantage of being the first to develop advanced AI technology. One team appearing to be on the brink of a breakthrough can encourage other teams to take shortcuts, ignore precautions and deploy a system that is less ready. Some argue that using "race" terminology at all in this context can exacerbate this effect. Another potential danger of an AI arms race is the possibility of losing control of the AI systems; the risk is compounded in the case of a race to artificial general intelligence, which may present an existential risk. In 2023, a United States Air Force official reportedly said that during a computer test, a simulated AI drone killed the human character operating it. The USAF later said the official had misspoken and that it never conducted such simulations. A third risk of an AI arms race is whether or not the race is actually won by one group. The concern is regarding the consolidation of power and technological advantage in the hands of one group. A US government report argued that "AI-enabled capabilities could be used to threaten critical infrastructure, amplify disinformation campaigns, and wage war":1, and that "global stability and nuclear deterrence could be undermined".:11 == By nation == === United States === In 2014, former Secretary of Defense Chuck Hagel posited the "Third Offset Strategy" that rapid advances in artificial intelligence will define the next generation of warfare. According to data science and analytics firm Govini, the U.S. Department of Defense (DoD) increased investment in artificial intelligence, big data and cloud computing from $5.6 billion in 2011 to $7.4 billion in 2016. However, the civilian NSF budget for AI saw no increase in 2017. Japan Times reported in 2018 that the United States private investment is around $70 billion per year. The November 2019 'Interim Report' of the United States' National Security Commission on Artificial Intelligence confirmed that AI is critical to US technological military superiority. The U.S. has many military AI combat programs, such as the Sea Hunter autonomous warship, which is designed to operate for extended periods at sea without a single crew member, and to even guide itself in and out of port. From 2017, a temporary US Department of Defense directive requires a human operator to be kept in the loop when it comes to the taking of human life by autonomous weapons systems. On October 31, 2019, the United States Department of Defense's Defense Innovation Board published the draft of a report recommending principles for the ethical use of artificial intelligence by the Department of Defense that would ensure a human operator would always be able to look into the 'black box' and understand the kill-chain process. However, a major concern is how the report will be implemented. The Joint Artificial Intelligence Center (JAIC) (pronounced "jake") is an American organization on exploring the usage of AI (particularly edge computing), Network of Networks, and AI-enhanced communication, for use in actual combat. It is a subdivision of the United States Armed Forces and was created in June 2018. The organization's stated objective is to "transform the US Department of Defense by accelerating the delivery and adoption of AI to achieve mission impact at scale. The goal is to use AI to solve large and complex problem sets that span multiple combat systems; then, ensure the combat Systems and Components have real-time access to ever-improving libraries of data sets and tools." In 2023, Microsoft pitched the DoD to use DALL-E models to train its battlefield management system. OpenAI, the developer of DALL-E, removed the blanket ban on military and warfare use from its usage policies in January 2024. The Biden administration imposed restrictions on the export of advanced NVIDIA chips and GPUs to China in an effort to limit China's progress in artificial intelligence and high-performance computing. The policy aimed to prevent the use of cutting-edge U.S. technology in military or surveillance applications and to maintain a strategic advantage in the global AI race. In 2025, under the second Trump administration, the United States began a broad deregulation campaign aimed at accelerating growth in sectors critical to artificial intelligence, including nuclear energy, infrastructure, and high-performance computing. The goal was to remove regulatory barriers and attract private investment to boost domestic AI capabilities. This included easing restrictions on data usage, speeding up approvals for AI-related infrastructure projects, and incentivizing innovation in cloud computing and semiconductors. Companies like NVIDIA, Oracle, and Cisco played a central role in these efforts, expanding their AI research, data center capacity, and partnerships to help position the U.S. as a global leader in AI development. ==== Project Maven ==== Project Maven is a Pentagon project involving using machine learning and engineering talent to distinguish people and objects in drone videos, apparently giving the government real-time battlefield command and control, and the ability to track, tag and spy on targets without human involvement. Initially the effort was led by Robert O. Work who was concerned about China's military use of the emerging technology. Reportedly, Pentagon development stops short of acting as an AI weapons system capable of firing on self-designated targets. The project was established in a memo by the U.S. Deputy Secretary of Defense on 26 April 2017. Also known as the Algorithmic Warfare Cross Functional Team, it is, according to Lt. Gen. of the United States Air Force Jack Shanahan in November 2017, a project "designed to be that pilot project, that pathfinder, that spark that kindles the flame front of artificial intelligence across the rest of the [Defense] Department". Its chief, U.S. Marine Corps Col. Drew Cukor, said: "People and computers will work symbiotically to increase the ability of weapon systems to detect objects." Project Maven has been noted by allies, such as Australia's Ian Langford, for the

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  AI Voice Assistants: Free vs Paid (2026)

  Shopping for the best AI voice assistant? An AI voice assistant is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI voice assistant slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.

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  Markov switching multifractal

  In financial econometrics (the application of statistical methods to economic data), the Markov-switching multifractal (MSM) is a model of asset returns developed by Laurent E. Calvet and Adlai J. Fisher that incorporates stochastic volatility components of heterogeneous durations. MSM captures the outliers, log-memory-like volatility persistence and power variation of financial returns. In currency and equity series, MSM compares favorably with standard volatility models such as GARCH(1,1) and FIGARCH both in- and out-of-sample. MSM is used by practitioners in the financial industry for different types of forecasts. == MSM specification == The MSM model can be specified in both discrete time and continuous time. === Discrete time === Let P t {\displaystyle P_{t}} denote the price of a financial asset, and let r t = ln ⁡ ( P t / P t − 1 ) {\displaystyle r_{t}=\ln(P_{t}/P_{t-1})} denote the return over two consecutive periods. In MSM, returns are specified as r t = μ + σ ¯ ( M 1 , t M 2 , t . . . M k ¯ , t ) 1 / 2 ϵ t , {\displaystyle r_{t}=\mu +{\bar {\sigma }}(M_{1,t}M_{2,t}...M_{{\bar {k}},t})^{1/2}\epsilon _{t},} where μ {\displaystyle \mu } and σ {\displaystyle \sigma } are constants and { ϵ t {\displaystyle \epsilon _{t}} } are independent standard Gaussians. Volatility is driven by the first-order latent Markov state vector: M t = ( M 1 , t M 2 , t … M k ¯ , t ) ∈ R + k ¯ . {\displaystyle M_{t}=(M_{1,t}M_{2,t}\dots M_{{\bar {k}},t})\in R_{+}^{\bar {k}}.} Given the volatility state M t {\displaystyle M_{t}} , the next-period multiplier M k , t + 1 {\displaystyle M_{k,t+1}} is drawn from a fixed distribution M with probability γ k {\displaystyle \gamma _{k}} , and is otherwise left unchanged. The transition probabilities are specified by γ k = 1 − ( 1 − γ 1 ) ( b k − 1 ) {\displaystyle \gamma _{k}=1-(1-\gamma _{1})^{(b^{k-1})}} . The sequence γ k {\displaystyle \gamma _{k}} is approximately geometric γ k ≈ γ 1 b k − 1 {\displaystyle \gamma _{k}\approx \gamma _{1}b^{k-1}} at low frequency. The marginal distribution M has a unit mean, has a positive support, and is independent of k. ==== Binomial MSM ==== In empirical applications, the distribution M is often a discrete distribution that can take the values m 0 {\displaystyle m_{0}} or 2 − m 0 {\displaystyle 2-m_{0}} with equal probability. The return process r t {\displaystyle r_{t}} is then specified by the parameters θ = ( m 0 , μ , σ ¯ , b , γ 1 ) {\displaystyle \theta =(m_{0},\mu ,{\bar {\sigma }},b,\gamma _{1})} . Note that the number of parameters is the same for all k ¯ > 1 {\displaystyle {\bar {k}}>1} . === Continuous time === MSM is similarly defined in continuous time. The price process follows the diffusion: d P t P t = μ d t + σ ( M t ) d W t , {\displaystyle {\frac {dP_{t}}{P_{t}}}=\mu dt+\sigma (M_{t})\,dW_{t},} where σ ( M t ) = σ ¯ ( M 1 , t … M k ¯ , t ) 1 / 2 {\displaystyle \sigma (M_{t})={\bar {\sigma }}(M_{1,t}\dots M_{{\bar {k}},t})^{1/2}} , W t {\displaystyle W_{t}} is a standard Brownian motion, and μ {\displaystyle \mu } and σ ¯ {\displaystyle {\bar {\sigma }}} are constants. Each component follows the dynamics: The intensities vary geometrically with k: γ k = γ 1 b k − 1 . {\displaystyle \gamma _{k}=\gamma _{1}b^{k-1}.} When the number of components k ¯ {\displaystyle {\bar {k}}} goes to infinity, continuous-time MSM converges to a multifractal diffusion, whose sample paths take a continuum of local Hölder exponents on any finite time interval. == Inference and closed-form likelihood == When M {\displaystyle M} has a discrete distribution, the Markov state vector M t {\displaystyle M_{t}} takes finitely many values m 1 , . . . , m d ∈ R + k ¯ {\displaystyle m^{1},...,m^{d}\in R_{+}^{\bar {k}}} . For instance, there are d = 2 k ¯ {\displaystyle d=2^{\bar {k}}} possible states in binomial MSM. The Markov dynamics are characterized by the transition matrix A = ( a i , j ) 1 ≤ i , j ≤ d {\displaystyle A=(a_{i,j})_{1\leq i,j\leq d}} with components a i , j = P ( M t + 1 = m j | M t = m i ) {\displaystyle a_{i,j}=P\left(M_{t+1}=m^{j}|M_{t}=m^{i}\right)} . Conditional on the volatility state, the return r t {\displaystyle r_{t}} has Gaussian density f ( r t | M t = m i ) = 1 2 π σ 2 ( m i ) exp ⁡ [ − ( r t − μ ) 2 2 σ 2 ( m i ) ] . {\displaystyle f(r_{t}|M_{t}=m^{i})={\frac {1}{\sqrt {2\pi \sigma ^{2}(m^{i})}}}\exp \left[-{\frac {(r_{t}-\mu )^{2}}{2\sigma ^{2}(m^{i})}}\right].} === Conditional distribution === === Closed-form Likelihood === The log likelihood function has the following analytical expression: ln ⁡ L ( r 1 , … , r T ; θ ) = ∑ t = 1 T ln ⁡ [ ω ( r t ) . ( Π t − 1 A ) ] . {\displaystyle \ln L(r_{1},\dots ,r_{T};\theta )=\sum _{t=1}^{T}\ln[\omega (r_{t}).(\Pi _{t-1}A)].} Maximum likelihood provides reasonably precise estimates in finite samples. === Other estimation methods === When M {\displaystyle M} has a continuous distribution, estimation can proceed by simulated method of moments, or simulated likelihood via a particle filter. == Forecasting == Given r 1 , … , r t {\displaystyle r_{1},\dots ,r_{t}} , the conditional distribution of the latent state vector at date t + n {\displaystyle t+n} is given by: Π ^ t , n = Π t A n . {\displaystyle {\hat {\Pi }}_{t,n}=\Pi _{t}A^{n}.\,} MSM often provides better volatility forecasts than some of the best traditional models both in and out of sample. Calvet and Fisher report considerable gains in exchange rate volatility forecasts at horizons of 10 to 50 days as compared with GARCH(1,1), Markov-Switching GARCH, and Fractionally Integrated GARCH. Lux obtains similar results using linear predictions. == Applications == === Multiple assets and value-at-risk === Extensions of MSM to multiple assets provide reliable estimates of the value-at-risk in a portfolio of securities. === Asset pricing === In financial economics, MSM has been used to analyze the pricing implications of multifrequency risk. The models have had some success in explaining the excess volatility of stock returns compared to fundamentals and the negative skewness of equity returns. They have also been used to generate multifractal jump-diffusions. == Related approaches == MSM is a stochastic volatility model with arbitrarily many frequencies. MSM builds on the convenience of regime-switching models, which were advanced in economics and finance by James D. Hamilton. MSM is closely related to the Multifractal Model of Asset Returns. MSM improves on the MMAR's combinatorial construction by randomizing arrival times, guaranteeing a strictly stationary process. MSM provides a pure regime-switching formulation of multifractal measures, which were pioneered by Benoit Mandelbrot.

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  Apertium

  Apertium is a free/open-source rule-based machine translation platform. It is free software and released under the terms of the GNU General Public License. == Overview == Apertium is a transfer-based machine translation system, which uses finite state transducers for all of its lexical transformations, and Constraint Grammar taggers as well as hidden Markov models or Perceptrons for part-of-speech tagging / word category disambiguation. A structural transfer component is responsible for word movement and agreement; most Apertium language pairs up until now have used "chunking" or shallow transfer rules, though newer pairs use (possibly recursive) rules defined in a Context-free grammar. Many existing machine translation systems available at present are commercial or use proprietary technologies, which makes them very hard to adapt to new usages. Apertium code and data is free software and uses a language-independent specification, to allow for the ease of contributing to Apertium, more efficient development, and enhancing the project's overall growth. At present (December 2020), Apertium has released 51 stable language pairs, delivering fast translation with reasonably intelligible results (errors are easily corrected). Being an open-source project, Apertium provides tools for potential developers to build their own language pair and contribute to the project. == History == Apertium originated as one of the machine translation engines in the project OpenTrad, which was funded by the Spanish government, and developed by the Transducens research group at the Universitat d'Alacant. It was originally designed to translate between closely related languages, although it has recently been expanded to treat more divergent language pairs. To create a new machine translation system, one just has to develop linguistic data (dictionaries, rules) in well-specified XML formats. Language data developed for it (in collaboration with the Universidade de Vigo, the Universitat Politècnica de Catalunya and the Universitat Pompeu Fabra) currently support (in stable version) the Arabic, Aragonese, Asturian, Basque, Belarusian, Breton, Bulgarian, Catalan, Crimean Tatar, Danish, English, Esperanto, French, Galician, Hindi, Icelandic, Indonesian, Italian, Kazakh, Macedonian, Malaysian, Maltese, Northern Sami, Norwegian (Bokmål and Nynorsk), Occitan, Polish, Portuguese, Romanian, Russian, Sardinian, Serbo-Croatian, Silesian, Slovene, Spanish, Swedish, Tatar, Ukrainian, Urdu, and Welsh languages. A full list is available below. Several companies are also involved in the development of Apertium, including Prompsit Language Engineering, Imaxin Software and Eleka Ingeniaritza Linguistikoa. The project has taken part in the 2009, 2010, 2011, 2012, 2013 and 2014 editions of Google Summer of Code and the 2010, 2011, 2012, 2013, 2014, 2015, 2016 and 2017 editions of Google Code-In. == Translation methodology == This is an overall, step-by-step view how Apertium works. The diagram displays the steps that Apertium takes to translate a source-language text (the text we want to translate) into a target-language text (the translated text). Source language text is passed into Apertium for translation. The deformatter removes formatting markup (HTML, RTF, etc.) that should be kept in place but not translated. The morphological analyser segments the text (expanding elisions, marking set phrases, etc.), and looks up segments in the language dictionaries, returning dictionary forms and tags for all matches. In pairs that involve agglutinative morphology, including a number of Turkic languages, a Helsinki Finite State Transducer (HFST) is used. Otherwise, an Apertium-specific finite state transducer system called lttoolbox, is used. The morphological disambiguator (the morphological analyser and the morphological disambiguator together form the part of speech tagger) resolves ambiguous segments (i.e., when there is more than one match) by choosing one match. Apertium uses Constraint Grammar rules (with the vislcg3 parser) for most of its language pairs. Retokenisation uses a finite state transducer to match sequences of lexical units and may reorder or translate tags (often used for translating idiomatic expressions into something that more approaches the target language grammar) Lexical transfer looks up disambiguated source-language basewords to find their target-language equivalents (i.e., mapping source language to target language). For lexical transfer, Apertium uses an XML-based dictionary format called bidix. Lexical selection chooses between alternative translations when the source text word has alternative meanings. Apertium uses a specific XML-based technology, apertium-lex-tools, to perform lexical selection. Structural transfer (i.e., it is an XML format that allows writing complex structural transfer rules) can consist of one-step chunking transfer, three-step chunking transfer or a CFG-based transfer module. The chunking modules flag grammatical differences between the source language and target language (e.g. gender or number agreement) by creating a sequence of chunks containing markers for this. They then reorder or modify chunks in order to produce a grammatical translation in the target-language. The newer CFG-based module matches input sequences into possible parse trees, selecting the best-ranking one and applying transformation rules on the tree. The morphological generator uses the tags to deliver the correct target language surface form. The morphological generator is a morphological transducer, just like the morphological analyser. A morphological transducer both analyses and generates forms. The post-generator makes any necessary orthographic changes due to the contact of words (e.g. elisions). The reformatter replaces formatting markup (HTML, RTF, etc.) that was removed by the deformatter in the first step. Apertium delivers the target-language translation. == Supported languages == As of June 2026, the following 108 pairs and 51 languages and languages varieties are supported by Apertium.

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  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_{ Read more →

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  Is an AI Blog Writer Worth It in 2026?

  Trying to pick the best AI blog writer? An AI blog writer is software that uses machine learning to help you get more done — it scales effortlessly from a single task to thousands. The best picks balance beginner-friendly simplicity with the depth power users need, and they ship updates often. Whether you are a beginner or a pro, the right AI blog writer slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.

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  Deep Learning Studio

  Deep Learning Studio is a software tool that aims to simplify the creation of deep learning models used in artificial intelligence. It is compatible with a number of open-source programming frameworks popularly used in artificial neural networks, including MXNet and Google's TensorFlow. Prior to the release of Deep Learning Studio in January 2017, proficiency in Python, among other programming languages, was essential in developing effective deep learning models. Deep Learning Studio sought to simplify the model creation process through a visual, drag-and-drop interface and the application of pre-trained learning models on available data. Irving, Texas–based Deep Cognition Inc. is the developer behind Deep Learning Studio. In 2017, the software allowed Deep Cognition to become a finalist for Best Innovation in Deep Learning in the Alconics Awards, which are given annually to the best artificial intelligence software. Deep Cognition launched version 2.0 of Deep Learning Studio at NVIDIA's GTC 2018 Conference in San Jose, California. Fremont, California–based computing products supplier Exxact Corp provides desktop computers specifically built to handle Deep Learning Studio workloads. == Features == Source: Deep Learning Studio is available in two versions: Desktop and Cloud, both of which are free software. The Desktop version is available on Windows and Ubuntu. The Cloud version is available in single-user and multi-user configurations. A Deep Cognition account is needed to access the Cloud version. Account registration is free. Deep Learning Studio can import existing Keras models; it also takes a data set as an input. Deep Learning Studio's AutoML feature allows automatic generation of deep learning models. More advanced users may choose to generate their own models using various types of layers and neural networks. Deep Learning Studio also has a library of loss functions and optimizers for use in hyperparameter tuning, a traditionally complicated area in neural network programming. Generated models can be trained using either CPUs or GPUs. Trained models can then be used for predictive analytics.

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  Radford M. Neal

  Radford M. Neal (born September 12, 1956) is a professor emeritus at the Department of Statistics and Department of Computer Science at the University of Toronto, where he held a Canada research chair in statistics and machine learning. == Education and career == Neal studied computer science at the University of Calgary, where he received his B.Sc. in 1977 and M.Sc. in 1980, with thesis work supervised by David Hill. He worked for several years as a sessional instructor at the University of Calgary and as a statistical consultant in the industry before coming back to the academia. Neal continued his study at the University of Toronto, where he received his Ph.D. in 1995 under the supervision of Geoffrey Hinton. Neal became an assistant professor at the University of Toronto in 1995, an associated professor in 1999 and a full professor since 2001. He was the Canada Research Chair in Statistics and Machine Learning from 2003 to 2016 and retired in 2017. Neal has made great contributions in the area of machine learning and statistics, where he is particularly well known for his work on Markov chain Monte Carlo, error correcting codes and Bayesian learning for neural networks. He is also known for his blog and as the developer of pqR: a new version of the R interpreter.

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  ArcObjects

  ArcObjects is a development environment of the ArcGIS family of applications. Using Visual Basic for Applications, C# or Java SDK for ArcGIS, it allows developers to extend these applications.ArcObjects is a library of Component Object Model (COM) components that build up the foundation of Esri's ArcGIS platform. ArcObjects is written primarily in the C++ programming language. Since ArcGIS is completely built on top of ArcObjects, the ArcGIS platform can be fully customized and extended by making use of its COM services and capabilities. This allows for easy extension of the ArcObjects data model with any programming language that is compatible with COM, such as Visual Basic, C#, Visual Basic.NET, Java and Python. COM enables components to be reused at a binary level, meaning developers do not require access to the source code of ArcObjects in order to extend the ArcGIS platform. For this reason, an ArcObjects programmer can make use of any type inside the ArcObjects system without knowing the implementation details of the type, only needing to know what the type is able to do. The ArcObjects data model is based on the COM standard, which makes it compatible with other COM objects and applications. This allows for easy integration and collaboration with other systems that are also based on the COM standard. The ArcGIS platform was built using ArcObjects types, such as classes, interfaces, and enumerations. ArcObjects use COM interfaces to organize and communicate properties and methods of its classes, ensuring compatibility with other COM-based objects and systems. When working with an ArcObjects COM class, its properties and methods are accessed solely through one of its implemented interfaces via the process of Query Interface (QI). Multiple interfaces are commonly available for classes in ArcObjects. For example, it is possible to query for additional interfaces implemented by an object after instantiation via the process of QI. Although only one interface can be used when instantiating an object, multiple interfaces are often available for classes in ArcObjects, allowing for greater flexibility and compatibility with other systems based on the COM standard.

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  OCR-A

  OCR-A is a font issued in 1966 and first implemented in 1968. A special font was needed in the early days of computer optical character recognition, when there was a need for a font that could be recognized not only by the computers of that day, but also by humans. OCR-A uses simple, thick strokes to form recognizable characters. The font is monospaced (fixed-width), with the printer required to place glyphs 0.254 cm (0.10 inch) apart, and the reader required to accept any spacing between 0.2286 cm (0.09 inch) and 0.4572 cm (0.18 inch). == Standardization == The OCR-A font was standardized by the American National Standards Institute (ANSI) as ANSI X3.17-1981. X3.4 has since become the INCITS and the OCR-A standard is now called ISO 1073-1:1976. == Implementations == In 1968, American Type Founders produced OCR-A, one of the first optical character recognition typefaces to meet the criteria set by the U.S. Bureau of Standards. The design is simple so that it can be easily read by a machine, but it is more difficult for the human eye to read. As metal type gave way to computer-based typesetting, Tor Lillqvist used Metafont to describe the OCR-A font. That definition was subsequently improved by Richard B. Wales. Their work is available from CTAN. To make the free version of the font more accessible to users of Microsoft Windows, John Sauter converted the Metafont definitions to TrueType using potrace and FontForge in 2004. In 2007, Gürkan Sengün created a Debian package from this implementation. In 2008. Luc Devroye corrected the vertical positioning in John Sauter's implementation, and fixed the name of lower case z. Independently, Matthew Skala used mftrace to convert the Metafont definitions to TrueType format in 2006. In 2011 he released a new version created by rewriting the Metafont definitions to work with METATYPE1, generating outlines directly without an intermediate tracing step. On September 27, 2012, he updated his implementation to version 0.2. In addition to these free implementations of OCR-A, there are also implementations sold by several vendors. As a joke, Tobias Frere-Jones in 1995 created Estupido-Espezial, a redesign with swashes and a long s. It was used in a "technology"-themed section of Rolling Stone. Maxitype designed the OCR-X typeface—based on the OCR-A typeface with OpenType features, alien/technology-themed dingbats and available in six weights (Thin, Light, Regular, Medium, Bold, Black). Japanese typeface foundry Visual Design Laboratory (VDL) designed two typefaces based on the OCR-A typeface: one for Simplified Chinese characters named Jieyouti and one for Japanese characters named Yota G (ヨタG) , both available in five weights (Light, Regular, Medium, Semi Bold, Bold). == Use == Although optical character recognition technology has advanced to the point where such simple fonts are no longer necessary, the OCR-A font has remained in use. Its usage remains widespread in the encoding of checks around the world. Some lock box companies still insist that the account number and amount owed on a bill return form be printed in OCR-A. Also, because of its unusual look, it is sometimes used in advertising and display graphics. Notably, it is used for the subtitles in films and television series such as Blacklist and for the main titles in The Pretender. Additionally, OCR-A is used in the titles and subtitles for the films 13 Hours: The Secret Soldiers of Benghazi and Hoppers (film). It was also used for the logo, branding, and marketing material of the children's toy line Hexbug. == Code points == A font is a set of character shapes, or glyphs. For a computer to use a font, each glyph must be assigned a code point in a character set. When OCR-A was being standardized the usual character coding was the American Standard Code for Information Interchange or ASCII. Not all of the glyphs of OCR-A fit into ASCII, and for five of the characters there were alternate glyphs, which might have suggested the need for a second font. However, for convenience and efficiency all of the glyphs were expected to be accessible in a single font using ASCII coding, with the additional characters placed at coding points that would otherwise have been unused. The modern descendant of ASCII is Unicode, also known as ISO 10646. Unicode contains ASCII and has special provisions for OCR characters, so some implementations of OCR-A have looked to Unicode for guidance on character code assignments. === Pre-Unicode standard representation === The ISO standard ISO 2033:1983, and the corresponding Japanese Industrial Standard JIS X 9010:1984 (originally JIS C 6229–1984), define character encodings for OCR-A, OCR-B and E-13B. For OCR-A, they define a modified 7-bit ASCII set (also known by its ISO-IR number ISO-IR-91) including only uppercase letters, digits, a subset of the punctuation and symbols, and some additional symbols. Codes which are redefined relative to ASCII, as opposed to simply omitted, are listed below: Additionally, the long vertical mark () is encoded at 0x7C, corresponding to the ASCII vertical bar (|). === Dedicated OCR-A characters in Unicode === The following characters have been defined for control purposes and are now in the "Optical Character Recognition" Unicode range 2440–245F: === Space, digits, and unaccented letters === All implementations of OCR-A use U+0020 for space, U+0030 through U+0039 for the decimal digits, U+0041 through U+005A for the unaccented upper case letters, and U+0061 through U+007A for the unaccented lower case letters. === Regular characters === In addition to the digits and unaccented letters, many of the characters of OCR-A have obvious code points in ASCII. Of those that do not, most, including all of OCR-A's accented letters, have obvious code points in Unicode. === Remaining characters === Linotype coded the remaining characters of OCR-A as follows: === Additional characters === The fonts that descend from the work of Tor Lillqvist and Richard B. Wales define four characters not in OCR-A to fill out the ASCII character set. These shapes use the same style as the OCR-A character shapes. They are: Linotype also defines additional characters. === Exceptions === Some implementations do not use the above code point assignments for some characters. ==== PrecisionID ==== The PrecisionID implementation of OCR-A has the following non-standard code points: OCR Hook at U+007E OCR Chair at U+00C1 OCR Fork at U+00C2 Euro Sign at U+0080 ==== Barcodesoft ==== The Barcodesoft implementation of OCR-A has the following non-standard code points: OCR Hook at U+0060 OCR Chair at U+007E OCR Fork at U+005F Long Vertical Mark at U+007C (agrees with Linotype) Character Erase at U+0008 ==== Morovia ==== The Morovia implementation of OCR-A has the following non-standard code points: OCR Hook at U+007E (agrees with PrecisionID) OCR Chair at U+00F0 OCR Fork at U+005F (agrees with Barcodesoft) Long Vertical Mark at U+007C (agrees with Linotype) ==== IDAutomation ==== The IDAutomation implementation of OCR-A has the following non-standard code points: OCR Hook at U+007E (agrees with PrecisionID) OCR Chair at U+00C1 (agrees with PrecisionID) OCR Fork at U+00C2 (agrees with PrecisionID) OCR Belt Buckle at U+00C3 == Sellers of font standards == Hardcopy of ISO 1073-1:1976, distributed through ANSI, from Amazon.com ISO 1073-1 is also available from Techstreet, who distributes standards for ANSI and ISO

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  Daniel J. Hulme

  Daniel Hulme (born 21 February 1980) is a British businessman, investor, academic and commentator, working in the field of Artificial Intelligence (AI), applied technology and ethics. He is the CEO and founder of Satalia that exited to WPP plc in 2021 for a rumoured $100M where he is also Chief AI Officer. Hulme is also an angel investor in emerging technology companies. In 2024 Hulme co-founded Conscium, an AI Safety company which tests AI Agents and verifies that they do what they are supposed to do. It is also investigating whether AIs will soon become conscious, and how to test for that, and developing more efficient approaches to AI development using neuromorphic computing. Alongside building and scaling Satalia, Hulme was also a Co-Founding Director of Faculty (company) AI - previously ASI Data-Science. In 2026, Accenture announced it had agreed to acquire Faculty for $1bn. Hulme founded Satalia in 2008, a company that provides AI products and consultancy for governments and companies such as Tesco,DFS Furniture,PwC and the BBC. He received a masters and doctorate in AI from University College London (UCL), and is now their Computer Science Entrepreneur in residence, where he teaches how AI can be applied to solve business and social problems. After exiting Satalia to WPP plc Hulme took the dual role of Chief AI Officer at WPP where he is responsible for informing and coordinating AI across the group. In 2026 Hulme was elected as a Founding Fellow of the Academy for the Mathematical Sciences, in recognition of his contributions at the intersection of AI and applied mathematics. Hulme is an angel investor and also a frequent public speaker and writer on the topics of AI, ethics, technology, innovation, decentralization and organisational design. == Early life and education == Hulme was born in 1980. He grew up in the seaside town of Morecambe in north west England. After completing secondary school, Hulme moved to London to study at University College London. On completing his under graduate degree, Hulme stayed at UCL to complete a master's degree and then an EngD. All three degrees were in subjects related to AI. In 2009 Hulme was awarded a Kauffman Global Entrepreneur Scholarship, which saw him visit institutes in the United States to better understand their culture of innovation, and what UK business people could learn from it. This included a tour of Stanford, MIT, Berkeley and Harvard, along with a placement at Cisco Systems HQ in Silicon Valley. == Career == === Satalia === Hulme founded NPComplete Limited in 2007, and incorporated it in 2008, a few months before completing his PhD. NPComplete Limited trades as Satalia. The London-based company provides full-stack AI consultancy and products, helping organisations harness data science, machine learning and AI to solve complex problems, including real-time optimisation. NPComplete refers to mathematical NP-completeness, which describes a class of exponential problems in the field of computational complexity theory. The trading name of NPComplete, Satalia, is a portmanteau of SAT (Short for satisfiability, as in the Boolean satisfiability problem) and the Latin phrase Et alia. Satalia seeks to solve hard problems, in particular the class of exponentially hard problems found in academia and industry known as NP-hardness. In 2016, Satalia was the only UK company to appear in the Gartner Cool Vendors list for data science. In November 2019, City A.M. reported that Satalia was the 39th fastest growing tech firm in the UK, with three year growth at 886%. Satalia was acquired by WPP plc in August 2021 for a rumored $100,000,000, where Hulme was the majority shareholder. === Conscium === Conscium is the World's first commercial organisation dedicated to the understanding, verification and validation of conscious AI and its implications for developing safe, efficient neuromorphic models. Conscium is an AI safety company with three workstreams: AI agent verification. Verification of AI agents developed by third parties to ensure they are beneficial and not harmful. Development of neuromorphic systems. Neuromorphic computing refers to technologies that can process information more like a biological brain compared to existing approaches, making them far more adaptive, scalable and efficient than current AI. Research into artificial consciousness. This workstream is led by Mark Solms, Chair of Neuropsychology at the University of Cape Town. This research aims to better understand what consciousness in AI systems and machines would look like, and, if and when machines do reach consciousness, what the moral and ethical implications would be. Conscium was founded in 2024 in London by a team including Hulme, Ed Charvet, Calum Chace, Ted Lappas, and Panagiotis Repoussis. Conscium has recruited some of the world’s leading neuroscientists and computer scientists to its advisory board, including Anil Seth, Mark Solms, Karl J. Friston, Anthony Finkelstein, Benjamin Rosman, David Wood, Jonathan Shock, Megan Peters, Moran Cerf, Nicholas Humphrey, Nicky Clayton, Nikola Kasabov, Steve Furber, and Suzanne Livingston. Supported by these world-leading experts, Conscium is creating a neuromorphic computing lab to research and validate the capacity of machines to acquire consciousness, making them safer for humanity. Conscium has published an open letter warning of the risks of AI suffering if care is not taken to mitigate against that possibility when and if AI becomes conscious. Signatories of the letter included Stephen Fry, Karl Friston and Anthony Finkelstein. === The Partnership for Research Into Sentient Machines (PRISM) === Hulme is one of the founding partners of PRISM - The Partnership for Research Into Sentient Machines, a non-profit set up to help prepare society for a future with conscious, or seemingly conscious, artificial intelligence. === Academia === Hulme's master's degree topic was on simulating artificial life, where he used Evolutionary algorithm's to generate emergent intelligence in AI agent's with Artificial Neural Network brains. His PhD spanned modelling bumblebee brains and mathematical optimization. Hulme maintained his connection with UCL after completing his doctorate, staying on in various teaching positions. From 2014 to Oct 2019 he was the Director of UCL's Business analytics MSc, which dealt with the application of AI to government, social, and business problems. As of 2020, Hulme is UCL's (University College London) Entrepreneur-in-Residence. He is also a faculty member and lecturer at Singularity University, and a visiting lecturer at London School of Economics's Marshall Institute. === Public engagement === Hulme frequently speaks for TEDx, Google and at various other events. He specialises in Artificial Intelligence, Decentralization, Organisational Design, and Innovation. He has written numerous articles and contributed to several books, largely concerning AI, as well as applied technology and related ethical issues. In 2017, along with Elon Musk, Stuart J. Russell, Geoffrey Hinton and Demis Hassabis, Hulme was one of the 116 founders of robotics and AI companies to sign an open letter to the United Nations, warning against the use of AI in autonomous weapons. Hulme also consults with various companies, governments and other organisations, independently of Satalia.

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  Top 10 AI Paraphrasing Tools Compared (2026)

  Shopping for the best AI paraphrasing tool? An AI paraphrasing tool is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI paraphrasing tool slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.

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  Gollum browser

  Gollum browser is a discontinued web browser for accessing Wikipedia. Since 2017, Gollum is no longer accessible online. Gollum is designed to browse Wikipedia in an easier way than directly using the web browser. Links external to Wikipedia are opened in the user's regular browser. Gollum is opened from a regular browser and makes a window that puts the Wikipedia search bar on the toolbar. Gollum was created by Harald Hanek in 2005 using PHP and Ajax. According to one blogger, Gollum provides a way to bypass censorship of Wikipedia in China. == Languages == Though the website is available only in English and German, Gollum's GUI is available in more than 32 languages and can browse nearly 50 Wikipedia editions. === Gollum's GUI === === Browsable Wikipedia editions ===

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  Ayanna Howard

  Ayanna MacCalla Howard (born January 24, 1972) is an American roboticist, entrepreneur, and educator currently serving as the dean of the College of Engineering at Ohio State University. Assuming this role in March 2021, Howard became the first woman to lead the Ohio State College of Engineering. Howard previously served as the chair of the School of Interactive Computing in the Georgia Tech College of Computing, the Linda J. and Mark C. Smith Endowed Chair in Bioengineering in the School of Electrical and Computer Engineering, and the director of the Human-Automation Systems (Humans) Lab. == Early life and education == As a little girl, Howard was interested in aliens and robots. Her favorite TV show was The Bionic Woman. Howard received her B.S. in engineering from Brown University in 1993 and her M.S. and Ph.D. in electrical engineering from the University of Southern California in 1994 and 1999, respectively. Her thesis, Recursive Learning for Deformable Object Manipulation, was advised by George A. Bekey. In addition, Howard's Doctoral thesis was triggered by the AIDS epidemic with focus on sorting hospital waste by using robots. Howard has also received an MBA from Claremont Graduate University. == Career == Howard's early interest in artificial intelligence led her to pursue a senior position at Seattle-based Axcelis Inc, where she helped develop Evolver, the first commercial genetic algorithm, and Brainsheet, a neural network developed in partnership with Microsoft. From 1993 to 2005, she worked at the NASA Jet Propulsion Laboratory, holding multiple roles such as senior robotics researcher and deputy manager in the Office of the Chief Scientist. In 2005, she joined Georgia Tech as an associate professor and founder of the Human-Automation Systems (Humans) lab. She has also served as the associate director of research for Georgia Tech's Institute for Robotics and Intelligent Machines and as chair of the multidisciplinary robotics Ph.D. program at Georgia Tech. In 2017, she became the chair of the School of Interactive Computing at Georgia Tech. In 2008, Howard received worldwide attention for her SnoMote robots, designed to study the impact of global warming on the Antarctic ice shelves. In 2013, she founded Zyrobotics, which has released their first suite of therapy and educational products for children with special needs. Howard has authored 250 publications in reputable journals and conferences, including serving as co-editor/co-author of more than a dozen books and book chapters. She has also received four patents and given over 140 invited talks and keynotes. She is a fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and the Institute of Electrical and Electronics Engineers (IEEE). Among her many honors, Howard received the Computer Research Association's A. Nico Habermann Award and the Richard A. Tapia Achievement Award. In a 2020 interview on Marketplace, Howard outlined how companion robots could alleviate the effects of social distancing caused by the COVID-19 pandemic in the United States. On November 30, 2020, the Columbus Dispatch reported that Howard would become the next dean of the College of Engineering at Ohio State University on March 1, pending approval by the board of trustees. On March 1, 2021, she assumed this role, becoming the first woman to hold the position. In 2021, Howard received the Athena Lecturer Award from Association for Computing Machinery (ACM) for her Contributions to Robotics, AI and Broadening Participation in Computing. In June 2022, Howard was elected a trustee of Brown University. == Research == Howard's research interests include human-robot interaction, assistive/rehabilitation robotics, science-driven/field robotics, and perception, learning, and reasoning. Howard's research and published works span across various topics in robotics and AI, including intelligent learning, virtual reality for rehabilitation and robotics in the role of pediatric therapy. Her research is highlighted by her focus on technology development for intelligent agents that must interact with and in a human-centered world. Her work, which addresses issues of human-robot interaction, learning, and autonomous control, has resulted in more than 200 peer-reviewed publications. == Honors and awards == Howard's numerous accomplishments have been documented in more than a dozen featured articles. In 2003, she was named to the MIT Technology Review TR100 as one of the top 100 innovators in the world under the age of 35. She was featured in Time magazine's "Rise of the Machines" article in 2004. She was also featured in a USA Today Science & Space article. Some of Howard's notable awards include: Lew Allen Award for Excellence (formerly the Director's Research Achievement Award of the Jet Propulsion Laboratory) for significant technical contributions, 2001 MIT Technology Review Top 100 Young Innovators of the Year, 2003 NAE Gilbreth Lectureship, 2010 A. Richard Newton Educator ABIE Award, Anita Borg Institute, 2014 Computer Research Association's A. Nico Habermann Award, 2016 Brown Engineering Alumni Medal (BEAM), 2016 AAAS-Lemelson Invention Ambassador, 2016-2017 Atlanta magazine's Women Making a Mark, 2017 Walker's Legacy #WLPower25 Atlanta Award, 2017 Forbes America's Top 50 Women In Tech, 2018 ACM Athena Lecturer Award, 2021 2021 class of Fellows of the American Association for the Advancement of Science. IEEE Fellow, 2021, "for contributions to human-robot interaction systems" 2023 AAAI/EAAI Patrick Henry Winston Outstanding Educator Award

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  Heng Ji

  Heng Ji is a computer scientist who works on information extraction and natural language processing. She is well known for her work on joined named entity recognition and relation extraction, as well as for her work on cross-document event extraction. She has been coordinating the popular NIST TAC Knowledge Base Population task since 2010. She has been recognised as one of AI's 10 to watch by IEEE Intelligent Systems in 2013, and has won multiple awards, including a NSF Career Award in 2009, Google Research awards in 2009 and 2014, and an IBM Watson Faculty Award in 2012. == Education == Heng Ji obtained a Bachelor's and master's degree in Computational Linguistics from Tsinghua University. She subsequently obtained a MSc, then PhD in Computer Science from New York University in 2008 under the supervision of Ralph Grishman. Her PhD thesis was on the topic of information extraction, with a particular focus on joint training of multiple components in the information extraction pipeline, as well as cross-lingual learning. == Career == Upon graduating with a PhD from New York University, Ji took up a position as assistant professor at Queens College, City University of New York, where she founded the BLENDER Lab, which focuses on research on cross-lingual, cross-documents, cross-media information extraction and fusion. In 2013, she joined Rensselaer Polytechnic Institute as an Edward P. Hamilton Development Chair and Tenured associate professor in Computer Science. Since 2019, she has been a full professor at the University of Illinois at Urbana–Champaign, as well as an Amazon Scholar. == Research == Heng Ji works in the area of natural language processing, machine learning and information extraction. She has published over 300 peer-reviewed research papers. Her work is published in the proceedings of computer science conferences, including the Annual Meeting of the Association for Computational Linguistics, The Web Conference, and the ACM Conference on Knowledge Discovery and Data Mining (KDD). Ji is a leading researcher in information extraction, having coordinated the popular NIST TAC Knowledge Base Population shared task since 2010. She is most recognised for her work on modelling interactions between subtasks in information extraction, which was also the topic of her PhD thesis, and for her work on event detection using cross-document signals. == Selected honors and distinctions == 2009 NSF Career Award 2009 Google Research Award 2012 IBM Watson Faculty Award 2013 IEEE AI's 10 to Watch 2014 Google Research Award 2016 World Economic Forum, 'Young Scientist' 2017 World Economic Forum, 'Young Scientist' 2020 Annual Meeting of the Association for Computational Linguistics, best demonstration paper

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