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English: With the growth in AI generated art, many new AI/ML (Artificial Intelligence / Machine Learning) models have been implemented and connected to each other. This diagram shows the major AI/ML Datasets / Corpora, Classifier / Transformer Models, Generative Models, and End-User Applications as well as how they are related and their dependencies.
Arc diagrams were used by Brandes (1999) to visualize the state diagram of a shift register, by Djidjev & Vrt'o (2002) to show that the crossing number of every graph is lower-bounded by a combination of its cutwidth and vertex degrees, by Byrne et al. (2007) to visualize interactions between Bluetooth devices, and by Owens & Jankun-Kelly (2013 ...
Hiring and AI skills. As AI adoption accelerates, a new skills gap is emerging that will further impact the job market. Organizations need skilled talent with the knowledge and capabilities to ...
In March 2023, OpenAI asked the ARC to test GPT-4 to assess the model's ability to exhibit power-seeking behavior. [10] ARC evaluated GPT-4's ability to strategize, reproduce itself, gather resources, stay concealed within a server, and execute phishing operations. [11] As part of the test, GPT-4 was asked to solve a CAPTCHA puzzle. [12]
Vacuum World, a shortest path problem with a finite state space. In computer science, a state space is a discrete space representing the set of all possible configurations of a "system". [1] It is a useful abstraction for reasoning about the behavior of a given system and is widely used in the fields of artificial intelligence and game theory.
An artificial superintelligence (ASI) is a hypothetical type of AGI that is much more generally intelligent than humans, [23] while the notion of transformative AI relates to AI having a large impact on society, for example, similar to the agricultural or industrial revolution.
Recurrent neural networks (RNNs) are a class of artificial neural network commonly used for sequential data processing. Unlike feedforward neural networks, which process data in a single pass, RNNs process data across multiple time steps, making them well-adapted for modelling and processing text, speech, and time series.
Cognitive architectures can be symbolic, connectionist, or hybrid. [7] Some cognitive architectures or models are based on a set of generic rules, as, e.g., the Information Processing Language (e.g., Soar based on the unified theory of cognition, or similarly ACT-R).