Generative adversarial networks (GANs) have great successes on synthesizing data.

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Semantic channel equalization has emerged as a solution to address language mismatch in multi-user semantic communications. This approach aims to align the latent spaces of an encoder and a decoder which were not jointly trained and it relies on a partition of the semantic (latent) space into atoms based on the the semantic meaning. In this work we explore the role of the semantic space partition in scenarios where the task structure involves a one-to-many mapping between the semantic space and the action space. In such scenarios, partitioning based on hard inference results results in loss of information which degrades the equalization performance. We propose a soft criterion to derive the atoms of the partition which leverages the soft decoder's output and offers a more comprehensive understanding of the semantic space's structure. Through empirical validation, we demonstrate that soft partitioning yields a more descriptive and regular partition of the space, consequently enhancing the performance of the equalization algorithm.

In forthcoming AI-assisted 6G networks, integrating semantic, pragmatic, and goal-oriented communication strategies becomes imperative. This integration will enable sensing, transmission, and processing of exclusively pertinent task data, ensuring conveyed information possesses understandable, pragmatic semantic significance, aligning with destination needs and goals. Without doubt, no communication is error free. Within this context, besides errors stemming from typical wireless communication dynamics, potential distortions between transmitter-intended and receiver-interpreted meanings can emerge due to limitations in semantic processing capabilities, as well as language and knowledge representation disparities between transmitters and receivers. The main contribution of this paper is two-fold. First, it proposes and details a novel mathematical modeling of errors stemming from language mismatches at both semantic and effectiveness levels. Second, it provides a novel algorithmic solution to counteract these types of errors which leverages optimal transport theory. Our numerical results show the potential of the proposed mechanism to compensate for language mismatches, thereby enhancing the attainability of reliable communication under noisy communication environments.

We consider a multi-user semantic communications system in which agents (transmitters and receivers) interact through the exchange of semantic messages to convey meanings. In this context, languages are instrumental in structuring the construction and consolidation of knowledge, influencing conceptual representation and semantic extraction and interpretation. Yet, the crucial role of languages in semantic communications is often overlooked. When this is not the case, agent languages are assumed compatible and unambiguously interoperable, ignoring practical limitations that may arise due to language mismatching. This is the focus of this work. When agents use distinct languages, message interpretation is prone to semantic noise resulting from critical distortion introduced by semantic channels. To address this problem, this paper proposes a new semantic channel equalizer to counteract and limit the critical ambiguity in message interpretation. Our proposed solution models the mismatch of languages with measurable transformations over semantic representation spaces. We achieve this using optimal transport theory, where we model such transformations as transportation maps. Then, to recover at the receiver the meaning intended by the teacher we operate semantic equalization to compensate for the transformation introduced by the semantic channel, either before transmission and/or after the reception of semantic messages. We implement the proposed approach as an operation over a codebook of transformations specifically designed for successful communication. Numerical results show that the proposed semantic channel equalizer outperforms traditional approaches in terms of operational complexity and transmission accuracy.

A good automatic evaluation metric for language generation ideally correlates highly with human judgements of text quality. Yet, there is a dearth of such metrics, which inhibits the rapid and efficient progress of language generators. One exception is the recently proposed Mauve. In theory, Mauve measures an information-theoretic divergence between two probability distributions over strings: one representing the language generator under evaluation; the other representing the true natural language distribution. Mauve's authors argue that its success comes from the qualitative properties of their proposed divergence. Yet in practice, as this divergence is uncomputable, Mauve approximates it by measuring the divergence between multinomial distributions over clusters instead, where cluster assignments are attained by grouping strings based on a pre-trained language model's embeddings. As we show, however, this is not a tight approximation -- in either theory or practice. This begs the question: why does Mauve work so well? In this work, we show that Mauve was right for the wrong reasons, and that its newly proposed divergence is not necessary for its high performance. In fact, classical divergences paired with its proposed cluster-based approximation may actually serve as better evaluation metrics. We finish the paper with a probing analysis; this analysis leads us to conclude that -- by encoding syntactic- and coherence-level features of text, while ignoring surface-level features -- such cluster-based substitutes to string distributions may simply be better for evaluating state-of-the-art language generators.

As many of you will know, artificial intelligence is a passion of mine. I believe in its potential to boost productivity, solve problems, and make the world a better place. For me, it's more than just talk; I am building an entire business around AI and I stand with the users and creators of AI who see its potential and the exciting places it can take us. But not everyone is like us. Despite growing body evidence to the contrary, many people still see AI as a dark force; a development to be feared instead of celebrated.

The term Artificial Intelligence (AI) is a somewhat catch-all term that refers to the different possibilities offered by recent technological developments. From machine learning to natural language processing, news organisations can use AI to automate a huge number of tasks that make up the chain of journalistic production, including detecting, extracting and verifying data, producing stories and graphics, publishing (with sorting, selection and prioritisation filters) and automatically tagging articles. These systems offer numerous advantages: speed in executing complex procedures based on large volumes of data; support for journalistic routines through alerts on events and the provision of draft texts to be supplemented with contextual information; an expansion of media coverage to areas that were previously either not covered or not well covered (the results of matches between'small' sports clubs, for example); optimisation of real-time news coverage; strengthening a media outlet's ties with its audiences by providing them with personalised context according to their location or preferences; and more. But there is a flipside to the coin: the efficiency of these systems depends on the availability and the quality of data fed into them. The principle of garbage in, garbage out (GIGO), tried and tested in the IT world, essentially states that without reliable, accurate and precise input, it is impossible to obtain reliable, accurate and precise output.

OpenAI has come up with a language generator GPT-3, which is a successor of GPT-2. This newly developed AI was put forward to a few selected outside software developers for testing. GPT-2 released a year prior, and it let out convincing streams in regards to message in the extent of different styles when induced with an underlying sentence. The differentiating factor of GPT-3 is having 175 billion parameters(the qualities that a neural system attempts to upgrade during preparing), whereas GPT-2 had only 1.5 billion. GPT-3 is the most significant language model ever.

As many of you will know, artificial intelligence is a passion of mine. I believe in its potential to boost productivity, solve problems, and make the world a better place. For me, it's more than just talk; I am building an entire business around AI and I stand with the users and creators of AI who see its potential and the exciting places it can take us. But not everyone is like us. Despite growing body evidence to the contrary, many people still see AI as a dark force; a development to be feared instead of celebrated.