The report also provides the analysis of import/export, production and consumption ratio, supply and demand, cost, price, estimated revenue, and gross margins. The global Artificial Intelligence (AI) & advanced Machine Learning (ML) market size is expected to reach USD 471.39 Billion at a steady CAGR of 35.2% in 2028, according to latest analysis by Emergen Research. Artificial Intelligence (AI) and advanced Machine Learning (ML) technologies are witnessing increasing demand and deployment across various fields, such as in leading-edge medical diagnostics, advanced quantum computer systems, consumer electronics, and smart personal assistants. Machine Learning is a type of AI, which enables computers to learn without being initially programmed. Rising focus on development of computer programs that can teach themselves and change and evolve when exposed to new data, is a factor driving demand for these technologies.

The great success of transformer-based models in natural language processing (NLP) has led to various attempts at adapting these architectures to other domains such as vision and audio. Recent work has shown that transformers can outperform Convolutional Neural Networks (CNNs) on vision and audio tasks. However, one of the main shortcomings of transformer models, compared to the well-established CNNs, is the computational complexity. In transformers, the compute and memory complexity is known to grow quadratically with the input length. Therefore, there has been extensive work on optimizing transformers, but often at the cost of degrading predictive performance. In this work, we propose a novel method to optimize and regularize transformers on audio spectrograms. Our proposed models achieve a new state-of-the-art performance on Audioset and can be trained on a single consumer-grade GPU. Furthermore, we propose a transformer model that outperforms CNNs in terms of both performance and training speed. Source code: https://github.com/kkoutini/PaSST

Individual Treatment Effects (ITE) estimation methods have risen in popularity in the last years. Most of the time, individual effects are better presented as Conditional Average Treatment Effects (CATE). Recently, representation balancing techniques have gained considerable momentum in causal inference from observational data, still limited to continuous (and binary) outcomes. However, in numerous pathologies, the outcome of interest is a (possibly censored) survival time. Our paper proposes theoretical guarantees for a representation balancing framework applied to counterfactual inference in a survival setting using a neural network capable of predicting the factual and counterfactual survival functions (and then the CATE), in the presence of censorship, at the individual level. We also present extensive experiments on synthetic and semisynthetic datasets that show that the proposed extensions outperform baseline methods.

As cell phones have grown increasingly prevalent worldwide, with a projected global penetration level of 73 percent in 2020, research on wealth forecasting from digital trail data has concentrated on mobile phone metadata (GSMA, 2017). Machine learning algorithms based on call detail records (CDR) have recently been proved to yield meaningful estimations of prosperity and well-being at a fine geographical resolution. Machine Learning and Artificial Intelligence can be used to target poor populations effectively for humanitarian aid using digital indicators. The challenge of assessing who is qualified for humanitarian help and who is not is a key cause of problems in anti-poverty programme management. Typically, programmes target people based on administrative records like tax records or survey-based asset or consumption measurements.

"The biggest change in human history will take place in the next decade," warns Aidan Meller, a Briton who ran an art gallery for 20 years until he became a pioneer by launching the world's first creative robot, Ai-Da. Introduced in 2019 as "the first humanoid artist," Ai-Da not only creates poems, paintings and sculptures, but also draws inspiration from the highest cultural references. Her name is not random either; it is a tribute to Ada Lovelace, a British mathematician considered the first computer programmer, also known for being the only legitimate daughter of the poet Lord Byron. Ai-Da's next action will be at the Giardini of the Venice Biennale on April 23. It will be the first time in the 120-year history of the Biennale that a robot artist will exhibit their work alongside that created by humans.

The dynamics of the global commercial artificial intelligence market continues to change over time, thanks to the persistent advancements in technology. This research report offers a detailed and insightful assessment of the global commercial artificial intelligence market, taking primary trends and the future prospects of this market in consideration. Various segments of this market, based on a number of parameters, have also been evaluated to gain a clear overview of the dynamics in the worldwide commercial artificial intelligence market. The global commercial artificial intelligence market demonstrates a highly fragmented and competitive business landscape. The leading companies in this market, including NVIDIA, Intel, IBM, Google, Microsoft, AWS, General Vision, GE, Siemens, and Mitsubishi Electric, are all competing on the basis of R&D, innovations, and new product launches.

Natural language offers a highly intuitive interface for image editing. In this paper, we introduce the first solution for performing local (region-based) edits in generic natural images, based on a natural language description along with an ROI mask. We achieve our goal by leveraging and combining a pretrained language-image model (CLIP), to steer the edit towards a user-provided text prompt, with a denoising diffusion probabilistic model (DDPM) to generate natural-looking results. To seamlessly fuse the edited region with the unchanged parts of the image, we spatially blend noised versions of the input image with the local text-guided diffusion latent at a progression of noise levels. In addition, we show that adding augmentations to the diffusion process mitigates adversarial results. We compare against several baselines and related methods, both qualitatively and quantitatively, and show that our method outperforms these solutions in terms of overall realism, ability to preserve the background and matching the text. Finally, we show several text-driven editing applications, including adding a new object to an image, removing/replacing/altering existing objects, background replacement, and image extrapolation. Code is available at: https://omriavrahami.com/blended-diffusion-page/

Crop maps are crucial for agricultural monitoring and food management and can additionally support domain-specific applications, such as setting cold supply chain infrastructure in developing countries. Machine learning (ML) models, combined with freely-available satellite imagery, can be used to produce cost-effective and high spatial-resolution crop maps. However, accessing ground truth data for supervised learning is especially challenging in developing countries due to factors such as smallholding and fragmented geography, which often results in a lack of crop type maps or even reliable cropland maps. Our area of interest for this study lies in Himachal Pradesh, India, where we aim at producing an open-access binary cropland map at 10-meter resolution for the Kullu, Shimla, and Mandi districts. To this end, we developed an ML pipeline that relies on Sentinel-2 satellite images time series. We investigated two pixel-based supervised classifiers, support vector machines (SVM) and random forest (RF), which are used to classify per-pixel time series for binary cropland mapping. The ground truth data used for training, validation and testing was manually annotated from a combination of field survey reference points and visual interpretation of very high resolution (VHR) imagery. We trained and validated the models via spatial cross-validation to account for local spatial autocorrelation and selected the RF model due to overall robustness and lower computational cost. We tested the generalization capability of the chosen model at the pixel level by computing the accuracy, recall, precision, and F1-score on hold-out test sets of each district, achieving an average accuracy for the RF (our best model) of 87%. We used this model to generate a cropland map for three districts of Himachal Pradesh, spanning 14,600 km2, which improves the resolution and quality of existing public maps.

In speaker recognition, where speech segments are mapped to embeddings on the unit hypersphere, two scoring backends are commonly used, namely cosine scoring or PLDA. Both have advantages and disadvantages, depending on the context. Cosine scoring follows naturally from the spherical geometry, but for PLDA the blessing is mixed -- length normalization Gaussianizes the between-speaker distribution, but violates the assumption of a speaker-independent within-speaker distribution. We propose PSDA, an analogue to PLDA that uses Von Mises-Fisher distributions on the hypersphere for both within and between-class distributions. We show how the self-conjugacy of this distribution gives closed-form likelihood-ratio scores, making it a drop-in replacement for PLDA at scoring time. All kinds of trials can be scored, including single-enroll and multi-enroll verification, as well as more complex likelihood-ratios that could be used in clustering and diarization. Learning is done via an EM-algorithm with closed-form updates. We explain the model and present some first experiments.

Welcome to the brave new world of securo-feminism*. In the long tradition of systems of patriarchal violence representing themselves as the solution to patriarchal violence, the ongoing expansion of draconian "war on terror" measures is being advertised as an advance for women's rights. For instance, countries like the United Kingdom have extended anti-terrorism provisions to now not only strip citizenship from "terrorists", but also from (some of) those convicted of sexual abuse: a "double punishment" reserved exclusively for dual nationals and suspected dual nationals, predominantly Muslims and other racialised targets from former colonies in the Global South. Simultaneously, the British government itself is threatening the rights and safety of abuse survivors and others fleeing violence, with its proposed new bill to "secure the borders" by penalising asylum seekers for arriving by unauthorised routes (never mind that such penalties flagrantly violate international refugee law). In the United States, President Joe Biden's "feminist" credentials include the introduction of new justice mechanisms to address sexual assault within the military: packaged in the same piece of legislation escalating American "defence" spending to unprecedented heights, surpassing even the previous record set by his predecessor Donald Trump.