Generative AI, distributed enterprise and cloud-native platforms are amongst the top strategic technology trends for 2022, Gartner has predicted. David Groombridge, research vice president at Gartner, says with CEOs and boards striving to find growth through direct digital connections with customers, the priorities of a CIO must reflect the same business imperatives, which run through each of Gartner's top strategic tech trends for 2022. "CIOs must find the IT force multipliers to enable growth and innovation, and create scalable, resilient technical foundations whose scalability will free cash for digital investments," Groombridge says. "These imperatives form the three themes of this year's trends: engineering trust, sculpting change and accelerating growth." Gartner says one of the most visible and powerful AI techniques coming to market is generative AI – machine learning methods that learn about content or objects from their data, and use it to generate brand-new, completely original, realistic artefacts.

Artificial intelligence is a branch of computer science that deals with making intelligent machines and computer programs. It is a broad branch that includes machine learning and deep learning. John McCarthy, a professor emeritus at Stanford University, coined the term artificial intelligence in 1956. The applications of artificial intelligence include voice assistants like Alexa, Siri, and Google Assistant. It is also applied to deep learning models like Luther AI.

Omneky utilizes state-of-the-art deep learning to empower businesses to grow. In the past few years, foundation models have emerged as dominant forces in the world of AI. Foundation models are trained on vast datasets and are thus capable of performing a wide range of unsupervised tasks. They are revolutionizing the way AI works, as well as producing unintended consequences. What Can Foundation Models Do?

Artificial intelligence and machine learning pioneers are rapidly expanding on techniques that were originally designed for natural language processing and translation to other domains, including critical infrastructure and the genetic language of life. This was reported in the 2021 edition of the State of AI Report by investors Nathan Benaich of Air Street Capital and Ian Hogarth, an angel investor. Started in 2018, their report aims to be a comprehensive survey of trends in research, talent, industry, and politics, with predictions mixed in. The authors are tracking "182 active AI unicorns totaling $1.3 trillion of combined enterprise value" and estimate that exits by AI companies have created $2.3 trillion in enterprise value since 2010. One of their 2020 predictions was that we would see the attention-based transformers architecture for machine learning models branch out from natural language processing to computer vision applications.

Is music generation even something that a [deep] neural network can do? The goal of this project is to artificially generate piano music. Often neural networks are used to solve either a classification or a regression problem. Music on the other hand is a sequence of notes, each note played for a given duration. The notes can be interpreted as classes and their duration are numerical values.

How to represent a face pattern? While it is presented in a continuous way in our visual system, computers often store and process the face image in a discrete manner with 2D arrays of pixels. In this study, we attempt to learn a continuous representation for face images with explicit functions. First, we propose an explicit model (EmFace) for human face representation in the form of a finite sum of mathematical terms, where each term is an analytic function element. Further, to estimate the unknown parameters of EmFace, a novel neural network, EmNet, is designed with an encoder-decoder structure and trained using the backpropagation algorithm, where the encoder is defined by a deep convolutional neural network and the decoder is an explicit mathematical expression of EmFace. Experimental results show that EmFace has a higher representation performance on faces with various expressions, postures, and other factors, compared to that of other methods. Furthermore, EmFace achieves reasonable performance on several face image processing tasks, including face image restoration, denoising, and transformation.

Goal oriented dialogue systems have become a prominent customer-care interaction channel for most businesses. However, not all interactions are smooth, and customer intent misunderstanding is a major cause of dialogue failure. We show that intent prediction can be improved by training a deep text-to-text neural model to generate successive user utterances from unlabeled dialogue data. For that, we define a multi-task training regime that utilizes successive user-utterance generation to improve the intent prediction. Our approach achieves the reported improvement due to two complementary factors: First, it uses a large amount of unlabeled dialogue data for an auxiliary generation task. Second, it uses the generated user utterance as an additional signal for the intent prediction model. Lastly, we present a novel look-ahead approach that uses user utterance generation to improve intent prediction in inference time. Specifically, we generate counterfactual successive user utterances for conversations with ambiguous predicted intents, and disambiguate the prediction by reassessing the concatenated sequence of available and generated utterances.

In this study, we propose using a neural embedding model-graph neural network (GNN)- that leverages the heterogeneous features of urban areas and their interactions captured by human mobility network to obtain vector representations of these areas. Using large-scale high-resolution mobility data sets from millions of aggregated and anonymized mobile phone users in 16 metropolitan counties in the United States, we demonstrate that our embeddings encode complex relationships among features related to urban components (such as distribution of facilities) and population attributes and activities. The spatial gradient in each direction from city center to suburbs is measured using clustered representations and the shared characteristics among urban areas in the same cluster. Furthermore, we show that embeddings generated by a model trained on a different county can capture 50% to 60% of the emergent spatial structure in another county, allowing us to make cross-county comparisons in a quantitative way. Our GNN-based framework overcomes the limitations of previous methods used for examining spatial structures and is highly scalable. The findings reveal non-linear relationships among urban components and anisotropic spatial gradients in cities. Since the identified spatial structures and gradients capture the combined effects of various mechanisms, such as segregation, disparate facility distribution, and human mobility, the findings could help identify the limitations of the current city structure to inform planning decisions and policies. Also, the model and findings set the stage for a variety of research in urban planning, engineering and social science through integrated understanding of how the complex interactions between urban components and population activities and attributes shape the spatial structures in cities.

We propose a deep learning-based surrogate model for stochastic simulators. The basic idea is to use generative neural network to approximate the stochastic response. The challenge with such a framework resides in designing the network architecture and selecting loss-function suitable for stochastic response. While we utilize a simple feed-forward neural network, we propose to use conditional maximum mean discrepancy (CMMD) as the loss-function. CMMD exploits the property of reproducing kernel Hilbert space and allows capturing discrepancy between the between the target and the neural network predicted distributions. The proposed approach is mathematically rigorous, in the sense that it makes no assumptions about the probability density function of the response. Performance of the proposed approach is illustrated using four benchmark problems selected from the literature. Results obtained indicate the excellent performance of the proposed approach.

Self-supervised representation learning techniques utilize large datasets without semantic annotations to learn meaningful, universal features that can be conveniently transferred to solve a wide variety of downstream supervised tasks. In this paper, we propose a self-supervised method for learning representations of geographic locations from unlabeled GPS trajectories to solve downstream geospatial computer vision tasks. Tiles resulting from a raster representation of the earth's surface are modeled as nodes on a graph or pixels of an image. GPS trajectories are modeled as allowed Markovian paths on these nodes. A scalable and distributed algorithm is presented to compute image-like representations, called reachability summaries, of the spatial connectivity patterns between tiles and their neighbors implied by the observed Markovian paths. A convolutional, contractive autoencoder is trained to learn compressed representations, called reachability embeddings, of reachability summaries for every tile. Reachability embeddings serve as task-agnostic, feature representations of geographic locations. Using reachability embeddings as pixel representations for five different downstream geospatial tasks, cast as supervised semantic segmentation problems, we quantitatively demonstrate that reachability embeddings are semantically meaningful representations and result in 4-23% gain in performance, while using upto 67% less trajectory data, as measured using area under the precision-recall curve (AUPRC) metric, when compared to baseline models that use pixel representations that do not account for the spatial connectivity between tiles. Reachability embeddings transform sequential, spatiotemporal mobility data into semantically meaningful image-like representations that can be combined with other sources of imagery and are designed to facilitate multimodal learning in geospatial computer vision.