If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
On November 18, 2019, Skylum will release Luminar 4, the AI photo editing tool for anyone who makes pictures. At PhotoPlus in New York, I talked with Dima Sytnik, chief product officer and co-founder of Skylum about the new artificial intelligence tools in Luminar 4. We're working with engineers to bring you the best technologies possible to make your photo editing workflow flow easier and faster than ever. The new technological advances in Luminar 4 is our new AI engine. We call it Artificial Intelligent Engine Skylum 2.0 and we have 4 AI-powered filters in Luminar 4, which is very unique to the market. So, photographers now can achieve the results much faster and much easier than before, but the quality is very high.
This paper introduces a divide-and-conquer inspired adversarial learning (DA-CAL) approach for photo enhancement. The key idea is to decompose the photo enhancement process into hierarchically multiple sub-problems, which can be better conquered from bottom to up. On the top level, we propose a perception-based division to learn additive and multiplicative components, required to translate a low-quality image or video into its high-quality counterpart. On the intermediate level, we use a frequency-based division with generative adversarial network (GAN) to weakly supervise the photo enhancement process. On the lower level, we design a dimension-based division that enables the GAN model to better approximates the distribution distance on multiple independent one-dimensional data to train the GAN model. While considering all three hierarchies, we develop multiscale and recurrent training approaches to optimize the image and video enhancement process in a weakly-supervised manner. Both quantitative and qualitative results clearly demonstrate that the proposed DACAL achieves the state-of- the-art performance for high-resolution image and video enhancement. Despite many mobile camera technological advances we have today, our captured images often still come with limited dynamic range, undesirable color rendition, and unsatisfactory texture sharpness. Among many possible causes, low-light environments and under/overexposed regions usually introduce severe lack of texture details and low-dynamic range coverage, respectively. Another critical issue is the amplification (during the enhancement process) of noise in the dark and/or texture-less regions, where the enhancement may not even be necessary.
BELLEVUE, WA – September 17, 2019 -- Today, Skylum has announced two major new features coming to Luminar 4, set to be released this fall. AI Skin Enhancer and Portrait Enhancer will enable photographers to further develop and improve their portraits. These tools use machine learning to speed up the process, but contain detailed controls for even the most demanding photo editor. Previously, photographers would have to spend time selectively editing their photographs, manually adjusting various tools through selections and masking. With Luminar 4, these tedious tools are a thing of the past.
After AI Sky Replacement and AI Structure there is more artificial intelligence to be featured in the upcoming Skylum Luminar 4 (learn more): AI-powered portrait and skin enhancements. Portrait Enhancer is a collection of tools that help improve the photo of any person in a natural, yet pleasing way. These tools are brand new to Luminar 4 making it now possible to highlight and improve primary features of a person's face. Thanks to the AI technology, faces and skin are automatically detected throughout a photo. AI Skin Enhancer allows photographers to automatically remove various skin imperfections like acne, freckles and moles, in addition to smoothing the skin.
Artificial Intelligence (AI) is the augmentation and imitation of human activity and behavior to increase output or efficiency. Driven in large by technological advancements and an increase in implementation and demand, this burgeoning field has gained a lot of attention in the last few years. However, its underlying sciences have been in development for decades. By the 1950's, a generation of scientists discussed the concept of an artificial brain. In 1956, John McCarthy coined the term AI when he, along with other researchers, claimed in their proposal for the Dartmouth Research Project on AI that "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it".
The brain is responsible for cognition, behavior, and much of what makes us uniquely human. The development of the brain is a highly complex process, and this process is reliant on precise regulation of molecular and cellular events grounded in the spatiotemporal regulation of the transcriptome. Disruption of this regulation can lead to neuropsychiatric disorders. The regulatory, epigenomic, and transcriptomic features of the human brain have not been comprehensively compiled across time, regions, or cell types. Understanding the etiology of neuropsychiatric disorders requires knowledge not just of endpoint differences between healthy and diseased brains but also of the developmental and cellular contexts in which these differences arise. Moreover, an emerging body of research indicates that many aspects of the development and physiology of the human brain are not well recapitulated in model organisms, and therefore it is necessary that neuropsychiatric disorders be understood in the broader context of the developing and adult human brain. Here we describe the generation and analysis of a variety of genomic data modalities at the tissue and single-cell levels, including transcriptome, DNA methylation, and histone modifications across multiple brain regions ranging in age from embryonic development through adulthood. We observed a widespread transcriptomic transition beginning during late fetal development and consisting of sharply decreased regional differences. This reduction coincided with increases in the transcriptional signatures of mature neurons and the expression of genes associated with dendrite development, synapse development, and neuronal activity, all of which were temporally synchronous across neocortical areas, as well as myelination and oligodendrocytes, which were asynchronous. Moreover, genes including MEF2C, SATB2, and TCF4, with genetic associations to multiple brain-related traits and disorders, converged in a small number of modules exhibiting spatial or spatiotemporal specificity. We generated and applied our dataset to document transcriptomic and epigenetic changes across human development and then related those changes to major neuropsychiatric disorders. These data allowed us to identify genes, cell types, gene coexpression modules, and spatiotemporal loci where disease risk might converge, demonstrating the utility of the dataset and providing new insights into human development and disease.
The human cerebral cortex has undergone an extraordinary increase in size and complexity during mammalian evolution. Cortical cell lineages are specified in the embryo, and genetic and epidemiological evidence implicates early cortical development in the etiology of neuropsychiatric disorders such as autism spectrum disorder (ASD), intellectual disabilities, and schizophrenia. Most of the disease-implicated genomic variants are located outside of genes, and the interpretation of noncoding mutations is lagging behind owing to limited annotation of functional elements in the noncoding genome. We set out to discover gene-regulatory elements and chart their dynamic activity during prenatal human cortical development, focusing on enhancers, which carry most of the weight upon regulation of gene expression. We longitudinally modeled human brain development using human induced pluripotent stem cell (hiPSC)–derived cortical organoids and compared organoids to isogenic fetal brain tissue. Fetal fibroblast–derived hiPSC lines were used to generate cortically patterned organoids and to compare oganoids' epigenome and transcriptome to that of isogenic fetal brains and external datasets. Organoids model cortical development between 5 and 16 postconception weeks, thus enabling us to study transitions from cortical stem cells to progenitors to early neurons. The greatest changes occur at the transition from stem cells to progenitors. The regulatory landscape encompasses a total set of 96,375 enhancers linked to target genes, with 49,640 enhancers being active in organoids but not in mid-fetal brain, suggesting major roles in cortical neuron specification. Enhancers that gained activity in the human lineage are active in the earliest stages of organoid development, when they target genes that regulate the growth of radial glial cells. Parallel weighted gene coexpression network analysis (WGCNA) of transcriptome and enhancer activities defined a number of modules of coexpressed genes and coactive enhancers, following just six and four global temporal patterns that we refer to as supermodules, likely reflecting fundamental programs in embryonic and fetal brain. Correlations between gene expression and enhancer activity allowed stratifying enhancers into two categories: activating regulators (A-regs) and repressive regulators (R-regs).
While videos of giant robots welding vehicles are exciting to watch (sparks!), humans are an important part of the assembly of vehicles. However, as you would expect, doing the same thing over and over often leads to injuries. For workers reaching up all day, that motion can be especially hard on their shoulders. To help, Ford will be offering exoskeleton vests to folks in 15 assembly plants around the world. The exoskeleton vest doesn't have a motor or battery pack to make its wearer stronger.