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) …
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When we started building Cortex, a platform for deploying machine learning models in production, we knew we wanted it to run as a self-hosted service on any cloud account. We also knew that as a small engineering team, we should focus on a single cloud provider, so we chose AWS. Our thought process was simple: start with the cloud provider with the most users. We assumed that the most interesting machine learning use cases would be at larger companies, who are predominantly using AWS. We also felt that our decision was validated by other infrastructure companies like Elastic, Databricks, and Cockroach Labs who prioritized AWS before other cloud providers for their products.
Given a publicly available pool of machine learning models constructed for various tasks, when a user plans to build a model for her own machine learning application, is it possible to build upon models in the pool such that the previous efforts on these existing models can be reused rather than starting from scratch? Here, a grand challenge is how to find models that are helpful for the current application, without accessing the raw training data for the models in the pool. In this paper, we present a two-phase framework. In the upload phase, when a model is uploading into the pool, we construct a reduced kernel mean embedding (RKME) as a specification for the model. Then in the deployment phase, the relatedness of the current task and pre-trained models will be measured based on the value of the RKME specification.
A natural progression in the field of computer vision following unprecedented progress in image classification tasks is towards video and video understanding, especially how it relates to identifying human subjects and activities. A number of datasets and benchmarks are being established in this area¹. In parallel, further progress is being made in 2D image related computer vision tasks such as fine-grained classification, image segmentation, 3D image construction, robot vision, scene flow estimation and human pose estimation. As part of my final Data Science project at Metis bootcamp, I've decided to marry these two parallel tracks -- video and human pose estimation in specific -- to create a content-based video search engine. Since applying 2D human pose estimation for video search is a novel idea with "no proof of concept", I have simplified my approach by selecting a single performer, fixed location single camera video footage of Salsa dance videos.
Big thanks to technological advances in areas like genetics, imaging, cancer is now more likely to be caught at an earlier stage than it was decades ago. Though, the accuracy in medical imaging diagnosis is still low, with the professionals witnessing 20-30 percent wrong negatives in chest X-rays and mammographies. AI can prevent this, and the fact that healthcare is data-rich is an added benefit. The more data visible to them, the more likely they can uncover the hidden patterns inside it that can be used to perform diagnosis. Over time, many machine learning algorithms have been introduced, but traditional forms, like logistic regression, have demonstrated the most usefulness in clinical oncology research.
We propose DeepHuman, a deep learning based framework for 3D human reconstruction from a single RGB image. Since this problem is highly intractable, we adopt a stage-wise, coarse-to-fine method consisting of three steps, namely inner body estimation, outer surface reconstruction and frontal surface detail refinement. Once an inner body is estimated from the given image, our method generates a dense semantic representation from the inner body to encode body shape and pose and to bridge the 2D image plane and 3D space. An image-guided volume-to-volume translation CNN is introduced to reconstruct the outer surface given the input image and the dense semantic representation. One key feature of our network is that it fuses different scales of image features into the 3D space through volumetric feature transformation, which helps to recover details of the subject's outer surface geometry.