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) …
A common problem that Deep Learning is helping to solve lately involves time series classification. A classic approach to this kind of problem is generating features from the signals we have and training a machine learning model. The process of handcrafting features might take a great chunk of your project schedule. This architecture has proven to be effective in order to reduce the amount of time spent on feature engineering. In this article, we're going to train a couple of models to detect irregular heart rhythms.
The artificial intelligence community has a problem; their data sources are riddled with bias. "We live in an era of AI, and data requirements are increasing rapidly. AI & Machine Learning have the potential to shape industries like Healthcare, Mobility, Insurance, etc. Because of this, there is a significant rise in concerns about the ethical implications of AI and inherent bias, as these applications are used in life-threatening scenarios," said Sukesh Kumar Tedla, Unbiased founder and CEO. "We decided to build a transparent platform that ethically meets the data needs of the AI industry to address what we see as the AI industry's current shortcomings."
We introduce a framework for actively learning visual categories from a mixture of weakly and strongly labeled image examples. We propose to allow the category-learner to strategically choose what annotations it receives---based on both the expected reduction in uncertainty as well as the relative costs of obtaining each annotation. We construct a multiple-instance discriminative classifier based on the initial training data. Then all remaining unlabeled and weakly labeled examples are surveyed to actively determine which annotation ought to be requested next. After each request, the current classifier is incrementally updated.
A machine learning model's performance is only as good as the quality of the data set on which it's trained, and in the domain of self-driving vehicles, it's critical this performance isn't adversely impacted by errors. A troubling report from computer vision startup Roboflow alleges that exactly this scenario occurred -- according to founder Brad Dwyer, crucial bits of data were omitted from a corpus used to train self-driving car models. Dwyer writes that Udacity Dataset 2, which contains 15,000 images captured while driving in Mountain View and neighboring cities during daylight, has omissions. Thousands of unlabeled vehicles, hundreds of unlabeled pedestrians, and dozens of unlabeled cyclists are present in roughly 5,000 of the samples, or 33% (217 lack any annotations at all but actually contain cars, trucks, street lights, or pedestrians). Worse are the instances of phantom annotations and duplicated bounding boxes (where "bounding box" refers to objects of interest), in addition to "drastically" oversized bounding boxes.
And that's a problem that is extremely dangerous. Machine learning, the process of teaching computer algorithms to perform new tasks by example, is poised to transform industries from agriculture to insurance. But ML models can only be as good as the data on which they're trained. One much-hyped area where machine learning is going to bring about societal change is in the advent of self-driving cars. But with great power comes great responsibility; a poorly trained self driving car can, quite literally, lead to human fatalities.
We propose a discriminative latent model for annotating images with unaligned object-level textual annotations. This mapping allows us to relate image regions to their corresponding annotation terms. This allows us to cluster test images. Our training data consist of images and their associated annotations. But we do not have access to the ground-truth region-to-annotation mapping or the overall scene label.
We propose a probabilistic topic model for analyzing and extracting content-related annotations from noisy annotated discrete data such as web pages stored in social bookmarking services. In these services, since users can attach annotations freely, some annotations do not describe the semantics of the content, thus they are noisy, i.e. not content-related. The extraction of content-related annotations can be used as a preprocessing step in machine learning tasks such as text classification and image recognition, or can improve information retrieval performance. The proposed model is a generative model for content and annotations, in which the annotations are assumed to originate either from topics that generated the content or from a general distribution unrelated to the content. We demonstrate the effectiveness of the proposed method by using synthetic data and real social annotation data for text and images.
A non-parametric Bayesian model is proposed for processing multiple images. The analysis employs image features and, when present, the words associated with accompanying annotations. The model clusters the images into classes, and each image is segmented into a set of objects, also allowing the opportunity to assign a word to each object (localized labeling). Each object is assumed to be represented as a heterogeneous mix of components, with this realized via mixture models linking image features to object types. The number of image classes, number of object types, and the characteristics of the object-feature mixture models are inferred non-parametrically.
Learning from multi-view data is important in many applications, such as image classification and annotation. In this paper, we present a large-margin learning framework to discover a predictive latent subspace representation shared by multiple views. Our approach is based on an undirected latent space Markov network that fulfills a weak conditional independence assumption that multi-view observations and response variables are independent given a set of latent variables. We provide efficient inference and parameter estimation methods for the latent subspace model. Finally, we demonstrate the advantages of large-margin learning on real video and web image data for discovering predictive latent representations and improving the performance on image classification, annotation and retrieval.
We introduce a novel active learning framework for video annotation. By judiciously choosing which frames a user should annotate, we can obtain highly accurate tracks with minimal user effort. We cast this problem as one of active learning, and show that we can obtain excellent performance by querying frames that, if annotated, would produce a large expected change in the estimated object track. We implement a constrained tracker and compute the expected change for putative annotations with efficient dynamic programming algorithms. We demonstrate our framework on four datasets, including two benchmark datasets constructed with key frame annotations obtained by Amazon Mechanical Turk.