On a mission to create safer communities, companies, and environments, Vintra uses artificial intelligence to augment the established surveillance systems deployed to protect the spaces where people work, learn, and travel. Vintra works with private companies, higher education, and law enforcement to transform visual data from any camera -- including mobile sources like drones and dash cams -- into actionable, tailored and trusted intelligence with proprietary deep learning algorithms. Backed by investors including Bonfire Ventures, Vertex Ventures and London Venture Partners, Vintra has raised $4.8 million to date. Founded in 2016 by entrepreneur Brent Boekestein and leading computer vision experts, Dr. Ariel Amato and Dr. Angel Sappa, Vintra saw a critical need developing with the rise of security systems and proliferation of cameras, including mobile surveillance cameras that are burgeoning in use. As the use of surveillance and security cameras grows, the amount of data collected per year requires billions of hours of manual interpretation if done by human operators.

Artificial Intelligence has been spreading its wings since 1950s but has been increasingly hogging limelight in recent times. Leaders of the world's most influential technology firms including Amazon, Facebook, Microsoft, Google are emphasizing their enthusiasm for Artificial Intelligence (AI) and its applicability. AI has become more popular today thanks to increased data volumes, advanced algorithms, and improvements in computing power and storage. Artificial intelligence is poised to have a huge impact in automating business processes including streamlining efficiency and anticipating barriers to growth. There is growing interest in AI, ML and DL, and the field is getting immense popularity amongst classes and masses.

There's an irony around Artificial Intelligence (AI) work: it involves a lot of manual, trial and error effort to build predictive models with the highest accuracy. With a seemingly continuous emergence of machine learning and deep learning frameworks, and updates to them, as well as changes to tooling platforms, it's no wonder that so much AI work is so ad hoc. But still, why would a technology that's all about automation involve so much bespoke effort? The problem with all the manual work is twofold: first, it makes it almost impossible for people without training in data science to do AI work; and second, people with data science backgrounds themselves face a very inefficient workflow. That logjam is starting to clear now, though, with the emergence of automated machine learning (AutoML).

Question Answering (QnA) model is one of the very basic systems of Natural Language Processing. In QnA, the Machin Learning based system generates answers from the knowledge base or text paragraphs for the questions posed as input. Various machine learning methods can be implemented to build Question Answering systems. Create a Question Answering Machine Learning model system which will take comprehension and questions as input, process the comprehension and prepare answers from it. With the Concept of Natural Language Processing, we can achieve this objective.

Catfishing and the use of false profiles to scam and extort individuals online has become an unfortunate fact of our modern digital life. Even prison inmates have used such tactics to extort money from US servicemembers. Yet, catfishing has been limited to some degree by its traditional reliance on preexisting imagery and personas misappropriated from innocent user accounts to create the fake catfishing profiles. As deep learning approaches increasingly allow the creation of entirely artificial faces and voices, we are fast approaching an era in which catfishing risks overwhelming the world of online dating and being used as an intelligence tool. Automated approaches to generating entirely artificial human imagery have improved dramatically over the past few decades, from primitive systems useful only for the creation of background characters in large crowds to state-of-the-art systems capable of generating fake human faces almost indistinguishable from photographs.

Your greatest strength can become your biggest weakness says the old proverb and that certainly applies to deep learning models. The entire deep learning space was possible in part to the ability of deep neural networks to scale across GPU topologies. However, that same ability to scale resulted in the creation of computationally intensive programs that result operationally challenging to most organizations. From training to optimization, the lifecycle of deep learning programs requires robust infrastructure building blocks to be able to parallelize and scale computation workloads. While deep learning frameworks are evolving at a rapid pace, the corresponding infrastructure models remain relatively nascent.

Last November Synced ran an interview with Yoshua Bengio, in which the deep learning maverick, Université de Montréal Professor and MILA Scientific Director discussed his research and commented on the current state of deep learning and AI. In this follow-up piece we look at the talk Bengio gave late last year at Tsinghua University in Beijing. Challenges for Deep Learning towards Human-Level AI addressed difficulties Bengio and his collaborators are facing and efforts they have made to improve deep learning for human-like AI development. Research over the last decade has given us a much improved understanding of AI, such as why certain methods are helpful for model optimization and why deep learning is so useful. Researchers are showing great interest in deep learning and its potential for application across many different fields.

Touch sensing is widely acknowledged to be important for dexterous robotic manipulation, but exploiting tactile sensing for continuous, non-prehensile manipulation is challenging. General purpose control techniques that are able to effectively leverage tactile sensing as well as accurate physics models of contacts and forces remain largely elusive, and it is unclear how to even specify a desired behavior in terms of tactile percepts. In this paper, we take a step towards addressing these issues by combining high-resolution tactile sensing with data-driven modeling using deep neural network dynamics models. We propose deep tactile MPC, a framework for learning to perform tactile servoing from raw tactile sensor inputs, without manual supervision. We show that this method enables a robot equipped with a GelSight-style tactile sensor to manipulate a ball, analog stick, and 20-sided die, learning from unsupervised autonomous interaction and then using the learned tactile predictive model to reposition each object to user-specified configurations, indicated by a goal tactile reading. Videos, visualizations and the code are available here: https://sites.google.com/view/deeptactilempc

We propose a simple yet effective technique to simplify the training and the resulting model of neural networks. In back propagation, only a small subset of the full gradient is computed to update the model parameters. The gradient vectors are sparsified in such a way that only the top-k elements (in terms of magnitude) are kept. As a result, only k rows or columns (depending on the layout) of the weight matrix are modified, leading to a linear reduction in the computational cost. Based on the sparsified gradients, we further simplify the model by eliminating the rows or columns that are seldom updated, which will reduce the computational cost both in the training and decoding, and potentially accelerate decoding in real-world applications. Surprisingly, experimental results demonstrate that most of time we only need to update fewer than 5% of the weights at each back propagation pass. More interestingly, the accuracy of the resulting models is actually improved rather than degraded, and a detailed analysis is given. The model simplification results show that we could adaptively simplify the model which could often be reduced by around 9x, without any loss on accuracy or even with improved accuracy. The codes, including the extension, are available at https://github.com/lancopku/meSimp

Singing voice conversion is a task to convert a song sang by a source singer to the voice of a target singer. In this paper, we propose using a parallel data free, many-to-one voice conversion technique on singing voices. A phonetic posterior feature is first generated by decoding singing voices through a robust Automatic Speech Recognition Engine (ASR). Then, a trained Recurrent Neural Network (RNN) with a Deep Bidirectional Long Short Term Memory (DBLSTM) structure is used to model the mapping from person-independent content to the acoustic features of the target person. F0 and aperiodic are obtained through the original singing voice, and used with acoustic features to reconstruct the target singing voice through a vocoder. In the obtained singing voice, the targeted and sourced singers sound similar. To our knowledge, this is the first study that uses non parallel data to train a singing voice conversion system. Subjective evaluations demonstrate that the proposed method effectively converts singing voices.