Today AGI is changing the world and Human expectations from the machine. Increasing variety and complexity of human-machine networks is failing non-deterministic behaviours of both the end. We may need to focus on two major aspects:
Trust building in the new agency method
Perceived risk and probable mitigation
Using Deep Reinforcement Learning for autonomous system control entails finding the right combination of parameters for achieving optimal quality. Finding global optimum is often challenged by several factors such as high dimensionality, non-linearity and noisy state and action spaces along with convergence issues. Conditional generative adversarial networks (C-GANs) are used to learn the underlying distribution of the solutions generated by the optimization algorithm, and then generating unseen, more optimized solutions to the original optimization problem using the generative model. These solutions are used to sample the episodes for training the agent in subsequent episodes thereby reducing the variance and enhancing the probability of more optimal solutions.
Natural disasters devastate local communities and make search and rescue (SAR) slow for those directly affected. Especially in harsh conditions, the search and rescue of people can take several days, which may be life-threatening in some cases. Technology that allows faster analysis will improve search and rescue efforts both in search time and resource management. We developed a deep neural machine (DNM), a novel artificial intelligence technology that processes UAV imagery for detection of people and other objects of interest in harsh environments, specifically for aquatic and nautical search and rescue. The DMN architecture utilizes convolution, batch normalization, and MISH activation (CBM) blocks, cross-stage partial (CSP) network blocks for residual features, and spatial pyramid pooling (SPP) blocks for merging different levels of features. Augmented versions of various datasets with synthetic weather scenarios are used for effective training of the object detection system for enhanced performance in changing environments. The DNM based autonomous vision system provides robust detection of humans and other objects of interest in aquatic and nautical environments.
Traditionally, each time series were treated in isolation and as a result, traditional forecasting has always looked at the history of a single time series along with fitting a forecasting function. But recently, because of the ease of collecting data, many companies have started collecting large amounts of time series from similar sources, or related time series. This opens up an opportunity for a new and exciting modelling paradigm – Global Forecasting Models. Instead of fitting a separate forecast function for each time series individually, we fit a single forecast function to all the related time series. This paradigm has proven itself in the industry as well as in forecasting competitions in the past few years. We will look at why Global Forecasting Models are revolutionizing time series forecasting and discuss ways to conceptualize and improve global forecasting models.
Federated learning is a growing field in Data Science, still businesses are not much aware of it. Most of the businesses and organizations are
concerned about privacy of the their data. Federated learning can be one such important technology here with which many businesses will be willing
to integrate ML solutions without any data privacy concern. Along with privacy, FL is helpful in many other use cases. I’d like to explain integration of
Federated Learning in various domains like manufacturing, pharma, healthcare, edge-devices etc. This talk will include the technical aspect of Federated
learning and it will shed some light on business aspect too.
ML deployments vary largely from regular software deployments. Any ML system evolves with data. So, along with code, models and datasets both need to be version-controlled. So, the regular CI/CD workflows might not work right off the bat for maintaining ML reliably.
In this talk, we’ll discuss different flavors of incorporating CI/CD into an ML system with varying degrees of automation, technical complexity, tooling, and stage of development. By the end of the session, the participants will have a conceptual framework of how CI/CD can be effectively approached for an ML project development.