Artificial intelligence and machine learning have been the two hot trending technologies for quite a while now. However, the interest in these new-age technologies peaked significantly right after the onset of coronavirus, when the world was looking forward to digital tools and platforms for sustenance. According to an IDC report, the global revenue of the AI market touched US$ 156.5 billion, marking a jump of 12.3% from 2019. This trend is only bound to rise further up in the next few years. Of all the diverse fields of artificial intelligence, robotics has gained massive popularity across industries, and rightly so.

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Apply now to join Dr. Kirk Borne's live interactive course, starting on November 10. In this live course with Dr. Kirk Borne, you will build up your theoretical and practical knowledge so that you can understand how to identify ML problems, choose the right algorithms for solving the problem at hand, build robust models in Python, and explain your models and communicate their results in common business language for colleagues, clients, and executives. Dr. Kirk Borne is a data science leader and research astrophysicist. He was a Principal Data Scientist, Data Science Fellow, and Executive Advisor at Booz Allen Hamilton. He was also a professor of Astrophysics and Computational Science at George Mason University where he designed one of the first Data Science programs.

This course will teach machine learning concepts with Tensorflow. In this course, you will explore a large dataset using Datalab and BigQuery, and learn how to use Pandas in Datalab, and sample a dataset for local development. Then you will develop a machine learning model in Tensorflow and operationalize the model. In the end, this course explains how to preprocess data at scale for machine learning and lets you train a machine learning model at scale on the Cloud AI Platform.

The objective of this training program is to help trainees to master all the skills that are required to work with Minitab. The training program will help the trainee to perform all the statistical analysis with Minitab. It is also intended to make the trainees cover all the topics that fall under the domain of Minitab. Topics like Minitab GUI and Descriptive Statistics, Statistical Analysis using Minitab, Correlation Techniques in Minitab and Predictive Modeling using Excel will be covered in this training module and Project on Data Analytics using Minitab and Project on Minitab – Regression Modeling will be covered in the project module. The goal of this course is to help an individual to achieve knowledge of working with Minitab to perform time series analysis and forecasting of data in all sorts of statistics based problems.

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This paper surveys vision-language pre-training (VLP) methods for multimodal intelligence that have been developed in the last few years. We group these approaches into three categories: ($i$) VLP for image-text tasks, such as image captioning, image-text retrieval, visual question answering, and visual grounding; ($ii$) VLP for core computer vision tasks, such as (open-set) image classification, object detection, and segmentation; and ($iii$) VLP for video-text tasks, such as video captioning, video-text retrieval, and video question answering. For each category, we present a comprehensive review of state-of-the-art methods, and discuss the progress that has been made and challenges still being faced, using specific systems and models as case studies. In addition, for each category, we discuss advanced topics being actively explored in the research community, such as big foundation models, unified modeling, in-context few-shot learning, knowledge, robustness, and computer vision in the wild, to name a few.

Existing deep learning-enabled semantic communication systems often rely on shared background knowledge between the transmitter and receiver that includes empirical data and their associated semantic information. In practice, the semantic information is defined by the pragmatic task of the receiver and cannot be known to the transmitter. The actual observable data at the transmitter can also have non-identical distribution with the empirical data in the shared background knowledge library. To address these practical issues, this paper proposes a new neural network-based semantic communication system for image transmission, where the task is unaware at the transmitter and the data environment is dynamic. The system consists of two main parts, namely the semantic coding (SC) network and the data adaptation (DA) network. The SC network learns how to extract and transmit the semantic information using a receiver-leading training process. By using the domain adaptation technique from transfer learning, the DA network learns how to convert the data observed into a similar form of the empirical data that the SC network can process without retraining. Numerical experiments show that the proposed method can be adaptive to observable datasets while keeping high performance in terms of both data recovery and task execution.