AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.

Humans are emotional creatures. Multiple modalities are often involved when we express emotions, whether we do so explicitly (e.g., facial expression, speech) or implicitly (e.g., text, image). Enabling machines to have emotional intelligence, i.e., recognizing, interpreting, processing, and simulating emotions, is becoming increasingly important. In this tutorial, we discuss several key aspects of multi-modal emotion recognition (MER). We begin with a brief introduction on widely used emotion representation models and affective modalities. We then summarize existing emotion annotation strategies and corresponding computational tasks, followed by the description of main challenges in MER. Furthermore, we present some representative approaches on representation learning of each affective modality, feature fusion of different affective modalities, classifier optimization for MER, and domain adaptation for MER. Finally, we outline several real-world applications and discuss some future directions.

The representation of the personal context is complex and essential to improve the help machines can give to humans for making sense of the world, and the help humans can give to machines to improve their efficiency. We aim to design a novel model representation of the personal context and design a learning process for better integration with machine learning. We aim to implement these elements into a modern system architecture focus in real-life environments. Also, we show how our proposal can improve in specifically related work papers. Finally, we are moving forward with a better personal context representation with an improved model, the implementation of the learning process, and the architectural design of these components.

Billionaire entrepreneur Mark Cuban's foundation is hosting an Artificial Intelligence boot camp in several cities which include Birmingham. The initiative is for any current high school student in the Birmingham area interested in learning more about Artificial Intelligence and Machine Learning. No prerequisite courses and no knowledge of coding is required. Students must commit to four half-day boot camp sessions from 10 am-4 pm. The dates are October 23rd, October 30th, November 6th and November 13th.

Online education has surpassed the conventional classroom paradigm as the new standard. The need for learning AI mobility solutions has increased as a result of COVID. For the intellectual ecosystem, it opens up a world of possibilities. Teachers increasingly use sophisticated applications and linked gadgets to teach their students. They can do this because of the combination of live video, animations, live chat, and other features.

Purvanshi Mehta shares her invaluable insights into the importance of finding your niche in Machine Learning and how best to go about it.

TensorFlow is a state-of-the-art, open source machine learning framework created by Google to design, build, and train Machine Learning and Deep learning models. TensorFlow has a comprehensive and flexible ecosystem of tools and community resources that make it easy to develop and train ML and Deep Learning models. I know the options out there; prerequisites and the skills you need to acquire to overcome the learning blocks. So, Please refer to the Closing Notes section at the tail end of this piece, where you will find helpful resources for bootstrapping your intellectual abilities. My goal in this piece is to help you find some interactive courses from the Notable Educators that will edify you with a solid understanding of TensorFlow.

Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind, according to ibm.com. One of the most important technologies of the 21st century, AI is already being used in dozens of processes, including speech recognition, customer service and automated stock trading. And the economic impact of AI promises to be immense. According to a 2018 report by McKinsey Global Institute, AI has the potential to add 16 percent or around $13 trillion to current global economic output by 2030. Needless to say, it is important for young people to learn about AI and take advantage of the economic and career opportunities it creates.

When Waymo opens its office in Pittsburgh, it will do so partly with the team and expertise of a Carnegie Mellon University spinoff company. Waymo announced last month it would open an engineering office in Pittsburgh and bring on board RobotWits, a robotics company focusing on autonomous vehicles that was founded by Maxim Likhachev, an associate professor in the School of Computer Science's Robotics Institute. "Pittsburgh is one of the main hubs for autonomous driving technology development in the U.S.," said Tushar Chandra, head of Waymo's Behavior team, noting the city's top academic institutions and long history of autonomous vehicle innovation. "You can see why we'd be excited about Pittsburgh's engineering and technology talent and world-class expertise in robotics." Waymo was Google's self-driving car project before it branched off as an independent company in 2016.

For the code explained in each lecture, you can find a GitHub link in the resources section.