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 1st International Workshop on Adaptive Cyber Defense was held as part of the 2021 International Joint Conference on Artificial Intelligence. This workshop was organized to share research that explores unique applications of Artificial Intelligence (AI) and Machine Learning (ML) as foundational capabilities for the pursuit of adaptive cyber defense. The cyber domain cannot currently be reliably and effectively defended without extensive reliance on human experts. Skilled cyber defenders are in short supply and often cannot respond fast enough to cyber threats. Building on recent advances in AI and ML the Cyber defense research community has been motivated to develop new dynamic and sustainable defenses through the adoption of AI and ML techniques to both cyber and non-cyber settings. Bridging critical gaps between AI and Cyber researchers and practitioners can accelerate efforts to create semi-autonomous cyber defenses that can learn to recognize and respond to cyber attacks or discover and mitigate weaknesses in cooperation with other cyber operation systems and human experts. Furthermore, these defenses are expected to be adaptive and able to evolve over time to thwart changes in attacker behavior, changes in the system health and readiness, and natural shifts in user behavior over time. The Workshop (held on August 19th and 20th 2021 in Montreal-themed virtual reality) was comprised of technical presentations and a panel discussion focused on open problems and potential research solutions. Workshop submissions were peer reviewed by a panel of domain experts with a proceedings consisting of 10 technical articles exploring challenging problems of critical importance to national and global security. Participation in this workshop offered new opportunities to stimulate research and innovation in the emerging domain of adaptive and autonomous cyber defense.

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.

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.

AI is designed to make us more efficient and productive, but study after study shows that it can also lead us to biased decision-making. This course includes invaluable lectures, articles, case studies, and ready-to-use documents to help you (and your company) get your arms around these pervasive and rapidly evolving issues. Throughout the course, we'll explain artificial intelligence in simple terms as we focus on how and where it is lurking in your HR systems. You'll acquire essential knowledge and tools to help you gain confidence in speaking about AI system-driven biases in a way that puts your HR service providers on notice and helps you hold them accountable. We'll end the course with a brief discussion on what's next in the field of AI. (Hint, hint: It includes an avalanche of legislation!)

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

The idea of CartPole is that there is a pole standing up on top of a cart. The goal is to balance this pole by moving the cart from side to side to keep the stick balanced upright. We consider the environment won if we balance it for 500 frames and fail once the pole is tilted more than 15 degrees from totally vertical or the cart moves more than 2.4 units from the middle position. For every frame that we go with the pole "balanced" (less than 15 degrees from vertical), our "score" gets 1, and our target is a score of 500. Now, however, how can we do this?