Artificial Intelligence is transforming the business world as a whole with all its applications and potential, with visual-based AI being capable of digital images and videos. Visual-based AI, which refers to computer vision, is an application of AI that is playing a significant role in enabling a digital transformation by enabling machines to detect and recognize not just images and videos, but also the various elements within them, such as people, objects, animals and even sentiments, emotional and other parameters-based capabilities to name a few. Artificial intelligence is now further evolving across various industries and sectors. Transport: Computer vision aids in a better experience for transport, as video analytics combined with Automatic number plate recognition can help in tracking and tracing violators of traffic safety laws (speed limits and lane violation etc.) and stolen or lost cars, as well as in toll management and traffic monitoring and controlling. Aviation: Visual AI can help in providing prompt assistance for elderly passengers and for those requiring assistance (physically challenged, pregnant women etc.); it can also be useful in creating a new "face-as-a-ticket" option for easy and fast boarding for passengers, in tracking down lost baggage around the airport as well as in security surveillance on passengers and suspicious objects (track and trace objects and passengers relevant to it).
Artificial intelligence (AI) means a smart computer system like humans to solve complex problems. Machine learning (ML) is allowing machines to learn from available data so that they can give a precise output. AI is used almost everywhere in facial recognition, medical, online gaming, sports, automobile, insurance, airways, defence, government and private companies. Decision making on various attributes of our daily lives is being outsourced to artificial intelligence and machine-learning algorithms as they are motivated by speed and efficiency in the decision-making process. However, slowly, public and government are beginning to realise the dangers and complexity of programmes developed using AI and ML and the need for proper checks and balances in the way such programmes are used and developed. Therefore, it is important to take fairness into consideration while consuming the output from these programmes.
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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.
There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.