Saturday, July 20, 2024

3 Ways To Responsibly Shape The Future of AI



Qi Yang (Getty Images)

The world is facing a fundamental technological shift, with artificial intelligence (AI) already transforming how we live, work, and learn. The Fourth Industrial Revolution brings with it a new horizon of opportunities. It will require visionary partnerships by the leaders of today in ways that will maximize benefits for future generations.

History shows that societies thrive when systems benefit not only the privileged, but rather when core systems like healthcare, sanitation and education are available equitably and sustainably to all. Equitable access to technology is no different.

An area where the impact of AI is, and will continue to be, acutely felt is education. It is today’s children that will disproportionately face the opportunities, and risks, that emergent technologies present. So, how can we ensure that AI is a lever for learning….Story continues

By: Bo Viktor Nylund

Source:  3 Ways To Responsibly Shape The Future of AI

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Machine learning is the study of programs that can improve their performance on a given task automatically.It has been a part of AI from the beginning. There are several kinds of machine learning. Unsupervised learning analyzes a stream of data and finds patterns and makes predictions without any other guidance.[49] Supervised learning requires a human to label the input data first, and comes in two main varieties: classification (where the program must learn to predict what category the input belongs in) and regression (where the program must deduce a numeric function based on numeric input).

In reinforcement learning, the agent is rewarded for good responses and punished for bad ones. The agent learns to choose responses that are classified as “good”. Transfer learning is when the knowledge gained from one problem is applied to a new problem. Deep learning is a type of machine learning that runs inputs through biologically inspired artificial neural networks for all of these types of learning. Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization.

Natural language processing (NLP) allows programs to read, write and communicate in human languages such as English. Specific problems include speech recognitionspeech synthesismachine translationinformation extractioninformation retrieval and question answeringEarly work, based on Noam Chomsky‘s generative grammar and semantic networks, had difficulty with word-sense disambiguation unless restricted to small domains called “micro-worlds” (due to the common sense knowledge problem). Margaret Masterman believed that it was meaning and not grammar that was the key to understanding languages, and that thesauri and not dictionaries should be the basis of computational language structure.

Modern deep learning techniques for NLP include word embedding (representing words, typically as vectors encoding their meaning), transformers (a deep learning architecture using an attention mechanism), and others. In 2019, generative pre-trained transformer (or “GPT”) language models began to generate coherent text,and by 2023, these models were able to get human-level scores on the bar examSAT test, GRE test, and many other real-world applications. Machine perception is the ability to use input from sensors (such as cameras, microphones, wireless signals, active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Computer vision is the ability to analyze visual input.

The field includes speech recognition, image classification, facial recognitionobject recognition,object tracking, and robotic perception.Affective computing is an interdisciplinary umbrella that comprises systems that recognize, interpret, process, or simulate human feeling, emotion, and mood. For example, some virtual assistants are programmed to speak conversationally or even to banter humorously; it makes them appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction. However, this tends to give naïve users an unrealistic conception of the intelligence of existing computer agents. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal sentiment analysis, wherein AI classifies the affects displayed by a videotaped subject.

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