"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
Artificial Intelligence is making waves in the tech world. It's the next exciting frontier, and it's already transforming industries from healthcare to finance. But what is artificial intelligence? To answer those questions, let's dive into the basic concepts of AI: machine learning and deep learning.
A cutting-edge artificial intelligence (AI) system that can accurately predict the areas of an image where a person is most likely to look has been created by scientists at Cardiff University. Based on the mechanics of the human brain and its ability to distinguish between different parts of an image, the researchers say the novel system more accurately represents human vision than anything that has gone before. Applications of the new system range from robotics, multimedia communication and video surveillance to automated image editing and finding tumors in medical images. The Multimedia Computing Research Group at Cardiff University are now planning to test the system by helping radiologists to find lesions within medical images, with the overall goal of improving the speed, accuracy and sensitivity of medical diagnostics. The system has been presented in the journal Neurocomputing.
The work, led by Johns Hopkins University, Georgia Institute of Technology, and University of Washington researchers, is believed to be the first to show that robots loaded with an accepted and widely-used model operate with significant gender and racial biases. The work is set to be presented and published this week at the 2022 Conference on Fairness, Accountability, and Transparency. "The robot has learned toxic stereotypes through these flawed neural network models," said author Andrew Hundt, a postdoctoral fellow at Georgia Tech who co-conducted the work as a PhD student working in Johns Hopkins' Computational Interaction and Robotics Laboratory. "We're at risk of creating a generation of racist and sexist robots, but people and organizations have decided it's OK to create these products without addressing the issues." Those building artificial intelligence models to recognize humans and objects often turn to vast datasets available for free on the Internet.
Yes, it's not technology because, At its core, AI is the Ideology of teaching computers how to "learn, reason, perceive, infer, communicate, and make decisions like humans do." The initial goal is called machine learning, where the machine (a computer) begins to make decisions with minimal programming. Instead of manually writing rules for how the computer should interpret a set of data, machine learning algorithms (i.e., sets of instructions for solving particular problems) allow the computer to determine the rules itself. Beyond machine learning lies an even bigger goal, deep learning. Deep learning uses more advanced algorithms to perform more abstract tasks such as recognizing images and detecting the early stage of the disease and saving millions of lives.
AI is taking on an increasingly important role in many Microsoft products, such as Bing and Office 365. In some cases, it's being used to power outward-facing features like semantic search in Microsoft Word or intelligent answers in Bing, and deep neural networks (DNNs) are one key to powering these features. One aspect of DNNs is inference--once these networks are trained, they use inference to make judgments about unknown information based on prior learning. In Bing, for example, DNN inference enables multiple search scenarios including feature extraction, captioning, question answering, and ranking, which are all important tasks for customers to get accurate, fast responses to their search queries. These scenarios in Bing have stringent latency requirements and need to happen at an extremely large scale.
Machine Learning: A specific application of AI that lets computer systems, programs, or applications learn automatically and develop better results based on experience, all without being programmed to do so. Machine Learning allows AI to find patterns in data, uncover insights, and improve the results of whatever task the system has been set out to achieve. Deep Learning: A specific type of machine learning that allows AI to learn and improve by processing data. Deep Learning uses artificial neural networks which mimic biological neural networks in the human brain to process information, find connections between the data, and come up with inferences, or results based on positive and negative reinforcement. Neural Networks: A process that repeatedly analyses data sets to find associations and interpret meaning from undefined data.
Building artificial intelligence (AI) that aligns with human values is an unsolved problem. Here we developed a human-in-the-loop research pipeline called Democratic AI, in which reinforcement learning is used to design a social mechanism that humans prefer by majority. A large group of humans played an online investment game that involved deciding whether to keep a monetary endowment or to share it with others for collective benefit. Shared revenue was returned to players under two different redistribution mechanisms, one designed by the AI and the other by humans. The AI discovered a mechanism that redressed initial wealth imbalance, sanctioned free riders and successfully won the majority vote. By optimising for human preferences, Democratic AI offers a proof of concept for value-aligned policy innovation.
Would you say Deep Learning models have become so good, that robust AI systems are no longer a dream, but a reality? Do you think you can safely use the latest models published by researchers in any real-world problem, like self-driving cars? Convinced that machines are already better than humans at processing and understanding images? Until I realized it is possible to deceive a state-of-the-art model, like DeepMind Perceiver, with a few lines of code. In this article, I will show you how you can do that in less than 10 minutes through a hands-on example.
Researchers have created a numerical model of psychology that aims to improve mental health. The system provides superior customization and outlines the shortest path toward a set of mental stability for any individual. Deep Longevity published a paper in Aging-US outlining a machine learning approach to human psychology in collaboration with Nancy Etcoff, Ph.D., Harvard Medical School, Authority on happiness and beauty. The authors created two numerical models of human psychology based on data from a US midlife study. The first model is a set of deep neural networks which predict respondents' chronological age and psychological well-being over 10 years using information from psychological surveys.
Percy Liang is director of the Center for Research on Foundation Models, a faculty affiliate at the Stanford Institute for Human-Centered AI and an associate professor of Computer Science at Stanford University. Humans are not very good at forecasting the future, especially when it comes to technology. Foundation models are a new class of large-scale neural networks with the ability to generate text, audio, video and images. These models will anchor all kinds of applications and hold the power to influence many aspects of society. It's difficult for anyone, even experts, to imagine where this technology will lead in the coming years.