Artificial intelligence (AI) is a broad branch of computer science concerned with the construction of intelligent machines capable of performing tasks that typically require human intelligence. While AI is an interdisciplinary science with many approaches, advances in machine learning and deep learning in particular are leading to a paradigm shift in almost every area of the technology industry.
Method and goal of AI:
Symbolic vs. Connectionist research in AI is based on two different and sometimes competing methods: the symbolic (or "top-down") approach and the connectionist (or "bottom-up") approach . The top-down approach aims to reproduce intelligence by analyzing cognitive functions independently of the biological structure of the brain in terms of symbol processing, hence the symbolic term. The bottom-up approach, on the other hand, is to create artificial neural networks that mimic the structure of the brain - hence the name "connectionists". To illustrate the difference between these approaches, consider the task of building a system equipped with an optical scanner that recognizes the letters of the alphabet.The bottom-up approach typically involves training an artificial neural network by presenting the letters one at a time and incrementally improving performance by "tuning" the network. (Fine-tuning regulates how different neural pathways respond to different stimuli.) In contrast, the top-down approach typically involves writing a computer program that compares each letter to geometric descriptions. Put simply, neural activities are the basis of the bottom-up approach, while
symbolic descriptions are the basis of the top-down approach.
In The Fundamentals of Learning (1932), Edward Thorndike, a psychologist at Columbia University in New York, first proposed that human learning consists of an unknown property of the connections between neurons in the brain.In "The Organization of Behavior" (1949), Donald Hebb, a psychologist at McGill University in Montreal, Canada, proposed that learning specifically involves the reinforcement of certain patterns of neural activity by measuring the probability (weighting) of induced neural firings between linked connections is increased. The concept of weighted connections is described in the next section, Connectionism.
In 1957, two staunch proponents of symbolic artificial intelligence—Allen Newell, a research scientist at RAND Corporation in Santa Monica, California, and Herbert Simon, a psychologist and computer scientist at Carnegie Mellon University in Pittsburgh, Pennsylvania—summated the top Bottom-down approach in the so-called physical-symbolic system hypothesis. This hypothesis states that the elaboration of symbolic structures is, in principle, sufficient to generate artificial intelligence in a digital computer, and that, moreover, human intelligence is the result of the same kind of symbolic manipulation.
Presence and future of AI:
How has artificial intelligence changed and shaped our world in the last five years? What influence will AI have on our lives in the years to come? These are the questions answered in the latest report from the One Hundred Year Study of Artificial Intelligence (AI100), an ongoing project at Stanford University that will examine the state of AI technology and its impact on the world over the next 100 years .
The 2021 report is the second in a series published every five years through 2116. The report, titled "Gathering Strength, Gathering Storms," explores the different ways AI is increasingly impacting people's lives in diverse contexts from movie recommendations and voice assistants to autonomous driving and automated medical diagnostics.
Barbara Grosz, Higgins Research Professor of Science at Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), is a member of the standing committee overseeing the AI100 project and Finale Doshi-Velez, Gordon McKay Professor in Computer Science , is part of a group of interdisciplinary researchers who authored this year's report.
We spoke to Doshi-Velez about the report, what he says about the role artificial intelligence is playing in our lives today and how it will change in the future.It can improve personalized care, close gaps in access to healthcare and reduce bureaucracy, but the risks are enormous
Artifical intelligence in medicine:
The news is bad:
“I'm sorry, but you have cancer Those unwanted words stay in your mind for a few minutes, and then your doctor begins to describe recent advances in artificial intelligence—advances that allow him to compare your case to that of every other patient who have ever had the same type of disease cancer. He says he has found the most effective and appropriate treatment for the specific genetic subtype of the disease in the person's genetic make-up: truly personalized medicine. And the prognosis is good.
Artificial intelligence (AI) will radically change medicine and potentially improve the experience for doctors and patients.We cover the key findings of a two-year weekly effort to monitor and share key developments in medical AI. We consider prospective studies and advances in medical image analysis that have narrowed the gap between research and implementation. We are also exploring several promising avenues for cutting-edge medical AI research, including non-imaging data sources, unconventional problem formulations, and human-AI collaboration. Finally, we consider serious technical and ethical challenges on issues ranging from data scarcity to racial bias. Once these challenges are overcome, the potential of AI can be harnessed to make healthcare more accurate, efficient, and accessible to patients around the world.
Artifical intelligence with cyber security:
AI is transforming cybersecurity by analyzing vast amounts of risk data to accelerate response times and improve resource-constrained security operations.
The main difference between cybersecurity and artificial intelligence is that cybersecurity is about protecting computer systems and the networks connecting them from data theft whereas artificial intelligence is about using intelligent machines to perform specific tasksbased on execute data.
Artifical intelligence and in game:
“Artificial intelligence in games is a vast area of research that includes movement and action planning , interactive storytelling, procedural generation, computational creativity and more. It is important that there finally be a scholarly textbook that brings together current thinking on all these issues in one place and makes connections between them...[The authors] have the deep expertise needed to weave these disparate subjects into a cohesive whole.” (Jeff Orkin, inventor of goal-based action planning, head of AI at F.E.A.R.)
“This book makes a tremendous contribution to this exciting dynamic field of research... The community service will be felt for many years to come. The book offers newcomers an easier and more comprehensive look than before, while providing an irreplaceable intelligence and games resourcefor existing artificial scientists who wish to exploretopicbeyond their immediate area of interest.
Peace heart
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