Goto

Collaborating Authors

Results


Is Fine Art the Next Frontier of AI?

#artificialintelligence

In 1950, Alan Turing developed the Turing Test as a test of a machine's ability to display human-like intelligent behavior. "Are there imaginable digital computers which would do well in the imitation game?" In most applications of AI, a model is created to imitate the judgment of humans and implement it at scale, be it autonomous vehicles, text summarization, image recognition, or product recommendation. By the nature of imitation, a computer is only able to replicate what humans have done, based on previous data. This doesn't leave room for genuine creativity, which relies on innovation, not imitation.


Artificial Intelligence in Healthcare

#artificialintelligence

Artificial intelligence refers to simulating the behavior of humans, so that machines can be programmed to perform intelligent behavior and mimic human actions. It is a branch of computer science dealing with building smart machines which can perform actions, typically needing human intelligence. With the availability of huge data, faster computation power, and technology advancement in machine learning and deep learning is providing a paradigm shift in across all the sectors. Artificial Intelligence (AI) in healthcare leverages complex algorithms to emulate human behavior in the data exploration, analysis and training the models, and comprehension of complicated medical and healthcare data. In this article, we will review the key applications of artificial intelligence in the healthcare sector.


Artificial Intelligence in Healthcare

#artificialintelligence

Artificial intelligence refers to simulating the behavior of humans, so that machines can be programmed to perform intelligent behavior and mimic human actions. It is a branch of computer science dealing with building smart machines which can perform actions, typically needing human intelligence. With the availability of huge data, faster computation power, and technology advancement in machine learning and deep learning is providing a paradigm shift in across all the sectors. Artificial Intelligence (AI) in healthcare leverages complex algorithms to emulate human behavior in the data exploration, analysis and training the models, and comprehension of complicated medical and healthcare data. In this article, we will review the key applications of artificial intelligence in the healthcare sector.


Neural network transforms 124-year-old film into crisp HD

#artificialintelligence

When it premiered in 1896, the silent short film "L'Arrivée d'un train en gare de La Ciotat" was a cinematic wonder. Compared to today's motion pictures, though, the quality of the black-and-white clip is downright primitive. But now, YouTuber Denis Shiryaev has found a way to show what the film might have looked like if it had been recorded using more modern technology -- by using artificial intelligence to upscale it to 4K resolution at 60 frames per second. "L'Arrivée d'un train en gare de La Ciotat," which translates to "Arrival of a Train at La Ciotat," depicts just what its title implies: a train arriving at a station. The original clip released in 1896 is jerky and grainy, and the features of the people at the station in the film are near-impossible to decipher.


Artificial Intelligence (AI) in HIV/AIDS

#artificialintelligence

GNW - Data corresponding to global AI markets and their employability in HIV/AIDS and main medical issues - Discussion of recent achievements and breakthrough therapies related to HIV/AIDS disease segments - Underlying technological trends and major issues related to the utilization of AI for diagnosis and treatment of HIV/AIDS - Coverage of artificial neural networks and deep learning as primary AI algorithm types and their feasible healthcare applications within this field Summary: Artificial intelligence (AI) is a term used to identify a scientific field that covers the creation of machines aimed at reproducing wholly or in part the intelligent behavior of human beings. These machines include computers, sensors, robots, and hypersmart devices. GNW About Reportlinker ReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.


Realizing the full potential of AI in the workplace

#artificialintelligence

Artificial intelligence (AI) is one of the signature issues of our time, but also one of the most easily misinterpreted. The prominent computer scientist Andrew Ng's slogan "AI is the new electricity"2 signals that AI is likely to be an economic blockbuster--a general-purpose technology3 with the potential to reshape business and societal landscapes alike. Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don't think AI will transform in the next several years.4 Such provocative statements naturally prompt the question: How will AI technologies change the role of humans in the workplaces of the future? An implicit assumption shaping many discussions of this topic might be called the "substitution" view: namely, that AI and other technologies will perform a continually expanding set of tasks better and more cheaply than humans, while humans will remain employed to perform those tasks at which machines ...


The Neuroscience of Spatial Navigation and the Relationship to Artificial Intelligence

#artificialintelligence

Recent advances in artificial intelligence (AI) and neuroscience are impressive. In AI, this includes the development of computer programs that can beat a grandmaster at GO or outperform human radiologists at cancer detection. A great deal of these technological developments are directly related to progress in artificial neural networks—initially inspired by our knowledge about how the brain carries out computation. In parallel, neuroscience has also experienced significant advances in understanding the brain. For example, in the field of spatial navigation, knowledge about the mechanisms and brain regions involved in neural computations of cognitive maps—an internal representation of space—recently received the Nobel Prize in medicine. Much of the recent progress in neuroscience has partly been due to the development of technology used to record from very large populations of neurons in multiple regions of the brain with exquisite temporal and spatial resolution in behaving animals. With the advent of the vast quantities of data that these techniques allow us to collect there has been an increased interest in the intersection between AI and neuroscience, many of these intersections involve using AI as a novel tool to explore and analyze these large data sets. However, given the common initial motivation point—to understand the brain—these disciplines could be more strongly linked. Currently much of this potential synergy is not being realized. We propose that spatial navi...


Philosophers On GPT-3 (updated with replies by GPT-3) - Daily Nous

#artificialintelligence

Nine philosophers explore the various issues and questions raised by the newly released language model, GPT-3, in this edition of Philosophers On, guest edited by Annette Zimmermann. Introduction Annette Zimmermann, guest editor GPT-3, a powerful, 175 billion parameter language model developed recently by OpenAI, has been galvanizing public debate and controversy. As the MIT Technology Review puts it: “OpenAI’s new language generator GPT-3 is shockingly good—and completely mindless”. Parts of the technology community hope (and fear) that GPT-3 could brings us one step closer to the hypothetical future possibility of human-like, highly sophisticated artificial general intelligence (AGI). Meanwhile, others (including OpenAI’s own CEO) have critiqued claims about GPT-3’s ostensible proximity to AGI, arguing that they are vastly overstated. Why the hype? As is turns out, GPT-3 is unlike other natural language processing (NLP) systems, the latter of which often struggle with what comes comparatively easily to humans: performing entirely new language tasks based on a few simple instructions and examples. Instead, NLP systems usually have to be pre-trained on a large corpus of text, and then fine-tuned in order to successfully perform a specific task. GPT-3, by contrast, does not require fine tuning of this kind: it seems to be able to perform a whole range of tasks reasonably well, from producing fiction, poetry, and press releases to functioning code, and from music, jokes, and technical manuals, to “news articles which human evaluators have difficulty distinguishing from articles written by humans”. The Philosophers On series contains group posts on issues of current interest, with the aim being to show what the careful thinking characteristic of philosophers (and occasionally scholars in related fields) can bring to popular ongoing conversations. Contributors present not fully worked out position papers but rather brief thoughts that can serve as prompts for further reflection and discussion. The contributors to this installment of “Philosophers On” are Amanda Askell (Research Scientist, OpenAI), David Chalmers (Professor of Philosophy, New York University), Justin Khoo (Associate Professor of Philosophy, Massachusetts Institute of Technology), Carlos Montemayor (Professor of Philosophy, San Francisco State University), C. Thi Nguyen (Associate Professor of Philosophy, University of Utah), Regina Rini (Canada Research Chair in Philosophy of Moral and Social Cognition, York University), Henry Shevlin (Research Associate, Leverhulme Centre for..


Artificial Intelligence to Power the Future of Materials Science and Engineering

#artificialintelligence

From the Paleolithic Age to the coming fourth industrial revolution, the millions of years of human history is mainly marked by materials. Material science is mainly to explore the relationship between materials structure, process, properties, and application. The discovery of new materials will play a greater role in promoting the development of human society. After several centuries of development, a large amount of data has been accumulated in the field of materials science.1 However, the inherent limitations of human cognitive ability make it difficult for human beings to absorb and process the massive literature and data produced every day.2 Only a small part of data (compared with the whole data volume) can be analyzed in a certain subdivision field.


Artificial General Intelligence will not resemble human intelligence

#artificialintelligence

AGI, Artificial General Intelligence, is the dream of some researchers -- and the nightmare of the rest of us. While AGI will never be able to do more than simulate some aspects of human behavior, its gaps will be more frightening than its capabilities. Will humans be interacting with seemingly intelligent robots in ten years? Yes, and we already are. Will robots be ubiquitous in our lives, with human-like abilities such as emotions, unsupervised learning?