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.
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 ...
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...
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..
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.
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?
In the previous story, we discussed the basis of Deep Learning, and it's a necessity. Today, we will dig up the story of CNN. Whenever we see any object, the data of the object is collected by eyes and then passed to the cerebral Cortex through neurons. The cerebral Cortex has a part called the Visual Cortex, especially available for processing the image data. Visual Cortex is composed of multiple layers to process the image data and give the output.
The IQ test questions aren't challenging enough; so they added various shapes, lines of varying thickness, and colors, as distractions. The best performing model is the WReN model! This is due to the Relation Network module designed explicitly for reasoning about the relations between objects. After removing distractions, the WReN model performed notably better at 78.3%, compared with 62.6% with distractions! Mathematical reasoning is one of the core abilities of human intelligence.
WIRE)--NTT Research, Inc., a division of NTT (TYO:9432), today announced that a research scientist in its Physics & Informatics (PHI) Lab, Dr. Hidenori Tanaka, was the lead author on a technical paper that advances basic understanding of biological neural networks in the brain through artificial neural networks. Titled "From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction," the paper was presented at NeurIPS 2019, a leading machine-learning, artificial intelligence (AI) and computational neuroscience conference, and published in Advances in Neural Information Processing Systems 32 (NIPS 2019). Work on the paper originated at Stanford University, academic home of the paper's six authors when the research was performed. At the time, a post-doctoral fellow and visiting scholar at Stanford University, Dr. Tanaka joined NTT Research in December 2019. The underlying research aligns with the PHI Lab's mission to rethink the computer by drawing inspirations from computational principles of neural networks in the brain.
What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.