AI News, Hybrid Artificial Intelligence Approaches for Predicting Critical ... artificial intelligence
To Understand The Future of AI, Study Its Past
Symbolic approaches to AI seek to build systems that behave intelligently through the manipulation of symbols that map directly to concepts—for instance, words and numbers.
Connectionist approaches, meanwhile, represent information and simulate intelligence via massive networks of interconnected processing units (commonly referred to as neural networks), rather than explicitly with symbols.
But because they are not explicitly programmed by humans, neural networks are 'black boxes': it is generally not possible to pinpoint, in terms that are meaningful to humans, why they make the decisions that they do.
As a serious academic discipline, artificial intelligence traces its roots to the summer of 1956, when a small group of academics (including future AI icons like Claude Shannon, Marvin Minsky and John McCarthy) organized a two-month research workshop on the topic at Dartmouth College.
At the same time that symbolic AI research was showing early signs of promise, nascent efforts to explore connectionist paths to AI were shut down in dramatic fashion.
The book set forth mathematical proofs that seemed to establish that neural networks were not capable of executing certain basic mathematical functions.
Symbolic AI reached its mainstream zenith in the early 1980s with the proliferation of what were called “expert systems”: computer programs that, using extensive “if-then” logic, sought to codify the knowledge and decision-making of human experts in particular domains.
These systems generated tremendous expectations and hype: startups like Teknowledge and Intellicorp raised millions and Fortune 500 companies invested billions in attempts to commercialize the technology.
Amid the ashes of the discredited symbolic AI paradigm, a revival of connectionist methods began to take shape in the late 1980s—a revival that has reached full bloom in the present day.
In 1986 Geoffrey Hinton published a landmark paper introducing backpropagation, a new method for training neural networks that has become the foundation for modern deep learning.
In just the past decade, a confluence of technology developments—exponentially increased computing capabilities, larger data sets, and new types of microprocessors—have supercharged these connectionist methods first devised in the 1980s.
Recognizing the promise of a hybrid approach, AI researchers around the world have begun to pursue research efforts that represent a reconciliation of connectionist and symbolic methods.
XAI is providing funding to 13 research teams across the country to develop new AI methods that are more interpretable than traditional neural networks.
few years ago, it was not uncommon for AV researchers to speak of pursuing a purely connectionist approach to vehicle autonomy: developing an 'end-to-end' neural network that would take raw sensor data as input and generate vehicle controls as output, with everything in between left to the opaque workings of the model.
Together, the approach goes beyond what current deep learning systems can do.” Taking a step back, we would do well to remember that the human mind, that original source of intelligence that has inspired the entire AI enterprise, is at once deeply connectionist and deeply symbolic.
As philosopher Charles Sanders Peirce put it, “We think only in signs.” Any conception of human intelligence that lacked either a robust connectionist or a robust symbolic dimension would be woefully incomplete.
Combined artificial intelligence modeling for production forecast in a petroleum production field
RESUMEN Este artículo presenta los resultados en el uso de una metodología que combina dos modelos de Inteligencia Artificial (IA) para predecir la producción de crudo, agua y gas en un campo petrolero colombiano.
El procedimiento de imputación de datos propuesto es el elemento clave para corregir elementos falsos o para completar posiciones vacías en los datos operacionales empleados para identificar modelos para un campo de producción de petróleo característico.
- On 17. oktober 2021
Predicting weather impact using cloud-based machine learning
Nearly two billion people worldwide rely on AccuWeather forecasts to help them plan their day, protect their assets, and stay safe. AccuWeather has been ...
MIT 6.S093: Introduction to Human-Centered Artificial Intelligence (AI)
Introductory lecture on Human-Centered Artificial Intelligence (MIT 6.S093) I gave on February 1, 2019. For more lecture videos on deep learning, reinforcement ...
[Renault] Coupling virtual sensors with artificial intelligence using Simcenter
In this video Vincent Talon from Renault Nissan explains how the company address not only thermal engine (gasoline and Diesel) engineering challenges but ...
Artificial Intelligence, Technology and the Future of Law - Keynote
Professor Dana Remus of the UNC School of Law gave the keynote address at the conference "Artificial Intelligence, Technology and the Future of Law" hosted ...
AI Responsibility (Cloud Next '19)
We recognize that powerful AI technology raises equally powerful questions about its use and may have a significant impact on society for many years to come.
AI and the Art of Ingenuity: Computational Creativity
SYNOPSIS: Will a computer ever be more creative than a human? In this compelling program, artists, musicians, neuroscientists, and computer scientists ...
AIOps: Why Now & Where do I Start? - AIxchange - David Myers
Learn more about this event at THANK YOU TO OUR EVENT PARTNERS CA TECHNOLOGIES - .
Jia Li: Machine learning and artificial intelligence could transform health care and education
Developing machine learning capabilities will require heavy investment and the cultivation of a generation of developers with a background in data science.
In 2019 latest IT Trend | Machine Learning (ML) & Artificial Intelligence (AI)
In my point of view the most trending will be Machine Learning (ML) and Artificial Intelligence (AI). In addition to this the enterprises will also try to adopt Hybrid ...
What’s next for IoT? Artificial Intelligence, of course | The Element Podcast - E07
Are your IT resources are increasingly supporting internet-of-things sensors and networking in the field or on the manufacturing floor? You may already be ...