A discovery system is an artificial intelligence system that attempts to discover new scientific concepts or laws. The aim of discovery systems is to automate scientific data analysis and the scientific discovery process. Ideally, an artificial intelligence system should be able to search systematically through the space of all possible hypotheses and yield the hypothesis - or set of equally likely hypotheses - that best describes the complex patterns in data. During the era known as the second AI summer (approximately 1978-1987), various systems akin to the era's dominant expert systems were developed to tackle the problem of extracting scientific hypotheses from data, with or without interacting with a human scientist. These systems included , Automated Mathematician, Eurisko, which aimed at general-purpose hypothesis discovery, and more specific systems such as Dalton, which uncovers molecular properties from data. The dream of building systems that discover scientific hypotheses was pushed to the background with the second AI winter and the subsequent resurgence of subsymbolic methods such as neural networks. Subsymbolic methods emphasize prediction over explanation, and yield models which works well but are difficult or impossible to explain which has earned them the name black box AI. A black-box model cannot be considered a scientific hypothesis, and this development has even led some researchers to suggest that the traditional aim of science - to uncover hypotheses and theories about the structure of reality - is obsolete. Other researchers disagree and argue that subsymbolic methods are useful in many cases, just not for generating scientific theories. (Wikipedia).
A Web-scale system for scientific knowledge exploration | AISC
Discussion Lead: Ramya Balasubramaniam Facilitators: Ehsan Amjadian , Karim Khayrat For more details including paper and slides, visit https://aisc.a-i.science/events/2019-05-02/
From playlist Machine Learning for Scientific Discovery
AI for Engineers: Building an AI System
Artificial intelligence (AI) is a simulation of intelligent human behavior. It is designed to perceive its environment, make decisions, and take action. Get an overview of AI for engineers, and discover the ways in which artificial intelligence fits into an engineering workflow. You’ll lea
From playlist 深度学习(Deep Learning)
Testing and Online Experimentation
Join Data Science Dojo and Statsig for a conversation on experimentation and testing. Learn how leading companies like Facebook use experimentation to build better products and accelerate their growth with 10x as much testing. Web experimentation can range from simple projects like design
From playlist A/B Testing & Beyond
Using AI to accelerate scientific discovery
Dr. Demis Hassabis, Founder and CEO of DeepMind Abstract: The past decade has seen incredible advances in the field of Artificial Intelligence (AI). DeepMind has been in the vanguard of many of these big breakthroughs, pioneering the development of self-learning systems like AlphaGo, the
From playlist Talks | AI for science
Kendrew Lecture 2021 pt1 - Using AI to accelerate scientific discovery - Demis Hassabis
MRC Laboratory of Molecular Biology John Kendrew Lecture 2021 part 1 Using AI to accelerate scientific discovery Speaker: Demis Hassabis, Founder and CEO, DeepMind. Abstract: The past decade has seen incredible advances in the field of Artificial Intelligence (AI). DeepMind has been in t
From playlist Talks | AI for science
Epic Meaning Business Strategy Product Strategy Derireability, visibility, feasibility
From playlist AI Product Development (hands on)
Caltech Science Exchange: What Is AI?
Artificial intelligence is transforming scientific research as well as everyday life from communications to transportation to health care and more. In this video, learn about the history of AI and what it means for our world. The field of artificial intelligence arose from the idea that m
From playlist Caltech Science Exchange
A Framework for Developing Deep Learning Classification Models
5-min ML Paper Challenge Presenters: https://www.linkedin.com/in/lisamariepritchett/ https://www.linkedin.com/in/geoffrey-hunter-29325a7a/ Clustering with Deep Learning: Taxonomy and New Methods https://arxiv.org/abs/1801.07648 Clustering methods based on deep neural networks have proven
From playlist AISC - 5-min Papers
Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning |
For slides and more information on the paper, visit https://aisc.ai.science/events/2020-02-25 Discussion lead: Sai Raghavendra Maddhuri Discussion facilitator(s): Rouzbeh Afrasiabi
From playlist Machine Learning for Scientific Discovery
Sparks! | Hiroaki Kitano | Nobel Turing Challenge
Talk broadcast live at CERN on 18 September 2021 during the first edition of Sparks! The Serendipity Forum at CERN Hiroaki is the CEO of Sony AI Inc. and president and CEO of Sony Computer Science Laboratories. He has been involved in AI, systems biology and intelligent robots research. Hi
From playlist Sparks! Serendipity Forum at CERN| First Edition: Future Intelligence | 2021
Sparks! Serendipity Forum: Future Intelligence
The Sparks! public event will consist of a series of short talks and debates about the current and future trends that will define the field of AI and how it will impact our society as we know it. The talks will be given by AI experts taking part in our Sparks! Forum. The event will be web
From playlist Sparks! Serendipity Forum at CERN| First Edition: Future Intelligence | 2021
We’re Teaching Robots and AI to Design New Drugs
September Pin of the Month: https://store.dftba.com/collections/scishow/products/scishow-pin-of-the-month-chandra-x-ray-observatory-september It might sound like a concept from science fiction, but artificial intelligence is already facilitating the development process behind some pharma
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The promise of AI with Demis Hassabis - DeepMind: The Podcast (S2, Ep9)
Hannah wraps up the series by meeting DeepMind co-founder and CEO, Demis Hassabis. In an extended interview, Demis describes why he believes AGI is possible, how we can get there, and the problems he hopes it will solve. Along the way, he highlights the important role of consciousness and
From playlist DeepMind: The Podcast - Season 2
Principled Engineering: AI and Drug Development
The use of artificial intelligence in drug discovery, when coupled with new genetic insights and the increase of patient medical data of the last decade, has the potential to bring novel medicines to patients more efficiently and more predictably. Watch a discussion of the promise and pote
From playlist Principled Engineering
How AI Can Save Lives | SciShow Compilation
Visit http://brilliant.org/scishow/ to get started learning STEM for free, and the first 200 people will get 20% off their annual premium subscription. Artificial intelligence is rapidly becoming an integral part of our everyday lives. Here’s a number of ways in which it manages to make l
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DDPS | Computational Scientific Discovery: Heuristic Search for Communicable Laws and Models
Description: Scientific discovery was long viewed as a uniquely human creative activity, but digital computers have now reproduced many facets of this process. In this talk, I examine previous research on this problem, which posits that discovery involves heuristic search through a space o
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Session 1 – Discoveries from the lab
AI UK co-chairs, Michael Wooldridge and Rachel Franklin and the Turing's Chief Scientific Officer, Mark Girolami opened the In the Lab stage. They shared top discoveries that have changed the world and discussed future possibilities. This recording is from AIUK 22 - The Alan Turing Insti
From playlist AI UK 2022 - IN THE LAB STAGE
AI In Healthcare 2022 | Artificial Intelligence In Healthcare | AI For Beginners | Simplilearn
"In this video, we will cover role of AI in healthcare in 2022. This video has answered all the questions about how AI has changed the healthcare system. The topics we will be covering are: 1. Drug and Research - 0:00 In this segment we have covered in detail about how Artificial Intelli
Data Science as a Catalyst for Scientific Discovery Michelle Gill, Ph.D. (BenevolentAI)
Michelle Gill explains how data science methodologies and tools can be used to link information from different scientific fields and accelerate discovery in a variety of areas, including the biological sciences. Subscribe to O'Reilly on YouTube: http://goo.gl/n3QSYi Follow O'Reilly on:
From playlist JupyterCon in New York 2018