Coronavirus: Through The Lens Of AI
In a global pandemic such as COVID-19, technology, artificial intelligence, and data science have become critical to helping societies effectively deal with the outbreak. In this talk, I will discuss three case studies of how AI is being used in Corona Virus research. The first part of the talk will discuss about how deep learning model detected COVID-19 caused pneumonia from computed tomography (CT) scans with comparable performance to expert radiologists. To be more specific, I will discuss about UNet++ architecture that was implemented by researchers for evaluating lung infection in COVID-19 CT images. The second part of the talk will be devoted to recent attempts in natural language processing to generate new insights in support of the ongoing fight against this infectious disease. There is a growing urgency for these approaches because of the rapid acceleration in new coronavirus literature, making it difficult for the medical research community to keep up. To be precise, BERT literature search engine for COVID-19 literature.will be discussed .
The third part of the talk deals with deep learning based generative modeling framework to design drug candidates specific to a given target protein sequence. One of the most important COVID-19 protein targets is the 3C-like protease for which the crystal structure is known. We present different deep learning models designed for generating novel drug molecules with multiple desirable properties. The deep learning framework involves Variational Autoencoder, Generative Adversarial Networks, Reinforcement Learning, and Transfer Learning. The generated molecules might serve as a blueprint for creating drugs that can potentially bind to the viral protein with high target affinity, as well as high drug-likeliness. Last but not the least, this talk will also touch upon how the world community responded by making the data available to the researchers which enabled the data scientists to explore and support the scientific community.
Outline/Structure of the Talk
1. AI for COVID-19: Promises and Challenges (2 min)
2. COVID-19 imaging and Object segmentation using U-net++ architecture (4 min)
3.Natural Language Processing using BERT to generate new insights in support of the fight against COVID19 (4 min)
4. Molecular machine learning approaches to tackle the corona crisis at molecular scale (9 min)
- Variational Autoencoder and Generative Adversarial Networks for Generative Chemistry
- Representation of small molecules suitable for Neural networks
- Drug-target prediction from ML perspective
5. Summary (1 min)
- After attending this session, the attendees will ...
- understand U-net++ and its applications in analyzing biomedical images
- understand how BERT algorithm is used in analyzing scientific documents
- be familiar with in machino drug design using Generative Adversarial Networks
Beginners who wants to learn how AI is being applied in Corona pandemic. Intermediates who wants to learn the basics of computer vision, NLP and molecular machine learning.
Prerequisites for Attendees
Basics of Deep Learning and Big Data
schedule Submitted 6 months ago
People who liked this proposal, also liked:
Parthiban Srinivasan - AI meets IP: There is Nothing Artificial about itParthiban SrinivasanCEOVingyani
schedule 6 months agoSold Out!
Artificial intelligence is a global phenomenon, a technology that has arrived. No industry will be untouched by the changes and disruption these technologies bring. With the rapidly changing innovation landscape, patent offices are discussing the interplay between AI and patents. Patent analysts will have to respond to this changing environment by being more global in their perspective.
The machine learning techniques revolutionizing AI are deep learning and neural networks, and these are the fastest growing AI techniques in terms of patent filings: deep learning showed an impressive average annual growth rate of 175 percent from 2013 to 2016, reaching 2,399 patent filings in 2016; and neural networks grew at a rate of 46 percent over the same period, with 6,506 patent filings in 2016.
The three AI functional applications with the highest number of patent families are computer vision, natural language processing and speech processing. These represent 49 percent, 14 percent and 13 percent of all patent families related to AI, respectively. This underlines the importance of these three functional applications to the field of AI.
The presentation will focus on these aspects and will highlight recent developments in AI methods and the breadth of AI applications that are of importance to patent searchers, analysts, and decision-makers.
Parthiban Srinivasan - The Global AI Strategy LandscapeParthiban SrinivasanCEOVingyani
schedule 6 months agoSold Out!
AI has the potential to deliver additional global economic activity of around $13 trillion by 2030, or about 16% higher cumulative GDP compared with today. Securing this economic growth, combined with the soft power an AI leadership role will bring any nation, makes it a major social and economic policy priority. In the last couple of years, many countries released strategies to promote the use and development of AI. No two strategies are alike, with each focusing on different aspects of AI policy: scientific research, talent development, skills and education, public and private sector adoption, ethics and inclusion, standards and regulations, and data and digital infrastructure. This talk will discuss the key policies and goals of each nation' strategy, as well as related policies and initiatives that have announced since the release of the initial strategies. The race to become the global leader in AI has officially begun!