Who will speak at AI Journey - meet the speakers of the big conference on artificial intelligence
Updated: Nov 19, 2021
AI Journey conference starts tomorrow. Participants will have 20+ thematic streams, where they will be able to listen to speeches and discussions on a variety of topics. 250+ world-class experts will speak at the conference, and with some of them
Joseph Marc Blumenthal had time to talk. Here's what they said.
Distinguished Research Engineer and Chief Architect for AI and Big Data, Founder and Member of the Steering Committee of the Apache Spark Project, Mentor of the MXNet Project, Intel
In my speech, I will talk about the BigDL program. It is an open source Intel tool that provides an end-to-end AI pipeline for big data to scale distributed AI. It allows information processors and machine learning engineers to create familiar Python notebooks on their PCs, which can then be scaled for use on large clusters and processed large amounts of information in a distributed manner. The tool will be useful for professionals who want to apply AI technologies to work with large-scale distributed datasets in a production environment. BigDL is used in practice by many large companies, such as Mastercard, Burger King, Inspur, JD.com and others.
At the conference, I would like to hear reports on how data analytics, machine learning, deep learning are used in practice. I believe that studying this issue is important for communities that are engaged in big data and AI, because AI technologies are now being used not only in experimental applications, but also in real products.
Chief Scientific Adviser, Institute for Artificial Intelligence AIRI, Director of the Dalle Molle Institute for Artificial Intelligence Research Laboratory (IDSIA)
The topic of my speech is the most popular artificial neural networks. I would probably be naive and propose to evaluate the popularity of a particular neural network by the number of scientific citations. However, it often depends on the popularity of the problem being solved by this network. I think my talk will be of interest to those who already know a little about machine learning and artificial neural networks. But it will be quite understandable to those who do not consider themselves an expert in these topics.
More specifically, I will highlight the five most cited neural networks that are used in various applications. The first is the so-called Long Short-Term Memory (LSTM), the most cited neural network of the 20th century. The second is ResNet, the most cited neural network of the 21st century and, in fact, an open version of our Highway Net - the first truly deep feedforward neural network with hundreds of layers.
Next are AlexNet and VGG Net - the second and third most cited neural networks of the 21st century. Both build on our earlier DanNet, which became the first deep convolutional network in 2011. She won the computer vision competition and performed better than humans on tasks. The fourth point is about Generative Adversarial Networks. Today, they are used to create realistic images and works of visual art. And this is one variation of our work Adversarial Artificial Curiosity, first published in 1990. In it, two neural networks fought each other in a minimax game. And finally, the fifth point - variations of the so-called Transformers, which are now widely used for natural language processing (NLP, Natural Language Processing). Linear Transformers are formally equivalent to Fast Weight Programmers - our 1991 development.
In the 2010s, a community of distinguished machine learning researchers, engineers, and practitioners feverishly pushed these developments forward. As a result, absolutely amazing things turned out that have influenced the lives of billions of people around the world. I hope that many people, especially young people, will want to learn more about modern AI and deep artificial neural networks and watch the speeches.
President of the Intuitive Foundation
On AI Journey, I will talk about the use of AI in surgery. As in other areas of medicine, AI helps the surgeon rather than replaces the doctor. And it will be interesting for me to share the nuances of the technology application.
AI can be useful in several surgical areas today. Machine learning technologies that analyze preoperative and intraoperative images can be used to improve the spatial orientation of the surgeon during surgery. In other words, the doctor will better understand what is happening with the patient. In addition, algorithms are already helping surgeons detect and interpret pathologies.
Also, AI “trainers” can recognize the current stage of the operation and generate hints for the doctor regarding the next stages. And for nurses and technicians in perioperative patient management, algorithms help to properly prepare equipment and materials for the next stage of the procedure.
I know that some doctors are still skeptical about the use of AI in medicine. It seems to me that there is no need to worry: practice shows that the use of algorithms can improve patient outcomes, reduce training time, and even extend the careers of surgeons themselves. These technologies are not something to be afraid of.
At the conference, I would like to hear a variety of points of view and learn about new opportunities for using AI in medicine. For example, about those that have not even occurred to me yet.