Interview with Dr. Steve Deng, Chief AI Scientist for MATRIX AI Network
We had the pleasure to speak this week with Dr. Steve Deng, Chief AI Scientist for MATRIX AI Network(MAN), a global open-source, public, intelligent blockchain-based distributed computing platform and operating system that combines artificial intelligence (AI) and blockchain. MATRIX AI Network was created to make blockchains faster, more flexible, more secure, and more intelligent.
Professor Deng is an Associate Professor at the School of Software, Tsinghua University, where he has served as faculty member since 2008.
Professor Deng’s research interests include machine learning, industry data analytics and computer architecture. He has authored over 50 papers. His textbook, “Structural VLSI Design and High-Level Synthesis,” is used by Tsinghua and other universities.
Professor Deng has served as Principal Investigator (PI) and Co-PI for numerous national level research projects. Since 2016, he has served as Vice Principle Architect of China Railway Rollingstock Corporation’s Prognostics Health Management for High-Speed Trains Project.
Professor Deng’s work on deep-learning based image detection was ranked #1 in many prestigious challenges including PASCAL VOC and VSCOCO; beating out teams from Google, Intel, Facebook and Microsoft. He has received numerous awards including “Best Paper” at the International Conference on Computer Design in 2013, the “NVIDIA Partnership Professor” Award and the Tsinghua University “Key Talent” Award.
In February, Matrix AI Network launched its main net with the following core feature set:
- Auto-Coding Smart Contracts– Matrix Smart Contracts use Natural Language Programming (NLP) and adaptive deep learning-based templates to auto-code. Currently, English and Chinese languages are supported.
- AI-powered Cybersecurity – Matrix’s Secure Virtual Machine uses AI-backed vulnerability detection with fault-tolerant protocols to recognize transaction vulnerabilities, identify loopholes and correct errors, while using a generative adversarial network (GAN) to continuously simulate attacks and exploitation of the network and identify and patch potential bugs and loopholes.
- Adaptive Blockchain Parameters– Matrix enables the seamless integration of public and private chains with the capability for multi-chain collaboration. Support for adaptive self-optimization and the ability to adjust blockchain parameters without the creation of a hard fork. Matrix uses an evolutionary algorithm built to support enterprise and government ecosystems.
- Value-Added Green Mining– Matrix incorporates an innovative mining mechanism to run its Markov-Chain Monte Carlo (MCMC) algorithms. All nodes connected to the Matrix AI Network that are not actively engaged in mining and verification tasks are available to solve AI tasks by lending their excess computing power in exchange for token rewards. This leads to more affordable, scalable and secure AI processing. Instead of using a traditional hash algorithm, Matrix is supporting deep learning algorithms. This combination will boost the performance and affordability of operations on the Matrix AI Server.
- Dynamic Delegation Network– Proprietary network hierarchy created with a distributed random clustering process without centralized control enables hybrid PoS + PoW consensus mechanism – alternatively described as either HPoW or Hyper PoW – to enable high system throughput speeds (starting at 5,000 TPS, fully encrypted) and reduces transaction latency.
Matrix boasts a world-class technical team and has announced an impressive number of strategic alliances and partnerships, including the Distributed AI Alliance; Tsinghua University Bayesian Computing Lab; Peking University Cancer Hospital; Advanced Telecommunication Chain Industry Alliance; and China Standards and Innovation Alliance’s Artificial Intelligence and Blockchain Council. Matrix AI Network is one of the most exciting companies to watch right now as blockchain and AI converge.
Read our exclusive interview below to see what Dr. Deng thinks about the future of blockchain, artificial intelligence, and motivating his technical team.
What are your thoughts about the current state of blockchain?
Blockchain was able to quickly capture the global imagination, and now it’s become fashionable to say the promise for industry applications has fallen short. The reality is it’s an early technology, and we never really viewed blockchain as stand-alone solution. Other fields like AI and Bayesian statistics also saw waves of enthusiasm followed by disillusionment. We see blockchain as a valuable new tool within a larger computing infrastructure.
How many persons are there in Matrix’s technical team now? And what’s the future plan for team expansion?
Our technical team has over 40 members. These include experts in AI, deep learning, natural language processing, data science, hardware optimization, blockchain protocol, cybersecurity, P2P computing, systems integration and various programming languages. We continue to attract top AI talent. With our technology platform built out, we are attracting experts with more industry specific expertise.
Tell us a little about your team’s culture. What are some key characteristics define your team?
Our top leadership and many team leaders have roots in academia. Many regularly publishing scholarly research and participating in academic conferences. We also have a strong international culture, with many of our team members being multi-lingual and having work experience in many countries. We also work very closely with developers and tech communities in more than 10 countries to localize materials and build partnerships, and continue to engage a larger world-wide community.
Tell us a little about your personal motivations with the project. What keeps your team driven?
The majority of our team is motivated by strengthening AI for social good. Our mission in that effort is building open a distributed computing infrastructure that is safe and robust enough to integrate with massive enterprise data ecosystems. This is necessary to expand the ownership and scale of data to be applied in areas like research and social good.