According to a Tencent report, there were close to 300,000 AI engineers in the world in 2017; and it stated that we are short by millions to drive the Data Science projects currently. To gauge what Chief AI Scientists think of AI Technologies and their AI DevOps teams, we spoke to Dr. Steve Deng of Matrix AI Network.
We had a chance to sit down with Dr. Deng this week to learn a little more about AI, blockchain, and the future of Matrix AI Network following our recent coverage of Adobe Analytics, Salesforce Blockchain CRM, and Google’s decision to acquire Data Visualization and Intelligence platform, Looker.
About Dr. Steve Deng
Dr. Steve Deng is the 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 a 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 a 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.
What are your thoughts about the current state of AI?
Outside of a narrow band of applications, in areas like research and AI for social good, there is a chronic lack of computing power and the data isn’t large enough to start seeing really transformative applications in AI.
How do you see blockchain and AI evolving into other industries? Like VR and AR, for example?
The industry applications for blockchain and AI are well documented. AI applications in consumer applications are well-funded and relatively saturated. We’re focused on areas with high-impact, high specialization, and low adoption in industries with a high growth ceiling. Our current focus is on AI IoT applications in renewable energy and public safety, as well as new computation and data-pooling solutions for AI-assisted medical diagnosis.
What do you think about ICOs as a fundraising instrument? Do you think they’re here to stay?
We would recommend anyone to research evolving regulatory frameworks, consult with leading VCs and good legal counsel specializing in this complex domain.
What advice do you give to those people who want to start a Blockchain or AI project?
These are broad areas of study. Ask focused questions and look for opportunities to create real value.
How can Distributed Apps (dApps) move more into the mainstream?
Distributed applications can connect to the blockchain, but that’s essentially just a new form of validating data. The applications themselves need to be high quality, have a social utility or solve real-world problems. They also need to be based on a full modern technology stack to do much of anything useful.
Building a strong ecosystem is also crucial for a project to be successful, how are you building your ecosystem?
We’re starting from the data supply chain, which is the lifeblood of AI and increasingly complex domain for big data that enables things like business intelligence. We have a highly built out computing infrastructure and partnerships to create true end-to-end solutions.
Most data ecosystem now have many multi-party, often tri-sector contributors, and we’re seeing a lot of pushback about data monetization and ownership. We’re developing high-performance and flexible solutions to smoothly and safely connect these diverse data stakeholders and satisfy privacy and business needs. Matrix technology is a Venn diagram of multiple industry ecosystems.
For developers, what kind of features make the Matrix AI Network main net attractive?
We’re a unique platform for incubating AI capabilities with our AI server and green mining AI accelerators. For anyone interested in building out an application or platform for AI services with secure and open end-to-end data sharing on the backend, we have a unique value proposition. Our NLP-based auto-coded smart contracts also lower the barrier to entry to multiple users to safely deploy smart contracts. Our high throughput, AI-optimized functionality, and growing range of integrated services are a great platform for new projects. We also have specialized expertise in areas like IoT and Gaming.
How do you improve network efficiency while ensuring safety?
This lies in our system architecture and consensus mechanism.
MATRIX AI Network introduces a novel hybrid consensus mechanism.
The PoW is performed in a significantly smaller network of delegates, which are selected with a randomly distributed voting algorithm. The probability of a node to be selected is proportional to its PoS. The “winner” delegate shares the PoW reward with other nodes in its cluster. This proprietary network hierarchy created with a distributed random clustering process without centralized control enables hybrid PoS + PoW consensus mechanism – dubbed HPoW or Hyper PoW – and reduces transaction latency.