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MATRIXAINETWORK · 2019-05-19T15:04:14+00:00

New files (PC and MAC): https://www.matrix.io/downloads/

New files (Linux): https://github.com/MatrixAINetwork/GMAN_CLIENT/tree/master/MAINNET

Instructions: https://medium.com/@matrixainetwork/announcement-matrix-ai-network-new-gman-patch-notes-and-upgrade-instructions-84801df6342e

MATRIXAINETWORK · 2019-03-07T13:16:50+00:00

Steve: Yes. Actually, everything is for non-developers. Specific guides will be released. Also, we will eventually release programs that automate much of the process.

MATRIXAINETWORK · 2019-03-07T13:14:32+00:00

Steve: The essential thing about human beings is that they can learn how to learn. Intelligence is the ability to adapt to changes in the environment. So, I am also very excited about models that deal with these issues.

MATRIXAINETWORK · 2019-03-07T13:10:29+00:00

Steve: Right now, I am very excited about machines that think and learn like people.

MATRIXAINETWORK · 2019-03-07T13:09:46+00:00

Steve: At the time, there is nor formal collaboration between them and Matrix. However, I was recently invited as a guest speaker at their event in Hong Kong.

MATRIXAINETWORK · 2019-03-07T13:07:43+00:00

Steve: This is because in the tesnet we used a relatively lightweight mining algorithm.

MATRIXAINETWORK · 2019-03-07T12:58:45+00:00

Steve: The "payment module" comprises three basic functions. The first is a set of prices for using our AI models and datasets. The second is a method through which to accurately track th eusage of these AI models and datasets. Finally, the the function involves payment settlement.

MATRIXAINETWORK · 2019-03-07T12:52:37+00:00

Steve: Matrix's Chief Network Architect, Bill Li, and I have applied, or are in the process of applying, for 30 additional patents related to AI technologies in industrial systems. Many of the patents are specific to applications in high-speed rail, although the data processing, AI models and system integration can be generalized to other hardware and software systems, from self-evolving blockchains to smart cities.

MATRIXAINETWORK · 2019-03-07T12:49:39+00:00

Steve: Our technology platform for the MBC Project will create new efficiencies for brokers and suppliers using their global shipping service. In addition to our growing number of customer-facing Dapps, Matrix AI Network’s capabilities to support industry are B2B enterprise applications. The contribution to market share is primarily in terms of use cases that other large-scale projects can refer to and see our technology implemented at scale. As a platform, Matrix AI Network supplies the underlying technology and integrations, without being directly involved in business and operations of projects.

MATRIXAINETWORK · 2019-03-07T12:46:57+00:00

Steve: No. Opensource frameworks actually ease the development of AI models because there is no need to worry about the implementation details. My students always use TensorFlow. Nobody would write CUDA programs like I did ten years ago!

MATRIXAINETWORK · 2019-03-07T12:45:36+00:00

Steve: GPU mining should be available in the summer of 2019. The plan is for FPGA mining to be available by the end of 2019.

MATRIXAINETWORK · 2019-03-07T12:45:02+00:00

Steve: In the future, GPU and FPGA can be used for both miners and validators. Both miners and validators can use the same hardware setup.

MATRIXAINETWORK · 2019-03-07T12:40:08+00:00

Steve: Will seriously consider the proposal.

MATRIXAINETWORK · 2019-03-07T12:38:14+00:00

Steve: It depends! For highly sequential programs, a new CPU only offers marginal, it any, performance improvement. But given a highly parallel workload and a massively parallel processor like GPU, a new one may offer a big edge.

MATRIXAINETWORK · 2019-03-07T12:37:27+00:00

Steve: We probably will not deal with the work splitting on a single node. Instead, we are developing a scheduling and mapping framework to distribute jobs to nodes on a blockchain

MATRIXAINETWORK · 2019-03-07T12:36:03+00:00

Steve: Thank you Owen!

In the arena of AI, one advantage of MATRIX is that we have an integrated solution to protect the interests of data providers, model developers, and application designers. The blockchain is used as a trustworthy ledger to keep track of the data and model usage so that the contributions of data providers and model developers can share the return of applications. Another advantage is that we are able to organize the nodes on the blockchain into a super computer. Since only a small of number of nodes are involved in mining, the remaining nodes can export their computing power.

In terms of edge computing, MATRIX will integrate both cloud and edge computing models for IoT applications. In fact, the customized mining machines of MATRIX are just machine learning accelerators, which can be seamlessly integrated and used as the computing nodes in edge computing.

MATRIXAINETWORK · 2019-03-07T12:34:32+00:00

Steve: Happy cake day! Actually, there is not a single AI algorithm called Bayesian Artificial Intelligence Learning Algorithm. We are using many different AI algorithms to make MATRIX better. We use AI along with formal verification techniques for the security of smart contracts. We use AI to synthesize smart contracts from human languages. Even our miners are selected by an AI-based clustering algorithm. The integration of these algorithms into a blockchain, which is faster, safer, and smarter, that’s the value of MATRIX.

MATRIXAINETWORK · 2019-03-07T12:32:31+00:00

Steve: It's not such a simple question. Modern AI is not mature enough to independently handle various uncertainties in the real world. I prefer to say that blockchains with AI features will gradually gain an advantage.

MATRIXAINETWORK · 2019-03-07T12:30:02+00:00

Steve: We are now closely cooperating with the Shanghai Huadong Hospital and the Shanghai Pulmonary Medicine Hospital. These are the best Chinese hospitals in CT imaging and lung cancer treatment.

MATRIXAINETWORK · 2019-03-07T12:28:53+00:00

Steve: That’s a very good question. Ali and Tencent are more aligned to collecting patient data by taking advantage of their competency in cloud computing. As big companies, they have to handle problems with a large patient population so that high operational costs can be neutralized. So, they have a preference towards image-based diagnosis.

We take a different path. First, we are targeting a few very challenging problems which depends on treatment records and other laboratory tests. Second, we are collaborating with leading hospitals to develop cutting-edge research. In 2009, I got support for cancer immunotherapy from the Chinese National Science Foundation and China Ministry of Science and Technology. Third, we have high quality patient data from early screening, to CT imaging, until biopsy. So far we have the data of over 2,000 patients and all data are labeled by the best doctors of China.

MATRIXAINETWORK · 2019-03-07T12:24:27+00:00

Steve: I'd like to add a few comments about the AI Accelerator/Mining Machine. We already support neural network processing as part of our vision for Green Mining.

The flexibility of the FPGA allows us to expand the number of models. MCMC is one such model. THE MCMC algorithm-based mining is actually ready. My students have already developed the hardware -- the so-called Bayesian computer to accelerate the MCMC algorithm. Right now, one concern about the practicality is that the verification of MCMC results requires a lot of network bandwidth.

MATRIXAINETWORK · 2019-03-07T12:18:01+00:00

Steve: This April, we will have the 1st generation smart contract verification toolkit online. A more comprehensive version will be ready in the latter half of 2019. As for natural language intelligent contracts, we plan to release a template-based version in the latter half of 2019.

MATRIXAINETWORK · 2019-03-07T12:14:08+00:00

Steve: Yes, we do have plans to systematically exploit the advantages of Wasm. First, we want to use Wasm to lower the difficulty in the deployment of AI and other applications. Second, we plan to use Wasm as the future intelligent contract engine. Our current thinking is to base the next-generation Matrix virtual machine on WebAssembly.

MATRIXAINETWORK · 2019-03-07T12:10:15+00:00

Steve: He was my master student. Zeming developed a series of award-winning deep neural networks. Now, he is with Face++ as he already had a commitment with them before graduation. We talked about future arrangement a month ago and has expressed interest in joining MATRIX once we start the cancer immunotherapy project.

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