Kepler brings blockchain for AI, ARM releases two AI chip designs, Coinbase commerce plugin, and more in today’s top stories around machine learning, blockchain, and data science news.
1. Kepler Technologies plans to build a decentralized ecosystem for the development of AI and robotics.
Kepler Technologies, a blockchain-based startup, plans to use blockchain-based solutions for the development of AI and robotics projects. Their decentralized platform is backed by smart contracts and powered by proprietary analytical algorithms. Every proposal presented to Kepler Technologies will be recorded to the blockchain through its’ innovative Proof-of-Creation network protocols. The platform’s innovative KEP token is the driver behind all voting and funding on the ecosystem. The KEP tokens will be used to incentivize behaviors on the platform in a decentralized way. It will also let users buy products at a discount, vote for or against projects to be developed, and funding proposals that are accepted.
The company has also established a blockchain tech incubation platform for the users to exhibit their ideas and connect with global investors to gain financial support. Innovators can connect with each other from anywhere in the world to develop their project.
2. ARM releases two new AI chip designs for mobile devices
ARM, has released designs for two new AI processors for delivering large amounts of computational capabilities to mobile devices.
The first, ARM Machine Learning (ML) Processor, which will speed up general AI applications from machine translation to facial recognition. The second, ARM Object Detection (OD) Processor is a second-generation design optimized for processing visual data and detecting people and objects. The company said that its Arm ML processors can handle more than 4.6 trillion operations per second while drawing very little power. Devices using the ARM ML processor will be able to perform ML independent of the cloud. The OD processor is expected to be available to industry customers at the end of this month, while the ML processor design will be available sometime in the middle of the year.
3. Coinbase unveils a new plugin For Ethereum, Bitcoin, and other cryptocurrencies
Coinbase, the popular crypto broker, has launched a new PayPal like plugin service for cryptocurrency merchants. This feature allows them to seamlessly integrate crypto payments by adding a Coinbase Commerce button. The plugin is available for Ethereum, Bitcoin, Bitcoin Cash and Litecoin. Previously, their merchants’ service was directly integrated with Coinbase, requiring a Coinbase account. Now it’s just a seamless crypto integration option, no different than paying through credit card, or Paypal.
4. IBM plans to use blockchain technology to aid the government
IBM wants to use blockchain technology in US governance processes to help make services more secure. According to, IBM’s vice-president of blockchain technology, Jerry Cuomo, “US government should employ the digital ledger technology for services such as paying taxes, creating secure identities, tracking food and drug shipments, among other purposes”. He preferred integrating blockchain into existing government projects and programmes rather than creating new projects based on the technology. The federal and state governments in the US are already working on several experimental projects based on blockchain, with some states working on implementing blockchain-based drivers licenses and identification cards. IBM itself is working with the Centers for Disease Control and Prevention in implementing blockchain to increase the speed of CDC’s ability to develop new drugs.
5. Hyperband, Hyperparameter Optimization for PyTorch
A new PyTorch implementation of Hyperband is in development. HyperBand is a hyperparameter optimization algorithm that exploits the iterative nature of SGD and the embarrassing parallelism of random search. Unlike Bayesian optimization methods which focus on optimizing hyperparameter configuration selection, HyperBand poses the problem as a hyperparameter evaluation problem. It adaptively allocates more resources to promising configurations while quickly eliminating poor ones. This allows it to evaluate orders of magnitude more hyperparameter configurations. It is described in the paper Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization by Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh and Ameet Talwalkar. The implementation details are available in the GitHub repo.