Google to Improve Mobile Machine Learning Models

The team used multi-criteria optimization to achieve high speed and accuracy in the neural network MnasNet
09 August 2018   1702

The neural network MnasNet uses a training method with reinforcement to select the appropriate mobile device architecture.

The MnasNet system contains:

  • object management based on a recurrent neural network for learning and selecting the architecture of the model;
  • he coach who builds the models and teaches their accuracy;
  • an output machine that calculates the speed of the model on the phones using TensorFlow.

The team used multi-criteria optimization to achieve high speed and accuracy. In addition, scientists used the learning algorithm with reinforcement and remuneration function. Thus, MnasNet finds Pareto optimality for each platform. For each mobile device, individual features are created that require a specific architecture.

Automated Neural Design Approach
Automated Neural Design Approach

To achieve the optimal balance between the flexibility of search and the scope of feasible solutions, a hierarchical approach has been applied. It represents a convolutional neural network in the form of a series of blocks. After, sequential search to assign layers of architecture to each block is being used. Due to this, each layer uses different operations and connections. In this case, the layers of blocks are copied by force.

MnasNet blocks
MnasNet blocks

Scientists have included the search speed of architecture in the function of rewarding the search algorithm.

MnasNet Blocks
MnasNet Blocks

According to the developers, the system picks up the model 1.5 times faster than MobileNetV2 and 2.4 times faster than NASNet, while taking into account the optimal accuracy and speed. As a proof, the result was demonstrated by the example of working with ImageNet image database.

BNC to Monitor BTC Community's Mood

The system called Twitter Sentiment analyzes over 34M BTC-related Twitter posts each week, using AI to track the mood of the community
22 January 2020   188

Blockchain-based New Zealand-based research firm Brave New Coin (BNC) has unveiled a new system for measuring the mood of the Bitcoin community based on Twitter messages.

According to BNC, the new Twitter Sentiment rating system analyzes over 34 million BTC-related Twitter posts each week. The company uses artificial intelligence (AI) algorithms that look for records containing the words bitcoin, $ BTC and BTC and others.

BNC notes that user sentiment continues to be a “significant” factor in the price and dynamics of digital assets, and a new technique has been developed to track these sentiments. According to the BNC, it took 18 months to launch the Bitcoin Twitter Sentiment. The data obtained is divided into seven categories - Opinion, Technical Information, Inside the Network, Advertising, Bots, Macros and Hacking.

For the week ending January 17, the most common entries were in the Opinion category - their number was 30.42% of all data received. In second place was the category Technical Information, and in third inside the network (includes information on mining and hashrate).

BNC spokeswoman Pierre Ansaldi said that during the first quarter of this year, the company will also launch community sentiment analysis tools for other crypto assets.