Table of Contents:
New York Proposes Blockchain Study for Election Security
New York State is taking a significant step towards integrating blockchain technology into its election processes. Assemblymember Clyde Vanel introduced Bill A07716 on April 8, which mandates the state Board of Elections to evaluate the potential of blockchain in securing voter records and election results. The bill is currently under review by the Assembly Election Law Committee.
The proposed study aims to assess how blockchain technology can enhance the security and integrity of election data. Experts in blockchain, cybersecurity, voter fraud, and election recordkeeping will contribute to the study. The Board of Elections is required to produce a comprehensive report within one year, detailing the potential benefits and challenges of implementing blockchain in elections.
Notably, this is not the first instance of blockchain being explored for election security. In March, the Bitcoin network was used to secure the results of the Williamson County, Tennessee, Republican Party Convention. However, experts caution that while blockchain offers tamper-resistant storage, it does not guarantee the accuracy of the data input into the system.
"Blockchain-based voting systems could foster more transparency and public trust in the election process," said Brian Rose, an independent mayoral candidate in London.
Assemblymember Vanel has been a strong advocate for blockchain initiatives. Earlier this year, he introduced legislation to combat cryptocurrency fraud and protect investors. He has also emphasized the need for the blockchain industry to better educate regulators and policymakers.
| Key Details | Information |
|---|---|
| Bill Number | A07716 |
| Introduced By | Clyde Vanel |
| Objective | Study blockchain for election security |
| Report Deadline | 1 year |
Summary: New York's proposed blockchain study aims to enhance election security by leveraging tamper-proof technology. The initiative highlights the state's proactive approach to integrating innovative solutions into public systems.
Anyscale Launches Comprehensive Ray Training Programs
Anyscale, a leading AI application platform, has unveiled a series of training programs to enhance proficiency in Ray, its open-source compute framework. These programs cater to AI and machine learning engineers, offering both free eLearning and instructor-led courses to help scale AI applications effectively.
The free eLearning course, "Introduction to Ray," includes 42 lessons spanning three hours. It covers the fundamentals of Ray, including modules like Ray Data, Ray Train, Ray Tune, Ray Serve, and Ray Core. For more hands-on learning, Anyscale offers a virtual instructor-led course titled "Introduction to Ray and Anyscale," which spans two half-day sessions. This course focuses on model training, hyperparameter optimization, data processing, and large-scale deployment.
For advanced learners, the "Ray for Practitioners" course provides an in-depth five-day program. Participants gain expertise in running end-to-end machine learning workloads with Ray and Anyscale. The course includes expert-led sessions, practical exercises, and real-world applications, showcasing how companies like Instacart, Amazon, and Canva utilize Ray to scale their operations.
Additionally, Anyscale offers customized private training sessions tailored to specific organizational needs. These sessions, available both in-person and virtually, cover essential Ray modules and provide focused learning opportunities for teams.
- Free eLearning: 42 lessons, 3 hours
- Instructor-Led Course: 2 half-day sessions
- Advanced Training: 5-day program
- Private Training: Customized for teams
Anyscale encourages participants to stay connected via LinkedIn and Twitter for updates and exclusive content. Subscribing to their newsletter provides additional resources to support the AI/ML learning journey.
Summary: Anyscale's new training programs offer a comprehensive pathway for AI and machine learning engineers to master Ray, from beginner to advanced levels. These initiatives aim to empower professionals to build scalable AI applications effectively.
Sources:
