An interview with Chris Dyer.

https://www.semanticscholar.org/paper/Recurrent-Neural-Network-Grammars-Dyer-Kuncoro/1594d954abc650bce2db445c52a76e49655efb0c

04 - Recurrent Neural Network Grammars, with Chris Dyer

In this episode of the Data Show, I spoke with Leo Meyerovich, co-founder and CEO of Graphistry. Graphs have always been part of the big data revolution (think of the large graphs generated by the early social media startups). In recent months, I’ve come across companies releasing and using new tools for creating, storing, and (most importantly) analyzing large graphs. There are many problems and use cases that lend themselves naturally to graphs, and recent advances in hardware and software building blocks have made large-scale analytics possible.

Starting with his work as a graduate student at UC Berkeley, Meyerovich has pioneered the combination of hardware and software acceleration to create truly interactive environments for visualizing large amounts of data. Graphistry has built a suite of tools that enables analysts to wade through large data sets and investigate business and security incidents. The company is currently focused on the security domain—where it turns out that graph representations of data are things security analysts are quite familiar with.

Graphs as the Front End for Machine Learning

Inverse Reinforcement Learning and Inferring Human Preferences is the first podcast in the new AI Alignment series, hosted by Lucas Perry. This series will be covering and exploring the AI alignment problem across a large variety of domains, reflecting the fundamentally interdisciplinary nature of AI alignment. Broadly, we will be having discussions with technical and non-technical researchers across a variety of areas, such as machine learning, AI safety, governance, coordination, ethics, philosophy, and psychology as they pertain to the project of creating beneficial AI. If this sounds interesting to you, we will hope that you join in the conversations by following or subscribing to us on Youtube, Soundcloud, or your preferred podcast site/application.

In this podcast, Lucas spoke with Dylan Hadfield-Menell, a fifth year Ph.D student at UC Berkeley. Dylan’s research focuses on the value alignment problem in artificial intelligence. He is ultimately concerned with designing algorithms that can learn about and pursue the intended goal of their users, designers, and society in general. His recent work primarily focuses on algorithms for human-robot interaction with unknown preferences and reliability engineering for learning systems. 

Topics discussed in this episode include:

-Inverse reinforcement learning
-Goodhart’s Law and it’s relation to value alignment
-Corrigibility and obedience in AI systems
-IRL and the evolution of human values
-Ethics and moral psychology in AI alignment
-Human preference aggregation
-The future of IRL

AIAP: Inverse Reinforcement Learning and Inferring Human Preferences with Dylan Hadfield-Menell

From my session on #Blockchain based #Cybersecurity Architectures presented in the Malabar Cybersecurity Summit organised by RedTeam Hacker Academy in Calicut. This presentation navigates through the fundamental concepts of Blockchain, Tamper Evidence properties of Blockchain Data Structure, Blockchain Architectures, Possibilities of Immutable Ledger, Importance of Blockchain for Digital Identities, IoT Security etc.

Blockchain for the Security of Digital Interactions, Processes and Things | Gokul Alex

This past year, we have witnessed what some are calling a “Cambrian Explosion” in zero-knowledge proof systems. New proof systems based on a variety of cryptographic assumptions are popping up every week. And while zero-knowledge systems are known for their privacy-preserving characteristics, they have proven particularly useful for scaling blockchains through off-chain computations.

We're joined by Eli Ben Sasson, Co-founder and Cheif Scientist in the East at Starkware. His company is developing a full proof-stack, which leverages STARKs. Pioneered by Eli, STARKs are zero-knowledge cryptographic proofs, which are succinct, transparent, and post-quantum secure. StarkWare has demonstrated how zkSTARKs may be leveraged to provide off-chain scalability by generating proofs of computational integrity which may be verified on-chain.

Topics discussed in this episode:
- Eli's trajectory and transition from academia to the startup world
- The origin story of Starkware and the founding team
- The current explosion of zero-knowledge proof research
- An overview of zero-knowledge proof systems and how they work
- What are STARKs and what are their properties
- How STARKs are different from SNARKs and Bulletproofs
- What are zk-Rollups and how they are used by StarkWare to achieve scalability
- The StarkDEX experiment and scalability benefits it had demonstrated
- The issue of data availability with layer-2 scaling solutions
- StarkWare's business model and the solutions they are building for customers

Links mentioned in this episode: 
- StarkWare: https://bit.ly/2W4sxXJ
- StarkWare resources: https://bit.ly/2Jfh82l
- StarkWare blog: https://bit.ly/33W9XDQ
- StarkWare Twitter: https://bit.ly/2BArGVs
- StarkWare Sessions conference videos: https://bit.ly/2pDMGrD
- Epicenter Podcast SFBW Week Meetup: https://bit.ly/2N1PQxv
- DeFi Hackathon sponsored by Cosmos: https://bit.ly/35YnJbd
- SF Blockchain Week 2019 (use the code EPICENTER for 20% off tickets): https://bit.ly/2BzSIfz
- Macro.WTF: https://bit.ly/2MAdths

Sponsors: 
- Vaultoro: Trade gold to Bitcoin instantly and securely starting at just 1mg - http://vaultoro.com
- B9Lab: Level up and become a Solidity smart contract auditor – 5% off the with the code EPICENTER - https://solidified.b9lab.com/epicenter

This episode is hosted by Sebastien Couture & Sunny Aggarwal. Show notes and listening options: https://epicenter.tv/310

#310 Eli Ben-Sasson: StarkWare – Productizing zk-STARKs to Provide Blockchain Scalability & Privacy

Blockchain Educator and Quantum Computing Researcher

Blockchain for the Security of Digital Interactions, Processes and Things | Gokul Alex

Gokul Alex

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