Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Building the Internet Computer: A Glimpse into an Ambitious Adventure

In course

CS-451: Distributed algorithms

Computing is nowadays distributed over several machines, in a local IP-like network, a cloud or a P2P network. Failures are common and computations need to proceed despite partial failures of machines or communication links. This course will study the fo ...

Description

This lecture delves into the creation of the 'Internet Computer' by the ICP protocol, aiming to transform the Internet into a distributed OS hosting software and services. It explores the concept of WebAssembly 'canisters' for secure code execution, consensus algorithms, state machine replication, and challenges in deriving randomness. The talk also covers the 'Beyond Blockchains' perspective by Adi Seredinschi, focusing on verifiable distributed systems and organizations, including Tendermint and Cosmos. Adi discusses consensus algorithms, the Tendermint BFT consensus, system models, rounds, and the Inter-Blockchain Communication (IBC) problem statement.

This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.

Watch on Mediaspace

Instructor

Rachid Guerraoui

Rachid Guerraoui has been affiliated with Ecole des Mines of Paris, the Commissariat à l'Energie Atomique of Saclay, Hewlett Packard Laboratories and the Massachusetts Institute of Technology. He has worked in a variety of aspects of distributed computing, including distributed algorithms and distributed programming languages. He is most well known for his work on (e-)Transactions, epidemic information dissemination and indulgent algorithms. He co-authored a book on Transactional Systems (Hermes) and a book on reliable distributed programming (Springer). He was appointed program chair of ECOOP 1999, ACM Middleware 2001, IEEE SRDS 2002, DISC 2004 and ACM PODC 2010. His publications are available at http://lpdwww.epfl.ch/rachid/papers/generalPublis.html

Official source

Related concepts (32)

Randomness

In common usage, randomness is the apparent or actual lack of definite pattern or predictability in information. A random sequence of events, symbols or steps often has no order and does not follow an intelligible pattern or combination. Individual random events are, by definition, unpredictable, but if the probability distribution is known, the frequency of different outcomes over repeated events (or "trials") is predictable. For example, when throwing two dice, the outcome of any particular roll is unpredictable, but a sum of 7 will tend to occur twice as often as 4.

Randomness test

A randomness test (or test for randomness), in data evaluation, is a test used to analyze the distribution of a set of data to see whether it can be described as random (patternless). In stochastic modeling, as in some computer simulations, the hoped-for randomness of potential input data can be verified, by a formal test for randomness, to show that the data are valid for use in simulation runs. In some cases, data reveals an obvious non-random pattern, as with so-called "runs in the data" (such as expecting random 0–9 but finding "4 3 2 1 0 4 3 2 1.

Statistical randomness

A numeric sequence is said to be statistically random when it contains no recognizable patterns or regularities; sequences such as the results of an ideal dice roll or the digits of π exhibit statistical randomness. Statistical randomness does not necessarily imply "true" randomness, i.e., objective unpredictability. Pseudorandomness is sufficient for many uses, such as statistics, hence the name statistical randomness. Global randomness and local randomness are different.

Randomized algorithm

A randomized algorithm is an algorithm that employs a degree of randomness as part of its logic or procedure. The algorithm typically uses uniformly random bits as an auxiliary input to guide its behavior, in the hope of achieving good performance in the "average case" over all possible choices of random determined by the random bits; thus either the running time, or the output (or both) are random variables.

Algorithmically random sequence

Intuitively, an algorithmically random sequence (or random sequence) is a sequence of binary digits that appears random to any algorithm running on a (prefix-free or not) universal Turing machine. The notion can be applied analogously to sequences on any finite alphabet (e.g. decimal digits). Random sequences are key objects of study in algorithmic information theory. As different types of algorithms are sometimes considered, ranging from algorithms with specific bounds on their running time to algorithms which may ask questions of an oracle machine, there are different notions of randomness.

Related lectures (32)

Quantum Random Number Generation

Explores quantum random number generation, discussing the challenges and implementations of generating good randomness using quantum devices.

Causality in an Indeterministic World

Delves into causality in an indeterministic world, challenging traditional views and exploring the implications of quantum physics on randomness and reality.

Classical Delegation of Quantum Computation: Further Works

Delves into classical delegation of quantum computation, discussing challenges and open questions in verifying quantum devices.

Algorithms to Live By: Optimal Stopping and Logarithmic Regret

Explores optimal stopping, explore/exploit, Bayes' Rule, and logarithmic regret in bandit solutions.

Quantifying Randomness and Information in Biological Data