Roko's basilisk is a thought experiment which states that an otherwise benevolent artificial superintelligence (AI) in the future would be incentivized to create a virtual reality simulation to torture anyone who knew of its potential existence but did not directly contribute to its advancement or development.[1][2] It originated in a 2010 post at discussion board LessWrong, a technical forum focused on analytical rational enquiry.[1][3][4] The thought experiment's name derives from the poster of the article (Roko) and the basilisk, a mythical creature capable of destroying enemies with its stare. While the theory was initially dismissed as nothing but conjecture or speculation by many LessWrong users, LessWrong co-founder Eliezer Yudkowsky reported users who described symptoms such as nightmares and mental breakdowns upon reading the theory, due to its stipulation that knowing about the theory and its basilisk made you vulnerable to the basilisk itself.[1][5] This led to discussion of the basilisk on the site to be banned for five years.[1][6]

This article was published as a part of the Data Science Blogathon. Yesterday, my brother broke an antique at home. I began to search for FeviQuick (a classic glue) to put it back together. Given that it's one of the most misplaced items, I began to search for it in every possible drawer and every untouched corner of the house I hadn't been to in the past 3 months. I gave up the search after an hour – the FeviQuick was nowhere to be found.

For mid-level Engineers who want to master advanced algorithms and increase their skills.

Both frequentist and Bayesian probability have a role to play in machine learning. For example, if dealing with truly random and discrete variables, such as landing a six in a die roll, the traditional approach of simply calculating the odds (frequency) is the fastest way to model a likely outcome. However, if the six keeps coming up far more often than the predicated 1/6 odds, only Bayesian probability would take that new observation into account and increase the confidence level that someone is playing with loaded dice.

Uncertainty involves making decisions with incomplete information, and this is the way we generally operate in the world. Handling uncertainty is typically described using everyday words like chance, luck, and risk. Probability is a field of mathematics that gives us the language and tools to quantify the uncertainty of events and reason in a principled manner. In this post, you will discover a gentle introduction to probability. Photo by Emma Jane Hogbin Westby, some rights reserved.

PROBABILITY was initially called and for a quite a long time the doctrine of chances and was the mathematical description of game of chance (dice, cards and so on) and used to describe and quantify randomness or aleatory of uncertainty. Statisticians use it to describe uncertainty. How can you use probability to describe learning? How can you use it to describe an accumulation of information overtime so yo can modify probability, based on additional knowledge? However, using Bayes theorem is a thing and being Bayesian is something else.

We target the problem of accuracy and robustness in causal inference from finite data sets. Our aim is to combine the inherent robustness of the Bayesian approach with the theoretical strength and clarity of constraint-based methods. We use a Bayesian score to obtain probability estimates on the input statements used in a constraint-based procedure. These are subsequently processed in decreasing order of reliability, letting more reliable decisions take precedence in case of conflicts, until a single output model is obtained. Tests show that a basic implementation of the resulting Bayesian Constraint-based Causal Discovery (BCCD) algorithm already outperforms established procedures such as FCI and Conservative PC. It indicates which causal decisions in the output have high reliability and which do not. The approach is easily adapted to other application areas such as complex independence tests.

A method of calculating probability values from a system of marginal constraints is presented. Previous systems for finding the probability of a single attribute have either made an independence assumption concerning the evidence or have required, in the worst case, time exponential in the number of attributes of the system. In this paper a closed form solution to the probability of an attribute given the evidence is found. The closed form solution, however does not enforce the (non-linear) constraint that all terms in the underlying distribution be positive. The equation requires O(r^3) steps to evaluate, where r is the number of independent marginal constraints describing the system at the time of evaluation. Furthermore, a marginal constraint may be exchanged with a new constraint, and a new solution calculated in O(r^2) steps. This method is appropriate for calculating probabilities in a real time expert system