Randomness

What is it, and why does it matter?

Probability & randomness. We can find probability everywhere in the world; there's always a measureable chance between certain outcomes coming to fruition, whether that be in our day to day lives, our beusiness applications or decisions we take.

With enough data we often know the probability of an outcome, while the occurrance of the outcome itself is in fact, random.

Randomness is a critical input to products and decision making

  • Encryption

    Used across cybersecurity, including generation of encryption keys, in authentication systems and across SSL. They are a fundamental base to secure computing systems.

  • AI

    Used throughout the training & execution of AI, including to help improve generalisation (ensuring the algorithm is not biased to the data it's trained on).

  • Modelling

    Used when undertaking modelling activities (e.g. market movements, drug discovery). Injecting randomness can help the model to account for the randomness encountered in the real world.

Don’t settle for flawed randomness

Today’s sources of randomness are 'pseudorandom'. Its flaws are holding back the work we do.

  • All current implementations of random number generation today are deterministic. It’s impossible to output independent variables.

  • It’s difficult to access certain ‘rare numbers’. Some of the numbers within the range you want don’t as appear as frequently as they should, whilst others appear more frequently.

  • Many tools use their own variation of a random number generator algorithm, leading to a lack of consistency. It’s not just across platforms, but even within your own architecture.