By PAUL VON BÜNAU Sorting the realistic AI possibilities from the delusional fantasies is crucial…
In the lab, artificial intelligence is taking adult strides. But outside the lab, it’s just taking baby steps. So this is a time for exploration. Here we share our mistakes, what we’ve learned and what we’d do next time.
Defined deliverables: the key to successful AI proofs-of-concept
By PAUL VON BÜNAU Vague setups condemn the majority of AI proofs-of-concept to being inconclusive.…
The full toolkit: why AI teams need to think broadly to spot the key opportunities
By JEANETTE KNIPP If you only have a hammer, everything looks like a nail. All…
Early stopping: how to avoid the AI proof-of-concept trap
By PAUL VON BÜNAU Too many AI projects fail Proof of Concept testing due to…
Ideation in AI: five ways to make the workshops work
By PAUL VON BÜNAU Too often, ideation is performed without structure or scrutiny. We must…
Mundane, but magical: the power of database matching
By MONA SCHIRMER With its reputation for being dull and straightforward, database matching is hardly…
A private function: how privacy-enhancing technology unlocks data analysis potential
Can data be analysed to the full without compromising individuals’ privacy? Lisa Martin, strategy associate…
Cutting through the laptop installation jungle: How our MacBook setup repository helps our Data Scientists to organise their tools in only half a day
By HONG ZHU For me, one of the most frustrating parts of learning to program…
How to achieve maximum office happiness: Turning the allocation of office seats into an integer programming problem
By HANNAH MARTIN The allocation of office seats can be tackled with either elbows, consideration…
A Modern Visualization of Group Comparisons
By JEANETTE KNIPP More and more companies, governmental institutions and researchers employ data-centric methods to…
(Errors in) Communicating Statistics: Base rate neglect
By LUIS DREISBACH When the results were in on last June 26th, most of those…