You don’t, and that”s the point.
It’s the contract that holds you together. Not your history. Not your friendship. Not anything else.
The contract. Offer. Acceptance. Consideration. That’s it.
And what does that paper mean when things start moving fast, when you jump from one meeting to the next?
And what does it mean when the client, or perhaps a representative of the client, has ideas? Where does the contract end?
Fourth episode in a series on the ins and outs of tech consulting.
How do you begin a consulting job?
In thunder, lightening or in rain?
It is chaotic at the start. You move to new offices, meet new people and start to appreciate new responsibilities. Our podcast has been hired by a German firm – a logistics provider to the coffee industry – to review their software systems and make recommendations. They arrive on site, after a long flight, and quickly discover that they did not fully appreciate the environment in which they would have to work.
First episode in a series on tech consulting.
It’s not a question of smarts. It’s a question of how it got that way. A lot of the smart apps are fairly straight forward – classic computing ideas repackaged in a clever case. You start taking the p hone apart and you a lot of ideas that aren’t that clever experience t for the effort that it took to get them into that little case.
So we ask the question “How?” And of course, Anna has her ideas. Maybe she’s right. Maybe she’s not. But someone had to decide why the map app was important and why it was smart.
Anna and her crew take apart the smartphone as part of her series on Artificial Intelligence.
It’s rarely a direct replacement. Rarely do smart machines actually change the workplace as they were intended. It’s always a change to some side occupation, an occupation that you may not connect with smart technology. as
Our series on smart machines and work considers the contributions of John McCarthy, one of the founders of the field of Artificial Intelligence. McCarthy had grand plans for his work. He hoped to build a machine that could do the kind of logical reasoning that Euclid did for geometry. The question is, of course, “Was McCarthy’s work a success?” but the answer is not measured by the parallel query “How many geometers did McCarthy put out of work?”
Machine Intelligence is going to take over the world, right? We know this? Driverless cars. Computer bosses. All the information that you could ever know captured in a few silicon chips? Can the new generation of workers look ever look forward to having a satisfying career again? The Podcast How We Manage Stuff is about to embark on a study of machine intelligence, as our very intelligent intern, Anna, explains.
So what’s so bad about Knowledge Engineering? You’re just systematizing what your company knows. Just trying to bring some order to the chaos of the corporate world. However, it always requires a compromise. Squeezing a 7 and a half foot into a 6 and a half Louboutin, as Anna would say. In no situation are the problems of the knowledge engineered boyfriend. To ask the question of the ages, it is better to be a 70% with a 20% probability or a 20% boyfriend with a 70% probability.
Big Data. It gives you facts. It tells you what is really happening. It has Volume, Varity, Velocity, Veracity and, of course, Vexation of Spirit. Vexation can come with the data, no matter how much you have of it, tells you more about the structure that gathered it than the truth of any matter. When you have that, you have a problem called Simpson’s Paradox – the bane of Big Data.
Once you have a character, it is impossible to control him or her. In the winter of 2016, Anna-the-Intern made a pitch to the (even then) notorious Elon Musk. She thought that he might like to be the next host of the podcast as the then current hosts were not pulling their weight.
She waited. No call. No email. No tweet. No nothing.
But it was a good performance with we felt worthy to preserve in our archive.