Machine Intelligence Toronto

Title

The Chief Science Officer at a Machine Intelligence startup

Christopher Berry

Abstract

By the end of this hour you can expect to learn:

• Why startups fail;

• What an Elevator Pitch is;

• What the Library of Smith is;

• What Jobs Theory (JTBD) is;

• Basic Software as a Service (SaaS) economic axioms (with simulation);

• The key Chief Science Officers' Job To Be Done at a machine intelligence startup;

• How to increase the odds in your favour; Why is this relevant:

Because a machine intelligence startup is a beast, and often features a strong CSO.

Relevant to you if you answer yes to one or more of these questions:

• How many of you work in machine intelligence or want to?

• How many of you intend to take on leadership positions in the next five years?

• How many of you are, or will become, Chief Science Officers?

• How many of you have experienced a failed startup?

• How many of you are planning to found a startups centered on machine intelligence in the next five years? -How many of you have founded a machine intelligence startup?

Bio

Christopher Berry

I'm a data scientist. I'll probably always be one at this point. Whenever I co-found a startup, I take on the title of Chief Science Officer. I turn data into product, make product more intelligent, and build teams of data scientists and engineers.

Thank you to Georgian Partners (http://georgianpartners.com/) for the extra space!

Both audiences, those who are interested in machine intelligence / data science, and those who are practitioners of machine intelligence / data science are invited, and can both expect to learn something new.

Attendees can expect to learn what machine intelligence is, its applications, and what's going on in Toronto's data science community. Significant getting to know you time, and Q&A time is deliberately set aside.