Our guest in this episode is Shamus Rae. Shamus is the co-founder of Engine B, a startup which aims to expedite the digitisation of the professional services industry (in particular the accounting and legal professions) and level the playing field, so that small companies can compete with larger ones. It is supported by the Institute of Chartered Accountants in England and Wales (the ICAEW) and the main audit firms.

Shamus was ideally placed to launch Engine B, having spent 13 years as a partner at the audit firm KPMG, where he was Head of Innovation and Digital Disruption. But his background is in technology, not accounting, which will become clear as we talk: he is commendably sleeves-rolled-up and hands-on with AI models. Back in the 1990s he founded and sold a technology-oriented outsourcing business, and then built a 17,000-strong outsourcing business for IBM in India from scratch.

Selected follow-ups:
https://engineb.com/
https://www.icaew.com/

Topics addressed in this episode include:
*) AI in many professional services contexts depends on the quality of the formats used for the data they orchestrate (e.g. financial records and legal contracts)
*) "Plumbing for accountants and lawyers"
*) Why companies within an industry generally shouldn't seek competitive advantage on the basis of the data formats they are using
*) Data lakes contrasted with data swamps
*) Automated data extraction can coexist with data security and data privacy
*) The significance of knowledge graphs
*) Will advanced AI make it harder for tomorrow’s partners to acquire the skills they need?
*) Examples of how AI-powered "co-pilots" augment the skills of junior members of a company
*) Should junior staff still be expected to work up to 18 hours a day, "ticking and bashing" or similar, if AI allows them to tackle tedious work much more quickly than before?
*) Will advanced AI will destroy the billable hours business model used by many professional services companies?
*) Alternative business models that can be adopted
*) Anticipating an economy of abundance, but with an unclear transitional path from today's economy
*) Reasons why consulting reports often downplay the likely impact of AI on jobs
*) Some ways in which Google might compete against the GPT models of OpenAI
*) Prospects for improved training of AI models using videos, using new forms of reinforcement learning from human feedback, and fuller use of knowledge graphs
*) Geoff Hinton's "Forward-Forward" algorithm as a potential replacement for back propagation
*) Might a "third AI big bang" already have started, without most observers being aware of it?
*) The book by Mark Humphries, "The Spike: An Epic Journey Through the Brain in 2.1 Seconds"
*) Comparisons between the internal models used by GPT 3.5 and GPT 4
*) A comparison with the globalisation of the 1990s, with people denying that their own jobs will be part of the change they foresee

Audio engineering assisted by Alexander Chace.

Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration

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