In 2018, the Turing Award – arguably equivalent to a Nobel Prize for computing – bestowed $1 million on three researchers for their work developing AI.
Godfathers of AI
Since then, the highly respected trio have taken up prominent positions in the world of AI research in industry and academia, and became known as the ‘Godfathers of AI’.
This week, two of those three have gone public on their regrets about what they’ve achieved.
Geoffrey Hinton quit Google, warning of the dangers the technology he helped create may pose to humanity, having called for an international ban on autonomous intelligent weapons.
Yoshua Bengio, professor at the University of Montreal and founder of Element AI, says he felt ‘lost’ over his life’s work and believes the military should not have access to the technology.
Only Yann LeCun, Facebook’s Chief AI scientist and professor at NYU, opposed the proposal from several thousand leading AI developers for a six month ban on the development of the tech.
This extreme anxiety sits in stark contrast to the optimism that greeted the arrival of AI and the excitement many of us have felt experimenting with ChatGPT and Bard.
For those of us who’ve spent years puzzling over the retirement planning conundrum, the prospect of technology that could source and update assumptions and projections to feed into a retirement planning model in an instant, and then manipulate data to give fully personalised proposals, is thrilling.
The planning profession has been making huge strides in this area of analysis, particularly using cash flow models. But the sheer number of variables means a two-dimensional graphic model is unlikely to fully capture the underlying maths involved.
Longevity, inflation, tax, bond returns, annuity rates, equity returns, charges, monthly spending, initial capital, dependents’ prospects, your partner’s data, state benefits, home maintenance budgets, debt, minimum income constraints, long term care... the list of relevant planning factors is very, very long. And then, a planner has to map individual and shared preferences too.
In our recent webinar on retirement planning, Chartered Planner and Pension Transfer Specialist, Kate Shaw told us how she sets about the challenge without the benefit of AI, but with her expertise and experience to guide the client.
Central to her approach was the need to ‘meet the client where they were’ – listening to what they want, feel, need, and hope to achieve.
I’m not sure AI would like such an open-ended philosophy. How would it seek to optimise its advice?
Would it take all the client’s data, search the internet for appropriate predictions and spit out a best course of action to ensure the lowest possible chance of dying with no money, having spent the most? Isn’t that the goal?
But what about alternative optimisations? What about maximum happiness before 85, with a minimum guaranteed income thereafter. Or greatest chance of maintaining habitual spending for the longest period, after which you’ll take your chances? They’re all options we might consider.
And what if the AI has other plans?
Given the complexity of the maths, what if this ‘tool of the future’ suggests a plan that maximises the share price of asset managers or insurance companies? And you can’t spot that. Or maximises the ‘unforeseen’ likelihood of having to take out equity release? Or simply minimises availability of dependence on state benefits, if it were a government approved model to fill the advice gap?
The more I think about it, the more I like the idea of talking to people like Kate who are prepared to ‘meet me where I am.’ And, more importantly, have the courage to admit it might all be so complex that we just have to be prepared to adapt now and again.
Listen to Pat in conversation with Kate Shaw here.
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