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LYNNE WALSH reports on the Future of Work event at the weekend, which saw a panel of expert speakers discuss automation, deskilling and the effects of Covid-19 on the workplace
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IN THE week which saw an international report warning that the pandemic would continue to drive down wages, a panel of expert speakers focused on the Future of Work.
Against a backdrop of the International Labour Organisation (ILO) data showing that the crisis was “likely to inflict massive downward pressure on wages in the near future,” the Communist Party of Britain brought together activists and academics to take a forensic look at the causes of low wages, precarious work and shoddy treatment of workers.
Launching the ILO report last week, director-general Guy Ryder said: “The growth in inequality created by the Covid-19 crisis threatens a legacy of poverty and social and economic instability that would be devastating.
“Our recovery strategy must be human-centred. We need adequate wage policies that take into account the sustainability of jobs and enterprises, and also address inequalities and the need to sustain demand.
“If we are going to build a better future we must also deal with some uncomfortable questions about why jobs with high social value, such as carers and teachers, are very often linked to low pay.”
A key focus at the online seminar was the “gig economy,” that spurious phrase aimed at hoodwinking workers into notions that they are more in control of their labour. In reality, it has meant insecurity, stress, ill-health and penury for many.
Dr Leonardo Impett, specialist in artificial intelligence (AI) and currently assistant professor of computer science at the University of Durham, brought a Marxist perspective to the way that tomorrow’s world of work is being steered.
Debunking some popular beliefs about the use of AI — and the ability of robots — Impett said that the media’s shorthand use of the term AI actually referred to “machine learning.”
This was how computers or robots learned from experience, either from hand-labelled examples given to them — supervised learning — or their own past mistakes — reinforcement learning.
“That second one is only of any use in cases where you know if you’ve won or lost, like if you’re playing a chess game or tennis, say.
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