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Artificial intelligence software reduces productivity and harms the working environment, according to a study from the freelance platform, Upwork. 
The findings appear to question how AI is being deployed at present, with a lack of understanding between staff and management on aims and expectations. The data suggests a long way to go before the advantages of the evolving technology are fully utilized. 
The research was carried out on behalf of Upwork by Walr, earlier this year. 2,500 workers from the US, Canada, the UK, and Australia were polled. The respondents consisted of 1,250 C-suite executives, 625 full-time, salaried employees, and 625 freelancers, made up of different demographics between the ages of 18 and 78. 
One of the headline findings from the survey points to an immediate disconnect. Management is expecting and encouraging workers to use AI to increase productivity but the demand for more comes at a cost. 
“Seventy-one percent are burned out and nearly two-thirds (65 percent) report struggling with increasing employer demands,” according to the survey. “Alarmingly, one in three employees say they will likely quit their jobs in the next six months because they are burned out or overworked.”
Bosses are also asking staff to expand their skill sets (35%), take on a wide range of responsibilities (30%), return to the office (27%), work more efficiently (26%), and work more hours (20%).
Workplace leaders expect generative AI will help boost productivity compared to existing levels (96%), with 39% of companies in the study already deploying AI tools, and encouraging the use of the tech.
Failing to unlock the productivity value of AI
The dividend has not yet arrived, nor has the feeling of benefit with ordinary workers. Around half (47%) of workers already using AI say they don’t know how to reach the productivity gains expected.  77% stated AI tools have had the opposite effect, diminishing productivity and adding further to the workload.
An interesting contradiction of sorts is that  65% of employees share expectations on machine learning making them more productive, but the reality stands out from the perception. 
40% of workers feel too much is being asked of them with AI. The lack of preparation is heightened with 37% of C-suite leaders at firms using AI rating their workforce as skilled and ready to use the tools. That contrasts with just 17% of workers who agree with the statement. 
Almost all (96%) execs see AI driving productivity but just over a quarter (26%) have implemented training courses, and a lowly 13% have reported a “well-implemented AI strategy. 
The survey shows the disconnect in the workplace over AI and is reflected by the comments of  Kelly Monahan, managing director of The Upwork Research Institute who stated: 
“Our research shows that introducing new technologies into outdated work models and systems is failing to unlock the full expected productivity value of AI.” 
“While it’s certainly possible for AI to simultaneously boost productivity and improve employee well-being, this outcome will require a fundamental shift in how we organize talent and work.”
Image credit: Via Ideogram
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Artificial intelligence is here already, it is tipped to own the future, but a new theory puts forward the case that AI “inbreeding” could lead to self-inflicted demise.
From Google to Microsoft, OpenAI, and many smaller startups at the other end of the scale, LLMs and the AI tools they train have changed technology as we know it. AI is here to stay, it is already making a significant impact on our online world and everything that runs through it. 
Further evolution appears inevitable but at what cost, and to what extent? We know already it is extremely expensive to provide the engine room that powers AI, with the server-heavy system just about coping. The advances in AI are remarkable but so is the depth of resources required for the big tech companies to underpin the system.
Irreversible defects in the resulting models
“Model collapse” considers that we might already have witnessed the peak of AI models, presenting the theory that as more and more web content becomes AI-generated, the tech will eventually “inbreed” on existing trained data leading to flaws as human-led data becomes increasingly scarce.
A new paper published in Nature delves further into this concept, which includes the following explanation:
“We find that indiscriminate use of model-generated content in training causes irreversible defects in the resulting models, in which tails of the original content distribution disappear,” states the abstract. “We refer to this effect as ‘model collapse’ and show that it can occur in [Large Language Models] as well as in variational autoencoders (VAEs) and Gaussian mixture models (GMMs).”
Big tech firms are drawn to the bright, shiny advances of AI, and the spike in shares that it propels. That is not the same as underlying profit which eventually will take its toll, and what of the climate cause? AI is power-intensive, requiring data center expansion and further demands on resources.
The dial is unlikely to be shifted anytime soon by the concept of “model collapse” but it is another interesting question that will be answered further down the line.
Image credit: Via Ideogram
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