How Generative AI may impact the future of work; ChatGPT for businesses and Meta’s SeamlessM4T
In January, McKinsey wrote that one in 16 workers may have to switch occupations by 2030, which is more than 100 million workers across the eight economies it studied and referred to in its report on the future of work. Here are its key findings: That job growth will be more concentrated in high-skill jobs (for example, in healthcare or science, technology, engineering, and math (STEM) fields), while middle- and low-skill jobs (such as food service, production work, or office support roles) will decline. A few job categories could see more growth than others.
As examples, the report cited the rise of e-commerce created a demand for warehouse workers; investments in the green economy increased the need for wind turbine technicians; the rise of ageing populations in many advanced economies increased demand for nurses, home health aides and hearing-aid technicians; and the importance of teachers and training instructors over the coming decade.
The report, however, qualified that jobs at grocery stores could be at risk with outlets increasingly installing self-checkout counters. The report added that there might be a need for fewer clerks and robotics to process routine paperwork, which may lessen demand for some office workers. You may read the full report here.
In July, McKinsey wrote about ‘Generative AI and the future of work in America’. Among other things, the report said activities that account for up to 30% of hours currently worked across the US economy could be automated by 2030—a trend accelerated by Generative AI. The reason is that unlike traditional machine learning (ML) that can analyze data patterns to make predictions, Generative AI foundational models and large language models (LLMs) can learn the structure of almost any information--be it text, images, video, proteins, DNA, physics, etc.--and generate new content with the help of ‘prompts’.
The report added, though, that generative AI is enhancing the way STEM, creative, and business and legal professionals work rather than eliminating a significant number of jobs outright. Automation’s biggest effects are likely to hit other job categories, with office support, customer service, and food service employment likely to decline. Federal investment to address climate and infrastructure, as well as structural shifts, will also alter labour demand.
The net-zero transition will shift employment away from oil, gas, and automotive manufacturing and into green industries for a modest net gain in employment. Infrastructure projects will increase demand in construction, which is already short of almost 400,000 workers today. We also see increased demand for healthcare workers as the population ages, plus gains in transportation services due to e-commerce, the report said.
As people leave shrinking occupations, the economy could reweight toward higher-wage jobs. Workers in lower-wage jobs are up to 14 times more likely to need to change occupations than those in highest-wage positions, and most will need additional skills to do so successfully. Women are 1.5 times more likely to need to move into new occupations than men, according to the report. You may read the full report here.
Last month, I wrote about ‘The big jobs debate: Who’s at risk from GenAI?’, where I emphasized that while previous tech waves impacted the blue-collar worker, the white-collar employee’s future is more threatened. Here are a few highlights from the article: An OpenAI report this March suggests that four in five US workers (80%) could have at least 10% of their tasks automated by Generative AI, and one in five (19%) could see at least half of their responsibilities affected.
Goldman Sachs predicts that generative AI could expose the equivalent of 300 million full-time jobs to automation, while a Microsoft report says 74% of Indian workers are worried that AI will replace their jobs. Jobs of content creators, artists, media persons, coders, customer care agents, bank tellers, postal service clerks, data entry operators, and paralegals appear to be most impacted.
According to a 1 August McKinsey report, those working in the technology and financial services sector are the most likely to expect disruptive change from Generative AI simply because industries relying most heavily on knowledge work are likely to see more disruption while potentially reaping more value at the same time. These would include tech companies, banking, pharmaceuticals, medical products, and education.
Emad Mostaque, CEO of Stability AI, known for its text-to-image generator tool Stable Diffusion, reportedly told UBS analysts in June that Indian engineers working in the information technology sector would be badly impacted as AI deployment by multinationals would lessen the work being outsourced.
Surveys conducted for the Future of Jobs Report by the World Economic Forum (WEF) corroborate this trend, suggesting that the highest job growth in 2023-2027 will be for agricultural equipment operators, drivers of heavy trucks and buses, and vocational education teachers, followed by mechanics and machinery repairers and business development professionals.
For instance, WEF expects jobs for agricultural professionals to rise by 30% in the coming five years, spurred by the increasing use of agricultural technologies and investments in climate change. The education sector, too, is expected to see an increase in jobs, with more people taking up courses to upgrade their skills in AI and other technologies.
As Martin Ford, author of Rule of the Robots: How Artificial Intelligence Will Transform Everything, told the BBC, “The white-collar employee’s future is more threatened than the Uber driver because we still don’t have self-driving cars, but AI can certainly write reports.” You may read the full article here.
ChatGPT for businesses
On 28 August, OpenAI launched the much-awaited ChatGPT Enterprise, which offers features including security and privacy, unlimited GPT-4 access, longer context windows for processing longer inputs, advanced data analysis capabilities, and customization options. Early adopters of ChatGPT Enterprise include companies such as Block, Canva, Carlyle, The Estée Lauder Companies, PwC, and Zapier.
Picture courtesy of Mint
In May, I wrote on ‘Generative AI: Why boardrooms must embrace the next frontier of innovation’ in which I pointed out that senior executives, including chief executive officers, chief revenue officers, chief information officers, and chief technology officers of companies are waking up to the transformative yet disruptive generative AI multimodal models and tools like ChatGPT, Dall-E, Mid-Journey, Stable Diffusion, Bing, Bard, and LLaMA.
On the one hand, they fear that any inertia on their part will cost them dear. On the other hand, they are reluctant to go full steam ahead since the current limitations of these models could compromise the security, integrity, and accuracy of their internal company data.
Nevertheless, Generative AI is already being discussed in global boardrooms. While AI was discussed by 17% of CEOs in the January-March quarter of this calendar year, spurred by the release of ChatGPT and the discussions around its potential use cases, Generative AI was specifically discussed by 2.7% of all earnings calls, and conversational AI was mentioned in 0.5% of all earnings calls--up from zero mentions in the October-December quarter, according to the latest ‘What CEOs talked about’ report by IoT Analytics--a Germany-based markets insight and strategic business intelligence provider.
You may read the full article here.
Meta’s SeamlessM4T
Meta has publicly released SeamlessM4T (Massive Multilingual Multimodal Machine Translation), a multimodal model that supports nearly 100 languages for input (speech + text), 100 languages for text output and 35 languages (plus English) for speech output.
Source: Meta
You may download the model from GitHub. Arguing that existing translation systems have limited language coverage, creating barriers for multilingual communication, and rely on multiple models, often causing translation errors, delays, and deployment complexities, Meta claims that SeamlessM4T addresses these challenges “with its greater language coverage, accuracy and all-in-one model capabilities”. You may read more about it here.
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