1. Resume Builder survey reveals business leader expectations for AI in 2024

Of 750 business leaders surveyed by Resume Builder, 96 per cent of those from companies hiring in 2024 say candidates will benefit from having AI skills on their resumes. Some 83 per cent report that AI skills will help current employees retain their jobs, while 44 per cent said that AI will lead to layoffs in 2024.

2. Digital customer experience teams are embracing AI, CMSWire finds

A report by CMSWire surveying companies on their digital customer experience (DCX) revealed that DCX teams are embracing generative AI (GenAI) in their work and customer journeys. With the leading choice being ChatGPT, 39 per cent are using some form of GenAI in their day-to-day work.

3. Using AI, Climate TRACE identifies GHG emissions from more than 353M assets

With the help of AI, the Climate TRACE coalition was able to expand its global dataset of assets that produce greenhouse gas emissions from 80,000 in 2021 to 352 million in 2022. The organisation says its inventory provides “unprecedented granularity that pinpoints nearly every major source of greenhouse gas (GHG) emissions around the world and provides independently produced estimates of how much each emits”. Its most recent data shows that global emissions increased by 1.5 per cent from 2021 to 2022.

4. Study explores the use of AI to guide future AI research

An international team of academics has used AI to predict the future direction of AI research itself. They write, “We envision a computer program that can automatically read, comprehend and act on AI literature. It can predict and suggest meaningful research ideas that transcend individual knowledge and cross-domain boundaries. If successful, it could greatly improve the productivity of AI researchers, open up new avenues of research and help drive progress in the field.”

5. Military agencies lead way in US government grants for AI research

An analysis by the Center for Security and Emerging Technology found that grants for AI research accounted for around 9 per cent of all US government research grants awarded over the past five years. However, AI grants from the Navy, Air Force and Army made up a much higher percentage of those departments’ total grants – around 25 per cent for each.

6. McKinsey: GenAI could make US racial economic gap worse

Unless deployed thoughtfully, GenAI could exacerbate the existing racial economic gap in the US by $43 billion a year by disproportionately automating jobs in which Black workers are overrepresented, according to research from McKinsey. Those jobs include office support, production work, food services, and mechanical installation and repair. “According to our analysis of 2022 data, some 24 percent of all Black workers are in occupations with greater than 75 percent automation potential, compared with just 20 percent of White workers,” McKinsey stated.

7. AI beliefs affect individual experiences – and also alter AI behaviour

People’s preconceived notions about AI tools can affect how helpful and trustworthy they find such tools to be, and also impact the AI’s behaviour, a team of US researchers found. When told the AI they’d be talking with was “caring”, 89 per cent of study participants reported they found that to be the case; by contrast, when told the AI was manipulative, fewer reported having a positive experience. “This work highlights the importance of AI narratives in society, as they can shape our expectations and thus our experiences with AI,” the researchers wrote.

8. ONS finds positive expectations for AI in healthcare, shopping and education

The ONS’ Opinions and Lifestyle Survey found that 43 per cent of respondents believe there are equal benefits and risks to using AI, with only 14 per cent expressing an expectation of more benefits than risks. Areas where respondents expect an improvement thanks to AI include access to healthcare (31 per cent), shopping experiences (27 per cent) and access to learning or education (25 per cent).

9. Study: Generating a single image with AI could use as much energy as a smartphone charge

Training machine learning (ML) models is far more energy- and carbon-intensive than the inference process – where ML applies that training to make predictions based on new data. But a team of researchers from Hugging Face and Carnegie Mellon University argues that it’s important to understand the energy and carbon costs of inference tasks because they occur far more frequently – “as many as billions of times a day for a model powering a popular user-facing product such as Google Translate”. The team’s study of various inference tasks found that the energy and carbon costs were highest for those involving image captioning and image generation – ranging from 0.06-2.9 kWh for 1,000 inferences. This means the least efficient image generation model uses nearly as much energy per image as a single smartphone charge.

10. US business use of AI is highest in information, professional services sectors

Only 3.8 per cent of all businesses in the US currently use AI to produce goods and services, although usage is 13.8 per cent among businesses in the information sector and 9.1 per cent in the professional, scientific and technical services sector, according to the US Census Bureau’s latest Business Trends and Outlook Survey. The survey also found that overall business use of AI is expected to grow to 6.5 per cent over the next six months.