Tools & Platforms
Using AI to advance skills-first hiring
The deployment of artificial intelligence is already transforming the world of work, with emerging effects that include the displacement of entry-level workers, rising demand for new AI skills, and fundamental changes in how tasks are executed across industries. AI’s undoubtedly dramatic future impact is not just a function of the technology, but also of how individuals and institutions choose to use it. So, how might we use this engine of disruption—and opportunity—to shift its trajectory toward inclusive outcomes in the labor market?
For decades, the bachelor’s degree has served as a proxy for employability for many middle- and high-wage jobs, effectively screening workers who are skilled through alternative routes (“STARs”) out of key economic mobility opportunities. Now, with the enhanced technological capabilities of generative AI, we can do better. We have the opportunity to replace that blunt proxy with something far more precise: a dynamic assessment of the actual skills of our workforce, as well as a better understanding of how workers acquire and deploy them in real time. Used this way, AI could just as well stand for “amplified intention.” If applied equitably and transparently, AI can help shift hiring systems from exclusion to inclusion—redefining opportunity in the 21st century labor market.
Wires crossed: Understanding the broken labor market
Before we assess how AI can change the world of work for the better, we must first examine the forces that led to today’s broken labor market. Today, over 70 million STARs form the backbone of the U.S. workforce. STARs were once the drivers and beneficiaries of a tremendous surge in upward economic mobility in the decades following World War II. However, in the early 2000s, upward mobility stalled, with deleterious effects on talent pipelines, family incomes, and the broader contract with the American working class. Much of this decline was the result of companies responding to changing technologies with talent management practices that had unintended and often unacknowledged consequences.
In Opportunity@Work’s recently published “State of the Paper Ceiling” report, we explain that between 2000 and 2019, a “paper ceiling”—invisible barriers such as degree screens, biased algorithms, stereotyping, and exclusive professional networking—caused STARs to lose access to almost 7.5 million jobs that had traditionally provided pathways to upward mobility. This happened despite the fact that over 30 million STARs demonstrated skills for higher-wage jobs, as employers changed core business practices and began to rely on flawed algorithms to sort through applications and identify and evaluate talent.
Rewiring the system: Progress in skills-first hiring proves what’s possible
“State of the Paper Ceiling” offers a perspective on how to reverse this harm and expand upward mobility, with implications for our use of AI. Consider the aftermath of two recent crises. After the 2008 financial crisis, the downward trend for STARs accelerated as “screening out” by degrees proliferated. However, following the pandemic-induced contraction of 2020, STARs fared somewhat better; although they still lost more ground and recovered less quickly than workers with degrees, the decline was less severe. These two events were characterized by different fiscal and monetary responses that impacted labor market opportunities and wages, while the 2020 recovery was also influenced by a growing skills-based hiring movement.
Over the past five years, Opportunity@Work has collaborated with partners across the nonprofit, public, and private sectors to launch the Tear the Paper Ceiling campaign, which highlights STARs’ contributions to the workforce and the barriers to mobility they face. Recognition of the “paper ceiling” has increased steadily since the inception of the campaign, with 38% of employers citing familiarity with the term and 15% of STARs self-identifying as STARs (both up from 0% pre-campaign). These shifting perceptions are a starting point for broader social change.
Changes in organizational culture and practice, especially in large companies and government entities, require realignment of incentives and resources. They also take time, but these shifts in perception are beginning to drive important changes in behavior. Employers, policymakers, and STARs themselves are contributing to new models of inclusive hiring. Over half of U.S. state governments have committed to modernizing hiring practices to enable more STARs to enter middle- and high-wage jobs in the public sector. In the private sector, companies are testing and iterating skills-based hiring practices. And across both the public and private sector, HR technology companies are evolving their solutions to support these skills-first employers.
‘Amplified intention’: Leveraging AI to rewire the labor market
The U.S. economy is entering a profound technological and economic transition, one that requires intention to maximize societal benefit and minimize risk. So, we should think of AI as “amplified intention”—a technology designed to observe, replicate, and accelerate actions. In today’s labor market, if we direct AI to observe the patterns of the past, it will replicate the paper ceiling and accelerate its exclusionary effects. Instead, we should direct AI technologies to observe and understand workers’ skills and replicate skills-based pathways to accelerate the tearing of the paper ceiling, exponentially opening up opportunities to STARs and connecting employers to the skilled talent they need.
What would that look like in practice? First, let’s support employers to use AI for inclusion. With the analytic power to review millions of job descriptions, job performance data, job transitions, and more, we have the ability to refine skill taxonomies for a better understanding of the skills needed for jobs and the many, varied ways those skills are attained. With this knowledge, we will be better equipped to support American employers to create new job categories, clarify skills-based pathways, and broaden access to jobs that require valuable but often overlooked skills.
Second, let’s help workers leverage AI to adapt. In a labor market where new skills are emerging at a high velocity, where AI technologies are augmenting existing skills in workers, and where skills can create an increasingly agile workforce positioned for a wider range of roles, AI can also equip workers with the tools to keep up, enabling them to pivot more easily across tasks as demands shift. These technologies may very well serve to enhance—rather than replace—workers’ existing strengths.
AI can supercharge a STARs-powered economy in this next generation. But because the broad use of AI promises to increase our reliance on algorithms, it is essential to consider the data and reasoning behind these algorithms in order to avoid the replication and amplification of past and present biases. If we succeed, this moment of disruption could serve as a moment of opportunity.
Chinese Big Tech player Baidu (NASDAQ: BIDU) has announced upgrades to its search engine, adding new artificial intelligence (AI) functionalities into the service, its biggest improvement in over a decade.
According to a CNBC report, Baidu is opting to innovate its search engine to remain viable, as studies highlight a trend of users turning to AI-powered chatbots for answers.
Baidu’s latest changes to its core search product will allow users to enter over a thousand characters in the search box. Previously, users were limited to only 28 characters, reducing search precision and requiring keyword prioritization.
Going forward, users can conversationally ask questions on the search engine, akin to how they interact with chatbots. Furthermore, Baidu is improving its voice search and image prompts.
Lastly, Baidu’s biggest upgrade is the integration of its AI chatbot into the search product. The integration will allow users to use AI to generate text, images, and video on Baidu Search.
Morning Star strategist Kai Wang disclosed that the changes to the Search product are designed to mirror how consumers interact with mainstream AI products. Baidu’s search users have fallen, with several users opting for AI chatbots for their search requirements.
Baidu Search faces stiff competition from China-based AI heavyweights like DeepSeek and Tencent (NASDAQ: TCTZF). Furthermore, short video platforms are turning their gaze to AI Search, slashing off a significant chunk of Baidu’s market share.
Despite the new pressure on Baidu from its rivals, the company took the lead with AI back in 2023 with the release of its Ernie Bot chatbot. In less than six months, Baidu racked up 100 million Chinese users to lead its peers, announcing several AI products to maintain its headstart in the local scene.
However, new entrants are catching up with Baidu with their range of AI products. The stiff competition has sent Baidu stock inching up by only 2.5% since the start of the year, while AI heavyweights Alibaba (NASDAQ: BABA) and Tencent have gained 30.5% and 20% respectively in the same window.
Google racing to innovate Search
Outside of China, Google Search (NASDAQ: GOOGL) is also facing challenges driven by the rapid adoption of AI chatbots. To stay ahead of the curve with emerging technologies, Google has rolled out new AI policies for its Search product, presenting AI summaries for queries ahead of website links.
Furthermore, Google says it integrates its AI mode directly into Search, allowing users to improve their queries and get conversational responses. The U.S.-based search giant has unfurled its independent AI chatbots, providing stiff competition to traditional AI companies like OpenAI and Anthropic.
Beijing schools to integrate AI into learning curriculum
A new report has confirmed that primary and middle schools will adopt AI classes into their existing curriculum to keep up with digitization.
According to an official document released by the Beijing Education Commission, the AI classes will begin in September at the start of a new academic year. Dubbed the Curriculum Outline for Artificial Intelligence Education in Primary and Secondary Schools in Beijing, the report suggests that the new AI classes are a trial before a main rollout.
For each academic year, pupils across primary and middle school will have at least eight class hours on AI. Upon full rollout, there are suggestions that the number of hours may increase, matching the hours in secondary schools.
The curriculum will attempt to achieve three key objectives. The Beijing Education Commission will focus on AI awareness and cognitive abilities, AI applications and innovation capabilities, ethics, and social responsibility.
The new curriculum attempts to step up from basic IT knowledge to promote critical thinking skills in pupils. Furthermore, the report notes that AI skills will form part of the comprehensive assessments of Beijing students.
Schools in the capital city of China will be free to teach AI courses independently or merge them with other subjects. The report name-checks information technology, science, and emerging technologies as potential courses for schools to integrate with AI.
“We expect that under the new guidance, an integrated AI educational innovation scenario from primary schools to middle schools could be built, which will better help the education sector seize the opportunities brought by the AI technological reforms,” Li Yuxin, principal of Beijing Bright Horizon Foreign Language Primary School, said.
Pundits have hailed the curriculum for aligning with the goals of general AI education, given its tailor-made design for elementary students. In May, the Chinese Ministry of Education launched new guidelines for AI use in classrooms, prohibiting students from submitting AI-generated text as their original work.
AI to become mainstay in global classrooms
Across several jurisdictions, regulators are bracing for the adoption of AI tools in classrooms. Technology firms are leading the charge via launching innovative products for students and teachers, with Khanmigo and Speechify emerging as frontrunners.
However, Japan’s regulators are limiting the use via key guardrails, including age restrictions and a blanket ban against their use in examinations. The United Nations also urges tighter AI restrictions in schools, citing a raft of ethical considerations, including age restrictions and the emotional well-being of younger students.
In order for artificial intelligence (AI) to work right within the law and thrive in the face of growing challenges, it needs to integrate an enterprise blockchain system that ensures data input quality and ownership—allowing it to keep data safe while also guaranteeing the immutability of data. Check out CoinGeek’s coverage on this emerging tech to learn more why Enterprise blockchain will be the backbone of AI.
Watch: AI is for ‘augmenting’ not replacing the workforce
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Chinese companies are doubling down on open-source artificial intelligence-powered models as part of a broader push to bring fast-evolving AI technology to more businesses and developers worldwide, and bolster its application in a diverse range of fields.
Experts said the open-source approach will lower the threshold for the development and application of AI, greatly reduce computing power costs, and foster the sharing of AI tech around the world, as well as boost collaboration and innovation.
The recent progress in open-source large language models has showcased China”s growing technological prowess and open attitude in the AI domain, given that Chinese AI startup DeepSeek’s open-source models have taken the world by surprise, they added.
Tech heavyweight Alibaba Group has stepped up efforts to enable broad access to its AI technology and innovations by releasing large language models from its Qwen family as open-source, and boasting China’s largest AI open-source community platform, ModelScope.
The company has made more than 200 generative AI models open-source in recent years. The models have multimodal capacities and can process and generate various types of content, covering text, images, audio and video.
ModelScope, which was launched in November 2022, hosts over 70,000 open-source models, and the user base has expanded from 1 million in April 2023 to 16 million as of June 30, serving 16 million developers from 36 countries around the world.
It supports developers in experiencing, downloading, fine-tuning, training and deploying models. Various types of open-source AI models have been included in the community.
“We aim to simplify and reduce the cost of developing, customizing and deploying AI models for developers and corporations, thereby enabling the creation of revolutionary AI applications that have a positive impact on society,” said Zhou Jingren, chief technology officer at Alibaba Cloud Intelligence, emphasizing they are committed to making AI models more accessible and easier to use.
Baidu Inc has recently open-sourced its multimodal LLM Ernie 4.5 series, consisting of 10 distinct variants. The model family includes mixture-of-experts (MoE) models with 47 billion and 3 billion parameters, the largest model having 424 billion parameters, alongside a 0.3 billion dense model. The Ernie 4.5, launched in March, is Baidu’s multimodal foundational model.
The company said the MoE architecture has the advantages of enhanced multimodal understanding and improved performance on text-related tasks. All models are trained with optimal efficiency using the PaddlePaddle deep learning framework, which enables highper-formance inference and streamlined deployment.
Experimental results show that the models achieve state-of-the-art performance across multiple text and multimodal benchmarks, particularly in instruction following, knowledge memorization, visual understanding and multimodal reasoning.
Zhu Keli, founding director of the China Institute of New Economy, said the open-source approach adopted by a string of Chinese AI companies will lower the technical threshold, speed up the popularization of AI tech across various sectors including automobiles, manufacturing, finance and education, and allow more enterprises and developers to participate in AI research and development.
Zhu believes technological innovation is unstoppable, and international cooperation serves as an important way to promote the development of AI tech, adding that China’s open and inclusive attitude helps promote the advancement of the global AI industry.
“Open source will allow resource-constrained startups, small businesses and entrepreneurial developers to access cutting-edge AI tech and build their own models more cost-effectively,” said Pan Helin, a member of the Expert Committee for Information and Communication Economy, which is part of the Ministry of Industry and Information Technology.
Pan said it will accelerate AI technological advancements and breakthroughs by enabling global developers to create customized industry-specific models, and foster a more competitive and diverse AI ecosystem.
Chinese AI companies have the ability to take the lead in global AI innovation, as they have sought an alternative AI development approach that emphasizes efficiency and open-source collaboration — which is different from their US counterparts — while reshaping the global AI landscape, Pan added.
The market size of the nation’s AI sector will reach 1.73 trillion yuan ($241.2 billion) by 2035, accounting for 30.6 percent of the global total, said market research firm CCID Consulting.
To enhance its uranium exploration in the Athabasca Basin, Stallion Uranium (TSX-V: STUD; OTCQB: STLNF) has partnered with Matthew J. Mason, enabling it to access proprietary technology for advanced data analysis. This agreement allows Stallion Uranium to utilize Haystack’s AI-driven geological targeting system. By integrating this platform, Stallion Uranium sharpens its precision in identifying targets and lowers the risks associated with exploration. It actively applies this technology to uncover previously undetected opportunities and optimize the value of its uranium holdings.
Haystack, headquartered in Vancouver, BC, provides an innovative mineral exploration platform called Matchstick TI, which operates with AI. Their predictive technology employs a proprietary algorithm developed over a decade in Cambridge, UK. By combining theoretical physics, data science, and pattern recognition, Matchstick TI achieves a 77% accuracy rate in predicting target locations using public data. This technology accelerates discoveries and minimizes financial risks.
Stallion Uranium plans to use this technology to confirm and define additional targets across its 1,700 sq km land position. Its team collaborates with leading data science and geoscience experts to ensure a thorough and innovative approach to target selection, positioning the company as a leader in technological advances within uranium exploration.
Matthew Schwab, CEO of Stallion Uranium, said: “The application of machine learning in mineral exploration is transforming the industry, and we are excited to integrate this powerful tool into our exploration strategy. By deploying advanced analytics, we aim to enhance our ability to identify high-priority targets, reduce exploration risk, and maximize the potential of our uranium assets.”
For more information visit www.StallionUranium.com.
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