The ability to sense what is happening in the body, a skill known as interoception, varies from person to person. But the good news, for those seeking to【C1】_____overthinking and other problematic behaviors, is that there is substantial【C2】__that interoception can be【C3】_____and that improving it can benefit our mental health.

【C4】_____any kind of physical exercise is likely to improve your mood, the ability to maintain sensory awareness may be the key to【C5】__habit’s grip. An investigation in 2023 tracked brain activity in 22 people who had to【C6】__to their breath.【C7】__, participants tracked either their breath or the pulsing of a circle on a screen. During this task, it【C8】__, following one’s breathing seemed to deactivate the higher cortical functions that researchers【C9】__with “doing” or “problem solving” brain activity. Becoming more【C10】__of one’s body seemed to【C11】_____the brain’s spotlight on other inputs.

In the face of stress, our attention is too often devoted to analyzing why we feel badly, leaving us【C12】_____in the habit of “ managing negativity,” with little【C13】__for insight or novelty in each passing moment.【C14】__, people can engage in what we call “ sense foraging,” or making time to focus on our varied physical sensations. Sticking with sensation【C15】__overthinking’s temptation allows curiosity, novelty and new learning to interrupt established【C16】_____.

To【C17】_____the benefits of sense foraging, we simply need to tune into our【C18】__more frequently.【C19】__we’re stressed or stuck, pausing to notice and feel the dynamic, vibrant world around us can provide a moment’s【C20】_____to our resilience, well-being, health and creativity. Where we go from there is up to us.

“We must crack down on low-value university degrees.” Who claimed that and when? It might have been Rishi Sunak last October. Or Sunak last July. Or Sunak the previous August. This time, it was Sunak on the election trail last week. Sunak promised to scrap “rip-off degrees” , replacing them with 100,000 vocational apprenticeships. Politicians keep repeating this argument, seeing it as tapping into popular hostility towards the “ university-educated liberal elite”.

What, though, is a “low-value” degree? For many policymakers, the worth of a degree is measured primarily by metrics such as the proportion of students who fail to complete their course and the number who land high-skilled, well-paid jobs. Ironically, though, the highest dropout rates at universities are in computer sciences, business and administrative studies, and engineering and technology. The lowest, apart from medicine, dentistry and veterinary science, are in languages and historical and philosophical studies. Stereotypes and reality don’t necessarily coincide.

Also often ignored is the impact of class on student experiences and outcomes. A study last year by London South Bank University’s Antony Moss showed that students who had been eligible for free school meals, an indicator of poverty, are less likely to complete their degree, achieve a good grade or get into a graduate-level job or further study. Improving the quality of courses barely challenged such inequality.

The background to all this is the colonisation of policymaking by a more instrumental view of education as valuable primarily because of its economic benefits, whether personal or national. It is a perspective that has turned universities into businesses, students into consumers and knowledge into a commodity. The notion of learning as being a good in itself, as a means of elevating the quality of our lives, is now scorned as hopelessly naive, or at least as something that should be the preserve of elite students.

The instrumental view of education is often presented as a means of advancing working-class students by training them for the job market. In reality, what it does is tell them to study whatever best fits them for their station in life. As the Observer’s Martha Gill observed last year, many politicians and commentators value university education as a means “ to elevate human lives and nourish the soul, regardless of any job market benefit”—but only for a certain class of people. “The sorts of students to whom we tend to apply a financial—rather than spiritual—calculation when it comes to higher education,” she wrote, “tend to be those from poorer backgrounds.”

For the affluent, education is about enriching the soul. For working-class students, it is viewed primarily as a route to the job market. They are seen as the kind of people who benefit from more “vocational” learning. As education analyst Jim Dickinson observes-. “When we say ’low-value’ courses, isn’t there a danger that we really mean ’low-value students’?”

The world’s largest philanthropy, the Bill & Melinda Gates Foundation, recently took a radical step aimed at giving preprints—freely available draft manuscripts that have not been peer reviewed—a much more prominent role in science. Starting in 2025, the foundation will require grantees to post as preprints all manuscripts that result from research it funds. It will also stop paying for researchers to publish their papers in journals that charge a fee to make papers free.

In an online Q&A., the foundation says the move to preprints will allow researchers to share research results as soon as they’re ready, and not wait weeks or months for journals to complete their review processes. The policy could also encourage authors and journals to publish only their best manuscripts in journals, reducing the workload on peer reviewers, who are volunteers. The philanthropy says its policy also avoids pitfalls of the business model of article-processing charges (APCs) , which has been blamed for incentivizing journals to churn out papers of limited value and supporting predatory journals that publish papers with no peer review at all.

But Kent Anderson, a scholarly publishing consultant and longtime critic of the open-access movement, says promoting preprints would be a mistake. Without a robust peer-review system, a surge of preprints could lead to researchers and the public becoming “ even more confused and puzzled about what constitutes reliable scientific findings,” he wrote on the Geyser, his online newsletter. He also doubts preprint servers will ever routinely sponsor meaningful peer review. Screening and checking manuscripts “ are just costly headaches,” he wrote, “ and the expense level needed for these would far exceed the small grants preprint servers receive.”

“The jury’s still a little bit out on preprints, but it’s certainly a place of great innovation and dynamism,” says Alondra Nelson, a social scientist at the Institute for Advanced Study. But, “It’s clear that we need to reimagine peer review in some ways,” she adds, in part because the steady growth of published research papers is taxing the researchers who volunteer to conduct the reviews.

The Gates foundation policy aligns with calls by some open-access advocates to de-emphasize the role of journal articles in quality control and professional evaluations. Those advocates envision a future in which employers evaluate and promote researchers based on a selection of their best manuscripts, including reviewed preprints—a change that might ease the pressure on scientists to publish in highly ranked journals that are paywalled or charge APCs.

The impending introduction of the European Union’s Artificial Intelligence Act represents the alliance’s latest attempt to cement its status as a regulatory powerhouse. Meanwhile, the United States lacks a cohesive AI regulatory framework. Instead, a surge of litigation has overwhelmed US courts, with leading AI firms being sued for copyright infringement, data-privacy breaches, defamation, and discrimination.

Given that litigation is expensive and often drags on for years, the EU’s strategy may appear more forward-looking. But the common-law system might actually prove to be a more effective mechanism for tackling the myriad challenges posed by generative AI. This is particularly evident in copyright law and the core question is whether the training of large language models (LLMs) should qualify as fair use, a classification that would exempt tech firms from compensating content creators. For its part, the EU’s AI Act includes a provision mandating the disclosure of copyrighted materials, enabling copyright holders to opt out of AI training databases.

But the EU’s sweeping regulation could backfire if European regulators fail to strike an appropriate balance between innovation and equity in addressing the question of fair use. For starters, restricting the use of copyrighted materials for LLM training could raise data-acquisition costs, potentially curbing the growth of the AI industry.

At the same time, a growing number of commentators and policymakers have warned that without ensuring fair compensation for content creators, the creative sector could collapse. And the future development of AI technologies depends heavily on the availability of high-quality, human-generated content. As studies have shown, training AI models on Al-generated data could corrupt them, potentially to the point of complete failure.

To be sure, striking the right balance between these two conflicting policy priorities will not be easy. Imagine a scenario in which data for AI training are abundant, particularly in emerging areas like text-to-video generation. Under these circumstances, regulation has little effect on the amount of data available to startups aiming to refine their LLMs. By adopting a more permissive approach to fair use, regulators could enable firms to improve the quality of their models, thereby boosting profits for both AI companies and content creators and enhancing overall consumer welfare.

But these dynamics can shift quickly when data for training AI models—particularly models that rely heavily on new content—are scarce. In such a scenario, permissive fair-use policies could weaken incentives to produce new content, thereby shrinking the pool of data available for AI training. Moreover, the growing sophistication of AI models could exacerbate the crisis of training data shortage by making creators overly reliant on AI for content generation.

So what we really need is a regulatory model that is both adaptable and tailored to specific contexts. The EU’s AI Act, whose broad mandate applies to all firms regardless of their specific industry sectors, combined with the pace of AI development and the competitive structure of the market, increases the likelihood of serious unintended consequences. Consequently, the common-law system, which is based on case-by-case verdict, may turn out to be a more appropriate institutional framework for regulating AI.

The industrial revolution was one of the most important events in human history. Over a handful of decades, technological breakthroughs kicked economic output off its centuries-long low plateau and sent populations, living standards and life expectancy soaring. Yet for all its vital importance, there is still disagreement over why all this took off.

One of the most compelling arguments comes from US economic historian Robert Allen, who argues that Britain’s successes in commerce in the 16th and 17th centuries pushed wages up and energy costs down, creating strong incentives to substitute energy and capital for labour and to mechanise manufacturing processes. Others place greater emphasis on the role of UK institutions, while some argue that innovative ideas emerged as a result of increasing interactions among growing and densifying populations.

Another interesting theory is that of economic historian Joel Mokyr, who argues that it was broader cultural change that laid the groundwork for the industrial revolution. Prominent British thinkers including Francis Bacon and Isaac Newton championed a progress-oriented view of the world, centred on the idea that science and experimentation were key to increasing human wellbeing.

While persuasive, Mokyr’s theory has until recently been only that; a theory. But a fascinating paper published last month by a quartet of economists puts some evidence behind the argument. The researchers analysed the contents of 173,031 books printed in England between 1500 and 1900, tracking how the frequency of different terms changed over time. They found a marked increase in the use of terms related to progress and innovation starting in the early 17th century.

Extending the same analysis to the present, a striking picture emerges: over the past 60 years the west has begun to shift away from the culture of progress, and towards one of caution, worry and risk-aversion, with economic growth slowing over the same period. The frequency of terms related to progress, improvement and the future has dropped by about 25 per cent since the 1960s, while those related to threats, risks and worries have become several times more common. That simultaneous rise in language associated with caution could well be not a coincidence but an equal and opposite force acting against growth and progress.

Ruxandra Teslo, one of a growing community of progress-focused writers at the nexus of science, economics and policy, argues that the growing scepticism around technology and the rise in zero-sum thinking in modern society is one of the defining ideological challenges of our time.

Some may counter that a rebalancing of priorities from perpetual advancement to caution is a good thing, but this could be a catastrophic mistake. As well as economic growth, the drive for progress brought us modern medicine, significantly longer and healthier lives, plentiful food supplies, dramatic reductions in poverty, and ever more and ever cheaper renewable energy. The challenges facing the modern world will be solved by more focus on progress, not less. If we are to avoid backsliding, advocates for innovation, growth and abundance must defeat the doomers.

[A] Honor the glory days and box up the prizes

[B] Get motivated from the past achievements

[C] Cultivate a spirit of giving instead of taking

[D] Take an honest inventory of your unique talents

[E] Utilize technologies to enhance personal skills

[F] Focus on abilities technology can never replace

[G] Take on new opportunities to stretch

Face Your Fear of Becoming Obsolete

Professionals across the career spectrum have moments where they fear they’re already obsolete, or becoming so. Whether you’re early in your career and facing a lifetime of technological and economic disruption, or later in your career and questioning your future relevance to the world, feelings of obsolescence don’t have to trap you in fear or futility. If you suspect that you’re haunted by deep fears of obsolescence, here are some ways to begin breaking free of them and reclaiming your agency, regardless of where you are in your career.

【G1】____________________________________

When we feel insecure about the relevance of our abilities, it becomes all too easy to see them through distorted lenses, either over-inflating their merits or undervaluing them. Gather tangible evidence of your contributions through feedback from colleagues and find out what they believe makes yours uniquely valuable. Ask experts you respect about which capabilities you should focus on strengthening to keep them valuable and relevant in the future.

【G2】____________________________________

For earlier-career employees particularly anxious about technologies like AI and robotics, don’t try and outrun their productivity or analytical powers. Instead, lean into human capabilities like empathy, curiosity, and resilience. As Tomas Chamorro-Premuzic states, “ We may not know what tomorrow’s jobs will look like, but we can safely assume that when people are more curious, emotionally intelligent, resilient, driven, and intelligent, they will generally be better equipped to learn what is needed to perform those jobs and provide whatever human value technology cannot replace.”

【G3】____________________________________

For those later in their careers clinging to past summits of grandeur, honor what you’ve achieved—and let go. Your obsessive focus on the past may be propelling you into the very obsolescence you fear. Seal up the “awards” you keep peering at longingly and look ahead. The intersection of nostalgia and relevance is a crossroads forcing you to choose one or the other. You can’t have both. Nostalgia might get you invited to share your reflections at a professional dinner, but it’s not going to get you assigned to the latest high-profile projects.

【G4】____________________________________

Demonstrating the ability to learn new things is one of the strongest signals you can send to the world about your relevance. More so, it sustains confidence in your ability to adapt to changing conditions, regardless of the stage of your career. Rather than seeing imminent change as a threat to your relevance, ask yourself, “What could this uncertainty be inviting me to learn?” While humans aren’t natural fans of uncertainty, it does create opportunities.

【G5】____________________________________

One potential nasty side effect of fearing our obsolescence is entitlement. Early-career professionals can become irritably insistent about being given prime assignments and opportunities to shine. By contrast, permanent employees feel they’ve “earned” their right to be seen as important simply because of their track record. This conflict between legacy and potential is counterproductive. Replace any hint of such sentiments with a genuine commitment to serving and contributing across generations. Regardless of where you are in your career, maintain a posture of humility, and graciously look for opportunities to help others shine.

In our fast-paced digital age, artificial intelligence (AI) is not just transforming our world; it’s reshaping how we see and engage with each other. AI has the power to unlock incredible opportunities, but it also holds a mirror to our society’s imperfections.【T1】Bias in AI, whether hidden or overt, is not just a fault in the system; it’s a reflection of our collective history, prejudices, and oversights.

The reality of the progress AI brings though, is that it’s built with foundational bias. When these algorithms are built on flawed data, they don’t just process information; they process our past biases, assumptions, and blind spots.【T2】It’s a scenario where limited and distorted inputs lead to biased and unjust outputs, perpetuating historical biases in a new digital guise. This isn’t just a replication of the past; it’s amplifying our history’s inequalities into the future.

Think about Al-based recruitment software with its preference to male candidates, or the U.S. justice system’s AI that recommended harsher sentences for Black offenders than white offenders convicted of the same crimes. These aren’t just coding errors; they are deep-rooted societal issues playing out on a new digital stage.【T3】Look what happens when AI chat-bots learn from a biased world—they can become a mouthpiece for hate within hours, spreading disinformation globally.

【T4】The potential of unbiased AI stretches beyond our imagination: From education systems that adapt to every learner’s needs to hiring practices that see beyond demographics to talent and potential. This isn’t just about correcting biases; it’s about unlocking new horizons of possibility and innovation in AI.

This is why we are designing the Algorithm for Equality Manifesto, by all of us, without bias. Embracing inclusive AI is our only chance to reshape the technological landscape. By eliminating biases in AI, we create a platform for true innovation and fairness. Imagine AI that doesn’t just reflect every voice but elevates it. This isn’t just an opportunity; it’s a responsibility and a promise for a better tomorrow.

【T5】As we move towards an increasingly globalized world, language translation tools powered by inclusive AI can break down communication barriers, fostering collaboration and understanding among people of different languages and cultures. This not only enhances our ability to work together on global challenges but also promotes cultural exchange and mutual respect.

If anyone using AI can simply question the results of AI created works,or help reshape AI’s response, we will be one step closer to an AI that’s truly reflective of our world.