Tag: Rise
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The rise of multidisciplinary research stimulated by AI
AI research tools such as OpenAI o1 have now reached test score levels that meet or exceed the scores of those who hold Ph.D. degrees in the sciences and a number of other fields. These generative AI tools utilize large language models that include research and knowledge across many disciplines. Increasingly, they are used for research project ideation and literature searches. The tools are generating interesting insights to researchers that they may not have been exposed to in years gone by.
The field of academe has long emphasized the single-discipline research study. We offer degrees in single disciplines; faculty members are granted appointments most often in only one department, school or college; and for the most part, our peer-reviewed academic journals are in only one discipline, although sometimes they welcome papers from closely associated or allied fields. Dissertations are most commonly based in a single discipline. Although research grants are more often multidisciplinary and prioritize practical solution-finding, a large number remain focused on one field of study.
The problem is that as we advance our knowledge and application expertise in one field, we can become unaware of important developments in other fields that directly or indirectly impact the study in our chosen discipline. Innovation is not always a single-purpose, straight-line advance. More often today, innovation comes from the integration of knowledge of disparate fields such as sociology, engineering, ecology and environmental developments, and expanding understanding of quantum physics and quantum computing. Until recently, we have not had an efficient way to identify and integrate knowledge and perspectives from fields that, at first glance, seem unrelated.
AI futurist and innovator Thomas Conway of Algonquin College of Applied Arts and Technology addresses this topic in “Harnessing the Power of Many: A Multi-LLM Approach to Multidisciplinary Integration”:
“Amidst the urgency of increasingly complex global challenges, the need for integrative approaches that transcend traditional disciplinary boundaries has never been more critical. Climate change, global health crises, sustainable development, and other pressing issues demand solutions from diverse knowledge and expertise. However, effectively combining insights from multiple disciplines has long been a significant hurdle in academia and research.
“The Multi-LLM Iterative Prompting Methodology (MIPM) emerges as a transformative solution to this challenge. MIPM offers a structured yet flexible framework for promoting and enhancing multidisciplinary research, peer review, and education. At its core, MIPM addresses the fundamental issue of effectively combining diverse disciplinary perspectives to lead to genuine synthesis and innovation. Its transformative potential is a beacon of hope in the face of complex global challenges.”
Even as we integrate AI research tools and techniques, we, ourselves, and our society at large are changing. Many of the common frontier language models powering research tools are multidisciplinary by nature, although some are designed with strengths in specific fields. Their responses to our prompts are multidisciplinary. The response to our iterative follow-up prompts can take us to fields and areas of expertise of which we were not previously aware. The replies are not coming solely from a single discipline expert, book or other resource. They are coming from a massive language model that spans disciplines, languages, cultures and millennia.
As we integrate these tools, we too will naturally become aware of new and emerging perspectives, research and developments generated by fields that are outside our day-to-day knowledge, training and expertise. This will expand our perspectives beyond the fields of our formal study. As the quality of our AI-based research tools expands, their impact on research cannot be overstated. It will lead us in new directions and broader perspectives, uncovering the potential for new knowledge, informed by multiple disciplines. One recent example is Storm, a brainstorming tool developed by the team at Stanford’s Open Virtual Assistant Lab (OVAL):
“The core technologies of the STORM&Co-STORM system include support from Bing Search and GPT-4o mini. The STORM component iteratively generates outlines, paragraphs, and articles through multi-angle Q&A between ‘LLM experts’ and ‘LLM hosts.’ Meanwhile, Co-STORM generates interactive dynamic mind maps through dialogues among multiple agents, ensuring that no information needs overlooked by the user. Users only need to input an English topic keyword, and the system can generate a high-quality long text that integrates multi-source information, similar to a Wikipedia article. When experiencing the STORM system, users can freely choose between STORM and Co-STORM modes. Given a topic, STORM can produce a structured high-quality long text within 3 minutes. Additionally, users can click ‘See BrainSTORMing Process’ to view the brainstorming process of different LLM roles. In the ‘Discover’ section, users can refer to articles and chat examples generated by other scholars, and personal articles and chat records can also be found in the sidebar ‘My Library.’”
More about Storm is available at https://storm.genie.stanford.edu/.
One of the concerns raised by skeptics at this point in the development of these research tools is the security of prompts and results. Few are aware of the opportunities for air-gapped or closed systems and even the ChatGPT temporary chats. In the case of OpenAI, you can start a temporary chat by tapping the version of ChatGPT you’re using at the top of the GPT app, and selecting temporary chat. I do this commonly in using Ray’s eduAI Advisor. OpenAI says that in the temporary chat mode results “won’t appear in history, use or create memories, or be used to train our models. For safety purposes, we may keep a copy for up to 30 days.” We can anticipate these kinds of protections will be offered by other providers. This may provide adequate security for many applications.
Further security can be provided by installing a stand-alone instance of the LLM database and software in an air-gapped computer that maintains data completely disconnected from the internet or any other network, ensuring an unparalleled level of protection. Small language models and medium-size models are providing impressive results, approaching and in some cases exceeding frontier model performance while storing all data locally, off-line. For example, last year Microsoft introduced a line of SLM and medium models:
“Microsoft’s experience shipping copilots and enabling customers to transform their businesses with generative AI using Azure AI has highlighted the growing need for different-size models across the quality-cost curve for different tasks. Small language models, like Phi-3, are especially great for:
- Resource constrained environments including on-device and offline inference scenarios
- Latency bound scenarios where fast response times are critical.
- Cost constrained use cases, particularly those with simpler tasks.”
In the near term we will find turnkey private search applications that will offer even more impressive results. Work continues on rapidly increasing multidisciplinary responses to research on an ever-increasing number of pressing research topics.
The ever-evolving AI research tools are now providing us with responses from multiple disciplines. These results will lead us to engage in more multidisciplinary studies that will become a catalyst for change across academia. Will you begin to consider cross-discipline research studies and engage your colleagues from other fields to join you in research projects?
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Rise in college applications driven by minority students
The number of first-year applicants this cycle is up 5 percent over January of last year, according to a new report from Common App, and overall applications rose 7 percent.
The growth was buoyed by a sharp uptick in underrepresented students: Latino applicants increased 13 percent, Black applicants by 12 percent and first-generation applicants by 14 percent. Asian applicants rose by 7 percent, while the number of white applicants didn’t change.
A Common App analysis also found that the number of applicants from low-income neighborhoods increased more than those from neighborhoods above the median income level—by 9 percent, compared to 4 percent. And the number of applicants who qualify for a fee waiver is up 10 percent so far.
Geographically, applicant trends seemed to follow broader demographic trends; they surged by 33 percent in the Southwest, with a 36 percent boost in Texas alone, while every other region remained relatively stable. The Western region saw applicants decline by 1 percent.
In general, students are applying to about the same number of schools as last year, with only a 2 percent increase in applications per student. Public institutions have received 11 percent more applications, while private ones have received 3 percent more.
For the first time since 2019, domestic applicant growth outpaced that of international applicants, with the former increasing by 5 percent and the latter slowing to 1 percent. Certain high-volume countries experienced steep declines: The number of applicants from Africa fell by 14 percent, and Ghana in particular saw a 36 percent decrease. Applicants from other increasingly popular source countries for international students surged; Bangladesh, for instance, saw 45 percent growth.
The number of applicants who submitted test scores was about even with the number who didn’t. For the past four years, since test-optional policies were implemented in 2020, no-score applicants have significantly outnumbered those who submitted scores, but institutions returning to test requirements may be swinging the pendulum back.
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Fall and Rise | HESA
Fall and Rise
The question I am getting more often than any other these days is: “what are you hearing about cuts at colleges and universities?” And my answer for the most part has been: “damned if I know.”
The reason for my confusion is that publicly available details are few and far between. The HESA Towers team has been scouring the public record for details on institutional budget announcements; by our count, only 34 universities or colleges have so far announced anything concrete about their 25-26 budget plans and/or any planned cuts as a result of changing international student numbers. It’s possible more have been announced internally but just not caught the notice of the local press; we’ll be doing a lot more digging over the next couple of weeks. My guess is that many institutions are trying to avoid bad headlines by simply not going public about any plans to cut…but of course in the process, they are making it harder to convey to the public the magnitude of the downsizing being forced on the sector.
(This is a really interesting version of the Tragedy of the Commons!).
Some additional problems with the data: such information as one can glean from public sources is often skimpy and inconsistent: sometimes you get a figure for “loss of anticipated revenue,” sometimes you get a “projected deficit” (which sometimes is for 24-25, and other times for 25-26, and whether the figure is for operating budget or total budget take a bit of digging). Sometimes the numbers of programs being cut are announced but the identity of the programs is secret. Often you see that there will be budget cuts of $X million but there is no clarity about where those cuts will come from or the timeframe for the return to budget balance. In terms of job “cuts” as near as we can tell only five institutions have announced specific numbers for layoffs which have actually so far occurred, for a total of 214 lost jobs. You may have seen higher estimates from other sources, but these seem to include data on jobs which “will be affected” and it’s not 100% clear how many of these are permanent jobs which will be eliminated vs. permanent posts which will not be filled, or contract jobs which will not be renewed. All of these nuances may sound petty, but it’s really hard to get meaningful numbers unless you get this stuff right.
The story of how universities and colleges deal with the sudden loss of international student income (and the long-term consequences of provincial disinvestment) is the biggest and most consequential story in Canadian postsecondary education this century. How we deal with this collectively will shape the sector for over a decade, maybe even out to 2050. The HESA Towers team is working hard to document what is happening and help the sector make sense of fast-moving events and respond appropriately. So today I want to tell you about two initiatives we’re launching.
The first is a Retrenchment Watch, which will follow developments in institutional cutbacks not just in Canada, but around the world (albeit with a particular focus on the anglosphere). Higher education probably hit peak public funding around the globe over a decade ago, but what we’re now seeing is an actual contraction of the sector as a whole, happening via an un-coordinated set of decisions made by individual institutions according to local imperatives. Understanding how this is happening is of great importance, not just for posterity but for present-day decision makers. And we’ll be making this information freely available to all via Retrenchment Watch.
For the moment, the Retrenchment Watch is extremely bare bones, but we’ll be filling it out very quickly over the next few weeks, with the Canadian institutions first. If you want regular updates on who is cutting what as well as some basic pattern analysis, please fill out this form, and we’ll get you signed up to our newsletter so you’re always up-to-date.
The second is what we are calling “The Recovery Project.” We know that institutional leaders aren’t just thinking about surviving cuts, they’re also thinking about how to position their organizations to thrive in the aftermath. To help them, we’re launching a subscription research project looking at universities and colleges around the world who have faced serious financial sustainability problems over the past three decades and examining how they turned their fortunes around. In a crisis, there’s no time to re-invent the wheel: with this research institutions can understand better what works, when and why. By spreading the cost of research collectively across many institutions, we can offer this premium product—which involves monthly reports and webinar sessions for all members—at a huge discount to individual schools (and if your school is a member of the University Vice-President’s Network, we’ll be offering an even bigger discount).
If you’re interested in joining this project, my colleague Tiffany MacLennan has been working to bring this information together. Email her at [email protected] and we’ll get back to you ASAP with a prospectus.
There’s no disguising how the sector is taking a beating right now. It will recover. The only question is how quickly, and which institutions will be at the forefront.