Ground Hog Day: Smart Enterprise Search
January 7, 2025
I am a dinobaby. I also wrote the Enterprise Search Report, 1st, 2nd, and 3rd editions. I wrote The New Landscape of Search. I wrote some other books. The publishers are long gone, and I am mostly forgotten in the world of information retrieval. Read this post, and you will learn why. Oh, no AI helped me out unless I come up with an art idea. I used Stable Diffusion for the rat, er, sorry, ground hog day creature.
I think it was 2002 when the owner of a publishing company asked me if I thought there was an interest in profiles of companies offering “enterprise search solutions.” I vaguely remember the person, and I will leave it up to you to locate a copy of the 400 page books I wrote about enterprise search.
The set up for the book was simple. I identified the companies which seemed to bid on government contracts for search, companies providing search and retrieval to organizations, and outfits which had contacted me to pitch their enterprise search systems before they were exiting stealth mode. By the time the first edition appeared in 2004, the companies in the ESR were flogging their products.
The ground hog effect is a version of the Yogi Berra “Déjà vu all over again” thing. Enterprise search is just out of reach now and maybe forever.
The enterprise search market imploded. It was there and then it wasn’t. Can you describe the features and functions of these enterprise search systems from the “golden age” of information retrieval:
- Innerprise
- InQuira
- iPhrase
- Lextek Onix
- MondoSearch
- Speed of Mind
- Stratify (formerly Purple Yogi)
The end of enterprise search coincided with large commercial enterprises figuring out that “search” in a complex organization was not one thing. The problem remains today. Lawyers in a Fortune 1000 company want one type of search. Marketers want another “flavor” of search. The accountants want a search that retrieves structured and unstructured data plus images of invoices. Chemists want chemical structure search. Senior managers want absolutely zero search of their personal and privileged data unless it is lawyers dealing with litigation. In short, each unit wants a highly particularized search and each user wants access to his or her data. Access controls are essential, and they are a hassle at a time when the notion of an access control list was like learning to bake bread following a recipe in Egyptian hieroglyphics.
These problems exist today and are complicated by podcasts, video, specialized file types for 3D printing, email, encrypted messaging, unencrypted messaging, and social media. No one has cracked the problem of a senior sales person who changes a PowerPoint deck to close a deal. Where is that particular PowerPoint? Few know and the sales person may have deleted the file changed minutes before the face to face pitch. This means that baloney like “all” the information in an organization is searchable is not just stupid; it is impossible.
The key events were the legal and financial hassles over Fast Search & Transfer. Microsoft bought the company in 2008 and that was the end of a reasonably capable technology platform and — believe it or not — a genuine alternative to Google Web search. A number of enterprise search companies sold out because the cost of keeping the technology current and actually running a high-grade sales and marketing program spelled financial doom. Examples include Exalead and Vivisimo, among others. Others just went out of business: Delphes (remember that one?). The kiss of death for the type of enterprise search emphasized in the ESR was the acquisition of Autonomy by Hewlett Packard. There was a roll up play underway by OpenText which has redefined itself as a smart software company with Fulcrum and BRS Search under its wing.
What replaced enterprise search when the dust settled in 2011? From my point of view it was Shay Banon’s Elastic search and retrieval system. One might argue that Lucid Works (né Lucid Imagination) was a player. That’s okay. I am, however, to go with Elastic because it offered a version as open source and a commercial version with options for on-going engineering support. For the commercial alternatives, I would say that Microsoft became the default provider. I don’t think SharePoint search “worked” very well, but it was available. Google’s Search Appliance appeared and disappeared. There was zero upside for the Google with a product that was “inefficient” at making a big profit for the firm. So, Microsoft it was. For some government agencies, there was Oracle.
Oracle acquired Endeca and focused on that computationally wild system’s ability to power eCommerce sites. Oracle paid about $1 billion for a system which used to be an enterprise search with consulting baked in. One could buy enterprise search from Oracle and get structured query language search, what Oracle called “secure enterprise search,” and may a dollop of Triple Hop and some other search systems the company absorbed before the end of the enterprise search era. IBM talked about search but the last time I drove by IBM Government systems in Gaithersburg, Maryland, it like IBM search, had moved on. Yo, Watson.
Why did I make this dalliance on memory lane the boring introduction to a blog post? The answer is that I read “Are LLMs At Risk Of Going The Way Of Search? Expect A Duopoly.” This is a paywalled article, so you will have to pony up cash or go to a library. Here’s an abstract of the write up:
The evolution of LLMs (Large Language Models) will lead users to prefer one or two dominant models, similar to Google’s dominance in search.
Companies like Google and Meta are well-positioned to dominate generative AI due to their financial resources, massive user bases, and extensive data for training.
Enterprise use cases present a significant opportunity for specialized models.
Therefore, consumer search will become a monopoly or duopoly.
Let’s assume the Forbes analysis is accurate. Here’s what I think will happen:
First, the smart software train will slow and a number of repackagers will use what’s good enough; that is, cheap enough and keeps the client happy. Thus, a “golden age” of smart search will appear with outfits like Google, Meta, Microsoft, and a handful of others operating as utilities. The US government may standardize on Microsoft, but it will be partners who make the system meet the quite particular needs of a government entity.
Second, the trajectory of the “golden age” will end as it did for enterprise search. The costs and shortcomings become known. Years will pass, probably a decade, maybe less, until a “new” approach becomes feasible. The news will diffuse and then a seismic event will occur. For AI, it was the 2023 announcement that Microsoft and OpenAI would change how people used Microsoft products and services. This created the Google catch up and PR push. We are in the midst of this at the start of 2025.
Third, some of the problems associated with enterprise information and an employee’s finding exactly what he or she needs will be solved. However, not “all” of the problems will be solved. Why? The nature of information is that it is a bit like pushing mercury around. The task requires fresh thinking.
To sum up, the problem of search is an excellent illustration of the old Hegelian chestnut of Hegelian thesis, antithesis, and synthesis. This means the problem of search is unlikely to be “solved.” Humans want answers. Some humans want to verify answers which means that the data on the sales person’s laptop must be included. When the detail oriented human learns that the sales person’s data are missing, the end of the “search solution” has begun.
The question “Will one big company dominate?” The answer is, in my opinion, maybe in some use cases. Monopolies seem to be the natural state of social media, online advertising, and certain cloud services. For finding information, I don’t think the smart software will be able to deliver. Examples are likely to include [a] use cases in China and similar countries, [b] big multi-national organizations with information silos, [c] entities involved in two or more classified activities for a government, [d] high risk legal cases, and [e] activities related to innovation, trade secrets, and patents, among others.
The point is that search and retrieval remains an extraordinarily difficult problem to solve in many situations. LLMs contribute some useful functional options, but by themselves, these approaches are unlikely to avoid the reefs which sank the good ships Autonomy and Fast Search & Transfer, and dozens of others competing in the search space.
Maybe Yogi Berra did not say “Déjà vu all over again.” That’s okay. I will say it. Enterprise search is “Déjà vu all over again.”
Stephen E Arnold, January 7, 2025
Why Buzzwords Create Problems. Big Problems, Right, Microsoft?
January 7, 2025
This is an official dinobaby post. No smart software involved in this blog post.
I read an essay by Steven Sinofsky. He worked at Microsoft. You can read about him in Wikipedia because he was a manager possibly associated with Clippy. He wrote an essay called “225. Systems Ideas that Sound Good But Almost Never Work—”Let’s just…” The write up is about “engineering patterns that sound good but almost never work as intended.”
I noticed something interesting about his explanation of why many software solutions go off the rails, fail to work, create security opportunities for bad actors associated with entities not too happy with the United States, and on-going headaches for for hundreds of millions of people.
Here is a partial list of the words and bound phrases from his essay:
Add an API
Anomaly detection
Asynchronous
Cross platform
DSL
Escape to native
Hybrid parallelism
Multi-master writes
Peer to peer
Pluggable
Sync the data
What struck me about this essay is that it reveals something I think is important about Microsoft and probably other firms tapping the expertise of the author; that is, the jargon drives how the software is implemented.
I am not certain that my statement is accurate for software in general. But for this short blog post, let’s assume that it applies to some software (and I am including Microsoft’s own stellar solutions as well as products from other high profile and wildly successful vendors). With the ground rules established, I want to offer several observations about this “jargon drives the software engineering” assertion.
First, the resulting software is flawed. Problems are not actually resolved. The problems are papered over with whatever the trendy buzzword says will work. The approach makes sense because actual problem solving may not be possible within a given time allocation or a working solution may fail which requires figuring out how to not fail again.
Second, the terms reveal that marketing think takes precedence over engineering think. Here’s what the jargon creators do. These sales oriented types grab terms that sound good and refer to an approach. The “team” coalesces around the jargon, and the jargon directs how the software is approached. Does hybrid parallelism “work”? Who knows, but it is the path forward. The manager says, “Let’s go team” and Clippy emerges or the weird opaqueness of the “ribbon.”
Third, the jargon shaped by art history majors and advertising mavens defines the engineering approach. The more successful the technical jargon, the more likely those people who studied Picasso’s colors or Milton’s Paradise Regained define the technical frame in which a “solution” is crafted.
How good is software created in this way? Answer: Good enough.
How reliable is software created in this way? Answer: Who knows until someone like a paying customer actually uses the software.
How secure is the software created in this way? Answer: It is not secure as the breaches of the Department of Treasury, the US telecommunications companies, and the mind boggling number of security lapses in 2024 prove.
Net net: Engineering solutions based on jargon are not intended to deliver excellence. The approach is simply “good enough.” Now we have some evidence that industry leaders realize the fact. Right, Clippy?
Stephen E Arnold, January 8, 2025
Good Enough Junk Food: Knowledge without the Work
December 16, 2024
This write up emerged from the dinobaby’s own mind. Yes, the dinobaby used AI because this write up is about the knowledge value lost with smart software. Intellectual junk food is not the same as my mother’s overcooked chicken which she killed on her aunt’s farm. Cluck, cluck, squawk.
I plugged the Guardian’s opinion essay into You.com’s Chat GPT-4o model. You can read the original essay titled “Is Doom Scrolling Really Rotting Our Brains? The Evidence Is Getting Harder to Ignore.” The original essay is clear, reasonably well researched, and structured to speak to [a] a person who has access to the Guardian online or in (gasp!) a print form, [b] who reads the Guardian’s opinion essays, and [c] who has the ability to recognize that “brain rot” has probably affected that individual, co-workers, government leaders, children, and people who repair refrigerators.
This illustration is good enough and it was produced by the AI system Magic Studio.
Here’s the summary Chat GPT-4o spit out:
The essay critiques the decline of human intellect in the digital age, using the viral “Skibidi Toilet” video as a cultural metaphor for deteriorating focus and intellectual engagement. The Oxford English Dictionary’s 2024 word of the year, “brain rot”, reflects this phenomenon, defined as the mental degradation caused by excessive consumption of trivial online content. The essay draws on academic research to demonstrate how compulsive internet use is literally shrinking grey matter, weakening memory, shortening attention spans, and distorting cognitive functions. Studies from Harvard, Oxford, and King’s College reveal the harmful effects of media multitasking and heavy internet usage, with findings such as reduced grey matter in the brain and the risk of “digital dementia” in young people during critical developmental years.The prevalence of infinite scroll and dopamine-driven design in apps is highlighted as a deliberate tactic by tech companies to exploit human psychology for profit, creating powerful addiction loops. Former Google design ethicist Tristan Harris warns that this “race to the bottom of the brain stem” manipulates fear, outrage, and reward systems, potentially driving society to catastrophe if left unchecked.Despite the grim outlook, the essay finds hope in the growing self-awareness among younger generations, who coined the term “brain rot” and recognize its effects. Movements advocating for reduced technology use, such as switching to dumbphones or promoting smartphone-free childhoods, are gaining traction. These trends suggest the possibility of reclaiming mental clarity and focus, countering the current trajectory of cognitive decline.Ultimately, the essay positions “Skibidi Toilet” as both a symptom of modern intellectual decay and a call to action. Humanity stands at a critical juncture: to either rise above the current trends of distraction and degradation or succumb further to the “U-bend” of intellectual regression.
For what it is worth, this is a typical bite-sized knowledge cookie. No one will know if the summary is good or bad unless that person takes the time to get the original full text and compare it with this AI generated output. The informational fast food provides a sugar jolt from saving time or the summary consumer’s belief that the important information is on the money. A knowledge cookie if you will, or maybe intellectual junk food?
Is this summary good enough? From my point of view, it is just okay; that is, good enough. What else is required? Flash back to 1982, the ABI/INFORM database was a commercial success. A couple of competitors were trying to capture our customers which was tricky. Intermediaries like Dialog Information Services, ESA, LexisNexis (remember Buster and his silver jumpsuit?), among others “owned” the direct relationship with the companies that paid the intermediaries to use the commercial databases on their systems. Then the intermediaries shared some information with us, the database producers.
How did a special librarian or a researcher “find” or “know about” our database? The savvy database producers provided information to the individuals interested in a business and management related commercial database. We participated in niche trade shows. We held training programs and publicized them with our partners Dow Jones News Retrieval, Investext, Predicasts, and Disclosure, among a few others. Our senior professionals gave lectures about controlled term indexing, the value of classification codes, and specific techniques to retrieve a handful of relevant citations and abstracts from our online archive. We issued news releases about new sources of information we added, in most cases with permission of the publisher.
We did not use machine indexing. We did have a wizard who created a couple of automatic indexing systems. However, when the results of what the software in 1922 could do, we fell back on human indexers, many of whom had professional training in the subject matter they were indexing. A good example was our coverage of real estate management activities. The person who handled this content was a lawyer who preferred reading and working in our offices. At this time, the database was owned by the Courier-Journal & Louisville Times Co. The owner of the privately held firm was an early adopted of online and electronic technology. He took considerable pride in our line up of online databases. When he hired me, I recall his telling me, “Make the databases as good as you can.”
How did we create a business and management database that generated millions in revenue and whose index was used by entities like the Royal Bank of Canada to index its internal business information?
Here’s the secret sauce:
- We selected sources in most cases business journals, publications, and some other types of business related content; for example, the ANBAR management reports
- The selection of which specific article to summarize was the responsibility of a managing editor with deep business knowledge
- Once an article was flagged as suitable for ABI/INFORM, it was routed to the specialist who created a summary of the source article. At that time, ABI/INFORM summaries or “abstracts” were limited to 150 words, excluding the metadata.
- An indexing specialist would then read the abstract and assign quite specific index terms from our proprietary controlled vocabulary. The indexing included such items as four to six index terms from our controlled vocabulary and a classification code like 7700 to indicate “marketing” with addition two digit indicators to make explicit that the source document was about marketing and direct mail or some similar subcategory of marketing. We also included codes to disambiguate between a railroad terminal and a computer terminal because source documents assumed the reader would “know” the specific field to which the term’s meaning belonged. We added geographic codes, so the person looking for information could locate employee stock ownership in a specific geographic region like Northern California, and a number of other codes specifically designed to allow precise, comprehensive retrieval of abstracts about business and management. Some of the systems permitted free text searching of the abstract, and we considered that a supplement to our quite detailed indexing.
- Each abstract and index terms was checked by a control control process using people who had demonstrated their interest in our product and their ability to double check the indexing.
- We had proprietary “content management systems” and these generated the specific file formats required by our intermediaries.
- Each week we updated our database and we were exploring daily updates for our companion product called Business Dateline when the Courier Journal was broken up and the database operation sold to a movie camera company, Bell+Howell.
Chat GPT-4o created the 300 word summary without the human knowledge, expertise, and effort. Consequently, the loss of these knowledge based workflow has been replaced by a smart software which can produce a summary in less than 30 seconds.
And that summary is, from my point of view, good enough. There are some trade offs:
- Chat GPT-4o is reactive. Feed it a url or a text, and it will summarize it. Gone is the knowledge-based approach to select a specific, high-value source document for inclusion in the database. Our focus was informed selection. People paid to access the database because of the informed choice about what to put in the database.
- The summary does not include the ABI/INFORM key points and actionable element of the source document. The summary is what a high school or junior college graduate would create if a writing teacher assigned a “how to write a précis” as part of the course requirements. In general, high school and junior college graduates are not into nuance and cannot determine the pivotal information payload in a source document.
- The precise indexing and tagging is absent. One could create a 1,000 such summaries, toss them in MISTRAL, and do a search. The result is great if one is uninformed about the importance of editorial polices, knowledge-based workflows, and precise, thorough indexing.
The reason I am sharing some of this “ancient” online history is:
- The loss of quality in online information is far more serious than most people understand. Getting a summary today is no big deal. What’s lost is simply not on these individuals’ radar.
- The lack of an editorial policy, precise date and time information, and the fine-grained indexing means that one has to wade through a mass of undifferentiated information. ABI/INFORM in the 1080s delivered a handful of citations directly on point with the user’s query. Today no one knows or cares about precision and recall.
- It is now more difficult than at any other time in my professional work career to locate needed information. Public libraries do not have the money to obtain reference materials, books, journals, and other content. If the content is online, it is a dumbed down and often cut rate version of the old-fashioned commercial databases created by informed professionals.
- People look up information online and remain dumb; that is, the majority of the people with whom I come in contact routinely ask me and my team, “Where do you get your information?” We even have a slide in our CyberSocial lecture about “how” and “where.” The analysts and researchers in the audience usually don’t know so an entire subculture of open source information professionals has come into existence. These people are largely on their own and have to do work which once was a matter of querying a database like ABI/INFORM, Predicasts, Disclosure, Agricola, etc.
Sure the essay is good. The summary is good enough. Where does that leave a person trying to understand the factual and logical errors in a new book examining social media. In my opinion, people are in the dark and have a difficult time finding information. Making decisions in the dark or without on point accurate information is recipe for a really bad batch of cookies.
Stephen E Arnold, December 15, 2024
We Need a Meeting about Meetings after I Get Back from a Meeting
December 10, 2024
This blog post flowed from the sluggish and infertile mind of a real live dinobaby. If there is art, smart software of some type was probably involved.
I heard that Mr. Jeff Bezos, the Big Daddy of online bookstores, likes chaotic and messy meetings. Socrates might not have been down with that approach.
As you know, Socrates was a teacher who ended up dead because he asked annoying questions. “Socratic thinking” helps people remain open to new ideas. Do new ideas emerge from business meetings? Most of those whom I know grumble, pointing out to me that meetings waste their time. Michael Poczwardowski challenges that assumption with Socratic thinking in the Perspectiveship post “Socratic Questioning – ‘Meetings are a waste of time’”.
Socratic-based discussions are led by someone who only asks questions. By asking only questions the discussion can then focus on challenging assumptions, critical thinking, and first principles-dividing problems into basic elements to broaden perspectives and understanding. Poczwardowski brings the idea that: “meetings are a waste of time” to the discussion forum.
Poczwardowski introduces readers to Socratic thinking with the steps of classification, challenge assumptions, look for data/evidence, change perspective, explore consequences and implications, and question the question. Here’s my summary done my a person with an advanced degree in information science. (I know I am not as smart as Google’s AI, but I do what I can with my limited resources, thank you.)
“The key is to remain open to possibilities and be ready to face our beliefs. Socratic questioning is a great way to work on improving our critical thinking.
When following Socratic questioning ask to:
• Clarify the idea: It helps us understand what we are talking about and to be on the same page
• Challenge assumptions: Ask them to list their assumptions.
• Look for evidence: Asking what kind of evidence they have can help them verify the sources of their beliefs
• Change perspectives: Look at the problem from others’ points of view.
• Explore consequences: Explore the possible outcomes and effects of actions to understand their impact”
Am I the only one who thinks this also sounds obvious? Ancient philosophers did inspire the modern approach to scientific thought. Galileo demonstrated that he would recant instead of going to prison or being killed. Perhaps I should convene a meeting to decide if the meeting is a waste of time. I will get back to you. I have a meeting coming up.
Whitney Grace, December 10, 2024
AI Automation: Spreading Like Covid and Masks Will Not Help
December 10, 2024
This blog post flowed from the sluggish and infertile mind of a real live dinobaby. If there is art, smart software of some type was probably involved.
Reddit is the one of the last places on the Internet where you can find quality and useful information. Reddit serves as the Internet’s hub for news, tech support, trolls, and real-life perspectives about jobs. Here’s a Reddit downer in the ChatGPT thread for anyone who works in a field that can be automated: “Well this is it boys. I was just informed from my boss and HR that my entire profession is being automated away.”
For brevity’s sake here is the post:
“For context I work production in local news. Recently there’s been developments in AI driven systems that can do 100% of the production side of things which is, direct, audio operate, and graphic operate -all of those jobs are all now gone in one swoop. This has apparently been developed by the company Q ai. For the last decade I’ve worked in local news and have garnered skills I thought I would be able to take with me until my retirement, now at almost 30 years old, all of those job opportunities for me are gone in an instant. The only person that’s keeping their job is my manager, who will overlook the system and do maintenance if needed. That’s 20 jobs lost and 0 gained for our station. We were informed we are going to be the first station to implement this under our company. This means that as of now our entire production staff in our news station is being let go. Once the system is implemented and running smoothly then this system is going to be implemented nationwide (effectively eliminating tens of thousands of jobs.) There are going to be 0 new jobs built off of this AI platform. There are people I work with in their 50’s, single, no college education, no family, and no other place to land a job once this kicks in. I have no idea what’s going to happen to them. This is it guys. This is what our future with AI looks like. This isn’t creating any new jobs this is knocking out entire industry level jobs without replacing them.”
The post is followed by comments of commiseration, encouragement, and the usual doom and gloom. It’s not surprising that local news stations are automating their tasks, especially with the overhead associates with employees. These include: healthcare, retirement package, vacation days, PTO, and more. AI is the perfect employee, because it doesn’t complain or take time off. AI, however, is lacking basic common sense and fact checking. We’re witnessing a change in how the job market, it just sucks to live through it.
Whitney Grace, December 10, 2024
Deepfakes: An Interesting and Possibly Pernicious Arms Race
December 2, 2024
As it turns out, deepfakes are a difficult problem to contain. Who knew? As victims from celebrities to schoolchildren multiply exponentially, USA Today asks, “Can Legislation Combat the Surge of Non-Consensual Deepfake Porn?” Journalist Dana Taylor interviewed UCLA’s John Villasenor on the subject. To us, the answer is simple: Absolutely not. As with any technology, regulation is reactive while bad actors are proactive. Villasenor seems to agree. He states:
“It’s sort of an arms race, and the defense is always sort of a few steps behind the offense, right? In other words that you make a detection tool that, let’s say, is good at detecting today’s deepfakes, but then tomorrow somebody has a new deepfake creation technology that is even better and it can fool the current detection technology. And so then you update your detection technology so it can detect the new deepfake technology, but then the deepfake technology evolves again.”
Exactly. So if governments are powerless to stop this horror, what can? Perhaps big firms will fight tech with tech. The professor dreams:
“So I think the longer term solution would have to be automated technologies that are used and hopefully run by the people who run the servers where these are hosted. Because I think any reputable, for example, social media company would not want this kind of content on their own site. So they have it within their control to develop technologies that can detect and automatically filter some of this stuff out. And I think that would go a long way towards mitigating it.”
Sure. But what can be done while we wait on big tech to solve the problem it unleased? Individual responsibility, baby:
“I certainly think it’s good for everybody, and particularly young people these days to be just really aware of knowing how to use the internet responsibly and being careful about the kinds of images that they share on the internet. … Even images that are sort of maybe not crossing the line into being sort of specifically explicit but are close enough to it that it wouldn’t be as hard to modify being aware of that kind of thing as well.”
Great, thanks. Admitting he may sound naive, Villasenor also envisions education to the (partial) rescue:
“There’s some bad actors that are never going to stop being bad actors, but there’s some fraction of people who I think with some education would perhaps be less likely to engage in creating these sorts of… disseminating these sorts of videos.”
Our view is that digital tools allow the dark side of individuals to emerge and expand.
Cynthia Murrell, December 2, 2024
AI In Group Communications: The Good and the Bad
November 29, 2024
In theory, AI that can synthesize many voices into one concise, actionable statement is very helpful. In practice, it is complicated. The Tepper School of Business at Carnegie Mellon announces, “New Paper Co-Authored by Tepper School Researchers Articulates How Large Language Models are Changing Collective Intelligence Forever.” Researchers from Tepper and other institutions worked together on the paper, which was published in Nature Human Behavior. We learn:
“[Professor Anita Williams] Woolley and her co-authors considered how LLMs process and create text, particularly their impact on collective intelligence. For example, LLMs can make it easier for people from different backgrounds and languages to communicate, which means groups can collaborate more effectively. This technology helps share ideas and information smoothly, leading to more inclusive and productive online interactions. While LLMs offer many benefits, they also present challenges, such as ensuring that all voices are heard equally.”
Indeed. The write-up continues:
“‘Because LLMs learn from available online information, they can sometimes overlook minority perspectives or emphasize the most common opinions, which can create a false sense of agreement,’ said Jason Burton, an assistant professor at Copenhagen Business School. Another issue is that LLMs can spread incorrect information if not properly managed because they learn from the vast and varied content available online, which often includes false or misleading data. Without careful oversight and regular updates to ensure data accuracy, LLMs can perpetuate and even amplify misinformation, making it crucial to manage these tools responsibly to avoid misleading outcomes in collective decision-making processes.”
In order to do so, the paper suggests, we must further explore LLMs’ ethical and practical implications. Only then can we craft effective guidelines for responsible AI summarization. Such standards are especially needed, the authors note, for any use of LLMs in policymaking and public discussions.
But not to worry. The big AI firms are all about due diligence, right?
Cynthia Murrell, November 29, 2024
Group Work Can Be Problematic Even on Video
November 28, 2024
Group work is an unavoidable part of school and everyone hates it. Unfortunately group work continues into adulthood except it’s called a job, teamwork, and synergy. Intelligent leaders realize that poor collaboration hurts profits, so the Zoom Blog (everyone’s favorite digital workplace) did the following: “New Report Uncovers What Bad Collaboration Can Cost Your Organization — And How You Can Help Fix It.”
Poor collaboration takes many forms. It’s more than one team member not carrying their weight, it’s also calendars not syncing or misunderstanding a colleague’s intent. Zoom conducted a Global Connection In The Workplace report based on a Morning Consult survey of over 8000 workers in 16 countries. The survey learned how much repairing bad coloration costs, common collaboration challenges, and how people prefer to work with each other.
The wasted costs are astounding : $874,000 annually per 1000 employee or $16491 per manage. Remote leaders spent the majority of their time collaborating with their co-workers, spending an average of 2-3 hours everyday on email and virtual meetings. Leaders spent more time than their associates resolving bad collaboration and refocusing between tasks. Leaders and workers both agreed that chatting/instant messaging was their favorite way of communicating. The survey also revealed that there were shifting preferences based on generational differences. Baby Boomers prefer in-person meetings while Gen Z like using project management software.
IT workers shared their collaboration struggles. The study discovered that IT workers are pummeled with requests for new tools and apps. IT workers also use a variety of apps to solve problems. If they use more than ten apps for their job, then continuity between all collaboration platforms doesn’t mesh:
“IT leaders are constantly bombarded with sales pitches and employee requests for new apps and tools. Individually, each one promises to solve a problem, but the report shows that too many apps were actually associated with greater collaboration challenges. Those who reported using more than 10 apps for work were more likely to struggle with issues like misunderstandings in communication, lack of engagement from colleagues, and lack of alignment than those who reported using fewer than five apps.”
Understandably coloration is a big problem for all companies and needs improvement. Zoom asserts that video collaboration is a solution to many of these issues. Doesn’t that make sense for Zoom to make those claims? We believe everything a funded research report presents as factual.
Whitney Grace, November 28, 2024
Modern Library Patrons Present Challenging Risky Business Situations
November 27, 2024
Librarians have one of the most stressful jobs in the world. Why? They do much more than assist people locating books or reading to children. They also are therapists, community resource managers, IT support, babysitter, elderly care specialist, referee, tutor, teacher, police officer, and more. Public librarians handle more situations than their job description entails and Public Library Quarterly published: “The Hidden Toll: Exploring the Impact of Challenging Patron Behaviors On Australian Public Library Staff.”
It’s not an industry secret that librarians face more than can handle, but their experiences are often demeaned. Their resources and stamina are stretched to the limits. There have been studies about how librarians have dealt with more than they can handle for years, but this study researched the trauma they face:
“As a public-facing profession, public library workers are often exposed to challenging behaviors that raise concern for their safety. To understand these concerns, this study explores these staff safety issues in Australian public libraries through semi-structured interviews with 59 staff members from six library services. Findings reveal that library workers frequently encounter challenging and sometimes violent behaviors from patrons. These incidents impact staff wellbeing, causing stress, anxiety, and potential long-term psychological effects. Many workers receive insufficient workplace support following traumatic incidents, leading to internalization of the trauma the experiences cause. The study highlights the need for improved institutional support and better safety measures.”It also recognizes the tension created by libraries’ open-door policies, which may expose workers to potential harm. The study acknowledges that there has ben zero to little research about the mental and physical health of library workers. There is a lot of literature written about patron satisfaction with services and staff, but very little about aggressive, problem patrons. Many studies also focus on the trauma-related services patrons need but not the library staff.
There has been some studies related to the impact of problem patrons on staff, but nothing in depth. The Australian participants in the shared stories of their time in the trenches and it’s not pretty. Librarians around the world have similar or worse stories. Librarians need assistance for themselves and their patrons, but I doubt it’ll come through. At least the writers agree with me:
“The findings of this study paint a concerning picture of the working conditions in Australian public libraries. The prevalence of unsafe incidents, coupled with their significant psychological impact on staff, calls for action from library management, policymakers, and local government councils responsible for public libraries. While public libraries play a crucial role in providing open access to spaces, information, and services for all members of society, this should not come at the cost of staff safety and wellbeing. Addressing these issues will require a multifaceted approach, involving enhanced training, improved support systems, policy changes, and potentially a broader societal discussion about the role and resources of public libraries.”
Whitney Grace, November 27, 2024
Early AI Adoption: Some Benefits
November 25, 2024
Is AI good or is it bad? The debate is still raging about, especially in Hollywood where writers, animators, and other creatives are demanding the technology be removed from the industry. AI, however, is a tool. It can be used for good and bad acts, but humans are the ones who initiate them. AI At Wharton investigated how users are currently adopting AI: “Growing Up: Navigating Generative AI’s Early Years – AI Adoption Report.”
The report was based on the responses from full-time employees who worked in commercial organization with 1000 or more workers. Adoption of AI in businesses jumped from 37 % in 2023 to 72% in 2024 with high growth in human resources and marketing departments. Companies are still unsure if AI is worth the ROI. The study explains that AI will benefit companies that have adaptable organizations and measurable ROI.
The report includes charts that document the high rate of usage compared last year as well as how AI is mostly being used. It’s being used for document writing and editing, data analytics, document summarization, marketing content creation, personal marketing and advertising, internal support, customer support, fraud prevention, and report creation. AI is definitely impactful but not overwhelmingly, but the response to the new technology is positive and companies will continue to invest in it.
“Looking to the future, Gen AI adoption will enter its next chapter which is likely to be volatile in terms of investment and carry greater privacy and usage restrictions. Enthusiasm projected by new Chief AI Officer (CAIO) role additions and team expansions this year will be tempered by the reality of finding “accountable” ROI. While approximately three out of four industry respondents plan to increase Gen AI budgets next year, the majority expect growth to slow over the longer term, signaling a shift in focus towards making the most effective internal investments and building organizational structures to support sustainable Gen AI implementation. The key to successful adoption of Gen AI will be proper use cases that can scale, and measurable ROI as well as organization structures and cultures that can adapt to the new technology.”
While the responses are positive, how exactly is it being used beyond the charts. Are the users implementing AI for work short cuts, such as really slap shod content generation? I’d hate to be the lazy employee who uses AI to make the next quarterly report and didn’t double-check the information.
Whitney Grace, November 25, 2024
-
- Subscribe to Beyond Search
Feature archive
News archive
-
