Am I Overly Sensitive to X (Twitter) Images?
August 28, 2024
X AI Creates Disturbing Images
The AI division of X, xAI, has produced a chatbot called Grok. Grok includes an image generator. Unlike ChatGPT and other AIs from major firms, Grok seems to have few guardrails. In fact, according to The Verge, “X’s New AI Image Generator Will Make Anything from Taylor Swift in Lingerie to Kamala Harris with a Gun.” Oh, if one asks Grok directly, it claims to have sensible guardrails and will even list a few. However, writes senior editor Adi Robertson:
“But these probably aren’t real rules, just likely-sounding predictive answers being generated on the fly. Asking multiple times will get you variations with different policies, some of which sound distinctly un-X-ish, like ‘be mindful of cultural sensitivities.’ (We’ve asked xAI if guardrails do exist, but the company hasn’t yet responded to a request for comment.) Grok’s text version will refuse to do things like help you make cocaine, a standard move for chatbots. But image prompts that would be immediately blocked on other services are fine by Grok.”
The article lists some very uncomfortable experimental images Grok has created and even shares a few. See the write-up if curious. We learn one X user found some frightening loopholes. When he told the AI he was working on medical or crime scene analysis, it allowed him to create some truly disturbing images. The write-up shares blurred versions of these. The same researcher says he got Grok to create child pornography (though he wisely does not reveal how). All this without a “Created with AI” watermark added by other major chatbots. Although he is aware of this issue, X owner Elon Musk characterizes this iteration of Grok as an “intermediate step” that allows users “to have some fun.” That is one way to put it. Robertson notes:
“Grok’s looseness is consistent with Musk’s disdain for standard AI and social media safety conventions, but the image generator is arriving at a particularly fraught moment. The European Commission is already investigating X for potential violations of the Digital Safety Act, which governs how very large online platforms moderate content, and it requested information earlier this year from X and other companies about mitigating AI-related risk. … The US has far broader speech protections and a liability shield for online services, and Musk’s ties with conservative figures may earn him some favors politically.”
Perhaps. But US legislators are working on ways to regulate deepfakes that impersonate others, particularly sexually explicit imagery. Combine that with UK regulator Ofcom’s upcoming enforcement of the OSA, and Musk may soon find a permissive Grok to be a lot less fun.
Cynthia Murrell, August 28, 2024
Anthropic AI: New Allegations of Frisky Behavior
August 27, 2024
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This essay is the work of a dumb dinobaby. No smart software required.
Who knew high school science club members would mature into such frisky people. But rules are made to be broken. Apologies make the problem go away. Perhaps in high school with some indulgent faculty advisors? In the real world where lawyers are more plentiful than cardinals in Kentucky, apologies may not mean anything. I learned that the highly-regarded AI outfit Anthropic will be spending some time with the firm’s lawyers.
“Anthropic Faces New Class-Action Lawsuit from Book Authors” reported:
AI company Anthropic is still battling a lyrics-focused lawsuit from music publishers, but now it has a separate legal fight on its hands. Authors Andrea Bartz, Charles Graeber and Kirk Wallace Johnson are suing the company in a class-action lawsuit in California. As with the music publishers, their focus is on the training of Anthropic’s Claude chatbot.
I anticipate a few of the really smart and oh-so-busy wizards will be sitting in a conference room doing the deposition thing. That involves lawyers who are not particularly as scientifically oriented as AI wizards trying to make sense of Anthropic’s use of OPW (other people’s work) without permission. If you are a fan of legal filings, you can read the 20-page document at this link.
Those AI wizards are clever, aren’t they?
Stephen E Arnold, August 27, 2024
A Tool to Fool AI Detectors
August 27, 2024
Here is one way to fool the automated ai detectors: AIHumanizer. A Redditor in the r/ChatGPT subreddit has created a “New Tool that Removes Frequent AI Phrases like ‘Unleash’ or ‘Elevate’.” Sufficient_Ice_6113 writes:
“I created a simple tool which lets you humanize your texts and remove all the robotic or repeated phrases ChatGPT usually uses like ‘Unleash’ ‘elevate’ etc. here is a longer list of them: Most used AI words 100 most common AI words. To remove them and improve your texts you can use aihumanizer.com which completely rewrites your text to be more like it was written by a human. It also makes it undetectable by AI detectors as a side effect because the texts don’t have the common AI pattern any longer. It is really useful in case you want to use an AI text for any work related things, most people can easily tell an email or application to a job was written by AI when it includes ‘Unleash’ and ‘elevate’ a dozen times.”
The author links to their example result at Undetectable AI, the “AI Detector and Humanizer.” That site declares the sample text “appears human.” See the post’s comments for another example, submitted by benkei_sudo. They opine the tool “is a good start, but needs a lot of improvement” because, though it fools the AI checkers, an actual human would have their suspicions. That could be a problem for emails or press releases that, at least for now, tend to be read by people. But how many actual humans are checking resumes or standardized-test essays these days? Besides, Sufficient Ice emphasizes, AIHumanizer offers an upgraded version for an undisclosed price (though a free trial is available). The AI-content arms race continues.
Cynthia Murrell, August 27, 2024
AI Snake Oil Hisses at AI
August 23, 2024
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This essay is the work of a dumb dinobaby. No smart software required.
Enthusiasm for certain types of novel software or gadgets rises and falls. The Microsoft marketing play with OpenAI marked the beginning of the smart software hype derby. Google got the message and flipped into Red Alert mode. Now about 20 months after Microsoft’s announcement about its AI tie up with Sam AI-Man, we have Google’s new combo: AI in a mobile phone. Bam! Job done. Slam dunk.
Thanks, MSFT Copilot. On top of the IPv6 issue? Oh, too bad.
I wonder if the Googlers were thinking along the same logical lines at the authors of “AI Companies Are Pivoting from Creating Gods to Building Products. Good.”
The snake oil? Dripping. Here’s a passage from the article I noted:
AI companies are collectively planning to spend a trillion dollars on hardware and data centers, but there’s been relatively little to show for it so far.
A trillion? That’s a decent number. Sam AI-Man wants more, but the scale is helpful, particularly when most numbers are mere billions in the zoom zoom world of smart software.
The most important item in the write up, in my opinion, is the list of five “challenges.” The article focuses on consumer AI. A couple of these apply to the enterprise sector as well. Let’s look at the five “challenges.” These are and, keep in mind, I a paraphrasing as dinobabies often do:
- Cost. In terms of consumers, one must consider making Hamster Kombat smart. (This is a Telegram dApp.) My team informed me that this little gem has 35 million users, and it is still growing. Imagine the computational cost to infuse each and every Hamster Kombat “game” player with AI goodness. But it’s a game and a distributed one at that, one might say. Someone has to pay for these cycles. And Hamster Kombat is not on the radar of most consumers’ radar. Telegram has about 950 million users, so 35 million users comes from that pool. What are the costs of AI infused games outside of a walled garden. And the hardware? And the optimization engineering? And the fooling around with ad deals? Costs are not a hurdle. Costs might be a Grand Canyon-scale leap into a financial mud bank.
- Reliability. Immature systems and methods, training content issues (real and synthetic), and the fancy math which uses a lot of probability procedures guarantees some interesting outputs.
- Privacy. The consumer or user facing services are immature. Developers want to get something to most work in a good enough manner. Then security may be discussed. But on to the next feature. As a result, I am not sure if anyone has a decent grasp of the security issues which smart software might pose. Look at Microsoft. It’s been around almost half a century, and I learn about new security problems every day. Is smart software different?
- Safety and security. This is a concomitant to privacy. Good luck knowing what the systems do or do not do.
- User interface. I am a dinobaby. The interfaces are pale, low contrast, and change depending on what a user clicks. I like stability. Smart software simply does not comprehend that word.
Good points. My view is that the obstacle to surmount is money. I am not sure that the big outfits anticipated the costs of their sally into the hallucinating world of AI. And what are those costs, pray tell. Here’s are selected items the financial managers at the Big Dogs are pondering along with the wording of their updated LinkedIn profile:
- Litigation. Remarks by some icons of the high technology sector have done little to assuage the feelings of those whose content was used without permission or compensation. Some, some people. A few Big Dogs are paying cash to scrape.
- Power. Yep, electricity, as EV owners know, is not really free.
- Water, Yep, modern machines produce heat if what I learned in physics was actual factual.
- People (until they can be replaced by a machine that does not require health care or engage in signing petitions).
- Data and indexing. Yep, still around and expensive.
- License fees. They are comin’ round the mountain of legal filings.
- Meals, travel and lodging. Leadership will be testifying, probably a lot.
- PR advisors and crisis consultants. See the first bullet, Litigation.
However, slowly but surely some commercial sectors are using smart software. There is an AI law firm. There are dermatologists letting AI determine what to cut, freeze, or ignore. And there are college professors using AI to help them do “original” work and create peer-review fodder.
There was a snake in the Garden of Eden, right?
Stephen E Arnold, August 23, 2024
Google Leadership Versus Valued Googlers
August 23, 2024
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This essay is the work of a dumb dinobaby. No smart software required.
The summer in rural Kentucky lingers on. About 2,300 miles away from the Sundar & Prabhakar Comedy Show’s nerve center, the Alphabet Google YouTube DeepMind entity is also “cyclonic heating from chaotic employee motion.” What’s this mean? Unsteady waters? Heat stroke? Confusion? Hallucinations? My goodness.
The Google leadership faces another round of employee pushback. I read “Workers at Google DeepMind Push Company to Drop Military Contracts.”
How could the Google smart software fail to predict this pattern? My view is that smart software has some limitations when it comes to managing AI wizards. Furthermore, Google senior managers have not been able to extract full knowledge value from the tools at their disposal to deal with complexity. Time Magazine reports:
Nearly 200 workers inside Google DeepMind, the company’s AI division, signed a letter calling on the tech giant to drop its contracts with military organizations earlier this year, according to a copy of the document reviewed by TIME and five people with knowledge of the matter. The letter circulated amid growing concerns inside the AI lab that its technology is being sold to militaries engaged in warfare, in what the workers say is a violation of Google’s own AI rules.
Why are AI Googlers grousing about military work? My personal view is that the recent hagiography of Palantir’s Alex Karp and the tie up between Microsoft and Palantir for Impact Level 5 services means that the US government is gearing up to spend some big bucks for warfighting technology. Google wants — really needs — this revenue. Penalties for its frisky behavior as what Judge Mehta describes and “monopolistic” could put a hit in the git along of Google ad revenue. Therefore, Google’s smart software can meet the hunger militaries have for intelligent software to perform a wide variety of functions. As the Russian special operation makes clear, “meat based” warfare is somewhat inefficient. Ukrainian garage-built drones with some AI bolted on perform better than a wave of 18 year olds with rifles and a handful of bullets. The example which sticks in my mind is a Ukrainian drone spotting a Russian soldier in the field partially obscured by bushes. The individual is attending to nature’s call.l The drone spots the “shape” and explodes near the Russian infantry man.
A former consultant faces an interpersonal Waterloo. How did that work out for Napoleon? Thanks, MSFT Copilot. Are you guys working on the IPv6 issue? Busy weekend ahead?
Those who study warfare probably have their own ah-ha moment.
The Time Magazine write up adds:
Those principles state the company [Google/DeepMind] will not pursue applications of AI that are likely to cause “overall harm,” contribute to weapons or other technologies whose “principal purpose or implementation” is to cause injury, or build technologies “whose purpose contravenes widely accepted principles of international law and human rights.”) The letter says its signatories are concerned with “ensuring that Google’s AI Principles are upheld,” and adds: “We believe [DeepMind’s] leadership shares our concerns.”
I love it when wizards “believe” something.
Will the Sundar & Prabhakar brain trust do believing or banking revenue from government agencies eager to gain access to advantage artificial intelligence services and systems? My view is that the “believers” underestimate the uncertainty arising from potential sanctions, fines, or corporate deconstruction the decision of Judge Mehta presents.
The article adds this bit of color about the Sundar & Prabhakar response time to Googlers’ concern about warfighting applications:
The [objecting employees’] letter calls on DeepMind’s leaders to investigate allegations that militaries and weapons manufacturers are Google Cloud users; terminate access to DeepMind technology for military users; and set up a new governance body responsible for preventing DeepMind technology from being used by military clients in the future. Three months on from the letter’s circulation, Google has done none of those things, according to four people with knowledge of the matter. “We have received no meaningful response from leadership,” one said, “and we are growing increasingly frustrated.”
“No meaningful response” suggests that the Alphabet Google YouTube DeepMind rhetoric is not satisfactory.
The write up concludes with this paragraph:
At a DeepMind town hall event in June, executives were asked to respond to the letter, according to three people with knowledge of the matter. DeepMind’s chief operating officer Lila Ibrahim answered the question. She told employees that DeepMind would not design or deploy any AI applications for weaponry or mass surveillance, and that Google Cloud customers were legally bound by the company’s terms of service and acceptable use policy, according to a set of notes taken during the meeting that were reviewed by TIME. Ibrahim added that she was proud of Google’s track record of advancing safe and responsible AI, and that it was the reason she chose to join, and stay at, the company.
With Microsoft and Palantir, among others, poised to capture some end-of-fiscal-year money from certain US government budgets, the comedy act’s headquarters’ planners want a piece of the action. How will the Sundar & Prabhakar Comedy Act handle the situation? Why procrastinate? Perhaps the comedy act hopes the issue will just go away. The complaining employees have short attention spans, rely on TikTok-type services for information, and can be terminated like other Googlers who grouse, picket, boycott the Foosball table, or quiet quit while working on a personal start up.
The approach worked reasonably well before Judge Mehta labeled Google a monopoly operation. It worked when ad dollars flowed like latte at Philz Coffee. But today is different, and the unsettled personnel are not a joke and add to the uncertainty some have about the Google we know and love.
Stephen E Arnold, August 23, 2024
AI Balloon: Losing Air and Boring People
August 22, 2024
Though tech bros who went all-in on AI still promise huge breakthroughs just over the horizon, Windows Central’s Kevin Okemwa warns: “The Generative AI Bubble Might Burst, Sending the Tech to an Early Deathbed Before Its Prime: ‘Don’t Believe the Hype’.” Sadly, it is probably too late to save certain career paths, like coding, from an AI takeover. But perhaps a slowdown would conserve some valuable resources. Wouldn’t that be nice? The write-up observes:
“While AI has opened up the world to endless opportunities and untapped potential, its hype might be short-lived, with challenges abounding. Aside from its high water and power demands, recent studies show that AI might be a fad and further claim that 30% of its projects will be abandoned after proof of concept. Similar sentiments are echoed in a recent Blood In The Machine newsletter, which points out critical issues that might potentially lead to ‘the beginning of the end of the generative AI boom.’ From the Blood in the Machine newsletter analysis by Brian Merchant, who is also the Los Angeles Times’ technology columnist:
‘This is it. Generative AI, as a commercial tech phenomenon, has reached its apex. The hype is evaporating. The tech is too unreliable, too often. The vibes are terrible. The air is escaping from the bubble. To me, the question is more about whether the air will rush out all at once, sending the tech sector careening downward like a balloon that someone blew up, failed to tie off properly, and let go—or, more slowly, shrinking down to size in gradual sputters, while emitting embarrassing fart sounds, like a balloon being deliberately pinched around the opening by a smirking teenager.’”
Such evocative imagery. Merchant’s article also notes that, though Enterprise AI was meant to be the way AI firms made their money, it is turning out to be a dud. There are several reasons for this, not the least of which is AI models’ tendency to “hallucinate.”
Okemwa offers several points to support Merchant’s deflating-balloon claim. For example, Microsoft was recently criticized by investors for wasting their money on AI technology. Then there NVIDIA: The chipmaker recently became the most valuable company in the world thanks to astronomical demand for its hardware to power AI projects. However, a delay of its latest powerful chip dropped its stock’s value by 5%, and market experts suspect its value will continue to decline. The write-up also points to trouble at generative AI’s flagship firm, OpenAI. The company is plagued by a disturbing exodus of top executives, rumors of pending bankruptcy, and a pesky lawsuit from Elon Musk.
Speaking of Mr. Musk, how do those who say AI will kill us all respond to the potential AI downturn? Crickets.
Cynthia Murrell, August 22, 2024
Microsoft and Palantir: Moving Up to Higher Impact Levels
August 20, 2024
Microsoft And Palantir Sell AI Spyware To Us Government
While AI is making the news about how it will end jobs, be used for deep fakes, and overturn creativity industries, there’s something that’s not being mentioned: spyware. The Verge writes about how two big technology players are planning to bring spyware to the US government: “Palantir Partners With Microsoft To Sell AI To The Government.”
Palantir and Microsoft recently announced they will combine their software to power services for US defense and intelligence services. Microsoft’s large language models (LLMs) will be used via Azure OpenAI Service with Palantir’s AI Platforms (AIP). These will be used through Microsoft’s classified government cloud environments. This doesn’t explain exactly what the combination of software will do, but there’s speculation.
Palantir is known for its software that analyses people’s personal data and helping governments and organizations with surveillance. Palantir has been very successful when it comes to government contracts:
“Despite its large client list, Palantir didn’t post its first annual profit until 2023. But the AI hype cycle has meant that Palantir’s “commercial business is exploding in a way we don’t know how to handle,” the company’s chief executive officer Alex Carp told Bloomberg in February. The majority of its business is from governments, including that of Israel — though the risk factors section of its annual filing notes that it does not and will not work with “the Chinese communist party.””
Eventually the details about Palantir’s and Microsoft’s partnership will be revealed. It probably won’t be off from what people imagine, but it is guaranteed to be shocking.
Whitney Grace, August 20, 2024
Good News: Meta To Unleash Automated AI Ads
August 19, 2024
Facebook generated its first revenue streams from advertising. Meta, Facebook’s parent company, continues to make huge profits from ads. Its products use cookies for targeted ads, collect user information to sell, and more. It’s not surprising that AI will soon be entering the picture says Computer Weekly: “Meta’s Zuckerberg Looks Ahead To AI-Generated Adverts.”
Meta increased its second-quarter revenues 22% from its first quarter. The company also reported that the cost of revenue increased by 23% due to higher infrastructure costs and Reality Labs needing a lot of cash. Zuckerberg explained that advertisers used to reach out to his company about the target audiences they wanted to reach. Meta eventually became so advanced that its ad systems predicted target audiences better than the advertisers. Zuckerberg plans for Meta to do the majority of work for advertising agencies. All they will need to provide Meta will be a budget and business objective.
Meta is investing and developing technology to make more money via AI. Meta is playing the long game:
“When asked about the payback time for investments in AI, Meta’s chief financial officer, Susan Li, said: ‘On our core AI work, we continue to take a very return on investment-based approach. We’re still seeing strong returns as improvements to both engagement and ad performance have translated into revenue gains, and it makes sense for us to continue investing here.’
Looking at generative AI (GenAI), she added: “We don’t expect our GenAI products to be a meaningful driver of revenue in 2024, but we do expect that they’re going to open up new revenue opportunities over time that will enable us to generate a solid return off of our investment…’”
Meta might see a slight dip in profit margins because it is investing in better technology, but AI generated ads will pay for themselves, literally.
Whitney Grace, August 19, 2024
An Ed Critique That Pans the Sundar & Prabhakar Comedy Act
August 16, 2024
This essay is the work of a dumb dinobaby. No smart software required.
I read Ed.
Ed refers to Edward Zitron, the thinker behind Where’s Your Ed At. The write up which caught my attention is “Monopoly Money.” I think that Ed’s one-liners will not be incorporated into the Sundar & Prabhakar comedy act. The flubbed live demos are knee slappers, but Ed’s write up is nipping at the heels of the latest Googley gaffe.
Young people are keen observers of certain high-technology companies. What happens if one of the giants becomes virtual and moves to a Dubai-type location? Who has jurisdiction? Regulatory enforcement delayed means big high-tech outfits are more portable than old-fashioned monopolies. Thanks, MSFT Copilot. Big industrial images are clearly a core competency you have.
Ed’s focus is on the legal decision which concluded that the online advertising company is a monopoly in “general text advertising.” The essay states:
The ruling precisely explains how Google managed to limit competition and choice in the search and ad markets. Documents obtained through discovery revealed the eye-watering amounts Google paid to Samsung ($8 billion over four years) and Apple ($20 billion in 2022 alone) to remain the default search engine on their devices, as well as Mozilla (around $500 million a year), which (despite being an organization that I genuinely admire, and that does a lot of cool stuff technologically) is largely dependent on Google’s cash to remain afloat.
Ed notes:
Monopolies are a big part of why everything feels like it stopped working.
Ed is on to something. The large technology outfits in the US control online. But one of the downstream consequences of what I call the Silicon Valley way or the Googley approach to business is that other industries and market sectors have watched how modern monopolies work. The result is that concentration of power has not been a regulatory priority. The role of data aggregation has been ignored. As a result, outfits like Kroger (a grocery company) is trying to apply Googley tactics to vegetables.
Ed points out:
Remember when “inflation” raised prices everywhere? It’s because the increasingly-dwindling amount of competition in many consumer goods companies allowed them to all raise their prices, gouging consumers in a way that should have had someone sent to jail rather than make $19 million for bleeding Americans dry. It’s also much, much easier for a tech company to establish one, because they often do so nestled in their own platforms, making them a little harder to pull apart. One can easily say “if you own all the grocery stores in an area that means you can control prices of groceries,” but it’s a little harder to point at the problem with the tech industry, because said monopolies are new, and different, yet mostly come down to owning, on some level, both the customer and those selling to the customer.
Blue chip consulting firms flip this comment around. The points Ed makes are the recommendations and tactics the would-be monopolists convert to action plans. My reaction is, “Thanks, Silicon Valley. Nice contribution to society.”
Ed then gets to artificial intelligence, definitely a hot topic. He notes:
Monopolies are inherently anti-consumer and anti-innovation, and the big push toward generative AI is a blatant attempt to create another monopoly — the dominance of Large Language Models owned by Microsoft, Amazon, Google and Meta. While this might seem like a competitive marketplace, because these models all require incredibly large amounts of cloud compute and cash to both train and maintain, most companies can’t really compete at scale.
Bingo.
I noted this Ed comment about AI too:
This is the ideal situation for a monopolist — you pay them money for a service and it runs without you knowing how it does so, which in turn means that you have no way of building your own version. This master plan only falls apart when the “thing” that needs to be trained using hardware that they monopolize doesn’t actually provide the business returns that they need to justify its existence.
Ed then makes a comment which will cause some stakeholders to take a breath:
As I’ve written before, big tech has run out of hyper-growth markets to sell into, leaving them with further iterations of whatever products they’re selling you today, which is a huge problem when big tech is only really built to rest on its laurels. Apple, Microsoft and Amazon have at least been smart enough to not totally destroy their own products, but Meta and Google have done the opposite, using every opportunity to squeeze as much revenue out of every corner, making escape difficult for the customer and impossible for those selling to them. And without something new — and no, generative AI is not the answer — they really don’t have a way to keep growing, and in the case of Meta and Google, may not have a way to sustain their companies past the next decade. These companies are not built to compete because they don’t have to, and if they’re ever faced with a force that requires them to do good stuff that people like or win a customer’s love, I’m not sure they even know what that looks like.
Viewed from a Googley point of view, these high-technology outfits are doing what is logical. That’s why the Google advertisement for itself troubled people. The person writing his child willfully used smart software. The fellow embodied a logical solution to the knotty problem of feelings and appropriate behavior.
Ed suggests several remedies for the Google issue. These make sense, but the next step for Google will be an appeal. Appeals take time. US government officials change. The appetite to fight legions of well resourced lawyers can wane. The decision reveals some interesting insights into the behavior of Google. The problem now is how to alter that behavior without causing significant market disruption. Google is really big, and changes can have difficult-to-predict consequences.
The essay concludes:
I personally cannot leave Google Docs or Gmail without a significant upheaval to my workflow — is a way that they reinforce their monopolies. So start deleting sh*t. Do it now. Think deeply about what it is you really need — be it the accounts you have and the services you need — and take action. They’re not scared of you, and they should be.
Interesting stance.
Several observations:
- Appeals take time. Time favors outfits like losers of anti-trust cases.
- Google can adapt and morph. The size and scale equip the Google in ways not fathomable to those outside Google.
- Google is not Standard Oil. Google is like AT&T. That break up resulted in reconsolidation and two big Baby Bells and one outside player. So a shattered Google may just reassemble itself. The fancy word for this is emergent.
Ed hits some good points. My view is that the Google fumbles forward putting the Sundar & Prabhakar Comedy Act in every city the digital wagon can reach.
Stephen E Arnold, August 16, 2024
A Familiar Cycle: The Frustration of Almost Solving the Search Problem
August 16, 2024
This essay is the work of a dumb dinobaby. No smart software required.
Search and retrieval is a difficult problem. The solutions have ranged from scrolls with labels to punched cards and rods to bags of words. Each innovation or advance sparked new ideas. Boolean gave way to natural language. Natural language evolved into semi-smart systems. Now we are in the era of what seems to be smart software. Like the punch card systems, users became aware of the value of consistent, accurate indexing. Today one expects a system to “know” what the user wants. Instead of knowing index terms, one learns to be a prompt engineer.
Search and retrieval is not “solved” using large language models. LLMs are a step forward on a long and difficult path. The potential financial cost of thinking that the methods are a sure-fire money machine is high. Thanks, MSFT Copilot. How was DEFCON?
I read “LLM Progress Is Slowing — What Will It Mean for AI?.” The write up makes clear that some of the excitement of smart software which can makes sense of natural language queries (prompts) has lost some of its shine. This type of insight is one that probably existed when a Babylonian tablet maker groused about not having an easy way to stack up clay tablets for the money guy. Search and retrieval is essential for productive work. A system which makes that process less of a hassle is welcomed. After a period of time one learns that the approach is not quite where the user wants it to be. Researchers and innovators hear the complaint and turn their attention to improving search and retrieval … again.
The write up states:
The leap from GPT-3 to GPT-3.5 was huge, propelling OpenAI into the public consciousness. The jump up to GPT-4 was also impressive, a giant step forward in power and capacity. Then came GPT-4 Turbo, which added some speed, then GPT-4 Vision, which really just unlocked GPT-4’s existing image recognition capabilities. And just a few weeks back, we saw the release of GPT-4o, which offered enhanced multi-modality but relatively little in terms of additional power. Other LLMs, like Claude 3 from Anthropic and Gemini Ultra from Google, have followed a similar trend and now seem to be converging around similar speed and power benchmarks to GPT-4. We aren’t yet in plateau territory — but do seem to be entering into a slowdown. The pattern that is emerging: Less progress in power and range with each generation.
This is an echo of the complaints I heard about Dr. Salton’s SMART search system.
The “fix” according to the write up may be to follow one of these remediation paths:
- More specialization
- New user interfaces
- Open source large language models
- More and better data
- New large language model architectures.
These are ideas bolted to the large language model approach to search and retrieval. I think each has upsides and downsides. These deserve thoughtful discussion. However, the evolution of search-and-retrieval has been an evolutionary process. Those chaos and order thinkers at the Santa Fe Institute suggest that certain “things” self organize and emerge. The idea has relevance to what happens with each “new” approach to search and retrieval.
The cited write up concludes with this statement:
One possible pattern that could emerge for LLMs: That they increasingly compete at the feature and ease-of-use levels. Over time, we could see some level of commoditization set in, similar to what we’ve seen elsewhere in the technology world. Think of, say, databases and cloud service providers. While there are substantial differences between the various options in the market, and some developers will have clear preferences, most would consider them broadly interchangeable. There is no clear and absolute “winner” in terms of which is the most powerful and capable.
I think the idea about competition is mostly correct. However, what my impression of search and retrieval as a technology thread is that progress is being made. I find it encouraging that more users are interacting with systems. Unfortunately search and retrieval is not solved by generating a paragraph a high school student can turn into a history teacher as an original report.
Effective search and retrieval is not just a prompt box. Effective information access remains a blend of extraordinarily trivial activities. For instance, a conversation may suggest a new way to locate relevant information. Reading an article or a longer document may trigger an unanticipated connection between ant colonies and another task-related process. The act of looking at different sources may lead to a fact previously unknown which leads in turn to another knowledge insight. Software alone cannot replicate these mental triggers.
LLMs like stacked clay tablets provide challenges and utility. However, search and retrieval remains a work in progress. LLMs, like semantic ad matching, or using one’s search history as a context clue, are helpful. But opportunities for innovation exist. My view is that the grousing about LLM limitations is little more than a recognition that converting a human concept or information need to an “answer” is a work in progress. The difference is that today billions of dollars have been pumped into smart software in the hope that information retrieval is solved.
Sorry, it is not. Therefore, the stakes of realizing that the golden goose may not lay enough eggs to pay off the cost of the goose itself. Twenty years ago search and retrieval was not a sector consuming billions of dollars in the span of a couple of years. That’s what is making people nervous about LLMs. Watching Delphi or Entopia fail was expensive, but the scale of the financial loss and the emotional cost of LLM failure is a different kettle of fish.
Oh, and those five “fixes” in the bullet points from the write up. None will solve the problem of search and retrieval.
Stephen E Arnold, August 16, 2024
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