Tag Archives: ai

Curation is Our Future. But Can You Trust It?

 You can get information from anywhere. But the meaning of that information can come from only one place: you. Everything we take in from the vast ecosystem of information that surrounds us goes through the same singular lens – one crafted by a lifetime of collected beliefs and experiences.

Finding meaning has always been an essentially human activity. Meaning motivates us – it is our operating system. And the ability to create shared meaning can create or crumble societies. We are seeing the consequences of shared meaning play out right now in real time.

The importance of influencing meaning creates an interesting confluence between technology and human behavior. For much of the past two decades, technology has been focusing on filtering and organizing information. But we are now in an era where technology will start curating our information for us. And that is a very different animal.

What does it mean to “curate” an answer, rather than simply present it to you? Curation is more than just collecting and organizing things. The act of curation is to put that information in a context that provides additional value by providing a possible meaning. This crosses the line that delineates just disseminating information from attempting to influence individuals by providing them a meaningful context for that information. 

Not surprisingly, the roots of curation lie – in part – with religion. It comes from the Latin “curare” – “to take care of”. In medieval times, curates were priests who cared for souls. And they cared for souls by providing a meaning that lay beyond the realms of our corporal lives. If you really think about religion, it is one massive juxtaposition of a pre-packaged meaning on the world as we perceive it.

In the future, as we access our world through technology platforms, we will rely on technology to mediate meaning. For example, searches on Google now include an “AI Overview” at the top of the search results The Google Page explaining what the Overview is says it shows up when “you want to quickly understand information from a range of sources, including information from across the web and Google’s Knowledge Graph.” That is Google – or rather Google’s AI – curating an answer for you.

It could be argued that this is just another step to make search more useful – something I’ve been asking for a decade and a half now. In 2010, I said that “search providers have to replace relevancy with usefulness. Relevancy is a great measure if we’re judging information, but not so great if we’re measuring usefulness.” If AI could begin to provide actionable answers with a high degree of reliability, it would be a major step forward. There are many that say such curated answers could make search obsolete. But we have to ask ourselves, is this curation something we can trust?

With Google, this will probably start as unintentional curation – giving information meaning through a process of elimination. Given how people scan search listings (something I know a fair bit about) it’s reasonable to assume that many searchers will scan no further than the AI Overview, which is at the top of the results page. In that case, you will be spoon-fed whatever meaning happens to be the product of the AI compilation without bothering to qualify it by scanning any further down the results page. This conveyed meaning may well be unintentional, a distillation of the context from whatever sources provided the information. But given that we are lazy information foragers and will only expend enough effort to get an answer that seems reasonable, we will become trained to accept anything that is presented to us “top of page” at face value.

From there it’s not that big a step to intentional curation – presenting information to support a predetermined meaning. Given that pretty much every tech company folded like a cheap suit the minute Trump assumed office, slashing DEI initiatives and aligning their ethics – or lack of – to that of the White House, is it far-fetched to assume that they could start wrapping the information they provide in a “Trump Approved” context, providing us with messaged meaning that supports specific political beliefs? One would hate to think so but based on Facebook’s recent firing of its fact checkers, I’m not sure it’s wise to trust Big Tech to be the arbitrators of meaning.

They don’t have a great track record.

Can OpenAI Make Searching More Useful?

As you may have heard, OpenAI is testing a prototype of a new search engine called SearchGPT. A press release from July 25 notes: “Getting answers on the web can take a lot of effort, often requiring multiple attempts to get relevant results. We believe that by enhancing the conversational capabilities of our models with real-time information from the web, finding what you’re looking for can be faster and easier.”

I’ve been waiting for this for a long time: search that moves beyond relevance to usefulness.  It was 14 years ago that I said this in an interview with Aaron Goldman regarding his book “Everything I Know About Marketing I Learned from Google”:“Search providers have to replace relevancy with usefulness. Relevancy is a great measure if we’re judging information, but not so great if we’re measuring usefulness. That’s why I believe apps are the next flavor of search, little dedicated helpers that allow us to do something with the information. The information itself will become less and less important and the app that allows utilization of the information will become more and more important.”

I’ve felt for almost two decades that the days of search as a destination were numbered. For over 30 years now (Archie, the first internet search engine, was created in 1990), when we’re looking for something online, we search, and then we have to do something with what we find on the results page. Sometimes, a single search is enough — but often, it isn’t. For many of our intended end goals, we still have to do a lot of wading through the Internet’s deep end, filtering out the garbage, picking up the nuggets we need and then assembling those into something useful.

I’ve spent much of those past two decades pondering what the future of search might be. In fact, my previous company wrote a paper on it back in 2007. We were looking forward to what we thought might be the future of search, but we didn’t look too far forward. We set 2010 as our crystal ball horizon. Then we assembled an all-star panel of search design and usability experts, including Marissa Mayer, who was then Google’s vice president of search user experience and interface design, and Jakob Nielsen, principal of the Nielsen Norman Group and the web’s best known usability expert. We asked them what they thought search would look like in three years’ time.

Even back then, almost 20 years ago, I felt the linear presentation of a results page — the 10 blue links concept that started search — was limiting. Since then, we have moved beyond the 10 blue links. A Google search today for the latest IPhone model (one of our test queries in the white paper) actually looks eerily similar to the mock-up we did for what a Google search might look like in the year 2010. It just took Google 14 extra years to get there.

But the basic original premise of search is still there: Do a query, and Google will try to return the most relevant results. If you’re looking to buy an iPhone, it’s probably more useful, mainly due to sponsored content. But it’s still well short of the usefulness I was hoping for.

It’s also interesting to see what directions search has (and hasn’t) taken since then. Mayer talked a lot about interacting with search results. She envisioned an interface where you could annotate and filter your results: “I think that people will be annotating search results pages and web pages a lot. They’re going to be rating them, they’re going to be reviewing them. They’re going to be marking them up, saying ‘I want to come back to this one later.’”

That never really happened. The idea of search as a sticky and interactive interface for the web sort of materialized, but never to the extent that Mayer envisioned.

From our panel, it was Nielsen’s crystal ball that seemed to offer the clearest view of the future: “I think if you look very far ahead, you know 10, 20, 30 years or whatever, then I think there can be a lot of things happening in terms of natural language understanding and making the computer more clever than it is now. If we get to that level then it may be possible to have the computer better guess at what each person needs without the person having to say anything, but I think right now, it is very difficult.”

Nielsen was spot-on in 2007. It’s exactly those advances in natural language processing and artificial intelligence that could allow ChatGPT to now move beyond the paradigm of the search results page and move searching the web into something more useful.

A decade and a half ago, I envisioned an ecosystem of apps that could bridge the gap between what we intended to do and the information and functionality that could be found online.  That’s exactly what’s happening at OpenAI — a number of functional engines powered by AI, all beneath a natural language “chat” interface.

At this point, we still have to “say” what we want in the form of a prompt, but the more we use ChatGPT (or any AI interface) the better it will get to know us. In 2007, when we wrote our white paper on the future of search, personalization was what we were all talking about. Now, with ChatGPT, personalization could come back to the fore, helping AI know what we want even if we can’t put it into words.

As I mentioned in a previous post, we’ll have to wait to see if SearchGPT can make search more useful, especially for complex tasks like planning a vacation, making a major purchase onr planning a big event.

But I think all the pieces are there. The monetization siloes that dominate the online landscape will still prove a challenge to getting all the way to our final destination, but SearchGPT could make the journey faster and a little less taxing.

Note: I still have a copy of our 2007 white paper if anyone is interested. Just email me (email in the contact us page), give me your email and I’ll send you a copy.

The Adoption of A.I.

Recently, I was talking to a reporter about AI. She was working on a piece about what Apple’s integration of AI into the latest iOS (cleverly named Apple Intelligence) would mean for its adoption by users. Right at the beginning, she asked me this question, “What previous examples of human adoption of tech products or innovations might be able to tell us about how we will fit (or not fit) AI into our daily lives?”

That’s a big question. An existential question, even. Luckily, she gave me some advance warning, so I had a chance to think about it.  Even with the heads up, my answer was still well short of anything resembling helpfulness. It was, “I don’t think we’ve ever dealt with something quite like this. So, we’ll see.”

Incisive? Brilliant? Erudite? No, no and no.

But honest? I believe so.

When we think in terms of technology adoption, it usually falls into two categories: continuous and discontinuous. Continuous innovation simply builds on something we already understand. It’s adoption that follows a straight line, with little risk involved and little effort required. It’s driving a car with a little more horsepower, or getting a smartphone with more storage.

Discontinuous innovation is a different beast. It’s an innovation that displaces what went before it. In terms of user experience, it’s a blank slate, so it requires effort and a tolerance for risk to adopt it. This is the type of innovation that is adopted on a bell curve, first identified by American sociologist Everett Rogers in 1962. The acceptance of these new technologies spreads along a timeline defined by the personalities of the marketplace. Some are the type to try every new gadget, and some hang on to the tried and true for as long as they possibly can. Most of us fall somewhere in between.

As an example, think about going from driving a tradition car to an electric vehicle. The change from one to the other requires some effort. There’s a learning curve involved. There’s also risk. We have no baseline of experience to measure against. Some will be ahead of the curve and adopt early. Some will drive their gas clunker until it falls apart.

Falling into this second category of discontinuous innovation, but different by virtue of both the nature of the new technology and the impact it wields, are a handful of innovations that usher in a completely different paradigm. Think of the introduction of electrical power distribution in the late 19th century, the introduction of computers in the second half of the 20th century, or the spread of the internet in the 21st Century.

Each of these was foundational, in that they sparked an explosion of innovation that wouldn’t have been possible if it were not for the initial innovation. These innovations not only change all the rules, they change the very game itself. And because of that, they impact society at a fundamental level. When these types of innovations come along, your life will change whether you choose to adopt the technology or not. And it’s these types of technological paradigm shifts that are rife with unintended consequences.

If I was trying to find a parallel for what AI means for us, I would look for it amongst these examples. And that presents a problem when we pull out our crystal ball and try to peer ahead at what might be. We can’t know. There’s just too much in flux – too many variables to compute with any accuracy. Perhaps we can project forward a few months or a year at the most, based on what we know today. But trying to peer any further forward is a fool’s game. Could you have anticipated what we would be doing on the Internet in 2024 when the first BBS (Bulletin Board System) was introduced in Chicago in 1978?

A.I. is like these previous examples, but it’s also different in one fundamental way. All these other innovations had humans at the switch. Someone needed to turn on the electrical light, boot up the computer or log on to the internet. At this point, we are still “using” A.I., whether it’s as an add-on in software we’re familiar with, like Adobe Photoshop, or a stand-alone app like ChatGPT, but generative A.I.’s real potential can only be discovered when it slips from the grasp of human control and starts working on its own, hidden under some algorithmic hood, safe from our meddling human hands.

We’ve never dealt with anything like this before. So, like I said, we’ll see.

What If We Let AI Vote?

In his bestseller Homo Deus – Yuval Noah Harari thinks AI might mean the end of democracy. And his reasoning for that comes from an interesting perspective – how societies crunch their data.

Harari acknowledges that democracy might have been the best political system available to us – up to now. That’s because it relied on the wisdom of crowds. The hypothesis operating here is that if you get enough people together, each with different bits of data, you benefit from the aggregation of that data and – theoretically – if you allow everyone to vote, the aggregated data will guide the majority to the best possible decision.

Now, there are a truckload of “yeah, but”s in that hypothesis, but it does make sense. If the human ability to process data was the single biggest bottle neck in making the best governing decisions, distributing the processing amongst a whole bunch of people was a solution. Not the perfect solution, perhaps, but probably better than the alternatives. As Winston Churchill said, “it has been said that democracy is the worst form of Government except for all those other forms that have been tried from time to time.…’

So, if we look back at our history, democracy seems to emerge as the winner. But the whole point of Harari’s Homo Deus is to look forward. It is, he promises, “A Brief History of Tomorrow.” And that tomorrow includes a world with AI, which blows apart the human data processing bottle neck: “As both the volume and speed of data increase, venerable institutions like elections, parties and parliaments might become obsolete – not because they are unethical, but because they don’t process data efficiently enough.”

The other problem with democracy is that the data we use to decide is dirty. Increasingly, thanks to the network effect anomalies that come with social media, we are using data that has no objective value, it’s simply the emotional effluent of ideological echo chambers. This is true on both the right and left ends of the political spectrum. Human brains default to using available and easily digestible information that happens to conform to our existing belief schema. Thanks to social media, there is no shortage of this severely flawed data.

So, if AI can process data exponentially faster than humans, can analyze that data to make sure it meets some type of objectivity threshold, and can make decisions based on algorithms that are dispassionately rational, why shouldn’t we let AI decide who should form our governments?

Now, I pretty much guarantee that many of you, as you’re reading this, are saying that this is B.S. This will, in fact, be humans surrendering control in the most important of arenas. But I must ask in all seriousness, why not? Could AI do worse than we humans do? Worse than we have done in the past? Worse than we might do again in the very near future?

These are exactly the type of existential questions we have to ask when we ponder our future in a world that includes AI.

It’s no coincidence that we have some hubris when it comes to us believing that we’re the best choice for being put in control of a situation. As Harari admits, the liberal human view that we have free will and should have control of our own future was really the gold standard. Like democracy, it wasn’t perfect, but it was better than all the alternatives.

The problem is that there is now a lot of solid science that indicates that our concept of free will is an illusion. We are driven by biological algorithms which have been built up over thousands of years to survive in a world that no longer exists. We self-apply a thin veneer of ration and free will at the end to make us believe that we were in control and meant to do whatever it was we did. What’s even worse, when it appears we might have been wrong, we double down on the mistake, twisting the facts to conform to our illusion of how we believe things are.

But we now live in a world where there is – or soon will be – a better alternative. One without the bugs that proliferate in the biological OS that drives us.

As another example of this impending crisis of our own consciousness, let’s look at driving.

Up to now, a human was the best choice to drive a car. We were better at it than chickens or chimpanzees. But we are at the point where that may no longer be true. There is a strong argument that – as of today – autonomous cars guided by AI are safer than human controlled ones. And, if the jury is still out on this question today, it is certainly going to be true in the very near future. Yet, we humans are loathe to admit the inevitable and give up the wheel. It’s the same story as making our democratic choices.

So, let’s take it one step further. If AI can do a better job than humans in determining who should govern us, it will also do a better job in doing the actual governing. All the same caveats apply. When you think about it, democracy boils down to various groups of people pointing the finger at those chosen by other groups, saying they will make more mistakes than our choice. The common denominator is this; everyone is assumed to make mistakes. And that is absolutely the case. Right or left, Republican or Democrat, liberal or conservative, no matter who is in power, they will screw up. Repeatedly.

Because they are, after all, only human.

OpenAI’s Q* – Why Should We Care?

OpenAI founder Sam Altman’s ouster and reinstatement has rolled through the typical news cycle and we’re now back to blissful ignorance. But I think this will be one of the sea-change moments; a tipping point that we’ll look back on in the future when AI has changed everything we thought we knew and we wonder, “how the hell did we let that happen?”

Sometimes I think that tech companies use acronyms and cryptic names for new technologies to allow them to sneak game changers in without setting off the alarm bells. Take OpenAI for example. How scary does Q-Star sound? It’s just one more vague label for something we really don’t understand.

 If I’m right, we do have to ask the question, “Who is keeping an eye on these things?”

This week I decided to dig into the whole Sam Altman firing/hiring episode a little more closely so I could understand if there’s anything I should be paying attention to. Granted, I know almost nothing about AI, so what follows if very much at the layperson level, but I think that’s probably true for the vast majority of us. I don’t run into AI engineers that often in my life.

So, should we care about what happened a few weeks ago at OpenAI? In a word – YES.

First of all, a little bit about the dynamics of what led to Altman’s original dismissal. OpenAI started with the best of altruistic intentions, to “to ensure that artificial general intelligence benefits all of humanity.”  That was an ideal – many would say a naïve ideal – that Altman and OpenAI’s founders imposed on themselves. As Google discovered with its “Don’t Be Evil” mantra, it’s really hard to be successful and idealistic at the same time. In our world, success is determined by profits, and idealism and profitability almost never play in the same sandbox. Google quietly watered the “Don’t be Evil” motto until it virtually disappeared in 2018.

OpenAI’s non-profit board was set up as a kind of Internal “kill switch” to prevent the development of technologies that could be dangerous to the human race. That theoretical structure was put to the test when the board received a letter this year from some senior researchers at the company warning of a new artificial intelligence discovery that might take AI past the threshold where it could be harmful to humans. The board then did was it was set up to do, firing Altman and board chairman Greg Brockman and putting the brakes on the potentially dangerous technology. Then, Big Brother Microsoft (who has invested $13 billion in OpenAI) stepped in and suddenly Altman was back. (Note – for a far more thorough and fascinating look at OpenAI’s unique structure and the endemic problems with it, read through Alberto Romero’s series of thoughtful posts.)

There were probably two things behind Altman’s ouster: the potential capabilities of a new development called Q-Star and a fear that it would follow OpenAI’s previous path of throwing it out there to the world, without considering potential consequences. So, why is Q-Star so troubling?

Q-Star could be a major step closer to AI which can rationalize and plan. This moves us closer to the overall goal of artificial general Intelligence (AGI), the holy grail for every AI developer, including OpenAI. Artificial general intelligence, as per OpenAI’s own definition, are “AI systems that are generally smarter than humans.” Q-Star, through its ability to tackle grade school math problems, showed the promise of being artificial intelligence that could plan and reason. And that is an important tipping point, because something that can rationalize and plan pushes us forever past the boundary of a tool under human control. It’s technology that thinks for itself.

Why should this worry us? It should worry us because of Herbert Simon’s concept of “bounded rationality”, which explains that we humans are incapable of pure rationality. At some point we stop thinking endlessly about a question and come up with an answer that’s “good enough”. And we do this because of limited processing power. Emotions take over and make the decision for us.

But AGI throws those limits away. It can process exponentially more data at a rate we can’t possibly match. If we’re looking at AI through Sam Altman’s rose-colored glasses, that should be a benefit. Wouldn’t it be better to have decisions made rationally, rather than emotionally? Shouldn’t that be a benefit to mankind?

But here’s the rub. Compassion is an emotion. Empathy is an emotion. Love is also an emotion. What kind of decisions do we come to if we strip that out of the algorithm, along with any type of human check and balance?

Here’s an example. Let’s say that at some point in the future an AGI superbrain is asked the question, “Is the presence of humans beneficial to the general well-being of the earth?”

I think you know what the rational answer to that is.