Tag Archives: ai

There Are No Short Cuts to Being Human

The Velvet Sundown fooled a lot of people, including millions of fans on Spotify and the writers and editors at Rolling Stone. It was a band that suddenly showed up on Spotify several months ago, with full albums of vintage Americana styled rock. Millions started streaming the band’s songs – except there was no band. The songs, the album art, the band’s photos – it was all generated by AI.

When you know this and relisten to the songs, you swear you would have never been fooled. Those who are now in the know say the music is formulaic, derivative and uninspired. Yet we were fooled, or, at least, millions of us were – taken in by an AI hoax, or what is now euphemistically labelled on Spotify as “a synthetic music project guided by human creative direction and composed, voiced and visualized with the support of artificial intelligence.”

Formulaic. Derivative. Synthetic. We mean these as criticisms. But they are accurate descriptions of exactly how AI works. It is synthesis by formulas (or algorithms) that parse billions or trillions of data points, identify patterns and derive the finished product from it. That is AI’s greatest strength…and its biggest downfall.

The human brain, on the other hand, works quite differently. Our biggest constraint is the limit of our working memory. When we analyze disparate data points, the available slots in our temporary memory bank can be as low as in the single digits. To cognitively function beyond this limit, we have to do two things: “chunk” them together into mental building blocks and code them with emotional tags. That is the human brain’s greatest strength… and again, it’s biggest downfall. What the human brain is best at is what AI is unable to do. And vice versa.

A few posts back when talking about one less-than-impressive experience with an AI tool, I ended by musing what role humans might play as AI evolves and becomes more capable. One possible answer is something labelled “HITL” or “Humans in the Loop.” It plugs the “humanness” that sits in our brains into the equation, allowing AI to do what it’s best at and humans to provide the spark of intuition or the “gut checks” that currently cannot come from an algorithm.

As an example, let me return to the subject of that previous post, building a website. There is a lot that AI could do to build out a website. What it can’t do very well is anticipate how a human might interact with the website. These “use cases” should come from a human, perhaps one like me.

Let me tell you why I believe I’m qualified for the job. For many years, I studied online user behavior quite obsessively and published several white papers that are still cited in the academic world. I was a researcher for hire, with contracts with all the major online players. I say this not to pump my own ego (okay, maybe a little bit – I am human after all) but to set up the process of how I acquired this particular brand of expertise.

It was accumulated over time, as I learned how to analyze online interactions, code eye-tracking sessions, talked to users about goals and intentions. All the while, I was continually plugging new data into my few available working memory slots and “chunking” them into the building blocks of my expertise, to the point where I could quickly look at a website or search results page and provide a pretty accurate “gut call” prediction of how a user would interact with it. This is – without exception – how humans become experts at anything. Malcolm Gladwell called it the “10,000-hour rule.” For humans to add any value “in the loop” they must put in the time. There are no short cuts.

Or – at least – there never used to be. There is now, and that brings up a problem.

Humans now do something called “cognitive off-loading.” If something looks like it’s going to be a drudge to do, we now get Chat-GPT to do it. This is the slogging mental work that our brains are not particularly well suited to. That’s probably why we hate doing it – the brain is trying to shirk the work by tagging it with a negative emotion (brains are sneaky that way). Why not get AI, who can instantly sort through billions of data points and synthesize it into a one-page summary, to do our dirty work for us?

But by off-loading, we short circuit the very process required to build that uniquely human expertise. Writer, researcher and educational change advocate Eva Keiffenheim outlines the potential danger for humans who “off-load” to a digital brain; we may lose the sole advantage we can offer in an artificially intelligent world, “If you can’t recall it without a device, you haven’t truly learned it. You’ve rented the information. We get stuck at ‘knowing about’ a topic, never reaching the automaticity of ‘knowing how.’”

For generations, we’ve treasured the concept of “know how.” Perhaps, in all that time, we forgot how much hard mental work was required to gain it. That could be why we are quick to trade it away now that we can.

Exclusive Interview: Larry Cornett, Yahoo Director Of User Experience Design

Note: This is the 2nd of the Just Behave series from Search Engine Land. This one ran back in February, 2007. At the time of this interview, Yahoo Search was retooling and trying to gain some marketshare lost to Google. They had just launched Panama, their ad management platform. In our eyetracking study, we found Yahoo – more than any of the other engines – loaded the top of the SERP with sponsored ads. We felt this would not bode well for Yahoo’s user experience and we talked about that in this interview. Yahoo pushed back, saying in many cases, a sponsored ad was what the user was looking for. I remember thinking at the time that this didn’t pass our own usability “smell test.” Three years later, Yahoo Organic search would be shuttered and replaced with results from Microsoft’s Bing.

This week I caught up with Larry Cornett, the relatively new Director of User Experience Design at Yahoo and Kathryn Kelly, Director of PR for Yahoo! Search. Again, to set the stage for the interview, here are some high level findings from our eye tracking study that I’ll be discussing in more detail with Larry and Kathryn.

Emphasis on Top Sponsored Results

In the study we found Yahoo emphasized the top sponsored results more than either Microsoft or Google. They showed top sponsored results for more searches and devoted more real estate to them. This had the effect of giving Yahoo! the highest percentage of click throughs on top sponsored, but on first visits only. On subsequent visits the click through rate on these top sponsored ads dropped to a rate lower than what was found on Google or Microsoft.

Better Targeted Vertical Results

Yahoo’s vertical results, or Shortcuts, seem to be better targeted to the queries used by the participants in the survey. Especially for commercial searches, Yahoo did a good job disambiguating intent from the query and providing a researcher with relevant vertical results in the product search category.

How Searching from a Portal Impacted the Search Experience

When we look at how the search experience translated from a portal page where the query is launched to the results page, we found that Yahoo had a greater spread of entry points on the actual search results page. This brings up the question of how launching a search from a portal page rather than a simple search page can impact the user experience and their interaction with the results they see.

I had the chance to ask Larry and Catherine about the Yahoo search experience and how their own internal usability testing has led to the design and the experience we see today. Further I asked them about their plans for the future and what their strategy is for differentiating themselves from the competition, namely Microsoft and Google. One difference you’ll notice from Marissa’s interview last week is the continual reference to Yahoo’s advertisers as key stakeholders in the experience. At Yahoo, whenever user experience is mentioned, it’s always balanced with the need for monetization.

Here’s the interview:


Gord: First let’s maybe just talk in broad terms about Yahoo’s approach to the user experience and how it affects your search interface. How are decisions made? What kind of research is done? Why does Yahoo’s search page looks like it does?

Larry: I can give you a little bit. I have been here about 7 months, so I’m still fairly new to Yahoo. But I can tell you a little bit about how we move forward with our decision making process and the approach we are taking with Yahoo.. We try to strike a balance between user experience and the needs of business as well as our advertising population. We want to provide the best user experience for the users who are trying to find information and give them most efficient experience they can have and then also provide a good ecosystem for our advertisers. And we do a tremendous amount of research here: we do a lot of usability testing, we do surveys. We also do eye tracking studies. All those help inform us when thing are working really well for users and one we need to work on improving things.

Kathryn: And we do bucket testing on a lot of different features, on different properties, to get feed back from our users before ever implementing any new features.

Gord: Since we conducted the study we’ve noticed some changes on the search interface. Do changes tend to be more evolutionary or do you lump them together into a major revision and roll them out together?

Larry: I’d say we do both. We have essentially two parallel tracks. One is continuous improvement, so we’re always looking to improve the experience. They would be considered the evolutionary changes based on a lot of data we are looking at. Then we also have larger things that we’re definitely interested on a more strategic direction, that we look at in a longer term window for larger changes.

Gord: One of the things that we did spend a fair amount of time on in this study is this whole idea of perceived relevancy. If we set a side the whole question of how relevant are the actual results based on the content of those results and what shows, and look more how quickly scent is picked up by the user and how the results that they see are perceived to be relevant. Does that notion coincide with your findings from the internal research and how is that idea of the appearance of relevancy rather the actual relevancy play into the results you present?

Larry: Yes, that absolutely is similar to the types of research findings that we’ve had, specifically with some of the eye tracking studies. We also continue to make efforts on actual relevance so our Yahoo search team is constantly doing improvements to everything to have real relevance improve. But you right, that perceived relevance is actually the most important thing because, at the end of the day, that’s what the users are looking at and that’s what they walk away with. In terms of: Was my search relevant? Did I find what I was looking for?

I do like the concept that you have with the information scent, the semantic mapping. I think it definitely ties into the mental model that a user has when they approach search and they are doing a query. They looking for things that come back to match what they have on their mind, what they are looking for in the results, so the more they actually see those search terms and things they are having in their mind, in terms of what they’re expecting to see, the more relevant the search is going to be for them.

Gord: It comes down to the efficiency of the user experience too, how quickly they find what they are looking for, how quickly they think they find what they are looking for and how successful that click through is. Did the promise match up with what was actually delivered on the other end?

Larry: Yes absolutely.

Gord: One of the biggest differences we saw between Yahoo and the other engines was the treatment of the top sponsored ads. I think it’s fair to say that in both the percentages of the searches that ads were presented for and the number presented, you were more aggressive than MSN (now Live Search) and Google. Obviously I understand the monetization reasoning behind that, but maybe you can speak a little bit as far as the user experience.

Larry: Sure, I mean in many cases those results are, and even in your report you showed this, those results are exactly what the users are looking for. Very often what they see in that sponsored section actually is a good fit for the type of query they are doing, especially if you look at a commercial query. So it’s always finding that balance between monetization and showing organic results. We’re just trying to get the best results for the user based on what they are looking for.

Gord: I certainly agree with you with the fact that in a lot of cases the sponsored results were what they were looking for, but we couldn’t help but notice that there was always a little bit of suspicion or skepticism on the part of the user, both in how they scan the results, and even when they do click through to a result there seems to be a hesitancy to stop there. We found a tendency to want to check out at least the top organic listing as well. One thing with Yahoo is that, with the more aggressive presentation of top sponsored, their choices on the organic tend to get pushed closer and closer to the fold. Have you done any testing on that?

Larry: Yes, those are definitely things that we are exploring as we’re trying to improve the user experience. And we’ve done our own eye tracking as well. A lot of it does come down to a big difference between what’s above the fold and what’s below the fold. So we’re always being very careful when we’re exploring that, thinking about the dominant monitor resolution, settings that we’re looking at as people start to have more advanced systems and larger monitors and really trying to understand what they seeing when they given that first load of the search page.

Gord: We did notice that of all the three engines, Yahoo has the highest percentage of click throughs on top sponsored and first visits. A little more then 30 % of the clicks happened there. But we noticed that it dropped substantially on repeat visits, much more then it did on the other engines. Combine that with the fact that we saw more pogo sticking on Yahoo then we did on the other engines; someone would click through a top sponsored ad then click back to the search results. So, my question is; does the more aggressive presentation of top sponsored ads even out when you factor in the repeat visits and does those lower repeat click through rates negatively impact the monetization opportunities on those repeat visits?

Larry: I can’t get into too many details about that, because it starts to get into some of the business logic and business rules that we have. Especially looking at CTR (click through rates) and repeat visits, so yes, that’s probably a little more information that I actually have access to, myself.

Gord: OK, we’ll put that one off limits for now. Let’s shift gears a little bit. One of the other things that we noticed, that actually works very well on Yahoo, was Yahoo Shortcuts on the vertical results. It seems like you are doing a great job at disambiguating intent based on query and really giving searchers varied options in that vertical real state. Maybe you can talk about that.

Larry: You are absolutely right that those are very effective. And you’ll probably notice over time that we’re continuing to refine our Shortcuts, try to find even more appropriate shortcuts for different types of queries. A lot of that is based on the best end result, what the user is trying to find, and the more we can give them that information the better

Kathryn: And it’s faster too, right?

Larry: Exactly. So a lot of time people just want a quick answer; they don’t want to have to dig through a lot of web pages. They just want a very simple quick answer, so if we can provide that, then that is a great experience for them.
Kathryn: And we found that certain queries like movies, entertainment, weather, sports, travel, blend very nicely with those types of Shortcuts.

Gord: In talking to Google about this, they have fairly strictly monitored click throughs thresholds in both their top sponsored ads and on their vertical results; and if results aren’t getting clicked they don’t tend to show. It automatically gets turned off. What’s Yahoo’s approach to that? Are you monitoring CTRs and determining whether or not vertical results and top sponsored ads will appear for certain types of queries.

Larry: We definitely monitor that as well. We’re interested in tracking usage and so looking at the CTR, because we don’t want to be showing things that are not actually getting usage, so we do continually monitor the CTR and the Shortcuts.

Kathryn: Are you just referring to the Shortcuts or to all of the ads?

Gord: Both; top sponsored and vertical results or Shortcuts.

Kathryn: It’s the same for both.

Larry: Obviously we track CTR in both of those areas and look at that trend over time.

Gord: When we look at the visibility difference or the delta between those top sponsored ads and those side sponsored ads, when you factor in conversion rates, click through rates and everything else, is the difference as significant as it appears to be from an eye tracking study? How do you work with your advertisers to maximize their placement and to help them understand how people are interacting with that search real estate?

Larry: That is actually a separate team that works with those folks.

Gord: Is there overlap between the two departments? You would be on top of how the user is interfacing or interacting with the search results page. Do you share that information with that team and keep them up to date with how that real estate is been navigated?

Larry: Absolutely, it’s a very collaborate relationship, We are in communication constantly, they are giving us performance, we are giving them performance. So it’s always a very collaborative kind of relationship with that team. We definitely can give them recommendations and vice versa. We each have our own worlds that we own.

Gord: Maybe you can speak a little bit from the user’s perspective how Yahoo is when you position it against Microsoft Live Search and Google. What’s unique, why should a user be using Yahoo rather than the other two?

Larry: I’d say one of the key differentiators that you’ve seen released last year, and Terry Semel actually talks about this, is that we’re starting to introduce social search. If you look at Yahoo Answers, it’s one of the key examples of that. It’s a very exciting site that’s performing very well. There’s a lot of great press around it, and we starting to integrate that within the search experience itself, so you can do certain types of queries within search and at the bottom of the page you’ll see relevant best answers that are brought from Yahoo’s Answers. In many cases people look at that and say that it actually adds value. That’s one of the key differentiators here; there is definitely a social aspect to Yahoo Search.

Gord: As Yahoo Answers and that whole social aspect gains traction , is that something that either be moved up as far as visibility on the real state page, moved up into that Golden Triangle real estate or would it be rolled in almost transparently in what the results being shown are?

Larry: Anything is possible, but it’s something that we’re evaluating, so we’re constantly looking at data that comes from user studies and the live site performance, and so we’ll be making a decision about that as the year goes on.

Gord: One other thing that I think somewhat distinguishes Yahoo, especially from Google, is where the searches are launched from. When you look at user’s experience, obviously you are taking into consideration where those Yahoo searches are being launched from; a tool bar versus a portal versus the search page. How does that factor into the user experience?

Larry: You’re right. We definitely look at that type of data and really try to understand how those users might be different and their expectations might be different. So we’re constantly looking at that whole ecosystem because Yahoo is a very large network, with a lot of wonderful properties, so you have to understand how we all play together and what the relationship is between the properties back and forth.

Kathryn: Another thing is knowing where to put a search box on what property and what is going to work with the right mix of users for that property, because not every property is conducive to having a search box prominently displayed, and that’s something that we look at very closely.

Gord: Which brings up another question. One of the things that we speculated on the study is; does the intent of the user get colored based on the context that shows around that search box? If it gets launched from a very clean minimalist search page, there is little influence on intent, but if it gets launched from a portal, where there is a lot of content surrounding it, the intent can then be altered in between the click on the search box and ending up on the search page . Do you have any insight on that? Have you done your own studies on that impact?

Larry: We are definitely doing research within that area to understand the affect of the context and I don’t really have anything I can share at this point but I would say that there’s probably a lot of very interesting information to be derived from looking at that.

Gord: Ok. I’ll go out on the limb once more and say: Obviously if you can take the contextual messaging or what is surrounding that search box, and if it obviously correlates to the search, then I suppose that will help you in potentially targeting the advertising messaging that can go with that, right?

Larry: I think that’s fair to say.

Gord: Ok. I’ll leave it there. One question I have to ask comes down to the user interface. It seems as changes are made the differences between the three engines are getting fewer and fewer and it does seem like everyone is moving more to the standards that Google has defined. Is Google’s interface as it sits the de facto standard for a search results page now and if so, then in what areas does Yahoo differentiate itself? I’m talking more about the design of the page, white space, font usage, where the query bolding is…that type of thing.

Larry: There are a lot of really smart people in each of those companies. They also do their own user studies and they look at their metrics, so I think everyone is realizing over time that they’ve refined their search experience, what is working and what is not. So it’s not surprising to see some convergence in terms of the design and what seems to be most effective. I can’t really speak to whether Google is the de facto standard, but definitely they have a lot of eyeballs, so I think people do get use to seeing things in a certain way. I know from the Yahoo perspective that we want to do what’s best for our users. And I think we do have a user population that has certain expectations of us. I think a big part of that is the social search component, because people do think of Yahoo as a distinct company with its own brand, so there’s a lot that we want to do on that page that is completely independent of what other people might be doing because we want to do what’s best for our users.

Kathryn: And our users tend to be different than Google users. There is obviously overlap but we also have a distinct type of user than Google. We have to take that into consideration.

Gord: Can we go a little bit further down that road? Can we paint a picture of the Yahoo user and then explain how your interface is catering to their specific needs?

Larry: I can’t speak too much about it but one difference is that Yahoo is a lot of different things. We’re not just a search company, not just a mail company, not just a portal company; we serve a lot of needs. And we have a lot of tremendously popular, very effective properties that people use. Millions and millions come to the Yahoo network every day for a whole variety of reasons, so I think that’s one thing that’s different about the Yahoo user. They’re not coming to Yahoo for one purpose. There is often many, many purposes, so that’s something we definitely we have to take into consideration. And I think that’s one reason of many that we’re looking at social search. We know that our users are doing a lot of things in our network and it’s really effective if we’re aware of that.

Gord: So, rather then the task oriented approach with Google where their whole job is to get people in and out as quickly as possible, Yahoo Search supports that community approach where search is just one aspect of several things that people might be doing when they are engaged with the various properties?

Larry: We want to support whatever the user’s task is; and I think search is actually a very simple term and it encompasses a lot. People use search for a whole variety of reasons, millions and millions of reasons, so you have to be aware of what their intent is and, you talk a lot about that in your report, you support that. If they want to get in and out, that’s one task flow. If they want to have a place where they have access to data and information that is coming from their community, all the social information that they think is valuable, that’s another task flow. So, I think just being aware of the fact that search is multi-faceted, it’s not just a simple single type of task flow.

Kathryn: And another thing we talk about a lot is that Google is really about getting people off of their networks as fast as possible; we tend to want to keep people in our network and introduce them to other properties and experiences. So I think that’s also something that we take a look at.

Gord: So, what’s the challenge for Yahoo for search in the future, if you were looking at your whiteboard of the things that you’re tackling in 2007? We talked a little bit about social search, but as far as the user’s experience, what is the biggest challenge that has to be cracked over the next year or two?

Larry: We’ve been touching on that and I think the biggest challenge is really disambiguating intent. Really trying to understand what does the user want when they enter a few words into the search box. It’s not a lot to work with, obviously. So the biggest challenge is understanding the intent and giving them what they’re looking for, and doing that in the most effective way we can. Yes, probably not anything new but I’d say that is the biggest challenge.

Gord: And in dealing with that challenge, I would suspect that moving beyond the current paradigm is imperative in doing that. We’re used to interacting with search in a certain way, but to do what you’re saying we have to move quickly beyond the idea of a query and getting results back on a fairly static page.

Larry: There are certain expectations that users have, because search is search, and it’s been that way for many years, but I think you can see with our strategy with social search and what we’ve been doing with the integration of Yahoo Answers that it is a shift. And it’s showing that we believe for certain types of queries and for certain information that it’s very useful to bring it up, not just purely algorithmic results.

Gord: I’m just going to wrap up by asking one question, and I guess…somewhat of a self serving question, but with our eye tracking report, are there parts where we align with what you have found?

Larry: No…I found the report fascinating. I think you guys have done a wonderful job. It’s a very interesting read. There is a lot of great information there. And I think there is a lot that is in sync with some of our findings as well. So I think you definitely found some themes that make a lot of sense.

Gord: Thanks very much.

Next week, I talk with Justin Osmer, Senior Product Manager at Microsoft about the new Windows Live Search experience, how MSN Search fared in the eye tracking study, and how MSN Search evolved into the Live Search experience.

Bots and Agents – The Present and Future of A.I.

This past weekend I got started on a website I told a friend I’d help him build. I’ve been building websites for over 30 years now, but for this one, I decided to use a platform that was new to me. Knowing there would be a significant learning curve, my plan was to use the weekend to learn the basics of the platform. As is now true everywhere, I had just logged into the dashboard when a window popped up asking if I wanted to use their new AI co-pilot to help me plan and build the website.

“What the hell?” I thought, “Let’s take it for a spin!” Even if it could lessen the learning curve a little bit, it could still save me dozens of hours. The promise given me was intriguing – the AI co-pilot would ask me a few questions and then give me back the basic bones of a fully functional website. Or, at least, that’s what I thought.

I jumped on the chatbot and started typing. With each question, my expectations rose. It started with the basics: what were we selling, what were our product categories, where was our market? Soon, though, it started asking me what tone of voice I wanted, what was our color scheme, what search functionality was required, were there any competitor’s sites that we liked or disliked, and if so, what specifically did we like or dislike?  As I plugged my answers, I wondered what exactly I would get back.

The answer, as it turned out, was not much. As I was reassured that I had provided a strong enough brief for an excellent plan, I clicked the “finalize” button and waited. And waited. And waited. The ellipse below my last input just kept fading in and out. Finally, I asked, “Are you finished yet?” I was encouraged to just wait a few more minutes as it prepared a plan guaranteed to amaze.

Finally – ta da! – I got the “detailed web plan.” As far as I can tell, it had simply sucked in my input and belched it out again, formatted as a bullet list. I was profoundly underwhelmed.

Going into this, I had little experience with AI. I have used it sparingly for tasks that tend to have a well-defined scope. I have to say, I have been impressed more often than I have been disappointed, but I haven’t really kicked the tires of AI.

Every week, when I sit down to write this post, Microsoft Co-Pilot urges me to let it show what it can do. I have resisted, because when I do ask AI to write something for me, it reads like a machine did it. It’s worded correctly and usually gets the facts right, but there is no humanness in the process. One thing I think I have is an ability to connect the dots – to bring together seemingly unconnected examples or thoughts and hopefully join them together to create a unique perspective. For me, AI is a workhorse that can go out and gather the information in a utilitarian manner, but somewhere in the mix, a human is required to add the spark of intuition or inspiration. For now, anyway.

Meet Agentic AI

With my recent AI debacle still fresh in my mind, I happened across a blog post from Bill Gates. It seems I thought I was talking to an AI “Agent” when, in fact, I was chatting with a “Bot.” It’s agentic AI that will probably deliver the usefulness I’ve been looking for for the last decade and a half.

As it turns out, Gates was at least a decade and a half ahead of me in that search. He first talked about intelligent agents in his 1995 book The Road Ahead. But it’s only now that they’ve become possible, thanks to advances in AI. In his post, Gate’s describes the difference between Bots and Agents: “Agents are smarter. They’re proactive—capable of making suggestions before you ask for them. They accomplish tasks across applications. They improve over time because they remember your activities and recognize intent and patterns in your behavior. Based on this information, they offer to provide what they think you need, although you will always make the final decisions.”

This is exactly the “app-ssistant” I first described in 2010 and have returned to a few times since, even down to using the same example Bill Gates did – planning a trip. This is what I was expecting when I took the web-design co-pilot for a test flight. I was hoping that – even if it couldn’t take me all the way from A to Z – it could at least get me to M. As it turned out, it couldn’t even get past A. I ended up exactly where I started.

But the day will come. And, when it does, I have to wonder if there will still be room on the flight for we human passengers?

Paging Dr. Robot

When it comes to the benefits of A.I. one of the most intriguing opportunities is in healthcare. Microsoft’s recent announcement that, given a diagnostic challenge where their Microsoft AI Diagnostic Orchestrator (MAI-DxO) went head to head with 21 general-practice practitioners, the A.I. system correctly diagnosed 85% of 300 challenging cases gathered from the New England Journal of Medicine. The human doctors only managed to get 20% of the diagnoses correct.

This is of particular interest to me, because Canada has a health care problem. In a recent comparison of international health policies conducted by the Commonwealth Fund, Canada came in last amongst 9 countries, most of which also have universal health care, on most key measures of timely access.

This is a big problem, but it’s not an unsolvable one. This does not qualify as a “wicked” problem, which I’ve talked about before. Wicked problems have no clear solution. I believe our healthcare problems can be solved, and A.I. could play a huge role in the solution.

The Canadian Medical Association outlined both the problems facing our healthcare system and some potential solutions. The overarching narrative is one of a system stretched beyond its resources and patients unable to access care in a timely manner. Human resources are burnt out and demotivated. Our back-end health record systems are siloed and inconsistent. An aging population, health misinformation, political beliefs and climate change are creating more demand for health services just as the supply of those services are being depleted.

Here’s one personal example of the gaps in our own health records. I recently had to go to my family doctor for a physical that is required to maintain my commercial driver’s license. I was delegated to a student doctor, given that it was a very routine check-up. Because I was seeing the doctor anyway, I thought it a good time to ask for a regular blood panel test because it had been a while since I had had one. Being a male of a certain age, I also asked for a Prostate-Specific Antigen test (PSA) and was told that it isn’t recommended as a screening test in my province anymore.

I was taken aback. I had been diagnosed with prostate cancer a decade earlier and had been successfully treated for it. It was a PSA test that led to an early diagnosis. I mentioned this to the doctor, who was sitting behind a computer screen with my records in front of him. He looked back at the screen and said, “Oh, you had prostate cancer? I didn’t know that. Sure, I’ll add a PSA to the requisition.”

I wish I could say that’s an isolated incident, but it’s not. These gaps is our medical history records happen all the time here in my part of Canada. And they can all be solved. It’s the aggregation and analysis of data beyond the limits of humans to handle that A.I. excels at. Yet our healthcare system continues to overwork exhausted healthcare providers and keep our personal health data hostage in siloed data centers because of systemic resistance to technology. I know there are concerns, but surely these concerns can be addressed.

I write this from a Canadian perspective, but I know these problems – and others – exist in the U.S. as well.  If A.I. can do certain jobs four times better than a human, it’s time to accept that and build it into our healthcare system. The answer to Canada’s healthcare problems may not be easy, but they are doable: integrate our existing health records, open the door to incorporation of personal biometric data from new wearable devices, use A.I. to analyze all this, and use humans where they can do things A.I. and technology can’t.

We need to start opening our mind to new solutions, because when it comes to a broken healthcare system, it’s literally a matter of life and death.

The Question We Need to Ask about AI

This past weekend I listened to a radio call-in show about AI. The question posed was this – are those using AI regularly achievers or cheaters? A good percentage of the conversation was focused on AI in education, especially those in post-secondary studies. Educators worried about being able to detect the use of AI to help complete coursework, such as the writing of papers. Many callers – all of which would probably be well north of 50 years old – bemoaned that fact that students today are not understanding the fundamental concepts they’re being presented because they’re using AI to complete assignments. A computer science teacher explained why he teaches obsolete coding to his students – it helps them to understand why they’re writing code at all. What is it they want to code to do? He can tell when his students are using AI because they submit examples of coding that are well beyond their abilities.

That, in a nutshell, sums up the problem with our current thinking about AI. Why are we worried about trying to detect the use of ChatGPT by a student who’s learning how to write computer code? Shouldn’t we be instead asking why we need humans to learn coding at all, when AI is better at it? Maybe it’s a toss-up right now, but it’s guaranteed not to stay that way for long. This isn’t about students using AI to “cheat.” This is about AI making humans obsolete.

As I was writing this, I happened across an essay by computer scientist Louis Rosenberg. He is worried that those in his circle, like the callers to the show I was listening too, “have never really considered what life will be like the day after an artificial general intelligence (AGI) is widely available that exceeds our own cognitive abilities.” Like I said, what we use AI for now it a poor indicator for what AI will be doing in the future.  To use an analogy I have used before, it’s like using a rocket to power your lawnmower.

But what will life be like when, in a somewhat chilling example put forward by Rosenberg, “I am standing alone in an elevator — just me and my phone — and the smartest one speeding between floors is the phone?”

It’s hard to wrap you mind around the possibilities. One of the callers to the show was a middle-aged man who was visually impaired. He talked about the difference it made to him when he got a pair of Meta Glasses last Christmas. Suddenly, his world opened up. He could make sure the pants and shirt he picked out to wear today were colors that matched. He could see if his recycling had been picked up before he made the long walk down the driveway to pick up the bin. He could cook for himself because the glasses could tell him what were in the boxes he took off his kitchen shelf. For him, AI gave him back his independence.

I personally believe we’re on the cusp of multiple AI revolutions. Healthcare will take a great leap forward when we lessen our requirements for expert advice coming from a human. In Canada, general practitioners are in desperately short supply. When you combine AI with the leaps being made by incorporating biomonitoring into wearable technology, I can’t imagine how great things would not be possible in terms of living longer, healthier lives. I hope the same is true for dealing with climate change, agricultural production and other existential problems we’re currently wrestling with.

But let’s back up to Rosenberg’s original question – what will life be like the day after AI exceeds our own abilities? The answer to that, I think, is dependent on who is in control of AI on the day before. The danger here is more than just humans becoming irrelevant. The danger is what humans are determining the future of direction of AI before AI takes over the steering wheel and determines its own future.

For the past 7 decades, the most pertinent question about our continued existence as a species has been this one, “Who is in charge of our combined nuclear arsenals?” But going forward, a more relevant question might be “who is setting the direction for AI?” Who is it that’s setting the rules, coming up with safeguards and determining what data the models are training on?  Who determines what tasks AI takes on? Here’s just one example. When does AI decide when the nuclear warheads are launched.

As I said, it’s hard to predict where AI will go. But I do know this. The general direction is already being determined. And we should all be asking, “By whom?”

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.