Showing posts with label algorithms. Show all posts
Showing posts with label algorithms. Show all posts

Saturday, May 27, 2023

In the Midst of the Writer's Strike, Is AI Set to Take Centre Stage in Hollywood?

Sam Altman, CEO of OpenAI, mentioned something worth reflecting on during a fireside chat with Tobias Lütke (founder and CEO of Shopify) at Toronto's 2023 Elevate conference

"If you look at the prediction from maybe 10 years ago maybe even five, I think most experts would have say first AI comes for physical labor. It's going to drive trucks it's going to work in factories. Then it comes from the sort of easier parts of cognitive labor. Then it comes from the stuff that's really hard… Maybe it can write computer code someday - maybe not. And then maybe someday in the distant future (but probably never) it can do creative work. And of course it's gone the exact opposite direction…Almost everybody predicted this wrong."

The quote by Altman summarizes the amazement that many of us experience when interacting with Generative AI, witnessing how it can effortlessly generate high-quality content, whether it's telling a joke in the tone of your favourite comedian or writing a blog post in the style of a famous author.

Not everyone, however, is a fan. Its abilities, though in their infancy, have irked the creative crowd. I am talking about the writers in Hollywood, who are picketing as we speak.

AI: Taking Center Stage within the Scriptwriting Process?

They are worried about the use of artificial intelligence in the movie production process. The writers fear that producers may use AI to write scripts or fill in gaps in unfinished screenplays. This would result in an increased supply of scripts. When the supply of something goes up, the price goes down. That's the way capitalism works. Consequently, the increased use of AI could lead to a decrease in the need for scriptwriters, potentially affecting writers' earnings. While recognizing that this rapidly advancing technology could be a useful tool in some cases, the writers are demanding that production companies agree to certain safeguards regarding its use.

It’s tempting to think that this only applies to those picketing in front of Hollywood. The word “content” may send our minds to art, writing, and movie scripts. However, content generation is not just about the creative crowd. Consider that accountants, lawyers, and consultants are also content generators. Sure, they cater to a different genre and audience. And yes, their content is more likely to make you cry than laugh, it's still content at the end of the day. Moreover, this discourse extends far beyond the confines of movies, television, or streaming platforms, serving as an early warning sign, a proverbial canary in the coal mine, hinting at the impending profound transformations across all sectors and industries.

How good is this technology? Is it really worth protesting?

As they say, seeing is believing. We don’t have to speculate about AI’s script writing powers. We can try it out ourselves. Here is my attempt to do just that. Check it out and see for yourself:

   

Admittedly, the story was terrible.

It is certainly no competition for the quality and the caliber of those protesting in the Sunshine State. Both my wife and Efrim Boritz thought it needed some work. A lot of work. That being said, if we focus on the aesthetics of the story, however, we miss the point. The story, from start to finish, took a grand total of 35 minutes. Here how I generated the story.

Step 1: Fill-in the Narrative Arc

I took the five elements of Narrative Arc and completed it for my story. This was crafted in the following prompt:

Prompt: Draft the story using the following

“Exposition:  A group of auditors working on an audit file for a bank in 2006. There is a deadline of two weeks. Kevin is the hero. He is the senior on the audit

Rising Action: Kevin is asked to audit a mortgage-backed bonds held by the audit client. He tries to understand the structure. He calls the bond rating agency. Doesn't get answers. Tries to call the borrowers no answer. Billy his manager wants him to just get the work done. He wants him to stop asking questions.

Climax: Kevin concludes that the bonds are worthless and must write them down. Billy insists that they use the current trading price. Terry the partner on the file must step-in to address this argument.

Falling Action: Partner decides to go with Kevin. Billy quits and goes to work for Lehman Brothers.

Resolution (Denouement): After the financial crisis, Kevin becomes part of the internal education team teaching him about the importance of skepticism. Billy has lost his job and is having a tough time finding a new one.”

The result was an outline with each element a bit more flushed out. Here’s how “Exposition” looked like:

“In 2006, a group of auditors are working on an audit file for a major bank. The team has a tight deadline of two weeks to complete the task. Kevin, the hero of the story, is the senior auditor on the project. He is responsible for overseeing the audit and ensuring that the team meets their deadline.”

Step 2: Expand out the Story

Then I asked it to take the text and “Expand and write in Michael Lewis's style”. Here is what it came up with:

“In the bustling year of 2006, when the financial world was basking in the warm glow of seemingly endless prosperity, a group of auditors found themselves hunkered down in a fluorescent-lit conference room. Their mission: to dissect and analyze an audit file for a major bank, a task with a ticking clock – a mere two weeks to complete the assignment. The pressure was palpable, the atmosphere charged with a blend of urgency and apprehension.

At the helm of this ragtag ensemble stood Kevin, the protagonist of our tale. A senior auditor with a keen eye for detail and an unwavering sense of duty, Kevin bore the weight of overseeing the audit and steering his team towards the fast-approaching deadline. His steady hand and calm demeanor disguised the internal churn of responsibility and the need to maintain professional skepticism amidst a sea of financial jargon and convoluted transactions.

Kevin's days blurred together as he poured over documents, cross-referencing figures and questioning assumptions. He navigated the labyrinthine world of finance with an uncanny intuition, a skill honed by years of experience that allowed him to cut through the fog of complexity to reveal the hidden truths beneath. Surrounded by his team of auditors, a diverse group of individuals each with their own quirks and strengths, Kevin felt the weight of their collective success or failure resting squarely on his shoulders…”

Rinse and repeat for the remainder of the outline and you’ve got a fully fleshed out story.

We all agree that this is not a work of art, but it only took 35 minutes. Now, imagine what I could create if I spent 35 hours or 35 days on it. Imagine further and think what the studios could achieve with an artificial intelligence that was designed specifically to generate movie scripts.

Was AI-Enabled Scriptwriting Truly Unpredictable?

No, it was not. This sci-fi thriller has been in the making for 20 years. As Chris Steiner describes in "Automate This", Hollywood had access to an algorithm in 2004 that could predict the commercial viability of a script. He writes:

"In 2004, a major movie studio allowed an algorithm to scan nine scripts of unreleased movies. The results of the analysis, run by a new company named Epagogix, were then tucked away. The movies all eventually hit the screen, and when the last one was out of theaters, the movie studio went back to take a look at what the algorithm, which was supposed to tell them how much money each film would gross at the box office, had predicted. In three of the nine cases, the algorithm missed by a wide margin. The other six forecasts, however, were bizarrely accurate. On one movie that the studio expected $100 million or more on, the total gross was $40 million, a huge disappointment.1 The algorithm predicted $49 million. Another prediction was within $1.2 million. Epagogix was suddenly on its way to becoming an indispensable tool for studios to use in analyzing scripts—especially ones that may be attached to big budgets—before the movie gets made. Epagogix was conceived and built by two movie lovers, one of them a lawyer and the other from Wall Street’s favorite of disciplines: risk management. The point is to minimize the risk of producing a stinker like Disney did in 2012 when John Carter lost the studio nearly $200 million."

He then goes on to describe the algorithm that analyzes a script based on a comprehensive report created by humans who evaluate various aspects, such as setting, characters, plot, and moral dilemmas. Despite its advanced capabilities, the algorithm still relies on human judgment to evaluate the script's language, story, and characters. But Steiner asks, presciently:

"What if there were an algorithm that didn’t need people for input? What if there were algorithms that could create the script itself?”

The Deeper Truth about Hollywood’s Existing Algorithmic Approach

There is a deeper truth in what Steiner uncovered: audiences are quite predictable. This notion seemingly contradicts the long-held belief that humans demand an infinite canvas when it comes to creativity, a canvas where one could expect unexpected twists, novel ideas, and a constant reinvention of concepts and narratives. Instead, 20-year-old algorithms are capable of predicting what most people like to watch.

Hollywood is clearly gravitating towards sustaining innovation, instead of pursuing truly disruptive narratives. John Wick is on its fourth instalment. And they just released the tenth instalment of Fast and the Furious. Yes, Fast X. By opting to reiterate tried-and-true storylines, Hollywood ensures its economic prosperity. However, the result is a not-so-creative landscape where sequels are incessantly produced, extending familiar plotlines to an almost infinite degree. This approach provides a measure of security, given the inherent uncertainty of box office returns. Yet it also confines the industry within the bounds of proven narratives, potentially at the expense of groundbreaking, original storytelling.

Hollywood studios are not in the business of searching for avant-garde composers, they're searching for chart-topping artists—those who can consistently produce hits that climb the billboards. And as for box-office bombs, they're a crippling blow to the account books—a $200 million heartache they'd rather avoid.

What about the value of creativity?

What about the pursuit of cinematic excellence, the weaving of a narrative so profound that it moves its audience to tears, laughter, or introspection? That's a narrative they've relegated to the bohemian fringe of society, the ones we affectionately refer to as “starving artists”. That creative crowd can dabble in the intricate arts of filmmaking to their hearts' content, while the Hollywood studios stick to what they know best—churning out billion dollar blockbusters, over and over again

This cold commercial reality births a Faustian bargain—one where artistic vision bows before the altar of profitability. It's the invisible contract that underwrites every script, each casting call, and the red-carpet premieres. It's the unspoken rule, the little secret tucked beneath the glitz and glamor of Hollywood. More on this and Optimus Prime in our next post.

Author: Malik Datardina, CPA, CA, CISA. Malik works at Auvenir as a GRC Strategist that is working to transform the engagement experience for accounting firms and their clients. The opinions expressed here do not necessarily represent UWCISA, UW, Auvenir (or its affiliates), CPA Canada or anyone else. This post was written with the assistance of an AI language model. The model provided suggestions and completions to help me write, but the final content and opinions are my own.

 

Thursday, July 16, 2020

'The Algorithm Made Me Do It': How Racist-Tech led an African-American man sleeping in a filthy cell

We've heard of Fintech, maybe even Regtech, but have we heard of Racist-Tech?

In the past few weeks, the US sees the largest protests in its history. I am not referring to the protests where armed protestors show up to state-capitals without much reaction. Rather, these are the protests that were in response to the death of George Floyd. George Floyd who died after a police officer kneeled on his neck (with his hands in his pocket) for eight minutes and forty-six seconds. These protests, in contrast, have been met with a strong reaction.

A related incident occurred a few months before Mr. Floyd lost his life.

As reported in NPR, Robert Julian-Borchak Williams was picked up by police by January 2020 and when he got to the station, he was surprised to the lack of resemblance between him and the pictures of the suspect.

The officer's response? "So I guess the computer got it wrong, too." 

Regardless, "Williams was detained for 30 hours and then released on bail until a court hearing on the case, his lawyers say."

(For more on the story, check out this video)

The story is chilling, to say the least.  The knee jerk reaction is to think of Skynet and dark AI. But is that really what's happening here?

The social unrest speaks to how the desegregation struggles of the 1960s have not totally succeeded. The challenge is that racism is systemic. Within the institutions that hold society together, the gothic systems that existed in the 1950s somehow still exist until today. Sure, it's illegal for prosecutors, judges and cops to be racist. But then how do we explain the treatment of George Floyd and Robert Williams? Is there is no overall monitoring provisioning to ensure that the desired equality is achieved? For example, good monitoring controls over a system would assess the outcomes to see if the desired outcomes are achieved. There was a case that tested this idea. In McClesky v Kemp, where the defence team provided Dr. Baldus's study that statistically proved that the African American is 4.3 times more likely to get the death penalty, the "big data" analysis was rejected and Warren McClesky was put to death by the state. (And yes it controlled for 35 non-race variables).

In other words, data analysis shows there actually is a problem. However, the courts essentially denied this reality and pretended everything is okay.

What does this have to do with Racist Tech?

It means that the systems and the data are biased. Racist Tech will naturally grow out of such systems. AI and predictive policing models that use data from the court system - also pretending everything is okay - will inevitably lead to people like Mr. Williams getting caught up in the criminal justice system. Compared to George Floyd he only had to spend 30 hours in a filthy cell. But during that time he would have no idea whether it was going to be 30 hours or 30 months, given how long it takes to exonerate the innocent.

I was once asked at a conference whether we can look forward to a future where AI takes over. My response was to point out the real issues is with the human that run the technology.  If I had to answer that question today, I would simply ask them to call Mr. William who knows that the nightmare scenario is here already.

Author: Malik Datardina, CPA, CA, CISA. Malik works at Auvenir as a GRC Strategist that is working to transform the engagement experience for accounting firms and their clients. The opinions expressed here do not necessarily represent UWCISA, UW, Auvenir (or its affiliates), CPA Canada or anyone else.



Saturday, September 14, 2019

Gartner Hype Cycle 2019: What trends should CPAs care about?

Less than a month ago, Gartner released it's latest Hype Cycle for 2019.

But for we get to that, what is the Hype Cycle?

If you have heard terms like "peak of inflated expectations" or "trough of disillusionment" - you're already familiar with it. But if not check it out here on Gartner's site. As described in the link, it looks at technology going through a "bubblistic" growth curve, dividing the ascent of an innovation or technology into the following phases:





The Hype Cycle essentially captures the "herd mentality" that causes Bubbles to form in the Capitalist economic system. Efrim Boritz and I wrote a paper, "A Brief Review of Investment Bubbles throughout History", over a decade ago that analyzes the history of Bubbles going back to Tulipmania back in 1600s to the DotCom Bubble in 2000. In the paper we reference, John Cassidy's "Dot.con: The Greatest Story Ever Sold", as follows:

"According to Cassidy (2002) all speculative bubbles go through four stages: 1) displacement, when something changes people’s expectations about the future; 2) boom, when prices rise sharply and skepticism gives way to greed; 3) euphoria, when people realize the bubble can’t last but they want to cash in on it before it bursts; and 4) bust, when prices plummet and speculators incur great losses. "

This illustrates that it's not just Gartner that has understood this phenomenon, but it is something broadly understood about the maturity of a given technology within the context of a Capitalist economy.

Why should CPAs care about the Hype Cycle? 

CPAs working for companies that approve investments or provide strategic advice to business leaders will want to understand where a technology trend is before approving an investment in that solution. For example, a company who is not threatened by competition or other trends may want to wait till the technology hits the Slope of Enlightenment or later. For example, chiropractors and other health care professionals can benefit from calender and website building/hosting services that are commercially available instead of having to develop their own. Conversely, someone in a highly competitive space may want to get in much earlier to head-off the competition. For example, Blackberry was too late when it came to having an app ecosystem to compete with Andriod or iOS.

Delving into the 2019 Trends: What's new in AI & Analytics? 

In terms of the overall 2019 Hype Cycle, we see that AI is still hasn't surpassed the Peak of Inflated Expectations. This illustrates that the technology is in a Hype mode; meaning a lot has to be proven out before the full ROI of AI can be understood

Gartner Hype Cycle for Emerging Technologies, 2019

In the article, the following 5 trends were highlighted:


  • Sensing and mobility
  • Augmented human
  • Postclassical compute and comms
  • Digital ecosystems
  • Advanced AI and analytics

  • In terms of the trend that arguably has the most relevance to CPAs is advanced AI and analytics.

    In a supplementary link, Gartner highlights the importance of data literacy equating to speaking the same language in the organization:

    "Imagine an organization where the marketing department speaks French, the product designers speak German, the analytics team speaks Spanish and no one speaks a second language. Even if the organization was designed with digital in mind, communicating business value and why specific technologies matter would be impossible.

    That’s essentially how a data-driven business functions when there is no data literacy. If no one outside the department understands what is being said, it doesn’t matter if data and analytics offers immense business value and is a required component of digital business."

    They go on to state that there is an essentially a looming crisis with respect to this stating that by "2020, 50% of organizations will lack sufficient AI and data literacy skills to achieve business value". 

    This trend is important for two reasons. 

    Firstly, one of the continuing challenges for CPAs in the world of audit is to get their arms around the data. So such a stat testifies to the continuing challenge that auditors will face when designing and executing audit data analytics (ADAs). 

    Second, the decision of CPA Canada to go with Data Governance as a strategic area seems to be a good choice given this trend being highlighted by Gartner.

    Such trends highlights the need for CPAs to be proficient in data wrangling, extract/transact/load (ETL) and analytics. As the Gartner article notes:

    "Poor data literacy is ranked as the second-biggest internal roadblock to the success of the office of the chief data officer, according to the Gartner Annual Chief Data Officer Survey. Gartner expects that, by 2020, 80% of organizations will initiate deliberate competency development in the field of data literacy to overcome extreme deficiencies."

    It won't be easy to prove value with data governance because of the indirect link to cost savings or revenue enhancement. But when reputable analyst firms, such as Gartner, identify this as an important trend it makes it easier to convince the business that such investments are warranted. 

    Author: Malik Datardina, CPA, CA, CISA. Malik works at Auvenir as a GRC Strategist that is working to transform the engagement experience for accounting firms and their clients. The opinions expressed here do not necessarily represent UWCISA, UW, Auvenir (or its affiliates), CPA Canada or anyone else.




    Wednesday, March 28, 2018

    Audit, Audit, Audit harked Mark: Can CPAs come to Facebook's rescue?

    In an investigation by the Guardian and the New York Times, the alleged misdeeds of Cambridge Analytica were revealed.

    As noted in the Guardian article:

    "Christopher Wylie, who worked with a Cambridge University academic to obtain the data, told the Observer: “We exploited Facebook to harvest millions of people’s profiles. And built models to exploit what we knew about them and target their inner demons. That was the basis the entire company was built on.”... Documents seen by the Observer, and confirmed by a Facebook statement, show that by late 2015 the company had found out that information had been harvested on an unprecedented scale. However, at the time it failed to alert users and took only limited steps to recover and secure the private information of more than 50 million individuals."

    The following video from TheVerge sums up the issue:



    Although such allegations have received attention (in my opinion due to the association with Trump's campaign), the reality is that these allegations against Facebook are actually not new and reported in both the Intercept in early 2017 and the Guardian way back in 2015. 

    There was an ensuing backlash (as noted in the video above and here) that forced Facebook CEO, Mark Zuckerberg to respond. He both had a written response and gave the following interview on CNN:



    During the CNN interview, he mentioned the word "audit" 3 times[emphasis added]:
    • "So we're going to go now and investigate every app that has access to a large amount of information from before we locked down our platform. And if we detect any suspicious activity, we're going to do a full forensic audit"
    • "And we're now not just going to take people's word for it when they give us a legal certification, but if we see anything suspicious, which I think there probably were signs in this case that we could have looked into, we're going to do a full forensic audit."
    • "We know how much -- how many people were using those services, and we can look at the patterns of their data requests. And based on that, we think we'll have a pretty clear sense of whether anyone was doing anything abnormal, and we'll be able to do a full audit of anyone who is questionable."
    Can CPAs come to Mark's rescue? 
    Zuckerberg's repetitive use of the word audit should be read in conjunction with his "welcoming" of regulation:

    "I actually am not sure we shouldn't be regulated. You know, I think in general, technology is an increasingly important trend in the world, and I actually think the question is more what is the right regulation rather than yes or no, should it be regulated?"

    Zuckerberg would not be the first tech giant to opt for regulation as a business strategy.

    In Tim Wu's Master Switch, Theodore Veil also advocated for the concept of a regulated monopoly in the arena of telephones:

    "[Theodore] Vail died in 1920 at age 74, shortly after resigning as AT&T's president, but by that time, his life's work was done. The Bell system had uncontested domination of American telephony, and long-distance communication was unified according to his vision. The idea of an open, competitive system had lost out to AT&T's conception of an enlightened, licensed, and regulated monopoly. AT&T would remain in this form until the 1980s, and it would return in not so substantially different form in the 2000s. As historian Milton Mueller writes, Vail had completed the "political and ideological victory of the regulated monopoly paradigm, advanced under the banner of universal service."" [emphasis added]

    As Tim points out in his book, the move enabled AT&T didn't always use their monopolistic powers for good. They charged high long distance rates and even stifled innovation suppressing the answering machine due to potential conflict with its main business.

    Regardless, it shows that Facebook could be an early advocate for CPAs offering privacy related assurance services around its algorithms.

    AlgoTrust: A new service offering for CPAs? 
    The concept of AlgoTrust is something I have previously discussed in this post.

    The idea actually has support from multiple angles not least of which of comes from information security expert, Bruce Schneier:

    "...it is also worth noting that there are other experts who hold that algorithms - from a privacy perspective - need to be regulated. Bruce Schneier, a well-known information security expert who helped review the Snowden documents, in his latest book, Data and Goliath ... also calls for "auditing algorithms for fairness". He also notes that such audits don't need to make the algorithms public, which is it the same way financial statements of public companies are audited today. This keeps a balance between confidentiality and public confidence in the company's use of our data."

    Big Data versus Privacy: The monetization paradox
    Such an algo-audit could leverage the work done by AICPA and CPA Canada in the realm of privacy, specifically the Generally Accepted Privacy Principles. That being said, privacy audits have been a hard sell in the past. But what distinguishes the service here is that it would be auditing the algorithm for compliance with privacy "regulations".The reason regulations need to be put in quotes is that in substance privacy legislation is effectively eliminated if the consumer consents to use the service.  

    The challenge, therefore, is balancing the drive to monetize big data with the privacy needs of the people who use the service. For example, people who identify with the "left" may not want Steve Bannon or Trump accessing their data. Similarly, people who identify with the "right" may not want Obama accessing their social media data. The end result is that no one can access meaningful data due to privacy restrictions - resulting in a standard so restrictive that it eliminates that ability of companies like Facebook to monetize the treasure trove of data that they have collected.

    As noted in an earlier post, there is an inherent highlight the conflict between privacy and profiting from big data. The value of big data emerges from the secondary uses of big data. However, privacy policies require the user to consent to a specific use of data at the time they sign up for the service. This means future big data analytics are essentially limited by what uses the user agreed upon sign-up. However, corporations in their drive to maximize profits will ultimately make privacy policies so loose (i.e. to cover secondary uses) that the user essentially has to give up all their privacy in order to use the service.

    There is a lot of potential in attempting to create an assurance service to address Facebook's predicament, but as they say, the devil is in the details. 

    Author: Malik Datardina, CPA, CA, CISA. Malik works at Auvenir as a GRC Strategist that is working to transform the engagement experience for accounting firms and their clients. The opinions expressed here do not necessarily represent UWCISA, UW, Auvenir (or its affiliates), CPA Canada or anyone else

    Wednesday, July 27, 2016

    Reflections on the demise of Yahoo!

    By now we've all heard that Yahoo!'s web assets were bought by Verizon. According to the Wall Street Journal, Verizon paid $4.83 billion in cash for the assets. Yahoo itself will continue to hold the remaining assets but will eventually change its name and become an investment company. In total, the company was rumoured to be worth $6 billion.

    For us Gen Xers this is an interesting day: we witnessed the end of a company we saw as innovative and fresh just a "few" (i.e. read ~20) years ago.

    I was recently explaining to a young lad in his early 20s about life before the Internet: you had to find books at the library and it was almost impossible to connect socially with people beyond your classmates. So to use Yahoo or other search engines to access information or people was a completely new and mind-blowing concept.

    As I noted in this post commemorating Google's 17th anniversary:

    "It's especially memorable for those of us who were in university in the late 90s because we had access to high speed internet on campus unlike the painfully slow dial-up at home. 

    I remember my first job as a coop student at the UW Federation of Students (I can't believe this quote is still hanging around from that time!) when a co-worker was explaining to me how OpenText was the best search engine (of course using my NetScape Browser). Of course back then there was a number of search engines including, Yahoo, Lyco, Alta Vista, etc. However, I stuck to OpenText for a while then eventually switched, along with everyone else, to Google...Well Lycos, OpenText (as a search engine) and AltaVista may be long gone, but it looks like plaid is back!"

    So now we can add Yahoo! to the pile of "has beens" search engine.

    Beyond nostalgia, I had the following reflections on the Verizon of Yahoo based on the WSJ article above:
    • Verizon is no longer just pipes: Verizon has a strategy to move beyond just serving mobile and broadband services. Verizon is adding Yahoo to its existing portfolio of content plays, such as AOL. For Verizon, it's an overall strategy to make billions through content and advertising. Net neutrality can potentially limit their ability to use this vertical integration to undermine competition, but regardless it shows how being a "pipes-only" company is not enough. Of course it is a bit ironic that former rivals, Yahoo and AOL, are now sitting in the same tent.  
    • Big Data is monetized at the expense of privacy: The ability of Verizon to combine the data plays between its various content plays is a great illustration of a point that I have noted before: for big data achieve value it must water down privacy. Since there are synergistic values (i.e. instead of just being additive) of combining the data, it could be argued that it's something that a user should explicitly consent because a user may simply not want Verizon to use their Yahoo data this way.  
    • Remember the Internet Bubble? Yahoo! had a market capitalization of "more than $125 billion at the height of the dot-com boom in early 2000", which is quite a steep decline to $6 billion. I wonder if it ever produced the cash flows to justify that valuation. 
    • Algorithms win over people: WSJ today published a good read comparing the algorithmic approach of Google, in contrast manual effort required to index the Internet. This is similar to Amazon's who found that the algorithms to better than humans in getting people to buy things: "Amabot replaced the personable, handcrafted sections of the site with automatically generated recommendations in a standardized layout," according to The Everything Store, a new book exploring the history of Amazon. "The system handily won a series of tests and demonstrated it could sell as many products as the human editors."
    • Innovation and exponential thinking: On a separate note, but related note Yahoo could have bought Google for $3B in 2002 but it didn't. It's a great example of how Google embraced leading-edge technology to deal with the exponential growth of the Internet and Yahoo's inability to recognize Google's approach as the winning approach led to its demise.

    Yahoo! is now literally a shell of its former self - both in structure and the assets it holds. However, it's a good case study of how failing to identify exponential trends - and acting on them - can ultimately lead to disaster.