As a fund manager, I am always looking for ways to eliminate emotions (like fear and greed). During crazy times, like these, that is harder than seems.
But as a father whose son just got married, the equation was different. It's been six years since I originally shared this video; but as we near their wedding anniversary, I re-watched this video … and decided to share it again.
One of my son's instructions to the "Officiant" was to make me cry. I made it through the Ceremony; then, I got a chance to say a few words at the Wedding Reception. Here they are …
The words "I love you" are powerful. Most people consider it an expression of emotion. It can also be a promise …
When I first got out of Law School in the 1980s, "professionals" didn't type … that was your assistant's job (or the "typing pool," which was a real thing too).
At that point, most people couldn't have imagined what computers and software are capable of now. And if you tried to tell people how pervasive computers and 'typing' would be … they would have thought that you were delusional.
My career has spanned a series of cycles where I was able to imagine what advanced tech would enable (and how businesses would have to change to best leverage those new capabilities).
Malcolm Gladwell suggests that it takes 10,000 hours of focus and effort for someone to become an expert at something. While that is not necessarily true or accurate, it's still a helpful heuristic.
Today, we can do research that took humans 10,000 hours in the time it took you to read this sentence. Moreover, technology doesn't forget what it's learned – As a result, technological memory is much better than yours or mine. Consequently, the type and quality of decisions, inferences, and actions are better as well. Ultimately, we will leverage the increased speed, capacity, and capabilities of autonomous platforms. While that is easy to anticipate, the consequences of these discontinuous innovations are hard to predict. Things often take longer to happen than you would think. But, when they do, the consequences are often more significant and more far-reaching than anticipated.
Still, technology isn't a cure-all. Many people miss out on the benefits of A.I. and technology for the same reasons they didn't master the hobbies they picked up as an adolescent.
I shot a video discussing how to use technology to create a sustainable creative advantage. Check it out.
Many people recognize a "cool" new technology (like A.I.), but they underestimate the level of commitment and effort that mastery takes.
When using A.I. and high-performance computing, you need to ask the same questions you ask yourself about your ultimate purpose.
What's my goal?
What do I (or my systems) need to learn to accomplish my goal?
What are the best ways to achieve that goal (or something better)?
Too many companies are focused on A.I. as if that is the goal. A.I. is simply a tool. As I mentioned in the video, you must define the problem the right way in order to find an optimal solution.
Artificial Intelligence is a game-changer – so you have to approach it as such.
Know your mission and your strategy, recognize what you're committing to, set it as a compass heading and make deliberate movement in that direction.
I end the video by saying, "Wisdom comes from making finer distinctions. So, it is an iterative and recursive process… but it is also evolutionary. And frankly, that is extraordinarily exciting!"
This chart shows the real cost of gas based on inflation. It also goes back to 1992. It makes a pretty obvious case for inflation being the real culprit. That being said, the rise is still worth watching. On the one hand, the rise in gas prices isn't unprecedented; on the other hand, a surge this sharp has only happened five times in the past 30 years … and it's been over a decade since the last time it happened.
In case inflation wasn't stressing you out enough, Berkshire Hathaway's Vice Chairman, Charlie Munger, channeled Nouriel Roubini in his tirade on the dangers of inflation.
For context, the CPI has inflation rising at the fastest rate in over 40 years. Meanwhile, Munger's current working hypothesis is that our currency will become worthless over the next hundred years.
Munger paints a dire picture – but there are a lot of "what-ifs." The infusion of cash into the economy during the pandemic certainly pushed us in a dangerous direction (despite saving the economy). However, 100 years is a long time, and there are many steps we can take as an economy to slow that snowball. One of those includes the continuing scale of innovation. As new technologies arise, and the value chain of industries changes, so does the economics of our nation.
I often talk about Machine Learning and Artificial Intelligence in broad strokes. Part of that is based on me – and part of that is a result of my audience. I tend to speak with entrepreneurs (rather than data scientists or serious techies). So talking about training FLOPs, parameters, and the actual benchmarks of ML is probably outside of their interest range.
But, every once in a while, it's worth taking a look into the real tangible progress computers have been making.
Less Wrong put together a great dataset on the growth of machine learning systems between 1952 and 2021. While there are many variables that are important in judging the performance and intelligence of systems, their dataset focuses on parameter count. It does this because it's easy to find data that is also a reasonable proxy for model complexity.
One of the simplest takeaways is that ML training compute has been doubling basically every six months since 2010. Compared to Moore's Law, where compute power doubled every two years, we're radically eclipsing that. Especially as we've entered a new era of technology.
Now, to balance this out, we have to ask the question, what actually makes AI intelligent? Model size is important, but you also have factors like training compute and training dataset size. You also must consider the actual results that these systems produce. As well, model size isn't a 1-t0-1 with model complexity as architectures and domains have different inputs and needs (but can have similar sizes).
A few other brief takeaways are that language models have seen the most growth, while gaming models have the fewest trainable parameters. This is somewhat counterintuitive at first glance, but makes sense as the complexity of games means that they have more constraints in other domains. If you really get into the data, there are plenty more questions and insights to be had. But, you can learn more from either Giancarlo or Less Wrong.
And, a question to leave with is whether the scaling laws of machine learning will differ as deep learning become more prevalent. Right now, model size comparisons suggest not, but there are so many other metrics to consider.
Not everyone was born to be an innovator or inventor … the question is, can anyone learn?
In 2013, Time ran an interesting poll on the subject. It talks about reasons people invent, whether the qualities necessary are trainable, and the barriers to invention in many places.
Like most things, I believe there's a mix of nature and nurture in inventiveness and innovation.
Some people are simply smarter (or more curious) than others. But that, in-and-of-itself, isn't enough to make them rich or successful (or prone to use those talents for innovation). Many factors combine to shape a person and their destiny. Examples include their natural abilities, environment (including their parents, location in time and place, and access to the time and space necessary to invent), and the practical realities of their situation.
Government policy also influences the behavior of its citizens and industry. Policy significantly impacts access to talent, resources, opportunities, and protection. These are things that the U.S. does well.
We're currently in the most inventive period of history … and that's a staggering thought.
Luckily, I think it will only continue to grow as technology enables more.
