A couple of years ago, a colleague of mine told me the following story. Is it true? Maybe. But even it it’s not, it illustrates a critical point, and one that will significantly boost your success.

Here it is:

There was once a young man who had just graduated from college with an engineering degree. He did what most of his classmates did and got a job as an engineer. Within the next few years he got married and had his first child. And then another child.

But after working “for the man” for twenty years, he decided to start his own firm; an engineering consulting firm. While at first business was slow, over the years he built up his firm. In fact, 30 years after founding his company, he was generating millions of dollars in annual revenues.

But then, at the age of 72, he died.

And he left the business to his 71 year old wife.

Now his wife had never run a business in her life. In fact, lately, most of her days were spent at the country club golfing and dining with her friends.

But within a year, she doubled the firm’s profits.

How did she do it?

Simple. She visited the company a week after her husband passed and sat down with the management team.

And then she told them to do two things:

   1.  Make me a list of the 5 things that worked the best in the last 12 months
   2.  Make me a list of the 5 things that worked the least in the last 12 months

The list of things that worked included items like upselling current clients, getting new clients from partnerships they formed, and their program that hired and trained new sales reps.

The list of things that didn’t work included radio advertising, sponsoring trade shows, and a new service offering that just didn’t catch on.

So the widow made one demand. She said, “Do more of the 5 things that worked, and stop doing the 5 things that didn’t work.”

The management team listened. And within a year, the company’s revenues and profits both doubled.

Yes, it’s that simple.

But most of us don’t do this. Here’s why. Throughout our lives, we’ve been told to improve our weaknesses and never quit. So, in business, when something doesn’t work, our first impression is typically to fix it.

However, that’s generally not the best solution. Rather, the best solution is typically to do more of what is already working. Those are the things that are proven to work. So why try to fix an unproven concept, when you already have figured out a winner?

Now, when it comes to marketing, I DO suggest to always tweak and test new ideas. Since creating new promotional vehicles which are profitable can give you competitive advantage. But in most other areas, you should focus more on your winners.

So, take out a pen and paper and:

   1.  Make a list of the 5 things that worked the best for you in the last 12 months
   2.  Make a list of the 5 things that worked the least for you in the last 12 months

And then make sure to focus the vast majority of your efforts on doing more of what’s worked.

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The Secret Formula to
Building a $10 Million Company

If you want to build a $10 million+ company, you must focus on building Value. And to build Value, you need to follow a specific formula.

In this video, I layout the precise formula for you.

As you watch the video, you’ll see the important schematic below:

Don’t be overwhelmed by its complexity, by the time you see it, it’ll make perfect sense. And you’ll be able to follow it to dramatically grow your business.

Click here to watch the video now

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What does the word intelligence really mean in Artificial Intelligence? In the same way as with living beings, intelligence varies considerably. From ants to humans and everything in between, there is some form of intelligence guiding our actions. In its most distilled essence, intelligence is:

1. A means of receiving information from the world.
2. Processing it to learn and make assumptions.
3. Performing actions back into said world.

It’s this middle step, the reasoning part, where the magic happens. Being able to process information, faster, in bigger quantities, and make better inferences, are characteristics of those around the top of the intelligence ladder. The other two components, the world interacting with us and us interacting with the world, allow us to exercise our intelligence on different environments based on the available sensors and actuators (like eyes for humans and animals, cameras for machines, etc.)

Comparable to how we categorize different creatures based upon their intelligence for a more accurate study, AI can also be categorized to better understand it: Strong AI (or Artificial General Intelligence) and weak AI. Strong AI is usually considered to be sentient machines, the ones that have consciousness, and for now, the stuff of science fiction. Think of C-3PO from Star Wars, HAL 9000 from 2001: A Space Odyssey, Data from Star Trek: The Next Generation. Weak AI, however, is what most are referring in regards to current applications such as IBM’s Watson and DeepBlue. Interestingly enough, there is a behavior called the “AI Effect,” where people will stop considering a machine as intelligent the moment it is obvious why it made certain decisions. In fact, a succinct definition of this effect states:

“AI is whatever hasn’t been done yet.”

Very similar to watching a David Blaine special on Netflix where everything is magic until you look for videos on YouTube to learn how he does it. Because afterward, it is just a trick. So, like magic, it seems that for AI to not be reduced to a mere calculation it must remain unexplained, a fantasy. When Google Maps calculates directions to a destination, nobody thinks that it is more than just that, calculations. Yet it is in fact a program applying intelligence to solve a problem. And this is the most important part of any AI, the problem. What is the point of intelligence if not to solve something? Can intelligence even exist without a purpose? Find the best route from Austin to Houston, schedule the best flights to get to Madrid, predict when to buy and sell more Bitcoin, deliver cargo via self-driving truck, or even win at a game of chess. The examples are as numerous as possible goals can exist in the universe, but they all share something: a problem and the environment where that problem lives. A machine with consciousness doesn’t need to be told what its goal is, it will figure it out. By contrast, most current AI applications have a clear distinct goal and a domain to interact with. It is this distinction between strong and weak AI that significantly separates their paths.

For this reason (and for better or worse), we are far away from a fully conscious machine. Creating something that we ourselves don’t completely understand is a bit tricky. Independent of the body or enclosure of this machine, we can’t say it is sentient until it is able to continuously interact with the world, learn from it, and make its own assumptions from which to derive actions. It would have to choose its own path. If we are ever able to create a such a system, capable of truly inferring and reasoning, we might end up with something less like J.A.R.V.I.S. from Iron Man and more like a CHAPPiE, from Neill Blomkamp’s dystopian sci-fi, having a lot bigger existential crisis.

It might get stuck altogether in a spinning wheel of death considering it’s existence:

“Why do anything? What is my purpose or goal?”

We know our lifespans are limited by biology but non-organic beings won’t be able to escape death either, even if their expiration date has a few more zeroes. A recently booted-up fully sentient AI might start reasoning very quickly as it interacts and learns from the world only to reach a point where it dwells on its purpose. It could reason the possible ultimate fate of the universe (like its heat death or the big crunch), decide that there is no point in doing anything, and shut itself down. That is one of the beautiful things about being human, we don’t go down the rabbit hole of every action we take to decide if it is the best path or even a logical one. When you wake up in the morning, you might just feel like hitting the snooze button for 2 or 5 minutes or get up right away. And while you could reason the benefit of those extra minutes on how you’ll feel for the rest of the day, weighted against the chance of being late for work, and a myriad of factors to determine if staying in bed a bit more is worth it, we just play it by ear. An intelligent machine would use Decision Theory, (a combination of Probability Theory, and Utility Theory) to weigh all options and choose the optimal plan to maximize the chance of achieving a specific goal.

Here’s where you can see how something as seemingly ordinary as getting directions from one place to another is actually an example of AI. It starts by exploring every road from your origin to your destination, proceeds to discount the ones that would put you farther instead of closer with every step on every direction, and repeat until it finds the shortest route. Unfortunately, that path might have too much traffic right at this moment and therefore take longer than a less direct tollway. So now it makes a decision based on what has the most utility: saving time, saving gas, or saving money. Once you strip away the technical details almost every current example of AI becomes obvious. We could do it ourselves. And just like that, the magic is gone.

More advancements are happening to get closer to strong AI and maybe one day a true artificial consciousness. Systems that learn for themselves what the problem is and how to solve it. Netflix has a great documentary on Google’s AlphaGo, which not only learned how to play the board game of Go but developed its own strategy that left the experts baffled. When it played 5 matches against one of the world’s top Go players, not even the creators fully understood what it was thinking. Afterwards, it was concluded that AlphaGo planned its decisions to maximize the chance of winning, even if by only 1 point instead of trying to crush the opponent.

Arthur C. Clarke famously said:

“Any sufficiently advanced technology is indistinguishable from magic.”

With more examples on how “no one really knows what the most advanced algorithms do what they do,” maybe the definition of magic will change in the future from wizards casting spells to machines fixing all man-made problems.

Looking to share what I learn as I build my way through an entrepreneurial journey in Austin, TX.

If you want to read more about AI, coding, and general startup wisdom, follow me and find me on LinkedIn.

AI: The new “magic” was originally published in Austin Startups on Medium, where people are continuing the conversation by highlighting and responding to this story.

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