[Editor’s note: “Machine Learning Breakthroughs Have Sparked the AI Revolution” was previously published in July 2022. It has since been updated to include the most relevant information available.]
It’s October 1950. Alan Turing, the genius who cracked the Enigma code and helped end World War II, has just introduced a novel concept.
It’s called the “Turing Test,” and it’s aimed at answering the fundamental question: Can machines think?
The world laughs. Machines — think for themselves? Not possible.
However, the Turing Test sets in motion decades of research into the emerging field of Artificial Intelligence (AI).
This research is conducted in the world’s most prestigious labs by some of the world’s smartest people. Collectively, they’re working to create a new class of computers and machines that can, indeed, think for themselves.
Fast forward 70 years.
AI is everywhere.
It’s in your phones. What do you think powers Siri? How does a phone recognize your face?
It’s in your applications. How does Google Maps know directions and optimal routes? How does it make real-time changes based on traffic? And how does Spotify create hyper-personalized playlists or Netflix recommend movies?
AI is on your computers. How does Google suggest personalized search items for you? How do websites use chatbots that seem like real humans?
As it turns out, the world shouldn’t have laughed back in 1950.
The great Alan Turing ended up creating a robust foundation upon which seven decades of groundbreaking research has compounded. Ultimately, it resulted in self-thinking computers and machines not just being a “thing” — but being everything.
Make no mistake. This decades-in-the-making “AI Revolution” is just getting started.
That’s because AI is mostly built on what industry insiders call “machine learning” (ML) and “natural language processing” (NLP) models. And these models are informed with data.
Accordingly, the more data they have, the better the models get — and the more capable the AI becomes.
Machine Learning Breakthroughs
When I say “identity,” what do you think of?
If you’re like me, you immediately start to think of what makes you, well, you — your height, eye color; what job you have, what car you drive, what shows you like to binge-watch.
In other words, the amount of data associated with each individual identity is both endless and unique.
Those attributes make identity data extremely valuable.
Up until recently, though, enterprises had no idea how to extract value from this robust dataset. That’s all changing right now.
Breakthroughs in artificial intelligence and machine-learning technology are enabling companies to turn identity data into more personalized, secure and streamlined user experiences for their customers, employees and partners.
The volume and granularity of data is exploding right now. That’s mostly because every object in the world is becoming a data-producing device.
Dumb phones have become smartphones and have started producing a ton of usage data.
Dumb cars have become smart cars and have started producing lots of in-car driving data.
And dumb apps have become smart apps and have started producing heaps of consumer preference data.
Dumb watches have become smartwatches and have started producing bunches of fitness and activity data.
The AI Revolution
As we’ve sprinted into the “Smart World,” the amount of data that AI algorithms have access to has exploded. And it’s making them more capable than ever.
Why else has AI started popping up everywhere in recent years? It’s because 90% of the world’s data was generated in the last two years alone.
More data, better ML and NLP models, smarter AI.
It’s that simple.
And guess what? The world isn’t going to take any steps back in terms of this “smart” pivot. No. We love our smartphones, smart cars and smartwatches far too much.
Instead, society will accelerate in this transition. Globally, the world produces about 2.5 exabytes of data per day. By 2025, that number is expected to rise to 463 exabytes.
A New Era of Machine Learning
Let’s go back to our process.
More data, better ML and NLP models, smarter AI.
Thus, as the volume of data produced daily soars more than 185X over the next five years, ML and NLP models will get 185X better (more or less). And AI machines will get 185X smarter (more or less).
Most things a human does, a machine will soon be able to do better, faster and cheaper.
Given the advancements AI has made over the past few years with the help of data — and the exponential amount of it yet to come — I’m inclined to believe this.
Eventually, and inevitably, the world will be run by hyperefficient and hyperintelligent AI.
I’m not alone in thinking this. Gartner predicts that 69% of routine office work will be fully automated by 2024. And the World Economic Forum has said that robots will handle 52% of current work tasks by 2025.
The AI Revolution is coming — and it’s going to be the biggest you’ve seen in your lifetime.
Democratizing the Power of AI
You need to be invested in this emerging tech megatrend that promises to change the world forever.
Of course, the question remains: What AI stocks should you start buying right now?
You could play it safe and go with the blue-chip tech giants. All are making inroads with AI and are low-risk, low-reward plays on the AI Revolution. I’m talking Microsoft (MSFT), Alphabet (GOOG), Amazon (AMZN), Adobe (ADBE) and Apple (AAPL).
However, that’s not how we do things. We don’t like “safe” — we like “best.”
At present, enterprise AI software is being used very effectively by Big Tech. And it’s being used ineffectively or not at all by everyone else.
Today’s AI companies are changing that. And the best way to play the AI Revolution is by buying the stocks that are changing the paradigm in which they exist.
We have identified several AI stocks to buy for enormous long-term returns.
Again, these AI stocks aren’t the “safe” way to play the AI Revolution. They’re the best way to do it.
Play to Win
In fact, the best way to win big in the market is one that’s been a well-kept secret on Wall Street. The truth is that every stock follows a similar pattern. And understanding it is key to consistently scoring big profits.
Stocks can move three ways — up, down, or sideways. And that means there are four stages in a stock’s repeating lifecycle:
- Stage 1 — stocks are bottoming, or going sideways after declining.
- Stage 2 — stocks are breaking out, or moving higher after bottoming.
- Stage 3 — stocks are topping, or going sideways after rising.
- Stage 4 — stocks are declining, or going down after topping.
So, the way to make the most cash in the market? Buy when a stock enters Stage 2, ahead of a big breakout.
Now, leaving these interpretations up to human reasoning can be a great way for emotions to lead to sub-optimal market moves. Countless studies have proven that emotions trip up investors, resulting in losses or missed gains.
Instead, we believe that a high-power computer network running detailed, complex algorithms is a vastly superior approach. And we’ve just finished developing a new quant system that does just that.
This means we’re not finding trades based on hunches, gut feel, or emotions. We’re using computers that don’t get greedy or scared. They process cold, hard data, then produce impartial results.
And that’s how we reduce false breakouts and avoid most premature, whipsaw sell signals. This new system holds the key to creating massive wealth in any market climate.
On the date of publication, Luke Lango did not have (either directly or indirectly) any positions in the securities mentioned in this article.