Artificial Intelligence vs. Deep Learning vs. Big Data

Computing was some pretty exciting stuff for those of us back in the 80s who still remember the first time we booted up our 386DX. That’s right, the “DX”, not the “MX”. While nobody could really say what the advantages of the “DX” were, better at math or something, we still ponied up the extra $200 USD to pick up that 386DX 16Mhz along with a Super VGA graphics card, then hooked that bad boy up to CompuServe via our lightning fast 14,400 baud U.S. Robotics “Sportster” modem. That was well before Al Gore created the Internet, and a lot has changed since then. Personal computing just isn’t cool anymore and it’s all about “the cloud” and “big data” and “deep learning”. Confused by all this new nomenclature? So are we, so let’s go through and define some of these terms and what they mean for investors.

“The Cloud” – The idea here is that instead of purchasing applications then installing them onto a computer, you lease the applications on demand and access them over the internet. That’s it. That’s “the cloud”. You may have heard of the term “software as a service” with the cool abbreviation “SaaS”. That’s essentially the same thing. It’s centrally hosted software available via a subscription service. This ship has already sailed for investors with companies like CRM SaaS provider Salesforce.com showing a 10-year return of +780%. “The cloud” and “SaaS” are old news for investors looking for the next big thing in computing.

“Big Data” – This is simply what it says on the tin. “Big data” refers to massive data sets brought on by new technologies like the “Internet of Things” and genomics. These data sets are so large and so complex that we can’t analyze them using traditional applications. We need to build new applications to analyze all this “big data”. In a recent article, we took a look at the top 5 data storage companies by revenues for a “picks and shovels” play on this theme. Only one of those companies seems like a viable investment. Maybe the biggest player today in this space is Palantir Technologies, a $25 billion private company which helps makes sense of all this “big data’. While storing this data is one thing, analyzing it is an entirely different animal since 80% of all data today is not structured such as news articles, research reports, and enterprise data. This brings us to the next term.

“Deep Learning” – This is essentially where we can teach a computer to take all that unstructured big data and start to make sense of it using various methods like “artificial neural networks” which mimic the way the human brain works. Deep learning uses algorithms to look for complex relationships in all that “big data”, and then we further refine those algorithms as they go along to make them better. The ability for a computer to learn more over time based on experience, something the human brain does naturally, is also referred to as “cognitive computing”. We’ve all probably heard of the IBM cognitive computing platform called Watson which uses deep learning for translation, speech-to-text, and text-to-speech. While there aren’t any pure play “deep learning” stocks we’re aware of, there are quite a few startups today pursuing deep learning across any number of industries. Deep learning or cognitive computing is a form of artificial intelligence, which brings us to our next term.

Artificial Intelligence – This is where a computer can begin to process data and infer complex relationships just like a human being can. How do we measure that? The most popular method is referred to as a “Turing Test”, though some researchers dismiss this as something only hobbyists would be interested in tackling. While IBM is the leading holder of artificial intelligence patents (+500), there are many other startups in the artificial intelligence space such as a company we highlighted before called Vicarious which literally everyone is backing. Vicarious is creating software code that replicates the human brain while using relatively minuscule amounts of data and computing power. While using minuscule computing power makes sense today, this may not be a problem if we can nail quantum computing.

Quantum Computing – This is where we can use the wonders of quantum physics to build a computer that is exponentially more powerful than anything we have today. We could get into talking about “quantum entanglement” and the need to freeze things to absolute zero but who cares about any of that. What potential does quantum computing have and where are we at today? Just recently, Google announced that they used a D-Wave 2 to solve an optimization problem (with 1,000 variables) at a speed over 100 million times faster than a conventional computer. To put that into perspective, the D-Wave 2 can process in one second what it would take a conventional computer 10,000 years to process. How do you invest in quantum computing? There aren’t many games in town but we  do give you one way to invest in this article.

All of these technologies are predicted to have massive growth in the coming years, so how can retail investors make money here? Well the name IBM keeps coming up, so is it a viable play on some of these themes? With $92 billion in revenues for 2014, the contributions of “big data” and Watson are currently having a minuscule impact to the bottom line. With that said, we love IBM’s 3.8% yield which is protected by a payout ratio of less than 50%.

While the most promising companies remain private, there will be exit events like acquisitions and IPOs that will allow retail investors to put some skin in the game. The next place retail investors should look is for future tech IPOs that cover some of these themes. As we highlighted in a recent article, there are two “big data” startups that are likely to IPO in 2016 according to CB Insights. In fact, CB Insights has identified over 530 tech startups that may IPO in 2016, many of which are related to “big data”.