As each year wraps up experts pull their crystal balls from their drawers and start peering into it for a glimpse of what’s to come in the next one. At BigML, We have been following such clairvoyance carefully this past holiday season to compare and contrast with our own take on what 2017 will have in store, which can come across as quite unorthodox to some experts out there.
For the TL;DR crowd, our crystal ball is showing us a cloudy (no pun intended) 2017 Machine Learning market forecast with some sunshine behind the clouds for good measure. To put it more directly, enterprises need to look beyond the AI hype for practical ways to incorporate Machine Learning into their operations. This starts with the right choice of internal platform that will help them build on smaller, low hanging fruit type projects that leverage their proprietary datasets. In due time, those projects add up to create positive feedback effects that ultimately not only introduce decision automation on the edges, but help agile Machine Learning teams transform their industries.
Jumping back to our regularly scheduled programming, let’s start with a quick synopsis of the road traveled so far:
Machine Learning has already set on an irreversible path in becoming
impactful(VERY impactful) on how we’ll do our jobs throughout many sectors and eventually touching the whole economy.
But digesting, adopting and profiting from 36 years of Machine Learning advances and best practices has been a very bumpy ride for many businesses few have managed to navigate so far.
There are many “New Experts” that read a couple of books or take a few online classes and are suddenly in a position to “alter” things just because they have access to cheap capital. While top technology companies have been “collecting” as much experienced Machine Learning talent as possible to get ready ready for the up and coming AI economy, other businesses are at the mercy of Machine Learning-newbie investors and inexperienced recent graduates with unicorn ambitions. It is wishfully assumed that versatile, affordable and scalable solutions based on a magical new algorithm will materialize out of these ventures.
In 2017, we suspect that the ecosystem is going to start converging around the right approach, albeit after otherwise avoidable roadkills.
Before we get to the specific predictions, we must note that 2016 was a special year in that it presented us with the watershed event such that the planet’s Top 5 most valuable companies are all technology companies for the first time in history. All five share the common traits of large scale network effects, highly data-centric company cultures and new economic value-added services built atop sophisticated analytics. Whats more they have been heavily publicizing their intent to make Machine Learning the fulcrum of their future evolution. With the addition of revenue generating unicorns like Uber and Airbnb the dominance of the tech sector is likely to continue in the coming years that will benefit immensely from the wholesale digitization of the World economy.
However, the trillion dollar question is how legacy companies (i.e., non-tech firms with rich data plus smaller technology companies) can counteract and become an integral part of the newly forming value chains to be able to not only survive, but thrive in the remainder of the decade. Today, these firms are stuck with rigid rear view mirror business intelligence systems and archaic workstation-based traditional statistical systems running simplistic regression models that fail to capture the complexity of many real life predictive use cases.
At the same time, they sit on growing heaps of hard to replicate proprietary datasets that go underutilized. The latest McKinsey Global Institute report named The Age of Analytics: Competing in a Data-driven World reveals that less than 30% of the potential of modern analytics technologies outlined in their 2011 report has been realized — not even counting the new opportunities made possible by the advent of the same technologies in the last five years. To make matters worse, the progress looks very unbalanced across industries (i.e., as low as 10% in U.S. Healthcare vs. up to 60% in the case of Smartphones) at a time analytics prowess is correlated with competitive differentiation more than ever.
Even if it maybe hidden behind polished marketing speak pushed by major vendors and research firms (e.g., “Cognitive Computing”, “Machine Intelligence” or even doomsday-like “Smart Machines”), the Machine Learning genie is out of the bottle without a doubt as its wide-ranging potential across the enterprise has already made it part of the business lexicon. This new found appetite for all things Machine Learning means many more legacy firms and startups will begin their Machine Learning journeys in the 2017. The smart ones will separate themselves from the bunch by learning from others’ mistakes. Nonetheless, some old bad habits are hard to kick cold turkey, so let’s dive in with some gloomier predictions and end on a higher note:
The soul searching in the “Big Data” movement will continue as experts recognize the level of technical complexity that aspiring companies must navigate to piece together useful “Big Data” solutions that fit their needs. At the end of the day “Big Data” is tomorrow’s data but nothing else. The recent removal of the “Big Data” entry from the Gartner Hype Cycle is further testament to the same realization. All this will only hasten the pivot to analytics and specifically to Machine Learning as the center of attention so as to recoup the sunk costs from those projects via customer touching smart applications. Moreover, the much maligned sampling remains a great tool to rapidly explore new predictive use cases that will support such applications.
The education process of VCs will continue, albeit slowly and through hard lessons. They will keep investing in algorithm-based startups with the marketable academic founder resumes, while perpetuating myths and creating further confusion e.g., portraying Machine Learning as synonymous with Deep Learning, completely misrepresenting the differences between Machine Learning algorithms and Machine-learned models or model training and predicting from trained models1. A deeper understanding of the discipline with the proper historical perspective will remain elusive in the majority of the investment community that is on the look out for quick blockbuster hits. On a slightly more positive note, a small subset of the VC community seems to be waking up to the huge platform opportunity Machine Learning presents.
The media frenzy around AI and Machine Learning will continue at full steam as humored by Rocket AI type parties, where young academics will be courted and ultimately funded by aforementioned investors. Ensuing portfolio companies will find it hard to compete on algorithms as few algorithms are really widely useful in practice although some do slightly better than other for very niche problems. Most will be cast as brides at shotgun weddings with corporate development teams looking to beef up on Machine Learning talent strictly for internal initiatives. In some nightmare scenarios, the acquirers will have no clear analytics charter, yet they will be in a frantic hunt to grab headlines to generate the illusion that they too are on the AI/Machine Learning bandwagon.
Legacy company executives that opt for getting expensive help from consulting companies in forming their top-down analytics strategy and/or making complex “Big Data” technology components work together before doing their homework on low hanging predictive use cases will find that actionable insights and game-changing ROI will be hard to show. This is partially due to the requirement to have the right data architecture and flexible computing infrastructure already in place, but more importantly outperforming 36 years of collective achievements by the Machine Learning community with some novel approach is just a tall order regardless how relatively cheap computing has become.
Deep Learning’s notable research achievements such as the AlphaGo challenge will continue generating media interest. Nevertheless, its advances in certain practical use cases such as speech recognition and image understanding will be the real drivers for it to find a proper spot in the enterprise Machine Learning toolbox alongside other proven techniques. Interpretability issues, dearth of experienced specialists, its reliance on very large labeled training datasets and significant computational resource provisioning will limit mass corporate adoption in 2017. In its current form, think of it as the Polo of Machine Learning techniques, a fun time perhaps that will let you rub elbows with the rich and famous provided that you can afford a well-trained horse, the equestrian services and upkeep, the equipment and a pricey club membership to go along with those. Nevertheless, not quite an Olympic sport. So short of a significant research breakthrough in the unsupervised flavors of Deep Learning, most legacy companies experimenting with Deep Learning are likely to come to the conclusion that they can get better results faster if they pay more attention to areas like Reinforcement Learning and the bread and butter Machine Learning techniques such as ensembles.
Of course, Machine Learning is only a small part of AI. More attention to research and the resulting applications from startups in the fields of reasoning and planning under uncertainty and not only learning will help cover truly new ground beyond the better understood pattern recognition. Not surprisingly, Facebook’s Mark Zuckerberg has reached similar conclusions in his assessment of the state of AI/Machine Learning after spending nearly a year to code his intelligent personal assistant “Jarvis”, that was loosely modeled after the same in the Iron Man series.
Some businesses will see early shoots of faster and evidence-based decision making powered by Machine Learning, however humans will still be central to the decision making. Early examples of smart applications will emerge in certain industry pockets adding to the uneven distribution of capabilities due to differences in regulatory frameworks, innovation management approaches, competitive pressures, end customer sophistication and demand for higher quality experiences as well as conflicting economic incentives in some value chains. Despite the talk about the upcoming singularity and robots taking over the world, cooler heads in the space point out that it will take a while to create truly intelligent systems. In the meanwhile, businesses will slowly learn to trust models and their predictions as they realize that algorithms can outperform humans in many tasks.
A more practical and agile approach to adopting Machine Learning will quietly take hold next year. Teams of doers not afraid to get their hands dirty with unruly yet promising corporate data will completely bypass the “Big Data” noise and carefully pick low hanging predictive problems that they can solve with well proven algorithms in the cloud with smaller sampled datasets that have a favorable signal to noise ratio. As they build confidence in their abilities, the desire to deploy what they have build in product as well as to add more use cases will mount. No longer bound by data access issues, complex, hard to deploy tools these practitioners not only start improving their core operations but also start thinking about predictive use cases with a higher risk-reward profiles that can serve as the enablers of brand new revenue streams.
- PREDICTION #9:
MLaaS platforms will emerge as the “AI-backbone” for enterprise #MachineLearning adoption by legacy companies.
MLaaS platforms will emerge as the “AI Backbone” in accelerating the adoption of Agile Machine Learning practices. Consequently, commercial Machine Learning will get cheaper and cheaper thanks to a new wave of applications built on MLaaS infrastructure. Cloud Machine Learning platforms in particular will democratize Machine Learning by
- significantly lowering costs by eliminating complexity or front-loaded vendor contracts
- offering a preconfigured frameworks that packages the most effective algorithms
- abstracting the complexities of infrastructure setup and management from the end user
- providing easy integration, workflow automation and deployment options through REST APIs and bindings.
2017 will be the year, when developers start carrying the Machine Learning banner easing the talent bottleneck for thousands of businesses that cannot compete with the Googles of the world in attracting top research scientists with over a decade of experience in AI/Machine Learning, which doesn’t automagically translate to smart business applications that deliver business value. The developers will start rapidly building and scaling such applications on MLaaS platforms that abstract painful details (e.g., cluster configuration and administration, job queuing, monitoring and distribution etc.) that are better kept underground in the plumbing. Developers just need a well-designed and well-documented API instead of knowing what a LR(1) Parser is to compile and execute their Java code or knowing what Information Gain or the Wilson Score are to be able to solve a predictive use case based on a decision tree.
We are still in the early innings of “The Age of Analytics”, so there is much more to feel excited about vs. dwelling on bruises from past false starts. Here’s to keeping calm and carrying on with this exciting endeavor that will take business as we know it through a storm by perfecting the alchemy between mathematics, software and management best practices. Happy 2017 to you all!
1: The A16Z presenter seems to think every self-driving car has to learn what a stop sign is by itself, thus reinventing the wheel many times over instead of relying on tons of historical sensor data from an entire fleet of such vehicles. In reality, few Machine Learning use cases require a continuously trained algorithm e.g., handwriting recognition.