Machine Learning Overview: Understanding The 'Gold Rush'

Over the last twenty years, the advent of global network connectivity has combined with exponential increases in computing capabilities to transform data science from the dry study of actuarial tables and census records into a hyper-detailed locus of rapidly updating and high-volume information about the human race.

What we think, what we buy, what we believe, who we like, love and hate…these, and hundreds of thousands of previously unknown decisions, preferences and minor events in the continuum of a human life are now directly readable via our relationships with networked devices, or else inferable from the same data, to those corporations, governments or researchers that consider such insights useful, valuable, or even essential.

From weblogs through to fitness tracking data, historical data digitization via OCR, and the JSON feeds of IoT devices, the information piled up far more quickly than even the advancing technologies of data science could cope with — at least, initially. This machine learning overview will look at how data-centricity has come to power our society at large, and where machine learning, among other data processing technologies, is positioned in this new reality.

This revived interest in machine learning and artificial intelligence was, perhaps inevitably, accompanied by the return of the hyperbolic 'AI hype cycle'. Like its predecessors, this new strain of 'AI fever' was fed by three main contenders: 

• Academic projects that promised much, but were in fact highly speculative and unlikely to yield commercial results for many years (if ever).
• Existing vendors keen to associate their established technologies with the promise of machine learning, even where there was only a cursory association, or none.
• The more pragmatic exploiters of genuine, deployable machine learning solutions. Though they tended to overstate their case, at least they had a working product to put on the table, based on the current machine learning state-of-the-art, rather than technologies that might only bear fruit later on in the AI/ML development timeline. 

Momentarily we'll take a closer look at these three perennial proponents of the AI hype cycle, and then examine ways to identify real-world applicable machine learning technologies through the high signal-to-noise ratio of AI hype.

First, let's see how the machine learning gold rush played out in previous eras.

So here we are again, wondering if the third time is the charm. Investment and interest in machine learning and AI have ascended from the troughs of the mid-1990s to become an unavoidable business topic, most especially over the last ten years of GPU-assisted machine learning.

The defining traits of the two previous booms were that the theories and frameworks proposed would require either the most expensive systems of the age, or else dedicated, custom-built hardware — which was even more expensive, and would have required sustained market confidence in order to achieve economy-of-scale.

Instead, the current boom in machine learning has been fueled by a unique brand of opportunism, coincidence, and exploitation of technologies that were intended, at least initially, for other industry sectors.

Distributed and parallel computing frameworks such as Hadoop and Spark were developed initially to analyze the petabytes of data that were being generated from internet searches from the turn of the century onwards. This approach involved the marshalling of many low-cost computer units into high-volume storage clusters (distributed storage), and letting each unit calculate and contribute a portion of a workload back to a central organizing entity that would then collate the final sum of all the units' activity (parallel computing). Improvements in Graphic Processing Units (GPUs), driven by the gaming and leisure industries, would eventually offer machine learning an exponential boost in performance and capacity.

The open source revolution freed machine learning and AI R&D from the confines of the patent silo. In the process, it transformed the economic model of machine learning systems research into a service-driven approach, favoring those who can best build on, commodify and exploit the common locus of knowledge while still contributing back to it.

Above all, the current boom in machine learning is fueled by the spontaneous and relatively unrelated creation of huge digital data repositories that can be treated as datasets:

The digitization of information, from newspapers, archives, and historic texts to on-demand digitization of new documents — all cases where language can be indexed for the purposes of NLP.   

• The social media phenomenon, wherein some of the biggest players, including Facebook and Twitter, are actively engaged in machine learning research, and provide APIs that allow machine learning projects to exploit public data; or else where screen-scraping and other furtive approaches are nonetheless able to derive data from more 'closed' social media systems.
• The explosion in online video, where publicly-available audio can be analyzed as waveforms for research in speech recognition, NLP and other pursuits around categorization and comparison, and where frames can be utilized for image recognition research.
• Device-driven information flows, including coordinate and temporal information from mobile and IoT devices, which can be exploited for research around augmented and virtual reality development, as well as used in medical and statistical research.
• Network-based user analysis, including weblogs and Global Positioning System (GPS) data, for sentiment inference, recommender systems, fraud analysis systems, and targeted marketing.

Neither of the two previous machine learning booms delivered the sheer range of practical and applicable technologies that have conquered business markets over the last ten years. The machine learning state now is characterized with genuine commercial products and services rather than long-term ambitions that yield little fruit in the interim, as was the case in the two previous AI bubbles.

However, since the AI hype machine will likely continue to conflate old, current and putative technologies under attention-grabbing headlines, the business imperative is to stay on top of the terms, and seek out the signs of a genuine value proposition.

The Gartner Hype Cycle takes an annual look at the promises and deliveries for technologies related to machine learning, artificial intelligence and related terms.

While this is a useful crib to distinguish between legacy, high-value and speculative technologies, we should also learn to make these distinctions for ourselves as new products and frameworks emerge.

Therefore, let's now extend this machine learning overview to the three most common levels of capability in the AI hype cycle.

We can also think of these three tiers of analytical capability in terms of the famous creations of Arthur Conan Doyle:

Doctor Watson (left) is the tireless factotum and arch-researcher who lacks Sherlock Holmes' insight, but makes up for it with diligent trips to libraries and record offices, unearthing the core facts and figures that constitute the case.

Inspector Lestrade (center), is the plodding face of RPA-style automation, macros, conditional scripting and branching logic routines. He's been around a long time, and he gets the job done; but he needs a lot of help, and rarely innovates.

Sherlock Holmes (right) is the visionary genius susceptible to insights and new paradigms of thought that ordinary people can't aspire to. He's brilliant, but — like true AI/AGI — he's unavailable for the foreseeable future.

For our purposes, Doctor Watson represents the fruitful and achievable current state of machine learning. The genius he serves, for the moment, must remain our own.

In order to attract general attention, the AI/ML hype cycle often conflates the three classes of capability into one overly generic super-sector, freely using (and abusing) the distinctive terms for each type of technology.

In this way, we are being marketed to buy into established 'legacy' or older technologies to which the new buzzwords are being deceptively applied; or else invited to invest in nascent machine learning pursuits that are currently more speculative and academic than applicable.

Since the true meaning of such popular terms remains clouded, it's useful to think of the phrase 'machine intelligence' not as a simile for computer consciousness, but rather in the sense of 'business intelligence' — useful and actionable information that has been derived systematically through machine learning techniques from otherwise opaque and vast volumes of data.

Machine learning sectors with applications that yield 'machine intelligence' in this sense include the following.

One of the oldest lodestones of machine learning research, Natural Language Processing (NLP) seeks to derive the correct context and meaning for the written and spoken word across a range of languages.

The open-source NLP libraries that dominate this field are helping researchers and business applications to create and feed a number of machine learning sectors, including:

• Machine translation systems, which have latency requirements similar to self-driving vehicles (see below), and require massive datasets and computation to advance the state of the art.
• Speech recognition systems, a corollary of machine translation systems, essential for voice-based AI assistants, chatbot development and voice-based authentication frameworks, among other uses.
• Sentiment analysis, which seeks to gauge user state and intent from natural language, and must identify and overcome enigmatic linguistic eccentricities such as sarcasm.
• Spam filtering, wherein common language patterns are used to identify and block unwanted commercial communications, even where spam actors are using elliptical language to bypass the filters.
• Market intelligence systems such as QlikView and Tableau, capable of inferring and anticipating customer intent not only from customer behavior but also from user-generated content.
• Machine summaries of long and often complex documents that may lack metadata or structurally obvious natural language summaries.

Teaching computers to understand the world around them in visual terms, and in some cases to interact with it in a creative, useful and safe manner, is one of the compelling economic use cases for ML/AI research. The market for computer vision applications, hardware and services is estimated to reach a value of more than $26 billion by 2025, across sectors that include:

• Vision-based robotics systems: pre-programmed 'positional' robotics workflows were the driver of the factory automation revolution of the 1970s, as well as new and revolutionary motion-control systems in the movie industry at that time. Machine learning, however, promises not only to make robots capable of acting based on understanding the changing environment around them (object segmentation, see below), but also of learning autonomously from their own mistakes and successes.
• Facial recognition is among the best-funded sector in machine learning due to its high applicability in the context of national and private sector security solutions. It drives secondary pursuits such as image manipulation (see below) in much the same way that the high-value videogame industry has inadvertently driven machine learning via advances in GPU technologies.
• Self-driving vehicle (SDV) systems, which must collate and interpret multiple sources of input including external video (see 'Object segmentation' below), data from LIDAR and analogous systems, temperature and geolocation data — and manage these inputs in the same fraction of a second as afforded to a human driver.
• Object segmentation, which transcends the single mission of facial and object recognition by attempting to identify and classify multiple components of an image across domains (i.e. people, cars, roads, etc.), and is a critical component of SDV and vision-based robotics systems.
• Image manipulation, including style transfer, reconstruction, upscaling, colorization and synthesis, applicable both to individual images and to video footage.

Predicting structural, organizational or technical failure is among the most prized goals in machine learning: in 2019 the US Army commissioned an AI-driven system to anticipate technical failure in its aircraft, vehicle and weapons systems; industrial Supervisory Control and Data Acquisition (SCADA) systems are beginning to adopt machine learning; and ML is also being used to decide the Remaining Useful Life (RUL) of technologies, allowing them to be decommissioned before critical failures occur.

Various companies now offer security testing for application source code, backed up with machine learning techniques. Anti-malware and anti-virus products have long used heuristics and behavioral detection techniques to attempt to identify rogue code that's behaving in unusual ways, but machine learning can catalogue and define such behavior at a far quicker and more efficient rate.