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Machine learning and anticipating the ripple effect
Technology is sly in nature. Time and again, it reminds us of its knack for appearing in the blink-of-an-eye and disrupting the status quo with little hesitation. Yet confusing this slyness for spontaneity would be a mistake. Meaningful technology very rarely, if ever, just happens. Believing so risks devaluing the constant questioning and ceaseless experimentation involved when developing groundbreaking technologies. And despite the fact that transparency around these developments continues to rise, it still takes time to realize how a nascent technology could eventually impact our lives.
For these reasons, it’s fair to say that machine learning is foreign to most of us. Well it’s now time to embrace the forthcoming era of computing. New products have already begun to emerge, and the effects of these technologies loom ahead. Business models are adapting, and in the near future, brands must evolve to embrace Artificial Intelligence (AI) just as much as they welcomed previous technological eras—from the Internet’s inception to the rise of the smartphone.
Despite being seen as technology of the future, machine learning was being created decades ago. Alan Turing’s paper "Computing Machinery and Intelligence" helped pioneer machine learning by presenting a daring question: Can machines think? To no surprise, the question ignited a widespread debate, spanning ethical, philosophical, technological and economical fields.
Most critiques of machine learning concern the ability to authentically imitate human thought, hence a common determination to elevate the debate to something more tangible. No longer should this be a matter of a machine’s ability to think, but instead, whether machines can learn to mimic human capabilities of reason and action.
Fast-forward from Turing’s provocative essay 65 years, and this more tangible form of intelligence is gradually taking life. Devices today are beginning to recognize images, text, audio and behavior, and some have even begun to use their newfound awareness to do our daily biddings. The established tech giants are largely driving these innovations. They see the emerging technology as a new means to answer consumer demands.
Addressing the rising demand for convenience:
Facebook, Microsoft, Apple, Amazon and Google are developing “digital assistants” within their respective devices and/or apps to fulfill personalized requests from users.
Elevating efficiency, safety, and convenience within transportation:
Tesla has remotely updated its fleet of cars with early self-driving capabilities—allowing owners and their Model S to intelligently navigate traffic.
Bringing emerging technologies to all products and services:
Automated responses via Gmail, augmented reality translations of text within Google Translate and AI capable of mastering the ancient strategy game Go, demonstrates Google’s reign of being the zeitgeist of digital innovation.
That said, the machine learning timeline is ripe for a new crop of companies to emerge. Both industry veterans and keen entrepreneurs are hoping to launch into success as the technology continues to become consumer facing.
Answering the need for a universal machine learning language:
Osaro aspires to advance the application of machine learning by developing proprietary operating systems for industrial robots, drones, driverless cars and the internet of things.
Creating the universally accessible digital assistant:
Unlike current digital assistants, Viv is being developed by the founders of Siri to provide a platform-agnostic AI capable of autonomously fulfilling user tasks from research to execution.
Advancing the way we measure television content:
Parrot Analytics helps shows like Sherlock and House of Cards measure popularity by enlisting machine learning to identify activity on photo-sharing, online video, social media, file sharing and blog sites.
There’s no doubt, then, that the race to develop new age intelligence is underway. Yet the years of what Jarvis from Iron Man, Samantha from Her and TARS from Interstellar have depicted are still well ahead of us. The technology’s Achilles’ heel involves integrating functional machines into an ever-changing and complex world. So much so, that developers like Osaro rely on games, both old and new, to double as digital incubators; conquering a controlled environment is key before tackling our own.
Moreover, developing and applying these capabilities has only just begun in the grand scheme of things. Achieving scale is an issue–something that Facebook M most appropriately embodies. Still in its infancy, the “imitation game” digital assistant relies on human experts to take over when necessary, resulting in a resource-intensive model. Facebook has stated publicly that the service will not be available outside of the Bay Area for quite some time.
But as mentioned earlier, it would be unwise to allow this early timeline to breed complacency. Near the end of 2015, Elon Musk and Sam Altman announced plans to continue the open sourcing of machine learning. OpenAI, their non-profit startup, seeks to act as a checks and balances for the rise of artificial intelligence. Furthermore, the new startup complements other open source initiatives to benefit the technology in several ways.
The thought behind the open sourcing is to democratize the entirety of machine learning to anyone interested. It empowers developers, regardless of their industry, to explore potential applications in their respective markets. And it further sparks competition between companies, both established and aspiring, to come out on top with best-in-class technology.
As the saying goes, a rising tide lifts all boats.
The effects of these technologies now loom ahead of us. Access to these technologies will unleash curious minds to experiment, innovate and ultimately shift the status quo. New offerings will emerge. Business models will evolve. And in the future, companies will value the role of Artificial Intelligence (AI) just as much as any other part of their brand strategy.
How will brands evolve?
Machine learning is pushing us towards better championing the contexts, attitudes and behaviors nuanced to each individual. With brands already increasing their focus on the individual to offer proactive value and create more meaningful experiences, the ability to identify and fulfill user-specific tasks will shift the technology from novelty to becoming the brand advocate personified.
Digital assistant Facebook M, for example, seeks to become the ultimate companion by handling activities on your behalf. From placing food orders to handling tedious customer service call centers, Facebook is on track to redefine what it means for a user to engage with both their services and those that they compete with.
How will brands be constructed?
Brands that embrace machine learning will be presented with new opportunities to engage their customers. From the Internet to the App Store, digital innovations throughout history have shown that the world of brands needs to (and does) evolve constantly to match consumer attitudes and behavior.
Machine learning will help us create new categories of consumer knowledge by greater understanding context, attitudes and behavior.
Could / should brands adapt dynamically to each consumer? How autonomous could/should this be? Google Now seeks to address this challenge.
Will digital assistants become the ultimate brand ambassadors? Their ability to masterfully match user needs could shift the technology from a singular touch point to core brand asset. IBM Watson provides an apt example today.
What are the challenges?
No new technology is ever free of obstacles. Machine learning will continue to face both existing and unprecedented challenges as the technology progresses:
Will open source software from established companies like Google, Facebook and Apple continue to be the easiest way for others to provide machine learning capabilities? How could reliance on a third-party platform affect a brand’s autonomy?
Will brands become stickier thanks to offering increasingly personalized interactions? Or will intelligent, quicker and autonomous on-demand services shift consumers to seek functionality over—and lessen—brand loyalty?
How will consumers respond to products and services that adopt human characteristics? Will this jeopardize or reinforce authenticity?
What measures will companies take in order to protect increasingly personal consumer data? Will risks of data breaches limit what capabilities can be used and what data can be captured?
We’re only just beginning to identify the potential outcomes introduced by machine learning. Those who begin to consider how machine learning will affect their brand today stand to become the leaders of tomorrow. It’s time to start thinking atypically.