Virtual reality may be generating most of the buzz today, but another major tech shift looms much closer on the horizon: machine learning.
The technology has already made inroads with the public through platforms such as Amazon’s Echo and Google’s Deep Dream Generator. But its influence will extend beyond voice-controlled speakers and AI-enhanced art, effecting a sea of change for businesses of all sizes. It will be a few years before we witness machine learning’s breakthrough moment, but it’s coming — and it will change everything.
A tool for raising human potential
The biggest obstacle to human advancement isn’t intelligence — it’s fatigue. Humans could be incredibly effective given endless timelines, budget, and energy. Until we’ve reached the age of immortality and abundance, however, people need to sleep. Machines, on the other hand, don’t. They can run constant calculations on business forecasts, potential strategies, and new product launches.
Amazon is already harnessing the power of integrated strategy machines. It relies on 21 data science systems to optimize its supply chain functions and profits, forecast inventory and sales, and generate product recommendations. These automated processes have helped Amazon become a $247.6 billion company, and they will become increasingly important to smaller businesses as machine learning becomes more prominent.
Machine learning’s breakthrough moment
Humans already rely on technology to get business done efficiently and accurately. Consider what an accountant’s job would look like without Excel, for example. When that program was introduced, it transformed the role, skyrocketing capacity and facilitating increasingly sophisticated analyses.
Machine learning will have a similarly transformative impact on all businesses once it has its big breakthrough moment. The technology remains hidden in the recesses of academia and the R&D labs of Silicon Valley. Amazon’s Echo and platforms such as x.ai, which serve as virtual assistants, have brought pieces of machine learning to the masses. But these operate within a narrow band of application; technology only climbs toward exponential use once it becomes fully accessible to the public.
As machine learning becomes part of the common lexicon, demand for these services will rise. For example, when I used the Deep Dream Generator, which uses AI algorithms to transform photos into art, the sunset never looked stranger, more exciting, or more indicative of the many forms technology can take. Platforms like this are already fueling public interest in machine learning technology — and the ability to better use this technology in a business setting will unleash an eager audience upon the market.
The most valuable business tool
The digital universe increases 40 percent each year, and experts predict that the number of digital bits will equal the stars in the universe by 2020. Human energy is finite, and humans are constantly overwhelmed by this growing influx of customer data, transaction details, performance metrics, and profit forecasts. Decision makers will drown under the weight of this data without a tool to manage it.
No one believes that artificially intelligent machines will be omniscient oracles (not yet, anyway). But they will filter out the noise of our digital reality and identify the most important data so that people — and businesses — can use it effectively.
Algorithms are invaluable when it comes to filtering and analyzing data, but as machine learning becomes more prominent, companies will also rely on them to hone processes. For instance, Algorithmia offers a marketplace of codes that can apply to any intelligent application. It provides microintelligence on different slices of data, helping companies improve their models through seamless incorporation. Intel is also working to empower companies of all sizes to use machine learning platforms to accelerate business solutions by uncovering insights hidden under loads of data.
A bright but unknown future
I recently visited NASA’s Jet Propulsion Laboratory in California, where scientists demonstrated dark matter with a jar of jelly beans.
The jar was mostly filled with black jelly beans illustrating dark matter, with a few pops of color representing what we can observe. The astronomers subtracted the mass of the colored beans from the jar’s total, which represented the mass of the universe, a known quantity. The difference is dark matter. Scientists don’t have to see it to know it’s there.
The same goes for businesses: Intelligent computers will fill in the gaps by identifying patterns that are invisible to humans. To know what we don’t know, we have to start taking indirect measurements to create a canvas of questions we haven’t thought to ask yet.
To prepare for the looming machine learning era, business leaders should start thinking about how the technology can add value now. Here’s how:
1. Envision the future. Help designers and creative technologists think about their roles in the context of automation. What happens when code writes code or when machines design interfaces? Investigate design methods that emphasize creating sustainable innovation through design thinking and orchestration.
2. Get comfortable with the technology. Challenge yourself — and others — to get familiar with the underlying technology and its capabilities. You don’t have to be an expert, but material knowledge is essential to effective design. Choose the method that works best for your company and implement it.
Google, for example, is helping its engineers get their feet wet with its Machine Learning Ninja Program. For six months, select employees are incorporated into Google’s machine learning team, learning the ropes and working on projects.
3. Think bigger. Go beyond asking what problems machine-driven platforms can solve. Explore these questions instead: What area should clients invest in? What problems should the platforms solve? Better questions get at more valuable investments. Designers should be the essential practitioners asking and answering those questions. Find the answers that solve your company’s specific challenges, and put them into action.
Machine learning will relieve humans of the burden of exhaustive exploration and take businesses to new heights. Rather than invest our time and energy in making the leaps of genius we work tirelessly to discover, machines will make those connections for us. Then, it’s up to our teams to create the products and solutions.
Shanon Marks, president of MU/DAI, simplifies technology through design with his team. When he isn’t working, he can be found flying a small plane or surfing at his favorite break.
This article was written by Shanon Marks and MU/DAI from VentureBeat and was legally licensed through the NewsCred publisher network.