Leveraging machine learning for business isn’t as simple as downloading Google’s TensorFlow and sitting back. Instead, stakeholders should understand and plan machine learning development and recognize differences in the core ideas behind machine learning. Then, organizations must determine if they will use an off-the-shelf version, build their own, or outsource machine learning to a team of experts.
When it comes to machine learning, organizations need to understand the correct approach for their business need – and it doesn’t always mean it has to be the most complex.
Breaking it down: Differences between AI, Machine Learning, and Deep Learning
To understand this rapidly transforming space, it’s important to understand the relationship between Artificial Intelligence (AI), Machine Learning, and Deep Learning.
AI is the overarching umbrella that covers all three forms of intelligence, and is, simply put, a machine exhibiting human intelligence. While technology hasn’t yet realized BB-8 levels of AI, it has achieved other aspects, like image recognition, as demonstrated by Facebook’s facial recognition capabilities as one example.
A subset of AI, Machine Learning parses data with algorithms, learns, and then makes a decision or prediction based on that learning. This powerful, pattern-based approach moves programming out of “if-then” binary logic and enables machines to learn themselves, which unlocks the potential for automation.
Diving deeper, Deep Learning breaks down tasks into “neutral networks,” mimicking the human brain. Of course, this imitation only goes so far, because it is not physical proximity that connects these networks, but data connections and directions. Each tiny component of a task is then broken down and examined, and a probability is assigned. For example, the ability to select every dog meme uploaded to a social networking site would be a deep learning exercise in neutral network assessment. At first, it’s likely many incorrect images would be selected, and some correct images would be missed. But over time, accuracy would improve. Neural networks require extensive training to increase the precision of probability.
Machine Learning in Business
According a report by ATKearney, machine learning has exploded in this decade, driven by four major factors:
- Exponential computing power. This ignited neural network growth and improved the rate at which data is processed.
- Lower costs and higher returns. Faster computing speeds equal more efficiency and therefore, lower costs. Not to mention capital needs dropped with the costs of transistors (down 33 percent annually, according to ATKearney’s report). These gains ultimately translate to higher potential returns.
- Growing talent pool. Media coverage, capital investment, and cultural focus on AI and machine learning have spurred AI-focused programs at universities, which then turn out graduates who are savvy with the technology.
- Increased comfort. While many may not realize it, the general public’s comfort with AI-powered devices has greatly increased, as smart digital assistants like Siri and Alexa permeate homes and offices.
Machine Learning encompasses four key areas, each which have the potential to tangibility impact business by improving product or service quality, increasing revenue, and cutting costs.
- Cognitive Machine Learning. This is all about recognition: audio, video, speech, text, or photos.
What does this mean for business? Organizations can index massive volumes of documents, process photos, use chatbots to communicate directly with customers for increased availability, or analyze emotional overtones across communication.
- Predictive Machine Learning. By analyzing historical data, predictive machine learning harnesses the power of hindsight to predict future outcomes. We’ve already seen this software at work. That handy recommendation list from your Netflix account? Predictive Machine Learning. And, the next generation may include Tesla’s self-driving car, which is powered by camera inputs and substantial amounts of data from other drivers.
What does this mean for business? In the health care industry, this has myriad applications for predicting health outcomes based on personal and family health history. In sales organizations, predictive machine learning could streamline forecasting to take into account outside factors, such as weather, in product sales.
- Optimizing Machine Learning. This approach is all about improvement. Through algorithms, optimizing machine learning strives for efficiency, be that the best, the shortest, or the quickest path depending on the issue at hand. Another area that is more prolific than many realize, this technology is what drives (excuse the pun) the mapping data many use to skip traffic and reach their destination more quickly.
What does this mean for business? Process improvement is a core focus organizations can use to eliminate wasted time, money, and resources, and leveraging a machine to make those assessments can help with cost evaluations, healthcare interventions, or operational process reviews.
- Classifying Machine Learning. Primarily applied for large datasets, classifying machine learning segments or clusters data. This technology triggers a call to a credit card user to flag a suspicious purchase, because the data flags as an anomaly.
What does this mean for business? Outside the banking industry, the ability to identify abnormal behaviors, whether that is by customers, employees, or even hardware like servers, can bolster security and speed up customer service. Don’t forget improved spam filters, either, which not only improve productivity, but can also amp up security measures.
Machine Learning Development
Getting on board with machine learning is one thing; bringing the technology on board is another altogether.
First, an organization needs to have clean data. Tech Emergence makes this important distinction: although large amounts of data make for more robust data, clean and current data may actually be more important. For example, an organization with a host of data from the past two decades, all housed in various Excel and Word documents, will need to first have its data cleaned and consolidated before it could be used for machine learning. Similarly, outdated data provides little value, as algorithms should be trained on current information, not stale and outdated records.
Then, there is the training. Many experts recommend beginning with supervised learning, which means the AI is trained on a labeled dataset expressly intended for training, akin to a student covering up the answer key to take a practice test, but checking responses against the key afterward. For organizations to leverage supervised learning, they will need datasets that are both robust and labeled. Remember, as the name suggests, machine learning requires actual learning, which means the algorithms achieve maximum precision through both correct examples and frequent practice – not unlike a student learning algebra.
These complex dataset requirements are what spur many organizations to outsource their machine learning to a team of experts, who can establish a deployment team, monitor the learning stages, and ensure the AI product runs efficiently. This is particularly important given the rising popularity of AI has flooded the market with AI-drenched software, sometimes, it would appear, simply for the sake of appearing cutting edge. Analysts at Gartner, Inc., predicted that by 2020 nearly every software product and service released will be saturated with AI, and on the strategy front, AI will climb to top 5 investment priorities among 30 percent of CIOs. But, a research vice president at Gartner cautioned, this AI-rich market is only valuable when solutions are designed for business problems, implemented only in response to a fad. As a result, it is only savvy, thoughtful machine learning development that can really bolster business.