AI, machine learning, and deep learning: Everything you need to know

There’s a lot of marketing buzz and technical spin on artificial intelligence, machine learning, and deep learning. Most of what’s out there is either too fluffy or too mathy, either too general or too focused on specific applications, too disconnected from business outcomes and metrics, and too undirected.

This article provides an overview of these related technologies by:

• Defining AI, machine learning, and deep learning, explaining the differences from traditional approaches, describing when to use them, and noting their advantages and disadvantages.
• Explaining how they complement business frameworks and enable business outcomes and metrics.
• Describing common types of machine learning and deep learning model training, algorithms, architectures, performance assessments, and obstacles to good performance.
• Providing examples of machine learning models and algorithms at work.
• Presenting a potential framework for AI implementation for business outcomes.

Why AI: AI in the business context

All organizations work to specific outcomes, and they juggle several business metrics and processes to achieve this, such as revenue, costs, time to market, process accuracy, and efficiency. Yet they have limited resources (money, time, people, and other assets). So, the problem boils down to making good decisions about resource allocation (what kind of resources, how many/much of them, what should they do, what capabilities do they need, etc.), and making those good decisions faster than competitors and faster than the market is changing.

Making these decisions is hard, but clearly, they become much, much easier when data, information, and knowledge are available. Assuming these inputs are available, they need to be aggregated and mined for nuggets. Analysts need time to pull tribal knowledge out of subject matter experts’ heads, to adjust to fluctuating business rules, to calibrate for personal biases where possible, and to spot patterns and to generate insights. Ideally, analysts and managers should (time permitting) assess multiple scenarios and run several experiments to increase confidence in their recommendations and decisions. Finally, the decisions need to be operationalized.

Enter AI, machine learning, and deep learning, which:

• Model the organization based on observations.
• Generate insights by simultaneously reviewing lots of factors and variables (far more than a person can achieve in a reasonable time period and cost constraint).
• Learn continuously as new observations are provided.
• Quantify the likelihood of outcomes (that is, predict what is likely to happen).
• Prescribe specific actions to optimize the business goals and metrics.
• Adjust rapidly to new business rules through faster retraining versus traditional slower reprogramming.

What makes AI, machine learning, and deep learning possible now is the proliferation of data volume and data types coupled with the lower costs of compute and storage hardware and tools. Web-scale companies (such as Facebook, Google, Amazon, and Netflix) have proven it works, and they are being followed by organizations in all industries. Combined with business intelligence, the trio of artificial intelligence, machine learning, and deep learning overcomes obstacles to decisioning, thereby facilitating organizations to achieve their business goals, as Figure 1 shows.

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AI, machine learning, and deep learning apply to everyone in metrics-driven organizations and businesses.

In its May 2011 publication “Big Data: The Next Frontier for Innovation, Competition, and Productivity,” McKinsey Global Institute stated that the gap for managers and analysts who know how to use the results of analytics stood at 1.5 million, an order of magnitude more than for those who produce the analytics (such as data analysts and data scientists).

Put another way, the chokepoint in the data value chain is not the data or the analytics; it’s the ability to consume the data/analytics in context and in an intelligent way for surgical action. This is an opportunity for business and process professionals to marry AI, machine learning, and deep learning to the business frameworks and concepts already understood so well. It’s a chance to define problems and hypotheses within those frameworks and concepts, and then to use AI, machine learning, and deep learning to find patterns (insights) and to test hypotheses that take too long to test, would otherwise be too expensive to identify and test, or are too difficult for people to carry out, as Figure 2 shows.

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Organizations and businesses are increasingly turning to AI, machine learning, and deep learning because, quite simply, business is becoming more complex. There are too many things occurring at one time for us people to process; that is, there are too many data points (both relevant and not-so-relevant) for us to synthesize. Looked at it this way, too much data can be a liability (analysis paralysis, anyone?).

But AI, machine learning, and deep learning can turn that pile of data into an asset by systematically determining its importance, predicting outcomes, prescribing specific actions, and automating decision making. In short, AI, machine learning, and deep learning enable organizations and businesses to take on the factors driving business complexity, among them:

• Value chains and supply chains that are more global, intertwined, and focused on microsegments.
• Business rules that rapidly change to keep pace with competitors and customer needs and preferences.
• Correct forecasting and deployment of scarce resources to optimize competing projects/investments and business metrics.
• Need to simultaneously drive towards both increased quality and customer experience while reducing costs.

In many ways, AI, machine learning, and deep learning are superior to explicit programming and traditional statistical analysis:

• The business rules don’t really need to be known to achieve the targeted outcome—the machine just needs to be trained on example inputs and outputs.
• If the business rules change such that the same inputs no longer result in the same outputs, the machine just needs to be retrained—not reprogrammed—allecelerating response times and alleviating people of the need to learn new business rules.
• Compared to traditional statistical analysis, AI, machine learning, and deep learning models are relatively quick to build, so it’s possible to rapidly iterate through several models in a try-learn-retry approach.

However, AI, machine learning, and deep learning do have disadvantageous, as Figure 3 shows. Among them, they are still based on statistics, so there is an element of uncertainty in the output. This makes the integration of AI, machine learning, and deep learning into the workflow tricky because high ambiguity in the machine’s decisions should likely be handled by a person. And to improve the machine’s accuracy, mistakes (and right answers) should be fed back to the machine to be used for additional training (learning).

Additionally, AI, machine learning, and deep learning models can be less interpretable; that is, it may not be clear how they arrive at their decisions. This is particularly true of complex deep learning models with many “layers” and “neurons”; such lack of clarity may be of particular concern in highly regulated industries. It should be noted that there is a lot of research focused in this area, so perhaps it won’t be a disadvantage in the future.

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Given the advantages and disadvantages, when might it be appropriate to use AI, machine learning, and deep learning? Here are some ideas:

• The juice is worth the squeeze: There’s a high-potential business outcome but traditional approaches are too cumbersome, time-consuming, or just not appropriate.
• Relevant data is available and accessible.
• Subject matter experts believe the data contain meaningful signal (that is, insight can be gleaned from the data).
• The problem definition ties to a machine learning or deep learning problem, such as classification, clustering, or anomaly detection.
• The success of use cases can be mapped to machine learning and deep learning model performance metrics, such as precision-recall and accuracy.

AI defined: The natural progression from BI to AI

AI, machine learning, and deep learning are a natural progression of business intelligence. Where BI describes and diagnoses past events, AI, machine learning, and deep learning try to predict the likelihood of future events and prescribe how to increase the likelihood of those events actually occurring. A simple example illustrating this is the GPS guiding you from point A to point B:

• Description: What route did the vehicle take, and how long did it take?
• Diagnosis: Why did the vehicle take a long time at a particular traffic light (assuming the GPS platform/tool tracks things like accidents and vehicle volume)?
• Prediction: If a vehicle is going from point A to point B, what is the expected ETA?
• Prescription: If a vehicle is going from point A to point B, what route should the vehicle take to achieve the expected ETA?

Prediction in AI

One example of prediction is sentiment analysis (the probability of someone liking something). Let’s assume you can track and store the textual content of any user posting (such as tweets, updates, blog articles, and forum messages). You can then build a model that predicts the user’s sentiment from his or her postings.

Another example is increasing customer conversion: people are more likely to sign up for subscriptions if they’re offered a chance to win a prize they want—so you can predict which prizes will lead to the highest number of conversions.

Prescription in AI

Prescription is about optimizing business metrics in various processes, such as marketing, sales, and customer service, and it’s accomplished by telling the prescriptive analytics system what metrics should be optimized. This is like telling the GPS what you want to optimize, such as least fuel consumption, fastest time, lowest mileage, or passing by the largest number of fast-food joints in case you get a craving for something. In a business setting, you might target increased conversion by 10 percent, sales by 20 percent, or net promoter score (NPS) by five points.

From there, the prescriptive analytics system would prescribe a sequence of actions that leads to the corresponding business outcomes you want.

Say you want to achieve a 10-percent conversion lift. The system may prescribe:

• Reducing the frequency of your direct mail marketing by 15 percent, while
• simultaneously increasing your Twitter and Facebook engagements by 10 and 15 percent, respectively, then
• when your aggregate social media engagement reaches 12 percent, start directing the public to your customer community portal for customer-to-customer engagement.

These prescriptive actions are like the turns that your GPS system advises you to take during the journey to optimize the goal you set.

The relationship among BI, statistics, and AI

Here’s one way to define the difference among BI, statistics, and AI:

• BI is traditionally query-oriented and relies on the analyst to identify the patterns (such as who are the most profitable customers, why are they the most profitable, and what attributes that set them apart, such as age or job type).
• Statistics also relies on the analyst to understand the properties (or structure) of the data to find information about the population in the data, but it adds mathematical rigor in extrapolating to generalization (such as if there is a difference between these customer segments in real life versus what is found in the sample data).
• AI, machine learning, and deep learning rely on algorithms (not analysts) to autonomously find patterns in the data and enable prediction and prescription.