AI Changed Dramatically in Only 9 Months

Productive uses for AI are closer at hand than ever due to the rise of AutoML.

Beginning in 2019, advancements in AI have replaced obstacles with tools to benefit from its power. Foremost among them is Automated Machine Learning (AutoML), which does not require programming or scripting of any kind. Here are some examples.

Business Analysts
“AI is the new BI” is a theme repeated for years by journalists and product marketers. AI is now simple enough that business analysts can make reliable predictions without being data scientists or programmers. Moving from visualizing trends with BI to predicting the future—record-by-record—with AI is transforming the way businesses run. AutoML is what makes that possible.

Citizen Data Scientists
Researchers and designers who need to understand patterns know their problems and data very well, but not necessarily the AI algorithms that can extract information they need. AutoML abstracts algorithm selection and use to produce results quickly.

Data Science Professionals
AutoML does not replace the need for true data scientists, whose expertise in solving complicated AI problems cannot be replicated. Nevertheless, AI pros use AutoML to glean insights on problems to make custom work more efficient. In addition, offloading simpler BI problems to AutoML keeps them focused where they are most needed.

Conclusion: AutoML makes achieving the benefits of AI simpler for everyone.

Why AI for Marketing and Sales?

Follow the money to see why marketing and sales are the most common applications for AI.

Instant Payback
Small improvements in marketing and sales can produce large returns quickly. Think of the impact of gaining a few percentage points on lead conversions, forecast accuracy, content targeting, and ad performance. Knowing which customers will buy, what they will buy, and when they will buy delivers value on both revenue and cost sides of the ledger.

Plenty of Data
More information than ever is available in CRM, marketing automation, and customer data platforms. AI—in the form of Automated Machine Learning (AutoML)—is really good at finding patterns in all that data to predict the future.

AutoML does not require programming or formula creation in order to make accurate predictions. Models can be made and refined rapidly. This is particularly important in supporting nimble marketing and sales processes.

AutoML insights for marketing and sales are easy to monetize and straightforward to execute. That makes them great places to amplify the benefits of AI.

Machine Learning vs. Statistics

Statistics and machine learning differ in method and purpose. Which is superior depends upon your goals.

Statistics is a subset of mathematics that interprets relationships among variables in data sets. Statisticians make inferences and estimate values based solely on data collected during a specific period, a rearward-looking view. Understanding how data was collected and the distributions of populations must be considered in model building. Statistics are useful where assumptions and probabilities must be mathematically auditable, such as when publishing a scientific paper on experimental observations.

Machine Learning (specifically, supervised learning) is a subset of computer science that uses past data to predict the future. The forward-looking view relies on training models using data sets of known outcomes and testing accuracy against test sets sequestered from the training data. The hold-back process proves that predictions on future data will be similarly accurate. Machine learning excels when there are large numbers of variables and records in data sets.

Conclusion: Use statistics for “court of law” explanations of what happened in the past. Use machine learning to make record-by-record predictions of future outcomes.