Artificial Intelligence (AI) is everywhere these days. It’s simultaneously heralded as both the greatest thing since sliced bread — freeing us from driving cars, diagnosing diseases better, and so on — and the worst thing imaginable— displacing millions of jobs, and a step towards the inevitable AI domination of humans.
Lost in this hyperbole are the many simple, yet effective, enabling innovations that AI makes possible. Just like we rely on machines in the physical world to excavate holes for buildings or transport people or cargo long distances, we increasingly rely on machine algorithms such as machine learning (ML) models in the online, networked world. These innovations enable us to keep our email from overflowing with spam and to index and catalog enormous volumes of text for simple and fast retrieval, along with a wide range of other efficiencies. A recent article in Harvard Business Review[1] touched on this, highlighting the risks of large – yet ambiguous – AI projects compared to the measured possibilities businesses can undertake.
Artificial intelligence is a hot topic right now. Driven by a fear of losing out, companies in many industries have announced AI-focused initiatives. Unfortunately, most of these efforts will fail. They will fail not because AI is all hype, but because companies are approaching AI-driven innovation incorrectly. And this isn’t the first time companies have made this kind of mistake.
In this blog series we’re going to dive deeper into several exciting examples where AI enables human workers to function at a far greater level of productivity than they would otherwise. The productivity gain is realized through three main mechanisms, which often overlap:
- Distillation — The ultimate summarizer. Crawling and analyzing enormous volumes of text, numbers, and data to generate a human-consumable concise summary.
- Categorization — The ultimate sorter and router. Finding global patterns in enormous datasets to allow you to organize data at large scales.
- Prediction — The ultimate assistant. Learning from human behavior and feedback to replicate and automate common tasks.
These mechanisms are the core conceptual elements of many AI applications, and they aren’t new. However, here we’re going to emphasize the machine-human interaction they involve.
All too often, we data scientists and engineers get lost in the technical details of our algorithms and code, forgetting about the human that is intended to benefit from the work we immerse ourselves in. And I’m not just talking about ignoring the end-user interface. When we scientists and engineers focus on how end-users can benefit from AI capabilities throughout the process — as viewed through the lens of to distilling, categorizing, and predicting — we can genuinely help make people more productive.
The three case studies we’re going to focus on will touch on each of these mechanisms in turn.
In the next article, we’ll take a look at the first mechanism, distillation. We’ll take as our example the customer journey challenge. We’ll explore how, through combinations of network analysis, temporal pattern mining, and interactive analysis we can build AI-assisted technologies that enable humans to answer these questions and identify service optimizations and cost reductions, and deliver a better customer experience.
Subsequent articles will touch on:
- Categorization – exploring how email triage can enable fixed resource teams to grow with volumes of email traffic
- Prediction – reviewing an example of how AI-assisted medical diagnosis can enhance medical care and accuracy
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Roy Wilds is the Chief Data Scientist at PHEMI Systems, a big data warehouse solutions company.
