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Demystifying artificial intelligence and machine learning - Intuit Developer Community Blog

January 16, 2020 | Brian Gorbett

Demystifying artificial intelligence and machine learning

Every business in the world should be an artificial intelligence company.

That’s quite a definitive statement, isn’t it? It’s also a true one.

I had the pleasure of recently speaking at a Startup Grind conference on this very topic, and today, I get to share that same message with you. As developers within the Intuit community, I hope you’ll enjoy my “demystification” of artificial intelligence (AI) and machine learning (ML).

The AI revolution

Before diving in to why every business in the world should be an AI company, let’s quickly define AI and ML.

The Gartner Glossary defines AI as “applying advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take action.” For our purposes today, I’m defining AI as taking data, learning from it, and redeploying outputs that help your customers.

This is what the Gartner Glossary writes about ML: “Advanced machine learning algorithms are composed of many technologies (such as deep learning, neural networks, and natural language processing), used in unsupervised and supervised learning, that operate guided by lessons from existing information.” ML is a subset of AI, and all ML counts as AI, but not all AI counts as ML; instead, ML learns over time.

AI and ML are hot topics right now, but I consider this time to be more than just implementing great technologies. I believe we’re in an AI revolution for several reasons.

  1. AI and ML have been around for a long time, but the challenge has been the lack of collected data to apply AI, or more specifically, ML. That’s changing as businesses begin to understand the power of data and the necessity of collecting mass quantities of data.
  2. ML requires neural networks that require high-intensity compute. ML does parallel processing. CPUs can’t do that, but GPUs can. They can take large chunks of data, split them up, and process them over thousands and thousands of cores so that you come up with the output. Because the cost and availability of GPU has come down and is widely available, you can now build ML models on your laptop.
  3. There have been great advances in algorithms. Note: people that want to get into AI and ML need to know math— it’s all equations and algorithms.

With these advances in AI and ML capabilities, every industry, not just technology, is going to be changed. These technologies are revolutionary. Between now and 2030, they will create an estimated $13 trillion of GDP growth. AI will also create 2.3 million jobs by 2020.

Interestingly, Gartner’s 2018 survey of 3,000 CIOs across the globe found that only one in 25 have started to deploy AI. The question is, why aren’t they jumping on the AI and ML bandwagon?

The answer lies in how broad the topic is and the lack of understanding of how to use AI in their businesses. For example, most distributors don’t need AI to know that adding another truck or two can increase their supply chain. What they do need AI and ML for is to collect the data from those trucks (for example, the distance they travel) to make better, more informed business decisions.

Gartner’s study provides great examples of how using AI benefits businesses and customers alike. Example include using chatbots to answer consumer questions, predicting when a key sensor in a machine needs to be replaced, forecasting when units will sell out, assisting healthcare providers diagnose and search images for early cancer detection, and more.

And to do all of this, businesses need to be collecting data. Lots of it.

Mounds of data and AI strategy

So, to properly utilize AI and ML, you need to set up a strategy.

#1: Build a team of AI and ML experts. These experts should not be people who may know a little about simple rule-based algorithms. They must understand math, specifically the linear regression algorithm. The team should include a software engineer, data scientists and developers.

#2: Start collecting mounds of data. Have people in your organization dedicated to collecting data.

Developers, your job is to help collect the data; you’re not data scientists. Data scientists are the ones who will know how to collect, gather, and organize data in a way that makes it easily consumable to both people and machines.

Also, and this is important, you must inform your customers that you’re gathering the data and what you’re doing with the data. In fact, in most countries, it’s the law.

#3: Do experiments and start building models. You can use companies like Kaggle to find the code and data to do your data science work.

AI and ML can bring powerful prosperity

Here’s a great quote: With the advent of AI, intelligent applications will be the fountain of the next generation of great software companies because they will be the new moats. This means that companies collecting data and using that data to improve their business processes will not get overtaken by their competitors. Essentially, every business should be an AI business.

However, to be clear, AI and ML aren’t magic wands. They won’t solve every business problem, but they are tools within your toolbox to solve many business needs.

My 13+ years at Microsoft and Google kept me at the very center of the technology industry – and Intuit is no different. I can tell you that Intuit has been leveraging the latest technology, including AI and ML, for years as they continue to fulfill their mission of powering prosperity around the world in a transparent and ethical manner.

Just as Intuit CEO Sasan Goodarzi mentioned in talking to Business Insider, “It’s actually a cultural change that we have to go through internally to understand the impact of artificial intelligence.” While we at Intuit approach all the amazing opportunities that AI delivers, we approach them with thoughtful consideration. AI is not something to do to just do. It is a technology that every company has to approach thoughtfully.

I’ve been gathering financial data from small businesses (which these businesses know about) and implementing business-changing innovations, such as QuickBooks Assistant. Unveiled in 2017 for iOS and Android users, and available for QuickBooks Online in 2018, QuickBooks Assistant is a chatbot that combines data-driven insights and natural language processing to ease business operations by asking financial questions or making requests, allowing users to uncover financial data points.

Pretty awesome stuff. So is the idea set forth by Sam Altman, former president of Y Combinator and CEO of Open AI. He says AI and ML have the potential to eliminate poverty, solve climate change, cure human disease, and educate people.

Every company in the world should be an AI business. Do you agree?

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