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Lean AI by Lomit Patel

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Chapter 1. Why Lean AI?

The hype around AI and ML has continued to gain momentum as every major industry from marketing to healthcare to manufacturing to transportation to finance to retail, and beyond have started to leverage advances in AI and AI-based applications to improve productivity and performance. Economists have hailed AI as a core enabling technology of the “fourth industrial revolution.1” With all of this excitement, every executive has started thinking about how their business will use AI to not only survive this revolution, but excel amongst their cohorts. According to PwC2, Artificial Intelligence will contribute $15.7 trillion to the global economy by 2030. Clearly, there is a huge opportunity for businesses to benefit from investing in AI. The MIT Sloan Management Review 2017 Artificial Intelligence Global Executive Study and Research Project found that 85% of executives believe that AI will help their businesses gain or sustain competitive advantage.

As with any technology that fuels the public and business imagination, there is a lot of confusion around the terms “machine learning (ML) and “AI.” Many people use AI and ML interchangeably, while others utilize them as discrete, parallel advancements. This confusion ripples into the common understanding of AI and ML, where much of the public discussing these advancements are unaware of the distinctions between the two.. For some, this dilution of the terms is overlooked in favor of creating hyper-excitement for advertising and sales purposes.

This chapter will provide you with working definitions for AI and ML that we’ll carry through the rest of the book, and we’ll also explore how these concepts support the framework of a Lean Startup.

What is artificial intelligence?

Artificial intelligence (AI) is often used as an umbrella term to describe types of technology that can simulate human intelligence. It’s the ability of a computer program or a machine to think and learn. It is also a field of study which tries to make computers “smart,” where computers work on their own without being encoded with commands.

John McCarthy came up with the name “artificial intelligence” in 1955. Artificial Intelligence is essentially building a machine or computer program to parse data to learn from different signals to make smart decisions on predictive actions to achieve a given goal or desired outcome. Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence.

The term “artificial intelligence” has a broader meaning. It’s the idea that machines and computers can complete tasks normally requiring human intelligence. AI as we know it today is symbolized with Human-AI interaction gadgets by Google Home, Siri and Alexa, by the machine learning powered video prediction systems that power Netflix, Amazon and YouTube, by the algorithms hedge funds use to make micro-trades that rake in millions of dollars every year. These technology advancements are progressively becoming important in our daily lives. In fact, they are intelligent assistants that enhance our abilities, making us more productive.

In contrast to machine learning, AI is a moving target, and its definition changes as its related technological advancements turn out to be further developed. What is and is not AI can without much of a stretch, be challenged, making machine learning very clear-cut on its definition. Possibly, within a few decades, today’s innovative AI advancements will be considered as dull as calculators are to us right now.

What is machine learning?

Let’s define machine learning (ML) since it is such a ubiquitous term these days.  Machine learning is a type of artificial intelligence developed around allowing computer system to progressively improve performance on a task by “learning” through a range of statistical approaches.  Put another way, machine learning is the development of algorithms that allow for more and more accurate predictions based on the incremental collection of data.

It also worth clarifying that artificial intelligence is not the same as machine learning because artificial intelligence is a broader concept. Machine learning is simply one of the most common and approachable applications of AI. The machine learning concept is about giving machines access to data to enable them to learn from it themselves. AI incorporates a bunch of technologies that include machine learning, deep learning, inference algorithms, natural language processing, neural networks, and computer vision — are starting to show real promise, despite the hype and confusion in the marketplace. Artificial intelligence is at a critical point in its evolution, especially as it relates to marketing automation.

Machine learning works best with large data-sets by examining and comparing the data to find common patterns and explore nuances. Machine learning automates model building for data analysis. The concept behind machine learning is that a computer can learn from the data it analyzes by identifying patterns. Ultimately, this technology can make decisions without humans. Essentially, it gives machines the ability to learn and adapt through experience. Here’s how it works: the system uses probability to make decisions or predictions based on the available data. It then uses feedback loops to find out if its prediction was right or wrong. Predictions get more and more accurate — and the system gets smarter.

An example of a startup that has successfully leveraged ML is IMVU, which works with third-party CRM platforms like Leanplum to help them collect a huge amount of customer data — from user profiles, location, revenue and usage stats, to which features users are interacting with, and much more. But on its own, this data only tells us what happened in the past. Machine learning, on the other hand, uses this information to predict the future for example which users are most likely to churn that need critical attention to keep them engaged to experience the value prop in the product. Data is useless if it doesn’t help you make better decisions in the future to achieve your desired business goals.

What is the Lean Startup?

Launching and growing a new startup has always been a challenging endeavor – which is why you need to be open to trying new and innovative approaches to increase your odds for success. The chances of being successful can be significantly increased simply by taking a rational and systematic approach to finding the best strategy for running the business. The Lean Startup has been one of the most successful systematic approaches to date that has been widely adopted across the globe, changing the way startups are built and new products are launched.

The Lean Startup is a methodology for developing businesses and products, which aims to shorten product development cycles and rapidly discover if a proposed business model is viable; this is achieved by adopting a combination of business-hypothesis-driven experimentation, iterative product releases, and validated learning. The Lean Startup method teaches you how to drive a startup-how to steer, when to turn, and when to persevere-and grow a business with maximum acceleration. It is a principled approach to get a desired product to customers’ hands faster.

Central to the lean startup methodology is the assumption that when startup companies invest their time into iteratively building products or services to meet the needs of early customers, the company can reduce market risks and sidestep the need for large amounts of initial project funding and expensive product launches and failures.

The same approach to iterative learning by continuously running experiments is practiced by the best growth teams, who take the same systematic testing and tweaking approach to make the business grow as fast as possible. The key is to take action— to try, to fail, to learn, and to win by learning faster than anyone else.

Taking the wisdom of The Lean Startup approach into the golden dawn of artificial intelligence, we can radically improve our chances of successful outcomes. It’s basically running experiments on steroids. A properly instrumented approach to modern artificial intelligence, machine learning and automation combine to offer companies large and small the ability to conduct far more experiments simultaneously. This speeds up the process of finding successful experiments -- some of which you’d never have taken the time to test in a pre-AI world. Incremental experiments that otherwise would have been sidelined for cost or complexity are now valid for observation in the world of autonomous marketing.

Three Key Drivers of Artificial Intelligence

Here are three key factors that have helped accelerate the recent advances in AI, which is leading to more major demand side and supply side partners adopting AI to offer more robust paid advertising options to growth teams to get better performance on their paid user acquisition budget.

Computing power

The price performance of computing power has grown exponentially in alignment with Moore’s Law.3 Exponential means that the computing speed doubles and/or the price drops by half year over year. In recent years, machine learning as one of the key drivers of AI advances has greatly benefited from GPUs (Graphics Processing Unit). GPUs are very performant for conducting vectorised numerical operations which are needed for all machine learning calculations. Google’s TPU (Tensor Processing Unit) is another example where (co) processors are optimized for machine learning problems. With great advances in quantum computing it is very likely that this trend will continue and accelerate. Quantum computers give us the ability to solve complex problems that are beyond the capabilities of classical computers like encryption, optimization, and other similar tasks. Quantum computers can analyze large quantities of data to provide artificial intelligence machines the feedback required to improve performance. Quantum computers are able to analyze the data to provide feedback much more efficiently than traditional computers and therefore accelerate the learning curve for artificial intelligence machines. Just like humans, artificial intelligence machines powered by the insights from quantum computers can learn from experience and self-correct. Quantum computers will help artificial intelligence expand to more areas of growth marketing and technology become much more intuitive very quickly.

Availability of data

Data is the fuel for AI and your own customer data is the most precious asset. There is an accelerated generation and availability of data fueled by the increased use of connected devices like mobile technology and social media. In fact, the number of internet users has grown over a billion in the last five years, more than half of the world’s web traffic now comes from mobile phones. Large amounts of data are essential to successfully do machine learning and therefore achieve a high accuracy of their predictions for growth marketing questions like which of your customers are most likely to purchase or churn in the near future. More users behavior actions are being tracked and shared than ever before, flooding out of the dozens of connected devices we use every day, and it shows no signs of slowing down. Knowledge is power and there’s a lot of knowledge trapped in your internal data as well as external sources. To best unlock that knowledge, you have to consider the type of data you need, where to look for it, how to get it, and how to build the right data models to analyze your business questions. And just as importantly, you need to continually update your data to re-train and enhance the algorithms. There’s certainly a lot that goes into data collection, but it’s worth it. As the lifeblood of AI, data is critical to helping you get the business insights you need to move your business forward.


Algorithms are used for calculation, data processing, and automated reasoning.” Whether you are aware of it or not, algorithms are becoming a ubiquitous part of our lives with the biggest focus on machine learning include data mining and pattern recognition. Algorithms can make systems smarter, for example movie recommendations from Netflix. However, there is a common principle that underlies all supervised machine learning algorithms for predictive modeling. Machine learning algorithms are described as learning a target function (f) that best maps input variables (X) to an output variable (Y): Y = f(X). The most common type of machine learning is to learn the mapping Y = f(X) to make predictions of Y for new X. This is called predictive modeling or predictive analytics and the goal is to make the most accurate predictions possible. The questions being raised about algorithms at the moment are not about algorithms per se, but about the way society is structured with regard to data use and data privacy with data breaches becoming more prevalent. It’s also about how models are being used to predict the future. Algorithms embedded into the technology through which we access so much information could be shaping what information we receive, how we receive it, even how we react to it. And AI might be shaping our behaviour, not as an unintended consequence of its use, but by design. Technology, often aided by AI, is exploiting human psychology to shape how we behave.

1 Professor Klaus Schwab, Founder and Executive Chairman of the World Economic Forum, characterized the Fourth Industrial Revolution by a range of new technologies that are fusing the physical, digital and biological worlds, impacting all disciplines, economies and industries, and even challenging ideas about what it means to be human.

2 2017 PwC’s Global Artificial Intelligence Study: Exploiting the AI Revolution.

3 Moore’s Law is a computing term which originated around 1970; the simplified version of this law states that processor speeds, or overall processing power for computers will double every two years. A quick check among technicians in different computer companies shows that the term is not very popular but the rule is still accepted.

4 CB Insights | The Data Flywheel: How Enlightened Self-Interest Drives Data Network Effects. June 2017

5 Artificial Intelligence for Marketers 2018: Finding Value Beyond the Hype,” October 2017. Author: Victoria Petrock. Copyright eMarketer.

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