Everything A CEO Needs To Know About AI

How AI works. What you can do with it. And how to get started.

AI is all about connecting A to B

Information on AI is often confusing and sometimes downright misleading (I’m looking at you, IBM). But AI for business is simple:

“99% of the economic value created by AI today is through one type of AI, which is learning A to B or input to output mappings.” Andrew Ng

Almost every business application of AI today is about learning to produce certain outputs from certain inputs:

How does AI learn to predict the correct output?
  1. You collect examples of input –> output pairs (the more the better).
  2. The AI algorithm learns the connection between input and output (that’s the magic).
  3. You apply the trained algorithm (the “model”) to new input data to predict the correct output.

That’s it! Almost everyone who uses AI to make $$ does it exactly like this.

Use AI when you have a lot of data

AI needs data

AI is powerful because it turns data into insights. But AI is less efficient at learning than people are (yes, way less efficient), so it needs a lot of data in order to learn. If you have lots of data, you should think about AI!

Data is a competitive advantage, not Algorithms.

That’s why Google and Facebook have no problem open sourcing their algorithms. But they definitely don’t open source their data. If you have a lot of data no one else has, then that’s the perfect opportunity to build a unique AI system.

The 3 simplest ways to find your AI use cases

So you have a lot of data. Now what do you do? Here are the 3 best ways I know to discover AI use cases:

1. Improve automated decision-making

Where do you have software that automates rule-based decisions?

For example:

  • Call Routing
  • Credit Scoring
  • Image Classification
  • Marketing Segmentation
  • Product Classification

There is a good chance AI can improve the accuracy of these decisions, because AI models can capture more of the underlying complexity that connects A to B. By comparison, when you write rules into software manually (the traditional way), you can only encode rudimentary dependencies.

For one of our clients (ImmobilienScout24), we increased revenue from emails by 250%. By simply replacing rule-based email segmentation with smarter, and more fine-tuned AI-based segmentation.

2. Things people can do in < 1 second

Another great heuristic I first heard from Andrew Ng is:

“Pretty much anything that a normal person can do in <1 sec, we can now automate with AI.” Twitter

So what are some things humans can decide in < 1 sec?

  • Who’s in that picture?
  • Do I have a good feeling about this potential customer?
  • Does this look like a problematic CT Scan?

Also many jobs are a sequence of < 1 sec decisions. Like driving:

  • Is that person going to cross the street?
  • Am I too close to the sidewalk?
  • Should I slow down?
  • … and many, many more.

Anything you can do in less than 1 second, AI can most likely do too (or it will be able to soon).

3. Get inspired by Kaggle competitions

Large corporations like Zillow, Avito, Home Depot, Santander, Allstate and Expedia are running data science competitions on Kaggle. These are challenges they want outside data scientists to solve. So these competitions give you an idea of what types of AI solutions they are working on. It’s really a great resource.

Have a look at the competitions and get inspired.

TL;DR: Finding AI Use Cases:

  • Upgrade decision-making that’s already automated
  • Automate things people do in < 1 sec
  • Get inspired by Kaggle competitions

Don’t wait until you have a Data Science Team

Building a good data science team is super hard (and expensive!)

Many companies struggle (and ultimately fail) to build an efficient data science team. Why is it so hard?

  • Misinformation about who to hire
  • Tough competition for talent
  • Few managers who can lead data science teams effectively

What’s more…

You don’t know yet whether you need a data science team

You might have a lot of data and a lot of ideas, but that doesn’t mean you need your own data science team. Only after you’ve built your first AI systems will you really know how much manpower you’ll need in the long run. So…

Build something that works – fast

Your first goal should be to pick the lowest hanging AI fruit and finish it quickly. This gets you miles ahead:

  • You’ll achieve tangible success that gets investors, the board, and your team excited;
  • You’ll get to know the taste of AI and get real-life experience of what works and what doesn’t;
  • You’ll have better ideas about where to use AI next.

With that first finished system under your belt, you are in a much better position to hire and train a data science team. Or maybe you find that you don’t want (or need) to hire a whole team, because that low-hanging fruit already accounted for 80% of what you can feasibly do with AI.

AI teams should work across the whole company

If you do build an AI team, you should build it for the whole company, not just one department. A horizontal AI team.

Don’t think in departments

AI experience is very transferable: To a data scientist, your CRM data looks almost the same as your inventory data. And to an algorithm, they look even more similar. So it makes a lot of sense to build one AI task force for the whole firm.

Side note: For that to work, you should also have your data in one place!

More life-saving tips

Don’t listen to people selling a “better algorithm”

Either they have no experience, or they’re trying to sell you open source for a markup. In AI, everyone is cooking with water, meaning they’re using publically available algorithms.

Focus on business experience & engineering quality

Work with someone who takes the time to really understand the business problem you’re trying to solve and has high standards when it comes to engineering quality. If you want lots of results with fewer headaches, then the plumbing is more important than the cute algorithm that flows through it.

Magic Triangle meetings 😋

The best ideas develop when you put three types of people in a room:

  1. Someone who’s in touch with the current business priorities (you?),
  2. Someone who knows the data you have (your database engineer), and
  3. Someone who has lots of practical experience building AI systems.

Together, these three people can make realistic plans, really fast.

TL;DR

So that’s it:

  • AI is about connecting A to B.
  • Look into processes that involve a lot of data and < 1 sec decisions.
  • Get a first win before you plan big.

Energy Transmission

Anticipating and Preventing Power Grid Failure

Massive power outages cause chaos for the general public, and they cost utility providers roughly $49 billion a year.

This wouldn’t be much of a problem if massive power outages were rare, but outages affecting more than 50,000 people have increased dramatically in recent years. This means utility companies need to find new ways of anticipating and managing these outages.

These days, smart grids are producing massive amounts of data, which means predicting and managing outages is easier than ever. Unlike traditional power grids, which are one-directional (meaning they only transmit power in one direction), smart grids are two-directional. They can capture data from every possible source in the grid at the same time as they’re providing electricity. They collect and monitor data from sources like smart meters, IoT devices, and power generation stations, providing a clear, real-time look at power usage.

Machine learning can use this data to anticipate and prevent massive power outages in the grid. Machine learning helps identify non-obvious patterns in the data that can be a precursor to grid failure, which helps maintenance teams preempt failure.

Balancing the Grid

Balancing the grid — making sure energy supply matches energy demand — is one of the most important jobs a transmission operator has. But renewable energy sources depend heavily on the weather, making them harder to predict.

Transmission operators spend millions each year fixing planning mistakes that lead to producing too much or too little power. In hybrid systems — which rely on both renewable energy sources and fossil fuels to generate electricity — these mistakes have to be corrected at the last minute by buying more energy or compensating power plants for the excess.

Machine learning is the most accurate method available to forecast the output of renewable energy. Advanced methods, like Long Short-Term Neural Networks (LSTMs), can weigh the many factors involved — wind, temperature, sunlight, and humidity forecasts — and make the best predictions. This saves money for operators and preserves resources for power plants.

Preventing Blackouts and Brownouts With Real-time Monitoring and AI Prediction

Power grids have a lot of obstacles to overcome in providing continuous energy to customers. Weather patterns, usage, internal failure, even wildcard incidents like lightning strikes and interference from wild animals can all affect power delivery.

Machine learning is increasingly being used to help predict potential brownout and blackout conditions. By feeding historical data into the AI and running Monte Carlo simulations to predict potential outcomes, grid operators can use machine learning to identify conditions that could lead to grid failure. And they can act accordingly.

Sensors like phase measurement units (PMU) and smart meters can provide usage information in real-time. When combined with both historical and simulation data, AI can help mitigate potential grid failure, using techniques like grid balancing and demand response optimization. Incidents that would otherwise have affected millions of people can be contained to a smaller area and fixed faster for less money.

Differentiate Power System Disturbances from Cyber Attacks

Cyber attacks are increasingly used to target important infrastructure, like shutting down hospitals with Ransomware attacks (when attackers break into the system and lock legitimate users out until a ransom is paid). With utility grids, a cyber attack can have widespread consequences and affect millions of users.

Detecting these attacks is critical.

Developers are using machine learning to differentiate between a fault (a short-circuit, for example) or a disturbance (such as line maintenance) in the grid and an intelligent cyber attack (like a data injection).

Since deception is a huge component of these attacks, the model needs to be trained to look for suspicious activity – things like malicious code or bots – that get left behind after the deception has occurred.

One such method uses feature extraction with Symbolic Dynamic Filtering (an information theory-based pattern recognition tool) to discover causal interactions between the subsystems, without overburdening computer systems. In testing, it accurately detected 99% of cyber attacks, with a true-positive rate of 98% and a false-positive rate of less than 2%. This low false-positive rate is significant because false alarms are one of the biggest concerns in detecting cyber attacks.

Balance Supply and Demand

Utility providers are looking for ways to better predict power usage while maintaining maintaining energy supply at all times. This becomes critical when renewable power sources (like solar or wind) are introduced into the grid.

Because these renewable power sources rely on elements beyond human control (like the weather), utility providers know they can’t always rely on renewables for continuous production. Knowing precisely when demand levels will peak allows utility providers to connect to secondary power sources (like conventionally generated electricity) to bolster the available resources and ensure constant service provision.

More and more utility providers are turning to machine learning for help. We can feed historical data into machine learning algorithms -- like Support Vector Machines (SVM) -- to accurately forecast energy usage and ensure sufficient levels and constant supply.

Detect Power Grid Faults

Current methods for detecting faults in the grid consume a lot of unnecessary time and resources. This creates a situation where power transmission is interrupted and customers are without electricity while faults are first located, then fixed.  

Machine learning can find faults quickly and more accurately helping you minimize service interruption for your customers.. Support Vector Machines (SVM) are combined with Discrete Wavelet Transformation (DWT) to locate faults in the lines using a traveling wave-based location method.

When we apply  DWT (a form of numerical and functional analysis that captures both frequency and location information) to the transient voltage recorded on the transmission line, we can determine the location of the fault by calculating aerial and ground mode voltage wavelets. So far, this method has detected fault inception angles, fault locations, loading levels, and non-linear high-impedance faults for both aerial and underground transmission lines.

Detect Non-Technical Power Grid Losses

In the energy world, “non-technical losses” means energy theft or fraud from the system.

There are two common types of non-technical losses. The first is when a customer uses more energy than the meter reports. The second involves rogue connections stealing energy from paying customers. To pull off this theft or fraud, bad actors can bypass smart meters completely or insert chips into the system that change how meters track energy use. Meter readers can also be bribed to report lower numbers (though thanks to smart meters, this is increasingly hard to do).

Because these non-technical losses cost $96 billion annually, utility providers are turning to machine learning to combat the problem.

We can help utility providers mine historical customer data to discover irregularities that indicate theft or fraud. These can be things like unusual spikes in usage, differences between reported and actual usage, and even evidence of equipment tampering.

Energy Distribution

Better Predict Energy Demand

Accurately predicting customers’ energy needs is critical for any utility provider. To date, we haven’t found an adequate solution for bulk energy storage, which means energy needs to be transmitted and consumed almost as soon as it’s produced.

We're using machine learning to increase the accuracy of these predictions. Historical energy use data, weather forecasts, and the types of businesses or buildings operating on a given day all play a role in determining how much energy is used.

For example, a hot summer day mid-week means more energy usage because office buildings run air conditioning at a high capacity. Weather forecasts and historical data can help identify those patterns in time to prevent rolling blackouts caused by air conditioners in the summer.

Machine Learning finds complicated patterns in the various influencing factors (such as day, time, predicted wind and solar radiation, major sports events, past demand, mean demand, air temperature, moisture and pressure, wind direction, day of the week, etc.) to explain the development of demand. Because machine learning finds more intricate patterns, its predictions are more accurate. This means energy distributors can increase efficiency and decrease costs when they buy energy – without having to make expensive adjustments.

Energy Generation

Predict Turbine Malfunction

Wind is a great renewable energy source, but wind turbine maintenance is notoriously expensive. It accounts for up to 25% of the cost per kWh. And fixing problems after they occur can be even more expensive.

Machine learning can help you get ahead of this problem. The goal is to reduce maintenance costs by catching problems before the turbine malfunctions. This is particularly important when wind farms are located in hard-to-access places, such as the middle of the ocean, which makes repair costs even higher.

Real-time data gathered with Supervisory Control and Data Acquisition (SCADA) can help identify possible malfunctions in the system far enough in advance to prevent failure.

For example, data from sensors found within the turbines – such as oil, grease, and vibration sensors – have been used to train machine learning models to identify precursors to failure, such as low levels of lubricant.

This method can train machine learning models to predict failures up to 60 days in advance.

Consumption / Retail

Accurately Predict Energy Prices

As personal power generation (using solar or wind power) gets easier and cheaper, consumers and businesses are increasingly producing their own power.

Personal power generation allows people to make, consume, and store their own energy. Depending on where they live, they may even be able to sell surplus power back to the local power utility.

Machine learning can help find the best time to produce, store, or sell this energy. Ideally, energy should be consumed or stored when prices are low and sold back to the grid when prices are high.

By looking at historical data, usage trends, and weather forecasts, machine learning models have made accurate predictions on an hourly basis. People with personal and business energy generation systems can use these predictions to make strategic decisions about whether to use, store, or sell their energy.

For example, Adaptive Neural Fuzzy Inference System (ANFIS) has been used to predict short-term wind patterns for wind power generation. This allows producers to maximize energy production and sell it when energy prices are at their peak.

Reduce Customer Churn

In open energy markets, where customers have a choice of utility providers, understanding which customers are going to churn out can be critical. Churn rates, which is the percentage of customers who stop using your service in a year, can be as high as 25%. Being able to predict churn and stay ahead of it is essential to survival.

Machine learning is helping utility owners predict when a customer is getting ready to churn out. By using techniques such as Cross-industry Standard Process for Data Mining (CRISP-DM), AdaBoost, and Support Vector Machines, as well as historical usage data, utility providers can identify key indicators of whether or not a customer is going to churn. These indicators include things like customer satisfaction, employment status, energy consumption, home ownership or rental status. A change in any of these can indicate a customer is getting ready to terminate their service.

When these indicators are identified far enough in advance, it’s possible to avoid churn by working with customers to solve any problems they’re experiencing.

Energy Trading

Predict Energy Prices

Just like natural gas and oil, wholesale energy is a market commodity. So naturally it's important for traders to be aware of market fluctuations and pricing when it comes to buying and selling energy.

To help make sense of the massive amounts of data used to make trading decisions, traders are increasingly turning to machine learning.

A mix of statistical analysis and machine learning can help commodity traders make better predictions. Classical statistical analysis techniques like time series analysis, Seasonal Autoregressive Integrated Moving Average (SARIMA), and regression models are used to deal with the data. And machine learning makes connections between the various data points.

What’s more, machine learning trains itself to make increasingly accurate predictions using the constant flow of real-time data.

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