Executive Briefing - Artificial Intelligence

Welcome to Executive Briefing for Artificial Intelligence (AI). Executives need to know a bit of all the technologies, particularly the new ones. This knowledge will be of help if you are a Country head responsible for New Business Development or Service Delivery Management or a Tech Enthusiast curious about the NexGen Technology.

This is a small attempt from me to give you a High Level Understanding of AI and prep you up for an engaging conversation. The focus will be more towards the Business Applications rather than the underlying technology, since this article's intended audience are Execs.

 This Executive Briefing on Machine Learning will make certain that you start appreciating that there is nothing unapproachable about the discipline. It will:

• Ensure you can navigate technical terms and topics related to Machine Learning
• Highlight the tools needed to create a stellar Machine Learning project
• Explain the steps to take to build a reliable team of experts and support staff

What is Machine Learning?

The terms Artificial Intelligence, Deep Learning and Machine Learning are often used interchangeably. They all refer to automated processes that rely on company data. So, how can we differentiate between them?

The simplest way to understand how Deep Learning and Artificial Intelligence correlate to our discipline of interest is to consider each term as one part of a Russian doll. Artificial Intelligence is the biggest of them all, and therefore the outermost shell. Machine Learning sits inside of it, and Deep Learning is packed inside both as the smallest ‘doll’. Logically speaking, all Machine Learning is a type of Artificial Intelligence, but not all Artificial Intelligence is Machine Learning.

Machine Learning Terminology:

• AI (Artificial Intelligence) — a broad concept. A Science of making things smart or, in other words, human tasks performed by machines (e.g., Visual Recognition, NLP, etc.). The main point is that AI is not exactly machine learning or smart things. It can be a classic program installed in your robot cleaner like edge detection. Roughly speaking, AI is a thing that somehow carry out human tasks.
• ML (Machine Learning) — an Approach (just one of many approaches) to AI that uses a system that is capable of learning from experience. It is intended not only for AI goals (e.g., copying human behavior) but it can also reduce the efforts and/or time spent for both simple and difficult tasks like stock price prediction. In other words, ML is a system that can recognize patterns by using examples rather than by programming them. If your system learns constantly, makes decisions based on data rather than algorithms, and change its behavior, it’s Machine Learning.
• DL (Deep Learning) — a set of Techniques for implementing machine learning that recognize patterns of patterns - like image recognition. The systems identify primarily object edges, a structure, an object type, and then an object itself. The point is that Deep Learning is not exactly Deep Neural Networks. There are other algorithms, which were improved to learn patterns of patterns, such as Deep Q Learning in Reinforcement task.

Below is a Venn Diagram of Data+Features+Algorithms and the mixture of all of these gives ML

Machine Learning Strategy

Your Machine Learning strategy must always consist of three components:

• Data
• Tools
• Team

Each one of these is essential to the running of your project. Think of them as cogs in a machine – if one of them is missing, the others won’t turn.

First, you need data to plug into the tools. Then, the Machine Learning tools must be there to execute analyses. Finally, you need a successful team by your side to ensure that you collect the right data and that you have chosen the most suitable algorithm for the job.

#1. Machine Learning Strategy - Data

Data is generated all the time. Every successful Machine Learning project requires an initial three stages related to data. Be mindful of them when you’re developing your Machine Learning strategy. They are:

1. Capture (through sensors, data points on your website, tracking devices on equipment)
2. Store (in storage facilities such as SQL or data lakes)
3. Process & Analyze (with relevant Machine Learning algorithms)

Issues with Data:

Data is a mess. Some of the most common problems for companies that do not have data science strategies in place are that the information stored is missing, corrupt, or just incorrect. The team might need time to find, gather, and clean your prior data, for it to function alongside your new units of information.

Data only tells you what you think is true. Biased data is one of the most challenging issues that you might come across. If a mortgage company is using a Machine Learning algorithm to replace its manual decision-making about whether or not to offer a loan, it will use the company’s historical data to predict the outcome for each new client. But the company’s prior data is biased.

Although it contains information about the loans that were granted and were not successfully repaid it does not offer any detail about the loans which weren’t approved – but that might have been successful.

Thus, the mortgage brokers continue to make the same errors in judgment because the machine has learned the same bad habits.

Data is in the wrong place. Organizations might have grown in such a way that data is kept in different departments, making them not easily accessible across the company. This disparity leads to data silos – information archives that exist separate to the rest of the organization. Silos are a common problem, and it can stop your project dead. Here are four possible reasons for silos:

1. Structural/functional: A sales division might choose to store their recent months’ sales in one location for easy access and to archive all previous months in another. This division makes it hard to analyze all sales data together.

2. Political: Data is power, and many managers know it. Some departments become data hogs, holding onto their information and unwilling to relinquish it to other staff.

3. Growth: Management changes, new tools enter the market, and operational strategies get updated. These upheavals can lead to disparate, unmanaged data.

4. Software vendor lock-in: This is potentially the most problematic issue. When you use external services to log your information, you leave your data at the mercy of that company.

Consider each of these issues when building the strategy. Leaving one of these areas unresolved may cause roadblocks to the project.

Finally, ensure that you leave enough time for quality assurance. Successful Machine Learning projects take time. As you can see from this section, data preparation, processing, and compilation take a significant chunk (roughly 80%) of project time.

A good rule of thumb is to quadruple the time requested by your team for model building

If your data scientist or Machine Learning engineer tells you that a model will take two weeks to build, multiply that amount of time by four to get the amount of time required for data preparation

#2. Machine Learning Strategy - Tools

There are lots of tools available to a data scientist – you might have heard of for-profit software such as SAS, SPSS, Matlab. These will help you to carry out your Machine Learning experiments.

Many organizations prefer to go with commercial tools because it gives them a feeling of security. What they’re creating is their intellectual property, and it cannot be easily shared or taken by other people online. But there is also a strong case for using open source tools and software. You might wince at the idea. Open source. Won’t that leave my data accessible to hackers? Will others be able to steal my information? If it’s free-to-use, surely it can’t be very good? These are common misconceptions of what open source actually means. Facebook and Google both use open source tools for their Machine Learning practices and even their AI algorithms. These tech giants have plenty of money to buy software if they wished to do so, but they use open source (specifically, Python). Why?

The value lies in data, not the tool.

So, why waste time inventing the wheel? Open source tools allow a Machine Learning team to start from an already developed base, to find solutions online, and to source talent.

Another benefit to using open source software is that they have massive online networks. With so many people across the world contributing to the development of tools, if the Machine Learning team has any questions or needs guidance, online help is readily available. The latter benefit also makes it easier to find talent, directly from the pool of people assisting you. Two industry-preferred open source tools dominate Machine Learning: R and Python.

#3. Machine Learning Strategy - Team

When you’re hiring a team of people to help with Machine Learning, avoid looking for the ‘unicorn’ who will magically solve your problems without really thinking through the specifics of what is needed. Career sites are flooded with completely unrealistic job descriptions, looking to find candidates with ten years’ experience, a PhD, and knowledge of 50 different tools and algorithms. These are red flags to the best data scientists – they signal desperate managers who don’t understand what Machine Learning does and how it can help to solve problems.

Select and structure your team the smart way.

Integrating Machine Learning in the Organization

One useful point of consideration at this stage is where your team will sit in the company. Which department will be the ‘parent’ for the team – Marketing? IT? Operations? Sales? There are two approaches for an executive to take. Both methods have their merits, and it is essential to consider both before settling on one:

1. Create a separate division

For large companies, many Machine Learning teams begin in IT and eventually branch out into a division in their own right. When this happens to your company, head up this division with a Chief Data Scientist. Ask them to sit in on executive meetings and advise how and where their team can add value to the organization. Enabling the division’s head to sit in on board meetings will improve communication across departments and prevent data siloing by giving your Data Science team authority. The only limit here is the cost of developing a Machine Learning division, but if the scientists are working to improve company operations, one should consider appraising this value against the staff costs.

2. Install experts into relevant departments

Rather than building a standalone Machine Learning team, one could consider dropping an engineer into every division that could utilize Machine Learning. This approach enables each scientist to help their target division, not to mention having an expert in each department will facilitate cross-company communication related to information archives. One disadvantage of this approach is a lack of assistance. Without support staff, your experts might become overwhelmed with their projects.

Conclusion

This course advocates taking the time to develop a Machine Learning strategy, the necessary components of which include data, tools, and a team, to ensure that its implementation will add value to your business. To recap the process:

• It is essential to identify a question before jumping into a project.
• You cannot run a Machine Learning project if there is no data, and understanding what your question is at the beginning of the process will help you to identify the data you need to collect.
• Hire the right people in the right number, depending on the size of your business and what it hopes to do with Machine Learning.
• Select an open source tool such as R or Python to kick-start your first Machine Learning project. Choose among the common algorithms of Machine Learning to begin with

What's Next?

Now that you are done with this article, then you can proceed to dive into the Machine Learning (ML) focusing on FIVE exciting new technologies that are touching millions of lives everyday. There is a separate article article on each of these FIVE since they warrant a good discussion on their own.

1. Computer Vision (CV)
2. Deep Learning (DL)
3. Reinforcement Learning (RL)
4. Natural Language Processing (NLP)
5. Robotic Process Automation (RPA)

And for each of these, we will:

• Get a bird’s eye view of the field
• Understand how each technology works
• See real-world applications and use cases of the technologies

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The above content is highly inspired by BLUE LIFE (https://www.bluelife.ai/). They provide corporate training and consulting services around Artificial Intelligence. 

The above article is not a paid post. But the credits for the language and thought process goes to BLUE LIFE.