collection and analysis is critical to the success of their factory project. FANUC Intelligent Edge Link & Drive (FIELD) is the company's solution for data collection to be later implemented using deep learning models. The AI Bin-Picking product relies on models created via the data collected from the FIELD project. Such data collection procedures form a critical backbone for any industrial process that needs to be automated.
FANUC has also enabled deep learning2 models for situations where there are too many parameters to be fine-tuned manually. Such models include AI servo-tuning processes that enable high-precision, high-speed machining processes that were not possible until recently. In the near future, your Apple iPhone case will probably be made using a machine similar to the one in Figure 1.1.
Most factories today are capable of utilizing these advancements with minor modifications to their processes. The gains that can be achieved from such changes will be able to exponentially elevate the output of any factory.
FIGURE 1.1 Example of a FANUC Robot3
Case Study #2: H&R Block
H&R Block is a U.S.-based company that specializes in tax preparation services. One of their customer satisfaction guarantees is to find the maximum number of tax deductions for each of their customers. Some deductions are straightforward, such as homeowners being able to deduct the mortgage interest on their primary residence. Other deductions, however, may be dependent on certain client-specific variables, such as the taxpayer's state of residence. Deduction complexity can then be further compounded by requiring multiple client-dependent variables to be considered simultaneously, such as a taxpayer with multiple sources of income who also has multiple personal deductions. The ultimate result is that maximizing deductions for a given customer can be difficult, even for a seasoned tax professional. H&R Block saw an opportunity to leverage AI to help their tax preparers optimize their service. In order to help facilitate the adoption process, H&R Block partnered with IBM to leverage their Watson capabilities.4
When a customer comes into H&R Block, the tax preparer engages them in a friendly discussion. “Have you experienced any life-changing events in the last year?,” “Have you purchased a home?,” and so on. As they talk, the tax preparer types relevant details of the conversation into their computer system to be used as reference later. If the customer mentions that they purchased a house last year, that will be an indicator that they may qualify for a mortgage interest deduction this year.
H&R Block saw the opportunity here to leverage the use of AI to compile, cross-reference, and analyze all of these notes. Natural language processing (NLP) can be applied to identify the core intent of each note, which then can be fed into the AI system to automatically identify possible deductions. The system then presents the tax professionals with any potentially relevant information to ensure that they do not miss any possible deductions. In the end, both tax professionals and their customers can enjoy an increased sense of confidence that every last applicable deduction was found.
Case Study #3: BlackRock, Inc.
Financial markets are a hotbed for data. The data can be collected accurately and in real time for most financial instruments (stocks, options, funds, etc.) listed on stock markets. Metadata (data about data) can also be curated from analytical reports, articles, and the like. The necessity for channeling the sheer amount of information that is generated every day has given rise to professional data stream providers like Bloomberg. The immense quantity of data available, along with the potential for trend prediction, growth estimations, and increasingly accurate risk assessment, makes the financial industry ripe for implementing AI projects.
BlackRock, Inc., one of the world's largest asset managers, deploys the Aladdin5 (Asset, Liability, Debt, and Derivative Investment Network) software, which calculates risks, analyzes financial data, supports investment operations, and offers trade executions. Aladdin's key strength lies in using the vast amount of data to arrive at models of risk that give the user more confidence in deploying investments and hedging. The project was started nearly two decades ago, and it has been one of the key drivers of growth at BlackRock. BlackRock's technology services revenue grew 19 percent in 2018, driven by Aladdin and their other digital wealth products.6 Aladdin is now used by more than 25,000 investment professionals and 1,000 developers globally, helping to manage around $18 trillion in assets.7 Aladdin embeds within itself the building blocks of AI through the use of applied mathematics and data science.
BlackRock is now setting up a laboratory to further study the applications of AI in the analysis of risk and data streams generated. The huge amount of data being generated is becoming a problem for analysts, since the amount of data a human can sift through is limited. The expectation of Rob Goldstein, BlackRock's chief operating officer, is that the AI lab will help increase the efficiencies in what BlackRock does across the board.8 By applying big data to their existing data trove, BlackRock will be able to generate higher alphas, a measure of excess return over other portfolio managers, according to David Wright, head of product strategy in Europe. With good data generated by Aladdin and a sufficiently advanced AI algorithm, BlackRock might just emerge as the leader in analyzing risk and portfolios.
How to Get Started
The journey to adopt AI promises to bring major changes to the way your organization thinks and approaches its future. This journey will involve the adoption of new methods and process improvements that will aid you in spotting the novel ways AI can be deployed to save costs and make available new opportunities.
As with any endeavor worth starting, we must make plans for how we intend to accomplish our goal. In this case, the goal is to adopt AI technologies to better our organization. The plan for achieving this goal can vary from organization to organization, but the main steps invariably remain the same (see Figure 1.2).
1. Ideation
The first step in any technology adoption journey must start with ideation and identifying your motivation. In this chapter, we will delve into answering questions such as “What problem are you trying to solve?,” “How does your organization operate today?,” and “How do you believe your organization will be able to benefit from AI technology?” Answering questions like these will kick-start your AI journey by establishing a clear set of goals. To properly answer these questions, you will also need a general understanding of the technology, which we will cover in the following section.
FIGURE 1.2 The AI Adoption Roadmap
2. Defining the Project
Once you have determined that the use of AI technologies can help improve your organization or solve a business problem, you must then get specific about what you hope to achieve. During
