we make. There is no one-size-fits-all solution, especially not in a field like AI that is constantly evolving.
Ahead of the recent boom in AI technologies, many organizations have already successfully implemented intelligent solutions. Most of these organizations followed an adoption roadmap similar to the one we will describe in this book. It is insightful for us to take a look at a few of these organizations, see what they implemented, and take stock of the benefits they are now realizing. As you read through these organizations' stories, keep in mind that we will be diving into aspects of each approach in more detail during the course of this book.
Case Study #1: FANUC Corporation
Science fiction has told of factories that run entirely by themselves, constantly monitoring and adjusting their input and output for maximum efficiency. Factories that can do just-in-time (JIT) ordering based on sales demand, sensors that predict maintenance requirements, the ability to minimize downtime and repair costs—these are no longer concepts of speculative fiction. With modern sensors and AI software, it has become possible to build these efficient, self-bolstering factories. Out-of-the-box IoT equipment can do better monitoring today than industrial sensors from 10 years ago. This leap in accuracy and connectivity has increased production threshold limits, enabling industrial automation on a scale never before imagined.
FANUC Corporation of Japan,1 a manufacturer of robots for factories, leads by example. Its own factories have robots building other robots with minimal human intervention. Human workers are able to focus on managerial tasks, whereas robots are built in the dark. This gives a whole new meaning to the industry saying “lights-out operations,” which originally meant servers, not robots with moving parts, running independently in a dark data center. FANUC Japan has invested in Preferred Networks Inc. to gather data from their own robots to make them more reliable and efficient than ever before. Picking parts from a bin with an assortment of different-sized parts mixed together has been a hard problem to solve with traditional coding. With AI, however, FANUC has managed to achieve a consistent 90 percent accuracy in part identification and selection, tested over some 5,000 attempts. The fact that minimal code has gone into allowing these robots to achieve their previously unobtainable objective is yet another testament to the robust capabilities of AI in the industrial setting. FANUC and Preferred Networks have leveraged the continuous stream of data available to them from automated plants, underlining the fact that data 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