Al Naqvi - Artificial Intelligence for Asset Management and Investment

Automation is the ability of a synthetic entity to perform work. Since work requires both intelligence and actions, automation involves both.

Thinking automation: Automation automates the thinking part where a synthetic entity can be intelligent (refer to the definition of intelligence presented above) autonomously. Here autonomously implies that it can make decisions to navigate through uncertainty on its own.

Action automation: Automation automates actions where non-thinking parts of work are automated. For instance, a car automates movement on land, an airplane automates mobility in the sky, a non-thinking computer automates work tasks such as spreadsheets, word processing, and others. Instead of walking, you ride in a car. Instead of flying (not sure how a human would fly, perhaps jump or fall is a better comparison), a human can fly in an airplane. A human driving a car, flying an airplane, or using a computer is benefiting from the automation in these artifacts even though he or she is using his or her own cognitive skills to operate these machines. These machines are not intelligent, but they are automated. That automation is the automation of action where an artifact can enable human work that requires interaction with the environment.

From a business process perspective, one can view a process as composed of intelligence-action sequences that accomplish work. In fact, it can be viewed as state transitions between thinking (decision making) to acting and doing them successively till the work sequence is finalized and the work goal is achieved. The action itself, or at least certain part of it, does not require any thinking. Going back to our example of you deciding to turn your car right, the “decision” in this case is to turn right, but an action follows. The action itself can be broken down into states of action to turn, as brain and hand coordination maintains what turning is. Thus, decision-oriented intelligence decides to turn right, and then brain, eyes, hands, and other sensors work collaboratively to help us make the right turn. We receive feedback, and our brain processes this feedback to turn the wheel in accordance with the feedback to keep the car on the road, accident free, and make the right turn. The think-act sequence itself is the second kind of decision—i.e., doing what needs to be done or monitoring an action-neural response (feedback) sequence once an action has begun.

Clearly, the automation from artificial intelligence requires the convergence of the two types of automations: automation of thinking (synthetic, or machine, thinking) and automation of action. It is a merger of the two where artifacts can think autonomously and take actions.

The acts of action can be many. Any time information is extracted from the environment and goes into a system (artifact, entity) as input and then when the environment is acted upon by the system as some form of output, these are actions. Thus, when the machine receives market data, it can be viewed as specific steps where no thinking is required by the machine. Then the machine thinks and makes a trading decision. The decision when communicated back to the environment where a trade is made is again an action and requires execution and not just thinking.

The enterprise software, then, is composed of a combination of “thinking” and “acting” software. The thinking-acting sequences imply integration of AI software with non-AI software to build work-task sequences. But these task sequences are not built around automating human-centric processes. In other words, automation requires rethinking the business models, and processes need to be built around machine work. Machines work differently than humans. In the next chapter, we will cover the design principles of AI-centric designs. At this stage, it is important to recognize that designing a modern investment management firm requires building an integrated software architecture of non-intelligent (legacy or traditional software) and intelligent (machine learning, rules-based) software.

Data is the lifeblood of machine learning. Without data, machine learning models fail to learn. In fact, not only do we need to have plenty of data, its quality needs to be good for the learning to be consistent with the goals of developing the artifact. Since each firm has its own data, the potential for each firm to perform in the AI era will be different.

Here are some of the capabilities needed for an AI-centric transformation:

What data do you have? What data do you not have but is needed? What data is needed but you cannot have? What data is essential for your core operation?

Having data is one thing, having quality data another. Data quality is essential to build powerful systems. Quality of data includes factors such as completeness, relevance, and timeliness.

You can partner with, buy, or build AI capabilities. Which one leads to establishing competitive advantage for your firm? Clearly, buying a solution implies that you are not the only one who has access to that one or more set of solution algorithms and data sets. This does not mean you should not consider buying certain solutions. However, for critical areas in your firm, it will be important to build (best alternative) and partner (second best) to create a custom capability set that is held only by your firm.

Of course, when it comes to data, you will need to buy it from various sources. But even for that, consider what data sensors and data collection mechanisms can be architected internally to save money and improve data quality.

Based upon the above discussion, we are now able to suggest how to architect competitive advantage for our firms. As we digest the above discussion, we can zero in on four core determinants of competitive advantage. These four determinants are the underlying engines that drive value for us and our clients. These are the technological constructs that we need to get right. Based on these four determinants, we architect various business processes and achieve work tasks. The following are the four determinants:

Design constructs: Design constructs are based on your firm's competitive and market positioning and strategy. Design constructs emerge from the deployment of capabilities that collectively define a firm's business model and orchestrate how the firm will structure itself.

Extent and quality of intelligence: The extent and quality of intelligence comes from the core intelligence-centric methodologies. It can be viewed as using the best algorithms for a particular problem set (and the available data; see below), with both effective and efficient training, which results in achieving training goals in a timely manner and with higher precision and recall. Since intelligence manifests at both the artifact level and the networked level connection of artifacts, the extent and quality of intelligence is relevant at both levels.

Sequence of intelligence and action: The sequence of intelligence and action means that such sequence chains are well defined, optimally placed, stacked for maximum efficiency, and flawlessly integrated. It implies that intelligent software and non-intelligent software (or electric or mechanical systems) function in an integrated, harmonious, synchronized, and efficient manner such that machines accomplish work tasks successfully and optimally.

Data: Data refers to a firm's ability to have quality data that it can feed to its learning algorithms. Both the quantity and quality of data are important. In addition, the span of data—that means the reality that data covers—is also significant. To clarify the span of data, think about a piece of relevant reality for which you do not have data. Without that data you have no understanding of the reality. As you learn more about the reality, you model the reality with a meaningful representation in terms of variables and features. This multidimensional view of the reality is what the span of data refers to.