The explosion of interest in Artificial Intelligence (AI), Machine Learning (ML), and Machine Intelligence (MI) has triggered the design and development of all sorts of autonomous systems, including digital marketing & ad campaigns, self-driving cars, drones, self-healing (autonomic) systems, autonomous manufacturing, deep space exploration probes, and more. In all of these systems, safety and risk mitigation are paramount. We present here a model for “safe driving” in these autonomous systems.
So, what is an autonomous system? A report on “Autonomous Manufacturing” offers these definitions and features:
- Autonomous systems have the ability to learn, adapt and take decisions.
- Autonomous systems are those that can gain information about their environment, adapt and make decisions without the need for human intervention. Such systems can lead to decisions being made that reduce waste and risk while increasing productivity and repeatability.
- Autonomous can refer to the control of a particular task or process, or an entire system.
- Autonomous systems have the capacity to replace, augment or complement human activity. They have the potential to work collaboratively with humans, machines or other autonomous systems and significantly advance both physical and intelligent capabilities.
- An autonomous system includes the hardware, software, sensors and communication technology that support the reasoning, planning and decision making required to achieve strategic self-directed goals.
- Features of an autonomous system include: collaborative, learning, decision-making, sensing, and sensor integration (i.e., data fusion).
We apply these concepts to a broader set of autonomous systems, borrowing liberally from the concept of a self-driving car: the self-driving organization, self-driving enterprise, and self-driving city (e.g., Smart Cities). We specifically examine five defining characteristics of the self-driving enterprise, their analytics implications, and their related organizational positioning. We then conclude with five rules for “safe driving”.
Before we examine safe driving in the self-driving enterprise, we look at a few more important characteristics of a self-driving (autonomous) system:
- Secure – the hardware, software, communications, and data streams in an operational autonomous system must be secured and protected from intrusion, tampering, theft, or other forms of hijacking.
- Compliant – the system must comply with all governing rules and regulations, as well as follow ethical principles of action, inaction, data collection, data use, and data dissemination.
- Stateful – the system must keep track of its state of interaction, to learn from prior actions, as well as to sustain and to improve on the safety, efficiency (speed to decision), and effectiveness (accuracy of decision) of its autonomous operations. (Note: The opposite of Stateful is Stateless. Stateless means there is no record of previous interactions and each interaction request must be handled based entirely on any information that comes with the request.)
- Embedded – the system is typically not stand-alone, but it must operate within a larger environment or under a wider set of exogenous constraints (including business goals and strategies that are not necessarily part of the operational components of the system, but are part of a greater purpose). Typically a specific autonomous system may be embedded in an environment with related systems (e.g., a self-driving car is an autonomous system, but must operate in an environment with other vehicles, not all of which are autonomous).
The five defining characteristics of the self-driving enterprise (including self-driving cars) are:
- Sensing, Streaming – receiving signals (inputs) from numerous sensors, with data continuously flowing from those sensors.
- Responsive – takes action in response to the input signals.
- Learning, Agile – builds knowledge (because it is a Stateful system) from past, present, and observed actions (the latter may come from other systems in the embedded environment) in order to see what’s coming and to take appropriate action in a timely manner (i.e., in online real-time mode, not in off-line batch mode).
- Contextual, Optimizing – takes into account additional (external) data sources about the context of its actions in order to build insights about optimal decision-making. Ubiquitous sensors in the Internet of Things (IoT) can provide a rich source of contextual information and insights.
- Deciding, Acting – uses all inputs (the 360 view of the situation) to build understanding of the environment in order to make a decision and take an action, with minimal risk and optimal outcomes (consistent with embedded business rules and appropriate decision logic).
Each of these five characteristics is associated with one of the five different levels of analytics maturity in the self-driving (autonomous) enterprise:
- Sensing, Streaming – Descriptive Analytics: observes, records, and keeps track of what events have been happening (hindsight).
- Responsive – Diagnostic Analytics: takes action based upon events that are currently happening in real-time (oversight).
- Learning, Agile – Predictive Analytics: goes beyond simple “reporting” of past events or reacting to present events, to predicting what is likely to occur next, based upon those past and current events – i.e., training data (foresight).
- Contextual, Optimizing – Prescriptive Analytics: goes beyond predictive models, to knowing how objects behave under various conditions, in order to understand objectively under what conditions will a given object in the embedded environment act (or react) in a certain way, and then seeking to set up those conditions and/or behaviors that will yield optimal outcomes (insight).
- Deciding, Acting – Cognitive Analytics: sense-making from all of the input data, knowledge, insights, and understanding, in order to decide on the NBA (Next-Best Action), and then taking the right action, at the right time, at the right place, in the right context. This may also include probing new, but safe, “what if” scenarios to explore whether there is a better NBA (right sight).
Next, each of the five defining characteristics and levels of analytics maturity correspond to a specific positioning of the self-driving (autonomous) enterprise:
- Sensing, Streaming – Descriptive Analytics = Passive.
- Responsive – Diagnostic Analytics = Reactive.
- Learning, Agile – Predictive Analytics = Proactive.
- Contextual, Optimizing – Prescriptive Analytics = Predictive Reactive.
- Deciding, Acting – Cognitive Analytics = Sense-making for NBA.
As our enterprise (organization, city, car, drone, supply chain, manufacturing process, or deep space exploration probe) moves through its operational processes, it applies machine learning algorithms:
- Supervised learning – search for, identify, classify, and respond to events, objects, or actions with known attributes and properties.
- Unsupervised learning – discover new categories of items (events, objects, or actions) in the environment; discover anomalous items; discover unusual associations of items and/or properties of items; discover emerging trends, patterns, or new directions (new leads) to evaluate and/or to follow.
These learning algorithms can be applied prudently at any of the five levels of analytics maturity: from descriptive, to diagnostic, predictive, prescriptive, or cognitive.
So, given this framework for an autonomous enterprise, we are now ready to introduce the five rules of safe driving in the self-driving enterprise. These rules are derived intentionally and explicitly from a tested and validated set of safe driving rules that are taught to student drivers and to professional drivers in driving schools: they are the five rules in the Smith System of Safe Driving. I learned these rules in my high school driving class a long time ago, and I still remember to apply them when I drive now, these many years later.
Each of the five rules in the Smith System of Safe Driving has parallel applications in autonomous systems (the self-driving enterprise) and has characteristics that map (more or less) to some of the five levels of analytics maturity. Here they are:
- “Aim high in steering” = View the path ahead as a whole, from a longer perspective, and not just a few steps ahead. Look to where you want to go, not only to where you are or have been (the Gretzky Principle: “Skate to where the puck is going to be, not to where it has been”) = Diagnostic, Predictive, Cognitive.
- “Get the big picture” = Stay aware of your surroundings (context) at all times. Note how others are behaving. Recognize that new situations will present new opportunities. Be contextual (the Yogi Berra Principle: “You can see a lot just by looking”) = Descriptive, Prescriptive, Cognitive
- “Keep your eyes moving” = Keep alert to the evolving conditions ahead. Dispose of eye-holding distractions quickly (i.e., interesting but unimportant items that present themselves in the environment) = Diagnostic (which contributes to Event Alert generation, based on business rules and machine learning algorithms).
- “Leave yourself an out” = Anticipate what choices others will take. Be ready for the unexpected. Leave space for a new, quick, safe decision. Optimize your reaction to events = Prescriptive.
- “Make sure they see you” = Reduce risk by removing assumptions about others’ expected behaviors. Connect, communicate, collaborate, share, trust, and show empathy with others. Make your NBA the right action = Prescriptive, Cognitive.
So, that’s it: our five rules for safe driving in the self-driving enterprise! Autonomous systems are becoming more common, and maybe even the norm in some operational environments (not to mention our future cities and roadways). Consequently, some common sense, smart analytics, risk-mitigating behaviors, and rules for safe driving can go a long way to make these systems adaptive, efficient, effective, and productive for all, including your enterprise.
To facilitate the design, development, and deployment of your own autonomous (predictive, prescriptive, and cognitive) systems, you should check out MapR’s data platforms, tools, and services that are available for numerous industries, applications, and use cases, including healthcare, energy, telecommunications, finance, IoT, and more. Also, explore the rich collection of resources in the MapR Converge Community to help you navigate the products and solutions that can help you achieve success in all of your data-driven activities.
This blog post was published August 17, 2017.