Part 1: Artificial Intelligence Defined
The most used terminology around it
“What is AI? What is Machine Learning? What is Cognitive Analytics? How do all these terms relate, or differ, from one another?
Stefan van Duin & Naser Bakhshi - March 2017 - Deloitte
1. Artificial Intelligence (AI)
In general terms, AI refers to a broad field of science encompassing not only computer science but also psychology, philosophy, linguistics and other areas. AI is concerned with getting computers to do tasks that would normally require human intelligence. Having said that, there are many points of view on AI and many definitions exist. Below, some definitions highlight its key characteristics.
Some general definitions
- “Artificial intelligence is a computerised system that exhibits behaviour that is commonly thought of as requiring intelligence.” (1)
- “Artificial Intelligence is the science of making machines do things that would require intelligence if done by man.” (2)
The founding father of AI, Alan Turing, defines this discipline as:
- “AI is the science and engineering of making intelligent machines, especially intelligent computer programs.” (3)
In these definitions, the concept of intelligence refers to some kind of ability to plan, reason and learn, sense and build some kind of perception of knowledge and communicate in natural language.
A chess computer could beat a human in playing chess, but that same computer program couldn’t solve a complex math problem. Virtually all current AI is narrow, meaning it can only do what it is designed to do. This means that for every problem, a specific algorithm needs to be designed to solve it. Narrow AIs are mostly much better than humans at the task they were made for: for example look at face recognition, chess computers, calculus, translation. The holy grail of AI is a General AI, a single system that can learn and then solve any problem it is presented. This is exactly what humans do: we can specialise in a specific topic, from abstract maths to psychology and from sports to art. We can become experts at all of them.
An AI system combines and utilises mainly machine learning and other types of data analytics methods to achieve artificial intelligence capabilities.
2. Machine Learning
Machine learning is the process in which a computer distills regularities from training data (4). If for example you want to write an algorithm to identify spam in e-mails, you will have to train the algorithm by exposing it to many examples of e-mails that are manually tagged as being spam or not-spam. The algorithm “learns” to identify patterns, like occurrence of certain words or combinations of words, that determine the chance of an e-mail being spam.
Machine learning can be applied to many different problems and data sets. You can train an algorithm to identify pictures of cats in photo collections, potential fraud cases in insurance claims, to transform handwriting into structured text, speech into text, etcetera. All these examples would require tagged training sets. Depending on the technique used, an algorithm can improve itself by adding a feedback loop that tells it in which cases it made mistakes.
The difference with AI however is that a machine-learning algorithm will never understand what it was trained to do. It may be able to identify spam, but it will not know what spam is or understand why we want it to be identified. And if there is a new sort of spam emerging, it will probably not be able to identify it unless someone (human) re-trains the algorithm.
Machine learning forms the basis of most AI systems. But while a machine-learning system may look intelligent', in our definition of AI, it in fact isn't.
3. Cognitive Analytics
Cognitive Analytics is a subfield of AI that deals with cognitive behaviour we associate with 'thinking' as opposed to perception and motor control. Thinking allows an entity to obtain information from observations, to learn and communicate.
A cognitive system is capable of extracting information from unstructured data by extracting concepts and relationships into a knowledge base. For example, from a text about Barack Obama, the relations from the figure below can be extracted using Natural Language Processing. 80 percent of all company data is unstructured and current Cognitive Analytics systems can search all of it to find the answer to your question.
Figure 3: a knowledge base extracted from text
The Cognitive System improves its performance over time in two major ways. Firstly, through interaction with humans, and using feedback from the conversation partner or by observing two interacting humans. Secondly, from all the data in the knowledge base, new knowledge can be obtained using inference.
Another important aspect of Cognitive Analytics is the ability to use context. Context enables a Cognitive Analytics system to infer meaning from language. For example, a chatbot can take into account the conversation history to infer who is referred to by the word he:
User: Who is Obama’s wife?
AI: Michelle Obama.
User: How old is he?
AI: Barack Obama is 55 years old.
For this simple exercise, the system needs to be aware of names that represent people, relationships between people, gender and the common sense to infer that Obama refers to Barack Obama. All of this contextual information is required to make the right inferences to answer both questions.
Since Cognitive Systems can use contextual information, can understand unstructured data and reason about information, they can communicate with humans as well. This enables the system to respond to a question posed in English, no longer requiring the time-consuming process of converting the question into a format the computer can work with. For example, a call center representative cognitive system can quickly answer a customer’s question about camping gear by using information from product descriptions, customer reviews, sales histories, topical blogs, and travel magazines (5).
Cognitive Systems can communicate through many media, including speech, image, video, sign language, graphs or any combination of these.
AI is an important enabling factor in the design and operationalisation of smart robots and other process-automation applications. In its simplest form, a robot may be a machine that is programmed to perform a simple task by following step-by-step instructions. It could consist of a rule-based engine that explicitly tells the system what to do when a certain condition occurs. A robot in a car factory is programmed like that and hardly considered intelligent.
But robotics exist in a variety of much more intelligent shapes, ranging from unmanned autonomous vehicles (UAV’s), drones, smart vacuum cleaners to intelligent chatbots and smart assistants etc. How advanced robots are is vivid if we look at robots developed by Boston Dynamics (6) and MIT’s Cheetah II (7). Another example is Amelia (8), an intelligent assistant with NLP capabilities. Key aspect of robotics is that it combines hardware (mechanical parts, sensors, screens etc.) with intelligent software and data to perform a task for which certain level of intelligence is required (e.g. orientation, motion, interaction etc.).
5. Smart Machines
The major theme in underlying the term Smart Machines is autonomy. Smart Machines are systems that –to some extend- are able to make decisions by themselves, requiring no human input. Cognitive Analytics systems as well as robots, or any kind of AI, can be called Smart Machines, as long they adhere to this rule. In the case of a robot, autonomy could consists of a capability to plan where it wants to go, what it wants to achieve and how to overcome obstacles. Rather than being human-controlled or simply following instructions, it could achieve higher-level goals like getting groceries, inspecting buildings and so forth. This is enabled by planning methods, self-preservation instincts on top of the skills that a normal robot already requires.
In the case of a Cognitive System, it will pro-actively try to learn new facts, gauge opinions and learn new common sense rules by engaging in active interaction with humans, asking questions and double-checking them with data found online. It will also actively inform decision makers about changes it has observed. For example, if customer opinions on social media suddenly swing. It could even act upon these changes, taking the example case, it could engage with the customers or share positive opinions on the social media outlets of the company.
Since Smart Machines are autonomous and intelligent, they might start communicating with each other. This leads to multi-agent systems that can make trades to improve their utility. The building-inspecting robot could ask a drone to inspect the roof for it, trading this favour for another favour, like transporting goods or simply currency.
A Cognitive System that becomes a Smart Machine can specialise in a specific area, becoming an expert in that area. Now, other Smart Machines can ask it for information in that area, and it will be able to provide more relevant answers more quickly than a general Cognitive System that is not specialised. Information brokers like this improve the overall utility of the whole network of Smart Machines.
The terms Machine Learning, Cognitive Systems, Robotics and smart machines are used often in relationship to AI, or sometimes even as synonyms. AI is a complex field of interest, with many shapes and forms.
(1) Preparing for the Future of Artificial Intelligence, NSTC, 2016
(2) 6. Raphael, B. 1976. The thinking computer. San Francisco, CA: W.H. Freeman
4) Stephen Lucci, 2016, Artificial intelligence in the 21st century : a living introduction