Adam Smith’s idea of the division of labor talks about allocating roles based on relative strengths. It is primarily targeted towards the division of physical labor. Computers and AI making predictions enable a cognitive division of labor between humans and machines based on their respective cognitive strengths. While machines are good at sifting through large volumes of data quickly (prediction), humans are better at interpreting complex information (judgement). Prediction and judgement are complements, as the use of AI prediction increases, the value of human judgement rises.
It is important to note that current AI techniques don’t actually bring intelligence but an important component of it — Prediction. Prediction is the process of filling missing information from previous data. For eg. ‘Alexa’ or ‘Google’ doesn’t know the capital of India, but is able to predict that when someone asks a question like this, they might be looking for New Delhi.
📝 Moravec’s paradox
Easy things for a five-year-old are the hard things for a machine, and vice versa. What’s easy for us (folding a towel, for instance) can be extremely difficult for machines. And what’s easy for machines (spotting hidden patterns in huge datasets, for instance) can be extremely difficult for us to do.
What machines do better
- Machines are much better at statistical reasoning and thereby predictions
- Machines can churn through huge volumes of data
- Machines are fast at churning this data
- Performing routine, repetitive and redundant tasks
- They can learn new skills quickly and in a distributed way
- They can operate non-stop
What humans do better
- Resolving ambiguous information
- Interpreting unusual and complex information
- Judgment in determining the payoff or utility of a particular outcome
- Making moral and ethical choices
📝 Distributed learning
Multiple machines can share information with each other instantaneously. This means that when one machine learns something new, all of them learn it. Machines can also spread out the learnings into chunks of information. 100 robots learning different parts of a problem in parallel for 1 hour is equivalent to each learning 100 hours worth of information in the same amount of time.
Machines helping humans
Machines are great at sifting through large amounts of data. This makes them great at detecting anomalies and generating predictions.
1. Detecting anomalies
Averaging lots of data points is one of the most powerful ideas in data science. To decide whether something is an anomaly you need to know what is expected on average and how far is the measured data point from this average a.k.a variability. In AI, this means scanning a stream of data points often in realtime and identifying those that don’t match the pattern. Successful detection can save money in case of credit card fraud detection and even lives in the case of detecting diseases.
2. Generating predictions
Most applications of AI do some sort of predictions based on previous data. Better predictions can enable new actions. Recommending what movies to watch, taking the next turn when driving, or suggesting the appropriate phrase when composing an email are all examples of predictions that AI systems make.
Humans helping machines
People are generally better than machines at making moral and ethical choices as well as analysing and judging complex problems. While an AI can predict better, a human can make better judgements.
Much like a child learning a new skill, AI systems need to be trained and nurtured. This training is often done by humans. In the case of self-driving cars, the AI is shown examples of humans driving decently to figure out how to drive. The person’s job is to curate these examples and correct the AI in case it makes mistakes.
2. Making judgements
We use judgement to determine a payoff, utility, or reward from an outcome. AI predictions reduce uncertainty while judgement assigns value. Judgement is needed to make a decision from a prediction. However in case of predictions that lead to an obvious course of action, AI can make the final decision.
3. Making ethical choices
Humans are extremely valuable when making ethical and moral choices. Sometimes the most profitable course of action might not be the most ethical. Predictions that AI makes could lead to discrimination. Humans can help in evaluating the non-quantifiable impact of AI systems and making policies accordingly.
Cobots: Human-machine symbiosis
Often humans and machines are seen as adversaries fighting for each other’s jobs. This view however neglects the powerful opportunities in collaboration between the two sides. Imagine a flexible, nimbler robot that works alongside humans to help perform generic, repetitive tasks. Or an algorithm that produces a variety of design options based on the designer’s preferences and goals. The designer can simply select the most optimum option and start using it as a reference or straight up modify it. This idea is sometimes also referred to as ‘Organic AI,’ where machines are flexible and easily partner with humans to perform tasks. Such teams can easily adapt to new data and novel market conditions. The machine can become an extension of the human mind and body.
For a long time, dedicated pieces of machinery have performed specific tasks, often away from humans. With the rise in computing, improvements in software and hardware, people started using machines as tools for work and pleasure. However, these tools weren’t very smart and humans had to operate them largely through command and control type instructions. Press the button to start, press again to stop, pull the lever to pick up…
Now as our tools become more interconnected and intelligent, machines and humans start looking like symbiotic partners in the system. This symbiotic relationship can enable new business models, create jobs that never existed, and impact existing jobs.