Human experience is too low a bar for AI

Human’s brain is a pretty impressive organ – about 100 billions neurons and 150 trillions synapses, make it an amazing machine that can think, create art, discover science.. they put a man in the moon (if you believed).

But then, it has its limits. Just try to multiply two 4 digits numbers, and it will take you ‘ages’ compared to simple calculator. If you do the same with 10 digits numbers, you’re most likely to give up quickly, saying – I can do that, but why bother; a computer can do it much faster and more accurate.

Artificial Intelligence is changing the rules

The last statement is now being reinforced with the recent research of AI and deep learning. Simply put –

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If your job is to take a decision between a finite number of options, a machine is more likely to do it better than you, sooner than you’d expect.

This rule applies to many of the tasks / occupations we have today –

  • Cab drivers (left, right, gass, break)
  • Brokers (hold, buy, sell)
  • Campaign managers (audience, creative, budget)
  • Medical Doctors (diagnosis, treatment, prognosis)

What?? Medical doctors? they have tons of experience…

Experience can be trained

Think of the training experience using the following example – given a set of symptoms, clinics and background, the MD’s job is to determine the diagnosis, treatment and prognosis. During their many years of study, young doctors are training their brain to be ‘wired’ correctly in order to find the right match between the two vectors – the input and the output. They are also learning what is the ‘penalty’ of wrong treatment and how to avoid it.

The more experience they get => the more incidents they see => the more accurate their conclusions are.

Now, what if we apply the same logic on machines?

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Artificial Neural Network

ANN is nothing new, since the late 40s, researches have been intrigued by the human’s brain and how to artificially mimic its behaviour. While research had been halted for few decades, latest interests in deep learning and the increase in parallel processing capabilities brought this discipline back to the front light.

Quite similar to the above example, ANNs are made out of 3 layers – input layer, output layer and (multiple, hence deep) hidden layer(s).

Without getting too technical, training the network is a process in which its goal is to find the optimum weights between each neuron in the different layers. The more examples you ‘feed’ the network (both correct and incorrect ones), the better it gets in finding those optimum weights and the more accurate the output eventually is.

Since the number of occurrence a machine can ‘eat’ is practically unlimited, it has an unfair advantage over the human’s brain, and thus its accuracy will eventually be on the upper hand.

The sad thing about ANN is that one cannot easily ‘reverse engineer’ them. It is not always trivial to identify the relationship between the input and output by simply following the thickest path in the network, especially on complex nets. Think of ANN as a black box that can resolve complex problems based on past occurrence, but not necessarily can be used to explain the reasoning behind it.

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AI is the Tractors for the White-Collar Jobs

Progress is being made ridiculously fast, as new startups are rising daily trying to take a bite of many of the different routine jobs in the market.

While Artificial Intelligence will probably reduce jobs, it will also created new ones and will make many processes far more effective and cheaper.

The future jobs will be different, for sure. Decision processing will be made automatically, just like that no one today will bother multiplying two 10 digits numbers. Humans role will not be around taking decisions, but more around setting up the gameplay – what are the possible inputs and what are the possible outputs.


With enough data, machine will always win humans in taking the right decisions, and it’s going to be dramatic to the employment market.

Nevertheless, decision-oriented-professions jobs, are not going anywhere yet, we will still need them for the foreseeing future. But in the era of Artificial Intelligence, regardless of the amount of experience the individuals possess, their jobs will be changed.

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MDs, for example, will then need to ‘help’ the system take the right decisions by feeding in the non-quantitative measurements (e.g. “patient is feeling pain in the chest and it makes him very stressful”) and by setting up the potential outputs – what are the different type of treatments, and diagnoses.

What’s left for us ? Setting up the gameplay is still on the human’s shoulders, and new professions will rise around this need.

It’s going to be fascinating, hold tight!!

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