Top Menu

Archive | AI

AI and humans must work together to turn dreams into reality

In May 1997, a world record was set for the first machine to defeat a world champion when Garry Kasparov, Russian chess grandmaster and former world chess champion, lost to Deep Blue in a rematch held in New York City.

While contemplating his loss to artificial intelligence, Kasparov postulated that humans and machines could potentially form a perfect synergetic relationship. And his theory came to fruition when, in 2005, a bunch of amateur chess players controlling three computers beat a team of grandmasters.

Humans possess intuition, strategy and experience. Machines are obedient and great at calculations, tactics and memorizing. We set the goals and formulate hypotheses. We determine the criteria and machines will perform the routine work. We then evaluate the results and insights. The consequence of this symbiotic relationship between humans and machines is work that is far superior to what humans alone can perform.

Newsweek cover showing Garry Kasparov before he challenges IBM’s Deep Blue computer to a chess rematch (Image Source:

Machines are good in computing. Machines are great in following instructions we have programmed into them. Humans, however, have purpose. We possess passion. We have dreams of a better world.

Deep Blue was victorious, but was it intelligent? No, no it wasn’t, at least not in the way Alan Turing and other founders of computer science had hoped. It turned out that chess could be crunched by brute force, once hardware got fast enough and algorithms got smart enough. Although by the definition of the output, grandmaster-level chess, Deep Blue was intelligent. But even at the incredible speed, 200 million positions per second, Deep Blue’s method provided little of the dreamed-of insight into the mysteries of human intelligence.

Kasparov versus Deep Blue, 1997 (Image source:

Instead of worrying about AI taking over our current jobs and rendering us all out of work, we should be thinking about what AI and machines still cannot achieve. For we will need their help to turn our far-reaching dreams into reality.

Rather than dreading artificial intelligence, we should fear our own complacency and limited ambitions. We need to overcome our own easily-contented attitude and seek out man-machine solutions to ever more challenging problems currently plaguing humanity.

Machines have calculations. We have understanding. Machines have instructions. We have purpose. Machines have objectivity. We have passion. We should not worry about what our machines can do today. Instead, we should worry about what they still cannot do today, because we will need the help of the new, intelligent machines to turn our grandest dreams into reality. And if we fail, if we fail, it’s not because our machines are too intelligent, or not intelligent enough. If we fail, it’s because we grew complacent and limited our ambitions. Our humanity is not defined by any skill, like swinging a hammer or even playing chess. There’s one thing only a human can do. That’s dream. So let us dream big.

Artificial Intelligence cannot touch you if you are unpredictable

Jobs in highly-predictable environments can be automated easily!

Technologies such as artificial intelligence will automate 50% of the current activities that our workers carry out today. This is especially true if they work in highly-predictable environments like accommodation and food services, manufacturing, transportation and warehousing, agriculture, retail and mining.

Jobs that are least susceptible to automation would be work that requires expertise in decision making and planning, creative tasks, people management and development, stakeholder engagement, and performing physical activities and operating machinery in unpredictable surroundings.

The advent of AI and automation doesn’t mean people are not needed in the workforce anymore. On the contrary, they will create new jobs that most of us cannot even envision yet. Many workers will be upskilled to work alongside smart machines.

The broad usage of machine learning across industries

Machine learning is finding its way into practical applications across many industries.

  • Agriculture: crop personalization depending on weather and environmental conditions
  • Finance: Fraud identification, personalized financial products
  • Pharmaceuticals: personalized health outcome prediction, customized remedies
  • Media: Personalized advertising, new consumer trend discovery

Source: What’s now and next in analytics, AI and automation