Recent studies on artificial intelligence-led unemployment certainly make uncomfortable reading.
A frequently cited report points out that some 47% of jobs in the US will be automated in the near future. Another study suggests that 45% of the daily tasks currently done by humans could be automated if current trends continue. It looks like human beings will become the horses of the past. And horses were never given the chance to vote on their future careers.
What AI Actually Is
In spite of the doomsday views, the reality is that the current capability of artificial intelligence (AI) is actually rather limited. It is important to understand that there isn’t really that much intelligence in AI. Intelligence refers to one’s capacity for logic, understanding, self-awareness, learning, emotional knowledge, planning, creativity and problem-solving. Yet, at the moment, machines can do very few of these things.
The true ability of AI lies in the way that it processes a huge amount of data sources and then, based on it, makes the right guesses in terms of decision output. Machines “learn” over time by repeating the same task, adjusting their information input and processing capabilities to become better at "guessing" the output.
It is true that machines these days can read, see images and hear natural speech with unprecedented levels of accuracy. For instance, at Nexus Frontier Tech, our Intelligent Scanner can already achieve 99.2% accuracy in reading typed or handwritten texts, regardless of whether they are presented in the digital or physical form. In this way, machines look very capable and smart. Yet, under the hood, the mechanism is not very different from how our brain works (hence the fact that AI development right now is mostly based on artificial neural networks). Our brains work by taking information from various sources, determine how important each piece is and mix them together to form a decision. What machines are doing is just taking in new data, assigning a weighting to the data in terms of importance, and making statistical guesses. They don’t “know” what the input and output really mean.
What AI Can Actually Do
Considering AI “learns” by going through the same routines again and again, what they can actually do is often limited to individual tasks. In other words, they are good at creating pockets of excellence but much less able to engage in full business transformation. AI is, at the moment, nothing much more than an app that supports existing business processes. In this sense, in the current business environment at least, it is not as much about AI as IA – ‘intelligent assets’ or ‘assistance’. It is just helping us approach business in smarter ways – in particular, dealing with standardised work. Given that AI can only take over a single, narrow aspect of work, ultimately, what it can automate away is often only portions of a job.
It is also important to remember that the success of using machine learning lies squarely in the success of how the human activities surrounding the new technology are organised. Coming up with the processes and workflows, together with putting the right people in charge (someone who can manage both machines and robots), is therefore key to effective AI adoption.
In this respect, it is really unlikely that a vast number of people will be losing their jobs anytime soon. Machines cannot create, market, deliver, feed, clean or fix itself. AI, like other machines, are just tools. And tools need to be used to create value. By people. Lately, there is a Chinese T-shirt manufacturer that signed a memorandum of understanding with the State of Arkansas to hire 400 people by paying them $14 an hour in the company’s new factory. What is their job? They are there to make sure the machines and robots do their jobs properly. Machines, therefore, cannot do much without human beings. And human beings cannot really do much without machines. So, instead of arguing whether machines or AI will eliminate our jobs, it would be much better for us to think about how to be best friends instead of competitors.
This article was originally published here.
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