Artificial Intelligence is booming. It is not that hard to believe, how just two decades ago Deep Blue a computer beat a chess grandmaster Gary Kasparov. AI is enhancing itself and is becoming better at numerous “human” jobs — diagnosing disease, translating languages, providing customer service — and it’s improving fast. This is raising reasonable fears amongst workers and upcoming students. The fear of automation replacing workers throughout the economy. According to The Guardian, 76% of Americans fear that their job will be lost to AI.
While it’s speculated AI will take over 1.8 million human jobs by the year 2020, however, the technology is also expected to create a 2.3 million new kinds of jobs, many of which will involve the collaboration between humans and AI. Research by the Harvard Business Review shows artificial intelligence is capable of performing several tasks better than humans in specific occupations, however, it is not capable of performing all tasks required for the job better than humans. Humans and Artificial Intelligence would form a more powerful alliance than working individually. A famous Russian proverb states “If you can’t beat them, join them.”
AI is omnipotent. Right now the machines are at its weakest stage, the narrow or weak AI still smarter than most of us. There are a total of three stages, narrow intelligence, AI general intelligence and the most powerful AI super-intelligence. There is minimal chance of us ever being superior to AI. They are accurate, fast and most importantly efficient. Automation does not get tired, it can do the same job millions of times. However, us humans have a few traits that machines can probably never have. Leadership, teamwork and the most important creativity.
Collaboration between humans and artificial intelligence is already happening. The Harvard Business Review completed research involving 1,500 businesses, coming to the conclusion that companies benefit the most when humans and machines are working together. Another research by BMW discovered that when their robot and human teams worked together, they were approximately 85% more productive compared to when they had robots working on one side of the factory, and their employees working on their old automated assembly line on the other side of the factory. James Wilson, an expert in the field of Artificial Intelligence describes, “Together, they really started to see those big productivity improvements that just weren’t possible through the old way of thinking about automation.”
A natural instinct of a person (making a joke, for example) can be tricky for machines, and what’s straightforward for machines (analyzing gigabytes of data) remains virtually impossible for humans. Business requires both kinds of capabilities. Moreover, business needs to learn how to take full advantage of this collaboration. Organizations must understand humans can most effectively augment machines, how machines can enhance what humans do best, and how to redesign business processes to support the partnership.
For the machines and humans to work together smoothly, we need to perform three crucial roles. We must train machines to perform certain tasks; explain the outcomes of those tasks, to others especially when the outcome is controversial; and sustain the responsible use of machines (by, for example, preventing robots from harming humans). This is yet another example of AI creating new jobs.
I am going to briefly explain these crucial rules, however, in my next blog, I’ll talk about it in more detail. To begin with, we have training. Machines are smart, but not smart enough to learn on their own. Just like teachers teach students algebra, Machine-learning algorithms must be taught how to perform the work they’re designed to do. Also, Artificial Intelligence must be trained to interact with humans. To do these two tasks, huge sets of training data have been accumulated. Some of the leading tech companies and other research organizations have already hired mature training staff and expertise. Let me give you a really common example. We all are familiar with Amazon’s Alexa right? How are they so smart? You ask it a question, outcomes an answer in a confident voice. Actually, it took countless hours of training to develop such a personality. They are already prepared for any questions as they have been pre-fed the answers. Without trainers, machines would not be the same as we know them today.
Another role created by AI are the explainers. Similar to trainers, explainers are extremely crucial. To understand this role, imagine this you are late for a meeting, you tell the driver to hurry, however, it is not a human driver driving the vehicle instead it is pre-programmed machine driving the car. There is a car accident a few hundred meters away, the machine does not know about it, so it continues on the projected route, and then bang! You are not hurt as the brakes were applied much more quickly than an average human being, however, your car is damaged and moreover you have missed the meeting. The robot does not say sorry, as it does not know what happened, but you are in a need of an explanation of why the accident happened and why the car does not take an alternative route. Here comes the need of an explainer. The worker calmly explains how the robot was not programmed to deal with road accidents, hence the reason for the crash. You see, artificial intelligence is fast and efficient in reaching conclusions. However, not everyone understands them.
In addition to explainers and trainers, the companies need one more important aspect, sustainers. Sustainers are employees that are constantly working to ensure that AI systems are working safely, properly functioning, and responsibly. Sustainers also referred to as safety engineers are finding new ways to prevent harm by automation. This is a really important job as without it the machines would not recognize the humans as an ally, instead of as a threat to them. Explainers and sustainers work together in the scenario when AI does cause harm, such as the example of fatal self-driving car accidents.
These were the three essential roles, we need to perform. This will not only create more jobs for us, but it will also be highly beneficial for the company itself.
Alright so AI can benefit us as a whole, but can it do the same to individuals? Sure, AI can boost our analytic and decision-making abilities and heighten creativity. You might be wondering, AI can’t be creative itself, how is it going to help us? “It’s easy for AI to come up with something novel just randomly. But it’s very hard to come up with something that is novel and unexpected and useful.” — John Smith, Manager of Multimedia and Vision at IBM Research.
Machines are actually assisting us in many ways. To start off, Artificial intelligence can boost our analytic and decision-making abilities by providing the right information at the right time. Also, it can heighten our creativity.
The latest study concluded by Adobe, which was based on interviews with creative professionals working in design, illustration and imaging, motion graphics and UX/UI design. “Creatives don’t fear being replaced by robots,” the report’s authors find. “Most do not fear that their jobs will be replaced by AI, although they do recognize that the ways they work and how they spend their time will change.”
AI does not have a human brain. It can most certainly solve a maths problem, that too really quickly. However, we can do something even better, we can find new ways to solve the problem. According to the book the Fourth Age by Bryon Reese, the developers of such automation still have a long way to go. If they solve one problem, for example, the problem of sight in automation, they meet with another problem hearing which is quite important if AI takes over the job call centre.
More importantly, machines are freeing our workload. How? You may ask. By interacting. Our collaboration with automation enables organizations to interact with employees and customers in more effective ways. A well-known example of this scenario is Siri, Apple’s smart assistant. Siri can facilitate communications between people or on behalf of people, such as by transcribing a meeting and distributing a voice-searchable version to those who couldn’t attend. Such applications are inherently scalable — a single chatbot, for instance, can provide routine customer service to large numbers of people simultaneously, wherever they may be.
Another example that has been fast-growing is the use of a virtual assistant named Aida. Used by a major Swedish Bank, it interacts with millions of people. Able to handle natural-language conversations, Aida has access to vast stores of data and can answer many frequently asked questions, such as how to open an account or make cross-border payments. She can also ask callers follow-up questions to solve their problems, and she’s able to analyze a caller’s tone of voice (frustrated versus appreciative, for instance) and use that information to provide better service later. Whenever the system can’t resolve an issue — which happens in about 30% of cases — it turns the caller over to a human customer service representative and then monitors that interaction to learn how to resolve similar problems in the future. With Aida handling basic requests, human reps can concentrate on addressing more-complex issues, especially those from unhappy callers who might require extra hand-holding.
To conclude today’s blog, it is certain that jobs will be lost to AI, but like said before AI is like another industrial revolution. I am sure we all have read about the industrial revolution if not this period of time was the transition to new manufacturing processes in Europe and the United States. Back then people were also not sure if their jobs were safe or not. Today, we have a similar situation where we are not certain but one thing is for sure if the industrial revolution can bring tons of new jobs, so can artificial intelligence. Automation still has a long journey ahead of itself, completing that journey AI will learn how to think and feel. This is still a dream for scientists.