The intelligence of the machine will have a huge impact on business and employment. See how McKinsey sees how artificial intelligence and machine learning will affect your business and what you can do.
For business people, artificial intelligence represents a series of challenges. At the technical level, artificial intelligence and machine learning are moving toward a complex direction, requiring a large amount of data to produce meaningful results. From a business perspective, many business leaders have difficulty figuring out the entry point of artificial intelligence, or even know how to start machine intelligence.
To make matters worse, the propaganda from technology vendors has caused constant noise in the market, and people are not sure what the real possibilities of artificial intelligence are.
To reduce this noise, we have invited many of the world's leading practitioners to share their experiences.
Below is the view of David Bray, head of the McKinsey Global Institute (MGI) and Eisenhower Foundation, who also serves as the chief technology officer of the Federal Communications Commission.
The McKinsey Global Institute has published a series of research reports on artificial intelligence, automation and employment. Below is an analysis of the automation of the work environment.
In the picture below, Chui and his team analyzed the industries that automation will change.
Another picture shows a list of jobs that may be replaced by machines and salary:
The conversation between Michael Chui and David Bray covers the relationship between corporate employees and automation, artificial intelligence, such as investment, planning, and even ethical considerations.
How do companies consider investing in artificial intelligence?
Michael Chiu: More and more companies are beginning to understand the potential of data analysis. Executives are beginning to understand that data and analytics are either the basis of competition or the services and products that customers, people and shareholders need.
Although there are real technical challenges, we have found that the real obstacle is the human side. How to get business-related experience from interesting experiments? Purchase algorithms and data to increase conversion rates in the next product; we can reduce maintenance costs or increase overall machine uptime. We can introduce more people into this field and we can find more suitable people.
Getting the scale value from experience is the place where the enterprise card is necked. How to gain experience, how to obtain data (whether in the form of machine learning or algorithms), how to incorporate model analysis into the actual work and processes of the enterprise, and thus change the way the scale operates? The analogy with military is: how to drive an aircraft carrier? The same is true for cargo ships, and they are hard to turn.
Understanding artificial intelligence, mastering the right talents, and then changing jobs on a large scale are the challenges companies face. There is a huge difference between companies that understand artificial intelligence and those that really want to implement artificial intelligence.
What is the acceptance problem of artificial intelligence and machine learning?
David Bray: The real secret of success is changing the way people do what they do in the organization. You can't just push the technology and not change the business process. I have seen experiments in the field of public services. They only experimented and did not change the scale of business of public services.
It's not just about technology, but about the existing processes, why companies need to do this, and then clearly understand the goals and how they become leaders in this change.
In some respects, artificial intelligence is only an extension of predictive analysis. This is a continuation of big data. It is not a new thing. Technology is always a possibility of change art.
Interestingly, we can use artificial intelligence to reflect our bias. If we accidentally use human data to create artificial intelligence, we know that human beings are biased. We will find artificial intelligence, and machine learning itself is biased.
Which business areas are best for artificial intelligence?
Michael Chiu: We conducted survey visits to 600 industry experts in different fields.
The first area is called the "deep learning" field, which is particularly well-suited for certain types of problems, such as model recognition, usually images.
The other is predictive maintenance. The ability to keep things from breaking; instead of waiting for it to break, then fix it and predict when it will break.
This is not only because of reduced costs, but more importantly, predictive maintenance does not cause the entire assembly line to stop working.
To some extent, this is an application of pattern matching. The sensor detects the type of part that will break and notifies you of predictive maintenance.
We find that in many industries, whether it's generators, buildings, HDC systems or the automotive industry, if you can make predictions before things happen, the value of maintenance will be revealed. This is one of the most powerful areas of machine learning.
Healthcare is another application of predictive maintenance, but it is replaced by the maintenance of human capital assets. We install sensors on the patient. Can we tell them that heart disease is about to happen? Will you get diabetes? Users should take actions that may not be so expensive and less damaging to the body, rather than turning it into an urgent medical action that goes through a very expensive, painful and urgent care.
David Bray: Let artificial intelligence and machine learning help the public, I think this will first appear in the city.
We have heard of the concept of a smart city. You can easily view road or power conditions for better preventive maintenance and then monitor to avoid power outages.
I think artificial intelligence and machine learning will appear in the city at the beginning.
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