Developments and Benefits
It's impossible to predict exactly what ML/AI will look like over a decade from now, but it is possible to form a rough idea of what shape key developments will take. Here are a few...
Information Access
ML/AI is already used extensively for information search. Steady progress is being made in understanding the contextual nuances of search queries. This trend will continue, and by 2030 we should be able to find just the right information for almost any search with ever increasing precision.
Autonomous Vehicles
Autonomous vehicle progress has been steady and has achieved significant milestones. The technology has been demonstrated to work successfully at a fundamental level. There remain a number of barriers to widespread adoption. However, the benefits and incentives to overcome these barriers are significant. By 2030 we should see a significant number of autonomous vehicles on the road.
Healthcare
Many countries, including the United States, have aging populations. This will put an significant stress on healthcare over the coming decade. ML/AI holds the possibility of filling the gap between available resources and healthcare demand in 2030. According to one set of experts, we'll trust AI more than doctors to diagnose disease. This will free doctors to spends more time on the things technology can't yet do.
Robotics and Automation
McKinsey predicts that by 2030, "60 percent of occupations have at least 30 percent of constituent work activities that could be automated." New jobs will also be created, but robotics and automation will continue to create significant shifts is how work is performed.
Education
ML/AI is disrupting the traditional model for successful education. A top futurist predicts the largest internet company of 2030 will be an online school and students will learn from robot teachers over the internet. ML/AI in education has the potential to create individually customized experiences that will enhance and speed the learning process.
Retail Shopping
Retail shopping is already being dramatically effected by ML/AI. The e-commerce share of total retail sales in the U.S. is rising rapidly. Customized on-line shopping experiences are growing in sophistication. Retail in 2030 may look very different than it does today and include interactive dressing room mirrors and a more on-demand at-home shopping experience.
Professional Services
ML/AI can digest information at a speed and scope that already exceeds human capabilities. Professional service providers in 2030 will use ML/AI to provide analyzed and summarized information needed for decision making. This will dramatically speed services delivery and lower services costs.
Financial Services
Financial services rely on collecting, storing and analyzing vast amounts of data. ML/AI is already replacing workers who perform many of the tasks related to these activities. One estimate is that up to 230,000 employees in capital markets will be replaced by ML/AI as we approach 2030. This will lower costs and improve delivery of services, but will also require significant staffing shifts in the industry.
Agriculture
Advances in robotics and sensing technologies are radically modifying agricultural practices. New ML/AI approaches include: automated harvesting, pest control, animal tracking, and soil conservation. By 2030, we should see significant increases in crop yields at lowered costs.
Challenges
ML/AI does pose challenges as it progresses spreads further into our lives and businesses. Here are a couple of examples...
Changing Jobs and Learning New Skills
As mentioned above, McKinsey predicts that by 2030, "60 percent of occupations have at least 30 percent of constituent work activities that could be automated." Overall, this would mean that about 20% of all jobs would be automated. New jobs will be created, with many of those requiring higher levels of education, training or skill.
New Legal Frameworks
As more work is performed by ML/AI, legal questions of responsibility and liability will arise. One example is autonomous car liability. Autonomous vehicles are expected to lower deaths caused by accidents. The Atlantic reports that automated cars could save up to 30,000 lives per year in the United States. But how responsibility for the deaths that do occur remains an open question.
An Example Individual Scenario
It can be difficult to imagine a total picture of what ML/AI will mean for our lives in 2030. One way to grasp this is to imagine use cases that demonstrate their impact. Here's one example...
John is sitting in his study at home when he receives a notification on his smartphone. It tells him that the biosensor imbedded in his arm has detected a slight irregularity in his heartbeat. John hasn't noticed any physical pain or abnormality, but he clicks the notice and sees the display of a heart rhythm pattern with annotations showing him where there might be an issue. It assures him that it's nothing immediately life threatening, but that he should consult a doctor. He's shown the location of the nearest clinic and asked if he'd like an appointment be made along with arrangements for a ride. He clicks yes and immediately sees that his ride is on the way and will arrive in 5 minutes. John grabs his jacket and goes outside to wait for the car. In a few minutes, an autonomous vehicle pulls up, he gets in and a friendly voice asks him if he's John and going to the AbleWay clinic. John says yes, sits back and turns on his phone to read more information he's been sent about his symptoms. He arrives shortly at the clinic, is welcomed by name and escorted immediately into an examination room. A couple of minutes later, Dr. Able enters carrying a tablet which he uses to show John a real-time display of his heart rhythm. Dr. Able explains what could be the cause and that the condition is something they should watch carefully to see if it continues. He prescribes a medication that should help correct the arrhythmia and tells John that the medication will be delivered to his home by the end of the day. John shakes Dr. Able's hand and walks out to the reception desk where he's told that the clinic has his insurance information and the car to take him home is waiting outside. John enters the car and starts his trip home feeling relieved that he knows more about his condition and taking steps to deal with it.
