The Economic Benefits of Artificial Intelligence
Will AI improve our lives beyond comparison, or will it steal our jobs and destroy humanity?
In this article, Toptal AI expert Necati Demir, PhD, moves past the evil artificial intelligence of science fiction to discuss the current algorithm-based prediction AI, the economics of AI, and the economic benefits of artificial intelligence.
Will AI improve our lives beyond comparison, or will it steal our jobs and destroy humanity?
In this article, Toptal AI expert Necati Demir, PhD, moves past the evil artificial intelligence of science fiction to discuss the current algorithm-based prediction AI, the economics of AI, and the economic benefits of artificial intelligence.
Necati holds a PhD in machine learning and has 14 years of experience in software development.
We see news about artificial intelligence (AI) everywhere; sometimes, we see the excitement around the benefits of AI, and sometimes we see articles that talk about how AI will replace or destroy our jobs. We also see articles talking about how AI will destroy humanity.
In this article, I will not discuss artificial general intelligence or an evil AI that wants to destroy humanity. Instead, I will focus on current AI, which is mostly based on algorithms that can make predictions, and discuss the economics of AI and how its adoption may affect business. The content of this article is highly influenced by two books I recommend: Prediction Machines: The Simple Economics of Artificial Intelligence and Human + Machine: Reimagining Work in the Age of AI.
This article is divided into three main parts:
- In The Evolution of Technology, I will briefly discuss the past and its similarities to the Age of AI.
- In AI Economics: Strategy and Business Models, I will discuss how having a higher accuracy of prediction may affect strategies and business models.
- In Human and AI Interaction, I will discuss how humans can complement AI and how AI can complement human efforts.
The Evolution of Technology
I would like to discuss the similarities between some historical events that are analogous to how we think of AI today and give examples of how the widespread usage of a particular technology changed our mindset. I’d like to explore how we got from basic arithmetic to specialized artificial intelligence development companies.
The Age of Electronic Computers
What computers do best is arithmetic. Before the computers we know now, the term “computer” was used for people who did literal computing, which we now call a “human computer.”
With technological advances, computing became cheaper and faster, and we started thinking of everything in terms of arithmetic. Photography is a good example—historically, modifying or applying visual effects to photos was a chemical reaction. Now, however, we use algorithms accessible to everyone through software suites to mathematically apply effects to photographs.
This is an excellent example of how we think when the cost of a good or service drops; we start thinking about how to solve our current problems using this new technology. It is the same for AI. The advantages of artificial intelligence are accessible to an increasing number of industries and individuals as the cost of using AI goes down.
The Internet Age
When internet use became commonplace it caused huge cost-reduction changes in many facets of various industries. The cost of distributing goods and services became cheaper and this triggered the birth of the e-commerce industry. Companies changed their strategies and either survived—or died. Once the cost of a good or service drops, we start using it more often; we can also see this pattern online. The price drop changes our mindset, and we move whole industries online. Think about search engines: We no longer use encyclopedias to search for information, but instead use Google or other search engines almost exclusively.
The Age of AI
The cost of AI is getting cheaper in terms of both computation power and tools. Each new tool or library is helping machine learning developers spend less time on prediction problems. The increased usage of GPU computing also illustrates the cost reduction of AI.
Cost reduction of AI is changing our way of thinking, which means we now think of various problems as prediction problems. Sales forecasts for a company’s next quarter are an obvious prediction problem, but developing an autonomous vehicle was not a prediction problem 15 years ago. Then, we were already using autonomous vehicles in controlled environments like factories, where the vehicle could be programmed by using if-else
programming conditions. Changing the mindset and looking at this as a prediction problem helped engineers to develop autonomous cars for consumer use.
Basically, an engineer taught an AI what a human would do in various conditions, and this enabled the development of onboard software that allows drivers to use autonomous cars for thousands (instead of hundreds) of miles. AI learned what a human would do and started predicting what it should do—a classic case of thinking about a problem in terms of prediction.
AI Economics: Strategy and Business Models
Here’s a key question: Will AI affect companies’ strategies and business models? If you only think of AI as a decision-making prediction tool, it may not be clear how it will affect pure strategy, but if you start to think of AI as a prediction tool that can forecast with high accuracy, that outlook may change the strategies themselves. There is an excellent illustration of this in Prediction Machines: The Simple Economics of Artificial Intelligence.
When we shop and purchase goods from Amazon, it ships the packages to our office or home— the shopping-then-shipping method. Amazon also has a recommendation engine that recommends items while you navigate the site. We don’t buy all the recommended items, but Amazon at least recommends items we might be interested in. Let’s assume that Amazon starts predicting what you will buy with high accuracy. If you start buying 80% of the recommended items, Amazon might decide to send the items before you even buy them—let’s call this shipping-then-shopping. This is an obvious change in business strategy because, once the items arrive at your home, you may send 20% of the items back, and current Amazon price modeling is not based on this assumption. Maybe Amazon will decide to send a truck to your city once a week to collect the returned items, and this will completely change how Amazon charges your credit card, how it packages the items, and how it handles the returned items. All this strategy change is the benefit of artificial intelligence, which has higher prediction accuracy.
I believe we can pursue more thought experiments like this by just thinking about what would happen if AI could predict with higher accuracy and make things cheaper.
Human and AI Interaction
How will human and AI interaction evolve in the future? Will we compete, or will we work together? I will focus on those questions by looking at Human + Machine: Reimagining Work in the Age of AI. According to the authors, there will be scenarios in which humans complement AI, and ones in which AI will complement humans.
Humans can complement AI in three areas: training, explaining, and sustaining.
Training AI Models
To make predictions, AI needs data to learn. This is called the training phase. In the future, we may have training agents focused exclusively on training AI based on the requirements of a particular business. If it’s a factory, a training agent could be responsible for training a robot; if it’s an e-commerce business, a training agent could be responsible for aggregating historical data.
Explaining AI-generated Results
We need to understand how and why AI provides a specific answer to a specific problem.
Generally, we face a trade-off between the explainability and the accuracy of AI. Black box AI methods have more accuracy than methods that can be easily explained. Even though tools have been developed to explain why a black box AI made a specific prediction, we may need a job role that can understand and explain the outcomes of AI.
Sustaining and Overseeing Deployed AI Systems
We need to be sure the AI is functioning as expected. In 2015, a robot in a Volkswagen factory grabbed a worker and fatally crushed him. We may need roles whose responsibility is to ensure that AI systems are working as expected.
AI Complementing Humans
The potential of AI gives humans “superpowers” because AI makes predictions faster and more precisely than humans ever could, and we can harness that knowledge. These superpowers add value to a given situation or action—one of the key advantages of artificial intelligence.
Amplifying Human Capabilities
AI tools help humans increase their capabilities. In Human + Machine: Reimagining Work in the Age of AI, the authors use the example of Autodesk’s Dreamcatcher software, which uses genetic algorithms to iterate through possible designs.
A designer wants to design a chair that is light, cheap, and strong with the help of this tool. AI creates designs based on the given criteria, and gives the results to the designer. The designer then chooses one of the designs and uses their creativity to add the finishing touches.
This is similar to the assistance that computers provided to us in the computer age—just at a new and exciting level of capacity in terms of what kinds of things AI can assist with.
Interacting With AI Products and Services
AI can act as a helpful assistant by interacting with people. Amazon’s Alexa, Google Home, and Apple’s Siri are prominent examples of this kind of interactive AI agent. As those agents are improved with each iteration, we started using them more often, and they become a part of our lives, like an indoor version of what we are doing with our smartphones outdoors. Those agents act as our private assistants, and they complement our abilities.
Augmenting Humans on the Factory Floor and Beyond
Examples of AI-fueled physical augmentation can be found in factories. Although many factories are currently operated by robots, they are primarily rule-based systems and put in a cage—just in case—for safety. Robots can help humans as co-workers and can be designed not to harm people while freely moving and working in a factory.
The Benefits of AI Outweigh the Drawbacks
Although some claim that human workers will be discarded in the future due to the efficiency of automation, Markus Schӓfer, Chief Technology Officer of Development & Procurement at Mercedes-Benz Group, says, ”We’re moving away from trying to maximize automation, with people taking a bigger part in industrial processes again.”
New technologies do bring monumental shifts in how we do things, but the invention of the plow did not eliminate the need for farmworkers, nor did the invention of the computer eliminate the need for mathematicians. As with all technological revolutions, the advent of AI will be utilized to help humankind reach a new paradigm, not to replace it entirely.
Further Reading on the Toptal Blog:
- A Deep Dive Into Reinforcement Learning
- The 10 Most Common Python Code Mistakes
- Adversarial Machine Learning: How to Attack and Defend ML Models
- Exploring Supervised Machine Learning Algorithms
- Machine Learning Number Recognition: From Zero to Application
- AI in QA: A Novel Framework for Delivering Quality Software Quickly
- Ask an AI Engineer: Trending Questions About Artificial Intelligence
- 5 Pillars of Responsible Generative AI: A Code of Ethics for the Future
Understanding the basics
What are the applications of artificial intelligence?
The applications of artificial intelligence range from autonomous cars to translation, from chatbots to image recognition. Digital assistants like Siri and Alexa are typical examples of AI applications, and, with recent increased efficiencies in AI, we should see more AI applications in the future.
How can AI improve businesses?
You can use AI in two different areas in your business: You can build your business on AI, which means your core value proposition will be a technology which is based on AI, or you can leverage AI methods to improve business and increase sales volume.
Where was artificial intelligence invented?
Artificial intelligence is a field of computer science that studies machines (algorithms) that work and react like humans. The term was coined by computer scientist John McCarthy in 1955. AI has subfields like machine learning, natural language processing, and computer vision.
What are predictive algorithms?
Predictive algorithms predict an outcome based on given inputs. They operate in either a learning or prediction phase. In the learning phase, historical data of input-output pairs is passed to the algorithm and it maps a relationship between the two. In the prediction phase, only inputs are given and the algorithm predicts an output.
Summit, NJ, United States
Member since November 17, 2015
About the author
Necati holds a PhD in machine learning and has 14 years of experience in software development.