What is an AI Factory?

Jan 25, 2021
5 min read

Introduction

Artificial Intelligence (AI) is a broad branch of computer science capable of running systems independently and intelligently. In the current world, AI has become a significant component in many sectors, such as healthcare, manufacturing, finance, etc. Marco Lansiti and Karim Lakhani have written in their book Competing in the Age of AI, no field of human endeavor will remain independent of artificial intelligence.

Artificial Intelligence is defining a new age in business, the AI age. It has offered colossal growth and success to companies like Amazon, Facebook, Google, Alibaba, etc. Start-ups are actively incorporating AI in their business for understanding demands and establishing a new market.

What is an AI Factory?

The combination of people, data, product, process, and platform to drive business values is an AI factory. AI technologies are based on algorithms trained from previous observations to make better outcome predictions. This technology is known as machine learning. It is capable of creating AI factories.

The useful data is collected from sources (internal and external both), and machine learning algorithms are trained to make predictions and give output on tasks. The algorithms can automate tasks such as content recommendation, testing of new hypotheses, etc. The predictions made by machine learning algorithms are beneficial in diagnosing disease and its treatment. These algorithms can work with little or no human intervention.

The AI factory improves the system by adding new features to existing products or by building new products. This enhances company performance, increases growth, and offers a better market.

Lansiti and Lakhani have written in their book Competing in the Age of AI, “The AI factory creates a virtuous cycle between user engagement, data collection, algorithm design, prediction, and improvement.”

Entrepreneurs have been calculating, studying, and making changes for better results. But the process is evolving drastically with AI factories. The fields of computer vision and natural language processing are getting recognition due to AI factories.

As per the authors of the book, Competing in the Age of AI, Ant Financial provides a wide range of financial services to millions of customers with just 9000 employees and an AI factory. Similarly, Bank of America is serving millions of customers with an AI-driven virtual assistant, Erica.

The Infrastructure of an AI Factory

Data is a crucial element for machine learning algorithms. It can be considered as a fuel of AI factories. Collection of useful data and feeding it into frameworks and models is the foremost step. Data acts as a case or example that the algorithms require to solve any problem and make predictions. The performance of machine learning algorithms depends on the quality and quantity of data.

Data act as the input for the algorithms to train systems and give output. The accuracy and performance of algorithms are highly dependent on the quality of the data. Hence, the pre-processing of data is done before feeding it to the machine learning algorithms.

Machine learning dataset resources are responsible for gathering the required data type for the processing. Depending upon the task, data is collected and transformed. Machine learning algorithms work on four types of data: text, numerical, time series, and categorical. Sometimes information is gathered from external sources like the stock market, social media, etc.

In a recent pandemic, machine learning is helping healthcare workers in diagnosing COVID-19 patients. AI systems have also played a role in finding its cure.

Data pipeline consolidates data from different sources. It is a set of processes and components that collects, cleans, integrates, processes, and stores the AI system’s data. The requirement of manual effort is significantly less with the data pipeline. They work in a sustainable, systematic, and scalable way.

Few challenges involved in AI Factory are:

·      Establishment of correct metrics

·      Establishment of supervised algorithms

·      To tackle the running experiments

·      Result validations

Top Companies using AI Factories

AI capabilities like demand planning, intelligent maintenance, and quality inspection have already been used in various sectors. Some of them are consumer products, automotive, aerospace, defense, and industrial manufacturing.

The top companies already in the path of using AI factories (based on the study done by Capgemini) are:

·      General Motors

·      BMW

·      Carlsberg

·      Nokia

·      Kellogg’s

·      Nissan

·      Thales SA

·      Canon

·      Bombardier

·      Boeing

Netflix is another example that is using AI, machine learning. The company uses artificial intelligence algorithms to suggest movie recommendations to customers, personalized thumbnails, streaming quality, etc.

Microsoft’s AI factory program is designed to support start-ups and companies by offering technical support, software licensing, etc. Few start-ups associated with this program are:

·      AB Tasty

·      Recast.AI

·      Scortex

·      DC Brain

·      Case Law Analytics

·      Craft AI

·      Hugging Faces

Benefits of AI in the Manufacturing Market and the Impact of AI on Labor Jobs

Artificial Intelligence offers 24/7 manufacturing in factories without human labor. They have the potential to perform with less or without electricity. Robots are ending workplace accidents and giving significant production at less cost.

AI is the future of factories involved in high-risk manufacturing. Cost-reduction, quick decision-making, and security are the broad advantages of AI factories.

Since there are so many advantages with AI, the question arises, will AI impact current labor jobs?

A study on over 1K companies globally on the impact of AI implementation on labor jobs revealed that laborers might be required more than before. The collaboration of humans with AI systems can level-up the performance. This may also generate new categories of jobs.

The fully automated manufacturing units, in-fact, might reduce production. After observing a reduced number of vehicles, Tesla figured out the cause is full automation. "Yes, excessive automation at Tesla was a mistake," Elon Musk tweeted.

Hence, humans are required even in AI factories but with enhanced skills and knowledge.

Some Books to Refer to For a Better Understanding of AI Factory

Lansiti and Lakhani discuss the key factors involved in the AI factory in their book Competing in the Age of AI, such as data pipelines, developing algorithms, testing new data, making predictions, etc.

The book contains several valuable case studies to explain AI Factories. A common example of an AI factory given by authors is search engines, “A search engine like Google or Bing. As soon as the user types a few letters in the search box, algorithms dynamically predict the full search term based on prior search terms and the user’s past actions.”

“Human + Machine: Reimagining Work in the Age of AI” by Daugherty & Wilson is another book that has presented how organizations use AI in innovations and gain profitability.

Conclusion

The right implementation and correct combination of AI and humans can offer tremendous results. It can boost production, minimize cost, end accidents, offer new markets, increase growth, etc. Artificial intelligence is the future of factories.

Factories dealing in automobiles, logistics, and other technologies-based companies are in the forefront of adopting artificial intelligence. In a current competitive world, technology alone is no more sufficient for significant results. AI makes the system intelligent, gives smartness to perform effectively in less time.

As per the Forbes report, out of five business executives, four agreed that AI has already started transforming the workflows intelligently.

The future is smart, and it’s the beginning of a new era where every company will run by an AI factory.