Humans think, learn, and make decisions. That is what makes them intelligent.
The idea of Artificial Intelligence (AI) was first coined in 1956. Over the coming years, it is expected that around 70% of enterprises will implement AI-based solutions. AI is simply any code, technique, or algorithm that enables a machine to mimic, develop, and demonstrate human cognition or behaviour. With AI, a machine can learn from experience and perform a human-like task.
In today’s world, technology is used to understand customers’ needs, work out solutions, and serve them better. Because AI is likely to become a bigger part of our work environments, it is important to understand the pros and cons of AI, how it impacts news consumption patterns, the paradigm shift in news media’s approach toward AI-based solutions, and how publishers can take advantage of AI’s benefits while minimising its consequences.
Commenting on AI’s important role in our lives, former U.S. President Barack Obama stated that it promises to create a vastly more productive and efficient economy. Some AI-based initiatives showing that progress so far are:
- GPT 3
- Personal assistants
- Recommendation engines
- Google News Lab — journalism AI
- China AI robot as news anchors
AI can appear in numerous ways:
- Narrow or weak AI: When a machine can perform a specific task.
- General AI: When a machine can perform any intellectual task as humans do.
- Strong AI: When a machine can beat humans in many intellectual tasks.
AI is a broader umbrella. Machine learning and deep learning are its subsets.
Machine learning is a technique whereby we teach a machine how to make decisions with the help of input data. Algorithms are trained and learned from past examples in a model that maps features to a corresponding outcoming variable. This is designed on statistical models like support vector machine (SVM), decision tree, K–means clustering, and linear regression single variable.
Deep learning is a technique whereby we develop algorithms that replicate the human brain. The objective is to mimic the human brain. In deep learning, artificial neurons replace biological neurons connected internally as an artificial neural network. This is comprised of an input layer, more than one hidden layer for processing, and then an output layer. Deep learning needs more data and high-end computing machines to perform effectively.
Understanding how AI can be useful for news media houses surfaces growing concerns over long-term profitability of both print and digital news media. More than ever before, publishers need to reinvent their models in ways that would allow them to reach their target audiences more efficiently.
AI is transforming news publishers working at many levels. It is analysing users’ consumption patterns and sharing insights that help publishers understand users’ interests in real time. Data analytics helps companies with personalisation when trying to predict what kind of content an individual would like to consume. Natural language processing, speech recognition, facial recognition, tonal analysis, and sentiment analysis turn data into actionable intelligence. This is real-time predictive modelling for anticipating demand and segmentation.
Based on this, news publishers receive assistance to deliver a better user experience with micro-targeting, which leads to better engagement, more time spent, and visitors returning with better frequency. Further, the automation process helps publishers increase efficiency and reduce time and manpower costs. The same manpower can be deployed in handling more complex and strategic work.
AI’s negative consequences
However, AI can be misused in the sense that predictions about humans can be made based on their consumption pattern. This enables AI-powered media to run propaganda using algorithms to unsuspecting consumers in a trusted way, as they will be unable to differentiate it from reality.
Additionally, there is also an increased risk of misinformation through recommendation engines. While the content recommendation feature remains a core use case for media companies, it often creates so-called “filter bubbles,” where a customer is only given news stories based on their consumption and biased toward their existing beliefs. This isolates consumers from differing viewpoints and perspectives.
We can predict that, in the future, content creation may happen from AI-powered algorithms imitating human cognitive abilities. So news publishers need to understand and harness the benefits of AI. In this regard, some important steps need to be initiated.
We must remember AI is not a plug-and-play solution that can deliver results overnight. This is a cutting-edge technology requiring efficient manpower, technical support, and an organization’s approach and culture supporting AI adoption.
AI needs a top-down approach: Top management must acknowledge the importance and urgency of AI initiatives while spelling out the benefits for all departments and manpower.
“Technology is not the biggest challenge, culture is,” noted Tim Fountaine and team in Building the AI-Powered Organization, published in Harvard Business Review in 2019. Organisations need to ensure all team members are well informed and open to the adoption of AI in their tasks.
Organisations can adopt a three-tier framework for effective AI strategy:
- Experiment-oriented approach with the agile working culture.
- Strategic flexibility with interdisciplinary collaboration.
- Database decision-making.
Further, effective adoption of technology is important and applicable to every department of an organisation to create a value chain. Publishers can define KRAs for all four key layers in their organisation: the leadership team, analytics team, implementation team, and frontline team.
Thus, AI can empower teams as it is capable of labelling and analysing large data sets in real time. The same is possible by humans but machines help us do so faster and on an infinitely larger scale.
We can say that AI can facilitate our teams to concentrate on strategic undertakings with the help of data and analysis. News publishers started adopting AI a bit later than other industries, and a lot of new data scientists compare it against companies like Google and Facebook without realising they are comparing apples and oranges.
Lastly, the technology is not meant to eliminate our traditional working culture. Rather, it can work as a bridge as companies adopt modern working strategies. In a nutshell, we can say that AI, machine learning, and deep learning are tools to empower our teams for a better and brighter future.