What does Artificial Intelligence (AI) mean?

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include:

• Speech recognition
• Learning
• Planning
• Problem solving
• Self programming 
• Organizing
• Maintaining system

“Creative minds won’t be replaced by machines”

Regarding loss of reporters’ jobs, they acknowledge that AI technology that can generate plausible prose and mimic writers’ tone is already available. However, “the narrative arc and a best-seller’s make-up have yet to be reduced to an algorithm.” AI, according to the report, is not going to replace writers; it will assist them in doing their work more efficiently.

 Problem: The first step i.e., defining the problem is best done by analyzing organizational areas, tasks, processes and services, that can be optimized by data-driven or automated steps. The user’s point of view should be taken into account and clear, measurable goals defined.

1. Culture: “AI is to be understood as an iterative and experimental process whose results can vary according to data quality, and whether or not the problem has been clearly defined,” suggest the authors. This makes company-wide buy-in an important part of the process. Moreover, cross functional perspectives can contribute deep insights in developing AI solutions. 
2. Knowledge: The next step involves building a collaborative AI team of business professionals, data scientists, and technology experts. An interdisciplinary team will be able to identify and analyze the relevant data, create and manage the business case and action plan, and set up and manage the relevant IT infrastructure.
3. Data: The efficacy of AI systems depends upon the quality of data that it’s trained on. “Not only does data make AI smarter, but it improves its accuracy, and an increase in data fuels other AI technologies,” write the authors. The fourth step involves carrying out an inventory of internal data sources to determine if the organization has data relevant to the identified problem. The paper also suggests publishers to identify external sources that can be used to collect relevant data. 
4. Learn: Next, publishers can gain initial experience by using, testing, and learning from available plug-and-play AI applications or open source solutions. The authors recommend taking small steps to stay agile so that they can react quickly and flexibly to changing conditions. The feedback gained from testing should then be used to iteratively develop the model until it satisfactorily solves the problem.
5. Organization: AI tools continue to improve with training and usage. So it should be an ongoing process. Additionally, the authors recommend promoting the use of AI across the organization. The goal is to create an ecosystem that adapts the results of the ongoing AI experiments to develop an organization-wide data strategy. It will enable the company to systematically leverage data and AI to address various other issues or develop services.

The authors comment, “For those companies implementing AI in the right way at the right time, the systems set to disrupt technology-based industries become the very tools with which they’ll climb their way to the top. 

“Given the varying stages of development of different AI technologies, it is too early to definitively state how they will change the publishing industry–but without question the impact will be immense.”

Tomorrow’s news organizations will be part human and part machine. This transformation, augmenting human intelligence with machines, will be crucial for the future of news media. To maintain their integrity and trustworthiness, news organizations themselves need to able to define how their AI solutions are built and used. And the only way to fully realize this is for the news organizations to start building their own AI solutions. The sooner, the better — for us all.