“Artificial Intelligence” by Maryann Sciallo (Avlino)

Avlino focuses mainly on telecommunications and maritime logistics. In what can AI benefit these sectors?

Both maritime logistics and telecommunications are capital-intensive industries that require high investments in essential facilities, infrastructure, and equipment.

Typically, capital-intensive industries have high operating expenses and need a large output volume to provide an adequate return on investment. In addition, they are extremely vulnerable to unexpected disruptions or economic downturns as a result of these high fixed costs.

AI applications act as a driver of operational excellence, seeking to enhance processes and improve efficiencies. But how does that help? AI and machine learning techniques enable the collection, aggregation, and preparation of large amounts of real-time data that feeds applications rich in intelligent algorithms to assess scenarios, gain insights and drive continuous operational improvements through semi-autonomous and autonomous processes. They also empower personnel in critical operations, extending their capabilities to make faster, more accurate decisions, which impact the entire supply chain.

The capacity to dynamically optimize assets and processes across real-world business objectives significantly increases productivity, which we consider the most relevant benefit of AI ​​in capital-intensive industries.

In addition, the key differentiating aspects of AI versus other systems is its continuous self-adaptation to each industry ecosystem, that allows businesses to overcome vulnerabilities, to increase resilience and sustainability.


What are the biggest challenges of working with AI?

Working in artificial intelligence implies a multitude of challenges, but we can pinpoint the top three most relevant to the work we develop.

One of the first challenges that usually arises is the need to work with unstructured data, which is difficult to process and analyze despite having immense value for businesses. Unstructured data can be broken down, processed, and stored as clean analytical data, but the techniques employed must be precise not to affect quality and accuracy, ultimately compromising the analytics workflow.

Another challenge we face is collecting and processing real-time data streams. With traditional batch processing, data collection issues can be identified before performing other steps in the data pipeline. But with real-time data streams, any failures in the collection will spread to the next steps in the pipeline. Therefore, it’s necessary to implement specific measures to avoid inaccurate analysis and possible breakdowns along the line. Simultaneously, there’s the issue of dealing with large volumes of data coming from different systems and platforms, which requires the consolidation of the extracted data sets in a unified and manageable big data architecture.

Finally, one of the most crucial challenges is the unknown nature of how models predict outcomes. How a specific set of inputs can create a solution to different problems is still hard to understand. AI is like a black box for many people, and they don’t feel comfortable when they don’t know how a decision was made. This causes a trust issue that requires some effort in educating the public.


Future perspectives for a phenomenon like this? Is there room for more innovation?

Naturally, our outlook for the future of AI does not fit Hollywood’s representations, in which the rise of machines leads to the destruction of humankind! Instead, we do believe AI will substantially and positively transform the way we entertain ourselves, study, and work.

Although we’re not on the verge of “human-level AI,” or artificial general intelligence, we’ve reached the threshold where humans can team up with machines to deliver results that combine speed and precision with human intuition and curiosity.

By reshaping the way machines learn, it’s possible to teach them to emulate a growing share of capabilities once considered “uniquely human.” If we add the evolution of hardware to that, we can take AI far beyond its current role. Several companies are building AI accelerators, and FPGA chips are also fast approaching that race, to support artificial intelligence that handles real-world complexity.

In our perspective, the future of AI encompasses two critical aspects: greater specialization – through the development of differentiated solutions for specific domains – and proactivity – where AI applications evolve from reactively taking orders to proactively making changes.

And to complete your question, as long as human intelligence is behind artificial intelligence, there will always be room for more innovation.


How useful can AI adoption be for companies and society in general?

AI technologies have finally evolved to a level of functionality that offers capabilities with significant potential for value creation. The adoption of AI ​​in businesses promises substantial performance gains at all organizational levels.

Some benefits include efficiency and productivity gains, where AI tackles tasks at a pace and scale that humans can’t match — at the same time, freeing these workers to move on to higher-value tasks. When creating new products, shorter development cycles reduce the time it takes to go from design to commercial, providing a more immediate ROI. Customer service is improved from more personalized interactions between organizations and each customer. In logistics, AI can optimize delivery equipment routing, improving fuel efficiency and reducing delivery times.

These are just a few examples taken from universal activities that AI impacts, such as making better decisions, optimizing operations, improving processes, seeking new markets, or developing products.

The widespread adoption of AI does raise some ethical concerns, but several organizations have already sprung up to monitor and advise on best practices. In addition, various government entities are taking steps to ensure trustworthy AI. The United Nations also introduced the AI ​​for Good Foundation, which aims to promote activities to maximize the benefits of Artificial Intelligence technologies and innovations.

AI is already considered instrumental in combating climate change. Its ability to reduce human error in many sectors makes everyday life safer, and the technology is currently facilitating major advances in health. So, despite its challenges, AI can help solve many of our society’s most challenging social, economic, and environmental problems.

29 September, 2021