Unexpected Ways AI is Helping Businesses
With the advent of the digital revolution and the emergence of constant technological innovations with social impacts, such as social networking and e-commerce, there has been a noticeable change in corporate behavior around the world. While many CEOs and leaders once viewed the purposes of computer automation with suspicion and caution, there is now substantial evidence that helps almost unquestionably prove the benefits of the data-driven age.
In the recent past, it would be difficult to associate AI with applications other than humanoid robots and search engines, much because of the success of sci-fi movies and the digital disruption caused by Google. But today there are countless examples of promising applications assisted by AI. The following are real-world cases of innovative solutions that are helping many companies effectively grow their businesses in unexpected ways.
Forest Conservation Using Environmental Sounds
Wildfires have been a recurring issue when it comes to the Brazilian Amazon rainforest. Just earlier this year, nearly half of the nearly 145,000 occurrences of fire outbreaks in Brazil are associated with the Amazon. There is also the problem of illegal exploitation, which implies increasing deforestation rates with each passing year. Given how easily we deal with satellite imagery daily, it seems natural to think of AI-based solutions to help combat these problems.
Digital satellite image processing is such a widespread area in computing that implementing AI-based solutions to combat forest fires is not really where the problem lies. But there is a novel solution developed by Rainforest Connection that uses sounds captured in real-time from the forest environment to detect evidence of illegal deforestation. Even with the benefits of AI, the crux of the problem is often the actionable policies adopted or not by the rulers.
Social Good Actions Based on Social Network Monitoring
Since the conception of social networks, there has been a revolutionary break in the paradigm of communication between people. While this disruptive innovation has contributed to more intense and frequent social interactions, we have also noticed a detachment between individuals. Social networks and digital media are subject to various influences, which can often camouflage social upheavals or culminate in opinion polarizations.
Among the main trends of AI in recent years is the automatic analysis of large volumes of data extracted from social networks. This type of analysis can handle many types of data at the same time, mostly unstructured, such as text, images, sounds, and videos. Because geographic data is embedded in user interactions, post monitoring ultimately allows for a multitude of applications that can be used for strategy definition and decision making.
Facebook recently performed a pilot project aimed at using bots to detect potential suicide cases in its social network. Although it is still difficult to deal with tasks involving natural language, being able to detect anomalies in interactions between people and point to evidence of psychosocial disorders is a big step towards the common good. This case is a genuine example of how AI can help society, as many companies are only interested in marketing based on user sentiment analysis.
Improving Human Interactions Through Data-Driven Processes
Whether negotiating with clients, developing major projects, or maintaining the workplace environment, it is common for impact-driven companies to have frequent interactions between people through e-mail, phone calls, and video conferencing. This data traffic is an inexhaustible source of insider information that can be used to identify opportunities to improve the human interactions that are taking place, thereby contributing to business progress.
Conversational interaction analysis serves as an essential tool to help companies create a shared concept of reality, as language is a complex system and subject to different interpretations. Applying the concept of conversational intelligence (C-IQ), we aim to know what, why and how interactions are being performed, seeking to connect, navigate and grow with others. Nowadays we have automated solutions to accomplish this kind of task like the following example.
Gong.io is one of the current examples of award-winning companies using IA to apply C-IQ to large data volumes. The service offered by the company promises to help improve customer interaction by thoroughly analyzing audio from phone calls and video conferences, as well as texts from emails. Large corporations like LinkedIn, Asana, Pinterest, and General Electric use or have used Gong to help sales teams improve customer interactions.
Generative Design and the Generation of Artificial Faces
Evolutionarily, there is no denying the ability of nature to generate the best structures for the most varied conditions. We know this is an extremely lengthy process, but it ensures the best adaptability of individuals based on natural selection. Based on this concept, would you be able to say what would be the benefits to humanity if we could implement AI systems inspired by evolutionary models? Although the concept of generative models is relatively new, there are already several companies effectively exploiting it in the most unexpected ways.
Design is one of the many areas in which generative models have been applied in the corporate environment. Tools such as Autodesk Within, Autodesk Inventor and Fusion 360 allows you to identify the best combination of variables such as aesthetics, functionality, and structure, which allows us to create extremely efficient designs based on factors such as strength and material economy. It is not difficult to find examples of generative designs that resemble real structures that we find in nature. Airline companies are using generative designs to develop cabin partitions structures stronger than the originals and with half of the weight. There is even a case of a drone chassis whose structure, designed through a generative process, is surprisingly similar to that of a flying squirrel's pelvis.
Another current example of generative models application is the generation of artificial faces from images of real people. Using the generative adversarial network (GAN) concept, we can create models that learn from each other how to perform specific tasks based on a large amount of unstructured data. Besides making it possible to generate artificial pictures of people, animals or objects, GANs also make it possible to even edit people's poses in photos, insert people's into videos or manipulate video audio... Scary! There is also conversion applications text to image, and others that allow us to identify the age of people.
The purpose of this article is to present examples of promising applications that are not commonly cited when addressing AI. We want the reader to think outside the box and be motivated to invest in that kind of solution, whatever the problem. There are several other applications as interesting as the topics we cover: bespoke marketing strategies, workplace healthcare, hiring processes, etc.
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Implementing AI in a business requires a strong commitment to innovation, and since it is an emergent field and only now brought to business on a large scale, many challenges are posed on the organization side. AI represents the next wave of digital disruption, but only with a strong strategic focus and openness to new ways to approach the business can this journey produce value from AI, and enjoy a critical advantage over organizations that don’t employ AI.
A great part of the challenges posed in these steps are addressed consistently on the Data Strategy Document, a document that defines and materialize the strategic approach for a data-driven business. The AI implementation benefits largely by the fundamental work made in this document. Our strategic AI implementation is always based on this document.