Eye on AI - September 18th, 2020
Welcome to Aigora's "Eye on AI" series, where we round up exciting news at the intersection of consumer science and artificial intelligence!
This week, we’ll be diving deep into food packaging tech by looking at advances in beta-packaging, XR, and Big Data, then conclude with an article by Bernard Marr on simple ways business leaders can make use of Natural Language Processing solutions.
Food Packaging Advances Through Beta, XR, AI and Big Data
We begin with news on food packaging tech. In the article “Beta, XR, AI, and Big Data Advance Food Packaging”, IFT contributor Claire Koelsch Sand describes how food packaging science is progressing through beta-packaging, XR, AI and Big Data to create more effective, eco-friendly packaging.
“Beta-packaging provides agility,” writes Sand. “Food packaging materials are typically selected with the expectation that they remain mostly static or constant, although active packaging offers some degree of flux. Use of material science, bio-sensitive compounds, and design, however, allow beta-packaging that provides an expansive degree of agility.”
Imagine if a package were able to sense when a moisture-sensitive food is experiencing an unexpected increase in moisture, then trigger a response that increases the package’s barrier properties, releases antimicrobial agents, or contracts to limit excess space to better preserve the food –– that’s one example of beta-packaging. Extended Reality (XR) can be used to design and test packaging in an XR production environment (a replica environment) before actual manufacturing takes place, saving time, reducing costs and improving production efficiencies and package design, while AI helps companies move to more planet-based design by creating more environmentally-friendly packages that are more efficient and effective for customers. Perhaps most interestingly, big data packaging helps democratize food and minimize food inequality.
“... in the food packaging field, when a social framework is applied, big data can contribute to democratizing food access and enable values-based interactions,” notes Sand. “Through the analysis of access to nutrition by region, big data has established the existence of food access inequality. With this information, the packaging industry can go even further than addressing access to nutrition into the realm of personalized access to nutrition to ensure that individualized nutrient profiles are delivered.
Should these new advances be applied at scale, we can expect to see significant packaging improvements and an altogether more eco-friendly packaging process, or what folks used to call a ‘win-win.’
Simple Ways Businesses Can Leverage Natural Language Processing
Let’s finish with some practical business advice by AI colmnest Bernard Marr. In his article “4 Simple Ways Businesses Can Use Natural Language Processing,” Marr outlines four simple ways businesses can take advantage of Natural Language Processing. Natural Language Processing, or NLP for short, is what helps computers read, edit and summarize text, while Natural Language Generation (NLG) allows computers to generate their own speech; in other words, Alexa understands voice requests through NLP, then responds using NLG.
“Think of all the information out there in the form of emails, WhatsApp messages, Twitter updates, news articles, books, spoken conversations, and so on,” writes Marr. “NLP allows machines to unlock all this information and extract meaning from it. Traditionally, extracting meaning from language was incredibly difficult for machines…. Thanks to AI technologies such as machine learning, coupled with the rise of big data, computers are learning to process and extract meaning from text – and with impressive results.”
The reason NLP has become a critical technology trend, according to Marr, is because so much of the world’s information is stored in natural human language form. And speech recognition, sentiment analysis, automatic summarization, and chatbots are all ways businesses can use NLP to their advantage.
Imagine being able to learn the statistics about a specific team’s performance with a single question, or analyzing the emotional response to your brand by dissecting user comments through social media and online, giving you an up-to-date pulse on your brand. These are just some of the things Marr points to in his article, which I highly recommend for a detailed read.
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