How Machine Learning Can Disrupt Cooking


It’s no secret that the majority of blue collars jobs that existed ten years ago have been eradicated due to automation. Breakthroughs in machine learning will enable the proliferation of apps that can predict and navigate traffic, operate self-driving cars and surgery steady robotics, and improve web crawlers to rate the quality & context of articles like this one. There’s much more to disrupt.

Last year, our country general manager for emerging markets listed nine applications for machine learning. While he’s listed an impressive range of problems and the solutions created by us and others to solve them, there is one area that will be a game changer: transactions. By reducing the friction that corporations and consumers face with transactions, a ripple effect of economic growth can ensue from the implementation of systems that enforce personalized policies and track reputations.

Meal-kit delivery services like Plated have started cropping up all over the Middle East to help disrupt cooking, aided by machine learning. In the recent past, people would first decide what they wanted to eat based on what they knew how to cook, visit a store for the ingredients, return home and cook it. As we push the limits of machine learning, businesses like Plated will enable self-driving cars to deliver the ingredients as its being decided which meals is best suited for your dietary needs. Startups like Blue Apron are also appearing in pockets of this region, offering busy people the option to receive pre-assembled meals when and where they want it.

Smart machine learning will disrupt how the quality of ingredients are judged as well, an impetus to a good meal. By certifying quality, its saves the grocer and the customer the trouble of returning or discarding spoiled items. Granted, ML would be fixated on the optics and unable to determine just how rotten the fruit, vegetable or meat is on the inside, but in doing so it cuts off the time taken by shoppers during their optical shortlisting process. To further ensure quality control, customers would be able to share feedback after confirmed purchase, with smart tools determining shills from actual customers.

In almost every market, machine learning has the potential to reduce costs for quality product search, purchase, and delivery. It goes a step further by ‘learning’ and enabling personalization for the buyer, reputation tracking for the supplier, and quality control for the middleman. The spillover effect, experienced over time, will undoubtedly capacitate marginal growth across every industry. The impact will be gradual and not as sexy as self-driving cars, yet it is one with the most democratized perks for the little guy.

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Ahsan Khalid

About the Author Ahsan Khalid

Ahsan Khalid is the director of technology at Centric DXB. He is a trusted partner for clients as well as internal cross-disciplinary teams to ensure quality relationships. He can be reached on [email protected]