Over the past few decades, there has been exponential growth in automation. In the coming years, robots will have taken over most of the tedious and repetitive tasks. Automation began with production lines in factories, following the assembly line’s invention after the First World War. Today, easy access to the power of computers has facilitated the use of automation for the collection of information and decision-making. And now we’re in the realm of Artificial Intelligence (AI).

An AI system can analyze its environment and use the information to act. AI systems can learn new things, and while this skill is not requisite to their operation, it proves extremely useful when applied correctly.

One thing to remember is that AI is not the preserve of large or complex business entities; in fact, all businesses can benefit from using AI to simplify problems and then delegating them to computers. The end-goal is to make the company more efficient and profitable.

Let’s look into a few examples that show AI in action:

Case 1: AI-based systems in Agriculture

Modern farming with AI-based systems

Modern farming now leverages integrated solutions based on sensors, cloud technologies, and Artificial Intelligence to improve results. These solutions are being used to assess crop-growing conditions in the present and in several days in advance.

Such solutions collect, analyze, and transform microclimatic data and returns actionable tips through web and mobile apps. Understanding microclimatic conditions is especially vital when growing crops on irrigation. Using AI-based integrated systems, farmers can get an accurate view of the three important date ranges: the present, the near future, and the past – as far back as the system has had on-farm data.

With insights and reliable predictions, farmers can make the best operational decisions – planning labor and logistics, optimizing water and farm inputs, and other activities – to guarantee high yields. More importantly, they can review past conditions to help plan for the future and mitigate weather and disease risks.

Case 2: Natural Language Processing (NLP) Analyzing Documents

Natural Language Processing

An AI system can be configured to learn from a database, even where the developers lack prior knowledge of the subject. In one article published in The Nature [1], a research group used Natural Language Processing to analyze a collection of a whopping 3.3 million scientific abstract in over 1,000 journals with related research.

In the study, AI is used to assign multi-dimensional vectors to words within a text copy. As such, words appearing in similar contexts will show a high similarity between the respective vectors. This technique identified a high similarity between the word ‘thermo-electric’ and materials, which had never been cited within the context in the past. Therefore, the AI system can recommend functionally relevant content even before it has been discovered.

The project used AI to fill in knowledge gaps in material science by comparing contexts within which different words occurred. This is an example of how a vector similarity problem through machine learning replaced all the comprehension of chemistry needed to accomplish such a task.

Even more impressive is that all this became possible without any prior knowledge of the real problem. A human being would never be able to accomplish such an analysis within such an extensive database of material.

Case 3: Amazon GO: Opens Supermarket

Open supermarket

Maybe you’re wondering how the previous example contributes to business gains. What if we needed to find similarities between product names within a supermarket? Let’s think even further.

Have you ever envisioned a supermarket withi queues or cashiers? This is what Amazon proposes to do with Amazon GO [2]. This proposed grocery shop entity is entirely powered by AI systems that monitor the product that shoppers pick off the shelves. Customers then download the Amazon app, where they can easily see the charges for the items selected. They have to scan the app on an automated turnstile to enter or exit the grocery store.

The system uses sensors, computer vision, and machine-learning algorithms to track the items shoppers pick. Any products lifted and returned to the shelves are not added to the final bill.
The idea behind developing Amazon GO was to optimize costs and improve customer experience. Using the data generated by customers’ purchases, the supermarket can optimize stock levels so that the supermarket does not stock more or fewer products than necessary.

Looking Into the Future: Are You Ready?

As you have seen, artificial intelligence is already an everyday reality in several business segments, from intelligent data capture to making reliable predictions based on vast and diverse data sets.

How is your company responding to the brewing revolution? Are you being/Will you be left behind?

At Programmers, we have written about a set of accelerators businesses in different sectors are using to adapt their businesses to the future. Learn more about these tools to help you adopt AI and Data Science into your core processes.

References:
  1. https://doi.org/10.1038/s41586-019-1335-8
  2. https://www.supermarketnews.com/store-design-construction/amazon-go-goes-smaller