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When AI Gets Creative: Is This Cause for Concern?

For decades, robots have worked with humans. Within the automobile business, as an example, they've long been the foremost precise and reliable welders and painters. Sitting in one place and doing the same thing over and over again has traditionally been automation's sweet spot. But, with the explosion in computing, robots can now comprehend how to accomplish much more complicated, nuanced tasks. This includes work that needs to be done inside, like at a factory, or outside, like in the field. In agriculture, they are not only tilling fields, but they can also detect weeds and zap them with lasers. In hospitals, robots do everything from helping nurses get the needed supplies to assisting surgeons in directing their instruments more precisely. 

A 2020 World Economic Forum report predicted that artificial intelligence and automation would displace 85 million jobs globally within five years. However, it also made a prediction that advancements in technology would create would ninety-seven million new jobs—generally ones requiring a lot of skills and education. Today we'll take a glance at the kind of labor AI is ready to try and accomplish and what we can expect from AI in the future. 

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Using Machine Learning to Detect Dupes: Some Real-Life Examples

As companies collect more and more data about their customers, an increased amount of duplicate information starts appearing in the data as well, causing a lot of confusion among internal teams. Since it would be impossible to manually go through all of the data and delete the duplicates, companies have come up with machine learning solutions that perform such work for them. Today we would like to take a look at some interesting uses of machine learning to catch duplicates in all kinds of environments. Before we dive right in, let’s take a look at how machine learning systems work.

How Do Machine Learning Systems Identify Duplicates?

When a person looks at an image or two strings of data it would be fairly easy for them to determine whether or not the images or strings are duplicates. However, how would you train a machine to spot such duplicates? Perhaps a good starting point would be to identify all of the similarities, but then you would need to explain exactly what 'similar' means. Are there gradations to similarities? In order to overcome such challenges, researchers use string metrics to train machine learning models.

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How Do AI Systems Identify Duplicate Data?

When you compare two Salesforce records, or any other CRM for that matter, side-by-side, you can easily determine whether or not they are duplicates. However, even if you have a small number of records, let’s say less than 100,000, it would be almost impossible to sift through them one by one by one, and perform such a comparison. This is why companies have developed various tools that automate such processes, but, to do a good job, the machines need to be able to recognize all of the similarities and differences between the records. In this article, we will take a closer look at some of the methods used by data scientists to train machine learning systems to identify duplicates. 

How Can Machine Learning Systems Compare and Contrast Records? 

One of the main tools researchers use is string metrics. This is when you take two strings of data and return a that is low if the strings are similar and high if they are different. How does this work in practice? Well, let’s take a look at the two records below: 

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Making the Case for a Monolithic Architecture

When microservices became popular in the past decade, everybody hailed them as the nail in the coffin of monolithic architecture. Given all of the advantages of that microservices offer, it is easy to see why this was the case. Still, there are certain situations when a monolithic architecture would serve you best. In my previous article, I explored some of the challenges that microservices offer, and today I would like to take this topic one step further. Let's take a look at some of the benefits of the monolith so you can decide if it is right for you. 

Simpler Tracking and Monitoring 

Microservices is all about taking independent technologies and sticking them together. However, how do you know whether any or all of the services are performing the way they should be? While there are tools available that can help you with this, you will still need to spend time integrating them. This, by itself, could get very costly. While many people would say that one of the advantages that microservices offer is the fault tolerance, it will still be difficult to find those points of failure manually. This is because microservices, by their very nature, are decoupled and you will need to integrate some monitoring tools which will increase costs. 

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The Challenges of Using Microservices in Software Development

Businesses around the world rely on various software and IT products to help them achieve their business needs. In order to facilitate the development of such products, companies use a microservices architecture which simplifies a lot of processes across the board. Not only will this expedite time to market, but it also makes it easy for companies to test their products and provide maintenance as well. Still, companies will encounter many difficulties while working on microservices architecture and we would like to tell you about some of the most common issues you might encounter. 

Managing a Complex Infrastructure

One of the big advantages of microservices is the ability to deploy small independent units. However, as you start deploying more and more microservices, the entire system becomes more complex and you will need a lot of expertise to handle such complications. Also, even though each service is now smaller, it is not necessarily less complex, it simply shifted to the communication between each individual microservice. Therefore you will need to choose the service boundaries very carefully and have the necessary tools and expertise to handle such complexity. Most importantly, there needs to be good collaboration between everybody involved in the development process, otherwise the microservices will not be set up correctly.