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Machine Learning for Augmented Supply Chain Networks

We are being dependent on AI or Artificial Intelligence in many ways, it has taken over almost every sector we work in. Even our homes are becoming smart with the implementation of AI and reducing the human effort to a great extent by making the machines able to work like humans but in a better way. There isn’t a single industry left in the world that hasn’t adopted AI technology in its processes. Like that, the logistics are also not far from utilizing AI in their process. Human errors that cannot be controlled by any kind of poka-yoke or kaizen can be completely replaced by machine learning algorithms. Optimized processes can also be well achieved by analyzing the huge data that is impossible to compute at a manual level thus eliminating human error and saving time.

Ways Businesses are Using RPA to Stay Competitive in this Pandemic

We have been digital enough to leave the humans’ work on the machines and let others focus on some other important tasks, and this is not surprising for the businesses that are using RPA to a whole new level to stay competitive at this time. Without any doubt, the pandemic will end one day, and the businesses should thrive towards that reach point to keep the global economy alive, but for this, we need to stay safe and well until we get a permanent solution in our hands. This pandemic sets us locked down in our homes, we can’t get to the workplaces to operate our works directly, even we can’t let the other employees take the risk on their lives, and in this case, only machines can help us out. Yes, I am talking about Artificial Intelligence and Robotics. We have known enough their benefits on the business process and this is the perfect time to implement this on the business to stay competitive, as the market competition will not decrease any way whether it is pandemic or not. So, let’s take a brief look at the impact of RPA on global businesses.

The Future of Machine Learning

In this era of artificial intelligence, machine learning is the most trending topic ever. Machine learning is a component of Artificial Intelligence Technology. This enables the machines to use its complex algorithms and allows the machines to autonomously learn from the data sets, and continuously make decisions on a specific task by improving the efficiency of the machines. It mainly focuses on computer programs, with the primary aim to make them automatically able to learn from the data and make decisions without any human interference.

Automation is Everywhere: Autonomous Driving

Automation is everywhere surrounding us. With the advancements in the technologies, the computers with its calculation power and better sensors are controlling our daily life tasks, like we use washing machine, dishwasher, refrigerator in our home, also we see the automated doors in buses and shopping malls, these all are controlled by the computers and these all are the automation in our daily life. In a broader aspect, we can think about autonomous driving as an application of automation in our lives.

The DARQ Age: Coming Era for Technologies

The term DARQ consists of Distributed Ledger Technology as for D, Artificial Intelligence as for A, Extended Reality as for R, and Quantum Computing as for Q. The analytics and the researches show that almost 85% of businesses have experimented with one or more DARQ technologies into their businesses, and it is expected that DARQ technologies will provide a more competitive advantage that they have not experienced with the previously SMAC technologies, i.e., Social Media, Mobile-Friendly Application, Analytics, and Cloud Computing.

Natural Language Processing and RPA

NLP or natural language processing is a branch of computer technology. It actually smoothens the connection between human and machine by teaching the machines to understand natural human language both verbally and written. Don’t you think that why NLP, while using ML it is able to identify the data sets and make changes to it or perform some work on it? Then come to the main point.