ElectrifAi’s Vision for Ai in 2021
2021 is set to be quite a different year from 2020, and we are excited for the possibilities before us. Perhaps the biggest change we’ll see is a focus less on technology and the fact that you’re using artificial intelligence (Ai) and a lot more on the return on investment (ROI).
The questions that are being asked are: What business value does Ai bring? When do I see results? How disruptive is implementation going to be to my operations? All the normal things you would ask if you’re buying any other piece of software or tool for your company, but Ai will no longer have a special role of it must be better because it is Ai. It’s going to have to prove it.
Machine learning, which uses Ai to detect patterns, is currently a big trend. Horizontal solutions are becoming commoditized and that will accelerate to be nearly complete in 2021. What will be a much more important differentiator is deep verticalization.
Customers buying chemicals and polyethylene is not the same as someone who buys sunglasses when they go through a store. It’s not the same type of behavior and thus the same type of models will not work. Deep verticalization and really understanding the domain you’re working in has always been critical and is going to be unavoidably critical in 2021. The people who don’t have domain expertise will start falling to the wayside.
Seeing that people are looking for solutions now and they have different and more complicated problems due to COVID-19 disruptions, they need to address those problems right away. Machine learning is there with an answer.
There are two ways COVID-19 has disrupted businesses. One is many companies and industries have experienced significant downturn. And operational inefficiencies can no longer be tolerated. Companies have to find a way to automate processes. There is a limit to what you can automate without Ai. You must understand past data and the domain and apply that to automation.
Blind optimization is reaching its peak with RPA reaching a plateau. The realities of how much that will help are setting in. It’s still helpful and people still do it, but it has limits. Ai and machine learning lift those limits significantly higher and can be used even without RPA in many cases. You’ll see more of a push to that.
Then there is the other case of companies and industries, primarily in technology, who are facing explosive growth. When COVID-19 hit, those companies were able to fill a need that many other companies used to satisfy.
The way that machine learning comes in is that these companies cannot possibly scale by adding workers. If you’re doing 10 times the business, you’re then adding more workers and thus more supervisors. That is simply not the type of scaling that a company can do efficiently to grow their business. If suddenly 11 out of 12 workers were brand new this year, the business isn’t going to move forward. It’s not a sustainable growth rate.
But machine learning can help the business move forward because machine learning can really squeeze out inefficiencies. Many things can be automated that a single person’s skills can be magnified to 10 or 100 times more than they were doing before. Even something simple such as invoice processing where someone had been manually inputting can now type in 100 times more invoices than before with the use of Ai and machine learning technology. Thus, scaling without hiring a lot of new people.
Regarding data and privacy regulations in 2021, many platform providers will be stressed to comply. If your business model is to take your data and organize it on a separate platform, it will be difficult to follow all compliance regulations with all the different variations. Compliances comes from law, but then laws are interpreted by the courts and that can change what you’re required to do. Your IT department really needs to start tracking the latest legal rulings to determine what their rights and obligations are for data protection.
But there is a simpler solution. If you make your code available where the data is, if you don’t require people to re-platform their data, it can literally sit where it is. You bring the machine learning to those systems, then the compliance issues go away as you aren’t moving the data anywhere. You’re just analyzing it and keeping that analysis internal. That’s a much more powerful model than working on a separate platform.
Another trend poised to grow substantially in 2021 is computation at the edge of the network. That means instead of taking the data back to a data center or cloud, a lot of intelligence can be run in the field. For example, if there are cameras on farms, the intelligence can be run right in the camera. And that means much of the data that would have been moving around to be analyzed back in the data center is no longer necessary.
This is really driven by a handful of trends. One is hardware availability is increasing, price is decreasing, and it is becoming more powerful. Everything from the chips that are integrated into systems and all of that has really hit a tipping point where technology has become powerful enough to do Ai at the edge.
ElectrifAi has a particular algorithm in image processing that is very different technology that has never been used before. Much of the technology and image processing requires neural networks that require substantial compute resources. We have algorithms that take a different approach. And because of that, those algorithms can run at much lower compute power and uses less energy consumption. Thus, it is easier to use this technology in the field.
The technology can do increasingly more sophisticated things with the available hardware so that you can really start creating use cases that don’t suffer from some of the things that usually happen when you send your data back to the data center, like limited bandwidth reducing the quality or reduced latency that doesn’t allow the data to quickly react in the field.
With this new technology, the internet of things (IoT) will no longer be the IoT we know now. It will become the internet of smart things (IoST). Devices on the edge are contextually aware of their surroundings, making important decisions and allowing optimization to enable true edge computing. When people look back at 2021, they’ll see that’s really when smart devices at the edge took off.
2021 definitely has some exciting things to watch for and Ai technology will boom. Companies are recognizing more and more that Ai and machine learning are necessary for growth, to improve operational efficiencies, and increase customer satisfaction.
Have you begun your Ai and machine learning journey? If you are at the beginning stages or have been on this path for a while, ElectrifAi can meet you anywhere on your journey. Reach out to us today!