Artificial intelligence is a bit like a car. If you don’t fill up the tank, it’s not going to go anywhere. Artificial intelligence is fueled by data; therefore, it needs to be fed a consistent amount of data or else it’s not going to learn or be effective.
Artificial intelligence is about teaching the computer to do something for you. It takes a monumental amount of information and data to build models and teach the computer to learn on its own. That’s why data, and the collection of data, is critical when embarking on working with AI.
Collecting more data means creating more accuracy when building the models that will allow the machine to learn.
Step one is collecting as much data as you have access to. Then, you need to be able to cleanse, validate and make sense of that data ensuring it’s not biased or wrong. Inaccurate data will simply provide inaccurate results.
From here, companies can use the tools on the market, use open source technologies or algorithms, and start applying the machine learning and artificial intelligence techniques on the data.
Finally, businesses should seek the advice of experts. With so much information and data, companies don’t really know where to start. When they feed that much data into a system in search of results, they‘re overwhelmed by the vast amounts of different combinations and permutations of scenarios. Manually investigating each result is impossibly time-consuming and most companies don’t have the resources and personnel to dedicate to that level of analysis.
This information overload can lead to confusion and further hesitance on adopting AI.
Data scientists and consultants can help businesses determine how to sift through the vast amounts of data, take what’s necessary and ensure the artificial intelligence is maximizing its use.
AI and the business
Artificial intelligence is often thought of as computers thinking for themselves and being used as a tool to automate jobs. But it also offers implications in business to analyze industry trends, cost trends, understanding inventory and supply chain needs, shipping times and much more.
Having this technology available allows businesses to understand more factors that can influence their success. Artificial intelligence is thus a tool that can help organizations budget better, plan more efficiently and understand how to more effectively allocate resources.
This allows companies to make more informed decisions about where to invest research and development dollars, understand the way customers use their products and ensure they can optimize products and services before going into production.
AI can predict the trends of users and how they use the products, so businesses can keep up in an ever-changing market.
Businesses, especially manufacturers, are starting to see the benefits in using collaborative robots with AI embedded in them. These types of robots can work alongside the factory workers to do things like unloading goods from a conveyer belt, putting them into a pallet or moving them around. They can use computer vision to inspect the manufacturing of components coming off the production line. AI has the capabilities to spot defects and perform quality control beyond a human’s capacity.
Working with AI to predict the future
Both businesses and manufacturers alike can use machine learning to understand what’s happening with their products and make future predictions.
Any data, from sensor data to product data, can be used to teach the machines building the products. From this, companies can use this information to design products better and for predictive maintenance giving manufacturers the capability to prevent problems before they happen. As a simulation tool, artificial intelligence allows engineers and designers to fix issues before they become a costly problem to fix.
Predicting maintenance means a business’s operation can be proactive about scheduling. The earlier you can detect the potential failure, the more time and money that can be saved by reducing downtime.
At the same time, using artificial intelligence in the manufacturing process enables designers to see how varying components influence each other. This is critical especially as OEMs outsource more of the supply chain. Building products are pricey, AI can catch potential flaws in the making, and use all the collected data to predict if a product will pass the test.
Designing the best product possible
Working with artificial intelligence and machine learning allows businesses to have the computer do a lot of the heavy lifting. When designing a product or developing software, it’s quicker and more effective to run multiple, different scenarios and simulations. The results offer their own collection of information, which are analyzed down to a potential set of useful data.
The machine learns as it narrows down all the possible scenarios providing a result that would otherwise take months and considerable cost to discover using traditional methods. Human beings don’t have time to look at every single possibility, so giving artificial intelligence this task frees up the engineers’ and designers’ time to be spent on more worthwhile endeavors.
Artificial intelligence won’t always give a specific answer; often it is a means for creating a more effective outcome by exploring a much larger set of possibilities, rather than a machine that will do all the work. it provides recommendations and some suggestions, freeing you to focus more time on solving the problem, rather than figuring out what the problem is.
AI helps analyze the data faster and allows users to establish an idea. But making the final decision is where the technology can’t completely replace a human. You don’t want to merely leave it in the hands of the machine and see what happens.
If you can apply artificial intelligence and machine learning techniques, along with automated analyses, you can receive those insights faster and design products better. AI closes that transitional loop as you’re creating a product design, running simulations, building prototypes, manufacturing the product and delivering those products to the market. The artificial intelligence gives you the tools and techniques to understand how that product is behaving, so that you can design it better.
Working with AI in the future
Technology is rapidly evolving and accelerating. You can’t be afraid to try new things and innovate new ideas. You need to be looking at artificial intelligence and understand how to apply it within your specific field.
There’s almost no field that can’t benefit from the use of artificial intelligence. One of the most famous data science use cases for AI is the concept of the book-turned-movie Moneyball. The application of data science, which is a sub category of artificial intelligence, helped build a more successful and cost-effective baseball team. It changed the industry.
Combining artificial intelligence with the digital enterprise—especially across manufacturing, industrializing and engineering—enables the integration of global data to create actionable insights.
There is untold value in data. But unless you can do something with that data, there is no value. You can have a car, but without fuel, it’s not going to go anywhere. A business using artificial intelligence to collect and use the right data can help position their products far ahead of their competitors.
Want to learn more about the future of AI and artificial intelligence in the workplace? Check out other blog posts about this innovative new technology.
Watch our video: Working with AI in the Next Industrial Revolution