Machine learning is a subcategory of AI and has changed the way businesses operate. Moreover, it’s a trending technology that provides businesses with significant value, such as learning from the existing data. To say the least, it does solve many problems you might have.
Recent innovations in machine learning have indeed brought many excitements and fears concerning AI. Fears include people thinking that they’ll lose their job to AI, and the biggest excitement is how helpful it can be in employee productivity.
Well, let’s not wait any further because, in this article, we will discuss the kind of values you receive from using machine learning in your business.
Why is it a great idea to use machine learning?
From the beginning of the programming era, one of every man’s significant issues has been communication between the user and the device. Over the years, programming languages have evolved and reduced these issues. However, the problem is still there.
However, machine learning in business is evolving, which can solve this problem. In the modern digital age, things have changed, search engines, daily usage software, and calendars have all come to understand our everyday language.
Since the software can understand our languages, it makes things easier for business executives and professionals by allowing them to reduce typing efforts and convert them into speaking ones. For example, AI personal assistants can work every day. They work 24/7 and try to make things easier for businesses.
Chatbots are a popular type of automation. They have made it easier for businesses to stay active even when human beings aren’t around. Chatbots can function outside working hours and reply to customers outside working hours. Earlier chatbots were programmed to respond based on the rules they had to follow and the keywords included.
Machine learning and the advanced natural language processing features have allowed chatbots to be much more productive over the years. For example, Cortana, Siri, and more are all chatbots. However, newer chatbots can respond to consumer requests much better than older ones.
With the digital age evolving, the world has become more and more dependent on web-based technology. However, at the same time, there are a few risks associated with it:
- Data breaches
- Privacy concerns
- Stealing identities, and more
Businesses follow a few control mechanisms to ensure users and businesses are protected. Some of them include threat management, applications, data storage policies, firewalls, and more. However, you can find even more dedicated security teams in larger companies that continuously monitor, update, and fix online applications.
For instance, let’s use threat assessment as an example. Online applications continuously face many issues daily. Machine learning can predict future attacks by analyzing the data from past attacks and identifying vulnerabilities within the app. As a result, most development teams integrate Machine learning within the application testing phase to evaluate their vulnerabilities before releasing a new product.
High level of natural language processing (NLP)
Natural language processing (NLP) is a branch of AI where systems are developed for understanding human language. However, NLP has a lot to evolve, even though it has come a long way over the years. As of now, it’s able to process human requests at a certain level but still struggles to process more advanced requests and words that may sound similar to each other. For example, “I scream” and “Ice cream.”
NLP has been around for more than 40 years, and the demand for advancement in computing power and access to language data has significantly increased. Moreover, these requests are practically applied to chat interfaces, voice interfaces, and even text mining applications.
Machine learning helps in decision-making
Machine learning helps companies make better decisions from the valuable insights they gather. Humans have difficulties evaluating information and running potential scenarios at the speed and size machine learning does. In short, machine learning isn’t replacing people but helping them get things done faster.
Additionally, let’s not forget that AI has a lower error margin than human beings. The margin of error in AI is 3%, while it’s 5% in human beings, and when processing valuable data, that 2% difference matters a lot.
Improves your logistics
Logistics are the overall process within an organization, beginning from purchasing raw materials to shipment, selling the end product, and more. Overall, machine learning does an excellent job of improving the company’s logistics.
Currently, there are many international companies that are including machine learning in their manufacturing, delivering, and shipping stages. For example, shipping companies use machine learning features to analyze the breakdown patterns of trucks and ships and allow vehicles to be appropriately maintained in time.
A large company using these practices is Amazon which uses the power of anticipatory shipping to allow quick shipping to take place and satisfy the needs of customers quickly.
Improves AI algorithm through valuable data
In an academic environment, AI research is concerned with creating algorithms that can perform tasks on established datasets, including SQUAD, ImageNet, and more.
There are various ways to gather the correct data for training and maintaining machine learning algorithms in real-world apps. For instance, more and better data leads to much better predictions. The more valuable data you gather, the better predictions you’ll most likely make.
According to statisticians, gathering quality data at times may be time-consuming and costly, but there are more benefits than being only time-consuming. As you train your machine learning algorithms with more data, accuracy improvements will slow down. The third data point will provide much more valuable information than the 50th one, which, again, the 50th one is still more useful than the 100th.
However, when you use machine learning in business terms, things change, and it all depends on how much value you get from your predictions. So, if you see that more data improves your machine learning algorithms to a point where you have a significant advantage over your competitors, the investment should be worth it.
Moreover, you see tech giants such as Google and Facebook competing at such high levels to collect data and improve their machine learning algorithms.
Machine learning helps in financial management
Machine learning algorithms can also be used for financial analytics, such as:
- Predicting business expenses
- Conducting cost analysis
- Algorithmic trading
- Fraud detection
All of these financial analytics rely on historical data to predict future outcomes. The accuracy of these types of predictions depends on the data you gather and your machine learning algorithm.
For example, a straightforward ML algorithm will be enough for simple tasks and predicting business expenses. However, ML algorithms will have to go through multiple revisions and many years of data until accurate ML models are found for algorithmic trading. For instance, investors firmly depend on ML for predicting the market when investing.
Accurate and timely predictions allow businesses to manage their financial situation better and increase revenue in the long term. Taking the proper measures will lead to long-term cost savings.
Computer vision includes object, image, and facial recognition. You can use inexpensive APIs to gather valuable information, including the text summary, age and sex of individuals, emotion, recognized faces, inappropriate content, and the topic in the photo. For instance, if your digital media library has thousands of images, this can ease your library’s manual maintenance and allow you to find more ways to retrieve content.
Wrapping it up on Machine Learning
Well, that’s all for this article. These were our top reasons you should consider using machine learning for your business and how much value it provides for your business. Generally speaking, machine learning is underestimated, and many companies don’t even know much about it.
However, machine learning is the future and will be the primary reason AI becomes similar to a human being. Its natural language processing has come such a long way that it’s even starting to differentiate words and hiring different actors to speak different dialects and improve the quality of the language processing.
Last but not least, the future of business is machine learning. Because there is a low barrier to entry in the digital world and all companies are moving towards a digital world, machine learning can provide enormous benefits to our working methods. Moreover, even though many may think it’s getting rid of jobs, it only makes jobs easier for us and increases our productivity levels. After all, that’s what every business is aiming for, to be more productive and efficient.