Machine Learning Can Done Everything For You

Machine Learning Can Do Everything
For You

Machine learning Can Do For You

Machine learning can be described as a branch of Artificial Intelligence (AI) and computer science which focuses on the use of data and algorithms to copy how humans learn, gradually improving its accuracy to be exactly like a human. Machine learning is one of the biggest components in industry 4.0 which grows rapidly. This is due to the use of statistical methods, algorithms are trained to make classifications or predictions, and to uncover key insights in data mining projects. Machine learning begins with observation patterns, and learning by data, and involves human decision with minimal touch. The main goal of ML is to allow computers works like a human brain on its own and adjust their actions accordingly. There are 3 main components in the learning system of the ML algorithm which are the decision process, an error function, and updating process. ML has been used in the business industry since it starts to be well-known. So, how can machine learning help businesses nowadays? Based on Shawn Harris, ML can help businesses by “Do More with Less’ whereas Zebra (a manufacturing company in technologies) can help businesses by sensing, analyzing, and acting in real-time. Next, automatic routine tasks. It works like ChatBot.

In businesses, customer problems can be solved by chatbot (machine agent) but only by frequently asking the question will be answered. For example, ‘why does the delivery takes so long?’, ‘when can I get my refund?’ and more. It helps workers in the company in engaging customers for 24 hours. ML also can help businesses in finding areas that can maximize efficiency. Those who wanted to run businesses can find premises or building rent in Mudah.My where it provides many suggestions for entrepreneurs to rent or buy. Basically, on the website, you can choose the spaces, square feet, monthly payments, facilities, and more. With that information, it would suggest to you the best building or premises that you want. As we all know, there is no technology that can detect grouping/clustering customers but unsupervised machine learning can do it by approaching to divide data points into groups that are similar. So, the algorithm can define the cluster. In running businesses, there must be risks that we have to face. There is one platform, ChargeBack119, where it can gauge risk more effectively by integrating proprietary systems and within days, began identifying unnecessary revenue loss. Besides that, if you wanted to start a business but do not how, Looka could help you. What is so special about Looka? it can help you design the logo, bring your brand to life and market your brand. Machine learning also helps an entrepreneur in solving the problem that humans cannot do such as streamlining all the data analysis.

Nyansa is a software company that can help the entrepreneur in analyzing and correlating data across every layer of the network to make sure the best possible customer performance. Since machine learning is getting developed, it becomes cheaper and easier to be accessed by entrepreneurs and customers in processing customer data efficiently. Entrepreneurs can know what is the most product that customer want. Examples, are Lazada, Shopee, e-bay, and other e-commerce. The customer always gets the suggested product that they might buy while the entrepreneur can know the most product that always is looking for the customer via the affiliate system. From this, it is easier for entrepreneur to evaluate their customer and product demand. Additionally, ML also can help the entrepreneur by calling their staff to prepare the room for the meeting, telling you about the daily schedule, showing financial reports in the meeting room, starting the meeting room online, and more. All these activities can be done by Alexa (computer program). It really helps the entrepreneur to be more efficient, and productive.

As we all know law firm is a service business. Due to that, ROSS Intelligence helps lawyers in solving the hardest law that cannot be solved by a human, for example, reviewing all the past documents or cases where it takes more than 300,000 hours in just a few seconds. This legal service helps in lightning lawyers’ works. Lastly, ML can help the entrepreneur in making their customer more efficient through object detection which allows the customer in checking the items’ prices without a cashier. Without realizing it, we use to normalize using machine learning in our daily lives. For example, in the streaming service company business, Netflix is one of the most famous businesses nowadays. What is so special about Netflix is that they can recommend movies based on the movies you have watched. For example, you love to watch sci-fictions. Next time when you want to watch movies back, it suggests sci-fiction movies for you to watch. This can attract more people to use Netflix. In American multinational automotive, Tesla is using ‘imitation learning’ algorithms where all the data of millions of actual drivers around the world have been put into the cars’ sophisticated system. Owing to that, all those directions had translated into their super smart autonomous cars.

Their system encourages the elites to invest in their company since they see a big potential for buyers and investors in the future. In healthcare companies, machine learning always is used in X-Ray. This ML can see something that doctors cannot see which is the patient’s race where the scan detects whether the patient is Black, White or Asian. Due to that, x-ray price is expensive, RM 100 to RM 1000 which can increase business profit. Lastly, in an online service provider company, LinkedIn, machine learning relies on input by training data or knowledge graphs, to understand entities, domains, and the connections between them. Deep learning can be started by defined entities. Define entities is the name of people, what is people’s passion, age, locations, and so on. This is a platform where people can build their own profiles where they can develop their job and career, and allows job seekers to post their CVs and employers to post jobs. It helps the entrepreneur in searching for workers. In achieving the success of machine learning, there must be challenges to face. First, is the technological singularity. As we know Philosopher Nick Bostrum defines super-intelligence even though it does not happen in our society afterward, raised some questions. Would a driver less car never have an accident? If it does, who would be responsible for that? These question still does not have an answer. This challenge might be one of the obstacles for Tesla to grow as an automotive car.

Next, ML is a complex process. This is owing to ML is still new in the industry and frequently changing from time to time. The change makes it difficult because high risks of error might happen and automatically ML becomes complicated. The activities include analyzing data, removing data bias, training data, applying complex mathematical calculations, and so on. Thus, slow implementation. The aim of ML is about the accuracy of a human. It takes a long time to achieve because of slow programs, overloaded data, excessive requirements, and so on. Additionally, the process also has to be monitored and maintained to deliver great output. Besides, privacy. It tends to be discussed in society since our privacy has been saved as a database. It drives society to be concerned a lot about their privacy. Due to that, the policy has created such as GDPR legislation. This policy has created in 2016 and it protects the personal data of people in the European Union and European Economic Area. Accountability is also a challenge that ML must face.

Since AI does have specific policies to be monitored and practiced, the collaboration between ethicists and researchers has emerged. This is owing to govern the construction and distribution of AI models within society. Lastly, poor quality of data. One of the significant roles in ML is data and the biggest challenge that they have to face is slow data. This is due to data, they are not electronic, but electrochemical. So, it can trigger the algorithm to make inaccurate or faulty predictions. All these challenges automatically might impact the companies in making strategies for their company accurately since some of the problems cannot be solved by a human. Additionally, if machine learning is taking all human work, what would happen to society? Unemployed people would be increasing year by year. AI must take wise actions about all these impacts for the future by finding smart ways to overcome all those challenges. Since machine learning is still improving the accuracy to be exactly like the human brain in the future. Let’s discuss the future of machine learning. The global market in ML is increasing from $8.43 billion in 2019 to $117.19 billion by 2027.

Many companies have been using ML because they all see the potential of ML algorithms can make accurate predictions and help them in decision-making for their businesses. Since ML is getting used by people nowadays, we cannot imagine the future without it on a daily basis. So, ML must be continued since it has the power in bringing transformative changes across industries in the future. Quantum is one of the advancements of ML because it has the potential in boosting the capability of ML. It also allows simultaneous multi-state operations, fast data processing, improving the analysis of data, and increased business performance. By these, we can see that quantum has a big opportunity to be better in the future. The investment by several technology companies and the rise of quantum machine learning are not that far off, maybe in the future, we can have quantum computers for our daily lives. In conclusion, machine learning is a copy of the human brain and it helps a lot of entrepreneurs and customers in the business industry. Even though machine learning has been developed in the 4.0 industry, it does mean ML has no challenges to face. As an example, slow implementation, slow data, privacy of people, and so on is not an obstacle for ML to be grown. AI must do something to overcome all the challenges for a better future of machine learning.