Source: https://www.youtube.com/watch?v=0yCJMt9Mx9c

AI (artificial intelligence) is everywhere. It approves your bank loan and it predicts the weather, and the possibilities are endless.

AI is basically intelligence demonstrated by machines, as opposed to natural intelligence that is displayed by humans and animals. AI is self-taught and follows a handful of instructions to establish a unique list of rules and strategy. The aim of AI is to copy human behaviour in both decision making and completing tasks.

AI is established through different types of Machine Learning. When supervised, unsupervised and reinforcement learning are used together, they can create complex programs that can both self-regulate and teach each other.

There are numerous ways of creating artificial intelligence. However, the most effective way is to build them to mimic the relationship between neurons in the brain. These artificial neural networks can complete numerous tasks such as image recognition and prediction.

There are two types of AI:

What is the Difference Between AI and Machine Learning?

Even though the terms AI and Machine Learning are used interchangeably, they are different. Machine Learning performs calculations delegating the decision-making to humans while Artificial Intelligence provides the human with a few suggestions about the right decision – AI is the idea that machines are able to complete tasks in a smart and efficient way.

For example: Machine Learning is the train control room, and it calculates what train is going to be late tomorrow, whereas AI provides you with a bunch of options and actions you can perform to avoid the delay from happening.

On the other hand, Machine Learning is the way in which AI is implemented, and it is based on the idea that computer scientists should allow machines to learn by themselves by providing them with data.

AI and Machine Learning in Different Industries

As we have already covered, AI and Machine Learning are two different but complementary concepts that concur in bringing numerous benefits to various industries.

Their differences can be used to improve efficiency or accuracy in decision-making in your business.
https://www.redhat.com/en/blog/what-aiml-and-why-does-it-matter-your-business

AI and Machine Learning in City Planning

Architects and urban engineers have always employed the latest tech innovations to be more efficient and accurate when dealing with the design and the planning of urban infrastructures. For example, numerous software enable automation which results in time saving.

However, AI and Machine Learning algorithms are more powerful than any previous software. One main difference lies in the high amount of data and the number of variables considered and processed. Even though the final decision is left to professionals, the number of calculations and considerations performed is higher than any human’s.

Machine Learning algorithms can compute a high number of variables and data and provide an array of solutions around complex urban design work. A few of the benefits could be improved traffic flow, access to amenities, and footpath space.

On the other hand, AI can analyse various options and can identify different optimal solutions leading to improved conditions. However, there are a few areas in which AI cannot provide viable solutions. More specifically, in concerns around social sustainability and community relations a personal input is needed rather than a computational artificial approach.

AI and Machine Learning in Transport

Transportation and technology have always complemented each other. From carrying people to delivering goods from A to B, researchers and practitioners have focused on how to make this process more efficient.

As you might have guessed already, AI and Machine Learning are going to be essential to the development and improvement of the transportation industry. According to Forbes, AI and Machine Learning will potentially increase the value of the transportation industry by an additional $2T.

The main benefits of using machine learning in the transportation industry are the following:

Examples include:

Multiple surveys have highlighted the increasing importance that business leaders have started giving to AI and machine learning. According to a recent survey, 72% of leaders believe that AI empowers workers to focus on meaningful work and 34% believe that Artificial Intelligence ensures that more time is dedicated to meaningful work.

Machine learning is going to be essential in making HR practices more effective. From ensuring that enough staff is available to work to pairing senior staff with juniors and graduates, machine learning algorithms will enable them to tap into new capabilities and achieve higher results.

AI In Telecommunications

According to Mckinsey, the telecommunication industry is one of the most complicated ones to operate in. The complexity might be due to the fact that a highly dynamic and responsive approach is needed. This is where AI and Machine Learning can simplify the numerous tasks.

5G might be just the tool that could bring new solutions to the challenges that the telecommunication industry and society is facing. The main reason lies in the fact that 5G collected a high amount of data that various stakeholders can use to improve cities and the environment.

From cutting and managing C02 emissions to preventing low emission areas from becoming polluted, the combination of AI, machine learning and 5G is going to be a real game changer.

AI in Automotive

According to Forbes, the global autonomous market will reach $60 billion in 2030. Not a bad number. This predicted growth signals the increasing importance of self-driving cars. AI and machine learning are only going to increase that importance.

The main focal point of car manufacturers is going to be the need of creating accurate and unbiased datasets that can be used to create machine learning and AI algorithms to integrate small automations. One of the main contributions of AI and machine learning to the automotive industry is going to definitely be the ability to provide better in-car assistance. By using high-quality training data, AI will be able to interpret driver cues, recognise speech and voice as well as help people choose the right car for their needs.

AI in Managing Retail Assets

Retailers use AI and machine learning to optimise their inventories, build recommendation engines, ensure there are enough available members of staff, and enhance the customer experience with visual search.

From avoiding over-stocking which occurs in beauty and cosmetic retail shops to placing the inventory in the wrong place, AI and machine learning can reduce the costs of inventory by 30% and increase revenues by 2%.

Sounds like a dream right?

AI in Managing Healthcare Facilities

Health organisations put AI and machine learning to use in applications such as image processing for improved cancer detection and predictive analytics for genomics research.

However, AI and machine learning are going to be most effective in managing healthcare
devices.

Medical devices manufacturer are going to benefit from artificial intelligence and machine learning to improve the performance of their equipment which are ultimately going to improve the levels of patient care.

Artificial intelligence and machine learning is effective in learning from real life experience and continuously improving its performances based on experience.

Applications of AI and Machine Learning

To summarise, many companies in many industries are building applications that take advantage of the connection between Artificial Intelligence and Machine Learning – and you could too.

It is not only about the various changes that AI and Machine Learning are bringing into different industries, but it is also about the innovations they are bringing into different business operations.

Could they help you transform your processes and products?

‘Machine Learning’ is one the main keywords that has been buzzing around on LinkedIn over the past few years, and everyone wants to know what it’s all about.

To the average person, ‘Machine Learning’ may be more likely to conjure up imagery of a physical, automatic machine that completes repetitive tasks, like the machine that replaced Charlie’s dad in Charlie and the Chocolate Factory.

So, what exactly is Machine Learning?

Machine learning essentially involves humans teaching machines. Familiar examples to you may include computers or cleaning robots, and how they independently learn about the environment that surrounds us and them. In order to ‘learn’, machines look at patterns and differences, which they then use to make ‘guesses’. However, machines require lots of data before becoming increasingly precise with their guesses. Machines build models with the data they are given. Subsequently, they use the model to make predictions.

For example, platforms that offer recommendations to enhance your user experience use Machine Learning models to make predictions based on the data they have gathered from your past experiences. Clever!

One mainstream example could be IKEA having a sale and strategically locating the flash sale items in warehouses near densely populated cities. IKEA would employ Machine Learning to predict the location from where customers are more likely to place orders.

Types of Machine Learning

Let’s take a look at the different ways that humans teach machines to learn by themselves – which isn’t as ‘robots taking over the world’ as it sounds.

We can compare it to the different types of revisions humans do when preparing for exams, from studying by heart the content to creating mental maps.

There are four different types of Machine Learning:

But let’s dive into Supervised Learning.

Supervised Learning

As you can infer from the name, supervised learning happens under the supervision of a data scientist. This person provides the machine with explicit learning data and tells the machine what variable they want the machine to analyse and assess. The data scientist needs thousands of data points to enable the machine to understand and create its own models.

Supervised learning is used for many tasks, such as:

For example, a data scientist provides the model with 10 thousands of images of cats and dogs and then requires the model to divide the images in two categories.

The data scientists provide the machine with clearly labelled training data of images of cats and dogs, which the machine can then use in order to ‘learn’ and therefore assess the images and divide into the relevant categories.

From easily collecting data or creating data output based on historical data to optimising performance criteria, Supervised Learning presents loads of benefits. On the other hand, multiple companies struggle to implement supervised learning. From hiring and training costs of data scientists to collecting enough data, supervised learning is not for the faint hearted.

This is where outsourcing to experts comes in handy. TUBR makes Machine Learning accessible to different types of companies with different types of budgets. It cuts down numerous costs, from hiring and training costs of data scientists to data collection as TUBR only needs 20% of the entire dataset in less than a month.

Now, doesn’t that sound efficient?

Why Do We Need Machine Learning?

From a corporate perspective, there are numerous reasons why Machine Learning is important to the daily operations of a business. Machine Learning can give companies insight into future demand in order to effectively manage their assets, reduce waste and drive customer satisfaction and loyalty.

Machine Learning also allows the company to make predictions such as:

Finally, Machine Learning allows the company to process automation on repetitive tasks so that customers can focus on tasks that add value to the business.

Machine Learning In Business: A Case For Active Mobility

Machine Learning can bring numerous benefits to various industries. One of them is active mobility. Among the many challenges that affect this industry, an efficient use of resources is one of the most predominant one. From ensuring that operation and environmental costs are minimised, the active mobility industry is a perfect playing field for Machine Learning.

Recently, shared electric bikes, such as Line or Santander bikes, have become popular among commuters, bringing numerous environmental and socioeconomic benefits. However, the location of electric bikes and improving their usage is a major challenge for city councils and manufacturers.

In this scenario, a time-series Machine Learning approach could be implemented to predict the usage and the location of parking spaces to meet the demands of as many people as possible.

Think time-series Machine Learning could have an impact on your business? TUBR is here to help.

AI has the transformative power of streamlining business processes and introducing new changes to the environment, with or without the understanding of the people who have created the algorithm.

In a previous blog, we talked about how the differences between Machine Learning and AI are going to benefit different industries and what changes they are already introducing. However, it doesn’t matter if you sell paper or build cars, most likely you have an HR department and a procurement department. All businesses are connected by shared processes and operations, regardless of industry. They all need to hire new employees, keep their finances in check and provide the best customer service. As do you!

How can AI help in transforming your business? Let’s find out.

AI in Business Operations

Business operations are all about completing different types of tasks, to ensure that the company is able to generate revenues and acquire a large profit as efficiently as possible.

From ensuring that the office is fully stocked with supplies and brand material, to updating outdated guidelines, an operations manager has a lot on their plate.

This is where AI can transform your internal processes, streamlining operations so your employees can focus on more meaningful tasks that add value to the business, instead of wasting time.

AI can be integrated into your daily operations so that:

  1. Manual processes are reduced and automation is introduced
  2. Help decision making by providing operation managers with key insights
  3. Help operation managers comprehend where bottlenecks are located and how they
    can be addressed
  4. Improve accuracy

AI in Finance

When dealing with the finance department of your company, it’s not all about paying the bills and invoicing customers. The finance department is critical to understand risk management, measuring the performance and gaining important insights around the return on investment.

In this scenario, AI’s deep learning capabilities will be most effective. Deep learning is the result of supervised, unsupervised and reinforcement learning, and its aim is to arrive at specific and highly tailored solutions depending on the data processed.

Now imagine applying deep learning with the financial data of a company. The results are infinite and are going to provide highly accurate recommendations around the following:

  1. Augmenting investment research
  2. Improving investment performance
  3. Reinforce fraud detection
  4. Forecasting cash flows
  5. Reducing costs

AI in Marketing

There is such a high amount of customer and marketing data that AI algorithms everywhere will have an absolute field day. From information gathered at the supermarket checkouts, to posts liked on Instagram, people are offering their data on a silver platter and AI can use that data to improve their overall experience.

From offering a more intelligent and tailored service and products, marketers can now target individuals which was unfeasible before the development of AI.

In the past, the closest marketers got to tailoring their marketing practices was by segmenting the population based on demographics and psychographics. Nowadays, they have the tools and the capabilities of analysing the actions and the preferences of each individual tapping into new possibilities for their marketing practices.

AI in Logistics

If you are a retailer or a logistics provider and you want to ensure that all you are using your resources efficiently to get your goods from A to B, AI might be the technology you’re looking for.

AI is going to be effective in demand prediction. By providing retailers and logistics operators with the number of supplies and goods certain customers are likely to order, AI can prevent lack of inventory and potential loss of sales. Additionally, your warehouse management can also be streamlined, by measuring and keeping track of various variables.

AI in Customer Service

One of the most popular approaches to customer service and AI is the creation of chatbots, effective self-service and fraud detection.

The use of NLP (natural language processing) enables chatbots to mimic the conversation that customers would have with customer service agents to resolve their queries. Not only do chatbots answer frequently asked questions, but they also help customers navigate the website and the app.

Chatbots enable customer service agents to focus on more complicated issues and can enable companies to conduct sentiment analysis to understand customers’ feelings. In addition, chatbots enable the company to act in real-time and improve the customer experience overall.

To summarise, AI holds the power to transform, streamline and have a real, tangible impact on your business and the processes you perform every day. To discover how TUBR could turn your time-series data into predictions and results, visit our solutions.