The role of Machine Learning in simplifying the operations and procedure of companies is well-established.
The most popular examples of Machine Learning are Natural Language Processing (NLP) and Sentiment Analysis, the contributions of which have improved customer service and marketing practices.
One less discussed, but equally important (and groundbreaking) application of Machine Learning is Spatial Time-Series.
Spatial Time-Series combines time series analysis with spatial series analysis.
Time series refers to a peculiar way of analysing a set of data points collected over a specific time interval. This differs from data collection because it aims at recording data at consistent time intervals rather than randomly collecting data
Spatial series refers to a set of data points of a variable collected based on their position on two spatial coordinates. One of the most popular ways of collecting spatial data is by using GPS.
Therefore, Spatial Time-Series refers to a Machine Learning algorithm that is fed with data points collected over a determined time interval and defined by their spatial position on two coordinates. It has numerous applications in the corporate world which can bring operational and financial benefits to the organisation.
Spatial Time-Series is useful in predicting demand in a certain city or region depending on numerous factors.
From logistics companies to retailers, understanding when certain items are going to be most demanded and requested can have numerous benefits. From ensuring that there are enough items in stock to increase production in preparation of the high volume of orders, the financial and brand reputation benefits are clear.
For example, if you’re a kitchenware retailer and a new academic year is approaching in two months, you’ll benefit from conducting a Spatial-Time Series analysis. By providing the Machine Learning model with historic spatial and temporal data, you’ll be able to predict which university cities need which amount of kitchenware to satisfy the needs of incoming students.
Managing a warehouse is the centre of a company’s supply chain and logistics operations, and reducing storage costs is one of the most popular challenges that companies need to overcome.
From optimising processes to ensure customer satisfaction, it provides profitability. At the end of the day, any accountant knows that inventory is cash thus reducing storage costs is essential to increase the profit margin.
Usually, shortage costs are caused by errors in demand forecasting which can be avoided using Spatial Time-Series.
However, there other advantages that Spatial Time-Series presents:
Not bad, huh?
Every company understands that managing risk is essential to ensure the smooth operations of the firm. The ultimate goal is to understand and proactively manage them and optimise the success rate.
Machine Learning and Spatial Time-Series enable companies to predict what is going to happen in the future, giving them an opportunity to either prevent them or put mitigating solutions in place.
For example, city councils can use spatial time-series to understand what infrastructure policies they can implement to ensure that the cities are adaptable to future changes. This is particularly useful in case of predicting environmental threats such as flooding, heatwaves and snowfalls.
With the temperature of the planet rising and weather becoming more extreme, companies can use the latest technologies to implement more environmentally friendly initiatives.
Spatial Time-Series is a strategic tool that can bring numerous benefits. For example, city councils can use it to manage traffic, reduce carbon emissions, and identify strategic zones in which to place alternative methods of transport such as e-bikes.
Spatial Time-Series is all about ensuring that things work in the right place at the right time. We know how frustrating it can be reaching the wrong destination or getting to an appointment ten minutes late. On a corporate level, wrong destinations and delays have financial and brand reputation repercussions and Spatial Time-Series is here to avoid that.
Let’s speak about how Spatial Time-Series and TUBR can help you in overcoming any data challenges you’re facing! A quick email to seethefuture@gettubr.com is all you need.
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:
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.
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.
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.
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.
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.
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.
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?
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.
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?