Spatial Time Series is an interesting and curious concept. As discussed in our earlier blog all about Spatial Time Series, it’s all about feeding a Machine Learning algorithm with data points collected over a very specific interval of time and defined by their spatial position.
Though there are a lot of applications for this in the business world, the real power of Spatial Time-Series lies in its capabilities of bringing real change in the fight against climate change.
Exploiting the Machine Learning model to understand what changes will take place due to global warming can help policymakers to delineate a set of proactive measures to avoid natural disasters – rather than taking on reactive acts when the earth shakes or the tsunami hits.
According to this research paper, most of the data around biological effects of climate change are collected seasonally which lacks the spatial and the temporal changes that take place in a microenvironment.
Well, Spatial Time-Series is here to provide exactly that.
Spatial Time-Series is becoming increasingly important in understanding global warming, predicting future temperatures and helping with environmental decision making.
A group of researchers from Ghent University and Chinese Academy of Agricultural Sciences have published an interesting article in the Advances in Atmospheric Sciences journal.
Their research focused on implementing a time series modelling to examine one of the most important environmental variables, the monthly records of absolute surface temperature.
This demonstrated that the surface temperature is going to rise and that their model can be used along with other environmental models to implement short-term environmental decision-making.
This model has the capabilities of improving communication between policy makers, environmentalists and researchers to bring active solutions on the table in terms of land utilisation, rising sea levels and natural disasters.
Unfortunately, examples of extreme weather are becoming more and more frequent.
Just three months ago, India and Bangladesh were hit by heavy rains that caused the water levels of rivers to rise and flood all cities. It goes without saying that people were harmed and buildings were damaged.
Another example is brought by the extreme heatwave that Europe has experienced this past summer. This caused wildfires as well as extreme droughts.
In this context, Machine Learning and Spatial Time-Series can be the solution policy makers have been looking for to fight climate change and, most importantly, come up with preventive measures to fight natural disasters.
For example, one of the main benefits of using Spatial Time-Series is its ability to predict future outcomes. By analysing labelled data and identifying patterns, the most at-risk areas of future natural disasters will be predicted and policy makers will be able to implement proactive strategies to minimise the effects.
The goal of many companies is to reach “Net Zero” which means that they are trying to reduce the carbon emission that is generated by their operations.
According to the EPA, the total emissions of CO2 were around 5,981 Million Metric Tons of CO2 equivalent just in the USA. In this figure, the transportation industry contributed by 27% and is the largest source of greenhouse emissions.
The greenhouse gas in the transportation industry comes from the use of fossil fuels to operate vehicles.
Spatial Time-Series is a powerful tool to use to reduce the carbon emissions caused by transport and traffic. For example, if councils performed an analysis of the hourly or daily traffic volume data, they could understand the number of cars going from A to B and implement innovative strategies to reduce the traffic with effective urban planning that incentives the use of alternative means of travel.
According to the IPCC, creating new buildings and maintaining them accounts for one fifth of the world’s emissions.
Not a great figure that puts managing infrastructure and offsetting carbon emissions at the top of the areas to tackle in the fight against climate change.
Spatial Time Series can help in managing and reducing the environmental impact of buildings, especially in cities.
For example, councils could use Spatial Time Series to understand which areas in the city are the most carbon intensive because of the heating and ventilation of their buildings. This could help policy makers to promote alternative energy to heat or to use electricity.
To wrap up, climate change is a phenomenon that we are all dealing with. It is still not a reality that we have to accept and there are steps that can be taken to reduce or to mitigate its effect. The solution lies in new technologies and the power of using untapped data.
Spatial Time Series is a powerful tool which has the ability of helping save cities and people by predicting and helping decision makers in their jobs.
To outsource or not to outsource, that is the question.
Well, according to 70% of B2B decision-makers, delegating certain tasks and operations to an external company is the most effective way to ensure time-saving and cost-saving.
There are numerous challenges that businesses are facing everyday. These challenges are not only caused by big historical events such as Covid-19, but are also caused by shifts on a smaller scale, like higher fuel prices or national strikes.
There’s only so much a company can do. You can be a great logistics provider and you want to optimise your performances, but do you really think that creating an in-house machine learning algorithm is the best way to approach this or would you rather put the experts in charge?
Just some food for thought!
Adding machine learning solutions to your operations needs the expertise of highly trained technical staff. This is a rule set in stone. No amount of Coursera courses or self-teaching on YouTube will give you what you are looking for.
You could hire data scientists and build an in-house model, but have you conducted a research on Glassdoor to see how much is the average salary of a data scientist or machine learning developer?
In this case it is not about soft skills, it is all about technical hard skills that are developed through years of studying and training. The issue that you might be facing might be too complex for your staff to deal with.
This is why working with a predictive analytics platform that turns small data into actionable insights is the best decision for you. We have a great team of machine learning developers who are eager to get their hands on your data to help you meet your targets from the get go, without any training required, only your spreadsheets.
Big data has been a buzzword for quite some time now. The new buzzword is small data.
It means that the enormous amount of data that is around us is not of concern, your business data is. Outsourcing to TUBR means that you can execute highly targeted projects without impacting your core business.
Do you want to improve your customer service experience without consistent data, but it is peak season for Christmas deliveries and you do not know where to start?
TUBR is what you’ve been looking for. We have adopted a physics approach to understand how data is connected, validating gaps and recreate a system into a model in which external factors support the prediction.
This means that we find relationships between the data and actively fill the gap to provide you with predictions that are reflected in the external environment.
Multitasking is only enjoyable when your tasks are simultaneously eating and watching Netflix, not when improving your customer service experience while delivering Christmas presents.
By outsourcing your machine learning processes, costs will be reduced, profits will increase and your accountant will be happy.
Why?
Well…
These are just two prime examples of how much you will save by outsourcing to a company who is going to provide you with all the predictions you need, without going bankrupt.
There’s nothing more unifying all businesses than the fear of not complying to GDPR regulations.
A skilled and highly trained machine learning workforce understands the need of handling, storing and systematically managing small data on various platforms.
Outsourcing to a predictive analytics platform such as TUBR reassures you that the company’s sensitive information is kept secure and far from malicious eyes.
Ultimately, there’s many more reasons that go above and beyond the basics we’ve outlined here. TUBR is the company you want to work with if you want to reduce waste and optimise your assets efficiently and without breaking the bank.
Why don’t you give us a call?
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?
‘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.
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.
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.
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?
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 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.