Generative AI has been the ‘hot trend’ of the year, with 87% of companies surveyed by Bain & Company reporting that they are developing generative AI applications. Generative AI refers to a class of artificial intelligence systems designed to create new content—such as text, images, music, or code—by learning patterns from existing data. Common examples of generative AI include ChatGPT and even certain TikTok filters and effects.
Unlike traditional AI, which typically performs classification, prediction, or decision-making tasks, generative AI focuses on producing outputs that are similar to, but distinct from, the data on which it has been trained.
With the growing pressure on organisations to adopt and develop generative AI technology, some less-than-impressive creations have inevitably made their way into the public eye. Below are a few standout examples from recent years:
New Zealand supermarket Pak ‘n’ Save launched Savey Meal-bot, an AI tool designed to create recipes based on a user’s shopping list. The goal was to help customers reduce food waste and find creative uses for leftovers—though “creative” might be an understatement. Outputs included dishes like “Oreo vegetable stir-fry,” “bleach-infused rice surprise,” and an “aromatic water mix” that was, in fact, chlorine gas.
Two lawyers found themselves in hot water after using ChatGPT to draft a legal brief for a personal injury claim. Unfortunately for them, many of the citations the AI provided were either incorrect or entirely fabricated—something that only came to light after the brief had been submitted to a judge.
AI’s struggles with generating realistic images of people—particularly hands—are an ongoing challenge. Some argue this is due to limited data; photos tend to include fewer clear images of hands compared to faces, and those that do are highly nuanced. As a result, AI models often lack the data needed to accurately learn how to render them.
In February 2024, an unlicensed two-day “Willy Wonka Experience” was held in Glasgow, with tickets costing £35 each. Some families even drove hours to attend this supposed “celebration of chocolate.” Unfortunately, not only were the images used on the event’s website clearly AI-generated, but the event itself was such a disappointment that the police were called. The experience was ultimately cancelled after half a day, with full refunds promised. This serves as a reminder not just about the risks of false advertising with AI, but also about the importance of delivering a product that truly delights your customers.
While we’ve seen a fair share of generative AI ‘fails’ spread across the internet this year, for every public misstep, there are many more technologies that don’t even make it past the experimental stage. As with any scientific exploration, failure is rarely a true failure—each iteration brings valuable insights and sometimes leads to solutions you weren’t even looking for (Penicillin, anyone?).
It’s also important to remember that there are no magic bullets when it comes to AI. Few, if any, artificial intelligence systems succeed without human intervention or oversight, and not all AI will be relevant to your business. Just because you can use a technology doesn’t mean you should.
That said, this isn’t a cautionary tale. Technological advancements have created incredible opportunities for SMEs that were once only available to large enterprises. This is why finding the right technology partners is so crucial—they can help you understand the opportunities and limitations of different technologies and determine what is best for your business’s specific needs.
Learn more about how TUBR can work with your team to help grow your business here.
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?