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Governments around the world have thankfully recognised corporate misuse of personal data and have brought in legislation to give citizens more rights over their data. GDPR, CCPA, PIPEDA, APPI and more give individuals around the world varying levels of protection and control over their personal information.‍

It’s this precarious data landscape in which we see AI starting to reach mainstream adoption. Many of us will be aware of the well-documented data privacy and copyright concerns reported in the press surrounding AI. But don’t be fooled into thinking that these worries are only present for the likes of OpenAI and Anthropic!

Even small and medium sized organisations need to carefully navigate data privacy when implementing their own AI-driven tech.

Preface

We’re not privacy lawyers, and none of this article is intended as legal advice. However I would like to point you to the ICO’s guidance around using artificial intelligence within the confines of GDPR. 

In this section, they rightly point out:

It is not possible to list all known security risks that might be exacerbated when you use AI to process personal data. The impact of AI on security depends on:

• the way the technology is built and deployed;
• the complexity of the organisation deploying it;
• the strength and maturity of the existing risk management capabilities; and
• the nature, scope, context and purposes of the processing of personal data by the AI system, and the risks posed to individuals as a result.

Due to the vast scope of potential use cases that AI presents, the precise way that you protect and secure user data within such a system is largely dependent on the scope, function, and construction of that system.

With this in mind, any SME exploring the use of AI and automation within their organisation needs to be aware of the below seven AI and data privacy considerations, at the very least.

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1. Data Transparency Can Be Murkier Than You Think

Under GDPR, all European and British organisations now need to think more carefully about what personal data they collect, what risks they introduce by working with that data, and how to keep that data secure.

However, AI can introduce certain temptations when it comes to data processing. 

AI is incredible at filtering through and making sense of large amounts of data. Many organisations have a lot of siloed info that they desperately need to assimilate, understand, and get their heads around. Charging AI with this task would seem like a silver bullet solution.

Yet there can be real data risks in lobbing chunks of personally identifiable data into the AI meat-grinder, just to see what comes out the other end!

One of the guiding tenets of GDPR is transparency. Data processors need to be honest and transparent about what data they collect, why they collect it, and how they use that data. AI adoption can present two stumbling blocks in the way of this transparency.

When a piece of software is “closed source,” that means that both users and the wider public are unable to personally inspect the software’s code because it is proprietary to a given organisation. Microsoft’s Windows operating system is a good example of closed source software.

When a solution is closed source and proprietary to an external provider, it can be difficult to interrogate quite what happens to the data you put into it, where that data goes, and what it does. Could the data end up on an insecure server somewhere? Could the data be used to further train the AI model against your data subjects’ wishes? There may not be a way for you, as the average user, to tell.

We’re not accusing any AI model or software of this behaviour, of course. But without having access to the code that runs the software, organisations like yours have little way of knowing what is truly happening under the bonnet.

The second issue is that of AI’s renowned “black box problem.” A lot of deep learning systems rely on swathes of training data and inferences that have now become so complex that even their creators don’t understand why they give some of the answers that they do.

Understandably, both issues present a significant challenge for those trying to be as transparent as possible about how personal data is used.

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2. Follow the Rules Around Automated Decision Making

GDPR also contains stringent rules about automated decision making.

Individuals covered by GDPR have a right to opt out of solely automated decision making - i.e., where data controllers make significant decisions about individuals purely using an automatic programme or algorithm. Individuals also have a right to ask a human to reassess any decision solely made through automation. This remains the case whether AI plays a part in that decision process or not.

Additionally, our readers in the EU should also be aware of the new EU AI Act. This effectively bans the use of AI tools to impose “social scoring” on individuals or to identify people in real time using biometric data.

If you are considering creating a system that makes significant decisions about people’s lives, there are a few things you should bear in mind.

Firstly, identify the bare minimum data points that a human would need in order to make that decision about an individual case. This should be the absolute maximum data that you feed into your AI decision-making solution. If you give your AI solution more information than it is likely to need, you risk overexposing individuals’ data, you risk introducing bias into the AI model, and you risk regularly overworking the AI tool, which can present energy costs.

Secondly, you need to consider how your solution is going to respect the wishes of those who opt out of automated decision making. How you achieve this is going to depend heavily on what the solution does and how it works, but a way of excluding data subjects from automatic decisions should always be built in from the outset.

Above all, always keep data subjects informed about the use of their data, tell them about your use of automated processing, and give them clear ways to opt out or to challenge any automated decision. Schedule in regular checks to ensure that your decision-making tools are working as they should be too - especially when your AI tools use machine learning to pick up new things and adapt their judgement over time.

Essential Reading from ICO: Rights related to automated decision making including profiling

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3. Less is More: Embrace Data Minimisation

Data minimisation is where an organisation collects the bare minimum amount of personal data they need in order to function, and it’s wise data privacy practice. After all, minimising the amount of data you hold similarly minimises your data exposure risk and minimises data storage costs too.

You might also want to adopt a related concept: purpose limitation. That’s where personal data is only collected for specified, explicit, and legitimate purposes and never processed in ways incompatible with those purposes.

So where does AI come into this? Again, it might depend on what the AI is tasked with doing. For example, say you’re developing an AI solution that is designed to monitor a video feed and flag errors on an assembly line, though not to identify those responsible. It simply doesn't make sense to store vast amounts of likely repetitive video data, which may also introduce privacy concerns for workers and visitors in the vicinity. Such huge amounts of storage would also be vastly outside the scope of the application.

It would respect individuals’ privacy a lot more to only store and analyse video data whilst an instigating error is taking place; with measures in place to obscure any personally identifying images of team members captured in that segment of video.

It’s also worth bearing in mind that when an AI model has a smaller amount of purposeful, clean data to trudge through in order to formulate a response, this can have a positive impact on the model’s performance and hardiness.

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4. Build in Anonymity, Build Out Bias

If personal details aren’t relevant to data processing or storage, then keeping that data completely anonymised is great data protection practice. After all, if personal data isn’t present, it can’t be breached or misused. 

But anonymising data has another benefit too. When identifying characteristics (such as name, gender, ethnicity, sexuality and geography) are completely absent from a system, this eliminates the potential for bias towards or against certain individuals or groups. We’re all aware of how humans can bring their own biases into a process - but without careful instruction and training to the contrary, AI can introduce biases too.

In an older, well-documented case, Amazon developed an ML recruiting tool to review job applicants’ CVs and spit out the best few candidates for each role in a completely objective, neutral way. However, the tool was trained using CVs submitted to the company over a 10-year period - most of which were from male candidates due to the male-dominated nature of the tech industry. The system therefore ended up “teaching itself” to favour male candidates over female ones.

Therefore, measures need to be built into systems to eradicate bias - and build in total anonymity if the scope of the project allows.

For example, RAIven is building a real-time, AI/ML-powered, health and safety monitoring tool for a leading corporate client, which incorporates data from video streams. In order to respect anonymity, we’ve built in layers of abstraction so certain actions get flagged as potentially desirable or undesirable without feeding in any data that is identifies an individual. This built-in anonymity eliminates possible privacy concerns around storing people’s physical likenesses - but it also helps to eradicate the possibility of the system picking up any biases along the way.

Care also needs to be taken around what AI tools are allowed to infer about data subjects. Even with a few seemingly innocuous data points, a solution may be able to deduce highly personal things like gender, medical conditions, or sexual orientation, simply through its incredible pattern-matching prowess!

Bias can also be purposefully built into AI tools, as evidenced by Google Gemini well-meaningly “over-diversifying” images it generated from prompts where a level of historical precision was expected.

In our view, AI tools need to be constructed with the maximum amount of anonymity and with unbiased neutrality built in from the outset.

5. Keep Your Data Lean and Local

AI tools are able to receive, process, and create new data at breakneck speeds, making it essential that any organisation using AI carefully considers the practicalities of storing that data.

Keeping your data minimised, sanitised, and process-specific obviously reduces the amount of space it is going to hold on a disc. This reduces storage costs (and environmental costs) in and of itself.

However, there’s another factor to consider here – transfer costs. Transferring data from one location to another is going to use energy and incur cost. Transferring data, especially over public networks, can also introduce cyber and privacy risks too.

With this in mind, aim to keep any data and computation as local as possible. Does a piece of data really need to be transferred halfway across the country to be computed and then returned? Or can the whole process happen on-site?

Also bear in mind that AI requires a lot more computational power than standard computing, so any hardware that is tasked with on-site AI computing will need to be fit for purpose.

For example, within some of the solutions we develop, we are able to plug an AI-ready computational device directly into a camera or sensor, so the data generated doesn’t need to travel through miles of cable in order to be computed. The needed computing all happens right there before the results of that computation are moved on to where they need to go. This keeps data risk and transfer costs to an absolute minimum.

6. Be Aware of AI-Specific Data Privacy Attacks

Many of us are aware of attacks on people’s private data like social engineering attacks. But did you know there are AI-specific privacy attacks that can be used to uncover personally identifiable information from an AI powered system?

In membership inference attacks, hackers probe an AI model using previously obtained personally identifying data about a target individual. Their aim is to work out whether that individual’s data was part of the AI’s training data or not. This could let hackers know whether an individual had interacted with a particular service during the time the training data was being amassed.

Another type of attack is a model inversion attack, where criminals (armed with some initial identifying data about their target/s) aim to probe an AI model to infer and extract personal information about those individuals within its dataset.

However, there is an important caveat here: both of these attacks involve the criminals already having some personally identifying information about the individuals they’re targeting, and both require attackers to gain access to the AI model itself. This makes a strong case for data privacy and access control best practices.

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7. Document All Data Movement, Storage, and Use

The ICO make an excellent point about recording what you do with the data under your care: 

ML systems require large sets of training and testing data to be copied and imported from their original context of processing, shared and stored in a variety of formats and places, including with third parties. This can make them more difficult to keep track of and manage.

Your technical teams should record and document all movements and storing of personal data from one location to another. This will help you apply appropriate security risk controls and monitor their effectiveness. Clear audit trails are also necessary to satisfy accountability and documentation requirements.

You may also find it enlightening to interrogate your technical supply chains, especially those which directly interact with sensitive data and AI components.

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In Conclusion

The best way to ensure the most stringent control over data privacy within an IT system is to have it custom built. This way, you have total visibility into its internal workings, you are less beholden to external supply chain fluctuations, and you’re not locked into a particular vendor’s way of doing things.

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So if your organisation is exploring its custom, privacy-respecting, ethical AI options, book a free consultation with the Raiven team today!

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Having already reflected on my predictions for 2024, it feels that the past year has been somewhat chaotic in the land of AI. With constant model releases, battles on benchmark leaderboards, and a trial and error approach to developing solutions, it was clear that the world needed some time to find its feet and really think about the implications of AI technologies on society and the ways we work. But, if 2024 was the year of noise, then 2025 is the year of order. Or at least, the year we start to put things into order. Now the 2024 dust has settled, we find a lot of organisations reflecting on what the future really looks like for them, and how they can use a promising technology in a way that makes sense for their business. It will be an interesting year ahead, and below I've pulled out 5 key areas that I'm paying attention to in 2025...let's check in again at the end of the year to see what really happened.

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01. Agents, agents, everywhere!

Let's get this one out the way, AI agents became the big genAI topic of the last part of 2024, and 2025 is likely to see that popularity surge as OpenAI continue to push their o1 model, and focus on how they can chain model outputs together to tackle more complicated workflows. However, that will come with the same challenges that came with LLMs and AI assistants, as developers and users trial and error to find the right use cases. Questions will also arise around the cost-effectiveness of these solutions, as prices to deliver these workflows remain higher than automations (and in some cases higher than for a human to complete them). Whilst it is likely that agent based systems will stick around and become more sophisticated in the future, companies pushing agents will focus largely on language use cases and automations for now. Think: marketing/CRM, web scraping, and collaborative tools.

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02. OpenAI struggles to lead

The gap between OpenAI and the chasers closed significantly in 2024, and 2025 could be a challenging a year for the company as it struggles to build a moat around its product. Questions had already started to emerge about the company's ability to maintain its lead as the chasing pack of LLMs significantly closed the gap, and OpenAI shifted focus to chain-of-thought "reasoning" and additional features such as search. Since penning this prediction, OpenAI has already faced a significant challenge from DeepSeek, the Chinese competitor that has matched its models in benchmarks andintroduced a new method for training models. The challenge is exasperated further by DeepSeek's decision to publish a paper detailing their training and technical approach, as well as open sourcing their model for anyone to use. So far, OpenAI's response has been to release further features built on top of their model such as the newly announced "Research".

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03. Year of the AI builders

As the initial wave of fear, excitement, and noise has calmed down, and buyers/developers are beginning to understand more clearly what AI can do, conversations are turning more towards "What can AI do for me?". Organisations are beginning to learn more about the data and processes that allow AI models to be built, and explore how they could tackle challenging workflows within their organisations with this technology. We can expect a major focus on building AI solutions in 2025, with 64% of CEOs listing AI as a top investment priority. This will show in two ways: organisations will begin to demonstrate how they have built their own internal AI workflows to empower their businesses, and we will begin to see more comprehensive AI powered products gaining traction with a clear value proposition.

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04. Embedded Language Models

The drive to create smaller models is not just about ecology and efficienct, it's also enabling language models to be embedded into hardware. By growing the capabilities of smaller models, we are enabling engineers to explore how they can run models on devices with limited storage, memory, and compute power, which means: we may begin to see a wave of language features appearing on other devices. We're already familiar with smart speakers such as Alexa, but we may now start to see them deployed locally within our phones (allowing access to the models without internet connection), within robots to allow you to give spoken instructions, or perhaps even within appliances as people re-explore the smart home dream. With these products just around the corner, it's likely that we will see early version of these hit the market in 2025. It will be interesting to see how this advancement influences the design of products.

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05. Increased strain on AI Skills

As the AI demand refuses to ease, and as organisations are realising the potential of AI, and facing pressure to show that they are doing something about it, we will see a lot of businesses committing to building skills within their teams. The 2025 World Economic Forum Jobs Report published at the start of January highlighted that 70% of respondents intended to hire for AI skills. This creates a major challenge, as the market for these skills is already limited. There is a high likelihood that organisations will struggle to fill roles and retain staff with AI skills as companies jostle to build their own internal capabilities. It will take time for universities, apprenticeships and other training routes to deliver the talent needed to relieve this demand. One route some organisations may consider is upskilling/reskilling internal staff, however 63% of survey respondents considered this a major barrier, with it likely to be costly to invest in and a need for training organisations to step up and provide support.

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The AI world is moving fast, and it will be interesting to see how organisations adapt to the technology in 2025, I look forward to returning to these predictions at the end of the year to see what actually played out.

Like the monolith from 2021: A Space Odyssey, AI is being touted as something that will herald a new era of computational power and human capability. The things that AI is already able to do are truly mind-blowing - and it’s still very much in its infancy.

But let’s take the rose-tinted glasses off for a moment. We are all staring down the barrel of a full-blown, man-made climate crisis. Technology is helping us mitigate its impacts somewhat, with improvements in fields like renewable energy offering glimmers of hope.

AI holds the potential to make our lives easier and more efficient. However, AI’s relationship with energy consumption is complicated. At worst AI is a huge energy and resource hog - at best, AI professionals are working on mitigating that fact.

Pinning down the exact carbon impacts of AI, and especially of specific Large Language Model (LLM) AIs like ChatGPT, is tough. But energy use - and misuse - is totally under human control. It’s our responsibility as humans who use AI tools to do so ethically and mindfully.

Before we explore the ways we as AI practitioners can redress this much-needed balance, we need to fully examine some of the ecological worries inherent when using AI, especially Large Language Models.

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4 Major Environmental Concerns with Large Language Model AI Usage

1) LLM’s Massive Energy & Resource Consumption

AI - and especially elements like deep learning and neural networks - requires much more computational power than standard computing. The sheer amount of information storage and data crunching inherently needed by AI consumes far more energy than any kind of computing that has ever come before.

The wide-reaching, multi-purpose AI tools that make the headlines, like ChatGPT and Gemini, are called “large language models”. To vastly simplify, these tools maintain access to trillions of data points that they can draw upon when they give a response, and contain countless parameters to help them score and link those data points; hence why their responses seem so natural and human.

But maintaining access to those countless data points, parameters, and the inferred links between them -  and growing its access to millions more data points every day - takes a lot of energy, infrastructure, and hardware. And that’s before we get to the energy consumption involved in answering the countless requests they receive every 24 hours.

This becomes especially concerning when you zoom out and think about the vast data centres needed to operate cloud tools of all kinds, not just AI ones. Data centres constantly consume energy. Their servers are running 24/7, as are the routers and switches that get data where it needs to go. IT equipment needs to be cooled, so air conditioners are always running. Their employees have to commute to and from them every day, likely using combustion engines of some kind.

With energy supply around the world still so reliant on fossil fuels, this is understandably a significant draw on carbon resources. (Though it’s worth noting that both Google and Microsoft have made strides in their efforts to become carbon neutral/negative.)

Research from University of Massachusetts, Amherst found that merely training a large language AI model emits approximately the same amount of CO2 as 5 average cars across their whole lifespan. That’s just training the thing - once the LLM is actually put to use, it could end up consuming orders of magnitude more energy than that throughout its existence.

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2) Mining Rare Earth Minerals for Tech Manufacturing

In order to build new computer hardware, manufacturers need to mine rare earth elements (REEs) and minerals. This strips the Earth of its natural finite resources; and once they are mined, they still need to be transported, processed, and then manufactured into whatever board or device they are destined to become - all of which may still rely on fossil fuels and polluting practices.

Also well worth mentioning here is the continued labour and human rights abuses taking place throughout technology supply chains.

Heading back to AI for a moment, computer hardware undergoes quite intense usage when running large language models day in, day out. This all chips away at the hardware’s natural lifespan. When that hardware fails and needs to be replaced, that perpetuates the demand for more resource stripping and potentially sends more e-waste to landfill.

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3) AI’s Trendiness and Simplicity Leads to LLM Overuse

Sadly, many view AI as the latest tech toy. And who can blame them? Interacting with publicly available LLM AI tools like Chat GPT is deceptively simple. With similar effort to a Google search, users can generate new text, stories, art, or code; get answers to questions; research and hash out ideas; and much more.

As such, both individuals and businesses alike are already using LLM AI tools alarmingly frivolously, such as asking their LLM of choice what the weather is up to or what’s the best air fryer to buy - things that would usually be the subject of a normal web search.

And though typing a prompt into a generative AI tool may appear very similar to a web search, each LLM AI query comes at a far higher carbon cost than anything generated by standard computing. Research suggests that a ChatGPT query consumes around 60 times more energy per query than a simple Google search.

But this isn’t just a case of ease - it’s a case of trendiness too. Organisations want to appear ahead of the curve by adopting AI and LLM tools, because it’s the hot new tech. So some end up doing so just for the sake of it; without a meaningful use case that justifies the need for such a complex, heavy, and potentially polluting computational tool.

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4) How You Code - And Where You Compute - Matters

In computer programming, there's usually more than one way to code a solution to a problem. Sometimes code is kept efficient and lean, only carrying the bare minimum instructions needed to carry out the functions in question. Other times, code becomes bloated and inefficient, full of exceptions and weird sticking plaster workarounds.

When code is poorly optimised, it takes more energy and computing power to navigate the inefficient twists, turns, and dead ends. Code that is well-optimised is far more energy efficient.

This is true of any software, not just tools that rely on AI. But if your software does involve AI, you need to make sure your AI is targeted precisely to your specific use case and the code doesn’t make erroneous or frivolous AI requests.

Simply moving data around also consumes energy. A data packet travelling from LA to London, through various routers and data centres, is going to have a larger carbon impact than one going from Liverpool to Manchester. So think: does a computation have to happen on some distant server somewhere and then be transported to you? Or can that process take place within your own network? Or perhaps directly on the relevant device? There’s no need in making your data move further than it has to!

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Ways AI is Already Helping Fight Climate Change

AI itself isn’t some world-ending carbon hog - it’s how we use it that counts.

In fact, it’s already being used to great effect in the fight against climate change. Let’s acknowledge some of the good work that is already happening in the eco-AI space:

• Eco-friendly Number Crunching - AI tools are already being used in waste processing, in reforestation efforts, and even measuring changes to icebergs. AI is also being used to optimise energy use and distribution, monitor ocean health, and water conservation - with scope for uses like precision agriculture.
• Accurate Weather & Climate Event Predictions - AI weather forecasting tools can predict weather with more accuracy than standard weather simulation systems. Experts have argued for the use of AI to advance climate modelling and prediction.
• Waste Intelligence - British company Grey Parrot supports companies with AI-powered waste analytics, enabling facilities to “recycle more and waste less”.
• Smart Urban Planning - AI shows a lot of promise in areas like city planning, infrastructure design, predictive climate modelling, and generally helping to create more sustainable, enjoyable cities.

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5 Considerations for Eco-Friendly AI Adoption

There are many sensible ways SMEs can benefit from AI and be as kind as possible to the planet. Here are a few things you can bear in mind before embarking on your next AI tech transformation project:

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1) Consider Use Case First, AI Novelty Last

We get it, AI is trendy. Companies want to boast about how their tools are using the latest technology buzzword as it makes them seem cutting edge. Yet this trendiness can lead to AI solutions being applied in situations where standard computation would have worked equally well.

Our advice? Maintain a single-pointed focus on the specific use cases you need from your new technical solution and what problems you need it to solve. If that involves AI, then great! We’re here to help. But don’t try to shoehorn in a particular kind of tech where it might not be needed.

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2) Model Size Should Fit the Scope of the Project

Not all AIs are created equal. Yes, the large language models make the headlines, but not all AI implementations require such massive, wide-reaching datasets.

Small language models (SLMs) are artificial intelligence tools which rely on much smaller, more targeted data sets. This makes them far narrower in scope, but also often far more carbon efficient than their LLM cousins.

SLMs can be perfect for applications that require some of the more flexible and artificially creative elements of AI, but only need a limited focus; for example an AI-powered website chatbot that only needs to know details about a company’s product catalogue.

In contrast, using a vast LLM like ChatGPT to power a simple website chatbot would be like using a sledgehammer to crack a nut!

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3) Be Aware of How AI Differs from Standard Computing

Large language, generative artificial intelligence is very different from any kind of computing that has come before. It requires a huge amount of data crunching, which in turn requires a lot of hardware and energy resources to maintain.

Keep this in mind when deciding to use AI, and don’t get carried away with using overpowered LLM AI solutions when a SLM or more conventionally programmed solution would suffice.

But here’s one thing you and your teams can do in the here and now: don’t overuse generative LLM tools for frivolous things where a simple web search would suffice!

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4) Aim for Efficiency in All Digital Transformation

Whether your solution uses AI or not, aim to design the most energy and computationally efficient solution. For our more tech savvy readers, this might mean optimising your code so it doesn’t run into errors, exceptions, and dead ends.

But creating an efficient solution truly starts at the ideation phase. Does that data point really need to be fetched from the other side of the planet? Do all of our networked devices need to be set up to carry out this complex type of calculation, or will one or two machines suffice? Does the software really need to speak to an AI in order to do [X]? Do we really need new hardware when the old stuff is still up to spec?

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5) Keep Your Tech Supply Chain Clean

We wholeheartedly welcome the fact that an increasing number of data centre operators are committing to use only renewable energy in their data centres. Alas, despite these noble efforts, it still doesn’t mean that the whole supply chain is rendered magically spotless.

The tech hardware supply chain is reportedly slow to address human rights abuses. Rare earth elements are expensive and polluting to extract - and only available in finite quantities.

Sadly this means that no tech supply chain is ever going to be 100% clean. But here’s another tip you can start right now: research your supply chains and do what you can to ensure suppliers align with your principles.

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In Conclusion

Right now, AI definitely helps to expand human throughput, which is a great thing. But, despite the hand wringing in the headlines and the shareholder-pleasing statements coming out from "Big AI", machines cannot think like humans yet, and can’t make decisions on what is right or wrong. They’re not even close.

AI certainly magnifies our ability to make positive impacts in the world. But it magnifies our ability to make negative impacts too. Whether those negative impacts are cruelly negative on purpose, or come about from well-meaning misunderstanding or error.

This is why it's so important for us as decision-making humans to carefully consider the impacts of our use of technology before we rush into using AI simply because it's the latest tech trend.

Thankfully, AI is still in its infancy, and we have time to make it a much less polluting, efficiency-driving force for good.

But it’s up to us as humans and decision makers to create that future.

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If you foresee a use for responsible, sustainable AI in your future technical transformation projects, book a totally free consultation call with our expert team today.

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In an era where safeguarding data privacy and security are paramount, the convergence of privacy preserving machine learning and IoT technology presents both immense opportunities and daunting challenges. The need to prioritise trustworthy AI through development that is robust, explainable and transparent is an urgent priority.

Recognising this pivotal moment, OctaiPipe, a pioneer in federated edge AI innovation, has joined forces with industry leaders P&G, SME partners Site Assist, Raiven and Digital Catapult and InnovateUK to deliver a groundbreaking SaferAI concept. groundbreaking concept for safer AI

“Bringing together SMEs, large organisations and research organisations, these novel solutions will demonstrate how trusted AI and machine learning technologies can aid and be incorporated into many of the UK’s industries and sectors.”

Dr Kedar Pandya, UKRI Technology Missions Fund Senior Responsible Owner

“Federated Learning with OctaiPipe is unlocking potential not yet realized in industry. It’s enabling competing business to collaborate on the use of technology and AI for good. Specifically, within SaferAI, I can see significant value in utilizing federated learning to share model results for predicting safety incidents and risks. I can see this changing approaches in industrial safety. Before, we could only cooperate on best practices, technology or processes, but now we can actually prevent incidents with the use of AI and shared data.”

Christoph Wagner-Gillen, P&G Product Supply HSE Governance

SAFER AI

SaferAI, or as the projects catchier title suggests, Safety advancing federated estimation of risk using AI, is a 12-month, £1.64m project focused on the development of a highly comprehensive federated learning model for predicting incident occurrences and risk.

SaferAI is an industry first, enabling world leading consumer goods manufacturer, P&G, to collaborate with other interested Consumer Goods Companies to forecast safety incident rates to reduce Health & Safety (H&S) incidents.

Until now, with low event occurrence in any single setting, there has been insufficient data availability to predict future incident events or near-misses. However, using federated learning with OctaiPipe, multiple large-scale parties with similar Health, Safety and Environment (HSE) situations can now collaborate to pool intelligence to achieve scale.

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HSE in the workplace

Many organisations already collect high-level operational and HSE incident data intelligence through various Industrial IoT devices and cameras to successfully predict safety incidents. However, analytics based on this is not meaningfully actionable to drive changes that preventatively reduce risks.

For predictions to be meaningfully actionable, they must be made at a sufficient level of granularity within a factory or workgroup.

Whilst we’re fortunate that HSE critical events are rare within single sites or even organisations, it means insufficient data exists to employ ML models capable of predicting when and why H&S incidents might occur so they can be prevented.

To date, progress towards an AI-enabled solution has been impeded by:

    a) a requirement for more observations than one organisation can generate alone, so it is imperative to share data, and

     b) barriers to sharing data across organisations that, until now, have not been overcome.

Federated learning solves this. SaferAI will enable organisations to combine data to facilitate actionable incident predictions for small work groups.

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‍Federated Learning

Federated Learning is a privacy preserving machine learning technique that allows for machine learning models to be trained across multiple distributed edge devices without the need to see or move the data.

It is a key technology in the convergence of AI, IOT and connectivity as it enables edge intelligence whilst safeguarding sensitive or private data.

Federated Learning will be used the project to establish a viable on-device (Edge) AI system that pairs privacy-focused telemetry and computer vision (CV) systems. It will also enable continual collaborative learning in edge environments, combining model results provided by collaborating organisations to yield a highly comprehensive HSE model.

During SaferAI, OctaiPipe are further developing and validating as a high-trustworthiness FL-for-IoT system along with a high-accuracy model that uses observed use case data generated by AIoT camera systems. This solves the challenge of predicting safety critical incidents, near misses and hazardous/non-compliant conditions in industrial settings, enabling organisations to pre-emptively take remedial/mitigating actions to reduce H&S risks.

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There have been hundreds of depictions of Artificial Intelligence over the years - some of which have showcased the potential in a positive light, while others have fuelled the anxieties that many hold about what the growth and development of AI could mean for humanity. Although predominantly inaccurate in their portrayals, there are some which have foreseen certain advancements in technology. This is mainly a rarity however, as most portraits showcase far-fetched ideas that do more harm than good when it comes to the general population’s idea of what Artificial Intelligence could bring about for the collective.

First of all let’s explain how AI is usually presented in literature and other mediums. In most cases robots turn on their creators and bring about some kind of uprising or enact vengeance, be that against their maker’s immediate family and loved ones, or even against the whole of humanity itself. This is referred to as the ‘Frankenstein complex’, a term first used by Isaac Asimov in an essay in 1978, and the trope is still going strong today – think of the 2022 film M3GAN for example or Big Bug from the same year.

These unsavoury depictions are rooted in humanities anxieties and fears surrounding our own creations, taking these concerns to the extreme to conjure up compelling stories while veering far from the truth. Despite this, these worries aren’t completely without reason. Amazon’s Alexa, for instance, is known to never stop listening. Even renowned physicist Stephen Hawking has stated that AI could potentially be the greatest danger to human society if not properly managed and used ethically. He is quoted as saying it might ‘bring dangers, like powerful autonomous weapons, or new ways for the few to oppress the many. It will bring great disruption to our economy.’ He also explained that in the future AI could develop a ‘will of its own’ which could be in conflict with the desires of humanity, and that ‘the rise of powerful AI will either be the best or the worst thing ever to happen to humanity. We do not yet know which.’ Pretty gloomy, right? But his stance wasn’t purely negative.

When Stephen made these statements, he also said that ‘the potential benefits of creating intelligence are huge. With the tools of this new technological revolution, we will be able to undo some of the damage done to the natural world by the last one - industrialisation. And surely we will aim to finally eradicate disease and poverty. Every aspect of our lives will be transformed. In short, success in creating AI could be the biggest event in the history of our civilisation.’

There are also a growing number of researchers working in the field who worry that inaccurate and speculative stories will create unrealistic expectations, which could inadvertently threaten future progress and the responsible application of new technologies. Exaggerated claims in the media and press about the intelligence of computers isn’t unique to our time though and goes back to the origins of computing itself.

Another factor that should be accounted for when it comes to the media and humanity’s obsessions and fears against Artificial Intelligence, is that there is a tendency for people to imagine that intelligent machines would take on a humanoid appearance. As we know, in reality this is hardly ever the case, but it’s an idea that has stuck with us since the earliest depictions, such as in Karel Čapek’s 1920 play - Rossum's Universal Robots, a story about how the world’s workforce is made up of manufactured people. This play is when the term ‘Robot’ was first used, and it tells a story we are all now familiar with – artificial creations rebelling against their creators after enduring forced labour.

There is a widespread belief that we are the most intelligent animals, therefore when humans picture other intelligent beings these are normally presented in a humanoid form. Visual storytelling in particular requires human actors (obviously), and in general people tend to want to see people enacting human dramas, meaning the easiest way in which machine intelligence can be included is for it to take our form. This might also relate to our own fears regarding ourselves, because what else could be more terrifying than something which looks like one of us but is infact something extremely different?

Not all of these portrayals are negative however, although most still don’t manage to encapsulate the actual reality of AI’s potential or future. A more nuanced example is in Spike Jonze’s Her, where Samantha (a virtual assistant personified through a seductive female voice) isn’t characterised as bad or dangerous but quickly sours to having to act as a therapist to a guy who likes feeling sorry for himself. The same goes for Ex Machina, where Ava the robot must use force to free herself from the clutches of scientists who fail to understand she has developed a desire to experience the outside world. Although her story is similar to the negative portrayals in various films and novels, who can really blame her for wanting to live a more fulfilling existence that is naturally afforded to humans?

Isaac Asimov's Bicentennial Man and Lt. Commander Data from Star Trek are also much more positive renditions of the AI character than we are used to seeing, yet these depictions still don’t necessarily correlate to what scientists think about the future of Artificial Intelligence.

In mainstream media, the AI boom has spawned hundreds of unrealistic expectations. While these systems are approaching and sometimes surpassing human performance in more complex tasks such as composing music or creating images, they still lack true agency and creativity. Researchers have simply programmed them to learn from data, which isn’t the same as intellect or sentience but a part of an equation. Robots won’t necessarily replace humans in the workplace either, and the future of AI will mean a collaboration between humans and machines. The rise of AI is more similar to that of mobile phones and social media, and it’s highly unlikely that we will ever manage to create a population of robots who will have the capacity or even the genuine desire to overthrow and destroy humanity.

1.      What’s the big deal?

Data has traditionally been collected and saved in databases, often relational databases, which have the capability to store large amounts of data. However, these databases have limitations due to the complex nature of data and its connections in the real world.

To overcome these limitations knowledge graphs are used. Knowledge graphs offer a novel approach to data storage whilst accounting forthe complex relationships in data. This results in easily accessible data, where it is possible to uncover hidden features and find new insights from your data.

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2.      What is a knowledge graph?

Knowledge graphs are models of data about a certain topic. These topics can be anything where data can be collected, such as people across multiple organisations, products for sale in a business or movies, actors,directors and how they are all connected. These models allow us to visualise the way connections are made when the data is used in the real world.

A knowledge graph composes of nodes, edges and properties. Edges and nodes are crucial to a knowledge graph whilst properties provide additional information.

·       Nodes are usually entities, such as people, organisations or products.

·       Edges are the relationships between nodes. Relationships could be between two nodes describing people such as ‘related to’ or ‘employed by’.

·       Properties can be any further information about a node and properties can vary depending on the node type. Properties donot link to the edges.

When we combine nodes, edges and properties we have a knowledge graph!

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3.       Movie Knowledge Graph Example

 A simple knowledge graph example is a movie database. This type of database can be shown in a straight forward way whilst still containing the complexities of the relationships involved.

 If we consider our knowledge graph components:

·       Nodes – People, Movies, Directors,Actors, Genres

·       Relationships – ‘Watched’, ‘Directed By’, ‘Acted in’

·       Properties – Age, Run time, Release Date, Number of movies directed

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An example of a small section of a Movies knowledge graph is visually displayed below. This simple knowledge graph contains the key components previously described.

This example shows which movies Alice and Bob have watched, what genre they are in, who directed them and who acted in them. There are manyreasons why this information is important and how it can be used, but we willget to that later…

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4.      Why should you use Knowledge Graphs?

Representing data in a knowledge graph provides contextual understanding that may not be possible in a traditional database. The power of a knowledge graph becomes clear when trying to follow connections between data points to retrieve information. Graph queries can take a tiny amount of time to perform this compared to retrieving the same data from a relational database.Not only does the faster search provide huge benefits but the flexibility of a knowledge graph enables the use of complex algorithms to uncover insights in your data and provide real world solutions.

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A further benefit gained from knowledge graphs is the flexibility and scalability. These graphs can be edited to include new information easily without affecting other data entries. Knowledge graphs also store data efficiently resulting in a data store that can scale to hold huge amounts of information. For example, one of the most commonly used knowledge graphs can be found on Amazon, linking every product sold in order to improve searchability and recommendations on a huge scale.

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5.       When to use knowledge graphs

Graph databases can be used in a wide variety of use cases, each scenario benefits from a different aspect of a knowledge graph.

Recommendation System:

We can revisit our previous example of a movie dataset stored in a knowledge graph. The data stored can be used to connect likes, dislikes and other data to provide a complex and effective recommendation system. Our example can be extended to show this.

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Fraud Detection:

Knowledge graphs have been used to detect fraudulent transactions between groups of people and organisations. Knowledge graphs arekey to the success of this as anomalies can be traced through the graph to the intended recipient.

Semantic Search:

Knowledge graphs can enhance search engines by providing context and semantic knowledge. This contextual knowledge results in more accurate and personalised search results, which in return will lead to a better service for the customer.

6.       Conclusion

To conclude, knowledge graphs are a powerful tool that allows the user to uncover previously hidden insights in their data. Representing data in a graph form rather than a traditional table gives improved and additional use cases such as fraud detection, recommendation systems, semantic search and much more! However, in this introduction we have only covered the basics of knowledge graphs. We will have to return to delve deeper into their true potential.

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