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complex data

Making complex data available for the benefit of society

Written by Cynthia Hoza on . Posted in Beyond Data Podcast, Data Governance, Data Insights, Data Privacy, Data Privacy, Data Strategy, Data Visualization, Glossary, Machine Learning. No Comments on Making complex data available for the benefit of society

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In Calligo’s latest Beyond Data podcast, Tessa Jones (Chief Data Scientist) is joined by Dr Ellie Graeden, Research Professor (Center for Global Health Science and Security) at Georgetown University. Here we explore some of the episode’s highlights:

  • The inherent conflict of private data and the public good
  • Protecting individual rights within federated learning
  • The importance of effective communication and a common language
  • Designing systems and policies that work together
  • Focusing regulation on outcomes, not creating data siloes

At societal level, poor communication costs lives

Transitioning data across and between departments and data systems has historically been fraught with problems – who owns it? Who pays for it? Is it understandable and translatable into meaningful and actionable insights for the end user? 

Having worked extensively in disaster response, Dr Graeden has seen first-hand the potentially life-threatening issues that can arise when government departments’ data platforms produce incompatible outputs:

  • If 20,000 people need water, how many pallets need to be shipped?
  • If 10,000 electricity meters have been knocked out by a hurricane, how many people need feeding?

In such scenarios, identifying individuals amongst population-level data is crucial if the help provided is to be sufficient.

“We have to be able to really effectively move and communicate and share data that are relevant, in ways that they can get used by people all across the system”

Of course, any data system design should ensure privacy and protection for personal data. ‘Big data’ is still relatively new, and as such more powerful and widespread regulatory controls are now being introduced, although the US still does not have consistent requirements for how data should be handled. Fundamentally, meeting a population’s needs today, and planning for them tomorrow, requires the data of individual people to be analysed. Personal data must be shared quickly, effectively and all the while protecting individual rights. Data system design must therefore:

  • Include all players
  • Consider cultural constraints
  • Keep out bias
  • Ensure the right words and phrases are used
  • Focus on the ‘so what’, why does it matter?

“Every single thing we experience can be captured as data”

Even the most mundane moments in our daily lives leave a digital footprint, we shed data everywhere. But when does ‘my’ data become public, or the property of the software developer or the service provider? VR headsets collect ephemeral data that is analysed and applied for that one end user, but if that data is assumed to fall under GDPR the potential to use it for positive outcomes is severely limited. For example, should authorities be notified if content viewed and generated is illegal or harmful? And what if that chip can detect if the user is having a stroke, is that data classified as ‘health’ data? Can it be used to alert the individual to their medical emergency without contravening legislation? What if your mouse clicks can detect the early stages of Parkinson’s? Should you, could you, be told?

“If you’re treating this data as health data, then they have a very different set of regulatory constraints. HIPAA isn’t going to regulate those because it’s not a health care provider or a health insurer”

Piercing the veil

The conflict between personal protection and public good is everywhere, and Dr Graeden believes that some new data laws will create problems for federated learning. Legislation has clear boundaries (speed limits, blood alcohol levels) whereas science deals in spectrums, probabilities and unknowns.

Deleting an individual’s personal data from the model breaks the system, contradicting what regulators are trying to achieve. The solution is to prioritize outcomes, not processes – it doesn’t matter whether you write the rules with a pen and paper, or with AI, as long as you write the rules. Expanding the framework by setting gradients of data availability affords protection for individuals, whilst making data available that informs better decision making for public bodies.

“Data is nothing more, nothing less, than an abstract description of our world. A useful and powerful language that can tell us things that other languages don’t”

Data can no longer exist in siloes if it’s to be useful to society

There is now a healthy global appetite for the discussion around data, thanks in the main to two recent developments:

  • Covid gave us huge amounts of data about mortality levels, vaccination rates, hospitalisation trends – all of which were in the public consciousness every day
  • AI and ChatGPT – articles and debates about the pros and cons are everywhere, discussion is not just in the scientific community

The key challenges now for data scientists are expectation management and communication – we need to be clear about aims and specific about context, as well as knowing what to leave out to avoid overwhelm and misunderstanding. Unfortunately, scientists are not always great communicators (using complex terminology and detail, rather than common parlance and generalization) as Covid demonstrated:

  • Did having a vaccine mean you wouldn’t get sick? Or just less sick?
  • ‘Everyone should wear a mask’ became ‘wear a mask if you can’. This was due to limited supply, but it appeared that the science was not clear

“The scientific approach means you never have an answer… we are trained as scientists to focus on the fact that we don’t know”

In fact, the only answer is that the right data, used consistently and communicated clearly, will always allow us to be prepared, not reactive. To make decisions for the public good that protect every individual.

You can find out more about the common language of privacy in our Rosetta Stone eBook.

You can also watch Tessa’s fascinating podcast with Dr Graeden below.

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ai and natural learning

Unlocking the power of AI and Natural Learning

Written by Cynthia Hoza on . Posted in Beyond Data Podcast, Glossary, Machine Learning, Machine Learning. No Comments on Unlocking the power of AI and Natural Learning

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In Calligo’s latest Beyond Data podcast, co-hosts Sophie Chase Borthwick and Tessa Jones are joined by Alexander Visheratin, Artificial Intelligence Engineer at Beehive AI. Here we explore some of the episode’s highlights; the importance of Natural Learning Processing (NLP) and the pros and cons of output produced by examples like OpenAI’s ChatGPT-3.

“It can do anything, because it was trained on everything”

NLP models like ChatGPT are changing the way we search for data online. But if you average everything, the output will necessarily be average. And we have questions:

  • How ethical is the learning data that feeds these models, and how ethical was the process of collecting it?
  • How can global models be policed and regulated within individual countries?
  • What is the potential for small and specific training datasets to be manipulated by humans in a way that will limit and create biases in the algorithms?
  • Is it a ‘bug’ when a prompt doesn’t give us what we wanted? What we ask for is rarely what we actually get.

Confidence or competence?

One major drawback of the NLP process is that many models stopped learning at the turn of the decade, which as Alexander highlights, can easily lead to incorrect information being generated. “I asked one of the large models, ‘who is the president of the United States?’ and it answered very confidently, Barack Obama.” That confidence is interesting, because as humans we are predisposed to trust information that is given to us clearly and directly, with no hint of doubt.

Also, NLP models are built to prove or agree with the task given to them, and they sound so plausible. Alexander shares a specific example of Chat-GPT providing convincing output that could easily persuade someone unfamiliar with the facts.

“Andrew Ng, who is an Adjunct Professor at Stamford University, asked Chat-GPT to prove that CPU is better than GPU for deep learning. It was very confident and created a long paragraph of text proving it. Then he asked it to prove that some more primitive way of calculating is better than CPU, and it again provided very confident paragraph of text. He ended up basically ‘proving’ that an abacus is better than GPU for deep learning.”

In this age of misinformation, there is huge potential for NLP to spread misleading (or downright false) information very quickly to large audiences. ‘Facts’ which then become accepted, magnified and transmitted further.

Taking liberties with artistic license

There are obvious intellectual property issues when it comes to NLP and art generation. Asking an AI tool to create a piece in the style of a named artist will generate convincingly similar work. But if this output contravenes the artist’s morals or political views for example, it is easy to see how discomfort (and possibly even legal challenges) could follow. Conversely, when original artwork is produced that has been generated from hundreds of command iterations to finesse exactly the output required, can it still be seen as ‘art’? Is it the work of the individual using the AI tool, or the tool itself? But is this any different to the great works credited to Michelangelo that we know were produced in part by his students? Is the value of NLP in art actually more as an idea generator, a source of inspiration for the artist rather than the end point?

Alexander believes that creatives shouldn’t be afraid of natural learning. “I think NLP is more of a supplement, a good supplement, because it allows us to be more creative, pushing forward, advancing. It’s not like a replacement at all, it’s more like a co-worker or a supplemental ghost writer almost.”

Guard rails contain or keep out discriminatory language?

OpenAI were very upfront when ChatGPT first launched about the fact that the model would not allow misogynistic or racist material to be produced. Yet the very nature of the learning process saw AI models scraping huge amounts of learning data from the internet, much of which would inherently be of questionable bias and tone. Thus, what these models are drawing from as ‘normal’ is very much not.

“What Chat-GPT doesn’t allow, it feels like it doesn’t allow not because of how it was trained, but because of the huge amounts of guard rails that OpenAI built around it. So, they basically caged this model into all these sorts of limitations about stuff that it shouldn’t allow. But if you can get past these guard rails and into the model itself, it still has all these biases, like race, gender, all this stuff. It still has it, but they just try their very best to limit the way it can show it. Chat-GPT is essentially a celestial bureaucrat!”

NLPs provide assistance, not autonomy

Going forward, combining NLP output with factual SEO-sourced content feels like best practice when using AI tools. Alexander points out that this is quicker than finding the information yourself too and gives us the opportunity to validate what the models generate. Ultimately, he believes that directed and federated learning have fantastic potential, as long as we remain mindful of the risk of reverse engineering and privacy breaches. Using NLP as part of the solution, not the source of the only answer.

If you’d like to discuss the benefits of using Natural Learning Processing in your organization, please contact Tessa Jones to find out more.

You can also watch the fascinating podcast in full below.

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ai bias lgbtq+

AI bias is frequently failing the LGBTQ+ community

Written by Brendan Walsh on . Posted in Beyond Data Podcast, Data Ethics, Data Privacy, Glossary, Machine Learning, Machine Learning. No Comments on AI bias is frequently failing the LGBTQ+ community

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In our latest Beyond Data podcast, co-hosts Sophie Chase Borthwick (our Data Ethics & Governance Lead) and Tessa Jones (our Chief Data Scientist) invited Tomer Elias, Director of Product Management at BigID, to discuss how AI bias affects the LGBTQ+ community.

Here we explore some of the episode’s highlights – although you can also watch the full episode here.

Why is there bias?

When building an AI algorithm or AI solution, it is crucial to make sure it’s based on data sets that are both unbiased and diverse and, in terms of the LGBTQ+ community, this often falls short. Whatever the sector – work, health, entertainment – all will be subject to bias if the LGBTQ+ community is not taken into consideration when an AI solution is being created.

For Tessa Jones, one of the barriers to collecting sufficient data is that people might be reluctant to share information about their sexual orientation or their gender journey – particularly if they don’t know how this personal data will be used. Sophie Chase-Borthwick agrees that it quickly becomes a catch-22 situation:

“The biases that make you nervous of disclosing information are the very reason that you need to disclose said personal information in order to prevent bias and improve.

Knock-on effects

Drawing on his experience as a board member of an organization that supports LGBTQ+ employees, Tomer Elias explains how candidates are being let down by recruitment AI solutions and that the consequences are significant.

“A lot of people in the LGBTQ+ community are unemployed and that’s not because they’re lacking the professionalism and passion.”

Meanwhile, medical advances in the LGBTQ+ community are constantly evolving, and many algorithms do not take these changes into account.

“People who are transitioning are not getting the right treatments because the treatment providers are not well educated about it and the data is not diverse enough,” explains Tomer.

Tessa also raises the issue of health apps that require a user to state whether they are male or female.

“Even though the equations could be written differently to how you use different input, they’re just not and that means, you either have to pretend you’re something different or just not use that tool.”

Potential of AI to help overcome bias

While AI bias is clearly affecting the LGBTQ+ community, there are innovative ways it can be used to overcome it, too. Such as in recruitment.

“At the initial interview stage, AI could be used to scramble the voice so you would not know if the candidate was male or female or someone who has transitioned,” says Tomer.

He also poses the possibility for AI to help with the retention of LGBTQ+ employees.

“Technology could help employers know that the employee is happy and feels a part of the organization.”

Time to step it up… 

There are already many AI forces for good – including recommendation systems which can help LGBTQ+ people feel more emotionally supported and The Trevor Project that uses AI to predict which callers are more likely to commit suicide to ensure they get help.

Much more needs to be done. But the fact that people are starting to think about AI bias and the LGBTQ+ community is a step in the right direction.

“Now we’re talking about it and people are realizing the actual real-world implications, hopefully more people will feel comfortable expressing themselves and we can close some of that data gap so there is more information for the models to work off,” according to our Data Ethics & Governance Lead, Sophie Chase-Borthwick.

“It’s also super critical that we have diverse AI developers who are knowledgeable about people and bias,” adds Calligo’s Tessa Jones.

To hear more of our fascinating discussion on AI bias and how it affects the LGBTQ+ community, tune in to our latest Beyond Data podcast episode below.

 

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Global Food Waste – Can AI Offer a Solution?

Written by Brendan Walsh on . Posted in Beyond Data Podcast, Glossary, Machine Learning, Machine Learning. No Comments on Global Food Waste – Can AI Offer a Solution?

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In our latest Beyond Data podcast ‘Global Food Waste – Can AI Offer a Solution?’, we invited Data Science leader Shawn Ramirez to help us explore the global issue of food waste and discuss how AI has the power to make a difference. Co-hosts Sophie Chase Borthwick (our Data Ethics & Governance Lead) and Tessa Jones (our VP of Data Science, Research & Development) steered Shawn to share her insight and examples of where AI is helping combat this prevalent ‘human’ problem. Here we explore some of the episode’s highlights. 

What a waste… 

To say global food waste is a huge problem seems like an understatement. Nearly one third of all food around the world is currently being wasted. Estimates also suggest that 8-10% of global greenhouse gas emissions are associated with food that’s not consumed. In Shawn’s own words, there are some stark facts that are hard to ignore:  

“In the United States about 40% of food is wasted while, at the same time, 40 million people in the US are suffering from hunger, including 12 million children. If we could reduce and redistribute food waste by 15%, we’d actually feed more than half of those hungry people.  And it’s a similar story in Europe where 153 million tons of food is being wasted.” 

Not just about hunger… 

In addition to hunger, food waste is directly connected to all kinds of additional concerns – such as resource conservation, carbon emissions, and climate change. Clearly hugely passionate about the subject, Shawn explains why we all need to commit to change.   

“With our rising population, the situation is only going to get worse and, if we could reduce or redistribute that food waste, we could have a massive global impact.” 

Where is it happening? 

In the US, 60% of food is wasted before it even reaches the consumer. In Europe, 55% to 60% occurs in consumer households. Whereas in developing countries, most waste happens during agricultural production. Where you are in the world’s supply chains can make a big difference. 

What part can AI play? 

It’s increasingly clear that AI can and is starting to play an important role in turning the tide on food wastage. Exciting hi-tech innovations are enabling more sustainable farming – such as AI-enabled monitors, computer vision, remote sensing, as well as robots. Shawn highlights how this technology is revolutionizing vertical farming. 

“We are now seeing single vertical farms that produce the same amount of fruit and vegetables as an 80-acre farm and they are using 97% less water.”   

A Swedish company is transforming disused office buildings into autonomously controlled greenhouses and a company in Singapore has created the world’s first low carbon hydraulic water-driven vertical farming system.  

Throughout the supply chain, AI is becoming an indispensable planning tool. And Shawn has seen this first-hand, thanks to her time at Shelf Engine – an end-to-end grocery ordering solution using advanced AI. 

“We worked with grocery stores, using inventory simulations to optimize the freshness of food by predicting customer demand…Connecting data across the supply chain facilitates better informed decisions.”   

Knowledge is power

Then there’s a need for efficient and effective monitoring of what’s actually being wasted – something AI now has the capability of doing in granular detail. 

“AI-powered garbage cans equipped with weight sensors, cameras and computer vision have the ability to recognize and track the amount and type of food we’re throwing away.”  

As well as in the home, these can be used in restaurants, hotels, and other businesses – enabling people to think carefully about their waste, while helping companies effectively monitor and understand what’s being thrown away and when.   

You may have heard of Ikea partnering with Winnow Vision AI to track kitchen waste using computer vision technologies. Well, Ikea then used this data to implement changes resulting in a saving of 20 million meals. That amounted to 40,000 tons of carbon dioxide.  

Food for thought

The US Department of Agriculture has set a target of reducing food waste by 10% within the next decade. To achieve this, Shawn believes education in the capability of AI is the next vital step. 

“We want to see more organizations thinking about the food that they waste and realizing how they can make a massive difference by adopting different AI technologies.” 

To hear more of our valuable discussion on how AI has the power to reduce food waste, tune in to our latest Beyond Data podcast episode now. 

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The dark side of AI energy consumption – and what to do about it

Written by Brendan Walsh on . Posted in Beyond Data Podcast, Data Ethics, Data Privacy, Data Strategy, Glossary, Machine Learning, Machine Learning. No Comments on The dark side of AI energy consumption – and what to do about it

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Artificial Intelligence’s ability to augment and support progress and development over the past few decades is inarguable. However, when does it become damaging, contradictory even? In our latest Beyond Data podcast AI’s Climate Jekyll & Hyde – friend and foe, Tessa Jones (our VP of Data Science, Research & Development) and Sophie Chase-Borthwick (our Data Ethics & Governance Lead) discuss exactly this with Joe Baguley, Vice President and Chief Technology Officer, EMEA, VMware.

Our speakers explore the multifaceted topic of energy consumption and AI – from whether all applications are equal for energy consumption (or reflecting if there are some ‘better’ than others), to creating visibility and responsibility of energy consumption for all stakeholders. Here we try to give clarity to some of the grey areas that were discussed.

Should we consider all applications equal?

“AI and machine learning are about huge things, huge data sets, huge computation actions … all of those have huge implications in terms of energy,” Joe observes, before dropping in hugely sobering stats such as the total annual energy consumption of bitcoin being the same as Norway. Even when considering the often-touted argument of 57% of the energy for bitcoin mining using renewables, Joe counters: “But those renewables could have been used for something else, right? Those solar panels… and those hydropower stations and those wind turbines, we could be using them for something else.”

This raises the ethical question of whether there should be stricter governance, standards, and precedent set on more ‘moral’ applications for their energy consumption. Should we be more closely considering the difference in energy consumption between server farms that support minimizing food waste versus those that are focused on mining digital currency, for example?

“Is there an opportunity for [greater] regulation?” Tessa ponders. Would this regulation help challenge the current status quo for all applications’ energy consumption being considered equal? While Sophie observes: “We’ve had certain European nations start to put rules around data center expansion, where you’re allowed and not allowed to build because of the capacity there, which isn’t regulating the use of it. But it does have that knock-on effect that if you literally can’t build the data center support, you have to start thinking about other ways to build [models].”

When considering Sophie’s point on alternative ways to build models, Joe notes: “We’re using AI to deal with the symptoms, but maybe there’s some better ways we could be using AI to deal with the cause as well”.

And this all raises the next question – who should ultimately be making these ongoing moral calls for the environment and energy usage?

Embedding Environmental, Social, and Governance (ESG) by design

Environmental, Social, and Governance (ESG) is shorthand for a framework that helps stakeholders understand how an organization is managing risks and opportunities related to environmental, social, and governance criteria. Our speakers untangle the idea of ESG and how companies could use it to help triage the different applications they use.

Joe asks: “Is there an ESG-led marketing opportunity here? Your AI might be the same as my AI, but my AI is better from an ESG perspective. They both get the same results at the same time for the same cost, but this one’s better from an ESG perspective, in terms of sustainability, in terms of social good, in terms of environmental.”

By placing more emphasis on ESG as the criterion for measuring impact and success, it could help with embedding sustainability in the heart of the application’s deployment, rather than a siloed approach. Sophie agrees: “We have privacy by design, we have security by design. Why not have ESG by design?”

Following on from this thought, our speakers consider the cost implications of AI and ESG with Joe observing, “There’s a lot of businesses right now that can’t afford AI because it’s expensive…but I believe they will come to a tipping point where they can’t afford not to”.

Are we over-prioritizing accuracy?

“There’s a hyper-focus on the accuracy,” according to Tessa. “It ends up not even being about the motivation for green, it’s a motivation for fast training, fast tuning. Unfortunately, it’s how most data scientists are motivated; be faster without having to compromise their accuracy.”

Often, the increase in accuracy can be mapped on a logarithmic graph. Good gains at first, but quickly tapering off to minimal increase. Is it useful to be that much more accurate, often by points of a decimal? “Some are good, more must be better … people just keep going, as opposed to saying actually good enough is good enough,” Joe summarizes.

Instead of chasing marginally better accuracy each time, we should be considering the application in a holistic view. The increase in accuracy might be 0.01%, but would cost heavily for energy consumption – is it worth it? Should we be better at exposing these costs more vigorously throughout a team so everyone can feel more empowered and have the visibility to interrogate more closely?

To hear about how our speakers untangle these controversial questions and more, tune in now to Beyond Data podcast episode 3: AI’s Climate Jekyll & Hyde – friend and foe.

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Vehicle Autonomy; the good, the bad, and the complicated

Written by Brendan Walsh on . Posted in Beyond Data Podcast, Data Ethics, Data Insights, Data Privacy, Data Warehouse, Glossary, Machine Learning, Machine Learning. No Comments on Vehicle Autonomy; the good, the bad, and the complicated

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In our second Beyond Data podcast episode ‘Autonomous mass transportation and its impact on citizen privacy’, we will sit down with Beep’s Chief Technology Officer, Clayton Tino to explore the current landscape of autonomous vehicles (AVs), whether AVs truly can replace the human factor in public transportation, and how AV ethics can be holistically measured. Here we give you a snapshot of that fascinating discussion by digging into a few of the explored topics.

You can watch episode 1 here

When looking at AV ethics, there are two strands to consider:

1: The ethics programmed into the AV itself (e.g., how the AV ‘decides’ which course to take when it identifies a hazard, otherwise known as the ‘trolley car’ scenario).
2: The ethics surrounding embedding AVs into society (e.g., whether we can truly replace the human factor in AVs, or what level of surveillance AVs should have).

Going beyond the trolley car scenario

Often touted as the litmus test for AV ethics, the ‘trolley car’ or ‘trolley problem’ is a thought experiment where someone chooses between saving five people in danger of being hit by a runaway trolley by diverting the trolley to hit one person. This is extrapolated to AVs by using a scenario such as an AV traveling down the street when suddenly a group of pedestrians runs out. The AV must ‘choose’ between hitting the group or altering its course but by doing so, hitting a lone pedestrian.

The ‘Moral Machine’ experiment was an online survey of 2.3 million people worldwide that investigated the moral dilemmas faced by autonomous vehicles. The study found that moral principles guiding drivers’ decisions varied from country to country, and also women and men viewed ethical and moral situations differently. This made something like the trolley problem difficult to quantify and standardize worldwide.

Far from a simple ethics exercise…

On the surface, it seems a simple ethics exercise. But as Clayton Tino summises: “People like to think they have a preconceived notion of how they would behave, but I just don’t buy that. [A near miss] is a purely reactive response. We’re setting unrealistic expectations on the machine because we need to blame something when something goes wrong.” Tessa Jones (podcast co-host) agrees, observing: “AVs need some decision-making process, but I don’t have a decision making process myself.”

As Sophie Chase-Borthwick (podcast co-host) explains: “We expect our AVs to be guaranteed safe. But we know that any other vehicles are not 100% safe with a human behind them. So we have a higher expectation of what ‘safe’ looks like when it’s autonomous [as opposed to] to when it’s a human.”

In our opinion, the disproportionate emphasis placed on the trolley problem to solve the lion’s share of AV ethics is reductive and dangerous to advancing AV technology. It’s a useful piece of the puzzle but it’s a symptom when we should be focusing on fixing the cause.

In our podcast, we also explore the importance of accurate and timely hazard perception (both in humans and AVs). By improving hazard perception, it not only provides safety methods for AVs but can help reduce or mitigate entirely AVs even having to make the trolley problem decision in the first place. 

Can we ever truly replicate the human factor?

There are five levels in the maturity of autonomy of AVs – with Level 1 being no autonomy and Level 5 being a vehicle without a driver safely taking you to where you want to go.

For Clayton, Tessa and Sophie the debate centers on where the application of AVs could work best with the least blockers. They wonder whether public transportation seems an ideal choice, given how it could be geo-fenced, fixed route and hyper-local.

However, when considering AVs in the context of public transportation, they realize it’s important to look at the holistic service of public transportation, beyond just the driving. As Clayton pithily observes when considering AVs for school buses, “[Bus drivers] do a heck of a lot more than just drive the bus … they need to be aware of passenger safety and security, assistance…”.

For example, in London, there’s been some disputes between wheelchair users and pram users about who has first access to the space. Bus drivers (and others in charge of public transportation) are expected to act as mediators to settle these disputes. How would this be replicated in an AV with no human factor?

The answer could lie in more secure and closely governed surveillance. Having surveillance on public transport AVs could add a safety layer to minimize vandalism, protect the users and ensure the AVs remain a reliable and safe choice. Our podcasters observe the marked differences between privacy in the US and Europe but with the introduction of GDPR-style laws such as the California Consumer Protection Act (CPPA), there will inevitably be more scrutiny on how the surveillance data is used and stored.

However, as is often the case with autonomy when it comes to public transport there’s no easy decision. By removing the human factor, there need to be other allowances made to fill the gap. Companies and governments need to work hard to make sure both the users and their data are protected and that these allowances do not harm the end-users or misuse them for commercial purposes.

Our podcast delves more into the nuances and pitfalls when considering the commoditization of a public service, such as public transportation. Generally, the people who need it most are vulnerable, and unless there’s a significant level of transparency, can users be fully aware and able to consent to the wider implications of being surveilled?

To hear more about how we untangle and much more, watch our episode on ‘Autonomous mass transportation and its impact on citizen privacy ’. 

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The top ‘AI for Business’ influencers you need to follow

Written by Brendan Walsh on . Posted in Glossary, Machine Learning. No Comments on The top ‘AI for Business’ influencers you need to follow

The need for Artificial Intelligence within businesses is becoming far more apparent. As companies become more data-driven, they want to ensure they extract all possible value and insights from their data.

Whether the objective is to gain or maintain a competitive edge, improve customer experience, reduce costs, or increase productivity, automation and AI has the power to transform businesses, making it the buzzword within the business world.

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In the news: Five stories about Artificial Intelligence

Written by Brendan Walsh on . Posted in Glossary, Machine Learning. No Comments on In the news: Five stories about Artificial Intelligence

Artificial Intelligence, and Machine Learning in particular, have without doubt become the hottest topics of discussion throughout the business and technology worlds.

Development, breakthroughs, benefits, ethics, data privacy – there is so much information being published almost daily about AI.

Which naturally makes it confusing for those taking their first steps into discovering how and where AI might make a difference to their business.

To help, we have tried to distil all the AI commentary from the last few months to just the five most useful articles. These handpicked stories provide an overview on AI, guidance on how to implement and scale it across your business, and then some specific examples of how it is transforming financial services – one of the industries that we feel stands to gain the most from the use of machine learning.

Combined with our material on how to discover where in your business would stand to gain the most from AI, these five articles give you a complete overview on how to use machine learning to catch up with the competition.

  1. The State of AI in 2019

AI is exciting; it’s evolving by the day; the capacities are seemingly endless. Has all the noise around AI softened the impact of this great revolution?

James Vincent’s article dispels the misunderstandings surrounding AI and shows how broad the term is, from smart homes to healthcare, all the way to improving business processes. He explains the process of machine learning and how it is a fundamental subfield of AI, and highlights the amusing misunderstandings of its power and dangers.

Moreover, while The Verge shamelessly re-uses the much-repeated example of using machine learning to recognise images of cats, the point James is making is serious – automated decision-making can give businesses a competitive edge. But only provided it is deployed correctly, with suitable respect to data privacy, bias and sensible business objectives.

This useful article highlights the dangers of not addressing these fundamentals, and concludes by quoting Kai-Fu Lee, a renowned AI researcher, that we are currently in the “age of implementation” – only emphasising that those who are not investigating how and where to implement AI to improve business processes’ accuracy and effectiveness will soon be left behind.

  1. Seven ways to jump-start AI

With all the PR about how AI can change your business, it’s unsurprising that businesses are jumping headfirst into this technology. However, for many, this haste has meant the projects have not all been plain sailing, resulting in them meeting more challenges than advantages.

We see this problem in many of the businesses we speak to about AI, where they have previously tried to deploy machine learning into business processes, but have not seen the benefits they expected. This is why we liked this article from Information Week, as it describes a business’ more pragmatic route to jump-starting an artificial intelligence project. All the speed, but less of the haste.

The article provides seven key lessons, based on the barriers organizations have faced when implementing AI without sufficient prior consideration; from ensuring you have mapped out exactly the business challenges you want AI to address, to ensure you have the right team in place, the right data, and buy-in across the company.

These steps echo exactly our own thoughts and practices for deploying AI, which is why our AI Value Discovery Service has been designed to discover where the technology will be most impactful to your business, whilst addressing the obstacles that would otherwise derail the project. For more on the thinking behind this service, and a sneak peek of the process it goes through, download our free white paper here.

  1. Five takeaways on scaling machine learning

According to a recent Gartner survey, 37% of organizations have already implemented AI into their day to day business, with many other businesses looking to introduce the technology. This article from InfoWorld highlights the ways that large organizations like Facebook and Twitter have maintained the advantages machine learning first gave them by scaling its use from a small number of uses cases far wider across the business.

Whilst it sounds daunting especially for SMEs who do not have the same resources as these two tech giants, or have even deployed their first project, it also shows smaller businesses how to make sure their first use case is not simply a “point solution”, is inherently scalable, and that maximum value is planned for from the outset

  1. How Artificial Intelligence is helping financial institutions

AI and machine learning has the ability to transform businesses within the financial sector, and this Forbes article discusses the competitor advantages the technology has to offer. From chatbots and personalised customer service to providing 24/7 banking services and preventing and detecting fraud and money laundering, AI is in widespread use protecting and serving the financial services industry.

However, the article does touch on a key barrier within smaller financial companies: the high salaries of AI expertise. This creates two trends – a tendency to look outside the business for experienced support, plus a lack of tolerance for AI projects that fail to add value.

Our artificial intelligence and machine learning services not only give smaller financial institutions access to this expertise, but our practical approach ensures that no technology is deployed before a clear financial case is scientifically discovered.

  1. How AI is revolutionizing financial services

Building off the Forbes article above that looks at where AI is currently proving valuable for financial services, McKinsey Global Institutes predicts that from the $5.6 billion that banks are expected to spend on implementing AI in 2019, the financial industry could see a return of upwards of $250 billion.

The additional angle this article covers is the potential compliance challenges businesses face when deploying AI, especially if machine learning is to determine credit risks for potential new customers. The main question being asked is whether AI’s output is transparent enough given regulators’ requirements for fully explicable decision-making – the so-called black box problem – which in turn leads to concerns over whether AI can truly be unbiased if it naturally dependent on the data it is given. Or more accurately, data that humans have chosen to give it. There is no silver bullet to this, but some solutions include ensuring the team managing the AI project is diverse, although this inherently requires an even greater salary spend.

These five stories provide an excellent primer for businesses investigating the opportunity that AI presents to their business. And the key theme across them all is clear: finding the right use case for your AI project is more than half the battle.