Have you ever seen or participated in a project where a great tool was developed and then no one ever used it? It’s a story that is repeated time and time again: when a project is delivered and adoption is not as high as would have been desired. How is it possible if the solution works perfectly and fully meets the specifications?

Maybe because the interface looked confusing to the final users, maybe because they didn´t perceive the value that it would bring, maybe because the users didn’t feel confident about what the solution was doing in the back-end. Or maybe, it is a combination of all these, plus many other reasons. 

Successful data solutions must ensure the involvement of all parties in the development phase or risk becoming “a cool demo collecting dust in a drawer”. Human-centred solutions make all parties feel involved and proud of the process, which naturally drives adoption.

This article includes some steps and thoughts about what’s needed to ensure that a tool has widespread adoption once it’s finished. And by adoption I don’t mean making the use of the tool compulsory (which could also get 100% adoption!) but adoption because the users feel involved in the creation process and see value in the solution.

More than 2.5 quintillion bytes of data were generated daily in 2020 (source: GS Analytics). To put this in perspective, a quintillion is a million million million or, more simply, a 1 followed by 18 zeros. It is therefore not surprising that a significant amount of this data is subject to errors. Data scientists are aware of this and routinely check their databases for values that stand out from the rest. This process referred to as “outlier identification” in the case of numerical variables, has become a standard step in data preprocessing.

The search for outliers

The search for univariate outliers is quite straightforward. For instance, if we are dealing with human heights and most of the individuals´ measurements are expected to range between 150cm and 190cm, then heights such as 1,70cm and 1700cm must be understood to be annotation errors. Aside from such gross outliers, which should definitely be cleaned when performing data preprocessing tasks, there is still room for outliers that are inherent to the type of data we are dealing with. For instance, some people could be 140cm or 200cm tall. This type of outlier is typically identified with rules of thumb such as the absolute value of the z-score being greater than 3. Unless there is an obvious reason (such as an annotation error), these outliers should not be removed/cleaned in general, still, it is important to identify them and monitor their influence on the modelling task to be performed.

Multivariate outliers

A more difficult problem arises when we are dealing with multivariate data. For example, imagine that we are dealing with human heights and weights and that we have obtained the data represented in the scatterplot below. The individual marked in red is not a univariate outlier in either of the two dimensions separately, however, when jointly considering both height and weight this individual clearly stands out from the rest.

You can argue about how much the pandemic had to do with the increasing pace at which Artificial Intelligence (AI) was adopted throughout 2021, but what you cannot argue with is that Covid has pushed leaders to accelerate research and work in this field. Managing uncertainty for the past two years has been a major reason for our clients to keep a data-driven business model as their top strategic priority to stay relevant and competitive, empowering them to actively and effectively respond to rapidly shifting situations. However, all of us are faced with a myriad of technology solutions and tools of increasing technical complexity. To help navigate this sheer amount of information, I have prepared a brief summary of my own perspective on what lies ahead for the next year. When putting this article together I found summarising the more than 30 conversations I had when recording our Data Stand-up! podcast really helpful. I spoke with successful entrepreneurs, CIOs, CDOs, Lead Data Scientists from all around the world, and all of them brought a great share of perspectives on the question: Where is data going in 2022 when it comes to supporting business strategies. So, what does 2022 have in store for us? Let‘s dive in!

1. Data Lake Houses

Putting it simply, there have been two “traditional” ways to operationalise data analytics at a business level in terms of the underlying infrastructure used and the type of data being fed:

• Structured datasets and Data Warehouses: This is about retrieving datasets that display a consistent schema (i.e. data from business applications such as CRMs) that is imported into a Data Warehouse storage solution that then feeds Business Intelligence tools. These “Warehousing architectures” particularly struggle with advanced data use cases. For instance, their inability to store unstructured data for machine learning development is a downside that cannot be overlooked. Furthermore, proprietary Data do not match well with some open-source data science and engineering tools like Spark.
• Unstructured, semi-structured datasets and Data Lakes: Data lakes were designed to store unprocessed data or unstructured data files such as pictures, audio or video that cannot fit as neatly into data warehouses. Retrieving raw data and importing it directly into a Data Lake without any cleansing or pre-processing in between becomes handy when dealing with these files. The majority of data being generated today is unstructured so it is now imperative to use tools that enable processing and storing unstructured sets. Data lake’s drawback is the difficulty in maintaining data quality and governance standards, sometimes becoming “Data Swamps” full of unprocessed information lacking a consistent schema. This makes it difficult to search, find and extract data at will.

The reality is that both scenarios need to “coexist”, integrating and unifying a Data Warehouse and Data Lake becomes a requirement as analytics teams need structured and unstructured data both indexed and stored. Any modern company needs the best of both worlds by building a cost-efficient resilient enterprise ecosystem that flexibly supports its analytical demands. Meaning, any Data Engineer should be able to configure data pipelines and grant retrieval access to Data Scientists regardless of underlying infrastructure in order to perform their downstream analytics job duties. This is the idea and vision behind The“Data Lakehouse”:

A unified architecture that provides the flexibility, cost-efficiency, and ease of use of Data Lakes with the data granularity, consistency, reliability, and durability of data warehouses, enabling subsequent analyses, ML and BI projects.

There are a few providers out there that offer top-notch Data Lakehouse solutions. Databricks seem to be leading the race and is the industry leader as it was the original creator of the Lakehouse architecture (i.e. Delta Lake). Amazon Web Services (AWS) is another winning horse with a Lakehouse architecture (i.e. Lake Formation + AWS Analytics). Snowflake is also a relevant provider of this emerging “hybrid” infrastructure. I predict that the Data Lakehouse architecture will continue to be in the spotlight in 2022 as companies will also focus on Data Engineering even more than previously. There is already a huge demand for data architects and engineers in charge of platforms, pipelines and DevOps.

2. Low-code and No-code AI. Is it really the future?

Data Science is not just a research field anymore and it has been many years since it was validated as a powerful tool that every area of the business wants a piece of. However, the market continues to struggle to keep up with the filling of new openings as talent demand still exceeds supply. Low-code or no-code platforms were and still are one of the promising solutions to turn this around as they empower non-technical business professionals to “act” as Data Scientists. Moreover, these tools present an added benefit: More people across the organisation may begin to understand what can be done with data and, therefore, know better what questions can be realistically asked. Some well-known solutions such as DataRobot, H2O AutoML, BigML or ML Studio allow the development of practical data applications with little to no programming experience but…

Is it realistic for people who haven’t learned how to code to implement functional and safe analytical systems or AI solutions? Yes, but only if these non-technical professionals are guided and supported.

These days you may find a marketing executive building an NLP solution for sentiment analysis or a Hypermarket operations manager building a demand prediction system, but I must share a word of caution based on recent experience. Codeless does not mean maths-less. Background knowledge of the processes and mathematics behind data transformation, feature engineering and algorithms is needed for the correct ideation and implementation of effective solutions. These days you may find a marketing executive building an NLP solution for sentiment analysis or a Hypermarket operations manager building a demand prediction system, but I must share a word of caution based on recent experience. Codeless does not mean maths-less. Background knowledge of the processes and mathematics behind data transformation, feature engineering and algorithms is needed for the correct ideation and implementation of effective solutions. My take here:

These tools´ adoption will continue to grow and low-code solutions will continue to be a relevant trend in 2022. However, the definition of new roles ( QA, Coaches, Evangelists, etc.) surrounding the adoption of these tools will be needed too.

Many have quickly realised that the supervision and guidance of qualified data professionals is critical, more so when explainable and transparent AI is an upcoming legal prerequisite.

3. Augmented and hybrid human workforce

Employees have been understandably concerned about robots taking over during the last few years, especially when Gartner claimed that one in three jobs will be taken by software or robots, as some form of AI, by 2025. It seems common sense that organisations should have highlighted earlier that AI is only aimed to augment our capabilities, providing us with more time for creative and strategic thinking tasks, and not just replacing people. In my view, Machine Learning will now start to really enhance the lives of employees. Boring and repetitive admin tasks will fade into obscurity and soon be long gone. I believe that 2022 will be the year when we begin to see that AI, in the form of digital-co workers, is really welcomed by people at organisations. Whether you choose to call them robots, RPA systems, or digital co-workers, AI will allow us all to make quicker decisions, automate processes and process vast amounts of information at scale much faster.

In order to remain competitive, businesses of all kinds will have to start designing a hybrid workforce model where humans and “digital co-workers” work hand in hand.

We should still be realistic about expecting automation to fully replace some jobs, but I do hope that reinvented jobs and new positions will balance out all the jobs lost. Cultural adoption barriers still pose a major challenge, but despite popular pessimistic beliefs and potential drawbacks, the redefined augmented workforce is one of the key trends to keep an eye on during 2022 and beyond.

4. Efficiency vs complexity

Whilst a huge chunk of the research efforts and R&D data initiatives by FANGs are directed towards pushing the boundaries of narrow AI in the pursuit of General AI, developing, training and running complex models in this pursuit has inevitably had a negative collateral impact on the environment. Due to the computational power required to fuel some hyper-parameterized models, it is no surprise that data centres are beginning to represent a significant chunk of global CO2 emissions. For reference, back in 2018, the number of parameters in the largest AI models was 94 million parameters and this grew to 1.6 trillion in 2021 as these larger players pushed the boundaries of complexity. Today, these trillions of parameters are language and image or vision-based. Models such as GPT-3 can comprehend natural language, but also require a lot of computational power to function. This has motivated leading organisations to explore how they can effectively reduce their Machine Learning carbon footprint.

Big players have started to look at ways of developing efficient models and this has had an impact in the Data Science community as teams seem to now be looking for simpler models that perform as well as complex ones for solving specific problems.

A relatively simple Bayesian model may sometimes perform as well as a 3D-CNN while using significantly less data and computational power. In this context, “model efficiency” will be another key aspect of modern data science.

5. Multi-purpose modelling

It takes a lot of data sets, hard-to-get talent, costly computing resources and valuable time to ideate, develop and train AI models. Data teams are very familiar with the effort that it takes to deploy a model that works properly and accurately, hence Data Scientists understand that every aspect of the development work should be reapplied if possible in other modelling exercises. We have seen this happening in many industries and this trend seems to be pointing in the direction of training capable general-purpose models that are able to handle very diverse data sets and therefore solve thousands of different tasks. This is something that may be incremental over the next few years.

These multimodal models could be thought of and designed from the beginning to be highly efficient reapplicable tools.

These AI models would be combining many ideas that may have been pursued independently in the past. For instance, Google is already following this vision in a next-generation kind of data architecture and umbrella that they have named Pathways. You should not be surprised if you read about substantial progress in the research field of multi-purpose modelling in the next few months.

6. People Analytics

Dissatisfaction with job conditions, reassessments of work-life balance, and lifestyle alterations due to the hardships of the pandemic led to the Great Resignation, an informal name for the widespread phenomenon of hundreds of thousands of workers leaving their jobs during the COVID-19 era. Also called the Big Quit, it has often been mentioned when referring to the US workforce, but this trend is now international. All pandemic effects are still unpredictable but organisations have been forced to wake up and now seem committed to understanding their people. Companies are looking for effective ways to gain this comprehension of their employees. Many have come to the realisation that People Analytics could be the answer. In my view, there are two main drivers that have encouraged leaders to consider People Analytics:

• The KPIs that define business value have changed during the past years. In the past, it was related to tangible stuff such as warehouse stock, money in the bank, owned real estate, etc., but value nowadays is highly tied to having a talented workforce that can be an industry reference and that nurtures innovation. This relates to the previous trend about workforce changes where creativity will become more and more important hence the need to have a motivated and innovative team that thinks outside the box.
• Data Technology and AI now form the backbone of the strategic decision-making toolkit at most advanced companies.

People analytics has become a data-driven tool that allows businesses to measure and track their workforce behaviour in relation to their strategy.

People analytics is built upon the collection of individual talent data and the subsequent analysis of the same, allowing companies to comprehend the evolving workplace, but also surfacing insights that drive customer behaviour and engagement. Moreover, it assists the management and HR units to manage and steer the holistic people strategy by prescribing future actions. These actions may be related, but not limited to, improving talent-related decisions, improving workforce processes and promoting positive employee experience. People Analytics was only adopted by large enterprises with big budgets in the past and it has not been until recently that mid-size organisations joined in too. As of 2020, more than 70 percent of organisations were investing in people analytics solutions to integrate resulting insights into their decision-making. I am pretty certain that this percentage will increase significantly during the next months

7. Data marketplaces

If data is now understood as the new oil and the most valuable asset for any company, data marketplaces may become a mainstream way in 2022 when it comes to exchange and trade information. Even though some companies in specific sectors still jealously guard their data, others have spotted an opportunity in exchanging information. Some platforms such as Snowflake’s Data Marketplace allow businesses to become data providers, enabling them to easily share and monetise large data sets. For enterprises that generate large datasets or highly unique ones as part of their day-to-day activities, some companies may find that it is worthwhile to explore this route as a new way of generating additional revenue. In contrast, a few years back, it was common that medium and large businesses would fully outsource data analytics projects to an IT provider that would eventually use the 3rd parties data without consent. Now that everyone has understood that data is the most valuable asset, data will be exchanged and shared at will, but always with the expectation of something in return. Nevertheless, companies that aim to capitalise on this opportunity need to ideate a robust strategy for it by carefully assessing all legal and privacy implications. Similarly, they will have to build processes that automate the required data transformations so that data exports comply with existing regulations

The rise in AI applications will contribute to the widespread adoption of this trend. Complex models require vast amounts of data to be fed and many will also use these exchanges as a way of developing and training models.

2022 might be the year when the well-known statement by the Economist from 2017 about data being the new Oil will come closer to business reality with the first ‘commodity exchanges’.

Conclusions

There are almost certainly more than these 7 trends, but I have chosen to focus on the high-level ones in order to provide a rough prediction of what may shape corporate strategies and business plans around the world. Now to recap the 7 trends that we have discussed and that you could expect to see in the Analytics and the Artificial Intelligence space in 2022:

• Data Lakehouses as a hybrid architecture that allows efficient processing and analysis of structured, semi-structured and unstructured data sets.
• Low and no-code data solutions will continue to be a way to democratise Data Science, but new supervisor roles may appear around them.
• AI-enhanced workforce will continue to rise where analytical mechanisms and automation are the norm
• Model efficiency and simplicity will be a defining metric more than ever.
• Data Science teams will demonstrate significant interest in >Multi-purpose AI as a way to efficiently reutilise pieces of modelling work from previous tested developments.
• People Analytics will be one of the most sought after data initiatives that can realistically support business goals
• Data Marketplaces and data exchanges will present a new revenue opportunity for businesses that generate large or unique data sets

  So what data trends are here to stay and what is coming next? Truthfully, no one could tell you and this is just my opinion so we will have to play along and see!

In the first part of this article, we took a look at the Transformer model and its main use cases related to NLP, which have hopefully broadened your understanding of the topic at hand. If you have not read it yet, I suggest you give it a brief glance first since it will help you understand its current standing.

In the second part of this article, we will present novel model architectures and research employing Transformers in several fields unrelated to NLP, as well as showing some code examples of the capabilities of these remarkable new approaches.

 

APPLYING THE TRANSFORMER TO OTHER AI TASKS

As previously mentioned, the Transformer architecture provides a suitable framework designed to take advantage of long-term relationships between words. This allows the model to find patterns and meanings in the sentences, and makes it suited for many tasks in NLP. The most common ones are:

 

• Text classification into categories, such as obtaining the sentiment of a text
• Question answering, where the model can extract information from a text when prompted to do so
• Text generation, such as GPT-3
• Translation; Google Translate already employs this technology
• Summarization of a text into few words or sentences

 

After the success of the Transformer model applied to NLP tasks, people began to wonder: If it can find long-term relationships in the data and be trained efficiently, then could it be as efficient in other tasks besides  ? This is the start of the current movement of research, where this model is used as the backbone for many algorithms in AI and machine learning previously dominated by other techniques. Some amazing contributions in other AI fields are:

 

Lip reading and text transcription:

A recurring problem in society is related to text transcription, especially for hearing impaired people. The current advances are divided into two groups: using an audio track and transcribing it, or directly interpreting the words from the person’s lip movements. The latter problem is a lot harder to resolve since many factors are involved.

 

Potentially, this solution could provide help in situations where recording audio is impossible, such as in noisy areas. In this regard, most researchers use CNN or LSTM as the main model interpreting this as a pure Computer Vision task. Recently, some studies such as (https://ieeexplore.ieee.org/document/9172849) have been published where Transformer-based solutions to this problem are presented, providing better results than the current state-of-the-art solutions.

Novel applications of this powerful architecture set the bar for future AI advances.

If you have dug deep into machine learning algorithms, you will probably have heard of terms such as neural networks or natural language processing (NLP). Regarding the latter, a powerful model architecture has appeared in the last few years that has disrupted the text mining industry: The Transformer. This model has altered the way researchers focus on analysing texts, introducing a novel analysis that has improved the models used previously. In the NLP field, it has become the game-changer mechanism and it is the main focus of research around the world. This has brought the model wide recognition, especially through developments such as OpenAI’s GPT-3 model for the generation of text.

Moreover, it has also been concluded that the architecture of Transformers is highly adaptable, hence applicable to tasks that may seem totally unrelated to each other. These applications could drive the development of new machine learning algorithms that rely on this technology.

The goal of this article is to present the Transformer in this new light, showing common applications and solutions that employ this model, but also remarking on the new and novel uses of this architecture that take into account its many advantages and high versatility.

So, a brief introduction to the Transformer, its beginnings and the most common uses will be presented next. In the second part of this article, we will delve deeper into the new advances being made by the research community, presenting some exciting new use cases and code examples along the way.

It should be noted that AI solutions sometimes lack the responsibility and rigour  required when practising Data Science. The undesired effect is that models can retain the inherent bias of the data sets used to train them, and this can lead to fiascos such as Google’s Photos app. (https://www.bbc.com/news/technology-33347866). I recommend you check out my colleague’s Jesús Templado article on responsible AI and some hands-on criteria to follow when ideating, training or fine-tuning these models.

(https://medium.com/bedrockdbd/part-i-why-is-responsible-ai-a-hot-topic-these-days-da037dbee705).

Intro

Dramatic increases in computing power have led to a surge of Artificial Intelligence applications with immense potential in industries as diverse as health, logistics, energy, travel and sports. As corporations continue to operationalise Artificial Intelligence (AI), new applications present risks and stakeholders are increasingly concerned about the trust, transparency and fairness of algorithms. The ability to explain the behaviour of each analytical model and its decision-making pattern, while avoiding any potential biases, are now key aspects when it comes to assessing the effectiveness of AI-powered systems. For reference, bias is understood as the prejudice hidden in the dataset used to design, develop and train algorithms, which can eventually result in unfair predictions, inaccurate outcomes, discrimination and other similar consequences. Computer systems cannot validate data on their own, but are empowered to confirm decisions and here lies the beginning of the problem. Traditional scientists understand the importance of context in the validation of curated data sets. However, despite our advances in AI, the one thing we cannot program a computer to do is to understand context and we consistently fail in programming all of the variables that come into play in the situations that we aim to analyse or predict.

“A computer cannot understand context and we consistently fail in programming all of the variables that come into play in the situations that we aim to analyse or predict.”

Historical episodes of failed algorithmia and black boxes

Since the effectiveness of AI is now measured by the creators´ ability to explain the algorithm’s output and decision-making pattern, “Black boxes” that offer little discernible insight into how outcomes are reached are not acceptable anymore. Some historical episodes that brought us all here have demonstrated how critical it is to look into the inner workings of AI.

• Sexist Headhunting: We need to go back to 2014 to understand where all this public awareness on Responsible AI began. Back then, a group of Scottish Amazon engineers developed an AI algorithm to improve headhunting, but one year later that team realised that its creation was biased in favour of men. The root cause was that their Machine Learning models were trained to scout candidates by finding terms that were fairly common in the resumés of past successful job applicants, and because of the industry´s gender imbalance, the majority of historical hires tended to be male. In this particular case, the algorithm taught itself sexism, wrongly learning that male job seekers were better suited for newly opened positions.
• Racist facial recognition: Alphabet, widely known for its search engine company Google, is one of the most powerful companies on earth, but also came into the spotlight in May 2015.

Intro

In the first part of this article we discussed the potential harm and risks of some Artificial Intelligence applications that have demonstrated immense potential across many industries. We concluded that the ability to explain each algorithm’s behaviour and its decision-making pattern is now key when it comes to assessing the effectiveness of AI-powered systems.

In this second part we will be providing some tips, tools and techniques to tackle this challenge. Likewise, we will be commenting on promising initiatives that are happening in the EU and worldwide around responsible AI. Lastly, we will comment on how responsible AI is an opportunity rather than a burden for organisations.

 

Technical guidelines and best practices

As professionals that operate in this field and that can be held accountable for what we develop, we should always ask ourselves two key questions:

1. What does it take for this algorithm to work?
2. How could this algorithm fail, and for whom?

Moreover, those developing the algorithms should ensure the data used to train the model is bias-free, and not leaking any of their own biases either. Here are a couple of tips to minimise bias:

• Any datasets used must represent the ideal state and not the current one, as randomly sampled data may have biases since we live in an unfair way. Therefore, we must proactively ensure that data used represents everyone equally.
• The evaluation phase should include a thorough “testing stage” by social groups, filtering these groups by gender, age, ethnicity, income, etc. when population samples are included in the development of the model or when the outcome may affect people.

 

What tools Data Scientists have

There are tools and techniques that professionals from our field use when they need to explain complex ML models.

• SHAP (SHapley Additive exPlanation): Its technical definition is based on the Shapley value, which is the average marginal contribution of a feature value over all possible coalitions. In plain English: It works by considering all possible predictions by using all possible combinations of inputs and by breaking down the final prediction into the contribution of each attribute.
• IBM’s AIX360 or AI Fairness 360: An open-source library that provides one of the most complete stacks to simplify the interpretability of machine learning programs and allows the sharing of the reasoning of models on different dimensions of explanations along with standard explainability metrics. It was developed by IBM Research to examine, report, and mitigate discrimination across the full AI application lifecycle. It is likely that we will see some of the ideas behind this toolkit being incorporated into mainstream deep learning frameworks and platforms.
• What-IF-TOOL: A platform to visually probe the behaviour of trained machine learning models with minimal coding requirements.
• DEON: A relatively simple ethics checklist for responsible data science.
• Model Cards: Proposed by Google Research provides confirmation that the intent of a given model matches its original use case. Model Cards can help stakeholders to understand conditions under which the analytical model is safe and also safe to implement.

 

The AI greenfield requires strict boundaries

AI represents a huge opportunity for society and corporations, but the modelling processes should be regulated to ensure that new applications and analytical mechanisms always ease and improve everyone’s life. There is not any legal framework that helps to tackle this major issue, that sets boundaries and/or that provides bespoke guidelines. Likewise, there is not any international consensus that allows consistent ruling, audit or review of what is right and wrong in AI. In fact there is not even national consensus within countries.

Specific frameworks such as The Illinois’ Biometric Information Privacy Act (BIPA) in the US are a good start. The BIPA has been a necessary pain for tech giants as it forbids the annotation of biometric data like facial recognition images, iris scans or fingerprints without explicit consent.

There are ambitious initiatives such as OdiseIA that shed some light on what to do across industries and aim to build a plan to measure the social and ethical impact of AI. But this is not nearly enough because of the immediate need of international institutions to establish global consistency. If a predictive model recommends rejecting a mortgage, can the responsible data science and engineering team detail the logical process and explain to a regulator why it was rejected? Can the leading data scientist prove that the model is reliable within a given acceptable range of fairness? Can they prove that the algorithm is not biased?

The AI development process must be somehow regulated, establishing global best-practices as well as a mandatory legal framework around this science. Regulating the modelling process can mean several things: from hiring an internal compliance team that supports data and AI specialists to outsourcing some sort of audit for every algorithm created or implemented.

AI could be regulated in the same way The European Medicines Agency (EMA) in the EU follows specific protocols to ensure the safety, efficacy and adversarial effects for drugs.

 

Emerging legal initiatives: Europe leading the way

On 8th April 2019 the EU High Level Expert Group on Artificial Intelligence proactively set the Ethics Guidelines for Trustworthy AI that were applicable to model development. They established that AI should always be designed to be:

1. Lawful: Respecting applicable laws and regulations.
2. Ethical: Respecting human ethical principles.
3. Robust: Both from a technical and sustainable perspective

The Algorithmic Accountability Act in the USA that dates from November 2019 is another example of a legal initiative that also aimed to set a framework for the development of algorithmic decision-making systems and has also served as a reference to other countries, public institutions and governments.

Fast forward to the present day, the European Commission proposed on 21st April 2021 new rules and actions with the ambition of turning Europe into the global hub for trustworthy AI by combining the first-ever legal framework on AI and a new Coordinated Plan with Member States. This new plan aims to guarantee the safety and fundamental rights of people and businesses, while strengthening AI uptake, investment and innovation across Europe. New rules will be applied in the same way across all European countries following a risk-based approach, and an Intelligence Board will facilitate implementation and drive the development of AI standards.

 

The opportunity in regulation

Governance in AI, such as that which the EU is driving, should not be considered as an evil. If performed accurately, AI regulation will level the playing field, will create a sense of certainty, will establish and strengthen trust and will promote competition. Moreover, governance would allow us to legally frame the boundaries on acceptable risks and benefits of AI monetisation while ensuring that any project is planned for success.

“AI regulation will level the playing field, will create a sense of certainty, will establish and strengthen trust and will promote competition.”

Regulation actually opens a new market for consultancies that help other companies and organisations manage and audit algorithmic risks. Cathy O’Neil, a mathematician and the author of Weapons of Math Destruction, a book that highlights the risk of algorithmic bias in dozens of contexts, heads the Online Risk Consulting & Algorithmic Auditing (ORCAA), a company that was set up to help companies identify and correct any potential biases in the algorithms they use.

Counting on a potential international legislator or auditor would also allow those that achieve the “Audited player label” to project a positive brand image while remaining competitive. Using an analogy that relates to drug development: Modern society relies on medicines prescribed by doctors because there is an inherited trust in their qualifications, and because doctors believe in the compulsory clinical trial processes that each drug goes through before hitting the market.

 

Final thoughts

Simply put, AI has no future without us humans. Systems collecting the data typically have no way to validate the data they collect and in which context the data is recorded and collected. Data has no intuition, strategic thinking or instincts. Technological advancements are shaping the evolution of our society, but each and every one of us is responsible for paying close attention to how AI, as one of these main advancements, is used for the benefit of the greater good.

If you and your organisation want to be ahead of the game, don’t wait for regulation to come to you, but take proactive steps prior to any imposed regulatory shifts:

• It must be well understood that data is everything. Scientists strive to ensure the quality of any data set used to validate a hypothesis and go to great lengths to eliminate unknown factors that could alter their experiments. Controlled environments are the essence of well-designed analytical modelling.
• Design, adapt and improve your processes to learn how to establish an internal “auditing” framework. Something like a minimal viable checklist that allows your team to work on fair AI while others are still trying to squeeze an extra 1% accuracy from a ML model. Being exposed to the risk of deploying a biased algorithm that may potentially harm your customers, scientific reputation and your P&L is not appealing.
• Design and build repositories to document all newly created governance and regulatory internal processes so that all work is accessible and can be fully disclosed to auditors or regulators when needed, increasing external trust and loyalty to your scientific work.
• Maintaining diverse teams, both in terms of backgrounds, demographics and in terms of skills is important for avoiding unwanted bias. While in the STEM world women and people of colour remain under-represented, these may be the first people to notice these issues if they are part of the core modelling and development team.
• Be a promoter and activist for change in the field. Ensure that your communications team and technical leaders take part in AI ethics associations or debates of the like. This will allow your organisation to be rightly considered a force for change.

All these are AI strategic mechanisms that we use at Bedrock and that allow the legal and fair utilisation of data. The greatest risk for you and your business not only lies in ignoring the potential of AI, but also in not knowing how to navigate AI with fairness, transparency, interpretability and explainability.

Responsible AI in the form of internal control, governance and regulation should not be perceived as a technical process gateway or as a burden on your board of directors, but as a potential competitive advantage, representing a value-added investment that still is unknown for many. An organisation that successfully acts on its commitment to ethical AI is poised to become a thought leader in this field.

Intro

An Artificial Neural Network (ANN) is a statistical learning algorithm that is framed in the context of supervised learning and Artificial Intelligence. It is composed of a group of highly connected nodes called neurons that connect an input layer (input), and an output layer (output). In addition, there may be several hidden layers between the previous two, a situation known as deep learning.

Algorithms like ANNs are everywhere in modern life, helping to optimise lots of different processes and make good business decisions. If you want to read a more detailed introduction to Neural Network algorithms, check out our previous article, but if you’re feeling brave enough to get your hands dirty with mathematical details about ways to optimise them, you’re in the right place!

Optimisation techniques: Adaptive methods

When we train an artificial neural network, what we are basically doing is solving an optimisation problem. A well optimised machine learning algorithm is a powerful tool, it can achieve better accuracy while also saving time and resources. But, if we neglect the optimisation process, we can cause very negative consequences. For instance, the algorithm might seem perfect during the tests and fail resoundingly in the real world, or we could have incorrect underlying assumptions about our data and amplify them when we implement the model. For this reason, it is extremely important to spend time and effort optimising a machine learning algorithm and, especially, a neural network.

The objective function that we want to optimise, – in particular, minimise-, in this case is the cost function or loss function J, which depends on the weights \omega of the network. The value of this function is the one that informs us of our network’s performance, that is, how well it solves the regression problem or classification that we are dealing with. Since a good model will make as few errors as possible, we want the cost function to reach its minimum possible value.

If you have ever read about neural networks, you will be familiar with the classic minimisation algorithm: the gradient descent. In essence, gradient descent is a way to minimise an objective function – J(\omega) in our case – by updating its parameters in the opposite direction of the gradient of the objective function with respect to these parameters.

Unlike other simpler optimisation problems, the J function can depend on millions of parameters and their minimisation is not trivial. During the optimisation process for our neural network, it is common to encounter some difficulties like overfitting or underfitting, choosing the right moment to stop the training process, getting stuck in local minima or saddle points or having a pathological curvature situation. In this article we will explore some techniques to solve these two last problems.

CMS ALGORITHMS FOR ANALYTICS AND DARK MATTER DETECTION

In this second part of the previous introductory article, we’ll tackle the more in-depth description of data collection, object modelling, and data analysis at CMS. The general workflow behind these kinds of experiments is complex, but I’ll try to give a brief description of each part so you can get a general idea of the whole process.

Analysis of a collision and important concepts

We‘ve been talking about particles and detectors, but an important question remains unanswered: what exactly is colliding? The answer is protons, or more exactly, bunches of protons. Accelerated through the LHC by multiple solenoid setups, these bunches come across one another in the detectors, and a collision happens. This proton beam has no perpendicular component, meaning it only moves in a circular manner through the LHC, due to the solenoids’ magnetic field; this is done on purpose since studying the collision in the perpendicular plane means the conservation of energy applies and all momentum must be null, meaning if there’s an imbalance an invisible particle has been produced. A cross-section of the detector can be seen below, with its components (here). Some examples of these particles are neutrinos, but also possible dark matter or other theoretical particles. So now, what’s the next step?

HOW DATA ANALYSIS AT CERN CAN HELP DETECT DARK MATTER

A comprehensive guide of the CERN workflow in new Physics discoveries.

Are you interested in learning more about particle physics? You might have heard terms like neutrinos, quarks, or dark matter mentioned before and want to know more about them. However, the literature and articles involved mainly use terms and concepts that the reader is supposed to already know, which makes them inaccessible to anyone not in this field.

In this article, I’ll provide an easy-to-understand explanation about everything you’d need to know to understand the main points of these articles: the main results, how they’ve been obtained, and the methodology behind their collection. It will be divided into two parts:

• The first one will focus on the basic concepts regarding this subject: a basic Standard Model (SM) overview and a description of dark matter and the Compact Muon Solenoid (CMS) detector.
• The second one will focus on the details of data collection and analysis at CERN; this one is where the Data Science component will be.

My experience with this subject comes from my undergraduate project in Physics dedicated to a dark matter model verification with LHC Run 2 data, where my love for experimental particle physics was consolidated. I hope this article will help you understand the basics of these studies at CERN and get you started in further publications on this topic.

Standard Model Basics

We’ll start with a short description of the Standard Model (SM), the theory that describes the structure of matter and its interactions. It’s worth noting that the terminology model comes from the 1970s, when this theory didn’t have enough experimental evidence to support it as such, while nowadays it does.

The SM postulates the existence of two kinds of particles: fermions, which compose all visible matter, and baryons, that mediate the fundamental interactions (ElectroMagnetism, Strong and Weak forces, and Gravity; integrating this last one is still one of the biggest mysteries of modern physics). According to this theory, every interaction between two particles is mediated via the exchange of a boson. Below you can see a simple diagram of these particles and their classifications. The main difference between these two types of particles is their spin: fermions have half-integer spins, while bosons have integer spins, and this is the main reason the physics around these particles are so different.