Machine Learning (ML) research problems 

Machine Learning research has changed, but many AI and ML breakthroughs still struggle to translate into real-world problems and practical applications. There is plenty of information about Artificial Intelligence and Machine Learning online, yet much of it falls into one of two categories: high-level marketing content filled with buzzwords or highly technical materials that resemble TensorFlow courses rather than practical guidance for businesses. 

Another challenge is that many top Machine Learning researchers focus on novel architectures, new algorithms, and benchmark improvements instead of addressing common Machine Learning problems faced in real-world projects. This creates a gap between academic research and the development of reliable ML solutions that can learn from data, support business decisions, and deliver measurable outcomes. 

This article explores the current challenges in Machine Learning, why AI and ML techniques, applications, and algorithms are approached differently in academia and business, and how organizations can identify valuable Machine Learning opportunities and apply them in practice.

Why is Machine Learning research becoming more competitive? 

The rapid development of Machine Learning has transformed how AI research is conducted, making the field more competitive and changing how researchers discover, share, and validate new ideas. 

The field has grown, switched almost entirely to the conference publication model (while journal articles are rarely published now), and become more competitive. 

The rush to publish 

Researchers constantly rush to put their ideas out. However, they need more rigor to be more systematic before someone else presents a similar concept/method/architecture and steps all over them. Or they skip the conventional review and iterative improvement processes to make a conference deadline. 

As a result, we get surface-level productiveness in data science. A plethora of papers are being released quickly (which seemingly drives the progress in the data quality field further along). Still, many of them need more in-depth knowledge and are rife with errors, incremental or otherwise, of poor quality. Most of these works would only be submitted after. 

The importance of slow science 

Fundamental advances are a product of meticulous testing – a slow science requiring researchers to step back, carefully assess, and verify ideas and statements before putting them out. Sadly, many in the ML community have strayed away from this principle for the past few years for attention, bibliometrics, and other short-term success. 

What weakens the quality of AI and machine learning research? 

Several recurring practices can weaken the quality and practical value of AI and machine learning research, including unclear terminology, excessive technical complexity, unsupported claims, and limited attention to real-world applications. 

• Authors use technical jargon and mathematics excessively and unnecessarily, which obfuscates rather than clarifies their message. Presumably, this is done to impress. But, the confusion of technical and non-technical concepts, which we frequently see in recent publications, leaves the readers needing clarification instead. 

• They casually and carelessly throw around terms of art and misuse language (using novel terms with colloquial connotations and overloading established technical terms are the most common examples of this). 

• They speculate instead of explaining and often fail to distinguish between the two. 

• They emphasize the least important – but most sensational – aspects of their work without correctly identifying the sources of empirical gains (e.g., focusing on trivial architectural modifications in a neural net when the hyper-tuning of its parameters made all the gains possible.) 

• They don’t focus on real-world applications and high-impact results and only care about introducing novel concepts that might interest reviewers. The word “application” in a research paper, many believe, can lead to it being marginalized at conferences and not receiving much attention. 

Keeping these in mind, AI-related scientific works, particularly the ones speaking about the phenomenon of a Machine Learning algorithm, tend to be narrowminded and need an excellent logical foundation. The authors focus on following the existing trends rather than seeking truth.

What makes Machine Learning research reliable? 

Reliable ML research is based on clear terminology, strong empirical evidence, thorough evaluation of Machine Learning techniques, and results that can be replicated in real-world applications. High-quality research should explain how Machine Learning models solve specific problems, validate findings with reliable data, and demonstrate practical value beyond benchmark results. 

This is especially important as the rapid development of Machine Learning and artificial intelligence has led to a growing number of research papers, but not all of them provide meaningful insights for practical Machine Learning projects. A valuable AI research paper should connect theoretical analysis with real-world problems, evaluate multiple approaches, and avoid prioritizing novelty over reliability. 

• Include proper terminology – precise, empowering for the reader, not misleading, without unproven connotations, not conflated with related concepts that are distinct; 

• Highlight how theoretical analysis of the issue at hand relates to empirical or intuitive claims; 

• Only present conclusions when there’s enough factual evidence to back them up (this is particularly important for business applications of ML) 

• Provide intuition to help the reader comprehend the matter; conduct a careful empirical investigation in which multiple approaches are thoroughly evaluated and ruled out before the best one is selected. 

Organizations should focus less on adopting the newest architectures and more on building machine learning solutions that are reliable, explainable, and capable of delivering consistent results in real-world applications.

Why do proven Machine Learning models often outperform complex approaches? 

Proven Machine Learning models can often outperform complex approaches because practical results depend on more than algorithm complexity. Companies adopting AI do not always need the newest Machine Learning algorithms; in many cases, simpler models such as decision trees and random forests can achieve comparable AUC scores and deliver strong ROI without the extensive setup, computing resources, and training data requirements of deep neural networks. 

Ultimately, successful Machine Learning projects come down to scalability. Organizations need a clear plan for moving a well-performing model from a Jupyter notebook into a reliable real-world deployment that can support practical business applications. 

How to make the most out of an AI initiative and Machine Learning algorithms 

Successful AI initiatives depend on close collaboration between business leaders and data scientists, who combine business knowledge with technical expertise to define valuable Machine Learning applications, address real-world problems, and build practical ML solutions. This collaboration helps teams align on objectives, evaluate the feasibility of Machine Learning algorithms, and make better decisions throughout the AI development process. 

The importance of shared vision and understanding 

Shared vision and understanding are crucial. It’s common for business people and PMs to make all decisions regarding the overall direction of an AI project and for data scientists to oversee architecture selection, experimentation, and model deployment. This is logical. 

The former group has insight into creating business value, optimizing operational decisions in corporate settings, and implementing far-reaching organizational changes, which AI integration requires. The latter group possesses computer vision and a deep understanding of available and obtainable data. It can estimate the feasibility of engineering a Machine Learning method or algorithm in a reasonable time. 

Bridging the communication gap between business and data science teams 

Group communication occurs through joint discussions, presentations, and flip-chart sharing. Usually, the business side of things is handled first; senior management green-lights the idea at a high level, and only after that are the data scientists engaged. 

However, since the groups use different jargon terminology and have profoundly different backgrounds, they might still understand the critical aspects of the project differently. Getting on the same page early often leads to loops in re-defining the assignment or, if left unchecked, to release products that seemingly fulfill the plan but don’t meet the project’s and customers’ requirements.

Understanding project aspects: A collaborative approach 

In this manner, the whole, diverse project team’s expertise is captured, and all project aspects, from trivial to crucial, are considered. Technical details such as irrelevant features such as prediction targets are not just discussed between the data science team; they are explained clearly to the stakeholders, and the technology’s possible impact on organizational structures and decision optimization is discussed upfront. 

Business perspective: creating value and defining success 

From this point, specific questions about the business view of the opportunity are explored: 

• How does the technology create value for the organization? For example, which specific problem does it solve? Is the best use case a substantial improvement of an existing offering or a launch of a new one? 

• How is profound learning success defined? The data science team might be tempted to concentrate on metrics assessing the Machine Learning model’s predictions. However, these predictions usually have little to do with the quality of the AI’s operational decisions, which are typically assessed not by technical metrics but with the help of KPIs.  

Ignoring technical metrics is not suggested. It’s essential to understand that several layers of noisy data usually separate the prediction of a machine-learning model and the operational decision stemming from it.

How do organizational changes affect Machine Learning projects? 

Successful Machine Learning adoption requires organizations to prepare their teams, processes, and decision-making structures alongside the technology itself. After identifying a new business opportunity, companies need to define objectives, adjust workflows, and ensure employees have the skills required to work effectively with AI systems. 

Changes the organization will have to undergo to accommodate the shift in operational decision-making should also be considered. If machines fully or partially take over certain operations, how are employees trained so that their expertise and AI capabilities complement each other? Is additional training required? 

What technical factors affect Machine Learning implementation? 

At this stage, the focus shifts to the technical aspects of the Machine Learning process. Teams need to define what the AI system should predict, what data is required, and how the model can support the intended business objectives. 

• What predictions should the AI Machine Learning system output to fulfill the objectives? 

• What feature variables should be considered for the machine to output the most accurate predictions, and based on the potential features, which data sources should be considered first? 

Data considerations: quality and sources 

The quality of data directly affects the performance of Machine Learning models. Organizations need to ensure that input data is properly prepared, processed, and sufficient for training the model. This includes evaluating available storage and computing capacity and deciding where the model will operate, such as a private data center or cloud environment. 

Addressing constraints and security concerns 

How are case-specific constraints dealt with? Will the model have to make predictions within certain time frames? Can specific requirements in terms of data security and privacy be met? 

Monitoring success and handling deviations 

Finally, once success criteria have been defined, how is success monitored? Are there genuinely relevant metrics? Should there be a concrete plan for handling deviations from the allowed range of data points and incidents should they occur? 

The success of any AI initiative hinges on the seamless collaboration between business and technical teams. A shared vision, open communication, and a clear understanding of both business and technical objectives are essential for navigating the complexities of AI projects. By acknowledging and addressing the challenges outlined in this piece, organizations can pave the way for more effective and impactful AI implementations.

What still limits Machine Learning in real-world applications? 

Machine Learning has transformed entire sectors and industries, but its impact could grow faster if research placed greater emphasis on real-world applications rather than novel methods alone. 

As many have pointed out, recent research papers exhibit troubling patterns, including the misuse of language, excessive mathematical complexity that obscures the message, and a failure to identify the true sources of performance gains or distinguish explanations from speculation. This can limit their value for both the scientific community and organizations trying to apply machine learning to real business problems. 

If you want to learn more about applying AI to real-world business tasks, contact our experts for a consultation. 

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Matt Kovtun

Senior Data Scientist