Irina Kolesnikova
September 15th, 2023
A world where a prediction is no longer a shot in the dark but a well-calibrated beam of insight?
It exists.
Welcome to the realm of machine learning and machine learning development – a realm where managers evolve into visionaries, leveraging the alchemical blend of data and intelligence to usher their organizations into the digital dawn.
McKinsey’s insights illuminate the scope of this transformation, showcasing how AI’s capacity to process vast data volumes without rigid programming rules has redefined business operations. From optimizing supply chains to elevating customer experiences, AI has become the cornerstone of the competitive advantage.
Gone are the days when understanding machine learning was reserved for the select few wearing the cloak of technical expertise. Today, we lift the curtain to reveal a truth: the core concepts fueling the engine of machine learning are not arcane enigmas but approachable tenets, open to those who have a spark of curiosity – and an analytical mind.
For managers, embarking on the AI odyssey is not a stroll in the park; rather, it’s a thrilling expedition through complexity’s labyrinth.
In today’s competitive landscape, machine learning offers a revolutionary way for businesses, whether medium-sized or large, to understand, leverage, and optimize vast data sets. Think of it as a scalpel in a surgeon’s hand; while its use is specific, when applied correctly, it can produce remarkable results. It thrives on unearthing patterns and making predictions based on data, distinctly setting it apart from traditional software development.
Moreover, the realm of machine learning is vast, leading to significant advancements in processing images, sounds, and texts. This, in turn, propels industries like healthcare, finance, and marketing to heights previously unimagined.
Machine learning, a subset of artificial intelligence, emphasizes constructing algorithms that can learn and make decisions based on data. Machine learning isn’t just about computers learning to perform tasks. At its core, ML revolves around discovering patterns in data and predicting outcomes based on those patterns. A basic illustration is deducing that cold weather decreases ice cream sales. Yet, the true potential of machine learning lies in deciphering much more nuanced and concealed patterns.
At its core, machine learning involves crafting algorithms adept at determining these insights regardless of the end goal, elucidating the optimal data representation for these algorithms. Universal models like decision trees and deep learning can be tweaked or refined to understand specific tasks such as predicting consumer behavior or image recognition.
It offers the ability to assess multifaceted subjects, facilitating well-informed decisions swiftly and dependably. Its applications are vast, spanning from data science to other diverse sectors. Meteorologists, for instance, employ machine learning to scrutinize past climatic patterns to enhance weather prediction. Undoubtedly, machine learning has immense potential to transform myriad industries, including healthcare, defense, cybersecurity, entertainment, and more.
While traditional software is grounded in a deductive approach (where a knowledgeable individual sets specific rules, often coded as a series of ‘if’ statements to be applied to data), machine learning operates on an inductive paradigm. In machine learning, algorithms autonomously determine patterns or rules by examining vast datasets, and these discerned rules can then be utilized on additional data.
According to Deloitte Access Economics’ database, machine learning offers project benefits between US$250,000 and US$20 million. For some companies, ML represents a strategic, multi-year investment potentially yielding benefits in the billions. In the short term, however, machine learning benefits can include:
AI-powered chatbots and messaging systems offer timely, personalized customer interactions, reducing delays and enhancing support team efficiency.
Machines ensure precision, eliminating human errors when properly programmed. ML algorithms can handle repetitive tasks, reducing human intervention.
AI can optimize various business processes, from communication to manufacturing, boosting efficiency. It frees up employee time for more innovative tasks.
AI increases operational efficiency by automating tasks and reducing errors. For instance, chatbots operate 24/7, and AI can elevate productivity by up to 54%.
AI and ML process vast amounts of data efficiently, aiding objective decision-making by analyzing trends and predicting outcomes without human biases.
The genesis of any machine learning endeavor is discerning whether the idea at hand truly necessitates such a solution. Gaining a holistic grasp on the current scenario, along with the amendments needed in the process, data, application, and infrastructure, is paramount. This comprehension emerges from in-depth dialogues with both the business and technological stakeholders. Through these deliberations, you can roughly sketch the project’s timeline and budget, discern the potential business returns, gauge the intricacy from an ML lens, and sketch out preliminary next steps.
To build a proper AI and machine learning strategy, remember that each machine learning and AI project goes through several stages (read more about AI life cycle here):
Machine learning is particularly advantageous for tasks that are routine and predictable, have learning loop potential alongside minimal physical interaction, demand significant human labor, and occur frequently. Hence, to understand if the task is compatible, follow this checklist:
Routine and Predictable: Despite the potential complexity or length of a task, if it follows a predictable pattern without frequently encountered anomalies, it’s a contender for a machine learning project.
Learning Loop Potential: Can a feedback mechanism be implemented to enhance machine learning results perpetually? Yes: a prime example is the human-in-the-loop system. In this instance, a machine learning model detects potentially illicit political ads on social media. Human auditors then review these flags, acting on the complex ones and providing feedback. This iterative process thus refines the machine learning model continuously.
Minimal Physical Interaction: Although today’s technology has made strides, machine learning struggles with tasks involving intricate or vast interactions with the physical world. However, if the rewards outweigh the challenges (like autonomous vehicles), pursuing machine learning is a plausible choice.
Human Resources and Frequency: Processes demanding substantial manpower and recurring frequently might be prime candidates for machine learning automation, especially if they generate vast amounts of data.
Time-intensive Analysis: Some processes, like an auditor sifting through myriads of social media posts, would benefit from AI’s capability to swiftly analyze vast amounts of data, pinpointing areas requiring human focus.
Complex Data Relationships: Processes that require understanding numerous factors and their intricate relationships, such as predicting traffic mishaps or optimal police patrol locations, might be too multifaceted for humans. However, a well-trained machine-learning model can simplify these complexities.
However, one should be aware of the red flags for machine learning implementation:
These pointers are broad guidelines. Most organizations can harness machine learning potential in some form. The core considerations should be present need, potential ROI, and current data volume. For smaller companies or those in the nascent stages of data collection, manual data analysis might offer a more immediate value, paving the way for machine learning applications in the future.
Automating customer support has become a promising application of ML. Using natural language processing, chatbots can interpret human language and either provide answers or direct users to the right person.
For instance, Elisa, a major telecom company in Northern Europe, introduced their chatbot “Annika”, which manages 70% of incoming contacts and resolves 42% on the first try.
ML can actively monitor and predict equipment or product faults. When anomalies arise, the system notifies human supervisors to review or intervene, leading to reduced maintenance costs and increased uptime. The collaboration between Hepta Airborne and MindTitan exemplifies the advantages of this approach.
Businesses, especially in retail and transport, are employing ML to discern patterns in their pricing data. By analyzing factors from time of day to seasonal changes, ML provides insights into demand shifts. This information, when coupled with market and consumer data, allows for dynamic pricing. One can see this in action with Uber’s surge pricing during peak demand or heightened airline ticket costs during peak travel seasons.
Many businesses now employ recommendation systems to tailor offers to their clientele. Such systems take into account users’ browsing history to suggest relevant products.
Fuzu, a Helsinki company focused on job placements in East Africa, leveraged a recommendation system to align job seekers with potential employers, boosting their click-through rates by 30%.
When businesses embark on a data science project, there’s a common misconception that a machine learning engineer alone will suffice to handle everything.
However, efficiently tackling intricate tasks requires a diverse team with specialized roles. For those assembling a team from the ground up or working with a new group, discerning each member’s strengths can be challenging.
A more robust situation might be two data scientists to handle the core responsibilities with an additional five members offering specialized skills contributing at different stages, with machine learning as part of the process.
When starting a machine learning project, business leaders often face the choice between outsourcing a machine learning team or hiring one in-house.
In-house teams often face an uneven workload:
Outsourcing can address this workload imbalance. In addition, during the maintenance phase of machine learning projects, continuous intensive effort isn’t always required, which could result in wasted resources with an in-house team.
MindTitan CEO Kristjan Jansons observes that in-house teams can face significant delays, waiting for data or other dependencies. Outsourcing provides the flexibility to navigate these challenges, making it a recommended approach in over 90% of scenarios.
When selecting an outsourced team, look for:
The agricultural sector is experiencing an unprecedented transformation due to the implementation of computer vision technology. Through advanced techniques such as precision farming and livestock management, these tools are helping farmers automate tasks, increase efficiency, and obtain valuable insights for making better decisions on resource usage. Higher crop yields can be achieved through this technology. Labor can also be reduced while sustainability is improved – all leading to more productive farm life in general.
As demands for food continue to grow rapidly and environmental challenges become increasingly prominent globally, it’s clear that computer vision in agriculture has immense potential to tackle these challenges head-on with its impressive range of applications. To maintain adequate supply now and into the future, it makes sense to leverage the powerful machine learning capabilities inherent within computer vision systems, shaping what lies ahead for agriculture.
