Have you ever played chess or checkers against a computer? If you have, news flash – you’ve watched artificial intelligence at work. But what if the computer could get better at the game on its own just by playing more and analyzing its mistakes? That’s the power of machine learning, a type of AI that lets computers learn and improve from experience.
In fact, machine learning is becoming increasingly important in our daily lives. According to a report by Statista, revenues from the global market for AI software are expected to reach 126 billion by 2025, up from just 10.1 billion in 2018. From personalized recommendations on Netflix to self-driving cars, machine learning is powering some of the most innovative and exciting technologies of our time.
But how does it all work? In this article, we’ll dive into the concepts of machine learning and explore how it’s changing the way we interact with technology.
What is Machine Learning?
Machine learning is a subset of artificial intelligence (AI) that focuses on building algorithms that can learn from data and then make predictions or decisions and recognize patterns. Essentially, it’s all about creating computer programs that can adapt and improve on their own without being explicitly programmed for every possible scenario.
It’s like teaching a computer to see the world through a different lens. From the data, the machine identifies patterns and relationships within it. Based on these patterns, the algorithm can make predictions or decisions about new data it hasn’t seen before.
Because of these qualities, machine learning has plenty of practical applications. We can train computers to make decisions, recognize speech, and even generate art. We can use it in fraud detection in financial transactions or to improve healthcare outcomes through personalized medicine.
Machine learning also plays a large role in fields like computer vision, natural language processing, and robotics, as they require the ability to recognize patterns and make predictions to complete various tasks.
Concepts of Machine Learning
Machine learning might seem magical, but the concepts of machine learning are complex, with many layers of algorithms and techniques working together to get to an end goal.
From supervised and unsupervised learning to deep neural networks and reinforcement learning, there are many base concepts to understand before diving into the world of machine learning. Get ready to explore some machine learning basics!
Supervised Learning
Supervised learning involves training the algorithm to recognize patterns or make predictions using labeled data.
- Classification: Classification is quite straightforward, evident by its name. Its goal is to predict which category or class new data belongs to based on existing data.
- Logistic Regression: Logistic regression aims to predict a binary outcome (i.e., yes or no) based on one or more input variables.
- Support Vector Machines: Support Vector Machines (SVMs) find the best way to separate data points into different categories or classes based on their features or attributes.
- Decision Trees: Decision trees make decisions by dividing data into smaller and smaller subsets from a number of binary decisions. You can think of it like a game of 20 questions where you’re narrowing things down.
- Naive Bayes: Naive Bayes uses Bayes’ theorem to predict how likely it is to end up with a certain result when different input variables are present or absent.
Regression
Regression is a type of machine learning that helps us predict numerical values, like prices or temperatures, based on other data that we have. It looks for patterns in the data to create a mathematical model that can estimate the value we are looking for.
- Linear Regression: Linear regression helps us predict numerical values by fitting a straight line to the data.
- Polynomial Regression: Polynomial regression is similar to linear regression, but instead of fitting a straight line to the data, it fits a curved line (a polynomial) to capture more complex relationships between the variables. Linear regression might be used to predict someone’s salary based on their years of experience, while polynomial regression could be used to predict how fast a car will go based on its engine size.
- Support Vector Regression: Support vector regression finds the best fitting line to the data while minimizing errors and avoiding overfitting (becoming too attuned to the existing data).
- Decision Tree Regression: Decision tree regression uses a tree-like template to make predictions out of a series of decision rules, where each branch represents a decision, and each leaf node represents a prediction.
Unsupervised Learning
Unsupervised learning is where the computer algorithm is given a bunch of data with no labels and has to find patterns or groupings on its own, allowing for discovering hidden insights and relationships.
- Clustering: Clustering groups similar data points together based on their features.
- K-Means: K-Means is a popular clustering algorithm that separates the data into a predetermined number of clusters by finding the average of each group.
- Hierarchical Clustering: Hierarchical clustering is another way of grouping that creates a hierarchy of clusters by either merging smaller clusters into larger ones (agglomerative) or dividing larger clusters into smaller ones (divisive).
- Expectation Maximization: Expectation maximization is quite self-explanatory. It’s a way to find patterns in data that aren’t clearly grouped together by guessing what might be there and refining the guesses over time.
- Association Rule Learning: Association Rule Learning looks to find interesting connections between things in large sets of data, like discovering that people who buy plant pots often also buy juice.
- Apriori: Apriori is an algorithm for association rule learning that finds frequent itemsets (groups of items that appear together often) and makes rules that describe the relationships between them.
- Eclat: Eclat is similar to apriori, but it works by first finding which things appear together most often and then finding frequent itemsets out of those. It’s a method that works better for larger datasets.
Reinforcement Learning
Reinforcement learning is like teaching a computer to play a game by letting it try different actions and rewarding it when it does something good so it learns how to maximize its score over time.
- Q-Learning: Q-Learning helps computers learn how to take actions in an environment by assigning values to each possible action and using those values to make decisions.
- SARSA: SARSA is similar to Q-Learning but takes into account the current state of the environment, making it more useful in situations where actions have immediate consequences.
- DDPG (Deep Deterministic Policy Gradient): DDPG is a more advanced type of reinforcement learning that uses neural networks to learn policies for continuous control tasks, like robotic movement, by mapping what it sees to its next action.
Deep Learning Algorithms
Deep Learning is a powerful type of machine learning that’s inspired by how the human brain works, using artificial neural networks to learn and make decisions from vast amounts of data.
It’s more complex than other types of machine learning because it involves many layers of connections that can learn to recognize complex patterns and relationships in data.
- Neural Networks: Neural networks mimic the structure and function of the human brain, allowing them to learn from and make predictions about complex data.
- Convolutional Neural Networks: Convolutional neural networks are particularly good at image recognition, using specialized layers to detect features like edges, textures, and shapes.
- Recurrent Neural Networks: Recurrent neural networks are known to be good at processing sequential data, like language or music, by keeping track of previous inputs and using that information to make better predictions.
- Generative Adversarial Networks: Generative adversarial networks can generate new, original data by pitting two networks against each other. One tries to create fake data, and the other tries to spot the fakes until the generator network gets really good at making convincing fakes.
Conclusion
As we’ve learned, machine learning is a powerful tool that can help computers learn from data and make predictions, recognize patterns, and even create new things.
With basic concepts like supervised and unsupervised learning, regression and clustering, and advanced techniques like deep learning and neural networks, the possibilities for what we can achieve with machine learning are endless.
So whether you’re new to the subject or deeper down the iceberg, there’s always something new to learn in the exciting field of machine learning!
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From the same author
Read the articleHave you ever played chess or checkers against a computer? If you have, news flash – you’ve watched artificial intelligence at work. But what if the computer could get better at the game on its own just by playing more and analyzing its mistakes? That’s the power of machine learning, a type of AI that lets computers learn and improve from experience.
In fact, machine learning is becoming increasingly important in our daily lives. According to a report by Statista, revenues from the global market for AI software are expected to reach 126 billion by 2025, up from just 10.1 billion in 2018. From personalized recommendations on Netflix to self-driving cars, machine learning is powering some of the most innovative and exciting technologies of our time.
But how does it all work? In this article, we’ll dive into the concepts of machine learning and explore how it’s changing the way we interact with technology.
What is Machine Learning?
Machine learning is a subset of artificial intelligence (AI) that focuses on building algorithms that can learn from data and then make predictions or decisions and recognize patterns. Essentially, it’s all about creating computer programs that can adapt and improve on their own without being explicitly programmed for every possible scenario.
It’s like teaching a computer to see the world through a different lens. From the data, the machine identifies patterns and relationships within it. Based on these patterns, the algorithm can make predictions or decisions about new data it hasn’t seen before.
Because of these qualities, machine learning has plenty of practical applications. We can train computers to make decisions, recognize speech, and even generate art. We can use it in fraud detection in financial transactions or to improve healthcare outcomes through personalized medicine.
Machine learning also plays a large role in fields like computer vision, natural language processing, and robotics, as they require the ability to recognize patterns and make predictions to complete various tasks.
Concepts of Machine Learning
Machine learning might seem magical, but the concepts of machine learning are complex, with many layers of algorithms and techniques working together to get to an end goal.
From supervised and unsupervised learning to deep neural networks and reinforcement learning, there are many base concepts to understand before diving into the world of machine learning. Get ready to explore some machine learning basics!
Supervised Learning
Supervised learning involves training the algorithm to recognize patterns or make predictions using labeled data.
- Classification: Classification is quite straightforward, evident by its name. Its goal is to predict which category or class new data belongs to based on existing data.
- Logistic Regression: Logistic regression aims to predict a binary outcome (i.e., yes or no) based on one or more input variables.
- Support Vector Machines: Support Vector Machines (SVMs) find the best way to separate data points into different categories or classes based on their features or attributes.
- Decision Trees: Decision trees make decisions by dividing data into smaller and smaller subsets from a number of binary decisions. You can think of it like a game of 20 questions where you’re narrowing things down.
- Naive Bayes: Naive Bayes uses Bayes’ theorem to predict how likely it is to end up with a certain result when different input variables are present or absent.
Regression
Regression is a type of machine learning that helps us predict numerical values, like prices or temperatures, based on other data that we have. It looks for patterns in the data to create a mathematical model that can estimate the value we are looking for.
- Linear Regression: Linear regression helps us predict numerical values by fitting a straight line to the data.
- Polynomial Regression: Polynomial regression is similar to linear regression, but instead of fitting a straight line to the data, it fits a curved line (a polynomial) to capture more complex relationships between the variables. Linear regression might be used to predict someone’s salary based on their years of experience, while polynomial regression could be used to predict how fast a car will go based on its engine size.
- Support Vector Regression: Support vector regression finds the best fitting line to the data while minimizing errors and avoiding overfitting (becoming too attuned to the existing data).
- Decision Tree Regression: Decision tree regression uses a tree-like template to make predictions out of a series of decision rules, where each branch represents a decision, and each leaf node represents a prediction.
Unsupervised Learning
Unsupervised learning is where the computer algorithm is given a bunch of data with no labels and has to find patterns or groupings on its own, allowing for discovering hidden insights and relationships.
- Clustering: Clustering groups similar data points together based on their features.
- K-Means: K-Means is a popular clustering algorithm that separates the data into a predetermined number of clusters by finding the average of each group.
- Hierarchical Clustering: Hierarchical clustering is another way of grouping that creates a hierarchy of clusters by either merging smaller clusters into larger ones (agglomerative) or dividing larger clusters into smaller ones (divisive).
- Expectation Maximization: Expectation maximization is quite self-explanatory. It’s a way to find patterns in data that aren’t clearly grouped together by guessing what might be there and refining the guesses over time.
- Association Rule Learning: Association Rule Learning looks to find interesting connections between things in large sets of data, like discovering that people who buy plant pots often also buy juice.
- Apriori: Apriori is an algorithm for association rule learning that finds frequent itemsets (groups of items that appear together often) and makes rules that describe the relationships between them.
- Eclat: Eclat is similar to apriori, but it works by first finding which things appear together most often and then finding frequent itemsets out of those. It’s a method that works better for larger datasets.
Reinforcement Learning
Reinforcement learning is like teaching a computer to play a game by letting it try different actions and rewarding it when it does something good so it learns how to maximize its score over time.
- Q-Learning: Q-Learning helps computers learn how to take actions in an environment by assigning values to each possible action and using those values to make decisions.
- SARSA: SARSA is similar to Q-Learning but takes into account the current state of the environment, making it more useful in situations where actions have immediate consequences.
- DDPG (Deep Deterministic Policy Gradient): DDPG is a more advanced type of reinforcement learning that uses neural networks to learn policies for continuous control tasks, like robotic movement, by mapping what it sees to its next action.
Deep Learning Algorithms
Deep Learning is a powerful type of machine learning that’s inspired by how the human brain works, using artificial neural networks to learn and make decisions from vast amounts of data.
It’s more complex than other types of machine learning because it involves many layers of connections that can learn to recognize complex patterns and relationships in data.
- Neural Networks: Neural networks mimic the structure and function of the human brain, allowing them to learn from and make predictions about complex data.
- Convolutional Neural Networks: Convolutional neural networks are particularly good at image recognition, using specialized layers to detect features like edges, textures, and shapes.
- Recurrent Neural Networks: Recurrent neural networks are known to be good at processing sequential data, like language or music, by keeping track of previous inputs and using that information to make better predictions.
- Generative Adversarial Networks: Generative adversarial networks can generate new, original data by pitting two networks against each other. One tries to create fake data, and the other tries to spot the fakes until the generator network gets really good at making convincing fakes.
Conclusion
As we’ve learned, machine learning is a powerful tool that can help computers learn from data and make predictions, recognize patterns, and even create new things.
With basic concepts like supervised and unsupervised learning, regression and clustering, and advanced techniques like deep learning and neural networks, the possibilities for what we can achieve with machine learning are endless.
So whether you’re new to the subject or deeper down the iceberg, there’s always something new to learn in the exciting field of machine learning!
Read the articleAs artificial intelligence and machine learning are becoming present in almost every aspect of life, it’s essential to understand how they work and their common applications. Although machine learning has been around for a while, many still portray it as an enemy. Machine learning can be your friend, but only if you learn to “tame” it.
Regression stands out as one of the most popular machine-learning techniques. It serves as a bridge that connects the past to the present and future. It does so by picking up on different “events” from the past and breaking them apart to analyze them. Based on this analysis, regression can make conclusions about the future and help many plan the next move.
The weather forecast is a basic example. With the regression technique, it’s possible to travel back in time to view average temperatures, humidity, and other variables relevant to the results. Then, you “return” to present and tailor predictions about the weather in the future.
There are different types of regression, and each has unique applications, advantages, and drawbacks. This article will analyze these types.
Linear Regression
Linear regression in machine learning is one of the most common techniques. This simple algorithm got its name because of what it does. It digs deep into the relationship between independent and dependent variables. Based on the findings, linear regression makes predictions about the future.
There are two distinguishable types of linear regression:
- Simple linear regression – There’s only one input variable.
- Multiple linear regression – There are several input variables.
Linear regression has proven useful in various spheres. Its most popular applications are:
- Predicting salaries
- Analyzing trends
- Forecasting traffic ETAs
- Predicting real estate prices
Polynomial Regression
At its core, polynomial regression functions just like linear regression, with one crucial difference – the former works with non-linear datasets.
When there’s a non-linear relationship between variables, you can’t do much with linear regression. In such cases, you send polynomial regression to the rescue. You do this by adding polynomial features to linear regression. Then, you analyze these features using a linear model to get relevant results.
Here’s a real-life example in action. Polynomial regression can analyze the spread rate of infectious diseases, including COVID-19.
Ridge Regression
Ridge regression is a type of linear regression. What’s the difference between the two? You use ridge regression when there’s high colinearity between independent variables. In such cases, you have to add bias to ensure precise long-term results.
This type of regression is also called L2 regularization because it makes the model less complex. As such, ridge regression is suitable for solving problems with more parameters than samples. Due to its characteristics, this regression has an honorary spot in medicine. It’s used to analyze patients’ clinical measures and the presence of specific antigens. Based on the results, the regression establishes trends.
LASSO Regression
No, LASSO regression doesn’t have anything to do with cowboys and catching cattle (although that would be interesting). LASSO is actually an acronym for Least Absolute Shrinkage and Selection Operator.
Like ridge regression, this one also belongs to regularization techniques. What does it regulate? It reduces a model’s complexity by eliminating parameters that aren’t relevant, thus concentrating the selection and guaranteeing better results.
Many choose ridge regression when analyzing a model with numerous true coefficients. When there are only a few of them, use LASSO. Therefore, their applications are similar; the real difference lies in the number of available coefficients.
Elastic Net Regression
Ridge regression is good for analyzing problems involving more parameters than samples. However, it’s not perfect; this regression type doesn’t promise to eliminate irrelevant coefficients from the equation, thus affecting the results’ reliability.
On the other hand, LASSO regression eliminates irrelevant parameters, but it sometimes focuses on far too few samples for high-dimensional data.
As you can see, both regressions are flawed in a way. Elastic net regression is the combination of the best characteristics of these regression techniques. The first phase is finding ridge coefficients, while the second phase involves a LASSO-like shrinkage of these coefficients to get the best results.
Support Vector Regression
Support vector machine (SVM) belongs to supervised learning algorithms and has two important uses:
- Regression
- Classification problems
Let’s try to draw a mental picture of how SVM works. Suppose you have two classes of items (let’s call them red circles and green triangles). Red circles are on the left, while green triangles are on the right. You can separate these two classes by drawing a line between them.
Things get a bit more complicated if you have red circles in the middle and green triangles wrapped around them. In that case, you can’t draw a line to separate the classes. But you can add new dimensions to the mix and create a circle (rectangle, square, or a different shape encompassing just the red circles).
This is what SVM does. It creates a hyperplane and analyzes classes depending on where they belong.
There are a few parameters you need to understand to grasp the reach of SVM fully:
- Kernel – When you can’t find a hyperplane in a dimension, you move to a higher dimension, which is often challenging to navigate. A kernel is like a navigator that helps you find the hyperplane without plummeting computational costs.
- Hyperplane – This is what separates two classes in SVM.
- Decision boundary – Think of this as a line that helps you “decide” the placement of positive and negative examples.
Support vector regression takes a similar approach. It also creates a hyperplane to analyze classes but doesn’t classify them depending on where they belong. Instead, it tries to find a hyperplane that contains a maximum number of data points. At the same time, support vector regression tries to lower the risk of prediction errors.
SVM has various applications. It can be used in finance, bioinformatics, engineering, HR, healthcare, image processing, and other branches.
Decision Tree Regression
This type of supervised learning algorithm can solve both regression and classification issues and work with categorical and numerical datasets.
As its name indicates, decision tree regression deconstructs problems by creating a tree-like structure. In this tree, every node is a test for an attribute, every branch is the result of a test, and every leaf is the final result (decision).
The starting point of (the root) of every tree regression is the parent node. This node splits into two child nodes (data subsets), which are then further divided, thus becoming “parents” to their “children,” and so on.
You can compare a decision tree to a regular tree. If you take care of it and prune the unnecessary branches (those with irrelevant features), you’ll grow a healthy tree (a tree with concise and relevant results).
Due to its versatility and digestibility, decision tree regression can be used in various fields, from finance and healthcare to marketing and education. It offers a unique approach to decision-making by breaking down complex datasets into easy-to-grasp categories.
Random Forest Regression
Random forest regression is essentially decision tree regression but on a much bigger scale. In this case, you have multiple decision trees, each predicting a certain output. Random forest regression analyzes the outputs of every decision tree to come up with the final result.
Keep in mind that the decision trees used in random forest regression are completely independent; there’s no interaction between them until their outputs are analyzed.
Random forest regression is an ensemble learning technique, meaning it combines the results (predictions) of several machine learning algorithms to create one final prediction.
Like decision tree regression, this one can be used in numerous industries.
The Importance of Regression in Machine Learning Is Immeasurable
Regression in machine learning is like a high-tech detective. It travels back in time, identifies valuable clues, and analyzes them thoroughly. Then, it uses the results to predict outcomes with high accuracy and precision. As such, regression found its way to all niches.
You can use it in sales to analyze the customers’ behavior and anticipate their future interests. You can also apply it in finance, whether to discover trends in prices or analyze the stock market. Regression is also used in education, the tech industry, weather forecasting, and many other spheres.
Every regression technique can be valuable, but only if you know how to use it to your advantage. Think of your scenario (variables you want to analyze) and find the best actor (regression technique) who can breathe new life into it.
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Source:
- IE University – Insights, Published on October 15th, 2024.
By Francesco Derchi
Purpose is a strategic tool for driving innovation, competitive advantage, and addressing AI challenges, writes Francesco Derchi.
Since the early 2000s, technology has dominated discussions among scholars and professionals about global development and economic trends, with the first wave of research regarding the internet’s impact on firms and society focusing on the enabling potential of technologies. The concept of “digital revolution,” as popularized by Nicholas Negroponte, became the new paradigm for broader considerations about the development of the firm’s macro environment, and how businesses could leverage it as an asset for creating competitive advantage. The following wave focused on the convergence of different technologies, such as manufacturing, and included the dynamics of coexistence between humans and machines. From the management side, the major challenges are related to defining effective digital transformation practices that could help to migrate organizations and exploit this new paradigm.
The current technological focus builds on these previous trends, particularly on artificial intelligence and more recently on the emergence of generative AI. The Age of AI is characterized by technology’s power to reshape business and society on a variety of levels. While AI’s pervasive impact is not new for firms, the mainstream adoption of ChatGPT for business purposes and the response to this ready adoption from big tech players like Microsoft, Google, and more recently Apple, shows how AI is reshaping and influencing companies’ strategic priorities.
From a research perspective, AI’s societal impact is inspiring new studies in the field of ethics. Luciano Floridi, now of Yale University, has identified several challenges for AI, characterizing them by global magnitudes like its environmental impact and has identified several challenges for AI security, including intellectual property, privacy, transparency, and accountability. In his work, Floridi underlines the importance of philosophy in defining problems and designing solutions – but it is equally important to consider how these challenges can be addressed at the firm level. What are the tools for managers?
Part of the answer may lie in the increasing and recent focus of management studies around “corporate purpose” and “brand purpose.” This trend represents an important attempt to deepen our understanding of “why to act” (purpose framing) and “how to act” (purpose formalizing and internalizing), while technology management studies address the “what to act” (purpose impacting) question. Furthermore, studies show that corporate purpose is critical for both digital native firms as well as traditional companies undergoing a digital transformation, serving as an important growth engine through purpose-driven innovation. It is therefore fair to ask: can purpose help in addressing any of the AI challenges previously mentioned?
Purpose concepts are not exclusively “cause-related” like CSR and environmental impact. Other types have emerged, such as “competence” (the function of the product) and “culture” (the intent that drives the business). This broadens the consideration of impact types that can help address specific challenges in the age of AI.
Purpose-driven organizations are not new. Take Tesla’s direction “to accelerate the world’s transition to sustainable energy” – it explicitly addresses environmental challenges while defining a business direction that requires constant innovation and leverages multiple converging technologies. The key is to have the purpose formalized and internalized within the company as a concrete drive for growth.
Due to its characteristics, the MTP plays a key role in digital transformation. This necessarily ambitious and long-term vision or goal – the Massive Transformative Purpose – requires firms, particularly those focused on exponential growth, to address emerging accelerating technologies with a purpose-first transformation logic. P&G’s Global Business Services division was able to improve market leadership and gain a competitive advantage over various start-ups and potential disruptors through its “Free up the employee, for free” MTP. This served as a north star for every employee, encouraging them to contribute ideas and best practices to overcome bulky processes and limitations.
My research on MTPs in AI-era firms explores their role in driving innovation to address specific challenges. Results show that the MTP impacts the organization across four dimensions, requiring commitment and synergy from management. Let’s consider these four dimensions by looking at Airbnb:
- Internal Impact: The MTP acts as the organization’s genetic code and guiding philosophy. It is key for leveraging employee motivation, with a strong relationship between purpose, organizational culture, and firm values. Airbnb’s culture of belonging highlights this, with its various purpose-shaping practices, starting with culture-fit interviews delivered during the recruitment process.
- Brand and Market Influence: The MTP contributes directly to building a strong brand and influencing the market. It allows firms to extend beyond functional and symbolic benefits to make the impact of the company on society visible. This involves addressing market demand coherently and consistently. Airbnb’s “Bélo” symbol visually represents this concept of belonging while their MTP features in campaigns like “Wall and Chain: A Story of Breaking Down Walls.”
- Competitive Advantage and Growth: The MTP drives innovation and can lead to superior stock market performance. In digital firms, it’s key in the creation of ecosystems that aggregate leveraged assets and third parties for value creation. The company’s “belong anywhere transformation journey” is a strategic initiative that formalized and interiorized the MTP through various touchpoints for all the different ecosystem members. As Leigh Gallagher details in her 2016 Fortune feature about the company, “When travellers leave their homes, they feel alone. They reach their Airbnb, and they feel accepted and taken care of by their host. They then feel safe to be the same kind of person they are when they’re at home.”
- Core Organization Identity: The MTP is considered part of the core dimension of the organization. More than a goal or business strategy, it is a strategic issue that generates a sense of direction and purpose that affects every part of the organization: internal, external, personality, and expression. This dimension also involves the role of the founder(s) and their personality in shaping the business. At Airbnb, the MTP is often used as a shortcut to explain the firm’s mission and vision. The founders’ approach is pragmatic, and instead of debating differences, time should be spent on execution. At the same time, the personalities of the three founders, Chesky, Gebbia, and Blecharcyzk, are the identity of the firm. They were the first hosts for the platform. Their credibility is key for making Airbnb a trustworthy and coherent proposal in a crowded market.
Executives and leaders of business in the current AI era should embrace three key principles. Be true: Purpose is an essential strategic tool that enables firms to identify and connect with their original selves, decoding their reason for being and embedding it into their identity. Be ambitious: The MTP allows for global impact, confronting major challenges by synthesizing business values and guiding innovation paths to address AI-related issues. Be generous: Purpose allows firms to explicitly address environmental and social issues, taking action on values-based challenges such as transparency, respect for intellectual property, and accountability. By following these principles, organizations and their leaders can maintain their direction and continue to advance in the AI era.
Read the full article below:
Source:
- Authority Magazine Medium, Published on September 15th, 2024.
Gaining hands-on experience through projects, internships, and collaborations is vital for understanding how to apply AI in various industries and domains. Use Kaggle or get a free cloud account and start experimenting. You will have projects to discuss at your next interviews.
By David Leichner, CMO at Cybellum
14 min read
Artificial Intelligence is now the leading edge of technology, driving unprecedented advancements across sectors. From healthcare to finance, education to environment, the AI industry is witnessing a skyrocketing demand for professionals. However, the path to creating a successful career in AI is multifaceted and constantly evolving. What does it take and what does one need in order to create a highly successful career in AI?
In this interview series, we are talking to successful AI professionals, AI founders, AI CEOs, educators in the field, AI researchers, HR managers in tech companies, and anyone who holds authority in the realm of Artificial Intelligence to inspire and guide those who are eager to embark on this exciting career path.
As part of this series, we had the pleasure of interviewing Zorina Alliata.
Zorina Alliata is an expert in AI, with over 20 years of experience in tech, and over 10 years in AI itself. As an educator, Zorina Alliata is passionate about learning, access to education and about creating the career you want. She implores us to learn more about ethics in AI, and not to fear AI, but to embrace it.
Thank you so much for joining us in this interview series! Before we dive in, our readers would like to learn a bit about your origin story. Can you share with us a bit about your childhood and how you grew up?
I was born in Romania, and grew up during communism, a very dark period in our history. I was a curious child and my parents, both teachers, encouraged me to learn new things all the time. Unfortunately, in communism, there was not a lot to do for a kid who wanted to learn: there was no TV, very few books and only ones that were approved by the state, and generally very few activities outside of school. Being an “intellectual” was a bad thing in the eyes of the government. They preferred people who did not read or think too much. I found great relief in writing, I have been writing stories and poetry since I was about ten years old. I was published with my first poem at 16 years old, in a national literature magazine.
Can you share with us the ‘backstory’ of how you decided to pursue a career path in AI?
I studied Computer Science at university. By then, communism had fallen and we actually had received brand new PCs at the university, and learned several programming languages. The last year, the fifth year of study, was equivalent with a Master’s degree, and was spent preparing your thesis. That’s when I learned about neural networks. We had a tiny, 5-node neural network and we spent the year trying to teach it to recognize the written letter “A”.
We had only a few computers in the lab running Windows NT, so really the technology was not there for such an ambitious project. We did not achieve a lot that year, but I was fascinated by the idea of a neural network learning by itself, without any programming. When I graduated, there were no jobs in AI at all, it was what we now call “the AI winter”. So I went and worked as a programmer, then moved into management and project management. You can imagine my happiness when, about ten years ago, AI came back to life in the form of Machine Learning (ML).
I immediately went and took every class possible to learn about it. I spent that Christmas holiday coding. The paradigm had changed from when I was in college, when we were trying to replicate the entire human brain. ML was focused on solving one specific problem, optimizing one specific output, and that’s where businesses everywhere saw a benefit. I then joined a Data Science team at GEICO, moved to Capital One as a Delivery lead for their Center for Machine Learning, and then went to Amazon in their AI/ML team.
Can you tell our readers about the most interesting projects you are working on now?
While I can’t discuss work projects due to confidentiality, there are some things I can mention! In the last five years, I worked with global companies to establish an AI strategy and to introduce AI and ML in their organizations. Some of my customers included large farming associations, who used ML to predict when to plant their crops for optimal results; water management companies who used ML for predictive maintenance to maintain their underground pipes; construction companies that used AI for visual inspections of their buildings, and to identify any possible defects and hospitals who used Digital Twins technology to improve patient outcomes and health. It is amazing to see how much AI and ML are already part of our everyday lives, and to recognize some of it in the mundane around us.
None of us are able to achieve success without some help along the way. Is there a particular person who you are grateful for who helped get you to where you are? Can you share a story about that?
When you are young, there are so many people who step up and help you along the way. I have had great luck with several professors who have encouraged me in school, and an uncle who worked in computers who would take me to his office and let me play around with his machines. I now try to give back and mentor several young people, especially women who are trying to get into the field. I volunteer with AnitaB and Zonta, as well as taking on mentees where I work.
As with any career path, the AI industry comes with its own set of challenges. Could you elaborate on some of the significant challenges you faced in your AI career and how you managed to overcome them?
I think one major challenge in AI is the speed of change. I remember after spending my Christmas holiday learning and coding in R, when I joined the Data Science team at GEICO, I realized the world had moved on and everyone was now coding in Python. So, I had to learn Python very fast, in order to understand what was going on.
It’s the same with research — I try to work on one subject, and four new papers are published every week that move the goal posts. It is very challenging to keep up, but you just have to adapt to continuously learn and let go of what becomes obsolete.
Ok, let’s now move to the main part of our interview about AI. What are the 3 things that most excite you about the AI industry now? Why?
1. Creativity
Generative AI brought us the ability to create amazing images based on simple text descriptions. Entire videos are now possible, and soon, maybe entire movies. I have been working in AI for several years and I never thought creative jobs will be the first to be achieved by AI. I am amazed at the capacity of an algorithms to create images, and to observe the artificial creativity we now see for the first time.
2. Abstraction
I think with the success and immediate mainstream adoption of Generative AI, we saw the great appetite out there for automation and abstraction. No one wants to do boring work and summarizing documents; no one wants to read long websites, they just want the gist of it. If I drive a car, I don’t need to know how the engine works and every equation that the engineers used to build it — I just want my car to drive. The same level of abstraction is now expected in AI. There is a lot of opportunity here in creating these abstractions for the future.
3. Opportunity
I like that we are in the beginning of AI, so there is a lot of opportunity to jump in. Most people who are passionate about it can learn all about AI fully online, in places like Open Institute of Technology. Or they can get experience working on small projects, and then they can apply for jobs. It is great because it gives people access to good jobs and stability in the future.
What are the 3 things that concern you about the AI industry? Why? What should be done to address and alleviate those concerns?
1. Fairness
The large companies that build LLMs spend a lot of energy and money into making them fair. But it is not easy. Us, as humans, are often not fair ourselves. We even have problems agreeing what fairness even means. So, how can we teach the machines to be fair? I think the responsibility stays with us. We can’t simply say “AI did this bad thing.”
2. Regulation
There are some regulations popping up but most are not coordinated or discussed widely. There is controversy, such as regarding the new California bill SB1047, where scientists take different sides of the debate. We need to find better ways to regulate the use and creation of AI, working together as a society, not just in small groups of politicians.
3. Awareness
I wish everyone understood the basics of AI. There is denial, fear, hatred that is created by doomsday misinformation. I wish AI was taught from a young age, through appropriate means, so everyone gets the fundamental principles and understands how to use this great tool in their lives.
For a young person who would like to eventually make a career in AI, which skills and subjects do they need to learn?
I think maybe the right question is: what are you passionate about? Do that, and see how you can use AI to make your job better and more exciting! I think AI will work alongside people in most jobs, as it develops and matures.
But for those who are looking to work in AI, they can choose from a variety of roles as well. We have technical roles like data scientist or machine learning engineer, which require very specialized knowledge and degrees. They learn computing, software engineering, programming, data analysis, data engineering. There are also business roles, for people who understand the technology well but are not writing code. Instead, they define strategies, design solutions for companies, or write implementation plans for AI products and services. There is also a robust AI research domain, where lots of scientists are measuring and analyzing new technology developments.
With Generative AI, new roles appeared, such as Prompt Engineer. We can now talk with the machines in natural language, so speaking good English is all that’s required to find the right conversation.
With these many possible roles, I think if you work in AI, some basic subjects where you can start are:
- Analytics — understand data and how it is stored and governed, and how we get insights from it.
- Logic — understand both mathematical and philosophical logic.
- Fundamentals of AI — read about the history and philosophy of AI, models of thinking, and major developments.
As you know, there are not that many women in the AI industry. Can you advise what is needed to engage more women in the AI industry?
Engaging more women in the AI industry is absolutely crucial if you want to build any successful AI products. In my twenty years career, I have seen changes in the tech industry to address this gender discrepancy. For example, we do well in school with STEM programs and similar efforts that encourage girls to code. We also created mentorship organizations such as AnitaB.org who allow women to connect and collaborate. One place where I think we still lag behind is in the workplace. When I came to the US in my twenties, I was the only woman programmer in my team. Now, I see more women at work, but still not enough. We say we create inclusive work environments, but we still have a long way to go to encourage more women to stay in tech. Policies that support flexible hours and parental leave are necessary, and other adjustments that account for the different lives that women have compared to men. Bias training and challenging stereotypes are also necessary, and many times these are implemented shoddily in organizations.
Ethical AI development is a pressing concern in the industry. How do you approach the ethical implications of AI, and what steps do you believe individuals and organizations should take to ensure responsible and fair AI practices?
Machine Learning and AI learn from data. Unfortunately, lot of our historical data shows strong biases. For example, for a long time, it was perfectly legal to only offer mortgages to white people. The data shows that. If we use this data to train a new model to enhance the mortgage application process, then the model will learn that mortgages should only be offered to white men. That is a bias that we had in the past, but we do not want to learn and amplify in the future.
Generative AI has introduced a new set of fresh risks, the most famous being the “hallucinations.” Generative AI will create new content based on chunks of text it finds in its training data, without an understanding of what the content means. It could repeat something it learned from one Reddit user ten years ago, that could be factually incorrect. Is that piece of information unbiased and fair?
There are many ways we fight for fairness in AI. There are technical tools we can use to offer interpretability and explainability of the actual models used. There are business constraints we can create, such as guardrails or knowledge bases, where we can lead the AI towards ethical answers. We also advise anyone who build AI to use a diverse team of builders. If you look around the table and you see the same type of guys who went to the schools, you will get exactly one original idea from them. If you add different genders, different ages, different tenures, different backgrounds, then you will get ten innovative ideas for your product, and you will have addressed biases you’ve never even thought of.
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