Artificial Intelligence (AI)

Artificial intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy.

Applications and devices equipped w

ith AI can see and identify objects. They can understand and respond to human language. They can learn from new information and experience. They can make detailed recommendations to users and experts. They can act independently, replacing the need for human intelligence or intervention (a classic example being a self-driving car).

But in 2024, most AI researchers, practitioners and most AI-related headlines are focused on breakthroughs in generative AI (gen AI), a technology that can create original text, images, video and other content. To fully understand generative AI, it’s important to first understand the technologies on which generative AI tools are built: machine learning (ML) and deep learning.

Key aspects of AI:

Mimicking Human Intelligence:
AI aims to create systems that can perform tasks that usually require human intelligence, such as learning, problem-solving, perception, and language understanding.

Learning from Data:
AI systems learn from data, identify patterns, and make predictions or take actions based on that data.

Adaptability:
AI can adjust to new inputs and learn from its experiences to improve its performance over time.

Various Applications:
AI has diverse applications, from simple tasks like automating repetitive processes to complex tasks like self-driving cars and medical diagnosis.

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Subset of Technologies:
AI is a broad field that includes various technologies like machine learning, deep learning, and natural language processing.
Machine learning
A simple way to think about AI is as a series of nested or derivative concepts that have emerged over more than 70 years:

How artificial intelligence, machine learning, deep learning and generative AI are related.

Directly underneath AI, we have machine learning, which involves creating models by training an algorithm to make predictions or decisions based on data. It encompasses a broad range of techniques that enable computers to learn from and make inferences based on data without being explicitly programmed for specific tasks.

There are many types of machine learning techniques or algorithms, including linear regression, logistic regression, decision trees, random forest, support vector machines (SVMs), k-nearest neighbor (KNN), clustering and more. Each of these approaches is suited to different kinds of problems and data.

But one of the most popular types of machine learning algorithm is called a neural network (or artificial neural network). Neural networks are modeled after the human brain’s structure and function. A neural network consists of interconnected layers of nodes (analogous to neurons) that work together to process and analyze complex data. Neural networks are well suited to tasks that involve identifying complex patterns and relationships in large amounts of data.

The simplest form of machine learning is called supervised learning, which involve

Deep learning

Deep learning is a subset of machine learning that uses multilayered neural networks, called deep neural networks, that more closely simulate the complex decision-making power of the human brain.

Deep neural networks include an input layer, at least three but usually hundreds of hidden layers, and an output layer, unlike neural networks used in classic machine learning models, which usually have only one or two hidden layers.

These multiple layers enable unsupervised learning: they can automate the extraction of features from large, unlabeled and unstructured data sets, and make their own predictions about what the data represents.

Because deep learning doesn’t require human intervention, it enables machine learning at a tremendous scale. It is well suited to natural language processing (NLP), computer vision, and other tasks that involve the fast, accurate identification complex patterns and relationships in large amounts of data. Some form of deep learning powers most of the artificial intelligence (AI) applications in our lives today.

Generative AI

Generative AI, sometimes called “gen AI”, refers to deep learning models that can create complex original content such as long-form text, high-quality images, realistic video or audio and more in response to a user’s prompt or request.

At a high level, generative models encode a simplified representation of their training data, and then draw from that representation to create new work that’s similar, but not identical, to the original data.

Generative models have been used for years in statistics to analyze numerical data. But over the last decade, they evolved to analyze and generate more complex data types. This evolution coincided with the emergence of three sophisticated deep learning model types:

- Variational autoencoders or VAEs, which were introduced in 2013, and enabled models that could generate multiple variations of content in response to a prompt or instruction.
- Diffusion models, first seen in 2014, which add “noise” to images until they are unrecognizable, and then remove the noise to generate original images in response to prompts.
- Transformers (also called transformer models), which are trained on sequenced data to generate extended sequences of content (such as words in sentences, shapes in an image, frames of a video or commands in software code). Transformers are at the core of most of today’s headline-making generative AI tools, including ChatGPT and GPT-4, Copilot, BERT, Bard and Midjourney.

Why Learning AI Skills Today Will Set You Apart in Tomorrow’s Job Market

AI Skills in Resume/CV

Including AI Skills in Your Resume/CV

When showcasing AI skills on your resume or CV, it’s essential to highlight both your technical abilities and practical experience. Key AI skills that employers look for include:

Programming Languages: Proficiency in Python, R, and Java is crucial for building AI models.
AI Frameworks: Experience with frameworks like TensorFlow, PyTorch, and Keras is highly desirable .
Projects and Case Studies: Showcase practical applications of your AI knowledge, such as developing a machine learning model, creating an AI-based recommendation system, or working on AI research projects. Quantify your contributions and the results whenever possible.

AI for Freelancing

Freelancers with AI expertise can offer a range of specialized services, from building AI-powered chatbots to conducting data analysis. Platforms like Upwork and Fiverr are seeing increasing demand for AI professionals, especially those proficient in machine learning and NLP . Freelancers can leverage these skills to provide personalized solutions for small businesses that cannot afford full-time AI teams.

Challenges in Presenting AI Skills

One common challenge in presenting AI skills is quantifying their impact. Employers often want to see measurable results, such as how an AI model improved operational efficiency or increased revenue. Additionally, AI is a rapidly evolving field, so staying updated with the latest advancements and showcasing continual learning is essential for remaining competitive .

AI Skills Training Programs

With the demand for AI skills growing, numerous training programs and courses have emerged to cater to various levels of expertise, from beginners to professionals. These programs help learners acquire practical knowledge and experience with AI technologies, equipping them for careers in the field. For beginners and students, programs that focus on foundational skills, such as programming languages and basic machine learning concepts, are highly recommended. For professionals, more advanced programs that cover specialized AI topics like deep learning, neural networks, and natural language processing (NLP) are ideal.

Some of the top AI training programs include:

AI for Everyone by Andrew Ng on Coursera: A beginner-friendly course designed to demystify AI for non-technical audiences, helping them understand the basics and applications of AI in business and society.
Deep Learning Specialization by Andrew Ng on Coursera: This program focuses on deep learning and neural networks, ideal for those who want to dive deep into advanced AI.
MIT’s AI and Machine Learning Professional Certificate Program: A comprehensive program for professionals looking to advance their AI skills. It covers topics like machine learning, ethics, and reinforcement learning.

Bridging the AI Skills Gap

To bridge the gap between the demand for AI skills and the availability of qualified professionals, several initiatives and certifications are making AI more accessible.

Online certifications have democratized AI education by offering structured learning paths, allowing individuals to gain industry-recognized credentials from the comfort of their homes. Platforms like Coursera, Udacity, and edX partner with leading universities and companies to offer AI certifications that validate learners’ skills. For example, Coursera’s AI Professional Certificate from IBM provides a hands-on learning experience, guiding learners through AI concepts, tools, and applications.

Initiatives such as AI4All focus on making AI accessible to underrepresented communities, helping bridge the gender and diversity gap in the AI workforce. Google’s TensorFlow for Education and Microsoft AI School offer free resources and cloud credits to students and professionals, ensuring that lack of access to technology is no longer a barrier to learning.

For those looking for workplace-based training, companies like DataCamp and Pluralsight offer team-based AI skills development programs that allow employees to learn AI at their own pace while collaborating with colleagues on projects. These platforms also provide assessments to identify skills gaps and recommend personalized learning paths, helping companies close internal skills gaps efficiently.

Conclusion and Call to Action

Invest in AI Skills Now

The rise of AI in every major industry has created a once-in-a-lifetime opportunity for professionals and students to carve out new career paths or enhance their existing roles. AI is no longer just for software developers or data scientists—its applications are far-reaching, impacting fields such as healthcare, finance, education, and even the arts. As AI continues to evolve, the demand for skilled professionals will only grow. This is why investing in AI skills now can future-proof your career, allowing you to stay ahead of the curve as technology advances.