How you could be learning AI

The Steam Engine Analogy

The introduction of the steam engine during the 19th century marked a significant turning point in history, as it revolutionized industry and transportation.

The introduction of the steam engine instantly made a number of blue-collar jobs obsolete.?It eliminated the artisanal production of home goods made by individuals. Before steam-powered locomotives and ships were developed, transportation relied heavily on horses. The steam engine also powered the mechanization of textile mills, leading to the displacement of handloom weavers and spinners.

The steam engine caused some panic when it was first introduced because it led to the disappearance of some traditional jobs, but it also created new opportunities for workers in a range of fields: from steam engine operators to engineers to design and improve the machines as well as factory workers to operate the new machines and perform the assembly line work.

AI is undoubtedly going to impact many industries including localization. This time the revolution is likely to affect many white-collar jobs.?

However, Artificial Intelligence is also projected to create 2.3 million jobs in the next few years.?

New Artificial Intelligence Jobs in Localization?

Here are some examples of jobs that are emerging in the AI-driven economy:

- AI Developers and Engineers who can design, build, and maintain AI systems.

- Data Scientists and Analysts with expertise in data science and analytics.

- AI Trainers who can train and supervise AI systems to operate effectively and ethically

- Cybersecurity experts who can protect these systems from cyber threats.

- Human-AI Interaction Designers who can create intuitive interfaces that allow humans to interact with these systems.

- AI Ethicists and policy experts who can develop ethical guidelines and policies for the use of AI technology.

While it is not immediately clear how those new jobs will materialize in the Localization industry now is the time to start learning about Artificial Intelligence, don’t you agree?

But where to start? What can you do to get ahead of the curb?

If you are interested in getting more knowledgeable about AI here is a selection of courses and books to help you. They offer different levels of technical depth and breadth, so it's important to choose the one that matches your learning goals and background. The majority of these are free and to assist you I have included more information on each:

Courses

Machine Learning Specialization - Stanford University (Coursera)

Machine Learning Specialization is a beginner-friendly online program offered on Coursera created in collaboration between Stanford Online and DeepLearning.AI. This program aims to teach learners the fundamentals of machine learning and how to use these techniques to build real-world AI applications.

The course is divided into three courses or modules, and it covers a range of topics, including supervised learning, unsupervised learning, deep learning, and practical applications of machine learning.

The first course focuses on the basics of machine learning, such as linear regression and logistic regression. Learners will build machine learning models using Python libraries NumPy and scikit-learn.

The second course is dedicated to unsupervised learning techniques, including clustering and dimensionality reduction, while the third course is focused on deep learning, including neural networks and convolutional neural networks.

The course is taught by Andrew Ng, a well-known AI pioneer, co-founder of Coursera, and founder of DeepLearning.AI Since its launch, this course has been taken by over 4.8 million learners since 2012.

Introduction to AI - University of Helsinki

The course is designed to introduce the basics of AI to anyone, regardless of their technical background, and it does not require any programming or computer science expertise. The course aims to make AI more understandable and combines theory with practical exercises, which can be completed at your own pace.

The course covers a range of topics, including the history and evolution of AI, what is possible (and not possible) with AI, and how AI affects our lives. It also introduces different types of AI, such as supervised and unsupervised learning, and discusses their applications. Additionally, the course covers problem-solving techniques, such as formulating a problem as a graph and applying search algorithms, and introduces game theory and the minimax algorithm.

As a proof of completion, each student is given an electronic certificate that includes a verification link. The course is part of multiple programs and can be applied to multiple specializations or professional certificate programs.

Overall, the "Introduction to AI" course by the University of Helsinki is an accessible and comprehensive online course that covers the basics of AI and its applications.

Introduction to Artificial Intelligence - Stanford University (Coursera)

The "Introduction to Artificial Intelligence" course offered by Stanford University on Coursera is a beginner-level course that introduces students to the basics of AI. This course is designed to be accessible to anyone, regardless of their technical background or experience with computer science. The course covers a range of topics, including machine learning, probabilistic reasoning, robotics, and natural language processing.

The course is part of multiple programs and is offered online through Coursera. Additionally, there are no prerequisites for this course, and it does not require any programming or computer science expertise.

The course is based on the Stanford graduate course CS221: Introduction to Artificial Intelligence and has been adapted to meet the needs of working professionals. This online course is taught by Stanford faculty members and provides students with opportunities to explore theoretical and project-based learning in natural language processing and understanding.

Overall, this course is an excellent starting point for anyone looking to gain an understanding of the foundational principles that drive AI applications. The course provides a rigorous introduction to the field, and students will have the opportunity to implement some of the systems they learn about throughout the course.

CS50's Introduction to Artificial Intelligence with Python - Harvard University (edX)

This course is designed to introduce learners to the fundamental concepts and algorithms of modern artificial intelligence, with a focus on the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. The course covers a variety of topics including search algorithms, knowledge representation, inference, and machine learning. Learners will have the opportunity to work on projects that involve building a game-playing agent, handwriting recognition system, and machine translation system. The course is self-paced and requires prior knowledge of Python programming language. Upon completion of the course, learners receive a certificate issued by edX and also by CS50. However, the course will not be available after 31 December 2023

Deep Learning Specialization - (Coursera)

If you want to take things a bit more seriously, the "Deep Learning" course by Coursera is a specialization consisting of five courses. The courses are taught by Andrew Ng and his team at deeplearning.ai. The specialization provides a comprehensive introduction to deep learning, including neural networks, convolutional neural networks, recurrent neural networks, hyperparameter tuning, regularization, optimization, and more.

The five courses in the specialization are as follows:

1.?????Neural Networks and Deep Learning

2.?????Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

3.?????Structuring Machine Learning Projects

4.?????Convolutional Neural Networks

5.?????Sequence Models

The specialization is designed for learners who have a basic understanding of programming and mathematics and want to gain expertise in deep learning to level up their careers. By the end of the specialization, learners will have developed the skills and knowledge necessary to build and apply deep learning models in a variety of applications. Additionally, the specialization provides career advice from deep learning experts in industry and academia.

The "Deep Learning" course by Coursera has been widely regarded as one of the most comprehensive and popular deep learning courses available online.

AI For Everyone - Andrew Ng (Coursera)

This is a non-technical course that provides a broad introduction to the field of AI. The course covers topics such as machine learning, deep learning, and neural networks. It is suitable for beginners who want to learn about AI and its applications.

Artificial Intelligence Engineer - (Udacity)

This is an advanced course that teaches you how to design and implement AI solutions. The course covers topics such as natural language processing, computer vision, and robotics. It is suitable for those who want to pursue a career in AI engineering.

Machine Learning - Stanford University (Coursera)

This is a technical course that provides a deep understanding of machine learning algorithms. The course covers topics such as linear regression, logistic regression, and neural networks. It is suitable for those who want to learn the fundamentals of machine learning.

Python for Data Science and Machine Learning Bootcamp - (Udemy)

This is a practical course that teaches you how to use Python for data science and machine learning. The course covers topics such as data analysis, visualization, and machine learning algorithms. It is suitable for those who want to learn how to use Python for AI applications.

Introduction to Artificial Intelligence - IBM (edX)

This is a comprehensive course that covers a wide range of topics related to AI. The course covers topics such as machine learning, natural language processing, and robotics. It is suitable for those who want to learn about AI from a broad perspective.

Books

Artificial Intelligence: A Guide to Intelligent Systems - Michael Negnevitsky

This book provides an introduction to the field of artificial intelligence, with a focus on intelligent systems. It covers topics such as knowledge representation, problem-solving, decision-making, and learning.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow - Aurélien Géron

This book is a practical guide to machine learning, with a focus on using the Scikit-Learn, Keras, and TensorFlow libraries. It covers topics such as data preprocessing, feature engineering, model selection, and deep learning.

Deep Learning - Ian Goodfellow, Yoshua Bengio, and Aaron Courville

This book provides a comprehensive introduction to deep learning, a subfield of machine learning that focuses on neural networks. It covers topics such as convolutional neural networks, recurrent neural networks, and generative models.

Superintelligence: Paths, Dangers, Strategies - Nick Bostrom

This book explores the potential impact of artificial intelligence on human society and the risks and opportunities associated with the development of super-intelligent AI. It covers topics such as the control problem, existential risks, and the possibility of a technological singularity.