Machine Learning vs Artificial Intelligence: What’s the Difference?

An increasing number of industries are starting to employ AI and machine learning technologies, including aviation and space, engineering and construction, medicine, and retail. Image from Unsplash

Rina Diane Caballar April 13, 2023

According to the Bureau of Labor Statistics, artificial intelligence and machine learning jobs offer a median annual wage of $114,520—with the highest 10 percent of workers making more than $176,780 per year.

In this course, participants learn core principles, concepts, and methods of continuous improvement and explore the history of continuous improvement efforts. In a series of lectures, Master Black Belt in Six Sigma Shane Wentz, Ph.D., enables learners to enhance, optimize, and stabilize business processes and to augment quality control through varied methodologies. Participants gain the skills and knowledge to reimagine internal processes, engage in advanced problem-solving, and empower employees. The course covers high-level foundational principles of Lean, Six Sigma, and DMAIC processes before diving deep into Lean Six Sigma methods. Learners will analyze the tools of process mapping, Kaizen (a continuous improvement method/tool deemed a prerequisite for Lean transformation success), Lean (a continuous improvement philosophy that emphasizes streamlined workflows and reducing waste), and the process improvement methodology driving DMAIC: Define, Measure, Analyze, Improve, and Control.

Entrepreneurs fostering new ventures outside of well-developed entrepreneurial ecosystems like Silicon Valley face significant challenges. These markets are unlike Silicon Valley, and they will likely remain so. That doesn't mean they can't nurture and sustain new businesses. In transitioning markets that lack abundant private sector financing, creative approaches from government officials, donors, and business leaders can fill the void to support entrepreneurial activity. Northeast Ohio (where Cleveland and Case Western Reserve University are located) has become a laboratory for such innovative solutions. During our time together in this course, we will explore some of these creative approaches to promoting regional entrepreneurship. Northeast Ohio has seen a massive infusion of government and donor resources for over ten years. In that time, Northeast Ohio has promoted regional development (including job creation and follow-on funding) through alternative methods of financing startups. It has not been easy. The region has made progress, but it remains to be seen whether it has struck upon an enduring formula. A dynamic entrepreneurial ecosystem takes many years, even decades, to develop. Government policymakers and donors typically seek quicker returns on their support programs, which makes long-term support for entrepreneurship challenging. This course includes case studies and first-person testimonials from entrepreneurs who have launched products and services ranging from medical devices to LED lights to whiskey. You will learn how these entrepreneurs discovered and procured resources from various programs and intermediary organizations. You will hear from business people still in the process of developing their companies as well as those whose ventures have flourished. Our focus will extend beyond Cleveland to selected markets around the world, including Greece, Vietnam, Tunisia, Argentina, Rwanda and China. You will learn about international incentives and other supports available to developing startups and small businesses across the globe. This course examines how different communities around the world approach implementing strategies and methods to support businesses. I will ask you to reflect on how to apply the principles we learn to growing entrepreneurship where you live. The social interactive features of the online platform will enable students from around the world to share ideas and learn from one another. Most MOOCs rebroadcast professors’ lectures; this course is different. Don’t expect to see me standing in front of the camera, talking and lecturing every module. Instead, the lectures will be relatively short in length. They have more of the feel of an engaging documentary than a static classroom setting. ## __What type of person takes the *Beyond Silicon Valley* course?__ - Active and aspiring businesspeople in communities looking to initiate new startup ecosystems or bolster existing ones - Current and prospective entrepreneurs who want to learn about funding sources, risk management, and the lifecycle of startups - Professionals who work in government agencies, nongovernmental organizations, philanthropic institutions, and various private funds that fund startups - Anyone curious about how to create and grow startup ecosystems outside the major tech hubs - The principles Beyond Silicon Valley teaches have been drawn from and applied to startups worldwide, from small villages to megalopolises. You'll study success stories spanning the globe from Vietnam to Kosovo to Botswana. ## __What will you learn in *Beyond Silicon Valley*?__ - How and where startups can find funding to get their businesses off the ground - What role government can play in encouraging startups and a healthy startup ecosystem - How anchor institutions such as universities and healthcare systems participate in startup ecosystems - How nongovernmental organizations support startups - How to raise funds from venture capitalists, angel investors, and accelerators - How to navigate challenging periods in the startup lifecycle - The importance of mentoring in successful startups

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Digital avatars, smart voice assistants, self-driving cars, emotionally intelligent robots: all rely on machine learning and artificial intelligence (AI). These two computing processes have become so integral to modern living that it's hard to remember they both seemed distant dreams not very long ago.

Most people understand that machine learning and AI involve machines that simulate the way the human brain works. But what, precisely, do the terms mean, and how do they differ? This article explores both questions. It also discusses the careers associated with each and the degrees required to enter and advance related computing professions.

Artificial intelligence and machine learning: what's the difference?

According to Andrew Moore, former dean of Carnegie Mellon University’s School of Computer Science and now head of Google Cloud AI, “Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence." These behaviors include decision-making, image and speech recognition, problem-solving, and translation.

Roomba, iRobot’s famous robot vacuum cleaner, offers an example of AI in action. The Roomba can scan a room’s size, identify any obstacles, and figure out the most efficient cleaning routes by itself. Newer models can even make a map of a house and figure out its floor plan.

Machine learning is a subset of AI. It involves learning from past data to make decisions or predictions. Using learning algorithms, machines process historical data, which they use to recognize patterns. These machines can then make decisions or predictions on their own.

Spotify uses machine learning to customize your recommendations and predict your musical preferences based on your previous activity and the songs on your playlists. That's just one of many, many real-world examples of this technology in action.

Machine learning and artificial intelligence: job markets

According to the Bureau of Labor Statistics, the U.S. job market for computer and information research scientists (the category into which AI and machine learning jobs are bucketed) is expected to grow at a whopping 21 percent rate between 2021 and 2031 (about three times the rate for the job market as a whole). That should create about 3,000 job openings in the field each year. The Stanford Institute for Human-Centered Artificial Intelligence (HAI) reports that the number of AI jobs worldwide is rising rapidly, with the US market leading the way.

LinkedIn's 2021 Jobs on the Rise report ranks artificial intelligence practitioners at the top of its digital transformation jobs list. It notes that "demand is strong… in fact, this was the top emerging trend from last year's report." ZipRecruiter reports a median salary for artificial intelligence engineers of almost $157,000 annually, with the top 25 percent earning over $165,000.

What are the most popular artificial intelligence and machine learning jobs?

The most common types of entry-level AI and machine learning jobs are in software engineering. The Bureau of Labor Statistics reports an average annual income for software developers of just under $131,000.

Other AI and machine learning jobs, with their average salaries, include:

Computer vision engineer: $131,000
Machine learning engineer $155,500
Research and development engineer: $100,000
Robotics software engineer: $126,000
Artificial intelligence software developer: $127,000

As you improve your skills and gain years of experience, you can level up to a senior role, perhaps as a manager leading a team or an architect focused on design and performance.

An increasing number of industries are starting to utilize AI and machine learning technologies, including aviation and space, engineering and construction, medicine, and retail. Finding a promising field and developing specialized skills could help you advance more quickly than you would with generalized AI and machine learning capabilities only.

What skills are needed for AI and machine learning jobs?

Machine learning and artificial intelligence jobs require strong analytical and problem-solving skills as well as solid math skills (including logic, probability, predictive analytics, and statistics). You’ll also need to know theory and fundamentals, especially when it comes to algorithms and data structures. You'll need an understanding of deep learning models and artificial neural networks and, of course, a solid grounding in computing and computer systems.

In terms of programming, you’ll be expected to navigate commonly used languages in AI and machine learning such as C++, Java, Python, and R. Additionally, you’ll be required to have knowledge of software development methodologies and tools.

Which cities and states have the most AI and machine learning jobs?

According to Indeed, New York City and San Francisco are the top employment markets for artificial intelligence and machine learning positions. No surprise there. Washington DC takes third place; again, no surprise given the concentration of intelligence, military, and other high-stakes government jobs in the city. The other big markets are, well, big markets for many professions: San Jose, Seattle, Boston, Los Angeles, Chicago, Dallas-Fort Worth, and Atlanta.

What are the educational requirements for AI and machine learning jobs?

Most AI and machine learning jobs require a bachelor’s degree in math or computer science. These programs teach the fundamentals of algorithms and logic, programming, and software engineering. A growing number of universities include AI and machine learning courses in their curricula, and some have specialized tracks in these fields. Carnegie Mellon University even offers a bachelor’s degree in artificial intelligence.

Many AI and machine learning companies prefer to hire candidates with a master’s degree in computer science, often with a specialization in AI or machine learning, which can be obtained either on-campus or online (the latter is offered by Columbia University’s Computer Science Master’s Degree in Machine Learning or the Lyle School of Engineering's MSCS-AI at Southern Methodist University).

Another way to specialize in AI or machine learning is through online courses or boot camps like Springboard’s AI/Machine Learning Career Track.

If you’re leaning toward an academic path, you’ll need to get a doctoral degree in AI or machine learning. A doctoral degree may also be required by research institutions or R&D departments of companies.

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