Written By Jess Feldman
AI has taken 2023 by storm, from generative chatbots like ChatGPT to AI-generated art. But artificial intelligence and machine learning aren’t that new! Lighthouse Labs’ resident data expert, Simon Dawkins, demystifies AI and machine learning, including what you need to know to land a role as an in-demand AI engineer (or other roles that are crucial in working with AI). If you’re ready to jump into AI, learn how Lighthouse Labs’ Data Science Bootcamp is preparing students to graduate ready to use machine learning on the job.
Are there major differences between AI and Machine Learning?
Machine learning and AI are the natural outgrowth of everything data scientists have been doing with statistics and statistical modeling. We've been doing what could have been called AI for 60 years — it's just changing names and buzzwords!
Artificial intelligence (AI) is the broad concept of creating systems, machines, or algorithms that can do things that would typically be done by a human. Basically, AI is anything we think of as requiring intelligence being done by something artificial.
Machine learning is a subset of AI that specifically focuses on developing algorithms and statistical models. Machine learning is essentially math-based AI that trains computers specifically to learn from data, make predictions, and inform decisions based on data. Machine learning is based on the fundamentals of relatively simple math. What differentiates machine learning is that the math is done on such a large scale that it’s impossible for humans to do in a practical timeframe.
AI is the buzzword right now, but what are the actual roots of artificial intelligence?
The terms AI and machine learning are often used interchangeably. AI is the umbrella that machine learning falls under. All machine learning is AI. Almost all AI work these days is machine learning because it's almost all being done with computers. In the context of the tech world and the news that we’re reading, AI is machine learning.
ChatGPT and other generative AI are the popular terms in 2023, but we’ve been working on the foundational technology for many years. ChatGPT is a large language model, which is a type of neural network. Neural networks are part of deep learning, which falls under the umbrella of machine learning and AI. They're called neural networks because they were originally designed in an attempt to mimic the way we think neurons work in brain tissue. A lot of natural language processing (NLP) is done with neural networks.
Are machine learning models getting more powerful?
Machine learning functionality is getting more powerful but there aren’t great technological advances happening. We're not being held back by computer capability at the moment. The big push in AI right now is advancement of ideas, techniques, and algorithms and not technological advancement. OpenAI with ChatGPT, for example, was a new idea that people have been working on for a long time, as a new way of creating a language model and training it. Generative models are becoming cheap, easy, and accessible. Even though AI is making art these days, it is actually quite taxing to use, computation-wise.
We're moving toward a time that every company realizes they could make use of AI, given how cheap, accessible, and relatively easy it's getting. It’s not hard to imagine most companies using a simple AI model for their business:
For example: A cafe on the corner could look at customer flow in relation to weather, events, and traffic and how to better schedule their employees so they can save money by not scheduling too many people.
There's no reason not to utilize AI for your business! It’s a matter of companies realizing and being willing to implement it. AI-potential is basically unlimited. You don’t have to be a tech company with a bunch of advanced technical employees to use AI.
What is the difference between an AI/Machine Learning Engineer and a Data Scientist?
The difference between a machine learning researcher, a data scientist, and an AI/machine learning engineer is that the AI/machine learning engineer is the one taking the ideas, processes, and algorithms developed by the other tech roles and actually turning that into production-ready code to run on millions of devices.
The engineer jobs require a bit more experience and hands-on familiarity with programming. A company has to have quite a few analysts, data scientists, database administrators and data engineers before they need one AI/machine learning engineer.
Can a total beginner really become an AI Engineer through an immersive data science bootcamp?
It depends on the company. I see a lot of job descriptions that are confusingly defined. I tell my data bootcamp students that if it falls in the lines of data, apply! Many job descriptions are written by people who don’t actually know what they mean — they’re just using buzzwords and terms they were told to include. Data scientists and data analysts tend to be entry-level, while roles that include “engineer” require more programming experience.
Are today’s employers looking to hire data professionals who understand AI?
Without question, it is an expectation that data professionals have a clear understanding of machine learning.
That said, good data collection costs a company money — machine learning isn't magic, it's math! Part of your job as a data professional may be to talk your boss down from trying to use machine learning for everything.
On the job, will entry-level data professionals be using machine learning and AI? Or is this for more of a mid-level or senior-level role?
They should be ready to use machine learning right away. Simply put, if anyone spends a week or two learning Python and another week reading about machine learning, they can implement something with no trouble at all. Any of our Lighthouse Labs Data Science graduates leave the program knowing how to do machine learning or machine learning-related work. The question is if machine learning is the tool needed for the job because it’s more expensive.
What do you need to know about machine learning to begin to use or create AI?
It’s task dependent, so it would depend on what kind of machine learning is needed for the task, which will inform the kind of AI you'll build. If it's sentiment analysis of customer comments, then we'll need to get into natural language processing using neural networks. If it's image recognition, then we need to get into image tools.
For those already working in the data field, what is your recommendation on how to keep your skills relevant for AI?
It’s honestly not that hard to do! Unless you totally disconnect yourself from all news and social media, it's hard to avoid finding out about new things you might want to get familiar with. On a more technical level a lot of libraries, like Python libraries and other programming frameworks, will tell you when an update is needed. It takes five minutes a month to stay up-to-date on what’s happening.
Does Lighthouse Labs cover machine learning and AI in its Data Science curriculum?
Machine learning and AI are covered throughout the data science curriculum. We cover programming languages, database languages, and data visualization. In the third week, we start talking about statistical models at a simple level for predictions.
We show students how to use machine learning tools, the appropriate use of ChatGPT, how to write good queries, and how to prompt engineering for LLMs. Of course, as a student, there's temptation to use ChatGPT to do your work instead of learning on your own, so we try to teach them to strike that balance — we remind them that they’re paying to learn, not to pretend.
How do Lighthouse Labs students use machine learning and AI in the bootcamp?
Lighthouse Labs breaks the program into eight projects. There are two end-to-end projects: a week-long, midterm project and a two-week, Capstone project, both on their topic of choice.
We also have six other smaller projects usually completed over 1-3 days that drill down into a specific part of the process, such as: dealing with databases, accessing data, filtering it, combining it with other data, and statistical modeling. There is an entire project focused on dashboarding and creating good data communication. A couple other projects focus on specific areas of machine learning.
What is your advice for students who are enrolling in the Data Science program and interested in AI?
It’s the same advice I give to anyone considering a bootcamp:
Jess Feldman is an accomplished writer and the Content Manager at Course Report, the leading platform for career changers who are exploring coding bootcamps.
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