This article was written by Educative's Co-founder and CEO, Fahim ul Haq.
Just as cloud computing transformed businesses and industries when it first appeared in the mid-2000s, machine learning and artificial intelligence are poised to do the same right now. In fact, if it wasn't already clear — the AI revolution is fully upon us. We've seen the impact on companies that didn't adopt cloud technologies where it was needed quickly enough: they ended up losing market share to more agile competitors.
“Companies rarely die from moving too fast, and they frequently die from moving too slowly.”
— Reed Hastings, Netflix Co-Founder
So how will the AI revolution affect the tech industry and its hiring practices?
OpenAI's latest iteration of their large language model, GPT-4, has generated an abundance of hype and has sent the tech industry into a frenzy to adopt this new technology.
The latest advancements in AI have sparked a flurry of activity across multiple industries: marketing, finance, retail, education, and even healthcare. AI is in everything from customer service chatbots that can answer inquiries to automated processes to analytics that can be used to provide valuable data insights.
We're seeing widespread changes being made to integrate AI into key parts of business operations (whether necessary or not), and it's no surprise why. The possibilities seem endless when it comes to discovering what machine learning and artificial intelligence can do to make our lives easier.
Unfortunately, we're also seeing plenty of news articles that fail to convey the true potential of AI technology in favor of highly clickable depictions of AI lingering ominously on the verge of quasi-sentience. To help separate fact from fiction, we will look at what AI is, what it's being used to accomplish, and how you can leverage it to your advantage.
Let's dive right in!
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Breaking down the GPT-4 hype
Only a few months after stopping the internet in its tracks with the debut of ChatGPT-3, OpenAI made waves once again on March 14th, 2023 when it unveiled its newest release: GPT-4.
GPT-4 is an advanced large language model developed by OpenAI and built on the GPT-4 deep learning architecture. GPT-4 is estimated to have about ten times as many parameters as GPT-3.5. OpenAI's previous language model, GPT-3, was trained on approximately 175 billion parameters.
Parameters, in the context of deep learning models such as GPT-4, refer to the biases in a model's neural network architecture that are learned during the training process. Parameters help the model identify patterns, relationships, and other features from the training data.
In other words, the more parameters, the better.
Despite its capabilities, GPT-4 has its limitations. AI models can only learn and make decisions based on the parameters they're trained with, so it's not uncommon for chatbots built on these deep learning architectures to occasionally generate false or nonsensical responses. Like any other AI model, GPT-4 is still susceptible to unreliability, hallucinations, and biases.
How much better is GPT-4?
Performance: GPT-4 outperforms existing large language models and most state-of-the-art models on traditional benchmarks. GPT-4 has demonstrated human-level performance on various professional and academic benchmarks, including scoring in the top 10% on a simulated bar exam.
Reliability: GPT-4 is more reliable than GPT-3.5 and can handle more nuanced instructions while generating better responses.
Steerability: GPT-4's steerability has improved, allowing developers to customize AI behavior using system messages. Steerability refers to the ability to guide or control the behavior of an AI system, particularly in generating responses or completing tasks according to specific user requirements.
Safety: OpenAI spent 6 months aligning GPT-4 to improve factuality, steerability, and adherence to guardrails compared to GPT-3.5.
Impact of ML/AI on the tech industry
ML and AI technology are already starting to have a widespread and transformative impact on the tech industry. OpenAI’s work with GPT is just a small slice of this revolution.
As new products and technologies continue to emerge, we'll see improvements in other areas of business as well:
Automation, optimization, and efficiency.
ML and AI will likely have the most significant impact in these areas. More tasks will be automated to improve efficiency and productivity, increasing demand for AI-powered tools.Enhanced user experiences.
As AI models become more sophisticated, they can handle more nuanced input, understand user preferences, and help provide personalized content and services.Data-driven decision-making. AI and ML are already being used extensively to harness the power of big data. Still, we can expect to see more accurate and reliable results that help drive better data-driven business decisions.
New products and services. Improvements in ML and AI technology will inevitably lead to the development of products and services previously impossible to offer.
Hiring and Job Outlook for ML/AI
The career outlook for ML and AI specialists is exceptionally positive.
We expect to see an increased demand for ML and AI specialists as the technology improves. Because ML/AI technology is so data-intensive, we can also expect to see a similar increase in the demand for data scientists and other data professionals.
Furthermore, because ML/AI is ideal for handling mundane, logic-based tasks, we'll likely see more of a focus on hiring for creative, higher-level skills.
According to the US Bureau of Labor Statistics, careers in these fields are expected to grow by 21% from 2021-2031, with the median salaries for these roles sitting at an impressive $131K per year.
How to leverage ML/AI to your advantage today
As the AI revolution matures, these specialized professionals will be in high demand for their expertise in developing and improving AI/ML technologies.
However, developers without specialized AI/ML skills should expect to benefit too. The advances in artificial intelligence and machine learning will create demand for developers capable of integrating and leveraging AI/ML technologies into various products and services using APIs or other tools. This will expand the range of applications and industries that can benefit from AI/ML advancements.
Learning to integrate ML/AI into the software you develop will be critical for the future, but for now, you can start harnessing its power and capabilities on a simpler level.
One of the easiest ways to start leveraging ML/AI is to start using AI-powered tools. Plenty of tools are already available, and you've probably used a couple of them already.
Virtual assistants like Siri, Alexa, and Bixby use natural language processing (NLP) to understand and respond to user commands.
AI-powered chatbots like ChatGPT-4 and Google Bard are used to respond to customer queries quickly and are also used to gather feedback or provide personalized recommendations.
Image generators like Midjourney and DALL-E 2 use text descriptions to generate digital images.
Here are some tips for using AI technology like ChatGPT-4:
Analyze and summarize text content. If you need to understand a highly technical article or want immediate feedback to improve your writing, ChatGPT-4 can be a great time-saver.
Brainstorm new ideas. Conversational AIs like ChatGPT-4 can be useful for brainstorming because they can quickly generate lists of creative ideas, topics, or solutions. This is a quick way to move forward when you're stuck and need some inspiration.
Improve communication. If you find yourself writing a lot of boilerplate responses, emails, or messages, it might save some time and stress to leave the bulk of the writing up to ChatGPT.
AI and ML technologies also offer teams and companies tangible benefits through task automation and enhanced decision-making, but they're also excellent tools for fostering innovation. There's a wealth of information and opportunities, and this is a great time to maintain a competitive edge by experimenting and discovering ways to fit ML and AI into the workplace.
Prepare for ML/AI interviews
Two things are true right now:
It is not too late to start learning how to become an ML or AI engineer — in fact, you're probably early in the grand scheme of things.
Learning how to harness ML and AI in your daily life will pay off in dividends as widespread integration of this technology continues.
Whether you want to work on the next major learning model or prefer more general dev roles, you'll need to learn to talk about ML and AI in interviews to future-proof your career.
Preparing for ML and AI interviews requires a solid understanding of core concepts, familiarity with relevant tools and frameworks, and the ability to demonstrate practical skills.
Here are some tips to help you prepare:
Adopt a structured approach to interview prep. You will maximize your chances of success by finding a structured course of study that fits into your schedule. Consistency is key!
Review fundamental ML/AI concepts. Refresh your knowledge of key concepts such as supervised and unsupervised learning, reinforcement learning, deep learning, neural networks, feature engineering, and more.
Understand popular algorithms. Familiarize yourself with standard algorithms used in ML, like linear regression, logistic regression, SVM, decision trees, and random forests.
Get hands-on practice with coding and problem-solving skills. Put the hours in to improve your coding skills in Python or another relevant programming language by working on projects and solving problems regularly.
Next Steps
As we witness the incredible advancements in AI and ML technologies such as GPT-4, it's becoming increasingly evident that the tech industry has entered a transformative period. Embracing these changes and leveraging them to your advantage will pave the way for exciting new opportunities and help you stay ahead of the curve in your career or among business competitors.
Whether you're considering a career as an ML/AI engineer, aiming to upskill in your current role, or simply curious about the impact of AI on the tech industry, now is the perfect time to explore the potential of these powerful technologies.
If you are ready to add ML fundamentals to your skillset, this course adapted from Oxford University Press is a great place to start:
Mastering Machine Learning Theory and PracticeIf you want to get yourself job ready as an ML engineer, I recommend our comprehensive learning path for software developers:
Become a Machine Learning EngineerAnd if you are brand new to coding, you can try this special path for absolute beginners:
Zero to Hero in Python for Machine Learning
Knowledge is power, and by staying informed and proactive, you'll be well-equipped to navigate an exciting new world.
As always, happy learning!
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