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Machine Learning vs. Artificial Intelligence: Which Is the Future of Data Science?

When we imagine the future of AI, we may think of the fiction we see in cinema: highly advanced robots that can mimic humans so well as to be indistinguishable from them. It is true that the ability to quickly learn, process, and analyze information to make decisions is a key feature of artificial intelligence. 

But what most of us have come to know as AI actually belongs to a subdiscipline called machine learning. Artificial intelligence has become a catch-all term for several algorithmic fields of mathematics and computer science. There are some key differences between them that are important to understand to maximize their advancement potential. 

Experts predict that investment in AI will continue to grow, including the adoption of AI as a Service platforms, which will make machine learning programs more accessible to users without advanced technical expertise. Therefore, it’s important to take a deeper dive into how these technologies work and how they can be used to positively impact the future of data science. 

AI vs. ML

In short, AI can be thought of as a field or a class of technology that aims to simulate human intelligence in machines. Machine learning, in contrast, is the subfield in which computers are taught to learn from past data. 

Things we may call AI, like facial recognition, speech recognition, and anomaly detection, all belong to the deep learning and reinforcement learning categories of machine learning. In these disciplines, computers are taught to learn patterns so they can eventually perform recognition or categorization tasks without human intervention.

A potential key to unlocking the next level of AI is through the continued development of reinforcement learning. While traditional machine learning programs learn through historical data, reinforcement learning programs learn through trial and error. RL can be thought of as a “mature” learning technology adept at optimization, that is, maximizing or minimizing a particular outcome. 

A program takes a series of actions, and subsequent actions are informed by the best outcomes of previous ones. This trial and error takes time, but technology is always getting faster. In the future, we can expect reinforcement learning programs to operate at a level that produces efficient results much faster. 

Although the dystopian fears about rogue AI are largely overblown, like any technology, AI and ML are not without implications and limitations. But these technologies can also provide great advantages for companies by offering them innovative ways of organizing and analyzing data.

The benefits of AI and ML

The benefits of AI and ML include:


Identifying opportunities and risks through machine learning has become critical in the field of cybersecurity. Machine learning programs can be used to help protect private data and keep security architecture operating smoothly. A good example of ML in cyber is Dynamic Application Security Testing (DAST), a program which communicates with web applications to identify potential security vulnerabilities in the app and the underlying architecture. 

According to the security analysts at Cloud Defense, “DAST is a type of black-box application testing that can test applications while they are running. When testing an application with DAST, you don’t need to have access to the source code to find vulnerabilities. You’ll then get notified if your project’s dependencies are affected by newly disclosed vulnerabilities.” This means vulnerability detection is becoming more efficient and comprehensive than ever. 

Once the scanner has identified a vulnerability, humans can then intervene and mitigate the issue. As “smart” as computers can be, ML programs do not have intuitions; they make decisions according to strict parameters and learned data. So, it’s still important for an IT expert to audit the scan after the process is complete to ensure maximal benefit.

Business logistics

The ability for a computer program to learn, organize, and analyze data on its own has led to the development of many business tools and applications. Market predictions, customer behaviors, and target demographics are just a few of the analytical areas in which machine learning can assist humans. 

Internally, companies can rely on machine learning algorithms to catch manual mistakes, increase speed and accuracy, and streamline business operations. Additionally, the prevalence of Big Data makes AI-driven marketing analytics a must for companies seeking to maximize their data analysis potential.

Customer outreach

With cloud data storage solutions increasing productivity and accessibility, more businesses are asking themselves how best to use customer data. As more data is collected, AI-powered analysis becomes more accurate, and B2B marketing efforts will see benefits from the information collected over time. 

We can expect to see customer interactions and preference-detection tailored with increasing speed. AI-based predictive analysis will give tech-savvy companies an undeniable advantage over their competition. 

The risks of AI and ML

The risks of AI and ML include:

The myth of sentient machines

There is a foreboding feeling that often accompanies the wonder at the speed and innovation of AI. Big names like Stephen Hawking, Elon Musk, and Bill Gates all warn of the potential dangers of AI  if humans don’t properly manage advancing technology. Popular books and movies have stoked the fear that machines will one day have minds of their own. There is some concern that destructive AI programs such as autonomous weapons could end up in the wrong hands. These concerns are not altogether misplaced. 

The two most recent US presidential elections, for example, brought to light how effective data mining algorithms can be in targeting social media users and the consequences of tampering with technology. 

But at its core, these interventions were not sentient machines; they were people using advanced technology for questionable purposes. The convenience and ubiquity of automation makes AI a powerful presence in our everyday lives, and, like anything, it must be managed through policy and ethics. 

Bad actors 

Another potential area of concern is cybersecurity. Cyberattacks are becoming increasingly complex and innovative. Just like any other artificial intelligence, AI-based malware AI is learning how to go up against AI-based cybersecurity tools too. We are entering into an era where the cybersecurity space may be a battle between good and bad machines. Fortunately, ML algorithms are good at anomaly detection. Cybersecurity professionals will have to continue to innovate in order to keep up with bad actors.

The future of data science

Currently, the limitations of artificial intelligence are related to the learning mechanism itself. Machines learn incrementally by basing future decisions on past data to produce a specific output. Humans, in contrast, are able to think abstractly, use context, and unlearn information that is no longer necessary.

Therefore, future machine learning algorithms will hopefully be able to engage in machine unlearning as well, particularly for digital assets like financial and personal data. This may be the next step in increasing security with AI and ameliorating some of its risks. 

Advances in AI will have a substantial impact on the future of data science, but machines are still not truly “intelligent” in the way humans tend to think of intelligence. Computers can put us to shame in terms of processing speed, but we have yet to create a program that is able to capture our own creative and logical abilities. Machines are a significant asset, but they are still only complimentary to human innovation. 

As we get ever closer to making fiction a reality, developments in AI are likely to happen in the disciplines of deep learning and reinforcement learning. These are some of the areas to watch when asking what’s next in the pursuit of artificial intelligence.