Table of contents
- What is artificial intelligence (AI)?
- Common AI applications
- What is machine learning (ML)?
- Common ML applications
- AI vs. ML: 3 key similarities
- 1. Continuously evolving
- 2. Offering myriad benefits
- 3. Leveraging Big Data
- AI vs. ML: 3 key differences
- 1. Scope
- 2. Success vs. accuracy
- 3. Unique outcomes
- Identifying the differences between AI and ML
Within the last decade, the terms artificial intelligence (AI) and machine learning (ML) have become buzzwords that are often used interchangeably. While AI and ML are inextricably linked and share similar characteristics, they are not the same thing. Rather, ML is a major subset of AI.
AI and ML technologies are all around us, from the digital voice assistants in our living rooms to the recommendations you see on Netflix.
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Despite AI and ML penetrating several human domains, there’s still much confusion and ambiguity regarding their similarities, differences and primary applications.
Here’s a more in-depth look into artificial intelligence vs. machine learning, the different types, and how the two revolutionary technologies compare to one another.
What is artificial intelligence (AI)?
AI is defined as computer technology that imitate(s) a human’s ability to solve problems and make connections based on insight, understanding and intuition.
The field of AI rose to prominence in the 1950s. However, mentions of artificial beings with intelligence can be identified earlier throughout various disciplines like ancient philosophy, Greek mythology and fiction stories.
One notable project in the 20th century, the Turing Test, is often referred to when referencing AI’s history. Alan Turing, also referred to as “the father of AI,” created the test and is best known for creating a code-breaking computer that helped the Allies in World War II understand secret messages being sent by the German military.
The Turing Test, is used to determine if a machine is capable of thinking like a human being. A computer can only pass the Turing Test if it responds to questions with answers that are indistinguishable from human responses.
Three key capabilities of a computer system powered by AI include intentionality, intelligence and adaptability. AI systems use mathematics and logic to accomplish tasks, often encompassing large amounts of data, that otherwise wouldn’t be practical or possible.
Common AI applications
Modern AI is used by many technology companies and their customers. Some of the most common AI applications today include:
- Advanced web search engines (Google)
- Self-driving cars (Tesla)
- Personalized recommendations (Netflix, YouTube)
- Personal assistants (Amazon Alexa, Siri)
One example of AI that stole the spotlight was in 2011, when IBM’s Watson, an AI-powered supercomputer, participated on the popular TV game show Jeopardy! Watson shook the tech industry to its core after beating two former champions, Ken Jennings and Brad Rutter.
Outside of game show use, many industries have adopted AI applications to improve their operations, from manufacturers deploying robotics to insurance companies improving their assessment of risk.
Also read: How AI is changing the way we learn languages
Types of AI
AI is often divided into two categories: narrow AI and general AI.
- Narrow AI: Many modern AI applications are considered narrow AI, built to complete defined, specific tasks. For example, a chatbot on a business’s website is an example of narrow AI. Another example is an automatic translation service, such as Google Translate. Self-driving cars are another application of this.
- General AI: General AI differs from narrow AI because it also incorporates machine learning (ML) systems for various purposes. It can learn more quickly than humans and complete intellectual and performance tasks better.
Regardless of if an AI is categorized as narrow or general, modern AI is still somewhat limited. It cannot communicate exactly like humans, but it can mimic emotions. However, AI cannot truly have or “feel” emotions like a person can.
What is machine learning (ML)?
Machine learning (ML) is considered a subset of AI, whereby a set of algorithms builds models based on sample data, also called training data.
The main purpose of an ML model is to make accurate predictions or decisions based on historical data. ML solutions use vast amounts of semi-structured and structured data to make forecasts and predictions with a high level of accuracy.
In 1959, Arthur Samuel, a pioneer in AI and computer gaming, defined ML as a field of study that enables computers to continuously learn without being explicitly programmed.
An ML model exposed to new data continuously learns, adapts and develops on its own. Many businesses are investing in ML solutions because they assist them with decision-making, forecasting future trends, learning more about their customers and gaining other valuable insights.
Types of ML
There are three main types of ML: supervised, unsupervised and reinforcement learning. A data scientist or other ML practitioner will use a specific version based on what they want to predict. Here’s what each type of ML entails:
- Supervised ML: In this type of ML, data scientists will feed an ML model labeled training data. They will also define specific variables they want the algorithm to assess to identify correlations. In supervised learning, the input and output of information are specified.
- Unsupervised ML: In unsupervised ML, algorithms train on unlabeled data, and the ML will scan through them to identify any meaningful connections. The unlabeled data and ML outputs are predetermined.
- Reinforcement learning: Reinforcement learning involves data scientists training ML to complete a multistep process with a predefined set of rules to follow. Practitioners program ML algorithms to complete a task and will provide it with positive or negative feedback on its performance.
Common ML applications
Major companies like Netflix, Amazon, Facebook, Google and Uber have ML a central part of their business operations. ML can be applied in many ways, including via:
- Email filtering
- Speech recognition
- Computer vision (CV)
- Spam/fraud detection
- Predictive maintenance
- Malware threat detection
- Business process automation (BPA)
Another way ML is used is to power digital navigation systems. For example, Apple and Google Maps apps on a smartphone use ML to inspect traffic, organize user-reported incidents like accidents or construction, and find the driver an optimal route for traveling. ML is becoming so ubiquitous that it even plays a role in determining a user’s social media feeds.
AI vs. ML: 3 key similarities
AI and ML do share similar characteristics and are closely related. ML is a subset of AI, which essentially means it is an advanced technique for realizing it. ML is sometimes described as the current state-of-the-art version of AI.
1. Continuously evolving
AI and ML are both on a path to becoming some of the most disruptive and transformative technologies to date. Some experts say AI and ML developments will have even more of a significant impact on human life than fire or electricity.
The AI market size is anticipated to reach around $1,394.3 billion by 2029, according to a report from Fortune Business Insights. As more companies and consumers find value in AI-powered solutions and products, the market will grow, and more investments will be made in AI. The same goes for ML — research suggests the market will hit $209.91 billion by 2029.
2. Offering myriad benefits
Another significant quality AI and ML share is the wide range of benefits they offer to companies and individuals. AI and ML solutions help companies achieve operational excellence, improve employee productivity, overcome labor shortages and accomplish tasks never done before.
There are a few other benefits that are expected to come from AI and ML, including:
- Improved natural language processing (NLP), another field of AI
- Developing the Metaverse
- Enhanced cybersecurity
- Low-code or no-code technologies
- Emerging creativity in machines
AI and ML are already influencing businesses of all sizes and types, and the broader societal expectations are high. Investing in and adopting AI and ML is expected to bolster the economy, lead to fiercer competition, create a more tech-savvy workforce and inspire innovation in future generations.
3. Leveraging Big Data
Without data, AI and ML would not be where they are today. AI systems rely on large datasets, in addition to iterative processing algorithms, to function properly.
ML models only work when supplied with various types of semi-structured and structured data. Harnessing the power of Big Data lies at the core of both ML and AI more broadly.
Because AI and ML thrive on data, ensuring its quality is a top priority for many companies. For example, if an ML model receives poor-quality information, the outputs will reflect that.
Consider this scenario: Law enforcement agencies nationwide use ML solutions for predictive policing. However, reports of police forces using biased training data for ML purposes have come to light, which some say is inevitably perpetuating inequalities in the criminal justice system.
This is only one example, but it shows how much of an impact data quality has on the functioning of AI and ML.
Also read: What is unstructured data in AI?
AI vs. ML: 3 key differences
Even with the similarities listed above, AI and ML have differences that suggest they should not be used interchangeably. One way to keep the two straight is to remember that all types of ML are considered AI, but not all kinds of AI are ML.
AI is an all-encompassing term that describes a machine that incorporates some level of human intelligence. It’s considered a broad concept and is sometimes loosely defined, whereas ML is a more specific notion with a limited scope.
Practitioners in the AI field develop intelligent systems that can perform various complex tasks like a human. On the other hand, ML researchers will spend time teaching machines to accomplish a specific job and provide accurate outputs.
Due to this primary difference, it’s fair to say that professionals using AI or ML may utilize different elements of data and computer science for their projects.
2. Success vs. accuracy
Another difference between AI and ML solutions is that AI aims to increase the chances of success, whereas ML seeks to boost accuracy and identify patterns. Success is not as relevant in ML as it is in AI applications.
It’s also understood that AI aims to find the optimal solution for its users. ML is used more often to find a solution, optimal or not. This is a subtle difference, but further illustrates the idea that ML and AI are not the same.
In ML, there is a concept called the ‘accuracy paradox,’ in which ML models may achieve a high accuracy value, but can give practitioners a false premise because the dataset could be highly imbalanced.
3. Unique outcomes
AI is a much broader concept than ML and can be applied in ways that will help the user achieve a desired outcome. AI also employs methods of logic, mathematics and reasoning to accomplish its tasks, whereas ML can only learn, adapt or self-correct when it’s introduced to new data. In a sense, ML has more constrained capabilities than AI.
ML models can only reach a predetermined outcome, but AI focuses more on creating an intelligent system to accomplish more than just one result.
It can be perplexing, and the differences between AI and ML are subtle. Suppose a business trained ML to forecast future sales. It would only be capable of making predictions based on the data used to teach it.
However, a business could invest in AI to accomplish various tasks. For example, Google uses AI for several reasons, such as to improve its search engine, incorporate AI into its products and create equal access to AI for the general public.
Identifying the differences between AI and ML
Much of the progress we’ve seen in recent years regarding AI and ML is expected to continue. ML has helped fuel innovation in the field of AI.
AI and ML are highly complex topics that some people find difficult to comprehend.
Despite their mystifying natures, AI and ML have quickly become invaluable tools for businesses and consumers, and the latest developments in AI and ML may transform the way we live.
The “race starts today” in search, said Microsoft CEO Satya Nadella at a special event today at Microsoft headquarters in Redmond, Washington. “We’re going to move fast,” he added, as the company announced a reimagined Bing search engine, Edge web browser and chat powered by OpenAI’s ChatGPT and generative AI.
The new Bing for the desktop is available on limited preview. And Microsoft says it is launching a mobile version in a few weeks. There will be no cost to use the new Bing, but ads will be there from the start, according to Yusuf Mehdi, corporate vice president and consumer chief marketing officer at Microsoft.
OpenAI CEO Sam Altman joined on stage at the event: “I think it’s the beginning of a new era,” he told the audience, adding that he wants to get AI into the hands of more people, which is why OpenAI partnered with Microsoft — starting with Azure and now Bing.
Microsoft announced new ‘AI-powered copilot’ experience
At the center of a new “AI-powered copilot” experience is a new Bing search engine and Edge web browser, said Mehdi.
Bing is running on a new, next-generation language model called Prometheus, he said, one more powerful than ChatGPT and one customizable for search (NOTE: So far, neither Microsoft nor OpenAI have referred to this more-advanced ChatGPT as the long-awaited GPT-4).
The Prometheus model, Mehdi said, offers several advances, including improvements in relevancy of answers, annotating answers with specific web links, getting more up-to-date information and improving geolocation, and increasing the safety of queries.
As a result, there have already been steady improvements on the Bing algorithm, he said. A few weeks ago, Microsoft applied AI to its core search index and saw the “largest jump in relevancy” over the past two decades.
Microsoft says it is ‘clear-eyed’ about unintended consequences of tech
In an introduction, Nadella said that, for Microsoft, these announcements are about being “clear-eyed” about the unintended consequences of technology, pointing to the company’s release of responsible AI principles back in 2016.
AI prompting, he explained, comes from human beings — Microsoft, he said, wants to take the design of AI products as a “first-class construct” and build that into our products. But that is insufficient, he added — the key is building AI that’s “more in line with human values and social preferences.”
Sarah Bird, Microsoft’s responsible AI lead, took the stage to emphasize that with technology this powerful, “I know we have a responsibility to ensure that it’s developed properly.” Fortunately, she added, at Microsoft “we’re not starting from scratch. We’ve been working on this for years. We’re also not new to working with generative AI.”
New Microsoft Bing experience
According to a Microsoft blog post, the new Bing experience is a culmination of four technical breakthroughs:
- Next-generation OpenAI model. We’re excited to announce the new Bing is running on a new, next-generation OpenAI large language model that is more powerful than ChatGPT and customized specifically for search. It takes key learnings and advancements from ChatGPT and GPT-3.5 – and it is even faster, more accurate and more capable.
- Microsoft Prometheus model. We have developed a proprietary way of working with the OpenAI model that allows us to best leverage its power. We call this collection of capabilities and techniques the Prometheus model. This combination gives you more relevant, timely and targeted results, with improved safety.
- Applying AI to core search algorithm. We’ve also applied the AI model to our core Bing search ranking engine, which led to the largest jump in relevance in two decades. With this AI model, even basic search queries are more accurate and more relevant.
- New user experience. We’re reimagining how you interact with search, browser and chat by pulling them into a unified experience. This will unlock a completely new way to interact with the web.
Announcements come as Google and Microsoft offer dueling debuts this week
The announcements come after Google and Microsoft, in separate surprise announcements, confirmed dueling generative AI debuts this week.
Yesterday, Google unveiled a new ChatGPT-like chatbot named Bard, as it races to catch up in the wake of ChatGPT’s massive viral success (growing faster than TikTok, apparently). In a blog post, CEO Sundar Pichai said that Bard is now open to “trusted testers,” with plans to make it available to the public “in the coming weeks.”
In addition, the company announced a streaming event called Live from Paris focused on “Search, Maps and beyond,” to be livestreamed on YouTube at 8:30 am ET on February 8th. According to the description: “We’re reimagining how people search for, explore and interact with information, making it more natural and intuitive than ever before to find what you need.”
It was only ten weeks ago that OpenAI launched what it simply described as an “early demo”; a part of the GPT-3.5 series — an interactive, conversational model whose dialogue format “makes it possible for ChatGPT to answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.”
ChatGPT quickly caught the imagination — and feverish excitement — of both the AI community and the general public.
Since then, the tool’s possibilities — as well as its limitations and hidden dangers — have been well established. Rumors around Microsoft’s efforts to integrate ChatGPT into its Bing search engine, as well as productivity tools like PowerPoint and Outlook, have circulated for weeks. And any hints of slowing down its development were quickly dashed when Microsoft announced its plans to invest billions more into OpenAI on January 23.