What Is Natural Language Processing (NLP)?

Artificial Intelligence: A Detailed OverviewInfographic - MarketingProfs

Science fiction is quickly becoming everyday reality. Chatbots, robots, digital assistants, automated vehicles, virtual assistants, and much more... Are the products of artificial intelligence (AI), which is already transforming entire industries.

An infographic by TechJury, provider of one-step tech guides and product reviews, provides a detailed overview of AI.

The infographic begins with a timeline of AI, starting in the mid-20th century with the "father of theoretical computer science and artificial intelligence," Alan Turing, who developed the "Turing test" for determining what qualifies as artificial intelligence.

The infographic goes on to outline various classifications of AI, provides examples of AI technology, highlights statistics about the AI market, and lists the companies and countries at the forefront of the AI race.

It concludes with AI's current impact on and uses of AI in 20+ industries, as well as future uses of AI.

Check out this thorough overview of the current state of AI, all in one (long) infographic:

AI Vs. ML: Artificial Intelligence And Machine Learning Overview

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The idea that machines can replicate or even exceed human thinking has served as the inspiration for advanced computing frameworks – and is now seeing vast investment by countless companies. At the center of this concept are artificial intelligence (AI) and machine learning (ML).

These terms are often used synonymously and interchangeably. In reality, AI and ML represent two different things—though they are related. In essence:

Artificial intelligence can be defined as a computing system's ability to imitate or mimic human thinking and behavior.

Machine learning, a subset of AI, refers to a system that learns without being explicitly programmed or directly managed by humans.

Today, both AI and ML play a prominent role in virtually every industry and business. They drive business systems and consumer devices. Natural language processing, machine vision, robotics, predictive analytics and many other digital frameworks rely on one or both of these technologies to operate effectively.

Also see: What is Artificial Intelligence 

Brief History of AI and ML

The idea of building machines that think like humans has long fascinated society. During the 1940s and 1950s, researchers and scientists, including Alan Turing, began to explore the idea of creating an "artificial brain." In 1956, a group of researchers at Dartmouth College began to explore the idea more thoroughly. At a workshop held at the university, the term "artificial intelligence" was born.

Over the following few decades, the field advanced. In 1964, Joseph Weizenbaum in the MIT Artificial Intelligence Laboratory invented a program called ELIZA. It demonstrate the viability of natural language and conversation on a machine. ELIZA relied on a basic pattern matching algorithm to simulate a real-world conversation.

During the 1980s, as more powerful computers appeared, AI research began to accelerate. In 1982, John Hopfield showed that a neural network could process information in far more advanced ways. Various forms of AI began to take shape, and the first artificial neural network (ANN) appeared in 1980.

During the last two decades, the field has advanced remarkably, thanks to enormous gains in computing power and software. AI and now ML is now widely used in a wide array of enterprise deployments. These technologies are used in natural language systems like Siri and Alexa, autonomous vehicles and robotics, automated decision-making systems in computer games, recommendation engines like Netflix, and extended reality (XR) tools, such as virtual reality (VR) and augmented reality (AR).

Machine learning in particular has flourished. It is increasingly used by government entities, businesses and others to identify complex and often elusive patterns involving statistics and other forms of structured and unstructured data. This includes areas as diverse as epidemiology and healthcare, financial modeling and predictive analytics, cybersecurity, chatbots and other tools used for customer sales and support. In fact, many vendors offer ML as part of cloud and analytics applications.

Also see: Best Machine Learning Platforms 

What Is the Impact of Artificial Intelligence?

A machine's ability to emulate human thinking and behavior profoundly changes the relationship between these two entities. AI unleashes automation at scale and enables an array of more advanced digital technologies and tools, including VR, AR, digital twins, image and facial recognition, connected devices and systems, robotics, personal assistants and a variety of highly interactive systems.

This includes self-driving cars that navigate real-world conditions, smart assistants that answer questions and switch lights on and off, automated financial investing systems, and airport cameras and facial recognition. The latter includes biometric boarding passes airlines use at departure gates and the Global Entry system that requires only a face scan to pass through security checkpoints.

Indeed, businesses are putting AI to work in new and innovative ways. For example, dynamic pricing models used by the travel industry gauge supply and demand in real-time and adjusts pricing for flights and hotels to reflect changing conditions.

AI technology is used to better understand supply change dynamics and adapt sourcing models and forecasts. In warehouses, machine vision technology (which is supported by AI) can spot things like missing pallets and manufacturing defects that are too small for the human eye to detect. Meanwhile, chatbots analyze customer input and provide contextually relevant answers on a live basis.

Not surprisingly, these capabilities are advancing rapidly—especially as connected systems are added to the mix. Smart buildings, smart traffic grids and even smart cities are taking shape. As data streams in, AI systems determine the next optimal step or adjustment.

Similarly, digital twins are increasingly used by airlines, energy firms, manufacturers and others to simulate actual systems and equipment and explore various options virtually. These advanced simulators predict maintenance and failures but also provide insight into less expensive and more sophisticated ways to approach business.

Also see: How AI is Altering Software Development with AI-Augmentation 

What Is the Impact of Machine Learning?

Machine learning has also advanced remarkably in recent years. Using statistical algorithms, machine learning unlocks insights that have traditionally been associated with data mining and human analysis.

Using sample data, referred to as training data, it identifies patterns and applies them to an algorithm, which may change over time. Deep learning, a type of machine learning, uses artificial neural networks to simulate the way the human brain works.

These are the primary ways to use ML:

Supervised learning, which requires a person to identity the desirable signals and outputs.

Unsupervised learning, which allows the system to operate independent of humans and find valuable output.

Semi-supervised learning and reinforcement learning, which involves a computer program that interacts with a dynamic environment to achieve identified goals and outcomes. An example of the latter is a computer chess game. In some cases, data scientists use a hybrid approach that combines elements of more than one of these methods.

Also see: The Future of Artificial Intelligence

A Variety of Algorithms

Several types of machine learning algorithms play a key role:

Neural Networks: Neural networks simulate the way the human brain thinks. They're ideal for recognizing patterns and they are widely used for natural language processing, image recognition and speech recognition.

Linear Regression: The technique is valuable for predicting numerical values, such as predicting prices for flights or real estate.

Logistic regression: This method typically uses a binary classification model (such as "yes/no") to tag or categorize something. A common use for this technology is identifying spam in email and blacklisting unwanted code or malware.

Clustering: This ML tool uses unsupervised learning to spot patterns that humans may overlook. An example of clustering is how a supplier performs for the same product at different facilities. This approach might be used in healthcare, for instance, to understand how different lifestyle conditions impact health and longevity.

Decision Tree: The approach predicts numerical values but also performs classification functions. It delivers a clear way to audit results, unlike other forms of ML. This method also works with Random Forests, which combine Decision Trees.

Regardless of the exact method, ML is increasingly used by companies to better understand data and make decisions. This, in turn, feeds more sophisticated AI and automation. For example, sentiment analysis plugs in historical data about sales, social media data and even weather conditions to adapt manufacturing, marketing, pricing and sales tactics dynamically. Other ML applications deliver recommendation engines, fraud detection and image classification used for medical diagnostics.

One of the strengths of machine learning is that it can adapt dynamically as conditions and data change, or an organization adds more data. As a result, it's possible to build an ML model and then adapt it on the fly. For example, a marketer might develop an algorithm based on a customer's behavior and interests and then adapt messages and content as the customer changes his or her behavior, interests or purchasing patterns.

Also see: Digital Transformation Guide: Definition, Types & Strategy

How are AI and ML Evolving in the Enterprise?

As mentioned, most software vendors—across a wide spectrum of enterprise applications—offer AI and ML within their products. These systems make it increasingly simple to put powerful tools to work without extensive knowledge of data science.

Yet, there are some caveats. For customers, in order to get the most out of AI and ML systems, an understanding of AI and some expertise is often necessary. It's also vital to avoid vendor hype when selecting products. AI and ML can't fix underlying business problems—and in some instance, they can produce new challenges, concerns and problems.

What are the Ethical and Legal Concerns?

AI and ML are at the center of a growing controversy—and they should be used wisely—and carefully. They have been associated with hiring and insurance bias, racial discrimination and a variety of other problems, including misuse of data, inappropriate surveillance and things like deep fakes and false news and information.

There's growing evidence that facial recognition systems are considerably less accurate when identifying people of color—and they can lead to racial profiling. Moreover, there are growing concerns about governments and other entities using facial recognition for mass surveillance. So far, there's very little regulation of AI practices. Yet Ethical AI is emerging as a key consideration.

What is the Future of ML and AI?

AI technologies are advancing rapidly, and they will play an increasingly prominent role in the enterprise—and our lives. AI and ML tools can trim costs, improve productivity, facilitate automation and fuel innovation and business transformation in remarkable ways.

As the digital transformation advances, various forms of AI will serve as the sun around which various digital technologies orbit. AI will spawn far more advanced natural speech systems, machine vision tools, autonomous technologies, and much more.

Also see: Top Digital Transformation Companies

Apple Intelligence: The MacStories Overview

After months of anticipation and speculation about what Apple could be doing in the world of artificial intelligence, we now have our first glimpse at the company's approach: Apple Intelligence. Based on generative models, Apple Intelligence uses a combination of on-device and cloud processing to offer intelligence features that are personalized, useful, and secure. In today's WWDC keynote, Tim Cook went so far as to call it "the next big step for Apple."

From the company's press release on Apple Intelligence:

"We're thrilled to introduce a new chapter in Apple innovation. Apple Intelligence will transform what users can do with our products — and what our products can do for our users," said Tim Cook, Apple's CEO. "Our unique approach combines generative AI with a user's personal context to deliver truly helpful intelligence. And it can access that information in a completely private and secure way to help users do the things that matter most to them. This is AI as only Apple can deliver it, and we can't wait for users to experience what it can do."

It's clear from today's presentation that Apple is positioning itself as taking a different approach to AI than the rest of the industry. The company is putting generative models at the core of its devices while seeking to stay true to its principles. And that starts with privacy.

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Because Apple Intelligence is limited to the iPhone 15 Pro line and M-series iPads and Macs, a lot of its processing can be done on-device, which has been one of the company's go-to methods for protecting user privacy for years. However, some intelligence features require models that cannot be run locally, forcing Apple to find a cloud-based solution. They call it Private Cloud Compute, and it involves drawing on the security foundation of Swift and running models on Apple-silicon powered servers that don't retain user information and run code that can be inspected by independent parties. Basically, Apple is seeking to extend the privacy and protection users expect on their iPhones to the cloud.

But what can Apple Intelligence actually do? The features offered by the system are broken down into three categories: language, images, and Siri.

Language

The language capabilities of Apple Intelligence power a wide array of features for understanding, editing, and generating text. Systemwide Writing Tools are available anywhere text is editable, offering to analyze and rework text on the user's behalf. The system can proofread your term paper, offering suggestions for improvement that you can evaluate one by one or accept all at once. It can also adjust the tone of the email you're working on to make it sound more friendly, professional, or concise.

Summarization tools allow you to select text and quickly get the most important information out of it. Apple Intelligence can also create a list of key points based on the text passed to it. You can record a meeting in Notes or a call in the Phone app (after the person on other end is notified that they're being recorded), and the system will automatically generate both a transcript and a summary of the recording when it's ended. In the Mail app, summaries are available right in your inbox or at the top of the message you're reading.

Another new Mail feature

The system's language tools aren't limited to generating text, though. They also enable features like Priority Notifications, which brings alerts that matter to you to the top of your notification list while summarizing the rest. A new Focus mode called Reduce Interruptions will only show you the notifications deemed as in need of immediate attention, while Priority Messages in Mail surfaces time-sensitive emails to the top of your inbox.

Images

When it comes to images, Apple Intelligence is equipped to enhance what you can do with your own pictures and allow you to create all-new images in some fun ways. The Clean Up tool in Photos uses generative models to remove unwanted elements from pictures, like a stranger in the background of a family portrait or a car that got in the way of your beautiful landmark photo. Simply draw a circle around what you want gone, and like magic, it disappears, with the system intelligently filling in the gap to create the impression that it was never there.

Photos also picks up some natural language features courtesy of Apple Intelligence. Search is no longer limited to predetermined categories of recognized images and OCR text. Simply describe the images you're looking for – including the names of people in it, colors, and any other details you like – and the intelligence system will find them in your library. It works for video, too, even pointing you to the specific part of the video that you're searching for.

You can create a memory movie from your photos with just a prompt. Tell the Photos app what you want a memory of, and it will compile photos into a movie matching your description. It will intelligently pick a soundtrack based on the prompt as well, or you can specify the type of song you're looking for.

On the generative side, Image Playground is the name of Apple Intelligence's image generation tool. It's built into apps like Messages, Notes, and Freeform, has its own standalone app, and will be available to developers as an API. Image Playground works like image generation tools you might be familiar with, but it includes some clever touches, too.

The UI features a text box where you can enter what type of image you'd like to generate. Above that is a row of suggested concepts and elements that are based on the context in which you're using Image Playground. For example, if you're in a Messages conversation with someone, the system will suggest including them in the image. You can mix and match different terms, and you'll be offered a preview of your generated image along with some alternative options.

Images can be generated in one of three styles: illustration, sketch, and animation. These images clearly aren't photorealistic and couldn't be passed off as real, likely by design. The purpose behind Image Playground is in the name: to have fun and be creative.

An extension of Image Playground in the Notes app is Image Wand. With this tool, you can circle a sketch you've drawn, and Apple Intelligence will analyze the sketch and its surrounding context to generate a beautiful, relevant image. Image Wand can be used without a sketch, too, to fill in blank space in a note based on the text around it.

The final piece of generative imagery in Apple Intelligence is Genmoji. Thanks to generative models, you never have to say, "How is there not an emoji for that?" again. Just tell the system what emoji you want, and it will create it for you, along with some backup options if the first attempt isn't quite right. You can even turn your friends into emoji based on photos of them. Genmoji can be sent as stickers, tapbacks, or inline emoji in Messages.

Siri

Thirteen years into its existence, Siri is getting a major overhaul with the power of Apple Intelligence. Not only does Siri look different, now encompassing a glow that emanates around the edges of your display when in use, but it works in new ways that leverage what your devices know about you to make tasks easier. (It's not an intelligence feature, but the redesigned Siri also allows you to quickly switch to typing input by double tapping the bottom of the screen.)

Siri is now more conversational, with the ability to retain context between requests so that you can speak to it more like a person rather than having to start over every time you make a request. It's also able to better understand your speech if you slip up or backtrack while talking to it. And it's been equipped with a great deal of product knowledge, so you can ask Siri how to do something on your phone, and it will be able to guide you through the process.

This is just scratching the surface of what an Apple Intelligence-enhanced Siri can do, and Apple has laid out plans for more capabilities coming over the next year. Because Siri has secure access to your personal on-device information, it will be able to use its knowledge about you and your data as context for fulfilling requests. You may not remember where you saved your flight information or whether your friend's latest music recommendation came via text or email, but Siri will, and it will surface that data for you based on your requests.

It will also be able to perform actions in and across apps. Based on an improved App Intents API coming within the next year, Siri will be able to control apps and even move content between them without any input from you beyond your request. And with onscreen awareness, Siri will be able to take actions based on what you're currently looking at, too.

The point where Apple Intelligence meets Siri is where it becomes truly personalized and adaptable to each user's needs. If the system proves to be as capable in practice as it is in Apple's demos, this new version of Siri could be a game-changer. We'll find out as these new Apple Intelligence features roll out over the coming year.

ChatGPT Integration

While Siri is great for interacting with your own data, users have needs and questions related to external information, too. Later this year, Apple Intelligence will integrate with ChatGPT to make its access to world knowledge and image- and document-understanding capabilities available to Siri users. ChatGPT will also power the Compose feature in Writing Tools, which will allow users to generate text and images based on prompts.

This integration uses the ChatGPT-4o model and is free of charge to users. There will be no need to download the ChatGPT app or create an account, though ChatGPT subscribers will be able to connect their accounts in order to access paid features.

It's also set up in a privacy-centered way. Each time a request is sent to ChatGPT, the system will ask for the user's permission beforehand. Users' IP addresses will be hidden when making ChatGPT requests, and their data will not be stored.

Apple intends to integrate Apple Intelligence with other artificial intelligence tools in the future, but the ChatGPT integration was the only one announced today.

AI for the Rest of Us

And that's Apple Intelligence. The approach is very much in line with what we were expecting from Apple: practical, personalized, and private. The company pulled off what they needed to with this announcement, stepping boldly into the generative AI game while putting their own spin on it. Now we just have to wait to see these features in action once developer beta testers get their hands on them later this summer.

Apple Intelligence will launch in beta this fall as part of iOS 18, iPadOS 18, and macOS Sequoia in U.S. English initially, with some features rolling out over the coming year.

You can follow all of our WWDC coverage through our WWDC 2024 hub or subscribe to the dedicated WWDC 2024 RSS feed.

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