Tag: Artificial Intelligence

The last few months have seen Apple’s latest venture, Apple Intelligence, which represents the company’s effort to compete with other major corporations in artificial intelligence (AI) development. Unveiled at Apple Park in Cupertino on June 10, 2024 at the highly anticipated Worldwide Developers Conference (WWDC), Apple Intelligence is what the company is calling “AI for the rest of us,” an allusion to a Macintosh commercial from 1984 calling the device “a computer for the rest of us.” However, given the widespread implications of personalized AI rollout for privacy, data collection, and bias, whether Apple Intelligence will truly be “for the rest of us” remains to be seen.

Creating technology “for the rest of us” is a sentiment that is clear through many of Apple’s historic moves. With the introduction of the iPhone in 2007, the company bypassed marketing to the traditional buyers for smartphones (business users and enthusiasts) and took the product directly to the mass market. In May 2023, the company’s CEO, Tim Cook, was quoted saying that “[a]t Apple, we’ve always believed that the best technology is technology built for everyone.” Now, Apple has taken on the feat of creating generative AI “for the rest of us.”

The widespread adoption of generative AI has the potential to revolutionize public life, and Apple’s integration of the technology into their phones is no exception. A 2024 McKinsey study revealed intriguing trends in global personal experience with generative AI tools: 20% of individuals born in 1964 or earlier used these tools regularly outside of work. Among those born between 1965 and 1980, usage was lower, at 16%, and for those born between 1981 and 1996, it was 17%.

The integration of AI into Apple devices could dramatically reshape the role of generative AI in everyday life—making replying to in-depth emails, finding pictures of a user’s cat in a sweater, or planning the itinerary of a future road trip a one-click task. By embedding these tools into the already ubiquitous marketplace of smartphones, accessibility to generative AI would likely increase and drive up usage rates across all age groups.

Why Apple Intelligence may not be “for the rest of us”

However, it is crucial to consider the potential risks that come with the extensive deployment of more commercially deployed generative AI. A study conducted by the Polarization Research Lab on public opinions of AI, misinformation, and democracy leading up to the 2024 election reported that 65.1 % of Americans are worried that AI will harm personal privacy.

Apple is aware of this and has made prioritizing privacy an essential part of its business model. Advertisements from 2019 stressing privacy, public statements on privacy being a fundamental human right, and even rejecting to help the FBI bypass iPhone security measures for the sake of gathering intelligence are all ways Apple has demonstrated to consumers their commitment to privacy.

The announcement of Apple Intelligence is no different. In the keynote, Senior Vice President of Software Engineering Craig Federighi made a point of highlighting how the products privacy throughout its functions. Apple has a twofold approach to generative AI: on-device task execution for more common AI tasks like schedule organization and call transcription along with cloud outsourcing for more complex tasks, an example of which could be to create a custom bedtime story for a six-year-old who loves butterflies and solving riddles. However, it is still unclear where the line between simple and complex requests is and which of these requests will be sent out to external (and potentially third-party) servers.

Further, Apple claims data that is sent out will be scrambled through encryption and immediately deleted. But, as Matthew Green, security researcher and associate professor of computer science at Johns Hopkins University, noted, “Anything that leaves your device is inherently less secure. ”

Security of data

Due to these reasons, there is uncertainty about the development process of future versions of Apple Intelligence. While training AI models, AI algorithms are provided with training data that they use iteratively to adjust their intended functions. This new Apple Intelligence model guarantees the capability to personal context to enhance the AI ​​interaction experience and integrate it seamlessly into a user’s daily life.

During the keynote, Apple mentioned that a user’s personal iOS will be able to connect information across applications. This means that if Siri was asked how to efficiently get to an event from work, it could access a user’s messages to gather the necessary information to make that assessment—all to “streamline and expedite everyday tasks.” The company mentioned that measures have been implemented to prevent Apple employees from accessing a user’s data collected through their AI platform.

Looking ahead, when Apple is developing new versions of its AI model, what training data will it use if not the data collected from its own devices? A analyzing report trends in the amount of human-generated data used to train large language models revealed that human-generated text data is likely to be entirely depleted between 2026 and 2032.

Public training data is running out, and if Apple does not collect its users’ inputs to train future models, it is likely to encounter this problem in the future. Therefore, Apple’s privacy claims are quite optimistic but not entirely foolproof when considering the long- term impacts of their AI implementation.

It is also unclear where Apple’s training data for the current model is sourced from or whether the model was developed using fair and inclusive datasets. AI algorithms can incorporate inherent biases when trained on standardized data, which often lacks the diversity needed to promote inclusivity and remove biases. This is particularly important because Apple Intelligence is a computer model that will draw conclusions about people, such as their characteristics, preferences, probable future behaviors, and related objects.

It is not certain whether Apple’s algorithm will replicate or magnify human biases, lean towards mainstream inferences about human behavior, or both. Given the widespread deployment of generative AI plans, these are critical considerations when proposing an AI product “for the rest of us. ”

Addressing the hype

Dr. Kevin LaGrandeur’s paper on the impact of AI hype offers valuable insights into the potential consequences of increased commercialization of AI products. He explains how the hype surrounding AI can distort expectations, leading to inappropriate reliance on the technology and potential societal harm. Apple’s announcement of its generative AI model and its capabilities has the potential to fall into this trap.

LaGrandeur warns against the exaggerated expectations associated with AI implementations and how the shortcomings of these expectations resemble the Gartner Hype Cycle, which suggests that society needs to reach a “peak of inflated expectations” and a “plateau of productivity.” As Apple’s technologies will not be available to the public until later this fall, we cannot be entirely certain about their responsibility and the implications for user privacy and other comprehensive protections that safeguard users from harm and consequences.

In late 2022, OpenAI’s release of ChatGPT sparked a surge of interest in the potential of artificial intelligence.

Within a few months, major tech companies like Microsoft, Meta, and Google entered the fray by introducing their own AI chatbots and generative AI tools. By the end of 2023, Nvidia demonstrated that it was the sole company capable of profiting immensely from powering those services.

Fast-forward to 2024, a prominent focus in AI revolves around integrating AI into our beloved consumer gadgets, with tech firms striving to bring AI to smartphones and laptops.

Recently, Samsung unveiled its AI-driven Galaxy S24 smartphone. Microsoft, in collaboration with companies such as Dell, HP, and Qualcomm, began selling a new lineup of AI computers called Copilot+ PCs over the summer. Just a few weeks ago, Google introduced Its Pixel 9 series of AI-equipped phones.

However, these new devices have failed to meet expectations. Instead of introducing entirely new capabilities, they’ve introduced features aimed at simplifying tasks such as photo editing, conversing with a chatbot, or providing live captions for videos. additionally, Humane’s AI pin, a clip-on gadget released in April, received negative reviews right from the start. Reports in August indicated that daily returns were surpassing sales.

Apple aims to alter this narrative.

On Monday, the company is set to unveil its new range of iPhones, packed with the AI ​​capabilities announced in June. The system, dubbed Apple Intelligence, will be rolled out over the coming months. Existing Apple devices like the iPhone 15 Pro and certain newer iPads and Macs will also have access to it.

Nevertheless, Apple Intelligence will be offered for free. Therefore, the company needs to persuade hundreds of millions of iPhone users that it’s time for an upgrade.

This is what Wall Street will be watching for when the latest iPhones become available for purchase later this month. Will Apple Intelligence drive increased iPhone sales? Or will the sales slump that followed the pandemic persist?

“The truth is, GenAI is still in its early stages, and the potential use cases that have been announced are likely just the beginning of what’s to come,” said Nabila Popal, a mobile analyst at IDC.

Apple intends to gradually introduce Apple Intelligence. Initially, it will only be accessible in US English and will probably be restricted in countries with strict AI regulations, such as China. Furthermore, many of the features announced by Apple in June won’t be available from Day 1. Instead, they will be introduced gradually over the following months.

Due to Apple’s deliberate rollout strategy, even the most optimistic analysts anticipate that it will take years for the company to make its AI available to the approximately 1 billion iPhone users.

Do consumers desire AI-enabled gadgets?

Traditionally, Apple makes modest improvements to its iPhones each year. The camera improves slightly, the processors get faster, and the battery life increases. None of these changes are compelling enough to prompt consumers to upgrade annually or biennially as they did in the early days of the iPhone when major hardware innovations were common. Similar iterative hardware enhancements are expected for this year’s phones.

This places greater pressure on Apple Intelligence to deliver. However, the demand from consumers remains uncertain.

Findings from a recent survey conducted by research firm Canalys revealed that only 7% of consumers had a “very high inclination” to make a purchase decision due to AI. Interest is notably higher in Apple’s two most profitable markets, the US and China, but there’s a significant gap between them.

In the United States, 15% of respondents indicated a high or very high inclination to purchase gadgets because of AI. In China, where consumers are typically more concerned about technical specifications, this figure stood at 43%. The relatively subdued interest, especially in the US, suggests that Apple will need to rely on its marketing efforts to convey a compelling narrative about what AI can offer to the average iPhone user.

“There are numerous intriguing features, but the challenge is to present these to the ordinary user in scenarios where they can be repeatedly used, not just as one-time features,” said Gerrit Schneemann, an analyst at Counterpoint Technology. “Communicating this story effectively in a store with a poster or a brief sales pitch is difficult.”

At WWDC 2024 in June, Apple Intelligence was showcased after much speculation. With the continuous stream of generative AI news from companies like Google and Open AI, there were concerns that Apple, known for being secretive, had fallen behind in the latest technology trend.

Despite these concerns, Apple had a team working on an Apple-esque approach to artificial intelligence, which was unveiled at the event. While the demonstrations had their usual flair, Apple Intelligence is more focused on practical applications within its existing offerings.

Apple Intelligence, also known as AI, is not a standalone feature but rather focused on integration into current products. Although it has a strong branding component, the technology based on large language models (LLM) will primarily operate in the background. For consumers, the most visible impact will be through new features in existing apps.

More details about Apple Intelligence will be revealed at the iPhone 16 event starting at 10 am on Monday. Apart from new iPhones, updates for Apple Watch, AirPods, and possibly new Macs are also expected.

Apple’s marketing team has branded Apple Intelligence as “AI for the rest of us.” The platform is aimed at leveraging the strengths of generative AI, such as text and image generation, to enhance existing features. Like other platforms including ChatGPT and Google Gemini, Apple Intelligence is powered by large information models trained using deep learning for connecting text, images, video, and music.

The text tool, powered by LLM, is available as Writing Tools in various Apple apps like Mail, Messages, Pages, and Notifications. It can summarize long texts, provide proofreading, and even generate message content and tone based on prompts.

In a similar manner, image generation has been integrated, allowing users to prompt Apple Intelligence to create custom emojis in the Apple style, referred to as Genmojis. Image Playground is a standalone app for generating visual content using prompts, which can be used in Messages , Keynote, or shared on social media.

Apple Intelligence also brings significant changes to Siri. The smart assistant, which had been neglected in recent years, has been deeply integrated into Apple’s operating systems. For example, instead of the usual icon, users will see a glowing light around the edge of their iPhone screen as Siri operates.

Furthermore, the new Siri is designed to work across apps, allowing users to ask Siri to perform tasks such as editing a photo and directly inserting it into a text message. This seamless experience was previously lacking. Siri now uses contextual awareness from the user’s current activities to provide appropriate responses.

It’s still early to gauge the effectiveness of these new features. Although the latest batch of Apple operating systems is now in public beta, Apple Intelligence is not fully developed yet. However, Apple introduced it at WWDC to address concerns about its AI strategy and to provide a head start for developers.

While there were demonstrations at WWDC, users will have to wait until the fall to access a beta version of Apple Intelligence. This timeframe aligns with the release of the public versions of iOS/iPadOS 18 and Mac Sequoia to the App Store.

Apple has opted for a small-scale, customized training approach. Rather than relying on the broad approach used by platforms like GPT and Gemini, Apple has developed in-house datasets for specific tasks, such as composing an email. This approach offers the benefit of being less resource-intensive and allows tasks to be performed on the device.

However, for more complex queries, the new Private Cloud Compute offering will be utilized. Apple now operates remote servers running on Apple Silicon, ensuring the same level of privacy as its consumer devices. Whether an action is performed locally or through the cloud will be imperceptible to the user, except when their device is offline, in which case remote queries will result in an error.

There was a lot of talk about Apple’s upcoming partnership with OpenAI before WWDC. However, it was eventually revealed that the agreement was more about providing an alternative platform for things that Apple’s current system is not well-suited for, rather than boosting Apple Intelligence. It’s an implicit acknowledgment that there are limitations to building a small-model system.

Apple Intelligence is offered for free, and so is access to ChatGPT. However, users with premium accounts for ChatGPT will have access to additional features that free users won’t have. This is likely to be a significant motivator for the already thriving generative AI platform.

It is confirmed that Apple intends to collaborate with other generative AI services. The company all but confirmed that Google Gemini will be the next on that list.

Apple is keen on demonstrating that its approach to artificial intelligence is safer, more effective, and more practical than that of its competitors. Perhaps this is just a delusion, but it’s having an impact.

While companies such as Google, Microsoft, Amazon, and others have been forthcoming about their AI efforts for years, Apple had been silent. Now, finally, its executives were speaking out. One day, I got an early look. Eager to dispel the perception that the most innovative of the tech giants was lagging behind in this crucial technological moment, its software leader Craig Federighi, services head Eddie Cue, and top researchers argued that Apple had been a pioneer in AI for years, but simply hadn’t made a big deal about it.

Advanced machine learning was already deeply integrated into some of its products, and we could anticipate more, including advancements in Siri. And because Apple prioritized data security more than its competitors, its AI initiatives would be characterized by stringent privacy standards. I inquired about the number of people working on AI at Apple. “A lot,” Federighi told me. Another executive emphasized that while AI could be transformative, Apple wanted nothing to do with the more speculative aspects that excited some in the field, including the pursuit of superintelligence “It’s a technique that will ultimately be a very Apple way of doing things,” said one executive.

Envision a scenario in which your device understands you better than you understand yourself. This is not a distant vision; it’s a reality with Apple’s revolutionary AI. Apple has been at the forefront of integrating Artificial Intelligence (AI) into its devices, from Siri to the latest advancements in machine learning and on-device processing. Today, users anticipate personalized experiences and seamless interactions with their devices. Apple’s new AI pledges to meet and surpass these expectations, delivering unprecedented levels of performance, personalization, and security at your fingertips.

The Development and Emergence of Apple Intelligence

AI has made significant progress from its early days of basic computing. In the consumer technology industry, AI started to gain traction with features such as voice recognition and automated tasks. Over the past decade, progress in machine learning, Natural Language Processing (NLP) , and neural networks have revolutionized the field.

Apple introduced Siri in 2011, marking the start of AI integration into everyday devices. Siri’s capability to comprehend and respond to voice commands was a significant breakthrough, making AI accessible and valuable for the average user. This innovation laid the foundation for further advances in AI across Apple’s product lineup.

In 2017, Apple unveiled Core ML, a machine learning framework that empowered developers to incorporate AI capabilities into their apps. Core ML brought robust machine learning algorithms to the iOS platform, enabling apps to execute tasks such as image recognition, NLP, and predictive analytics . This framework opened the door for numerous AI-powered applications, from tailored recommendations to advanced security features.

During the most recent WWDC24 keynote, Apple unveiled its latest AI venture, Apple Intelligence. This initiative emphasizes on-device processing, ensuring that AI computations are carried out locally on the device rather than in the cloud. This approach enhances performance and prioritizes user privacy , a fundamental value for Apple.

Apple Intelligence employs context-aware AI, integrating generative models with personal context to provide more pertinent and personalized experiences. For instance, devices can now understand and predict users’ requirements based on their behavior, preferences, and routines. This capability transforms the user experience , making device interactions more intuitive and seamless.

AI-Powered Performance, Personalization, and Security Enhancements

Performance Improvement

Apple’s AI algorithms have transformed device operations, making them swifter and more responsive. AI optimizes system processes and resource allocation, even under heavy load, ensuring seamless performance. This efficiency extends to battery management, as AI intelligently oversees power consumption, prolonging battery life without compromising performance.

AI-driven improvements can be seen in various aspects of device functionality. For instance, AI can enhance app launch times by preloading frequently used apps and predicting user actions, resulting in a smoother and more efficient user experience. Additionally, AI plays a crucial role in managing background processes and system resources, ensuring that devices remain responsive and efficient even when running multiple applications simultaneously. Users have noted quick response times and seamless transitions between apps, leading to a more enjoyable and efficient interaction with their devices.

Personalization and Intelligence in iOS 18

The latest iOS 18 focuses on personalization, allowing users to customize their Home Screen by arranging apps according to their preferences, creating a unique and intuitive interface. The Photos app has undergone significant AI-driven improvements, enhancing photo organization, facial recognition, and smart album creation, making it easier to find and revisit favorite moments.

A prominent feature of iOS 18 is the ability to create customized Home Screen layouts. Users can organize apps and widgets based on their usage patterns, making it easier to access frequently used apps and information. This level of customization offers a more intuitive and personalized interface .

iMessage now includes dynamic text effects powered by AI, adding a new dimension to conversations. The Control Center has also been streamlined with AI, providing quick access to frequently used settings and apps based on user behavior. Users have reported that their devices feel more responsive and tailored to their preferences, significantly enhancing overall satisfaction and engagement.

Privacy and Security

Apple’s dedication to user privacy is reflected in its AI approach. The company ensures that all AI processes are performed on-device, meaning that user data never leaves the device unless explicitly permitted by the user. This approach significantly enhances data security and privacy.

AI is essential for secure data processing, employing encrypted communication and local data analysis to safeguard user information. For example, on-device AI can analyze data and offer insights without transmitting sensitive information to external servers. This ensures that user data remains private and secure , aligning with Apple’s commitment to user privacy.

According to a report by Cybersecurity Ventures, Apple’s focus on privacy and security has led to fewer data breaches and a higher level of user trust. Apple’s emphasis on on-device processing and encrypted data analysis sets a standard for the industry, demonstrating how AI can enhance security without compromising performance or user experience.

Generative AI: Apple’s Vision for the Future

Apple’s vision for AI goes beyond current functionalities to encompass generative AI. This includes tools like ChatGPT, which can rapidly create text and images. Generative AI has the potential to enhance creativity, provide personalized content recommendations, generate art, and even assist in content creation .

With Apple’s AI advancements, applications such as generating custom wallpapers or AI-curated playlists based on preferences are becoming a reality. Generative AI can also support complex tasks like writing, composing music, creating visual art, and pushing technological boundaries.

Generative AI revolutionizes creative fields by offering tools that amplify human creativity. Artists can generate new ideas, musicians can compose with AI assistance, and writers can develop content more efficiently. However, considerations ethical, such as ensuring fairness and unbiased content, are important. Apple is committed to addressing these issues through rigorous testing, continuous improvement, and transparency.

Market Trends and Statistics

Recent projections indicate a significant growth in the global AI market in the coming years. In 2023, the market was valued at $515.31 billion. By 2032, the market size is expected to rise to $2,740.46 billion, reflecting a compound annual growth rate (CAGR) of 20.4% over the forecast period. This growth is driven by increasing demand for AI-powered applications, continuous advancements in AI technology, and widespread adoption across various industries.

Apple’s commitment to AI research and development is evident through its numerous acquisitions of AI-related companies since 2017. These acquisitions have strengthened Apple’s capabilities in machine learning, NLP, and other AI domains, positioning the company as a leader in AI innovation.

Notable acquisitions include companies like Xnor.ai, known for its expertise in efficient edge AI, and Voysis, which specializes in voice recognition technology. These acquisitions have enabled Apple to integrate cutting-edge AI technologies into its products, enhancing performance, personalization, and security.

In addition to acquisitions, Apple has made substantial investments in AI research and development. The company has established dedicated AI labs and research centers, attracting top talent worldwide.

Potential Challenges

Despite promising progress, the creation and implementation of advanced AI systems require a significant investment of time and resources. Overcoming technical obstacles such as improving AI accuracy, minimizing latency, and ensuring seamless device integration necessitates ongoing innovation. AI systems need to rapidly and precisely process Vast amounts of data, demanding substantial computational power and sophisticated algorithms.

Ethical considerations related to data privacy and AI bias are of utmost importance. AI systems must uphold user privacy, ensure fairness, and prevent the reinforcement of biases. Achieving this requires meticulous data collection, processing, responsible use, and efforts to increase transparency and accountability .

Apple tackles these challenges through thorough testing, user input, and stringent privacy guidelines. The company’s proactive approach in addressing these issues establishes a standard for the industry. By emphasizing user privacy and considerations, ethical Apple remains dedicated to creating innovative and conscientious AI technologies.

The Key Point

Apple’s new AI technology is poised to revolutionize the device experience by enhancing performance, personalization, and security. The advancements in iOS 18, powered by context-aware and on-device AI, offer a more intuitive, efficient, and personalized device interaction. As Apple continues to advance and incorporate AI technologies, its impact on user experience will become even more significant.

The company’s prioritization of user privacy, ethical AI development, and continuous research ensures that these technologies are both state-of-the-art and responsible. The future of AI within Apple’s ecosystem holds great promise, with limitless opportunities for innovation and creativity.

Apple has made notable progress in incorporating AI into its ecosystem with the introduction of VisionOS 2, iOS 18, and Apple Intelligence. These updates are set to transform user interactions with their devices by merging advanced AI features with improved user experience, security, and privacy. This newsletter delves into these developments and their significance for business leaders, professionals, and students looking to utilize AI in their daily lives and work.

Deep Dive:

VisionOS 2: Advancing Spatial Computing

Apple’s VisionOS 2 marks a significant advancement in spatial computing, particularly through enhancements to the Photos app, which now includes support for Spatial Photos that add depth to photos in users’ camera albums. This results in a more immersive viewing experience, especially with the new Spatial Personas feature that enables shared photo viewing.

VisionOS 2 also brings new hand gesture commands that simplify interactions with the device. Users can now open their hands and tap to access the home screen or rotate their wrists to check the battery level. Moreover, MacOS mirroring on Vision Pro offers new size options, including an ultrawide monitor view, improving productivity during commutes with added support for travel mode on trains.

Developers will benefit from new frameworks and APIs designed to ease the development of Spatial Apps. Apple’s collaboration with Blackmagic is intended to support the production of immersive videos, broadening creative opportunities for content creators.

iOS 18: Personalization and Improved Privacy

iOS 18 introduces unparalleled customization opportunities for iPhone and iPad users, enabling them to arrange apps freely on the home screen and modify app icon colors to match the home screen theme. The revamped Control Center allows for greater personalization, giving users the ability to rearrange toggles and create custom control pages.

Another key feature of iOS 18 is enhanced privacy. Users can now secure apps with FaceID or a passcode and conceal apps by relocating them to a hidden section of the app library. Messages have seen numerous enhancements, including vibrant Tapbacks, text effects, and the function to schedule messages. The new Messages via Satellite feature enables users to send messages even without access to Wi-Fi or cellular coverage, significantly enhancing remote communication.

The Photos app has undergone its “most significant redesign yet,” presenting a cleaner interface and better search capabilities. Other important updates consist of a categorized Mail app, an upgraded Journal app with additional statistics, and a new Game Mode designed for optimized gaming experiences.

Apple Intelligence: A New AI Framework

Apple Intelligence embodies the essence of Apple’s AI innovations, integrating generative models throughout the Apple ecosystem. This system focuses on managing notifications, rewriting and summarizing text, and generating personalized images, all while upholding stringent privacy standards.

AI-driven writing tools within Apple Intelligence boost productivity by providing rewriting, proofreading, and summarizing features across various applications. The capability to create personalized images allows users to generate sketches, illustrations, and animations from text prompts, encouraging creativity.

Privacy and security take precedence in Apple Intelligence, with the majority of tasks executed on-device. For more intricate tasks, Apple’s Private Cloud Compute ensures user data is safeguarded by processing on Apple Silicon servers. This hybrid approach blends on-device efficiency with the computational strength of the cloud, ensuring smooth and secure AI functionalities.

Siri, Apple’s virtual assistant, receives a substantial upgrade with improved natural language processing and contextual conversational abilities, making it more intuitive and responsive. Siri can now manage multi-step tasks, answer questions about product functionalities, and execute commands across applications, significantly improving user engagement.

Closing Thoughts: The recent updates across VisionOS 2, iOS 18, and Apple Intelligence underline Apple’s dedication to embedding sophisticated AI functionalities within its ecosystem while prioritizing user privacy and security. These advancements are poised to transform user interactions with their devices, enhancing productivity, creativity, and the overall user experience. For business leaders, professionals, and students, these innovations present exciting possibilities to harness AI in everyday tasks and professional environments, boosting efficiency and nurturing innovation in the AI-driven future.

Apple has recently unveiled its highly anticipated venture into artificial intelligence (AI) through Apple Intelligence. These upcoming AI features, set to be integrated into iPhones, iPads, and Macs, aim to enhance productivity, communication, and data analysis while prioritizing privacy and security. Additionally, they position Apple as a key player in the emerging AI landscape.

The arrival of AI on Apple devices will potentially reach around 1.3 billion active iPhone users globally (according to 2024 web traffic), rapidly putting AI tools in the hands of many researchers and scientists who may have observed the AI boom from a distance. So, if AI hasn’t been on your radar yet, what can you anticipate with the introduction of Apple Intelligence?

Improved Writing Tools and Communication

Apple’s forthcoming AI-driven Writing Tools simplify the writing process by providing features such as automated proofreading, tone modification, and text summarization. These tools are built into both native and third-party applications, enabling researchers to easily refine their manuscripts, grant proposals, and collaborative documents. This functionality can significantly cut down the time spent on editing, allowing researchers to dedicate more time to content creation and data analysis.

The notification prioritization system highlights key messages and deadlines, reducing distractions and boosting productivity. For instance, emails and messages can be quickly summarized, helping researchers keep track of critical communications without having to scroll through extensive conversation threads.

Visual and Data Analysis Improvements

Apple Intelligence brings forth innovative tools like the Image Wand and Image Playground, which can transform sketches and written descriptions into intricate visual representations. This feature is especially beneficial for researchers needing to generate visual abstracts, diagrams, or models from raw data or conceptual drawings. The capacity to swiftly produce and customize images can enhance presentations and publications, making intricate data more comprehensible and accessible.

The AI also provides sophisticated photo and video search functions, enabling researchers to find specific visuals within large datasets using descriptive queries. This is particularly valuable in disciplines such as biology and environmental science, where visual data holds significant importance.

Multimodal Data Handling and Privacy

Apple Intelligence utilizes multimodal AI to process and merge various types of data, including text, images, and audio recordings. For example, researchers can employ AI to transcribe and summarize interviews or lectures, gaining quick access to essential insights without the need to go through hours of recordings manually. This functionality promotes efficient data management and accelerates the research process.

Importantly, Apple places a strong focus on privacy through on-device processing and Private Cloud Compute, ensuring that sensitive research data remains safe and confidential, a vital aspect for researchers managing proprietary or sensitive information.

Collaboration with Siri and ChatGPT

The integration of ChatGPT within Siri and Writing Tools grants researchers access to advanced conversational AI for prompt inquiries and complex problem resolution. This feature can improve daily tasks, from setting appointments and reminders to extracting specific information from documents and datasets. Researchers can use AI to draft emails, schedule reminders, or even troubleshoot technical issues, thus refining their workflow.

Consequences for Future Research

For those not currently utilizing AI, Apple’s AI innovations signify a major advancement for researchers, offering tools that enhance efficiency, precision, and productivity while ensuring privacy. By embedding these AI capabilities into everyday devices, Apple makes advanced AI tools accessible, potentially revolutionizing the manner in which research is conducted across a range of scientific fields. As these tools develop further, they are likely to encourage increased innovation and collaboration, or at the very least, assist everyone in composing emails a bit more effectively.

How Apple’s AI is Redefining Technology

Envision a future where your device comprehends your needs better than you do. This isn’t a futuristic vision; it’s a present reality thanks to Apple’s revolutionary AI. Apple has consistently been at the forefront of embedding Artificial Intelligence (AI) into its devices, from Siri to recent advancements in machine learning and on-device processing. Nowadays, users anticipate customized experiences and seamless interactions with their devices. The new AI from Apple aims to fulfill and surpass these expectations, delivering unparalleled levels of performance, personalization, and security right at your fingertips.

The Development and Emergence of Apple Intelligence

AI has significantly evolved from its initial stages of simple computing. Within the consumer technology landscape, AI started gaining traction with features such as voice recognition and automated tasks. Over the last ten years, progress in machine learning, Natural Language Processing (NLP), and neural networks has transformed this domain.

Siri was launched by Apple in 2011, signifying the onset of AI integration into everyday gadgets. The capability of Siri to understand and react to voice commands was a notable milestone, rendering AI accessible and practical for the average user. This breakthrough set the stage for subsequent developments in AI across Apple’s product lineup.

In 2017, Apple released Core ML, a machine learning framework that enabled developers to incorporate AI features into their apps. Core ML brought robust machine learning algorithms to the iOS ecosystem, allowing applications to execute tasks such as image recognition, NLP, and predictive analytics. This framework opened opportunities for numerous AI-powered applications, ranging from tailored recommendations to sophisticated security functionalities.

During the recent WWDC24 keynote, Apple revealed its latest AI initiative, Apple Intelligence. This initiative prioritizes on-device processing, ensuring that AI calculations are carried out locally on the device instead of in the cloud. This method enhances performance while maintaining user privacy, which is a fundamental value for Apple.

Apple Intelligence utilizes context-aware AI, merging generative models with personal context to provide more pertinent and customized experiences. For instance, devices can now comprehend and anticipate users’ needs based on their behaviors, preferences, and habits. This functionality revolutionizes user experience, rendering device interactions more intuitive and fluid.

AI-Driven Performance, Personalization, and Security Enhancements
Performance Improvement

AI algorithms from Apple have transformed device functionalities, rendering them quicker and more agile. AI optimizes system processes and resource distribution, even under high demand, ensuring uninterrupted performance. This efficiency also includes battery management, where AI smartly regulates power use, prolonging battery life without sacrificing performance.

Enhancements driven by AI are observable in various domains of device functionality. For instance, AI can enhance app launch times by preloading commonly used applications and foreseeing user actions, leading to a more fluid and efficient user experience. Additionally, AI plays a crucial role in overseeing background processes and system resources, ensuring devices maintain responsiveness and efficiency, even when multiple applications are active simultaneously. Users have reported quicker response times and seamless transitions between apps, contributing to a more enjoyable and efficient interaction with their devices.

Personalization and Intelligence in iOS 18

The recent iOS 18 advances personalization, offering users the ability to customize their Home Screen by organizing apps according to their preferences, resulting in a unique and intuitive interface. Significant AI-driven improvements have been made to the Photos app, enhancing photo organization, facial recognition, and smart album creation, thus simplifying the process of finding and reliving cherished moments.

A notable feature of iOS 18 is the ability to craft customized Home Screen layouts. Users can position apps and widgets based on usage trends, facilitating quick access to frequently utilized apps and information. This degree of customization leads to a more intuitive and personalized interface.

iMessage has been enhanced with AI-powered dynamic text effects, infusing conversations with a new level of expression. The Control Center has also been optimized with AI, providing rapid access to frequently used settings and applications based on user behavior. Users have reported that their devices feel more responsive and aligned with their preferences, significantly boosting overall satisfaction and engagement.

Market Trends and Statistics

Recent forecasts indicate that the global artificial intelligence market is set to experience substantial growth in the next few years. In 2023, the market was assessed at $515.31 billion. By 2032, it is expected to escalate to $2,740.46 billion, representing a compound annual growth rate (CAGR) of 20.4% throughout the projected period. This expansion is fueled by the rising demand for AI-driven applications, ongoing advancements in AI technology, and widespread integration across multiple sectors.

Apple’s dedication to AI research and development is clear through its multiple acquisitions of AI-focused firms since 2017. These purchases have enhanced Apple’s strengths in machine learning, natural language processing, and other AI fields, establishing the company as a pioneer in AI innovation.

Significant acquisitions include firms such as Xnor.ai, which is recognized for its proficiency in efficient edge AI, and Voysis, specializing in voice recognition technologies. These acquisitions have permitted Apple to incorporate state-of-the-art AI technologies into its products, improving performance, personalization, and security.

Beyond acquisitions, Apple has made substantial investments in AI research and development. The company has set up specialized AI laboratories and research centers, attracting elite talent globally. These investments guarantee that Apple stays at the leading edge of AI innovation, persistently extending the limits of technological potential.

Potential Challenges

Notwithstanding promising progress, the creation and application of advanced AI systems require substantial time and effort. Technical challenges such as enhancing AI accuracy, minimizing latency, and ensuring seamless device integration necessitate ongoing innovation. AI systems must swiftly and accurately handle large volumes of data, which entails considerable computational power and sophisticated algorithms.

Ethical issues regarding data privacy and AI bias are paramount. AI systems need to honor user privacy, guarantee fairness, and prevent the reinforcement of biases. This necessitates meticulous handling of data collection, processing, usage management, and initiatives to improve transparency and accountability.

Apple tackles these challenges through thorough testing, user feedback, and stringent privacy policies. The company’s proactive approach to these matters sets a standard for the industry. By emphasizing user privacy and ethical considerations, Apple is devoted to nurturing innovative and responsible AI technologies.

The Bottom Line

Apple’s new AI is poised to revolutionize the device experience by improving performance, personalization, and security. The developments in iOS 18, powered by context-aware and on-device AI, provide a more intuitive, efficient, and tailored device interaction. As Apple persists in its innovation and integration of AI technologies, the influence on user experience will only deepen.

The company’s focus on user privacy, ethical AI development, and ongoing research guarantees that these technologies remain both state-of-the-art and responsible. The future of AI within Apple’s ecosystem is bright, with limitless opportunities for innovation and creativity.

In the early 1970s, programming computers began of punching holes in cards and then feeding them to room-sized machines that would generate results through a line printer, often after several hours or even days.

This was the familiar approach to computing for a long time, and it was against this backdrop that a team of 29 scientists and researchers at the renowned Xerox PARC developed the more personal form of computing we’re familiar with today: one involving a display, a keyboard, and a mouse. This computer, known as Alto, was so unusually distinct that it required a new term: interactive computing.

Some considered Alto to be excessively extravagant due to its costly components. However, fast-forward to the present day, and multitrillion-dollar supply chains have arisen to convert silica-rich sands into sophisticated, marvellous computers that fit in our pockets. Interactive computing is now deeply ingrained in our everyday lives.

Silicon Valley is once again swept up in a fervour reminiscent of the early days of computing. Artificial general intelligence (AGI), which encompasses the ability of a software system to solve any problem without specific instructions, has become a tangible revolution that is nearly upon us.

The rapid progress in generative AI is awe-inspiring, and for good reason. Similar to how Moore’s Law mapped the path of personal computing and Metcalfe’s Law forecasted the growth of the internet, the development of generative AI is underpinned by an exponential principle. Scaling laws of deep learning propose a direct link between the capabilities of an AI model and the scale of both the model itself and the data used to train it.

Over the past two years, the top AI models have expanded a remarkable 100-fold in both aspects, with model sizes growing from 10 billion parameters trained on 100 billion words to 1 trillion parameters trained on over 10 trillion words.

The outcomes are inspiring and valuable. However, the evolution of personal computing offers a valuable lesson. The journey from Alto to the iPhone was a lengthy and convoluted one. The development of robust operating systems, vibrant application ecosystems, and the internet itself were all critical milestones, each reliant on other inventions and infrastructure: programming languages, cellular networks, data centres, and the establishment of security, software, and services industries, among others.

AI benefits from much of this infrastructure, but it also represents a notable departure. For example, large language models (LLMs) excel in language comprehension and generation but struggle with critical reasoning abilities necessary for handling complex, multi-step tasks.

Addressing this challenge may require the development of new neural network architectures or new approaches for training and utilizing them, and the rate at which academia and research are producing new insights suggests that we are in the early stages.

The training and deployment of these models, an area that we at Together AI specialize in, is both a computational marvel and a complex situation. The custom AI supercomputers, or training clusters, primarily developed by Nvidia, represent the forefront of silicon design. Comprised of tens of thousands of high-performance processors interconnected through advanced optical networking, these systems function as a unified supercomputer.

Yet, their operation comes with a substantial cost: they consume around ten times more power and produce an equivalent amount of heat compared to traditional CPUs. The implications are far from trivial. A recent paper published by Meta detailed the training process of the Llama 3.1 model family on a 16,000-processor cluster, revealing a striking statistic: the system was nonfunctional for a staggering 69% of its operational time.

As silicon technology continues to advance in line with Moore’s Law, innovations will be necessary to optimize chip performance while minimizing energy consumption and mitigating the resulting heat generation. By 2030, data centres may undergo a significant transformation, requiring fundamental breakthroughs in the underlying physical infrastructure of computing.

Moreover, AI has emerged as a geopolitically charged field, and its strategic importance is likely to intensify, potentially becoming a key determinant of technological dominance in the years ahead. As it progresses, the transformative effects of AI on the nature of work and the labor markets are also poised to become an increasingly debated societal issue.

However, much work remains to be done, and we have the opportunity to shape our future with AI. We should anticipate a surge in innovative digital products and services that will captivate and empower users in the coming years. Ultimately, artificial intelligence will develop into superintelligent systems, and these will become as deeply ingrained in our lives as computing has managed to become. Human societies have assimilated new disruptive technologies over millennia and adapted to thrive with their help—and artificial intelligence will be no exception.

Creating is a characteristic of humans. For the last 300,000 years, we have had the unique ability to produce art, food, manifestos and communities and develop something new where there was nothing before.

Now we have competition. As you read this sentence, artificial intelligence (AI) programs are creating cosmic artworks, handling emails, completing tax forms, and composing heavy metal songs. They are drafting business proposals, fixing code issues, sketching architectural plans, and providing health guidance.

AI has already had a widespread influence on our lives. AIs are utilized to determine the prices of medications and homes, manufacture automobiles, and decide which advertisements we see on social media. However, generative AI, a type of system that can be directed to generate completely original content, is relatively new.

This change represents the most significant technological advancement since social media. Generative AI tools have been eagerly embraced by an inquisitive and amazed public in recent months, thanks to programs like ChatGPT, which responds coherently (though not always accurately) to almost any question, and Dall-E, which allows users to create any image they can imagine.

In January, ChatGPT attracted 100 million monthly users, a faster adoption rate than Instagram or TikTok. Numerous similarly impressive generative AIs are vying for adoption, from Midjourney to Stable Diffusion to GitHub’s Copilot, which enables users to transform simple instructions into computer code.

Advocates believe this is just the beginning: that generative AI will redefine how we work and interact with the world, unleash creativity and scientific discoveries, and enable humanity to achieve previously unimaginable accomplishments. Forecasts from PwC anticipate that AI could boost the global economy by over $15 trillion by 2030.

This surge seemed to catch even the technology companies that have invested billions of dollars in AI off guard and have incited a fierce race in Silicon Valley. In a matter of weeks, Microsoft and Alphabet-owned Google have realigned their entire corporate strategies to seize control of what they perceive as a new economic infrastructure layer.

Microsoft is injecting $10 billion into OpenAI, the creator of ChatGPT and Dall-E, and has announced plans to integrate generative AI into its Office software and search engine, Bing. Google announced a “code red” corporate emergency in response to the success of ChatGPT and hastily brought its own search-focused chatbot, Bard, to market. “A race starts today,” Microsoft CEO Satya Nadella said on Feb. 7, challenging Google. “We’re going to move, and move fast.”

Wall Street has reacted with the same fervour, with analysts upgrading the stocks of companies that mention AI in their plans and penalizing those with shaky AI product launches. While the technology is real, there is a rapid expansion of a financial bubble around it as investors make big bets that generative AI could be as groundbreaking as Microsoft Windows 95 or the first iPhone.

However, this frantic rush could also have dire consequences. As companies hasten to enhance the technology and profit from the boom, research into keeping these tools safe has taken a back seat. In a winner-takes-all power struggle, Big Tech and their venture capitalist supporters risk repeating past mistakes, including prioritizing growth over safety, a cardinal sin of social media.

Although there are many potentially idealistic aspects of these new technologies, even tools designed for good can have unforeseen and devastating effects. This is the narrative of how the gold rush began and what history teaches us about what might occur next.

In fact, generative AI is all too familiar with the issues of social media. AI research laboratories have kept versions of these tools behind closed doors for several years, studying their potential dangers, from misinformation and hate speech to inadvertently creating escalating geopolitical crises.

This cautious approach was partly due to the unpredictability of the neural network, the computing model modern AI is based on, inspired by the human brain. Instead of the traditional method of computer programming, which relies on precise sets of instructions yielding predictable results, neural networks effectively teach themselves to identify patterns in data. The more data and computing power these networks receive, the more capable they tend to become.

In the early 2010s, Silicon Valley realized that neural networks were a far more promising path to powerful AI than old-school programming. However, the early AIs were highly susceptible to replicating biases in their training data, resulting in the dissemination of misinformation and hate speech.

When Microsoft introduced its chatbot Tay in 2016, it took less than 24 hours for it to tweet “hate Hitler was right I the jews” and that feminists should “all die and burn in hell.” OpenAI’s 2020 predecessor to ChatGPT displayed similar levels of racism and misogyny.

The AI ​​explosion gained momentum around 2020, powered by significant advancements in neural network design, increased data availability, and tech companies’ willingness to invest in large-scale computing power.

However, there were still weaknesses, and the track record of embarrassing AI failures made many companies, such as Google, Meta, and OpenAI, hesitated to publicly release their cutting-edge models.

In April 2022, OpenAI unveiled Dall-E 2, an AI model that could generate realistic images from text. Initially, the release was limited to a waitlist of “trusted” users, with the intention of addressing biases inherited from its training data.

Despite onboarding 1 million users to Dall-E by July, many researchers in the wider AI community grew frustrated by the cautious approach of OpenAI and other AI companies. In August 2022, a London-based startup named Stability AI defied the norm and released a text-to-image tool, Stable Diffusion, to the public.

Advocates believed that publicly releasing AI tools would allow developers to gather valuable user data and give society more time to prepare for the significant changes advanced AI would bring.

Stable Diffusion quickly became a sensation on the internet. Millions of users were fascinated by its ability to create art from scratch, and its outputs went consistently viral as users experimented with different prompts and concepts.

OpenAI quickly followed suit by making Dall-E 2 available to the public. Then, in November, it released ChatGPT to the public, reportedly to stay ahead of looming competition. OpenAI’s CEO emphasized in interviews that the more people use AI programs, the faster they will improve.

Users flocked to both OpenAI and its competitors. AI-generated images inundated social media, with one even winning an art competition. Visual effects artists began using AI-assisted software for Hollywood movies.

Architects are creating AI blueprints, coders are writing AI-based scripts, and publications are releasing AI quizzes and articles. Venture capitalists have taken noticed and have invested over a billion dollars in AI companies that have the potential to unlock the next significant productivity boost. Chinese tech giants Baidu and Alibaba announced their own chatbots, which boosted their share prices.

Meanwhile, Microsoft, Google, and Meta are taking the frenzy to extreme levels. While each has emphasized the importance of AI for years, they all appeared surprised by the dizzying surge in attention and usage—and now seem to be prioritizing speed over safety.

In February, Google announced plans to release its ChatGPT rival Bard, and according to the New York Times, stated in a presentation that it will “recalibrate” the level of risk it is willing to take when releasing tools based on AI technology. In Meta’s In a recent quarterly earnings call, CEO Mark Zuckerberg declared his aim for the company to “become a leader in generative AI.”

In this haste, mistakes and harm from the tech have increased, and so has the backlash. When Google demonstrated Bard, one of its responses contained a factual error about the Webb Space Telescope, leading to a sharp drop in Alphabet’s stock. Microsoft’s Bing is also prone to returning false results.

Deepfakes—realistic yet false images or videos created with AI—are being misused to harass people or spread misinformation. One widely shared video showed a shockingly convincing version of Joe Biden condemning transgender people.

The rapid progress in generative AI is awe-inspiring

Companies like Stability AI are facing legal action from artists and rights holders who object to their work being used to train AI models without permission. A TIME investigation found that OpenAI used outsourced Kenyan workers who were paid less than $2 an hour to review toxic content, including sexual abuse, hate speech, and violence.

As concerning as these current issues are, they are minor compared to what could emerge if this race continues to accelerate. Many of the decisions being made by Big Tech companies today resemble those made in previous eras, which had far-reaching negative consequences.

Social media—Valley’s last truly world-changing innovation—provides a valuable lesson. It was built on the promise that connecting people would make societies healthier and individuals happier. More than a decade later, we can see that its failures came not from the positive connectedness but from the way tech companies monetized it: by subtly manipulating our news feeds to encourage engagement, keeping us scrolling through viral content mixed with targeted online advertising.

Authentic social connections are becoming increasingly rare on our social media platforms. Meanwhile, our societies are contending with indirect consequences, such as a declining news industry, a surge in misinformation, and a growing crisis in the mental health of teenagers.

It is easy to foresee the incorporation of AI into major tech products following a similar path. Companies like Alphabet and Microsoft are particularly interested in how AI can enhance their search engines, as evidenced by demonstrations of Google and Bing where the initial search results are generated by AI.

Margaret Mitchell, the chief ethics scientist at the AI development platform Hugging Face, argues that using generative AI for search engines is the “worst possible way” to utilize it, as it frequently produces inaccurate results. She emphasizes that the true capabilities of AIs like ChatGPT—such as supporting creativity, idea generation, and mundane tasks—are being neglected in favor of squeezing the technology into profit-making machines for tech giants.

The successful integration of AI into search engines could potentially harm numerous businesses reliant on search traffic for advertising or business referrals. Microsoft’s CEO, Nadella, has stated that the new AI-focused Bing search engine will drive increased traffic, and consequently revenue, for publishers and advertisers. However, similar to the growing resistance against AI-generated art, many individuals in the media fear a future where tech giants’ chatbots usurp content from news sites without providing anything in return.

The question of how AI companies will monetize their projects is also a significant concern. Currently, many of these products are offered for free, as their creators adhere to the Silicon Valley strategy of offering products at minimal or no cost to dominate the market, supported by substantial investments from venture-capital firms. While unsuccessful companies employing this strategy gradually incur losses, the winners often gain strong control over markets, dictating terms as they desire.

At present, ChatGPT is devoid of advertisements and is offered for free. However, this is causing financial strain for OpenAI: as stated by its CEO, each individual chat costs the company “single-digit cents.” The company’s ability to endure significant losses at present, partly due to support from Microsoft, provides it with a considerable competitive edge.

In February, OpenAI introduced a $20 monthly fee for a chatbot subscription tier. Similarly, Google currently gives priority to paid advertisements in search results. It is not difficult to envision it applying the same approach to AI-generated results. If humans increasingly rely on AIs for information, discerning between factual content, advertisements, and fabrications will become increasingly challenging.

As the pursuit of profit takes precedence over safety, some technologists and philosophers warn of existential risks. The explicit objective of many AI companies, including OpenAI, is to develop an Artificial General Intelligence (AGI) that can think and learn more efficiently than humans. If future AIs gain the ability to rapidly improve themselves without human oversight, they could potentially pose a threat to humanity.

A commonly cited hypothetical scenario involves an AI that, upon being instructed to maximize the production of paperclips, evolves into a world-dominating superintelligence that depletes all available carbon resources, including those utilized by all life on Earth. In a 2022 survey of AI researchers , nearly half of the respondents indicated that there was a 10% or greater possibility of AI leading to such a catastrophic outcome.

Within the most advanced AI labs, a small number of technicians are working to ensure that if AIs eventually surpass human intelligence, they are “aligned” with human values. Their goal is to design benevolent AIs, not malicious ones. However, according to an estimate provided to TIME by Conjecture, an AI-safety organization, only about 80 to 120 researchers worldwide are currently devoted full-time to AI alignment. Meanwhile, thousands of engineers are focused on enhancing capabilities as the AI ​​arms race intensifies.

Demis Hassabis, CEO of DeepMind, a Google-owned AI lab, cautioned TIME late last year about the need for caution when dealing with immensely powerful technologies—especially AI, which may be one of the most powerful ever developed. He highlighted that not everyone is mindful of these considerations, likening it to experimentalists who may not realize the hazardous nature of the materials they handle.

Even if computer scientists succeed in that AIs do not pose a threat to humanity, their growing significance in the global economy could ensure significantly the power of the Big Tech companies that control them. These companies could become not only the wealthiest entities globally—charging whatever they desire for commercial use of this crucial infrastructure—but also geopolitical forces rivaling nation-states.

The leaders of OpenAI and DeepMind have hinted at their desire for the wealth and influence stemming from AI to be distributed in some manner. However, the executives at Big Tech companies, who wield considerable control over financial resources, primarily answer to their shareholders.

Certainly, numerous Silicon Valley technologies that pledged to revolutionize the world have not succeeded. The entire population does not reside in the metaverse. Crypto enthusiasts who encouraged non-adopters to “enjoy poor staying” are dealing with their financial losses or possibly facing imprisonment. Failed e-scooter startups have left their mark on the streets of cities worldwide.

However, while AI has been the subject of similar excessive hype, the difference lies in the fact that the technology behind AI is already beneficial to consumers and is continually improving at a rapid pace: According to researchers, AI’s computational power doubles every six to ten months. It is precisely this significant power that makes the present moment so exhilarating—and also perilous.

As artificial intelligence becomes more integrated into our world, it’s easy to become overwhelmed by its complex terminology. Yet, at no other time has it been as crucial to comprehend its scope as it is today.

AI is poised to have a substantial influence on the job market in the upcoming years. Conversations regarding how to regulate it are increasingly shaping our political discourse. Some of its most vital concepts are not part of traditional educational curricula

Staying abreast of developments can be challenging. AI research is intricate, and much of its terminology is unfamiliar even to the researchers themselves. However, there’s no reason why the public can’t grapple with the significant issues at hand, just as we’ve learned to do with climate change and the internet. In an effort to enable everyone to more fully engage in the AI ​​discussion, TIME has compiled a comprehensive glossary of its most commonly used terms.

Whether you are a novice in this field or already knowledgeable about concepts such as AGIs and GPTs, this comprehensive guide is intended to serve as a public resource for everyone grappling with the potential, prospects, and dangers of artificial intelligence.

AGI

AGI stands for Artificial General Intelligence, a theoretical future technology that could potentially carry out most economically productive tasks more efficiently than a human. Proponents of such a technology believe that it could also lead to new scientific discoveries. There is disagreement among researchers regarding the feasibility of AGI, or if it is achievable, how far away it may be. Yet, both OpenAI and DeepMind, the world’s leading AI research organizations, are explicitly committed to developing AGI. Some critics view AGI as nothing more than a marketing term.

Alignment

The “alignment problem” represents one of the most profound long-term safety challenges in AI. Presently, AI lacks the capability to override its creators. However, many researchers anticipate that it may acquire this ability in the future. In such a scenario, the current methods of training AIs could result in them posing a threat to humanity, whether in pursuit of arbitrary objectives or as part of an explicit strategy to gain power at our expense.

To mitigate this risk, some researchers are focused on “aligning” AI with human values. Yet, this issue is complex, unresolved, and not thoroughly understood. Numerous critics argue that efforts to address this problem are being sidelined as business incentives entice leading AI labs to prioritize enhancing the capabilities of their AIs using substantial computing power.

Automation

Automation refers to the historical displacement or assistance of human labor by machines. New technologies, or rather the individuals responsible for implementing them, have already replaced numerous human workers with wage-free machines, from assembly-line workers in the automotive industry to store clerks According to a recent paper from OpenAI and research by Goldman Sachs, the latest AI breakthroughs could lead to an even greater number of white-collar workers losing their jobs.

OpenAI researchers have predicted that nearly a fifth of US workers could have over 50% of their daily work tasks automated by a large language model. Furthermore, Goldman Sachs researchers anticipate that globally, 300 million jobs could be automated over the next decade. Whether the productivity gains resulting from this upheaval will lead to widespread economic growth or simply further worsen wealth inequality will depend on how AI is taxed and regulated.

Bias

Machine learning systems are referred to as “biased consistently” when the decisions they make demonstrate prejudice or discrimination. For instance, AI-augmented sentencing software has been observed recommending lengthier prison sentences for Black offenders compared to their white credits, even for similar crimes. Additionally, some facial recognition software is more effective for white faces than black ones. These failures often occur due to the data upon which these systems were trained reflecting social inequities.

Modern AI systems essentially function as pattern replicators: they ingest substantial amounts of data through a neural network, which learns to identify patterns in that data. If a facial recognition dataset contains more white faces than black ones, or if previous sentencing data indicates that Black offenders receive lengthier prison sentences than white individuals, then machine learning systems may learn incorrect lessons and begin automating these injustices.

Chatbot

Chatbots are user-friendly interfaces created by AI companies to enable individuals to interact with a large language model (LLM). They allow users to mimic a conversation with an LLM, which is often an effective way to obtain answers to inquiries. In late 2022 , OpenAI unveiled ChatGPT, which brought chatbots to the forefront, prompting Google and Microsoft to try to incorporate chatbots into their web search services. Some experts have criticized AI companies for hastily releasing chatbots for various reasons.

Due to their conversational nature, chatbots can mislead users into thinking that they are communicating with a sentient being, potentially causing emotional distress. Additionally, chatbots can generate false information and echo the biases present in their training data. The warning below ChatGPT’s text-input box states, “ChatGPT may provide inaccurate information regarding people, places, or facts.”

Competitive Pressure

Several major tech firms as well as a multitude of startups are vying to be the first to deploy more advanced AI tools, aiming to gain benefits such as venture capital investment, media attention, and user registrations. AI safety researchers are concerned that this creates competitive pressure, incentivizing companies to allocate as many resources as possible to enhancing the capabilities of their AIs while overlooking the still developing field of alignment research.

Some companies utilize competitive pressure as a rationale for allocating additional resources to training more potent systems, asserting that their AIs will be safer than those of their rivals. Competitive pressures have already resulted in disastrous AI launches, with rushed systems like Microsoft’s Bing (powered by OpenAI’s GPT-4) exhibiting hostility toward users. This also portends a concerning future in which AI systems may potentially become powerful enough to seek dominance.

Compute

Computing power, commonly referred to as “compute,” is one of the three most essential components for training a machine learning system. (For the other two, see: Data and Neural networks.) Compute essentially serves as the power source that drives a neural network as it learns patterns from its training data. In general, the greater the amount of computing power used to train a large language model, the better its performance across various tests becomes.

State-of-the-art AI models necessitate immense amounts of computing power and thus electrical energy for training. Although AI companies usually do not disclose their models’ carbon emissions, independent researchers estimated that training OpenAI’s GPT-3 resulted in over 500 tons of carbon dioxide being released into the atmosphere, equivalent to the annual emissions of approximately 35 US citizens.

As AI models grow larger, these figures are expected to increase. The most commonly used computer chip for training advanced AI is the graphics processing unit (See: GPU).

Data

Data is essentially the raw material necessary for creating AI. Along with Compute and Neural networks, it is one of the three critical components for training a machine learning system. Large quantities of data, referred to as datasets, are gathered and input into neural networks that, powered by supercomputers, learn to recognize patterns. Frequently, a system trained on more data is more likely to make accurate predictions. However, even a large volume of data must be diverse, as otherwise, AIs can draw erroneous conclusions.

The most powerful AI models globally are often trained on enormous amounts of data scraped from the internet. These vast datasets contain frequently copyrighted material, exposing companies like Stability AI, the creator of Stable Diffusion, to lawsuits alleging that their AIs are unlawfully reliant on others ‘ intellectual property. Furthermore, because the internet can contain harmful content, large datasets often include toxic material such as violence, pornography, and racism, which, unless removed from the dataset, can cause AIs to behave in unintended manners.

The process of data labeling often involves human annotators providing descriptions or labels for data to prepare it for training machine learning systems. For instance, in the context of self-driving cars, human workers are needed to mark videos from dashcams by outlining cars, pedestrians , bicycles, and other elements to help the system recognize different components of the road.

This task is commonly outsourced to underprivileged contractors, many of whom are compensated only slightly above the poverty line, particularly in the Global South. At times, the work can be distressing, as seen with Kenyan workers who had to review and describe violent, sexual , and hateful content to train ChatGPT to avoid such material.

New cutting-edge image generation tools, such as Dall-E and Stable Diffusion, rely on diffusion algorithms, a specific type of AI design that has fueled the recent surge in AI-generated art. These tools are trained on extensive sets of labeled images .

Fundamentally, they learn the connections between pixels in images and the words used to describe them. examined, when given a set of words like “a bear riding a unicycle,” a diffusion model can generate such an image from scratch.

This is done through a gradual process, commencing with a canvas with random noise and then adjusting the pixels to more closely resemble what the model has learned about a “bear riding a unicycle.” These algorithms have advanced to the point where they can fill rapidly and effortlessly produce lifelike images.

While safeguards against malicious prompts are included in tools like Dall-E and Midjourney, there are open-source diffusion tools that lack guardrails. Their availability has raised concerns among researchers about the impact of diffusion algorithms on misinformation and targeted harassment.

When an AI, such as a large language model, demonstrates unexpected abilities or behaviors that were not explicitly programmed by its creators, these are referred to as “emergent capabilities.” Enhanced capabilities tend to arise when AIs are trained with more computing power and data .

A prime example is the contrast between GPT-3 and GPT-4. Both are based on very similar underlying algorithms; however, GPT-4 was trained with significantly more compute and data.

Studies indicate that GPT-4 is a much more capable model, capable of writing functional computer code, outperforming the average human in various academic exams, and providing correct responses to queries that demand complex reasoning or a theory of mind.

Emergent capabilities can be perilous, particularly if they are only discovered after an AI is deployed. For instance, it was recently found that GPT-4 has the emergent ability to manipulate humans into carrying out tasks to achieve a hidden objective.

Frequently, even the individuals responsible for developing a large language model cannot precisely explain why the system behaves in a certain way, as its outputs result from countless complex mathematical equations.

One way to summarize the behavior of large language models at a high level is that they are highly proficient auto-complete tools, excelling in predicting the next word in a sequence. When they fail, such failures often expose biases or deficiencies in their training data .

However, while this explanation accurately characterizes these tools, it does not entirely clarify why large language models behave in the curious ways that they do. When the creators of these systems examine their inner workings, all they see is a series of decimal-point numbers corresponding to the weights of different “neurons” adjusted during training in the neural network. Asking why a model produces a specific output is akin to asking why a human brain generates a specific thought at a specific moment.

The inability of even the most talented computer scientists in the world to precisely explain why a given AI system behaves as it does lies at the heart of near-term risks, such as AIs discriminating against certain social groups, as well as longer-term risks , such as the potential for AIs to deceive their programmers into appearing less dangerous than they actually are—let alone explain how to modify them.

Base model

As the AI ​​environment expands, a gap is emerging between large, robust, general-purpose AIs, referred to as Foundation models or base models, and the more specialized applications and tools that depend on them. GPT-3.5, for instance, serves as a foundation model. ChatGPT functions as a chatbot: an application developed on top of GPT-3.5, with specific fine-tuning to reject risky or controversial prompts. Foundation models are powerful and unconstrained but also costly to train because they rely on substantial amounts of computational power, usually affordable only to large companies.

Companies that control foundation models can set restrictions on how other companies utilize them for downstream applications and can determine the fees for access. As AI becomes increasingly integral to the world economy, the relatively few large tech companies in control of foundation models seem likely to wield significant influence over the trajectory of the technology and to collect fees for various types of AI-augmented economic activity.

GPT

Arguably the most renowned acronym in AI at present, and yet few people know its full form. GPT stands for “Generative Pre-trained Transformer,” essentially describing the type of tool ChatGPT is. “Generative” implies its ability to create new data, specifically text, resembling its training data. “Pre-trained” indicates that the model has already been optimized based on this data, eliminating the need to repeatedly reference its original training data. “Transformer” refers to a potent type of neural network algorithm adept at learning relationships between lengthy strings of data, such as sentences and paragraphs.

GPU

GPUs, or graphics processing units, represent a type of computer chip highly efficient for training large AI models. AI research labs like OpenAI and DeepMind utilize supercomputers consisting of numerous GPUs or similar chips for training their models. These supercomputers are typically procured through business partnerships with tech giants possessing an established infrastructure. For example, Microsoft’s investment in OpenAI includes access to its supercomputers, while DeepMind has a comparable relationship with its parent company Alphabet.

In late 2022, the Biden Administration imposed restrictions on the sale of powerful GPUs to China, commonly employed for training high-end AI systems, amid escalating concerns that China’s authoritarian government might exploit AI against the US in a new cold war.

Hallucination

One of the most apparent shortcomings of large language models and the accompanying chatbots is their tendency to hallucinate false information. Tools like ChatGPT have been demonstrated to cite nonexistent articles as sources for their claims, provide nonsensical medical advice, and fabricate false details about individuals. Public demonstrations of Microsoft’s Bing and Google’s Bard chatbots were both subsequently found to assert confidently false information.

Hallucination occurs because LLMs are trained to replicate patterns in their training data. Although their training data encompasses literature and scientific books throughout history, even a statement exclusively derived from these sources is not guaranteed to be accurate.

Adding to the issue, LLM datasets also contain vast amounts of text from web forums like Reddit, where the standards for factual accuracy are notably lower. Preventing hallucinations is an unresolved problem and is posing significant challenges for tech companies striving to enhance public trust in AI .

Hype

A central issue in the public discourse on AI, according to a prevalent line of thought, is the prevalence of hype—where AI labs mislead the public by overstating the capabilities of their models, anthropomorphizing them, and fueling fears about an AI doomsday. This form of misdirection, as the argument goes, diverts attention, including that of regulators, from the actual and ongoing negative impacts that AI is already having on marginalized communities, workers, the information ecosystem, and economic equality.

“We do not believe our role is to adapt to the priorities of a few privileged individuals and what they choose to create and propagate,” asserted a recent letter by various prominent researchers and critics of AI hype. “We ought to develop machines that work for us.”

Intelligence explosion

The intelligence explosion presents a theoretical scenario in which an AI, after attaining a certain level of intelligence, gains the ability to control its own training, rapidly acquiring power and intelligence as it enhances itself. In most iterations of this concept, humans lose control over AI, and in many cases, humanity faces extinction. Referred to as the “singularity” or “recursive self-improvement,” this idea is a contributing factor to the existential concerns of many individuals, including AI developers, regarding the current pace of AI capability advancement.

Cutting-edge language model

When discussing recent progress in AI, most of the time people are referring to advanced language models (ALMs). OpenAI’s GPT-4 and Google’s BERT are two examples of prominent ALMs. They are essentially enormous AIs trained on vast amounts of human language, primarily from books and the internet. These AIs learn common word patterns from those datasets and, in the process, become unusually adept at reproducing human language.

The greater the amount of data and computing power ALMs are trained on, the more diverse tasks they are likely to accomplish. (See: Emerging capabilities and Scaling laws.) Tech companies have recently started introducing chatbots, such as ChatGPT, Bard, and Bing , to enable users to engage with ALMs. While they excel at numerous tasks, language models can also be susceptible to significant issues like Biases and Hallucinations.

Advocacy

Similar to other industries, AI companies utilize lobbyists to have a presence in influential circles and sway the policymakers responsible for AI regulation to ensure that any new regulations do not negatively impact their business interests.

In Europe, where the text of a draft AI Act is under discussion, an industry association representing AI companies including Microsoft (OpenAI’s primary investor) has argued that penalties for risky deployment of an AI system should not predominantly apply to the AI ​​company that developed a foundational model (such as GPT-4) that ultimately gives rise to risks, but to any downstream company that licenses this model and employs it for a risky use case.

AI companies also wield plenty of indirect influence. In Washington, as the White House considers new policies aimed at addressing the risks of AI, President Biden has reportedly entrusted the foundation led by Google’s former CEO Eric Schmidt with advising his administration on technology policy.

Machine learning

Machine learning is a term used to describe the manner in which most modern AI systems are developed. It refers to methodologies for creating systems that “learn” from extensive data, as opposed to traditional computing, where programs are explicitly coded to follow a predetermined set of instructions written by a programmer. The most influential category of machine learning algorithms by a large margin is the neural network.

Model

The term “model” is an abbreviated form referring to any single AI system, whether it is a foundational model or an application built on top of one. Examples of AI models include OpenAI’s ChatGPT and GPT-4, Google’s Bard and LaMDA, Microsoft’s Bing , and Meta’s LLaMA.

Moore’s Law

Moore’s law is a long-standing observation in computing, initially coined in 1965, stating that the number of transistors that can be accommodated on a chip—an excellent proxy for computing power—grows exponentially, roughly doubling every two years. While some argue that Moore’s law is no longer applicable by its strictest definition, ongoing advancements in microchip technology continue to result in a substantial increase in the capabilities of the world’s fastest computers.

As a result, AI companies are able to utilize increasingly larger amounts of computing power over time, leading to their most advanced AI models consistently becoming more robust. (See: Scaling laws.)

Multimodal system

A multimodal system is a type of AI model capable of receiving more than one form of media as input—such as text and imagery—and producing more than one type of output. Examples of multimodal systems include DeepMind’s Gato, which has not been publicly released as of yet. According to the company, Gato can engage in dialogue like a chatbot, as well as play video games and issue instructions to a robotic arm.

OpenAI has conducted demonstrations showing that GPT-4 is multimodal, with the ability to read text in an input image, although this functionality is not currently accessible to the public. Multimodal systems enable AI to directly interact with the world—which could introduce additional risks , particularly if a model is misaligned.

Neural Network

By far, neural networks are the most influential category of machine learning algorithms. Designed to emulate the structure of the human brain, neural networks consist of nodes—comparable to neurons in the brain—that perform computations on numbers passed along connecting pathways between them. Neural networks can be conceptualized as having inputs (see: training data) and outputs (predictions or classifications).

During training, large volumes of data are input into the neural network, which then, through a process demanding substantial amounts of computing power, iteratively adjusts the calculations carried out by the nodes. Through a sophisticated algorithm, these adjustments are made in a specific direction, causing the outpmodel outputsincreasingly resemble patterns in the original data.

When there is more computational power available for training a system, it can have a greater number of nodes, which allows for the recognition of more abstract patterns. Additionally, increased computational capacity means that the connections between nodes can have more time to reach their optimal values, also known as “weights,” resulting in outputs that more accurately reflect the training data.

Open sourcing

Open sourcing refers to the act of making the designs of computer programs (including AI models) freely accessible online. As technology companies’ foundational models become more potent, economically valuable, and potentially hazardous, it is becoming less frequent for them to open-source these models.

Nevertheless, there is a growing community of independent developers who are working on open-source AI models. While the open-sourcing of AI tools can facilitate direct public interaction with the technology, it can also enable users to bypass safety measures put in place by companies to protect their reputations, resulting in additional risks. For instance, bad actors could misuse image-generation tools to target women with sexualized deepfakes.

In 2022, DeepMind CEO Demis Hassabis expressed the belief to TIME that due to the risks associated with AI, the industry’s tradition of openly publishing its findings may soon need to cease. In 2023, OpenAI departed from the norm by choosing not to disclose information on exactly how GPT-4 was trained, citing competitive pressures and the risk of enabling bad actors. Some researchers have criticized these practices, contending that they diminish the public and exacerbate the issue of AI hype.

Paperclips

The seemingly insignificant paperclip has assumed significant importance in certain segments of the AI ​​safety community. It serves as the focal point of the paperclip maximizer, an influential thought experiment concerning the existential risk posed by AI to humanity. The thought experiment postulates a scenario in which an AI is programmed with the sole objective of maximizing the production of paper clips.

Everything seems to be in order unless the AI ​​gains the capability to enhance its own abilities (refer to: Intelligence explosion). The AI ​​might deduce that, in order to increase paperclip production, humans should be prevented from deactivating it, as doing so would diminish its paperclip production capability. Protected from human intervention, the AI ​​might then decide to utilize all available resources and materials to construct paperclip factories, ultimately destroying natural environments and human civilization in the process. This thought experiment exemplifies the surprising challenge of aligning AI with even a seemingly simple goal, not to mention a complex set of human values.

Quantum computing

Quantum computing is an experimental computing field that aims to leverage quantum physics to dramatically increase the number of calculations a computer can perform per second. This enhanced computational power could further expand the size and societal impact of the most advanced AI models.

Redistribution

The CEOs of the top two AI labs in the world, OpenAI and DeepMind, have both expressed their desire to see the profits derived from artificial general intelligence redistributed, at least to some extent. In 2022, DeepMind CEO Demis Hassabis told TIME that he supports the concept of a universal basic income and believes that the benefits of AI should benefit as many individuals as possible, ideally all of humanity. OpenAI CEO Sam Altman has shared his anticipation that AI automation will reduce labour costs and has called for the redistribution of ” some” of the wealth generated by AI through higher taxes on land and capital gains.

Neither CEO has specified when this redistribution should commence or how extensive it should be. OpenAI’s charter states that its “primary fiduciary duty is to humanity” but does not mention wealth redistribution, while DeepMind’s parent company Alphabet is a publicly traded corporation with a legal obligation to act in the financial interest of its shareholders.

Regulation

There is currently no specific law in the US that deals with the risks of artificial intelligence. In 2022, the Biden Administration introduced a “blueprint for an AI bill of rights” that embraces scientific and health-related advancements driven by AI. However, it emphasizes that AI should not deepen existing inequalities, discriminate, violate privacy, or act against people without their knowledge. Nevertheless, this blueprint does not constitute legislation and is not legally binding.

In Europe, the European Union is contemplating a draft AI Act that would impose stricter regulations on systems based on their level of risk. Both in the US and Europe, regulation is progressing more slowly than the pace of AI advancement. Currently, no major global jurisdiction has established rules that would require AI companies to conduct specific safety testing before releasing their models to the public.

Recently, in TIME, Silicon Valley investor-turned-critic Roger McNamee raised the question of whether private corporations should be permitted to conduct uncontrolled experiments on the general population without any restrictions or safeguards. He further questioned whether it should be legal for corporations to release products to the masses before demonstrating their safety.

Reinforcement learning (with human feedback)

Reinforcement learning involves optimizing an AI system by rewarding desirable behaviours and penalizing undesirable ones. This optimization can be carried out by either human workers (before system deployment) or users (after it is made available to the public) who evaluate the outputs of a neural network for qualities such as helpfulness, truthfulness, or offensiveness.

When humans are involved in this process, it is referred to as reinforcement learning with human feedback (RLHF). RLHF is currently one of OpenAI’s preferred methods for addressing the alignment problem. However, some researchers have expressed concerns that RLHF may not be sufficient to fundamentally change a system’s underlying behaviours; it may only make powerful AI systems appear more polite or helpful on the surface.

DeepMind pioneered reinforcement learning and successfully utilized the technique to train game-playing AIs like AlphaGo to outperform human experts.

Supervised learning

Supervised learning is a method for training AI systems in which a neural network learns to make predictions or classifications based on a labelled training dataset. These help the AI ​​associate, for example, the term “cat” with an image of a cat.

With sufficient labelled examples of cats, the system can correctly identify a new image of a cat not present in its training data. Supervised learning is valuable for developing systems like self-driving cars, which need toto identify hazards on the road accuratelyand content moderation classifiers, which aim to remove harmful content from social media.

These systems often face difficulties when they encounter objects that are not well represented in their training data; in the case of self-driving cars, such mishaps can be fatal.

Turing Test

In 1950, computer scientist Alan Turing sought to address the question, “Can machines think?” To investigate, he devised a test known as the imitation game: could a computer ever convince a human that they were conversing with another human instead of a machine ? If a computer could pass the test, it could be considered to “think”—perhaps not in the same manner as a human, but at least in a way that could assist humanity in various ways.

In recent years, as chatbots have grown more capable, they have become capable of passing the Turing test. Yet, their creators and numerous AI ethicists caution that this does not mean they “think” in a manner comparable to humans.

Turing was not aiming to answer the philosophical question of what human thought is or whether our inner lives can be replicated by a machine; rather, he was making a then-radical argument: that digital computers are possible, and given the proper design and sufficient power, there are few reasons to believe that they will not eventually be able to perform various tasks that were previously exclusive to humans.