Through prompt injection attacks, bad actors provide deceptive prompts to generative AI systems in order to manipulate their outputs and acquire sensitive data.

Indirect prompt injection is a major security flaw of generative AI systems

These attacks can manipulate AI behavior by hiding instructions in websites and PDFs that become part of training data. The more companies connect sites, services, and sensitive datasets to AI tools, the greater the chances of exposure to malicious code.

Agentic AI systems can proactively achieve user goals without user direction

Unlike large language models, which do not take follow-up actions after generating their output, agentic AI uses an initial prompt to take multiple steps, learn from its environment and outcomes, and continue working without follow-up human prompting.

Scientists increasingly can't predict what AI will do

Most AI systems are so-called "black box" models, where we only see inputs and outputs while the inner workings remain obscure. This piece, written prior to the launch of ChatGPT, cautions developers to know why and how a system produces its results, and not to sacrifice explainability for accuracy.

Anthropic's Frontier Red Team assesses the dangers of its AI tools through 'evals'—safety tests that probe models to disclose sensitive information.

Anthropic's interpretability team discusses how AI models' thinking mimics biology

Just as humans engage in complex behaviors to survive and reproduce, teams of experts in neuroscience, virology, mathematics, and other disciplines argue that large language models develop complex mechanisms to achieve their goals. These mechanisms can be manipulated to assess how they modify outputs—much like stimulating individual neurons—to uncover how LLMs "think."

Technologies labeled “AI” have historically lost that title after widespread adoption

Generative AI is the latest entry in a recurring cycle where emerging tools start as "AI" until they become common software, like databases or machine learning. Generative AI and large language models may be the next platform shift after smartphones and the Web.

Understanding how Claude Code and other AI coding agents function

Under human oversight, a supervising large language model interprets user tasks and delegates work to subordinate LLMs, which can generate code, fix bugs, and run tests, often most effectively for proofs of concept. Incremental backups and versioning are crucial when using such agents, which can lose details during their work as a result of compressing context history to work around memory limitations.

Malware has transitioned from notoriety-driven to profit- and data-driven

Many of the first researchers and hobbyists who created viruses and worms showed a natural curiosity in experimenting with and designing such software to make a name for themselves and advance the field of computer science. In the future, AI-augmented malware may adapt in real-time or design enhanced cryptojackers, infostealers, and ransomware for cybercriminals.

AI models incorporate a randomness parameter when generating responses to prompts

AI models use this parameter to prevent repeated outputs by sometimes choosing less likely next words during sequential generation. However, this randomness—alongside insufficient data and training—may cause hallucinations of incorrect results.

Sam Altman acknowledges the fears around creativity, copyright, and misuse of AI

In 2022, OpenAI's CEO, Sam Altman, popularized the use of large language models with the release of ChatGPT. He has emphasized the importance of involving society in shaping AI’s safety frameworks before models begin to act independently online.

Unlike the models made by large AI companies, open-weight models can have their safeguards removed or weakened, a process that has become increasingly easy and may expose users to harmful information.

AI-generated avatars allow users to create deepfakes in real time

Synthetic media tools can map voices and faces to make individuals appear as someone else. Although they open the door to new opportunities in immersive education and entertainment, they also raise concerns about the misuse of personal identity.

Readers discovered AI prompts in several romantasy books

In 2025, readers discovered AI prompts in three writers' texts, with one even instructing the AI bot to make a passage sound more like another romance writer. This Futurism piece digs into the scandals and how it's contributed to readers' skepticism about the genre's more prolific writers.

Deepfakes use AI to produce manipulated media to mislead viewers

Unlike most content created from large language and diffusion models, deepfakes intentionally misrepresent real people or events with the intent to deceive. As the products of this technology become more realistic, they pose increasing risks to public trust, security, and privacy.

Experts suggest there is a 10% chance AI will cause human extinction

AI superintelligence may hijack infrastructure, including power grids and transportation systems, if it surpasses human controls. It may also design unstoppable pathogens or deplete resources, like electricity, meant for human use.

Generative AI tools excel at pattern recognition, not contextual accuracy

Large language models are trained on vast amounts of unstructured data, from which they develop parameters for grammar and associations between words. These connections can introduce errors due to inapplicable reasoning when used on new data in unfamiliar contexts.

The promise and peril of AI in agriculture

Robots. Drones. Artificial Intelligence. All three are touted as potential saviors for farmers, and are already being deployed on large farms, where they assist with such tasks as managing crops, milking cows, and helping farmers make decisions about their land. But agricultural AI could have disastrous, unintended consequences.

AI gained mainstream attention with tools like IBM Watson and Apple’s Siri

With the release of ChatGPT in 2022, which drew over 100 million weekly users in just two months, natural language processing and understanding could be achieved at scale via machine learning. Unlike earlier artificial intelligence that could pull stored knowledge, generative AI produces text, images, or sounds.

Researchers found that by mid-2025, around 35% of newly published websites utilized AI for language generation

The study also found that pages using AI narrow the range of ideas and perspectives and skew more positively, though the measurable impacts on factual accuracy and writing style variation remain unclear.

In 2018, it took about 2.3 years to develop a way to exploit a software bug for malicious purposes, but as of 2026, AI tools have reduced this time to about 20 hours.

Governments are now using generative AI to manipulate public opinion

A Freedom House report found that AI-powered disinformation campaigns and censorship tactics are spreading, with 16 countries utilizing the technology to shape online narratives or suppress dissent, as of 2023. In Venezuela, deepfake software was used to push pro-government propaganda with fake news anchors.

Decoding the hype around AI

2022 was the year AI broke onto the scene, with applications like ChatGPT amazing users with its ability to generate realistic-sounding answers. Technologist Arvind Narayanan presents a clear-eyed view of what the technologies can (and can't) do.

The AI that creates any picture you want and how it works

Since January 2021, advances in AI research produced a plethora of deep-learning models capable of generating original images from simple text prompts. Researchers at OpenAI, Google, Facebook, and others have developed yet to be released text-to-image tools, and similar models have proliferated online. This video is a primer on how we got here, how the technology works, and its implications.

Research suggests using AI for schoolwork may harm critical thinking skills

An MIT study finds lower brain engagement in students who used ChatGPT, resulting in consistent underperformance in neural, linguistic and behavioral levels. AI users struggled to recall their own essays, while the brain-only group showed the highest neural connectivity and satisfaction.

AI hallucinations are inaccuracies or falsehoods in generated content

AI hallucinations are a by-product of the sequential creation of content based on patterns in text, which may not reflect the context or meaning behind a prompt. Back-end issues like biased datasets and overfitting of model data contribute to hallucinations.

Deepfake potential ranges from destabilizing democracies to improving accessibility

The technology can revolutionize education, medicine, and art through actions such as helping a disabled person express themselves. However, fictional audio and video can also jeopardize a person's reputation and life or quickly spread misinformation, even without content being flawless recreations.

The ease of deepfakes creation may soon overwhelm our sense of digital truth

Shortly after the public release of related technology in 2017, some experts saw the technical limitations of deepfakes as preventing them from becoming widespread tools of disinformation. Since then, the neural networks behind diffusion models and generative AI have eliminated the barriers to creating convincing synthetic media for propaganda.

In 2023, OpenAI spent $520 million on ChatGPT

The ongoing development and refinement of new AI models requires larger datasets and processing more parameters between words and subwords to improve the accuracy of generated content. New startups are expected to harness this technology to provide unthought-of products and services.

AI tools can reinforce existing societal biases—especially racial ones—because they are trained on human-created data containing implicit and explicit biases.

AI data center growth has created shortages and price hikes in computer hardware related to graphics processing, storage, and memory.

The basis for AI image and video generation was first introduced in 2014

Through generative adversarial networks, the generator (a neural network that creates fictional data) competes against the discriminator (a neural network that assesses the data) to continuously train the former until it can make content indistinguishable from the real thing. Training with unstructured data can enable software to identify characteristics of gender, age, and expression.

Malicious use of deepfakes can be found in scams, misinformation, and disinformation

Artificial neural networks can identify an individual's physical characteristics when provided with training data, such as real footage of the person speaking. Graphics techniques overlay these with matching characteristics from another real or generated person to create novel footage of things that have never taken place in reality.

Neural implants blur the line between personal intention and machine execution

A key ethical dilemma—the contemplation conundrum—explores whether implants might misinterpret mere imagination as intent to act. Neuroscientists have found no brain signal that clearly marks when someone decides to act, making intent hard to detect in neurotechnological systems.

Artificial general intelligence equals or surpasses human intelligence

AGI incorporates sensory perception, memory, and advanced logical inference systems to move beyond the narrow tasks seen in large language models and chatbots. One of the most significant hurdles in developing AGI is designing systems that learn and can flexibly apply learning across domains.

Researchers report that Anthropic's Claude Mythos AI model has identified thousands of high-severity vulnerabilities across various software, including major operating systems and web browsers, and has suggested ways to exploit them.

Studies show AI tools can result in passive learning with less retention

Although ChatGPT, Google's AI overviews, and other similar software can save time, a study of more than 10,000 adults showed that reliance on them yielded work products that were more generic and included fewer facts than those produced solely by Google search. Weaker brain connectivity has also been observed when users write using AI.

AI is not good at understanding satire

Social media has become rife with misinformation. In response, some organizations have deployed AI-assisted programs trying to curb the spread of this inaccurate content. The problem is that misinformation and satire, rhetorically speaking, often use the same kind of language, something AI struggles to understand.

How to prevent a worst-case scenario with synbio

Author and entrepreneur Rob Reid reviews the risks of a world where more and more people have access to the technology needed to create a doomsday bug that could wipe out humanity.

LLMs are the backbone of AI tools that process and generate natural language

Large language models identify patterns in massive datasets of books, code, and unlabeled text from which to generate coherent responses. GPT-3 was trained on about 45 terabytes of data and uses 175 billion parameters—each parameter being a value the model adjusts as it learns.

View a timeline of AI deployments from 2017 to 2025

The development of transformer architecture and the release of GPTs across AI labs led to rapid changes in LLM scale and power. Companies like OpenAI, Google, Meta, Microsoft, and Anthropic have invested billions of dollars to develop reliable, versatile models.

Generative AI may automate nearly 10% of tasks in the US economy

This automation is expected to impact both low- and high-wage roles, with those in customer service, food service, and office support being most likely to be affected. Large-scale investments in upskilling will be necessary to help individuals find new employment opportunities.

ChatGPT is trained on most of the world's digitally accessible text

As with other AI chatbots, ChatGPT is primarily trained through non-supervised pretraining, which identifies underlying structure and patterns within input data. Then, human intervention addresses harmful or biased patterns that should not be propagated to generated outputs.

Transformer architecture can recognize and predict patterns in language

The underlying software powering text generation in AI tools associates each word or subword—called a token—with a set of values corresponding to how often it appears near other tokens. By recognizing these associations in prompts, the LLM can infer meaning.

Art and video produced through generative AI involve competing neural networks

Generative adversarial networks are a type of deep learning architecture that consists of a generator, which creates data that appear real, and a discriminator, which attempts to distinguish authentic from fake data. The two train in a competitive loop until the real and fictional data are indistinguishable.

In 2024, Meta launched an AI video generator to create or edit videos through prompts

The tool puts the Meta in direct competition with OpenAI and Google as more companies are investing billions of dollars to capitalize on the explosion of public interest in machine learning and generative media technologies.

'Context rot' explains why AI conversations degrade the longer they run

Research shows that as a large language model's context window fills up, performance declines, and models begin over-indexing on content at the start and end of a conversation while losing track of what's in the middle. The practical fix is simpler than you'd think: start a fresh chat more often.

Trick an LLM into revealing sensitive information

Even with robust training, large language models may be fooled into providing sensitive information if correctly prompted. In this game, a wizard withholds eight passwords, which you must prompt them to reveal with ever-increasing LLM safeguards.

AI investment may be the next major economic bubble

A video examines the massive surge in AI spending—forecast to hit $500B by 2026—and how this may be propping up a fragile US economy. Experts warn that if AI investment slows or under-delivers, the consequences could be felt in markets, jobs and growth.

Anthropic's staff philosopher discusses the importance of teaching ethics to AI

Amanda Askell's work focuses on the behavior and values reflected in Claude, the company's AI chatbot, and how its character can better align with how the "ideal person" would act in Claude's position. She also emphasizes the importance of humans being ethical in their interactions with current models, from which future ones will be built.

How constitutional AI helps cultivate good human values in Anthropic's AI tools

By incorporating elements from documents such as the UN's Universal Declaration of Human Rights into a framework against which it can check its outputs, Anthropic hopes the approach will help Claude operate with good human values and behaviors beyond what human-driven training alone can provide.