AI hallucinations are inaccuracies or falsehoods in the outputs of generative AI tools, which can result from insufficient training data, data biases, or incorrect assumptions.

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.

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.

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.

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.

Systems that can reproduce near perfect counterfeits of legitimate financial documents are increasingly prevalent and sophisticated. The international Association of Certified Fraud Examiners estimate organizations lose about 5% of revenue to fraud each year.

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.

Compared to machine learning - which is sort of like teaching a toddler how to recognize what a dog looks like by showing them images of one - generative AI is like teaching that same child how to actually draw a dog. Unlike machine learning, generative AI can actually generate. Read this article for a more in-depth comparison between these two types of artificial intelligence.

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.

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.

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.

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.

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.

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.

This interactive project from MIT challenges users to distinguish between AI-generated images and real ones.

Workers, particularly in marketing, are experiencing mental fatigue in the workplace due to expectations—often unspoken—that they should use AI to multitask beyond their comfort levels. Many feel they are underperforming if they don't embrace this technology. This situation differs from typical burnout. This podcast discussion suggests that this "brain fry" tends to intensify when employees are required to use more than three AI tools.

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.

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.

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.

Tools like ChatGPT, Gemini, and Perplexity can help organize chaotic calendars, troubleshoot bedtime routines, and assist with salary planning and moving. However, specific quotes, like insurance, and local cost comparisons can be flawed since AI draws from nonlocalized datasets.

Artificial intelligence (AI) is increasingly being integrated into healthcare, offering the potential to aid in diagnoses, answer patient queries, and streamline administrative processes. In a recent PBS interview, Dr. Isaac Kohane, editor-in-chief of the New England Journal of Medicine AI and chair of Harvard’s department of biomedical informatics, discusses the current state and future of AI in medicine.

Generative artificial intelligence has left no industry untouched, and banking isn’t an exception. Experts estimate that gen AI could add as much as $340B in value to the banking sector each year. For consumers, this involves new features to prevent fraud and customer service chatbots. This report dives into the revolution underway and seeks to define a “full stack” AI bank of the future. Read more here.

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.

Many marketers argue that AI is useful in an ad agency setting. Others attribute the significant layoffs the industry has faced in recent years, at least in part, to AI. Agency leaders and big tech firms alike have supported experimenting with artificial intelligence’s capacity to create content once reserved for agency creatives.

Realtime uses generative AI to create immediate data visualizations of news items on its regularly-updated feed, pulling from public data feeds. Peruse stories around sports, economics, politics, and more with the helpful aid of charts and graphs. See the shape of the news with this clever AI tool here.

Researchers are training AI to detect stress-related symptoms, helping physicians and other medical workers recognize and reduce stress from overwork and potentially avoid burnout.

Rodney Brooks wrote several decades ago about the boom and bust cycles of artificial intelligence. Starting from the 1950s excitement over ENIAC's multiplication through the second launch of then-novel neural networks, Brooks suggests AI progresses incrementally despite the hype.

AI models are trained on enormous datasets built from content scraped from websites across the internet. Copyright law includes a four-factor test to determine whether copies of content fall under fair use, but the law's enforcement has been inconsistent.

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.

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."

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.

In a classroom experiment, a journalist enrolled in an AP English Literature course submitted an essay written by ChatGPT, which confused characters and setting when comparing two texts. The tool was also unfamiliar with texts outside of its training data.

Up to 80% of schizophrenia patients may have auditory hallucinations. Recent research concluded that this symptom may be from misfired motor signals that feed into each other, essentially failing to suppress certain sensory neural activities and that resulting overexcitment fuels hallucinations. The finding suggests that, one day, magnetic pulses might be used to stimulate specific nerves to better regulate the errant signals and reduce or stop hallucinations.

Volunteers are provided with visual representations of signal abnormalities at various frequencies and tasked with categorizing them based on reference examples. Progressing through the tool unlocks more difficult images and additional features. Categorized images will be used to train machine learning algorithms for automated future classification.