New technology gleans the gist of stories a person hears while laying in a brain scanner
Functional magnetic resonance imaging (fMRI) captures coarse, colorful snapshots of the brain in action. While this specialized type of magnetic resonance imaging has transformed cognitive neuroscience, it isn’t a mind-reading machine: neuroscientists can’t look at a brain scan and tell what someone was seeing, hearing or thinking in the scanner.
But gradually scientists are pushing against that fundamental barrier to translate internal experiences into words using brain imaging. This technology could help people who can’t speak or otherwise outwardly communicate such as those who have suffered strokes or are living with amyotrophic lateral sclerosis. Current brain-computer interfaces require the implantation of devices in the brain, but neuroscientists hope to use non-invasive techniques such as fMRI to decipher internal speech without the need for surgery
Now researchers have taken a step forward by combining fMRI’s ability to monitor neural activity with the predictive power of artificial intelligence language models. The hybrid technology has resulted in a decoder that can reproduce, with a surprising level of accuracy, the stories that a person listened to or imagined telling in the scanner. The decoder could even guess the story behind a short film that someone watched in the scanner, though with less accuracy. Read More
Tag Archives: ChatBots
How Smart is ChatGPT?
ChatGPT, a language model developed by OpenAI, has become incredibly popular over the past year due to its ability to generate human-like responses in a wide range of circumstances.
In fact, ChatGPT has become so competent, that students are now using it to help them with their homework. This has prompted several U.S. school districts to block devices from accessing the model while on their networks.
So, how smart is ChatGPT?
In a technical report released on March 27, 2023, OpenAI provided a comprehensive brief on its most recent model, known as GPT-4. Included in this report were a set of exam results, which we’ve visualized in the graphic above. Read More
ChatGPT Answers Beat Physicians’ on Info, Patient Empathy, Study Finds
— Evaluators gave chatbot the better rating for responses to patient queries by a nearly 4:1 ratio
The artificial intelligence (AI) chatbot ChatGPT outperformed physicians when answering patient questions, based on quality of response and empathy, according to a cross-sectional study.
Of 195 exchanges, evaluators preferred ChatGPT responses to physician responses in 78.6% (95% CI 75.0-81.8) of the 585 evaluations, reported John Ayers, PhD, MA, of the Qualcomm Institute at the University of California San Diego in La Jolla, and co-authors.
The AI chatbot responses were given a significantly higher quality rating than physician responses (t=13.3, P<0.001), with the proportion of responses rated as good or very good quality (≥4) higher for ChatGPT (78.5%) than physicians (22.1%), amounting to a 3.6 times higher prevalence of good or very good quality responses for the chatbot, they noted in JAMA Internal Medicine. — Read More
#chatbots
How to Make ChatGPT Copy Your Writing Style
Key Takeaway: Published writers can ask ChatGPT to emulate their style by referencing existing work; anyone can submit samples of their own writing for emulation, or you can simply describe a style using plain language.
ChatGPT can generate excellent text on virtually any subject, but by default, it has a very bland (and obvious) tone. Instead of editing that text into your own style to use it, you can simply teach ChatGPT your style instead. Read More
What is Visual Prompting?
Landing AI’s Visual Prompting capability is an innovative approach that takes text prompting, used in applications such as ChatGPT, to computer vision. The impressive part? With only a few clicks, you can transform an unlabeled dataset into a deployed model in mere minutes. This results in a significantly simplified, faster, and more user-friendly workflow for applying computer vision.
Traditionally, building a natural language processing (NLP) model was a time-consuming process that required a great deal of data labeling and training before any predictions could be made. However, things have changed radically. Thanks to large pre-trained transformer models like GPT-4, a single API call is all you need to begin using a model. This low-effort setup has removed all the hassle and allowed users to prompt an AI and start getting results in seconds.
Similarly to what has happened in NLP, large pre-trained vision transformers have made it possible for us to implement Visual Prompting. This approach accelerates the building process, as only a few simple visual prompts are required. You can have a working computer vision system deployed and make inferences in seconds or minutes; this will benefit both individual projects and enterprise solutions. Read More
Visual Prompting Livestream With Andrew Ng
The Anatomy of Autonomy: Why Agents are the next AI Killer App after ChatGPT
“GPTs are General Purpose Technologies”1, but every GPT needs a killer app. Personal Computing needed VisiCalc, the smartphone brought us Uber, Instagram, Pokemon Go and iMessage/WhatsApp, and mRNA research enabled rapid production of the Covid vaccine.
One of the strongest indicators that the post GPT-3 AI wave is more than “just hype” is that the killer apps are already evident, each >$100m opportunities:
- Generative Text for writing – Jasper AI going 0 to $75m ARR in 2 years
- Generative Art for non-artists – Midjourney/Stable Diffusion Multiverses
- Copilot for knowledge workers – both GitHub’s Copilot X and “Copilot for X”
- Conversational AI UX – ChatGPT / Bing Chat, with a long tail of Doc QA startups
The fifth killer app is here, and it is Autonomous Agents. Read More
#chatbots
Snapchat sees spike in 1-star reviews as users pan the ‘My AI’ feature, calling for its removal
The user reviews for Snapchat’s “My AI” feature are in — and they’re not good. Launched last week to global users after initially being a subscriber-only addition, Snapchat’s new AI chatbot powered by OpenAI’s GPT technology is now pinned to the top of the app’s Chat tab where users can ask it questions and get instant responses. But following the chatbot’s rollout to Snapchat’s wider community, Snapchat’s app has seen a spike in negative reviews amid a growing number of complaints shared on social media.
Over the past week, Snapchat’s average U.S. App Store review was 1.67, with 75% of reviews being one-star, according to data from app intelligence firm Sensor Tower. For comparison, across Q1 2023, the Snapchat average U.S. App Store review was 3.05, with only 35% of reviews being one-star. Read More
Enhancing Vision-language Understanding with Advanced Large Language Models
The recent GPT-4 has demonstrated extraordinary multi-modal abilities, such as directly generating websites from handwritten text and identifying humorous elements within images. These features are rarely observed in previous vision language models. We believe the primary reason for GPT-4’s advanced multi-modal generation capabilities lies in the utilization of a more advanced large language model (LLM). To examine this phenomenon, we present MiniGPT-4, which aligns a frozen visual encoder with a frozen LLM, Vicuna, using just one projection layer. Our findings reveal that MiniGPT-4 possesses many capabilities similar to those exhibited by GPT-4 like detailed image description generation and website creation from hand-written drafts. Furthermore, we also observe other emerging capabilities in MiniGPT-4, including writing stories and poems inspired by given images, providing solutions to problems shown in images, teaching users how to cook based on food photos, etc. In our experiment, we found that only performing the pretraining on raw image-text pairs could produce unnatural language outputs that lack coherency including repetition and fragmented sentences. To address this problem, we curate a high-quality, well-aligned dataset in the second stage to finetune our model using a conversational template. This step proved crucial for augmenting the model’s generation reliability and overall usability. Notably, our model is highly computationally efficient, as we only train a projection layer utilizing approximately 5 million aligned image-text pairs. Our code, pre-trained model, and collected dataset are available at https://minigpt-4.github.io/. Read More
Paper
demo links here: Link1Link2Link3Link4Link5Link6
Web LLM runs the vicuna-7b Large Language Model entirely in your browser, and it’s very impressive
… Web LLM is a project from the same team as Web Stable Diffusion which runs the vicuna-7b-delta-v0 model in a browser, taking advantage of the brand new WebGPU API that just arrived in Chrome in beta.
I got their browser demo running on my M2 MacBook Pro using Chrome Canary, started with their suggested options:/Applications/Google\ Chrome\ Canary.app/Contents/MacOS/Google\ Chrome\ Canary --enable-dawn-features=disable_robustness
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