ChatGPT rival Anthropic’s Claude can now read a novel in under a minute

A new element of engagement. 

ChatGPT rival Anthropic’s Claude can now read a novel in under a minute.   By Kyt DOTSON

OpenAI LP rival chatbot maker Anthropic today said that it’s upgrading its own generative artificial intelligence with a much longer memory to take on the internet’s darlings ChatGPT and Google LLC’s Bard.

Since its debut late last year, generative AI has caught the attention of users and businesses for its ability to hold human-like conversations and pull insightful information from documents. 

Although these AIs are trained on vast amounts of data, one issue is that conversations with them are limited by the amount of memory that they can handle at one time, this is known as a “context window,” or how much text an AI can handle before it begins to “forget” what was being discussed earlier. This is the same reason that certain AI chatbots, such as Microsoft Corp.’s Bing Chat, have a limited number of responses built into them.

These AIs generally don’t operate in terms of words, and instead use tokens, which break down the text sent to them into parts of words and phrases, and Claude has been upgraded from 9,000 tokens to 100,000 tokens, which corresponds to approximately 75,000 words.

“This means businesses can now submit hundreds of pages of materials for Claude to digest and analyze, and conversations with Claude can go on for hours or even days,” Anthropic’s team said in the announcement.

In comparison, ChatGPT’s context window is approximately 4,000 tokens (or about 3,000 words) and OpenAI’s GPT-4 has a context window of 32,768 tokens (around 25,000 words).

To test the greatly enhanced memory for Claude, the Anthropic team fed the entire text of “The Great Gatsby” into Calude-Instant, a low latency version of the chatbot – but the developers modified one line to see if the AI would catch it. The AI read the entire novel and spotted the difference in under 22 seconds.

The novel is becoming a favorite testing ground for engineers building AI models, MosaicML Inc., a generative AI infrastructure and AI model provider, also used it to show off the capabilities of its MosaicML foundational model.

“Beyond just reading long texts, Claude can help retrieve information from documents that help your business run,” the Anthropic team wrote. “You can drop multiple documents or even a book into the prompt and then ask Claude questions that require synthesis of knowledge across many parts of the text.”

It is possible to work with smaller context windows and have an AI digest and provide insights for large documents. It means breaking them into smaller portions and having the AI summarize them down so that the summaries themselves can be stitched together into the smaller context window. The problem with doing this is that summarization necessarily leads to picking and choosing what to discard in a specific section of text and its context is then lost for comparison to later sections. That’s not necessary if the entire text can be held in memory at once.

With extremely large context windows this means that enormous documents can be processed all at once without losing sight of any data. It greatly simplifies digestion, summarization and explanation of dense material such as financial statements, research papers, legal filings, annual reports and more. Developers themselves could benefit from this by using it to read hundreds of pages of documentation and have the chatbot answer questions about technical documentation.  ... /

Blinkist for Speeding up Content Acquisition

This brought to mind some work I was involved with at the University of Pennsylvania's Language Laboratory.   We worked with more efficient ways to deliver books and reading material, and one of the experiments was to compress books and make them available for select classes.  Overall a quite simple technique. Then measured the effort/effort/use/value that was achieved.    In general this worked well with some kinds of class content, we touched on neural based techniques   The approach called Blinkist, mentioned below,  is apparently doing some something similar.  I have not tested this, nor has Engadget,  who originally posted the below.  Also I have not received any compensation for posting this.   But am intrigued by the application.  May take a further look.  

Read bestselling books in 15 minutes with Blinkist   in Engadget

Blinkist Premium offers thousands of condensed nonfiction books and podcasts that you can process in just 15 minutes, with 70 new titles added every month.

Every year, we tell ourselves that we need to read more. Perhaps we’ll crack open that book we were gifted months ago. Maybe pick up an Amazon bestseller will finally get us into the habit. And yet, the reading list keeps growing.

Between your professional and personal life, there’s little time for intellectually engaging pursuits. That’s where Blinkist comes in handy. This app contains condensed ideas from thousands of bestselling nonfiction books, so you can stay up to date with your daily reading while you go about your busy schedule. Right now, you can purchase a two-year Blinkist Premium subscription for just $99 — that’s a $285 discount.

Blinkist identifies the main ideas from popular podcasts and nonfiction books and condenses them into digestible, 15-minute text and audio files. You can read or listen to over 4,500 summarized bestsellers ranging in topics from personal development to psychology. This subscription gives you unlimited access to everything in the Blinkist library, including 70 new titles that are added every month.... "  ... ' 

AI for Reading Understanding

Been reading and exploring about what reading understanding means.  Here an update from Quanta Magazine on the toic.      We still have far to go when we have to deal with changing context, common sense and even inferring things like implications of cause and effect.   We did lots of work with 'sentiment analysis' long ago,  and its much easier to do now, with lots of easy to plug in capabilities, but the result is still statistically weak.  Shows how difficult a building a semi general purpose chatbot is.    We discovered that during several efforts.     Good read here at the link:

Machines Beat Humans on a Reading Test. But Do They Understand?
A tool known as BERT can now beat humans on advanced reading-comprehension tests. But it's also revealed how far AI has to go.

In the fall of 2017, Sam Bowman, a computational linguist at New York University, figured that computers still weren’t very good at understanding the written word. Sure, they had become decent at simulating that understanding in certain narrow domains, like automatic translation or sentiment analysis (for example, determining if a sentence sounds “mean or nice,” he said). But Bowman wanted measurable evidence of the genuine article: bona fide, human-style reading comprehension in English. So he came up with a test.

In an April 2018 paper coauthored with collaborators from the University of Washington and DeepMind, the Google-owned artificial intelligence company, Bowman introduced a battery of nine reading-comprehension tasks for computers called GLUE (General Language Understanding Evaluation). The test was designed as “a fairly representative sample of what the research community thought were interesting challenges,” said Bowman, but also “pretty straightforward for humans.” For example, one task asks whether a sentence is true based on information offered in a preceding sentence. If you can tell that “President Trump landed in Iraq for the start of a seven-day visit” implies that “President Trump is on an overseas visit,” you’ve just passed.

The machines bombed. Even state-of-the-art neural networks scored no higher than 69 out of 100 across all nine tasks: a D-plus, in letter grade terms. Bowman and his coauthors weren’t surprised. Neural networks — layers of computational connections built in a crude approximation of how neurons communicate within mammalian brains — had shown promise in the field of “natural language processing” (NLP), but the researchers weren’t convinced that these systems were learning anything substantial about language itself. And GLUE seemed to prove it. “These early results indicate that solving GLUE is beyond the capabilities of current models and methods,” Bowman and his coauthors wrote.   .... " 

More on Imminent Audible Captioning

Recently have been reading/hearing long texts in Audible and Kindle,  usually non-fiction.   So gaining an appreciation for the difference in the way we utilize recorded knowledge in varying contexts.  Having the option of having it read to us is good, but seeing it in context provides better retention and integration.     And also has implications for accessibility.   Which leads to this new article in ArsTechnica, considerable additional discussion there:

Seven of the nation's top book publishers sued Amazon subsidiary Audible on Friday, asking federal courts to block the company from releasing a new feature called Audible Captions that's due out next month. The technology does exactly what it sounds like: display text captions on the screen of your phone or tablet as the corresponding words are read in the audio file.

The publishers argue that this is straight-up copyright infringement. In their view, the law gives them the right to control the distribution of their books in different formats. Audio is a different format from text, they reason, so Audible needs a separate license.  ......

The caption feature "is not and was never intended to be a book," Audible explained in an online statement following the lawsuit. "Listeners cannot read at their own pace or flip through pages as they could with a print book or eBook." Instead, the purpose is to allow "listeners to follow along with a few lines of machine-generated text as they listen to the audio performance."

"We disagree with the claims that this violates any rights and look forward to working with publishers and members of the professional creative community to help them better understand the educational and accessibility benefits of this innovation," Audible added.  ... "

Captioning Audible Books

The idea below is a kind of captioning of audio from a a book being read to you.    Gives you two streams, audio and visual, for a given book.    I would find this useful, sometimes one or the other works better, also seems its useful for accessibility as well.  Sometimes I want to 'reread' a section of text I may not have understood audibly, and that's usually easier to visually scan than hear.  Also good for things that are best shown rather than described, like pictures or charts or equations.   Had thought a number of times that captions would be useful.   A recent book on Leonardo kept pointing me to a pdf for painting illustrations, those could have been made accessible in captions.

But it seems some publishers believe its giving too much away:

Publishers are pissed about Amazon’s upcoming Audible Captions feature
Some are asking for their books to be withheld from the feature
By Andrew Liptak  @AndrewLiptak  in TheVerge ... 

Barnes & Noble Crowdsources Reading with Browsery

Been a while that I have heard much from B&N, so this is interesting, can it recreate the bookstore virtually?   Can this alter the nature of reading, book selection?  With come expert comment:

Barnes & Noble’s crowdsourcing app engages readers and earns solid reviews  By Tom Ryan

Barnes & Noble has launched Browsery, an app that uses crowdsourcing to help readers discover new books.

The app basically features a bunch of questions that support “browsing, community, and conversation.” Customers can “like”, comment on or contribute answers to questions about books posted by the Browsery community. They may also like or comment on the answers of others or ask questions of their own.

For example, the Biography & Memoir section includes questions such as:

“Favorite memoirs that include recipes?”
“What are some interesting biographies for a film-lover?”
“What celebrity made you laugh the hardest?”

Launched in late March, one question has already drawn 84 responses. Clicking through enables users to “agree” with a suggestion and offer the reason why. The authors with the most “agrees” to the “laugh the hardest” question included Tina Fey, Amy Poehler and Trevor Noah. .... " 

Alibaba Claims to Beat Humans in Reading Test

More examples of advanced AI in China.

Alibaba's AI Outguns Humans in Reading Test    By Robert Fenner in Bloomberg

Alibaba has developed an artificial intelligence model that scored better than humans in a Stanford University reading and comprehension test.

Alibaba Group Holding Ltd. put its deep neural network model through its paces last week, asking the AI to provide exact answers to more than 100,000 questions comprising a quiz that’s considered one of the world’s most authoritative machine-reading gauges. The model developed by Alibaba’s Institute of Data Science of Technologies scored 82.44, edging past the 82.304 that rival humans achieved.

Alibaba said it’s the first time a machine has out-done a real person in such a contest. .... "