Technology
Why RAG won’t solve the AI generative hallucination problem

Hallucinations – essentially the lies that generative artificial intelligence models tell – pose an enormous problem for firms seeking to integrate the technology into their operations.
Because models haven’t any real intelligence and easily predict words, images, speech, music, and other data in keeping with a non-public schema, they often get it mistaken. Very bad. In a recent article in The Wall Street Journal, a source cites a case through which Microsoft’s generative AI invented meeting participants and suggested that conference calls covered topics that weren’t actually discussed during the call.
As I wrote a while ago, hallucinations could be an unsolvable problem in modern transformer-based model architectures. However, many generative AI vendors suggest eliminating them roughly through a technical approach called search augmented generation (RAG).
Here’s how one supplier, Squirro, he throws it: :
At the core of the offering is the concept of Recovery Augmented LLM or Recovery Augmented Generation (RAG) built into the solution… (our Generative Artificial Intelligence) is exclusive in its promise of zero hallucinations. Each piece of knowledge it generates is traceable to its source, ensuring credibility.
Here it’s similar tone from SiftHub:
Using RAG technology and fine-tuned large language models and industry knowledge training, SiftHub enables firms to generate personalized responses without hallucinations. This guarantees greater transparency and reduced risk, and instills absolute confidence in using AI for all of your needs.
RAG was pioneered by data scientist Patrick Lewis, a researcher at Meta and University College London and lead writer of the 2020 report paper who coined this term. When applied to a model, RAG finds documents which may be relevant to a given query—for instance, the Wikipedia page for the Super Bowl—using keyword searches, after which asks the model to generate a solution in this extra context.
“When you interact with a generative AI model like ChatGPT or Lama and ask a question, by default the model responds based on its ‘parametric memory’ – i.e. knowledge stored in its parameters as a result of training on massive data from the Internet,” he explained David Wadden, a research scientist at AI2, the artificial intelligence research arm of the nonprofit Allen Institute. “But just as you are likely to give more accurate answers if you have a source of information in front of you (e.g. a book or file), the same is true for some models.”
RAG is undeniably useful – it lets you assign things generated by the model to discovered documents to ascertain their veracity (with the additional advantage of avoiding potentially copyright-infringing regurgitations). RAG also allows firms that don’t need their documents for use for model training – say, firms in highly regulated industries comparable to healthcare and law – to permit their models to make use of these documents in a safer and temporary way.
But RAG actually stops the model from hallucinating. It also has limitations that many providers overlook.
Wadden says RAG is best in “knowledge-intensive” scenarios where the user desires to apply the model to fill an “information need” – for instance, to search out out who won the Super Bowl last 12 months. In such scenarios, the document answering the query will likely contain lots of the same keywords as the query (e.g., “Super Bowl,” “last year”), making it relatively easy to search out via keyword search.
Things get harder for reasoning-intensive tasks like coding and math, where in a keyword-based query it’s harder to find out the concepts needed to reply the query, much less determine which documents is perhaps relevant.
Even for basic questions, models can grow to be “distracted” by irrelevant content in the documents, especially long documents where the answer isn’t obvious. Or, for reasons still unknown, they could simply ignore the contents of recovered documents and rely as a substitute on their parametric memory.
RAG can be expensive when it comes to the equipment needed to deploy it on a big scale.
This is because retrieved documents, whether from the Internet, an internal database, or elsewhere, have to be kept in memory – at the very least temporarily – for the model to confer with them again. Another expense is computing the increased context that the model must process before generating a response. For a technology already famous for the large amounts of computing power and electricity required to even perform basic operations, this can be a serious consideration.
This does not imply RAG cannot be improved. Wadden noted many ongoing efforts to coach models to raised leverage documents recovered using RAG.
Some of those efforts include models that may “decide” when to make use of documents, or models that may opt out of search first in the event that they deem it unnecessary. Others are specializing in ways to index massive document datasets more efficiently and to enhance search through higher representations of documents—representations that transcend keywords.
“We’re pretty good at retrieving documents based on keywords, but we’re not very good at retrieving documents based on more abstract concepts, such as the checking technique needed to solve a math problem,” Wadden said. “Research is required to construct document representations and search techniques that may discover suitable documents for more abstract generation tasks. I feel it’s mostly an open query at this point.”
So RAG may help reduce models’ hallucinations, however it isn’t the answer to all hallucinatory problems of AI. Beware of any seller who tries to say otherwise.
Technology
Chanel Nicole Scott joins Black Network as a marketing director

At Black Network (ITBN), she announced that Chanel Nicole Scott will probably be his latest marketing director (CMO). ITBN, which presents the stories created by the diaspora and attending the diaspora, said that Scott’s nomination is a component of the brand’s vision to extend global visibility.
Scott brings over a decade of experience within the production of technology and media. Through its company, Chanel Scott Production House has developed Cheminstry, a multimedia platform that features a television program, books and card games specializing in navigation in relationships and private development.
Scott is the creator of the podcast who premiered at Black Network. She too writer of the book with the identical name, sharing personal anecdotes and advice on relationships. In a press release, the manufacturer expressed his enthusiasm to his latest role in ITBN.
“Being a part of a breakthrough company, such as in Black Network, is more than a professional opportunity – it is a cultural mission. We are moving, who controls the narrative and the way our stories are told. Time to restore power to our hands – and I have the honor to help in conducting this movement,” said the host of Podcast.
The television producer, film creator and founding father of ITBN, James Dubose, said that keenness, work ethics and achievements of Scott make him a helpful advantage for the developing network. Dubose discussed his vision of world expansion Black Network with Black company in 2024
“We want you to come to one place, and it is internationally, it is locally, we are every market that you can think about, the Caribbean and so on to come to come one place and stay” – he said.
The filmmaker also said that he wants to offer a platform for black creations, often neglected within the media of the mainstream to present his content. Established in 2023, ITBN is a free Avod streaming service that incorporates a premium content emphasizing black voices. Network Streams directly On your website, on Smart TV and via the applying, which is obtainable on iOS and Android devices.
(Tagstranslate) SmartApps (T) Chanel Nicole Scott (T) James Dubose (T) within the Black Network (T) stream service
Technology
As Musk manages his growing family: WSJ

Elon Musk says his duty is to “make new people.” Now Investigation of WSJ He suggests that he could start greater than 14 known children, and the sources claim that the actual number will be much higher. The report also describes how Musk keeps these details within the package.
In the middle of all this, based on the report, there may be a longtime Fixer Jared Birchall, which runs the Muska’s family office, but additionally supports the logistics of the developing Muska family, including by developing Hush contracts and serving as a board for moms of some children.
For example, Musk reportedly asked the conservative influence of Ashley St. Clair for signing a restrictive agreement after she gave birth to their son last autumn. Agreement: $ 15 million plus an extra $ 100,000 per 30 days, so long as the kid is 21 in exchange for her silence. She refused; He says that the contract worsens with every treason perceived. (She told the journal that the Muska team sent her only $ 20,000 after they bowed to Musk to comment on his article).
As for Birchall, which can also be CEO Press-IMPLANTU-IMPLANTU VENTURE NEURALK IA partner In AI Venture XAI in Musk, Muska’s private life management can simply be the third full -time job. According to the journal, in a single two -hour conversation with St. Clair, Birchall told her that the transition “legal path” with musk “always, always leads to a worse result for this woman than otherwise.”
Technology
Lime scooter and Ebike batteries will be recycled by Redwood Materials

The joint company Micromobility Lime has reached an agreement on sending batteries utilized in scooters and electronic bikes to Sewoi materials that extract and recycle critical minerals, comparable to lithium, cobalt, nickel and copper.
The agreement announced on Monday makes Redwood Materials the only real battery recycling partner for common scooters and e-bike bikes situated in cities within the United States, Germany and the Netherlands. The contract doesn’t cover every region where lime worksAn inventory covering cities throughout Europe, Asia and Australia.
In Lime up to now he had other recycling partnerships, especially with Sprout through his suppliers. However, for the primary time, the joint company Micromobility had direct relations with battery recycling in North America, which might directly process the fabric for recovery and returns it to the availability chain.
Redwood Materials, The Carson City, Startup from Nevada founded by the previous CFO Tesla JB Straubel, will get better battery materials when they can’t be used. After recovering and recycling, the materials will be re -introduced within the battery production process. This production system of a closed loop-which can reduce the demand for extraction and refining of minerals-is on the Redwood Materials business center.
The effort can also be consistent with its own goals of limestone sustainable development. Lime is geared toward decarbonization of operations by 2030. The company has made progress in reducing the range 1, 2 and 3 of emissions by 59.5% in five years of basic years 2019. Wapno plans to report the outcomes of carbon dioxide emissions 2024 in May.
“This cooperation means significant progress in the establishment of a more round supply chain, helping our batteries not only to recycled responsibly after reaching the end of their lives, but that their materials are returned to the battery supply chain,” said Andrew Savage, vice chairman for balanced development in Lime.
Lime also has partnerships from Gomi in Great Britain and Voltr in France and other European countries to gather these live battery cells for “Second Life” applications, including, amongst others, in the sphere of consumer electronics, comparable to portable speakers and battery packages.
Redwood Materials has contracts with other micromobility corporations, including Lyft, RAD Power Bikes and bicycle batteries and scooters specialized in recycling. Redwood, which collected over $ 2 billion in private funds, announced at first of this month, opened the research and development center in San Francisco.
(Tagstranslat) ebikes
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