Training is not the same as chatting: ChatGPT and other LLMs don’t remember everything you say
Are they storing up everything anyone says to their models and dumping that into the training run for their next model versions every few months?
Are they storing up everything anyone says to their models and dumping that into the training run for their next model versions every few months?
FTC Chair Lina Khan said Wednesday that companies that train their artificial intelligence (A) models on data from news websites, artists’ creations or people’s personal information could be in violation of antitrust laws.
These jokey posts from random Internet users probably wouldn’t be among the first answers someone saw when clicking through a list of web links. But with AI Overviews, those trolls were integrated into the authoritative-sounding data summary.
The philosophy, along with other selected cyberspace themes that are aligned with the official government narrative, make up the core content of the LLM, according to a post published on Monday on the WeChat account of the administration’s magazine.
OpenAI has signed a deal for access to real-time content from Reddit’s data API, which means it can surface discussions from the site within ChatGPT and other new products.
By default, and without requiring users to opt-in, Slack said its systems have been analyzing customer data and usage information (including messages, content and files) to build AI/ML models to improve the software.
The only change in my question was ‘John’ to ‘Jane’. No other details were specified.
Yet the output given by ChatGPT couldn’t have been more different.
The same companies telling the public that “AI is enabling new forms of connection and expression” should also be willing to offer an explanation when its systems are unable to handle queries for an entire race of people.
While the inner workings of these algorithms are notoriously opaque, the basic idea behind them is surprisingly simple. They are trained by going through mountains of text, repeatedly guessing the next few letters and then grading themselves against it.
Tech companies including OpenAI, Google and Meta have cut corners, ignored corporate policies and debated bending the law, according to an examination by The New York Times.
Researchers found that with some spare cash and enough technical know-how, even a “low-resourced attacker” can tamper with a relatively small amount of data that’s invasive enough to cause a large language model to churn out incorrect answers.
Google started offering image generation through its Gemini AI models earlier this month, but over the past few days some users on social media had flagged that the model returns historical images which are sometimes inaccurate.
Cryptocurrency? That’s _so_ 2022. Owners of fleets of GPUs, they’re now pivoting to AI, according to The Guardian…
“Data collected in mid-January on 44 top news sites by Ontario-based AI detection startup Originality AI shows that almost all of them block AI web crawlers, including newspapers like The New York Times, The Washington Post, and The Guardian…”
In some cases, biased selection criteria is clear – like ageism or sexism – but in others, it is opaque.
“The figures were notably larger for image-generation models, which used on average 2.9 kWh per 1,000 inferences. The average smartphone uses 0.012 kWh to charge, so generating one image using AI can use almost as much energy as charging your smartphone.”
“It’s reasonable to expect that by around 2030 there will be more than a billion people using AI day-to-day in their work, and perhaps another 3 or 4 billion using it via their smartphones…”
“ChatGPT’s claim that any bias it might ‘inadvertently reflect’ is a product of its biased training is not an empty excuse or an adolescent-style shifting of responsibility…”
A team of researchers at New York University wondered if AI could learn like a baby. What could an AI model do when given a far smaller data set—the sights and sounds experienced by a single child learning to talk?
If you want to implement Microsoft’s Copilot AI assistants, don’t expect the software giant’s channel to be much help – they’ve scarcely had a chance to use it, never mind develop meaningful expertise in the tool.
Data sets that are poorly thought out or insufficiently described increase the risk of ‘garbage in, garbage out’ studies and the propagation of biases, rendering outcomes meaningless or, even worse, dangerous.
To varying degrees, the models appeared to be using race-based equations for kidney and lung function, which the medical establishment increasingly recognizes could lead to misdiagnosis or delayed care for Black patients.
Researcher found that a modest amount of fine tuning – additional training for model customization – can undo AI safety efforts that aim to prevent chatbots from suggesting suicide strategies, harmful recipes, or other sorts of problematic content.
Perhaps the most fundamental limitation of today’s large language models is that they depend on knowledge that’s been generated by people. A sea change will come when the bots can generate knowledge for themselves.
Clegg, told Reuters that the “vast majority” of the training data used to develop them came from publicly available posts, including on Facebook and Instagram.
n this latest study, DeepMind researchers found “Take a deep breath and work on this problem step by step” to be the most effective prompt when used with Google’s PaLM 2 language model. The phrase achieved the top accuracy score of 80.2 percent in tests against GSM8K, which is a data set of grade-school math word problems.
If nine experts in privacy can’t understand what Microsoft does with your data, what chance does the average person have?
A recent column in COSMOS Magazine explored the need for folks to come up to speed – quickly – with the capabilities and pitfalls of AI Chatbots: