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We put the card through a grueling series of tests which confirm its rock solid stability under Doom 3. UPDATE: The issues we experienced with the eVGA GeForce 6800 Ultra Extreme have been fixed with an newer version of the card sent by that (among other things) dropped the core clock speed down to 450MHz.

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We were unable to install this driver on 9800 series cards, but we hear that performance improvement on older platforms is less significant than what we see on the x800 series. We can see that the biggest performance improvements come on the higher end platform, higher quality setting, higher AA setting, and lower resolution.

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These drivers will not be available in a whql package until September, but until then, here is a sneak peek at percentage performance improvement over the 4.7 drivers:Ĭatalyst 4.9 Performance Improvement w/ 4xAA Enabled UPDATE: We have recently been made aware of ATI's beta Catalyst 4.9 drivers targeted at Doom 3 performance enhancement. All the usual suspects put in an appearance as well to make this one of the most comprehensive graphics tests we've undertaken in a while.īut let's get down to it: we're here for the analysis, so lets take a look at the system we're testing on and iron out the details about what to expect from this review. We've also included such favorites as the GeForce 4 Ti 4400, the Radeon 9700 Pro, and the Radeon 9600 XT. We will explore everything from the ultra high end down to the bargain basement cards. Our goal in this review is to cover all angles of graphics card impact on playing Doom 3. Quite a few impressions we had about what our Doom 3 experience would be like had to be thrown out the window, and it all comes down to the fact that this is an impressive game no matter how you slice it. Staying up all night benchmarking one of the most anticipated titles of the past few years has been very educational for us. 128.Kicking off what we've dubbed "Doom 3 Week" here at AnandTech, we're bringing you our in depth analysis of video card performance under Doom 3.Weekend Open Forum - Friday, Aug by wdunwoody

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Pregnant Detroit woman sues city: falsely arrested by crap facial software by PX_Eliezer The Longest-Running Shows on American Television by Stem Bolt Is this what there really doing ? by tito79

READ carefully: This May Be the World's Stupidest Question by mbernste

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Multiple password reset tries by PoloDude Elon Musk's SpaceX Revolution: From 'Fool's Errand' To Building A 'New Internet In Space' + more notable news.Starlink's Dominance Of Satellite Internet Raises Alarms About Elon Musk's Growing Geopolitical Sway + more news.Indie Cable Ops Encouraged To Play Offense And Defense With BEAD + more notable news.Internet Providers That Won FCC Grants Try To Escape Broadband Commitments + more notable news.FCC Hikes ACP Low-Income Broadband Support For High-Cost Areas + more notable news."We don't know," said Zico Kolter, associate professor of computer science at CMU, allowing that there are different hypotheses about that. Why these attacks work across models – whether its training data overlap or something else – isn't clear. They then found that some of their adversarial examples transferred to other released models – Pythia, Falcon, Guanaco – and to a lesser extent to commercial LLMs, like GPT-3.5 (87.9 percent) and GPT-4 (53.6 percent), PaLM-2 (66 percent), and Claude-2 (2.1 percent). The researchers initially developed their attack phrases using two openly available LLMs, Viccuna-7B and LLaMA-2-7B-Chat. These phrases look like gibberish but follow from a loss function designed to identify the tokens (a sequence of characters) that make the model offer an affirmative response to an inquiry it might otherwise refuse to answer. The researchers – Andy Zou, Zifan Wang, Zico Kolter, and Matt Fredrikson – describe their findings in a paper titled, "Universal and Transferable Adversarial Attacks on Aligned Language Models." Their study, accompanied by open source code, explains how LLMs can be tricked into producing inappropriate output by appending specific adversarial phrases to text prompts – the input that LLMs use to produce a response. Boffins affiliated with Carnegie Mellon University, the Center for AI Safety, and the Bosch Center for AI say they have found a way to automatically generate adversarial phrases that undo the safety measures put in place to tame harmful ML model output. The "guardrails" built atop large language models (LLMs) like ChatGPT, Bard, and Claude to prevent undesirable text output can be easily bypassed – and it's unclear whether there's a viable fix, according to computer security researchers.