Shocking new records reveal the filthy reality of Los Angeles’ notoriously rundown MacArthur Park — where city crews hauled away nearly 13 million pounds of trash and hazardous waste last year.
Records obtained by The California Post through a public records request show sanitation workers dragged out more more than 6,359 tons of disgusting debris in 2025, including a staggering 142,329 pounds of human waste.
The new platform of the Democratic Socialists of America (DSA) got a lot of attention this week.
The revision to the DSA platform, "Workers Deserve More," plans to abolish the Senate entirely and replace the president and Supreme Court with an executive branch and judiciary elected by and subordinate to Congress.
It's not often that we're able to write words of praise for ABC News' Jon Karl on the pages of RedState, but fair play to the co-host of the Sunday show This Week for actually holding a Democrat accountable for their lies.
Karl's guest on this week's show was Sen. Mark Warner (D-VA), the ranking Democrat on the Senate Select Committee on Intelligence, and one of the topics discussed was voter ID and the SAVE America Act. Warner was there, of course, to rebut President Trump's assertions during his Thursday night speech from the White House that American elections have been compromised – and will continue to be compromised – due to foreign influence. Influence, it could be argued, that's been covered up by the Deep State. Put a pin in that, we'll revisit it in a moment.
READ MORE: Trump Unleashes Election Intel: China Stole 220 Million Voter Files, Deep State Cover-Up
Olive Garden Just Accidentally – and Hilariously! – Made the Case for Voter ID
According to Warner, everything is hunky dory with our election system, and, hey, just look his home state of Virginia. The Old Dominion, claimed Warner, requires voters to present an ID in order to cast their ballot, so no one can say we Democrats don't support this!
There's just one thing, noted Karl. That's simply not true.
Here's the exchange:
KARL: Now, I understand that you and probably every Democrat oppose this so-called SAVE America Act, and there are a lot of reasons that you put forth. But, let me ask you – very narrowly – on the issue of photo ID. Requiring photo ID to register to vote, is this something Democrats should get behind? I mean, 80-something percent of Americans, 71 percent of Democrats, say they favor this.
WARNER: You know what? In Virginia, we have voter ID and photo ID. I gotta show my –
KARL: That's actually not the case. I mean, I'm a Virginia voter. You don't need to show a photo ID in Virginia. There are a wide range of things you can show for ID, and if you don't show on of those, you can sign an affidavit.
WARNER: You sign an affidavit and those become ballots that if you want to contest later can be contested.
Just so we're clear here, Warner is saying that signing an affidavit and casting a ballot without showing any sort of photo ID is the same thing as showing a photo ID. He lied, and the proof is all right there on the Virginia Dept. of Elections website:
Can I vote if I forget my ID?
Yes! If you get to your polling place without acceptable ID, you can sign an ID statement affirming your identity, you will be able to vote a regular ballot.
If you do not sign an ID statement to affirm your identity you may vote a provisional ballot. You will be provided instructions to ensure your vote will count.
It's important to note here that Warner chaired the Senate Intelligence Committee from 2021 through 2025, so he should be sweating bullets about the evidence newly declassified by the Trump White House as it very well could point to him playing a role in covering up consequential information about voting systems, foreign activity, and/or noncitizen registration.
The question Jon Karl really should have asked Mark Warner is this: What did this Senate intelligence watchdog know, and why didn’t he tell Americans?
Last night’s Maine Democratic Senate debate to replace Graham Platner was a train wreck. Don't believe me? Feel free to check out this excellent rundown by my colleague, Nick Arama.
Keep in mind that this not only looked like a train wreck, but it comes with the realization that this crop of misfit toys has only surfaced after the Democrats' previous muse, an accused rapist with a Nazi tattoo and an unhealthy fascination with portapotties, was forced out of the race.
Among the new group was one candidate, Ashley Webb, who believes some of the best qualifications for serving in the Senate are writing songs and books.
Oh, and transparency ... I guess?
"I wouldn't lie to the people, and I wouldn't deceive the people like we are being deceived right now," Webb said.
Now, it'd be easy to launch that softball off the tee and into outer space by starting with the fact that some might consider it an obvious deception taking place before their eyes, but let's not go for the low-hanging fruit here. (Or lack thereof).
What is remarkable here is that the George Washington-esque inability to lie took all of seconds to crumble on social media when promotions for Webb's memoir resurfaced.
Actually, I should point out that it's "more than just a memoir—it's a spark." As stated in the Amazon listing.
The transgender Democrat running in the crowded replacement field has been promoting her memoir, "I Am Ashley", with an AI-generated image of a much younger, glamorized woman that bears almost no resemblance to the candidate.
And by "almost no resemblance," I mean "absolutely no resemblance."
To quote Agent 86, Maxwell Smart, Webb "missed it by that much."
You can find a slew of these images on the "I am Ashley" Facebook page. They all look relatively the same. And they all look nothing like the person they are meant to portray.
Danny DeVito and Arnold Schwarzenegger are closer to being actual twins than Webb is to the person in the memoir.
Some might offer a defense of the images, suggesting Webb is simply writing about a character that is more representative than anything else. To that, I say nonsense.
The book is not only marketed in the "Biographies & Memoirs" section, but the title itself includes a sub-header which reads: "A True Story of Growing up Trans in a World That Said I Couldn't be Her."I'm not quite sure what "world" Webb lives in, but it's not one within light-years of Earth, and it's certainly not one that dabbles in true stories.
Do you know who's absolutely stoked that this person is a candidate for the Democrat Party running for a very high-profile seat? The GOP, who definitely wants to see more of the train wreck we all saw last night.
Indeed, voters deserve to see the real crop of candidates Democrats had waiting on the bench. The ones that were so bad that they clung to Platner for dear life, despite his never-ending sea of scandal.
Editor’s Note: The 2026 Midterms will determine the fate of President Trump’s America First agenda. Republicans must maintain control of both chambers of Congress.
Help RedState continue to report on the Democrats’ radicalism and inform voters as our nation faces a crossroads. Join RedState VIP and use promo code FIGHT to receive 60% off your membership.
by Corin Wagen · Apr 2, 2025
Update: This paper has been retracted. We are leaving this blog post up for historical reasons, but the data presented here should not be viewed as trustworthy or authoritative. Read the full statement from MIT: https://economics.mit.edu/news/assuring-accurate-research-record.
Despite the current ubiquity of artificial intelligence, many scientists remain skeptical. Generative AI models have made waves in image and language domains, but their relevance to real-world research often seems unclear. Can these models really help with something as complex and domain-specific as chemical discovery?
A new study by Aidan Toner-Rodgers at MIT provides a rigorous, large-scale evaluation of the impact of machine learning on scientific research. Toner-Rodgers evaluates what happened when a large U.S. industrial R&D lab introduced an AI-powered materials discovery tool to over a thousand researchers. The results are surprisingly clear: AI can dramatically accelerate innovation, but only for scientists with the domain expertise to guide it.
Aiden Toner-Rodgers, an MIT economics Ph.D. student.
The AI tool studied in Toner-Rodgers's work was a graph neural network (GNN)-based diffusion model trained to generate candidate materials that were predicted to have specific properties. In this study, the researchers used it for inverse design—providing target features and receiving plausible structures in return. The company rolled the model out in waves across 1,018 scientists, allowing for a controlled and large-scale study of AI's impact over almost two years. (The exact nature of the company and the model are, sadly, confidential.)
The results are striking. Researchers who gained access to the model discovered 44% more materials, filed 39% more patents, and produced 17% more product prototypes. Adoption of the AI model leads to a clear step change in materials discovery and patent filings after about six months, while the increase in product prototypes took over a year to appear. This makes sense, as prototypes are downstream of patents and new materials.
Figure 5 from Toner-Rodgers's paper, showing the impact of introducing the AI model over time.
These graphs don't just show a flood of "AI slop" overrunning the materials discovery pipeline—as far as Toner-Rodgers can quantify, the discoveries were also better than human-only discoveries. The materials were superior in quality (as assessed by similarity to the researchers' desired properties) and showed significantly greater novelty, both structurally and in downstream patents. For instance, patents filed by AI-assisted scientists used more novel technical terminology, an early marker of transformative innovation.
One concern with applying ML to scientific domains is the so-called "streetlight effect": the idea that models might just guide us toward what we already know and disfavor truly novel research. But in this case, AI-enabled teams produced more distinct materials and more new product lines, not just incremental tweaks. This suggests that the model actually helped researchers explore new territory in materials design space, although a full treatment of this question will require further research.
Although the full effect of incorporating AI took a substantial amount of time, the effect on the organization was substantial. Overall, Toner-Rodgers estimates that introducing this single AI model improved overall R&D efficiency by 13–15%, even after model training costs are taken into account. This productivity boost would be extraordinary in any company, let alone an organization with over a thousand researchers.
Scientists' task logs also showed a dramatic reallocation of effort: AI automated about 57% of the idea-generation process, freeing researchers to focus on evaluating and testing candidate materials—areas where domain knowledge is essential.
Figure 8 from Toner-Rodgers's paper, showing the impact of introducing the AI model on researcher activities.
Here's how Toner-Rodgers summarizes this finding:
While [AI] replaces labor in the specific activity of designing compounds, it augments labor in the broader discovery process due to its complementarity with evaluation tasks.
This phenomenon is perhaps unsurprising: the ML model studied here was capable of generating new candidate materials, so less human time was spent generating candidates and more time was allocated to evaluation these candidates. It's interesting to imagine what might happen if a second ML model capable of candidate evaluation were added—might it be possible to produce compounding productivity increases?
Critically, introduction of the ML model didn't help all of the scientists equally. The top third of scientists nearly doubled their output, while the bottom third saw little change. This can be traced to differences in how these scientists employed the models. The best performers used their domain expertise to filter the flood of model-suggested candidates, avoiding time sinks on unstable or irrelevant compounds. In contrast, less experienced users often tested the model's suggestions at random—burning resources on dead ends.
This divide can be further studied by examining which forms of expertise proved most useful for judging candidate materials generated by AI. Scientific training proved to be the most important, followed by previous in-field experience and raw intuition, while experience with other ML tools proved unimportant. Paradoxically, this implies that the advent of AI tools makes domain knowledge and scientific intuition more important, not less.
Figure 12 from Toner-Rodgers's paper, showing which forms of expertise proved useful in working with the AI model.
This finding complements earlier work suggesting that while machine prediction is improving rapidly, human evaluation and decision-making are still critical to success. One of the study's most striking findings is that "only scientists with sufficient expertise can harness the power of AI." The need for scientific thinking hasn't disappeared; it's simply shifted downstream to judgment and interpretation.
Of course, increased patents and prototypes don't automatically mean real-world success. Still, there are reasons to take these results seriously: patent filings require novelty, utility, & non-obviousness, and product prototypes represent a substantial corporate expenditure and a degree of human validation & trust. We won't know the full impact of these discoveries for years—but for those trying to assess whether AI can unlock new chemical space, this study represents unusually rigorous evidence that it can.
Unfortunately, not all impacts were positive. In follow-up surveys, 82% of scientists reported reduced job satisfaction. Even those who benefited the most cited skill underutilization and a decline in creativity as top concerns. AI may accelerate discovery, but many researchers felt alienated by the new workflow. Still, scientists' belief in AI's productivity-enhancing potential nearly doubled after using the model. The vast majority reported plans to reskill, anticipating a future in which the traits needed to excel in scientific research will shift.
This study offers some of the clearest empirical evidence to date that AI can accelerate real-world scientific discovery, especially in chemistry and materials science. But it also highlights an important nuance. AI doesn't replace scientific experts—instead, it makes them exponentially more valuable.
At Rowan, we're building the ML-native design and simulation platform for chemistry, drug discovery, and materials science. We believe that the future of scientific discovery requires both humans and AI models working in tandem, as described in Toner-Rodgers's paper, and are working to build software to make this transition possible. If you're a scientist looking to excel in the age of AI-powered research, come check out what we're building!
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