🔥 BREAKING AI NEWS! Google’s former CEO has just unveiled FREE Superintelligent AI Agents that are outperforming PhDs! 😱🧠
In this AI Revolution episode, we cover:
🚀 The shocking capabilities of these AI agents
🎓 How they outperform PhD-level experts in multiple fields
💻 Real-world use cases and tools now available
🌐 What this means for the future of education, coding, and productivity
📉 Are traditional jobs and degrees at risk?
This could be one of the biggest AI releases of 2025 — and it's FREE to use! Tap in to see how it works and what it means for you.
🔔 Don’t forget to LIKE, COMMENT, and SUBSCRIBE for the most important AI updates!
#AIRevolution
#GoogleAI
#AIUpdate
#SuperintelligentAI
#AIvsPhD
#FreeAI
#OpenAI
#ArtificialIntelligence
#AItools
#AI2025
#NextGenAI
#AIforEveryone
#MachineLearning
#TechNews
#AIBreakthrough
#FutureOfWork
#AIExplained
#EmergingTech
#ProductivityAI
#AIvsHumans
In this AI Revolution episode, we cover:
🚀 The shocking capabilities of these AI agents
🎓 How they outperform PhD-level experts in multiple fields
💻 Real-world use cases and tools now available
🌐 What this means for the future of education, coding, and productivity
📉 Are traditional jobs and degrees at risk?
This could be one of the biggest AI releases of 2025 — and it's FREE to use! Tap in to see how it works and what it means for you.
🔔 Don’t forget to LIKE, COMMENT, and SUBSCRIBE for the most important AI updates!
#AIRevolution
#GoogleAI
#AIUpdate
#SuperintelligentAI
#AIvsPhD
#FreeAI
#OpenAI
#ArtificialIntelligence
#AItools
#AI2025
#NextGenAI
#AIforEveryone
#MachineLearning
#TechNews
#AIBreakthrough
#FutureOfWork
#AIExplained
#EmergingTech
#ProductivityAI
#AIvsHumans
Category
🤖
TechTranscript
00:00FutureHouse just dropped four super-intelligent AI agents, and they're free to use right now.
00:08Backed by Eric Schmidt, the former Google CEO, this isn't some closed beta.
00:12It's public, powerful, and already outpacing PhDs in key tasks.
00:17Named Crow, Falcon, Owl, and Phoenix, each agent handles a different part of the workflow,
00:22from answering complex questions to designing brand new molecules,
00:25cutting down work that used to take weeks into minutes.
00:28So let's talk about it.
00:30Alright, let's start with the vibe of the platform.
00:32FutureHouse claims each agent is specialized from the ground up for science, not chit-chat.
00:38Crow is the quick-draw generalist.
00:40Ask it a technical question, and bam, it sifts through open-access papers and spits out a concise, citation-studded answer.
00:47Falcon goes deeper, slurping in dozens or even hundreds of full-text articles,
00:52plus proprietary databases like OpenTargets, then weaving them into long-form review.
00:56Owl, rebranded, from a prototype called HasAnyone, works like a detective,
01:02checking if someone's already done that crazy experiment so you don't waste six months reinventing the pipette.
01:08And Phoenix, still labeled experimental, is the chemistry brainchild.
01:12It proposes fresh compounds, predicts reactions, and even cost checks whether it's cheaper to buy or synthesize a molecule.
01:19All four can be chained together, which FutureHouse says lets a single researcher juggle workloads that used to require entire teams.
01:28Now, none of this happens in a vacuum.
01:30Google's own AI co-scienced announcement back in February lit a fire, and remember Google's 2023 genome system?
01:38It bragged about 40 supposedly new materials, but a later analysis showed none were genuinely novel.
01:45That flop still haunts the field, highlighting how hallucinations and shaky reasoning can torpedo flashy demos.
01:53FutureHouse is clearly trying to avoid that trap.
01:56In every blog post, they hammer on transparent reasoning and multi-stage evidence gathering.
02:02You can literally click through each step, watch the agent pick search terms, rank journals by citation graphs,
02:09and see why, say, nature genetics made the cut, while someone's bio-archive preprint did not.
02:16In one demo, Falcon pulled 32 papers, decided 24 were truly relevant,
02:21then distilled 62 separate chunks of evidence into a final answer.
02:26And you, the user, could inspect every breadcrumb.
02:30That visibility matters because, let's be real, scientists are a skeptical bunch.
02:35Even Sam Rodriguez, Andrew White, and the rest of the FutureHouse leadership admit
02:39today's large language models melt down on high-precision tasks.
02:43LLMs can't even count nitrogens in a molecule reliably.
02:47So Phoenix leans on external chemistry tools, patent databases, solubility predictors,
02:53reaction simulators, basically calculators stapled onto an LLM brain.
02:56The team runs its own wet lab in San Francisco so they can close the loop,
03:01generate a hypothesis, synthesize compounds, test them, feed the data back, tweak the model, repeat.
03:06FutureHouse calls this their four-layer architecture.
03:09At the base are generic AI tools.
03:12Think AlphaFold or, yes, your favorite gradient descent wizardry.
03:17Above that sit assistants like Crow and Falcon that execute specific workflows such as protein annotate.
03:23One rung higher is the future AI scientist that will design experiments end-to-end.
03:30And hovering over everything is the human researcher, the quest giver.
03:34Steering the big questions like curing Alzheimer's or, in today's flagship demo,
03:39tackling polycystic ovary syndrome.
03:42Alright, PCOS.
03:43This example is how FutureHouse tries to prove the platform isn't vaporware.
03:48Michaela Hinks, who leads their science team, says she came in cold.
03:52No PCOS background, just genetics chops.
03:55Step 1.
03:56She fired up Falcon and asked for a comprehensive breakdown of PCOS definitions,
04:01symptoms, diagnostic criteria, underlying causes, the whole deal.
04:05The agent ran a torrent of queries, hoovered up full-text studies and clinical trial records,
04:10filtered duplicates, and produced an overview in minutes.
04:14The transparent trace showed not just titles and DOIs, but quality scores tied to citation networks.
04:20Then, Michaela went surgical.
04:22She switched to Crow and asked which genes keep popping up in PCOS genome-wide association studies.
04:29Crow rattled off big hitters, including the now infamous D&D1A,
04:33and linked each to multiple GWAS papers.
04:36But listing genes is table stakes, so next she pinged Owl.
04:41Has anyone used CRISPR screens to connect these GWAS hits to hyperandrogenism?
04:46Owl confirmed that, yeah, one group had shown overexpression of D&ND1A,
04:51boosting testosterone in vitro.
04:54However, nobody had nailed the mechanism.
04:56Boom, research gap.
04:58Identified in four short prompts instead of a weekend library crawl.
05:01Still curious, Michaela probed, do we know why increased D&D1A jacks up androgen levels?
05:09And the agents came up empty.
05:11That's the moment a wet lab experiment becomes justified.
05:14Future House argues most of us burn days or weeks to reach that point.
05:19Enter Phoenix.
05:21Given the open question, Michaela asked Phoenix to propose three novel,
05:25potentially drug-like compounds that might tamp down D&D1A-driven hyperandrogenism.
05:30Because no FDA-approved binders exist for the protein,
05:34Phoenix first mapped protein interaction partners, then fetched molecules that hit those partners.
05:40It checked each candidate's patent status, solubility, functional groups,
05:44even synthetic routes, tossing out anything derivative.
05:47By the end, it produced a little dossier for each molecule.
05:50Why it might modulate D&D1A, how much it would cost to procure,
05:54and whether you'd need to tweak it into a pro-drug down the line.
05:58Future House says tasks like patent searching or log-S prediction rely on specialized tools.
06:03Because again, pure LMs are comically bad at chemistry arithmetic.
06:09Now, metrics.
06:10Future House ran benchmarks, lit QA precision and accuracy,
06:14head-to-head against frontier search models such as XAI plus human PhD literature sleuths.
06:21On retrieval precision, correct answers over answered questions,
06:24and accuracy, correct over all questions, their agents topped the charts.
06:29They haven't published raw percentages in the press articles, but the claim is superhuman.
06:34To be fair, the blog concedes Phoenix is less battle-tested, so it may make mistakes,
06:39and is out there to gather public feedback.
06:42Rapid iteration is the strategy.
06:44Nobody's pretending perfection.
06:47That honesty matters, especially after the G-nome disappointment
06:51and the broader worry that hallucinations could slip unvetted claims into real experiments.
06:57Gale is the other buzzword.
06:59PubMed holds 38 million papers.
07:01ClinicalTrials.gov tracks more than 500,000 trials,
07:05and scientists juggle thousands of siloed tools.
07:08Future House argues individual labs can't scrape, store, or rate limit that much data.
07:13They lack the engineering muscle.
07:15So the nonprofit wants to be the infrastructure layer.
07:18API calls so your screening pipeline can auto-kick literature reviews
07:22or contradiction hunts every night at 2 a.m.
07:24Everything is free, publicly available, and open to feedback,
07:27with the source code for key components opened under permissive licenses.
07:32The board includes names like Scientist Entrepreneur, Adam Marblestone,
07:36and Schmidt's backing presumably keeps the lights on without VC pressure to monetize everything tomorrow.
07:42Of course, skepticism hasn't vanished.
07:44The TechCrunch write-up notes that so far, Future House hasn't produced an entirely novel discovery.
07:50No new alloy, no first-in-class drug, despite fancy agents.
07:53The nonprofit counters that even Google's G-nome, with its 2023 fanfare,
07:59couldn't deliver net new materials,
08:01so the bottleneck is partly experimental logistics, not just algorithm smarts.
08:05They run a physical lab precisely to cross that last mile.
08:09Still, until a peer-reviewed breakthrough pops out,
08:12the broader scientific community will stay cautiously optimistic at best.
08:16Future House's launch timing is intriguing, too.
08:19The AI sector is flush with capital.
08:21OpenAI and Anthropic both publicly brag about accelerating science.
08:26Yet many bench scientists complain that general-purpose chatbots are unreliable,
08:30hallucinate, or sight-retracted studies.
08:33Future House tries to thread that needle by restricting its agents to carefully curated corpora,
08:38weighing source provenance and exposing every reasoning step.
08:42In demos, you can even watch the agent ditch low-impact journals
08:46in favor of higher citation density or stronger experimental methods.
08:50It's nerdy, sure, but it might be the transparency researchers need to trust an AI collaborator.
08:56Now, about that decade-long roadmap.
08:58Future House envisions the full AI scientists as an autonomous system
09:02that can read every paper in biology, spot contradictions,
09:06devise hypotheses, design and execute experiments,
09:10evaluate the data, and iterate,
09:12basically compressing the scientific method.
09:15Humans remain in charge of the quest,
09:17the top-level framing of what matters.
09:19Whether that's curing Alzheimer's, decoding the brain,
09:23or universal gene delivery,
09:24the idea is to let machines handle the brute-force literature grinding
09:28and high-dimensional search,
09:30while humans judge significance and ethics.
09:33If it works, the company says,
09:34we could clear today's global backlog of unexplored opportunities,
09:39thousands of promising leads buried in PDF supplemental sections,
09:43simply because no one has time to compare them.
09:45All of this lands in a world already buzzing about AI ethics.
09:49Future House's insistence on open-sourcing and auditable reasoning
09:52is meant to address risk, but unintended consequences,
09:56say, a hallucinated synthesis route
09:58that actually produces a toxic byproduct, still loom.
10:01The nonprofit is betting that transparency
10:04plus community feedback beats closed-door secrecy.
10:08They've even plastered a big
10:09please-provide-feedback banner over Phoenix's chemistry tools.
10:13So what's next?
10:14Short-term, Future House wants to widen the platform's range
10:17in just raw lab data, not just published papers.
10:20Picture an agent that sees your single-cell RNA sequence results
10:24in real time and immediately correlates them
10:26against millions of past experiments.
10:28The team thinks that within a couple of years,
10:30most discoveries will carry an AI-driven component,
10:34whether at the literature stage,
10:35the analytical stage, or both.
10:37Long-term, if they manage to keep hallucinations,
10:40bias, and experimental error in check,
10:43they might really bend the curve of scientific productivity,
10:46which economists have shown has stagnated
10:48even as paper counts soar.
10:51And look, hype aside, the launch day invitation is simple.
10:56Go to platform.heo.io, sign up for free,
10:59query crow, fire falcon, let owl double-check your idea,
11:03then ask Phoenix if it can cook you a molecule.
11:06Whether you're in academia with a dropbox
11:08full of half-read PDFs or a biotech startup
11:11drowning in assay data,
11:13the promise is a turbo button you didn't have yesterday.
11:17Will it deliver?
11:18History says skepticism is healthy.
11:20But the alternative?
11:22Wading through 38 million PubMed articles,
11:24500,000 clinical trials,
11:26and who knows how many supplementary spreadsheets by hand,
11:29feels, frankly, impossible.
11:31If Future House's agents can shave even 10% off that grunt work
11:35without injecting hallucinated nonsense,
11:37that alone could make them worth the hype.
11:40And if, as the founders hope,
11:42an autonomous AI scientist emerges before 2035,
11:47well, we may be telling our grad students stories
11:49about the Dark Ages
11:50when we had to parse every p-value ourselves.
11:54So, are we really about to let AI outperform top researchers
11:58and take over the front lines of discovery?
12:01Let me know what you think in the comments.
12:03Thanks for watching,
12:03and I'll catch you in the next one.
12:05See you in the next one.