Midjourney Medical：体重計に乗るように臓器をスキャン

a quiet day.

AI News for 6/16/2026-6/17/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews' website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Top Story: Midjourney Medical

What happened

Midjourney unveiled a medical imaging/scanning system and then published a technical dive on it, triggering a mix of fascination, skepticism, and broader discussion about AI labs moving into hardware/medical devices.

• Midjourney’s official account posted “A technical dive inside our new ‘Midjourney Scanner’” in the main announcement tweet, which appears to be the core launch artifact for the project @midjourney.

• The launch was preceded or paralleled by discussion of a scanner whose tradeoffs were summarized as: radiation-free, magnet-free, fast, and low-cost, but requiring the person to sit in a water immersion tank and currently having coarser resolution than CT/MRI @iScienceLuvr.

• A demo appears to have been available in person: one attendee said, “I put my hand in the @midjourney demo scanner tonight”, framing it as a tangible prototype rather than a purely conceptual announcement @saranormous.

• The announcement generated strong enthusiasm from supporters who viewed it as evidence of unusually ambitious product direction from Midjourney, including comments like “this is so amazing” and “let inventors like @DavidSHolz invent” @saranormous.

• Others interpreted the launch competitively against more incremental AI hardware efforts; one reaction contrasted it with “boring lapel camera” bets and argued other AI labs should “slap yourself” if Midjourney is building this kind of thing @matvelloso.

• There was also lightweight technical commentary from people interested in imaging methods, including speculation about detector/emitter arrangements and real-time variants @johnowhitaker, plus teasing that some users seemed unusually prepared for the launch topic @johnowhitaker.

Facts vs opinions

Factual claims explicitly present in the tweet set

• Midjourney published a technical dive into a product called the “Midjourney Scanner” @midjourney.

• The scanner was described as:

Radiation-free

• Magnet-free

• Fast

• Low-cost

• Requiring a water immersion tank

• Having coarser resolution than CT/MRI

@iScienceLuvr

• A person physically tried a demo scanner with their hand @saranormous.

Interpretations/opinions/speculation

• Strongly positive reactions framed the scanner as visionary or “the future” @saranormous.

• Some observers took the launch as evidence that Midjourney is pursuing a more ambitious hardware roadmap than competing AI labs @matvelloso.

• One humorous reply escalated the idea into “next up is full cargo transport by midjourney,” clearly not a factual claim @yacinelearning.

• Independent technical commentary suggested possible future design directions, such as distributed scattered detectors and emitters or real-time systems, but these were not presented as features of Midjourney’s current scanner @johnowhitaker.

Technical details and inferred modality

The tweet corpus contains only a limited number of hard specs, but they are enough to outline the project’s positioning.

• No ionizing radiation: “Radiation-free” implies the system is not using X-rays/CT-style ionizing modalities @iScienceLuvr.

• No magnets: “Magnet-free” differentiates it from MRI, which relies on strong magnetic fields @iScienceLuvr.

• Water immersion tank: This is a major clue about the physical sensing setup. Water coupling is common in some acoustic and wave-propagation imaging systems because it improves transmission and coupling between emitters, tissue, and detectors @iScienceLuvr.

• Resolution below CT/MRI: The system is not being claimed, in these tweets, to outperform incumbent clinical imaging on resolution; in fact, an explicit limitation is that resolution is coarser than CT/MRI @iScienceLuvr.

• Speed/cost positioning: It is framed as fast and low-cost, suggesting the value proposition is likely accessibility, throughput, or portability rather than top-end image fidelity @iScienceLuvr.

There is also technically informed reaction about the likely sensing challenges:

• John Whitaker notes that systems based on light, ultrasound, electric current, etc. have a harder inverse problem than X-rays because signals do not travel in straight lines in the same way, making reconstruction more complex @johnowhitaker.

• He also suggests a future version with many scattered detectors and emitters rather than mechanically moving components, indicating that at least some readers infer the current system may involve motion/scanning geometry rather than fully parallelized capture @johnowhitaker.

Taken together, the public discussion points toward a non-CT, non-MRI modality with wave-based reconstruction and meaningful algorithmic/inverse-problem content, though the tweets here do not provide definitive modality labeling or performance tables beyond the stated tradeoffs.

Different perspectives

Supportive / optimistic

• The most enthusiastic camp sees this as exactly the kind of high-upside, weird, non-consensus invention AI founders should pursue, not just incremental chatbot/UI products. That tone is clear in “let inventors like @DavidSHolz invent” @saranormous.

• In-person demo reactions emphasized the visceral novelty of interacting with a real scanner, not just reading a paper or watching a video @saranormous.

• Some interpreted the move as a sign that Midjourney may be thinking beyond image generation and toward full-stack applied invention, possibly combining hardware, sensing, and AI reconstruction.

Neutral / technical-curious

• The most grounded reaction in the set is the concise pros/cons summary: radiation-free, magnet-free, fast, low-cost versus water immersion and lower resolution than CT/MRI @iScienceLuvr.

• Technically curious observers liked the strangeness of the modality while immediately identifying the physical and systems tradeoffs:

Non-straight-line propagation compared with X-rays

• Need for better real-time capture arrangements

• Questions about detector/emitter topology

@johnowhitaker

Opposing / skeptical / cautionary

Direct hostile criticism is limited in this tweet set, but skepticism is implicit in several points:

• Clinical utility skepticism: saying it has coarser resolution than CT/MRI is a substantive caveat, especially in medicine where image quality can directly affect diagnostic value @iScienceLuvr.

• Practicality skepticism: requiring a water immersion tank is a serious ergonomic and deployment constraint for routine clinical or consumer use @iScienceLuvr.

• Modality skepticism: technical comments about non-straight-line propagation hint at the usual challenge for alternative imaging systems: the physics and inverse reconstruction are hard, and the pretty demo may not automatically translate into robust, clinically reliable imaging @johnowhitaker.

Competitive framing

• One notable perspective was less about the scanner itself and more about what it says strategically: if Midjourney is attempting hardware-medical invention, then AI companies pursuing narrower wearable-camera concepts look conservative by comparison @matvelloso.

Context: why this matters

Midjourney is primarily known as an image-generation company. That makes a medical/scanner reveal noteworthy for several reasons:

• It suggests a willingness to move from generative media software into real-world sensing and hardware.

• Medical imaging is a domain where inverse problems, signal processing, reconstruction, and increasingly ML-based interpretation all matter; it is not an obvious adjacency, but it is a technically deep one.

• The scanner appears to be positioned not as “better than MRI/CT on all axes,” but as a potential entrant in the classic disruption lane: worse on a premium metric, better on cost/accessibility/operational burden.

• If the system is genuinely fast and low-cost, the most plausible implications are in:

screening or triage,

• settings where CT/MRI access is limited,

• repeat imaging where avoiding radiation matters,

• specialized anatomical use-cases where immersion-based setups are acceptable.

The launch also fits a broader 2025 pattern where AI-adjacent companies increasingly try to define themselves not just as model vendors, but as builders of new interfaces to the physical world. In that framing, Midjourney Medical is less about a single scanner and more about whether frontier AI-era startups can productize difficult sensing systems, not just generate content.

Implications and open questions

• Regulatory path: nothing in these tweets addresses approvals, validation studies, or whether this is research-only versus intended for clinical deployment. For medical relevance, those questions are central.

• Reconstruction stack: the phrase “technical dive” implies the company has discussed internals, but the tweet set here does not expose the actual algorithmic details. The likely crux is reconstruction quality under a constrained sensing setup.

• Use-case specificity: lower resolution than CT/MRI does not necessarily doom the system; many imaging tools win by being good enough for a narrow workflow. But no specific target indication appears in these tweets.

• Form factor challenge: a water immersion tank is acceptable for some scanning contexts and a major barrier for others. Whether this is a prototype artifact or a fundamental requirement matters.

• Throughput and cost realism: “fast” and “low-cost” are meaningful only relative to benchmarks—scan time, hardware cost, consumables, operator burden, and downstream interpretation overhead. Those numbers are not provided in the tweets here.

• AI’s role: the most interesting technical question may be whether Midjourney’s contribution is primarily in hardware design, inverse-problem reconstruction, learned denoising/super-resolution, automated interpretation, or an integrated stack spanning all of these. The social reaction suggests people are projecting a lot onto the project because Midjourney’s brand is associated with learned visual systems rather than classical medical devices

AI research, agents, and open models

• A notable research meta-point: Chinese open-source literature over the last year was highlighted as unusually high-ROI to follow, with the claim that the “alpha is insanely huge” @himanshustwts.

• PapersWithCode’s top trending paper was VibeThinker-3B, described as a 3B parameter model exploring verifiable reasoning in small LMs and allegedly landing in the performance tier of DeepSeek V3.2, GLM-5, and Gemini 3 Pro @NielsRogge.

• A computer-use paper, PreAct, was praised for compiling successful agent runs into a guarded replayable state machine, eliminating per-step LM calls on repeats and yielding 8.5x to 13x faster replay @dair_ai.

• Another RL/agent paper proposed LLM-as-Environment-Engineer, where the policy uses its own failures to redesign the next training environment; the associated benchmark is MAPF-FrozenLake @dair_ai.

• Omar Sar0 argued coding agents need verifiers and robust guardrails, not blind autonomous loops, reinforcing a trend toward constrained agentic execution @omarsar0.

• David Khourshid’s coding-agent take was more operational: AI-generated code still has to be read, and not reading it simply defers the debugging burden @DavidKPiano.

• On RL theory, John Schulman said PPO’s resurgence in the LLM era comes from effects not anticipated in the original paper, including the importance-ratio objective correcting biases from numeric error, async training, and forward-pass noise, while clipping alters entropy via a mechanism only later understood; he cites DAPO @johnschulman2.

• Relatedly, Chris Wolfe said recent post-GRPO analysis papers (e.g. DAPO, Dr. GRPO, GSPO, TIS) are exactly the kind of objective-analysis work he hopes to see for PPO in reasoning/agent contexts @cwolferesearch.

• John Carmack posted a detailed critique of Temporal Differences for visual representation learning, summarizing the method: train a frame encoder and a “motion encoder” on RGB frame differences so latent(frame1) + delta ≈ latent(frame2), with a 0.25 second stride; he questioned the DINO EMA anti-collapse choice and the soundness of the delta construction @ID_AA_Carmack.

AI infrastructure, inference, and product rollouts

• Xenova released a demo and kernels from the now-shut-down Fable 5 effort, claiming it had pushed Gemma 4 to 255 tok/s on WebGPU; the framing is that agentic kernel optimization could materially improve browser/on-device inference @xenovacom.

• Fal announced Kling 3.0 Turbo and O3 upgrades:

faster generation

• lower costs

• better lip-sync

• more stable motion

• stronger prompt/reference consistency in “Omni”

• up to 15s clips

• full 4K generation with Omni

• improved storyboard and multishot workflows

@fal

• Kling’s own account amplified the Fal rollout as a creator-facing quality/speed improvement @Kling_ai.

• GitHub Copilot’s Auto mode now uses a custom routing model to choose among models based on reasoning depth, code complexity, debugging difficulty, and tool orchestration needs; a blog post and a linked research paper were shared @pierceboggan, @pierceboggan.

• Kimi Code Web appears to be back online, per a brief ecosystem note @bigeagle_xd.

• Grok image generation projects were mentioned via grok.com/imagine, but with no substantive technical detail @chaitu.

Talent, labs, and competitive dynamics

• The biggest personnel story outside Midjourney: Noam Shazeer announced he is joining OpenAI, leaving Google after saying it was a difficult decision and praising his former team @NoamShazeer.

• Sam Altman celebrated the move, saying Noam was one of the people he had most wanted to work with since OpenAI’s beginning @sama, then joked about OpenAI being SOTA “in noams” @sama.

Commentary emphasized Shazeer’s significance as co-author of Transformer, T5, and Switch Transformer and pioneer of sparse MoE systems, with some calling