Hiroaki Kitano

[Sony AI @SonyAI_global]

@alicexiang, Global Head of AI Governance at Sony and Lead Research Scientist @SonyAI_global, writes in @TIME: data nihilism is driving one of the greatest wealth transfers in history. The solution is ethical innovation built on consent and shared value.

[Sony AI @SonyAI_global]

Lorenzo Servadei, Head of AI for Chip Design at Sony AI, speaks with Silicon Semiconductor on AI-powered EDA, #GENIE-ASI for analog subcircuit identification, and #Schemato for human-readable schematics.
Read the interview: https://t.co/iuyIABphRs
#SonyAI #ChipDesign

[Sony AI @SonyAI_global]

A defining month for Sony AI: Project #Ace reached the cover of @Nature, a new framework on #AI companionship, a full slate at #ICLR 2026, and a feature on the future of #chip design.
🔗Read the April Month in Review: https://t.co/dJ0JNPqXxX

[Hiroaki Kitano @HKitano]

元ソニーCTOの北野教授は「私たちは科学が得意なのだろうか」と問いかけた。“AIドリブン科学”時代に日本で起こること｜BUSINESS INSIDER https://t.co/44l5qMSmg6 @ＢＩＪａｐａｎより

[Sony AI @SonyAI_global]

What happens when a ball hits the net? In table tennis, net contact creates unpredictable trajectories. For Ace, Sony AI's physical AI research system, these rare events were one of the hardest real-world conditions to address.

[Sony AI @SonyAI_global]

Sense. Decide. Act. In milliseconds.

This is the story of a robot that can beat professional table tennis players.

Watch the short film: https://t.co/yjh5b1WZWQ

#SonyAI #Ace #Robotics

[Sony AI @SonyAI_global]

For 40+ years, building a robot that could rally with an elite human table tennis player at full speed was an unsolved problem. Sony AI's Ace research project set out to change that—and the results are now accepted for publication in @Nature and featured on the cover.

[Hiroaki Kitano @HKitano]

https://t.co/saQaEmegJ1

[Kanika @KanikaBK]

🚨I JUST READ SOMETHING SHOCKING.

Researchers just trained an AI to predict which scientific ideas will succeed before any experiment is run.

It is now better at judging research than GPT-5.2, Gemini 3 Pro, and every top AI model on the market.

And it learned by studying 2.1 million research papers without a single human scientist teaching it what "good science" looks like.

Here is what they did.

A team of Chinese researchers built two AI systems. The first, called Scientific Judge, was trained on 700,000 matched pairs of high-citation vs low-citation papers. Every pair came from the same field and the same time period. The AI's only job: figure out which paper would have more impact.

It worked.

The AI now predicts which research will succeed with 83.7% accuracy. That is higher than GPT-5.2. Higher than Gemini 3 Pro. Higher than every frontier model that exists.

Then they built the second system.
Scientific Thinker doesn't just judge ideas. It proposes them. You give it a research paper, and it generates a follow-up idea with high potential impact.

When tested head to head against GPT-5.2, Scientific Thinker's ideas were rated as higher impact 61% of the time. It is generating better research directions than the smartest AI models in the world.

It gets stranger.

They trained the Judge only on computer science papers.

Then they tested it on biology. Physics. Mathematics. Fields it had never seen. It still worked. 71% accuracy on biology papers it was never trained on. The AI didn't learn what makes good computer science. It learned what makes good science, period.

Then the researchers tested whether it could see the future. They trained it on papers through 2024, then asked it to judge 2025 papers. It predicted which ones would gain traction with 74% accuracy. The AI learned to spot winners before the scientific community did.

Here is what nobody is talking about. A 1.5 billion parameter model, tiny by today's standards, jumped from 7% to 72% accuracy after training. That is a 65-point leap. The ability to judge scientific quality isn't some emergent property of massive models. It can be taught to small, cheap, fast AI systems that anyone can run.

Every year, over 2 million papers flood scientific databases. Researchers spend months deciding what to work on next. Grant committees spend billions deciding what to fund.

An AI just learned to make those decisions faster, cheaper, and more accurately than any of them.

If an AI can now judge which ideas will shape the future of science, what exactly is left that only a human scientist can do?

[Hiroaki Kitano @HKitano]

ソニー・チーフテクノロジーフェロー×『Nature』編集長が語る「初期・中堅キャリアの女性研究者に光を当てる理由」 | Forbes JAPAN 公式サイト（フォーブス ジャパン） https://t.co/qzRHmWMyn5

[Sony AI @SonyAI_global]

AAAI 2026 roundup is live. Sony AI contributions span continual learning, robust autonomy, and #creator tools for editing music, #video, and #speech with a shared focus on reliability + control.
Read: https://t.co/DM50SrQ0lG
#AAAI2026 #AAAI #SonyAI #AIResearch

[Hiroaki Kitano @HKitano]

Let AI be a scientist (Hiroaki Kitano | TEDAI Vienna) https://t.co/yw9HpabZ00 via @TEDTalks

[Hiroaki Kitano @HKitano]

Ep 22 - The Nobel Turing Challenge: A New Dawn for Discovery https://t.co/oLRyAoKRCe

[Hiroaki Kitano @HKitano]

世界を変える異能集団の多様性マネジメント | DiamondQuarterly | ダイヤモンド・オンライン https://t.co/So6DxIlAHJ

[Hiroaki Kitano @HKitano]

https://t.co/gPMgwYJm7y

[Hiroaki Kitano @HKitano]

https://t.co/nUUoFhy6VT

[Hiroaki Kitano @HKitano]

This Scientist Thinks an A.I. Could Win a Nobel Prize by 2050 https://t.co/0JyWQVP8dX

[Hiroaki Kitano @HKitano]

@Mid_observatory https://t.co/5bwZ2lCiHc

[Hiroaki Kitano @HKitano]

@rkmt Organoid Intelligenceが出てきても同じことになるのか？

[Sony AI @SonyAI_global]

GT Sophy 2.1 is now available in Custom Race on all GT Sophy supported tracks and layouts in World Circuits (PS5 only). See you on the track! https://t.co/0Q3RMQmxRR

#SonyAI #AI #GTSophy #GranTurismo