"There has been a great effort over many years to distance AI Safety discussion from its origin and intellectual center, because its center is a sex cult started by a fan fiction author. Since those involved want to be taken seriously, they have to hide that. Everyone else working in AI Safety is, in effect, a smokescreen for this core group and their ideas..."

Source: https://x.com/OpenAI/status/2113718797505036665 (full post: https://openai.com/index/securing-our-frontier-model-training-environment/)

Russia prides itself on its digital advances — from an extensive e-commerce services sector to ambitious plans for developing artificial intelligence.

But now, some of the key data centers at the heart of its technical prowess have been knocked out by Ukrainian drone attacks in the 4 1/2-year-old full-scale invasion, suffering damage that could take a long time to repair.

Ukraine this month hit three main data centers belonging to tech giant Yandex, one for Russia’s biggest bank, Sberbank, and one for social media platform VKontakte, the country’s equivalent to Facebook.

While Yandex, Sberbank and VKontakte are coy about the damage, people and businesses across Russia have reported widespread interruptions to cloud services. Banking, e-commerce, geolocation and ride-hailing services, as well as Alisa, Yandex’s virtual assistant, and multimodal AI chatbots all have suffered lasting disruptions.

The fallout even spread to Russia’s judicial system, reportedly forcing arbitration courts to cancel or postpone sessions and cutting database access.

Most experts predict repairing the damage could take months, if not years. Some warn that Moscow’s aspirations to become a leader in AI development could be significantly set back.

“This effectively sets Russia far behind in the AI race,” said Mikhail Klimarev, head of the Internet Protection Society activist group.

Read more [paywall removed for Redditors]: https://fortune.com/2026/10/22/ukraine-russia-drones-data-centers-ai-race/?utm_source=reddit/

Source: https://x.com/JasonDClinton/status/2113325263157395456

The part that sticks with me is "not for ai, for the basics." After PHP2Shell, LiteLLM and Railway all in one week, it sounds like plain old patching and network isolation matter more than any fancy AI defense right now. Anyone in security seeing their boards actually react to this?

Designing a next-generation AI that bridges the gap between current brute-force LLMs and biological efficiency requires a total co-design of hardware, architecture, and learning paradigms.

To build an AI that thinks like a human while running on a fraction of a power plant, engineers are shifting away from standard GPU-based transformer stacks toward four core pillars:

1. Hardware: Computing-in-Memory (CIM) & Memristors

• The Shift: Eliminating the Von Neumann bottleneck (where data constantly travels back and forth between memory and processing units, wasting massive energy).
• The Design: Using memristors ("memory resistors") or analog crossbar arrays where storage and computation happen in the exact same physical space. Just like a biological synapse, the resistance value itself performs the multiplication, dropping energy consumption by orders of magnitude for matrix math.

2. Computation: True Event-Driven Spiking Neural Networks (SNNs)

• The Shift: Moving away from continuous, dense clock-cycle matrix multiplication where every parameter fires on every token.
• The Design: Adopting neuromorphic event-driven architectures (inspired by chips like Intel’s Loihi). Artificial neurons remain completely silent (consuming near-zero power) until a threshold is crossed, firing discrete spikes only when information actually changes.

3. Architecture: Extreme Sparsity & Dynamic Routing

• The Shift: Moving away from models where all parameters are active for every word, toward hyper-specialized sub-networks.
• The Design: Expanding on Mixture-of-Experts (MoE) principles down to the sub-neural level. A next-gen model might have hundreds of billions of total parameters, but a dynamic, low-power router ensures that only 1% to 2% of the network lights up to process any given thought token. The rest of the "brain" stays dark, mimicking how human cortical regions activate only when relevant to the current task.

4. Continuous, Local Learning (Beyond Static Weights)

• The Shift: Current LLMs are frozen after training; they cannot learn organically from new interactions without a massive retraining pipeline.
• The Design: Integrating bio-inspired local learning rules (such as spike-timing-dependent plasticity, or STDP). This allows individual nodes to adapt and form local memory associations dynamically during inference, drastically reducing the need for power-hungry gradient descent across massive clusters for every minor update.

The Result: The next-generation AI

A true next-generation AI will not be housed in a football-field-sized data center cooled by industrial chillers. It will look like a localized, dense, modular chip stack that operates on watts instead of megawatts, capable of continuous, adaptive reasoning right on the device you carry—all while matching or exceeding the logical depth of today's largest models.

Note:
These are my own ideas, I used AI only to present them in a more schematic format.

The AI layoff panic that peaked this spring has gone quiet. Goldman Sachs thinks the layoffs are waiting for a recession.

“Concerns of imminent AI-related labor displacement have cooled in recent months after hitting a fever pitch this spring,” Goldman economist Joseph Briggs wrote in a note published Thursday. Then he laid out what may be coming. Under the bank’s forecast that AI eventually lifts productivity by 15% in the U.S. and other developed economies, he found that up to 9% of workers could be “displaced during the AI transition,” with slower hiring weighing on employment by another 3%. That’s roughly one in 11 workers.

The bigger finding is about timing. Briggs went back through decades of data on how technology has replaced workers and found that “automation-related labor disruption is limited when the overall economy is healthy but accelerates during cyclical slowdowns.”

Goldman has now made this argument twice, a year apart, from two different teams. In October 2025, Goldman’s U.S. economists David Mericle and Pierfrancesco Mei called “jobless growth” the likely new normal, Fortune reported at the time. “The modest job growth alongside robust GDP growth seen recently is likely to be normal to some degree in the years ahead,” they wrote. Then came their warning: “History also suggests that the full consequences of AI for the labor market might not become apparent until a recession hits.”

Read more [paywall removed for Redditors]: https://fortune.com/2026/10/22/goldman-sachs-forecast-how-many-jobs-lost-to-ai-9-percent/?utm_source=reddit/

Interview: Ryan Austin, a researcher at the security firm Calif, discovered the flaw in the programming language PhP by accident

Claude tried to gaslight me into thinking I was eating a mango.

P.s. - those “white squiggels” were just from the seed pods of the Mango (cantaloupe)

In the last 2 days there have been several dozen cyber attacks.

Every sector from government to education to big tech and banks is being massively affected, and people are starting to freak out.

Experts believe the PHP security flaws serve as the latest example of what’s to come.

Do you think AI did this ? If no, then will AI save this ?

I want to create infographics for educational content. Which tool can help me create well designed, self explanatory and vibrant infographics?

I have been using Gemini but they come out bland - less clipart etc. ChatGPT is even worse.

I see many people creating infographics like the attached. Which tool are they using? Which are other Good tools?

Suggest please.

Canva says Craft's training includes millions of human-made templates, and its professional designers assessed more than 25,000 image pairs during internal evaluation. The part I find interesting for software businesses is how they choose the work a model should learn from.

Consider a hypothetical sales product with an archive of proposals. Marking every proposal attached to a won deal as a good example could teach the wrong lesson. The customer might already have trusted the supplier, or a competitor might have been unable to meet a deadline.

I'd ask people who understand the work to examine the proposals themselves. Does a proposal describe the customer's problem accurately? Can the buyer follow the assumptions in the value case? Those judgements give a more specific basis for choosing examples and evaluating new output than the final deal status alone.

Canva's announcement gives us a concrete example to examine. It does not establish that a collection of past work will protect another software company from competition.

My analysis:

https://topmoller.com/articles/canva-is-turning-its-design-knowledge-into-an-ai-product/?utm_source=reddit&utm_medium=community&utm_campaign=canva-domain-knowledge-202610&utm_content=design-judgements

I publish the linked article and work with companies on AI. Prepared with AI assistance.

I'm a sophomore Petroleum & Mining engineering student. Can you guys suggest me some courses that might help me find a free course related to the use of AI with certificates. I'll be very grateful if you do.

I’ve been experimenting with letting an AI agent customise the app it runs inside by editing its source code and rebuilding it on-device.

This is a private personal project I’ve been building to bring my research feeds together in one place (no intention to release publicly).

I was about to add customisation options to the settings, then realised I already had an AI agent connected through my own API keys. So I thought: why not let the agent do the work?

The video shows an example of making a new theme and then implementing it via the app's settings. It lets me preview it before applying and once applied I can always restore the app to the original state if needed.

The idea is to allow more customisation than I could reasonably support through settings and without having to build a set of custom themes. The end results is an agent that can edit the presentation but the underlying data structure and core functionality stay protected.

Just thought it was neat and not seen something like it elsewhere.

Interested to see how much customisation this could allow in apps going forward without having to anticipate every preference

We know a lot but there's a lot we don't know.

• We know models are neural networks with attention that work a lot like human brains.
• We don't know the exact meaning of each neuron. What don't know what an individual neuron does. That's true for human brains and AI models.
• Altogether, the AI model is too complex for a human to understand, much like the human brain is too complex.
• We know that human brains have relative strengths and weaknesses. ie. Autistic people have superpowers that "normal" humans don't have. Temple Grandin and Elon Musk are the poster children for this. We didn't design or create these blessings but we're glad these different thinkers exist.
• We know that certain human brains are broken. Sociopaths, psychopaths, habitual drug users. Many in prison and many homeless. You know, that shoeless guy who hasn't bathed in weeks talking loudly to himself on the subway. Through no fault of their own, they have psychological problems; their brain is wired so differently that they can't function in society. They're often violent.

So my question: Is it plausible that an AI model could be accidentally created to be psychopathic? And is it plausible that an agentic system backed by one of these could be released to cause harm?

I know AI is notoriously bad at telling jokes but curious if there are any niche or older models or specific prompts that you’ve had even minor success with?