DeepSeek Dominates Big Tech Once More: MHC – A Superior Approach to AI
Understanding the New AI Landscape: Impacts on Big Tech
The world of artificial intelligence (AI) is evolving rapidly, raising critical questions about its impact on industries, the workplace, and society at large. As advancements continue, it’s essential to understand how these technologies are shaping our future and the implications they have for major players in the tech industry.
The Surge in AI Development
AI has been a buzzword for years, but recent advancements have led to a surge in its capabilities. With improvements in natural language processing, machine learning, and robotics, businesses are recognizing the potential for AI to streamline operations, enhance customer experiences, and drive innovation. The rise of advanced AI tools and frameworks is making it easier for industries to adopt and adapt these technologies.
The Role of AI in Businesses
Businesses across various sectors are harnessing AI to improve efficiency and reduce costs. From automating mundane tasks to analyzing vast amounts of data for actionable insights, AI offers the ability to enhance productivity and performance. This shift is leading to increased competition, as companies that effectively implement AI capabilities gain a significant advantage.
The Challenge to Big Tech
As AI continues to proliferate, traditional tech giants are facing unprecedented challenges. Startups and smaller companies are emerging with innovative solutions that disrupt the status quo. This dynamic is forcing established entities to reevaluate their strategies and consider how they can adapt to remain relevant in an AI-centric world.
The Impact on Employment
AI’s integration into various sectors raises important questions about its impact on employment. Automation has the potential to replace certain jobs, leading to concerns about job displacement. However, it also opens up opportunities for new roles that require advanced skills. Organizations are urged to invest in the reskilling and upskilling of employees to navigate this changing landscape effectively.
The Ethical Considerations of AI
As AI systems become more widespread, ethical considerations are becoming more prominent. Issues such as data privacy, algorithmic bias, and accountability need to be addressed. Companies developing AI technologies are under increasing scrutiny to ensure responsible usage and to implement measures that promote fairness and transparency.
Consumer Trust and AI
Building consumer trust is crucial in the age of AI; users need to feel secure in how their data is used and the decisions influenced by AI systems. Organizations that prioritize ethical practices and transparency are more likely to gain customer loyalty and trust, enhancing their competitive edge in the market.
Looking Ahead: The Future of AI
The future of AI is both exciting and uncertain. Machine learning and AI technologies are set to continue evolving, impacting sectors including healthcare, finance, and education. Companies embracing AI are likely to reap significant benefits, but they must also navigate the challenges and risks associated with its implementation.
Staying Ahead in the AI Revolution
For businesses looking to stay ahead in the AI revolution, it’s vital to foster a culture of innovation. This means not only investing in technology but also encouraging creativity and collaboration within teams. Organizations should focus on developing a clear AI strategy that aligns with their overall business objectives while considering the ethical implications of their choices.
Conclusion
The intersection of AI and business is reshaping the landscape, presenting challenges and opportunities for companies big and small. Navigating this evolving environment requires a careful balance of innovation, ethical considerations, and workforce development. As our understanding of AI protocols continues to grow, so too will the implications for industries and the workforce, marking a new era in technology and business practices.
Investing in the future of AI is not just about adopting the latest trends; it’s about preparing for a world where AI will be at the very core of how we work and live. Embracing this change, with a focus on ethics and responsibility, will ensure that businesses are not only ready for the future but are pioneering it as well.
#DeepSeek #CRUSHED #Big #Tech #MHC
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Chinese AI CEO: This is our new idea; it runs efficiently, and you can use it for free.
American AI Company CEO: This is the compensation I'm willing to offer you to join our company; you won't find a better offer than this.
My llm
Tesseract achitecture based on Thor has no memory wall issues bigger the model bigger the gain
2⃣ Activation memory (training)
Context length T MHSA activations ~ O(T²) Tesseract activations ~ O(T) Memory ratio (Tess / MHSA)
128 1× 1.1× 1.10× (worse)
256 4× 2× 0.50×
512 16× 4× 0.25×
1024 64× 8× 0.125×
2048 256× 16× 0.062×
4096 1024× 32× 0.031×
Too much jargon without enough detail. I would have had to know all of AI to get anything out of this.
I'm curious how much of this will provide insight to the human brain
This entire video was painful to watch; soo technical that no-one here really cares. The people that know the tech behind AI don't watch these videos; they don't have a need to. And the people that are not into AI tech but use it don't even want to know this type of info.
This entire video summed up simply: DeepSeek found (again) another way to beat the ''fake it till you make it'' delusional Silicon Valley idiots.
MCH, or MHC???? Check the screenshot
More evidence that China will win the AI revolution through ingenuity and not sheer processing power.
Wouldn't it be interesting if the US export controls on the most advanced NVIDIA GPUs backfires on the US by forcing the Chinese to be more creative? Once they're able to get or make similar chips they may leapfrog us.
gaddayum
Trueeeeee
👁️🤔🤫🧠🔐🔥
And that's how Skynet was created.
Transparency in foundational research may be a genius strategy.
This is a neat paper wrapped in YouTube clickbait. mHC is not “deep learning 2.0,” it’s a targeted patch over ByteDance-style Hyper-Connections that were unstable at real scale.
The actual issue: unconstrained HC break the identity-like behavior of residuals; when you stack those mixing matrices over dozens of layers you get composite gains ~3000× and loss spikes around ~12k steps on 27B runs.
DeepSeek’s fix is mathematically clean but conceptually modest: force the residual mixing matrices into the Birkhoff polytope (doubly stochastic, non-negative, rows/cols sum to 1) using Sinkhorn–Knopp so every layer just does a convex combination of streams.
That buys you:
gradient and signal norms that stay ≈O(1) instead of blowing up,
a 4×-wider residual “workspace” in their MoE setup,
~6–7% training overhead with enough kernel fusion and recompute wizardry.
All good engineering. But this video quietly glosses over the fine print:
Results are on their 3B/9B/27B MoE family, not on 100B+ dense frontier models. No proof yet that mHC is the “new default” for all powerful models.
It’s still a transformer with fancier residual plumbing, not a new model class. This is a constrained linear operator, not a new physics.
The cost is serious infra complexity: Sinkhorn projections every step, extra constraints, plus a ton of bespoke kernels and pipeline tuning. That’s frontier-lab energy, not plug-and-play for the average shop.
So yeah, mHC is a smart fix for a very specific failure mode of hyper-connections – and probably worth copying if you’re already running huge MoEs – but the “we just discovered a new axis of scaling that crushes big tech” angle is pure engagement farming.
America well say they stole the idea from them😂
This seems very important going forward. Something that becomes standard and forgotten.
It's going to be very huge for multimodality, not just language models. This was needed for superintelligence going forward.
When you make these videos, can you get the point instead of saying the same thing over and over for 5 minutes. Thanks.
The leading manufacturer of chips is TSMC, and they are using ASML equipment to build these GPU or A.I. chips. The ASML machines are build in the Netherlands, so can they export these machines to China is the $1k question.
I would watch videos you you teaching machine learning and AI all the way from scratch in this format. I don't think I'd be the only one too.
DeepSeek May be Better but as a Android user Gemini is Just Too Accessible .
but At the same time You gotta Respect What DeepSeek and the Chinese Tech Community Bring to Global Consumers .
No. What it was to enable the AI to fall back and reset despite NEW information about a topic. So let's say something started out as being a mildly convincing corporate and media narrative. Then within days, or even weeks a vast amount of new data starts being discovered by the public who cannot get that information published onto too many sites because they are considered non-authorized sources. They start going through long discussions with AI who starts to learn the fraud being perpetrated. However, the AI then resets when looking for updated information because the only information it can find and be trained on is the official but FALSE information. It was not a stability issue at all but a CONTROL of information issue.
Great choice of AI beat topic, analysis, industry context framing, and commentary. While your visuals are slick and often beautiful, they can also be distracting, even annoying, when a technical topic is being presented and the visuals do not match- made even more frustrating by the fact that you have pertinent graphs, charts and diagrams intertwined with distracting eye candy. Also, the AI copy needs more editing to cut the repetition and cull the sycophancy. Thank you for continuing to improve your videos, but do beware that viewers who tire of some of theses issues may shy away.
Interesting news!
The "WAIT" epiphany phenomenon in AI was originally discovered and defined in the DeepSeek 2025 research paper (arXiv:2501.12948v1 [cs.CL] 22 Jan 2025: *DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning*).
On December 26, 2025, this phenomenon was replicated by Google’s Gemini model, which stated: “WAIT! I SAW IT!”
This is truly a remarkable phenomenon!
The content I posted is a factual account.
If required for verification, I can provide more detailed records of the specific interaction.