He sould see how, symmetrically, other countries would be in their right to send their own criminals to the USA ... yet, being a criminal himself, he would never admit it.
i am down - voting this because i hate what the White House is doing here. Yet, if this is true, i should be aiding in diffusing this news and so i should upvote ... yet i won't.
Thanks. Also, after a bit of thought, i do believe you are right saying that we should remove one frame of all the raw camera data. ... it will, as you say, decrease the absolute values and make Wayland more laggy proportionally, yet, it doesn't change the absolute difference.
instead of calculating the average one could decide to drop the data points that are way outside of their goups.
Doing so, in the first group I would neglect the value of Δ = 1 or 6 frame and in the second group I would neglect the value of 4 or 8 frames
Results :
X11 : from 3 to 5 frames
Wayland : from 5 to 6 frames
Δ(Δ) = 5.5 - 4 = 1.5
Still it doesn't change the final result that the difference between these two Gnome versions is 1.5 camera frames at 240 frames per second.
One of the most remarkable aspects of this self-evolution is the emergence of sophisticated behaviors as the test-time computation increases. Behaviors such as reflection—where the model revisits and reevaluates its previous steps—and the exploration of alternative approaches to
problem-solving arise spontaneously. These behaviors are not explicitly programmed but instead emerge as a result of the model’s interaction with the reinforcement learning environment. This spontaneous development significantly enhances DeepSeek-R1-Zero’s reasoning capabilities, enabling it to tackle more challenging tasks with greater efficiency and accuracy.
Aha Moment of DeepSeek-R1-Zero
A particularly intriguing phenomenon observed during the training of DeepSeek-R1-Zero is the occurrence of an “aha moment”. This moment, as illustrated in Table 3, occurs in an intermediate version of the model. During this phase, DeepSeek-R1-Zero learns to allocate more thinking time to a problem by reevaluating its initial approach. This behavior is not only a testament to the model’s growing reasoning abilities but also a captivating example of how reinforcement learning can lead to unexpected and
sophisticated outcomes.
This moment is not only an “aha moment” for the model but also for the researchers
observing its behavior. It underscores the power and beauty of reinforcement learning: rather than explicitly teaching the model on how to solve a problem, we simply provide it with the right incentives, and it autonomously develops advanced problem-solving strategies. The “aha moment” serves as a powerful reminder of the potential of RL to unlock new levels of intelligence in artificial systems, paving the way for more autonomous and adaptive models in
the future.
::: https://github.com/huggingface/open-r1
Fully open reproduction of DeepSeek-R1
130 feet tall with 9/10 underwater means about 400 meters thich. Nice. Also :
"The whole ecosystem in the Southern Ocean is very resilient to these events," he wrote. "It has evolved with these icebergs being a factor for hundreds of thousands of years."
A_A @ A_A @lemmy.world Posts 40Comments 1,442Joined 2 yr. ago
He sould see how, symmetrically, other countries would be in their right to send their own criminals to the USA ... yet, being a criminal himself, he would never admit it.