Sakana AI introduces The AI Scientist, a system enabling foundation models like LLMs to perform scientific research independently, automating the entire research lifecycle.
The highlighted articles cover a variety of topics, including algorithmic thinking for data scientists, outlier detection in time-series data, route optimization for visiting NFL teams, minimum vertex coloring problem solution, high-cardinality features, multilingual RAG (Rapidly-explainable AI) system development, fine-tuning smaller transformer models, long-form visual understanding, multimodal image-text models, the theoretical underpinnings of learning, data science stress management, and reinforcement learning.
First, using the demonstrations significantly outperforms the no demonstrations method
even with small k (k = 4), and performance drop
from using gold labels to using random labels is
consistently small across varying k, in the range of
0.8–1.6%.7
Interestingly, model performance does
not increase much as k increases when k ≥ 8, both
with gold labels and with random labels.
even with small k (k = 4), and performance drop
from using gold labels to using random labels is
consistently small across varying k, in the range of
0.8–1.6%.7
Interestingly, model performance does
not increase much as k increases when k ≥ 8, both
with gold labels and with random labels.
BrisquelyBrusque writes "I think what he's getting at is, we'll never have an algorithm that is
1. fast, distributed, easily deployed
2. interpretable
3. able to converge quickly for most problems
4. robust to noise, outliers, multicollinearity, class imbalance, and the curse of dimensionality
5. optimized for any combination of numeric variables and factors
6. self-supervised (no need for extensive parameter tuning)
7. capable of probability estimates as well as predictions
8. able to issue predictions for multiple targets
9. comfortable with structured, unstructured data (text, 2D, 3D, audio, tabular)
10. open-source
Besides, a recent analysis by Amazon Web Services found that 50 to 95% of all ML applications in an organization are based on traditional ML (random forests, regression models). That's why these application papers matter -- we're learning to make progress in certain areas where traditional ML fails."
1. fast, distributed, easily deployed
2. interpretable
3. able to converge quickly for most problems
4. robust to noise, outliers, multicollinearity, class imbalance, and the curse of dimensionality
5. optimized for any combination of numeric variables and factors
6. self-supervised (no need for extensive parameter tuning)
7. capable of probability estimates as well as predictions
8. able to issue predictions for multiple targets
9. comfortable with structured, unstructured data (text, 2D, 3D, audio, tabular)
10. open-source
Besides, a recent analysis by Amazon Web Services found that 50 to 95% of all ML applications in an organization are based on traditional ML (random forests, regression models). That's why these application papers matter -- we're learning to make progress in certain areas where traditional ML fails."
