An update on Google's efforts at LLMs in the medical field.

Great series on machine learning. Posting for anyone interested in more of the details on the AI's and LLM's and how they're built/trained.

In the hidden layer, the activation function will decide what is being determined by the neural network, is it possible for an AI to generate activation function for itself so it can improve upon itself?

Hi lemmings, what do you think about this and do you see a parallel with the human mind ?

... "A second, more worrisome study comes from researchers at the University of Oxford, University of Cambridge, University of Toronto, and Imperial College London. It found that training AI systems on data generated by other AI systems — synthetic data, to use the industry’s term — causes models to degrade and ultimately collapse" ...

cross-posted from: https://lemmy.world/post/811496

Huge news for AMD fans and those who are hoping to see a real* open alternative to CUDA that isn't OpenCL!

*: Intel doesn't count, they still have to get their shit together in rendering things correctly with their GPUs.

We plan to expand ROCm support from the currently supported AMD RDNA 2 workstation GPUs: the Radeon Pro v620 and w6800 to select AMD RDNA 3 workstation and consumer GPUs. Formal support for RDNA 3-based GPUs on Linux is planned to begin rolling out this fall, starting with the 48GB Radeon PRO W7900 and the 24GB Radeon RX 7900 XTX, with additional cards and expanded capabilities to be released over time.

and another commercially viable open-source LLM!

TLDR Summary:

• MIT researchers developed a 350-million-parameter self-training entailment model to enhance smaller language models' capabilities, outperforming larger models with 137 to 175 billion parameters without human-generated labels.

• The researchers enhanced the model's performance using 'self-training,' where it learns from its own predictions, reducing human supervision and outperforming models like Google's LaMDA, FLAN, and GPT models.

• They developed an algorithm called 'SimPLE' to review and correct noisy or incorrect labels generated during self-training, improving the quality of self-generated labels and model robustness.

• This approach addresses inefficiency and privacy issues of larger AI models while retaining high performance. They used 'textual entailment' to train these models, improving their adaptability to different tasks without additional training.

• By reformulating natural language understanding tasks like sentiment analysis and news classification as entailment tasks, the model's applications were expanded.

• While the model showed limitations in multi-class classification tasks, the research still presents an efficient method for training large language models, potentially reshaping AI and machine learning.

TLDR summary:

1. Researchers at MIT and Tufts University have developed an AI model called ConPLex that can screen over 100 million drug compounds in a day to predict their interactions with target proteins. This is much faster than existing computational methods and could significantly speed up the drug discovery process.

2. Most existing computational drug screening methods calculate the 3D structures of proteins and drug molecules, which is very time-consuming. The new ConPLex model uses a language model to analyze amino acid sequences and drug compounds and predict their interactions without needing to calculate 3D structures.

3. The ConPLex model was trained on a database of over 20,000 proteins to learn associations between amino acid sequences and structures. It represents proteins and drug molecules as numerical representations that capture their important features. It can then determine if a drug molecule will bind to a protein based on these numerical representations alone.

4. The researchers enhanced the model using a technique called contrastive learning, in which they trained the model to distinguish real drug-protein interactions from decoys that look similar but do not actually interact. This makes the model less likely to predict false interactions.

5. The researchers tested the model by screening 4,700 drug candidates against 51 protein kinases. Experiments confirmed that 12 of the 19 top hits had strong binding, including 4 with extremely strong binding. The model could be useful for screening drug toxicity and other applications.

6. The new model could significantly reduce drug failure rates and the cost of drug development. It represents a breakthrough in predicting drug-target interactions and could be further improved by incorporating more data and molecular generation methods.

7. The model and data used in this research have been made publicly available for other scientists to use.

A team from Israel has developed an AI model that translates Cuneiform, a 5000-year-old writing system, into English within seconds. This model, developed at Tel Aviv University, uses Neural Machine Translation (NMT) and has fairly good accuracy. Despite the complexity of the language and age, the AI was successfully trained and can now help to uncover the mysteries of the past. You can try an early demo of this model on The Babylon Engine and its source code is available on GitHub on Akkademia and the Colaboratory.

Meta AI has revealed their first AI model, I-JEPA, which learns by comparing abstract representations of images, not the pixels. This self-supervised learning model fills in knowledge gaps in a way that mirrors human perception. I-JEPA is adaptable and efficient, offering robust performance even with a less complex model. Excitingly, the code for this pioneering technology is open-source. Check it out on GitHub!