Why the big discussion on 24 September at #European_Parliament is important. Digital regulation is a deserving effort but just not enough, and will not breach Big Tech Castle. Need more and different. https://t.co/zWdDr9zNlq
Sometimes, the obvious must be studied so it can be asserted with full confidence:
- LLMs can not answer questions whose answers are not in their training set in some form,
- they can not solve problems they haven't been trained on,
- they can not acquire new skills our knowledge without lots of human help,
- they can not invent new things.
Now, LLMs are merely a subset of AI techniques.
Merely scaling up LLMs will *not* lead systems with these capabilities.
There is little doubt AI systems will have these capabilities in the future.
But until we have small prototypes of that, or at least some vague blueprint, bloviating about AI existential risk is like debating the sex of angels (or, as I've pointed out before, worrying about turbojet safety in 1920).
https://t.co/Qkyqo4WhO0
Cuántas películas interesantes han salido con el cine francés. Cambiemos de país.
Hagamos un hilo aquí entre todos recomendando películas de cine ITALIANO🇮🇹.
Mi recomendación, (obviamente):
TTT models could be next Frontier in Generative AI : A promising Architecture Test-Time-Training claims that TTT models can not only process far more Data than Transformers but also do so by not consuming as much compute Power ▶️
#AI#BigData#MachineLearning#Python#javascript #TensorFlow
#HTML #LLM #Web3 #Cloud #DataScience #Robotics #Coding #Programming #100DaysOfCode
https://t.co/IaVW8zIzwl
It took me 5 years to master all 24 of these machine learning concepts. In the next 24 days, I'll teach them to you one by one (with examples of how I've used them in business cases). Here's what's coming:
1. Linear Regression
2. Clustering
3. Decision Tree
4. Neural Networks
5. Reinforcement Learning
6. Logistic Regression
7. Naive Bayes
8. Supervised Learning
9. Support Vector Machine
10. Probability
11. Random Forest
12. Variance
13. Evaluation Metrics
14. Bagging
15. Data Wrangling
16. Dimensionality Reduction
17. K-nearest Neighbors Algorithm
18. Programming
19. Regularization
20. Statistics
21. Binomial Distribution
22. Bootstrap Sampling
23. Exploratory Data Analysis
24. Data Collection
There you have it- my top 24 concepts on Machine Learning. The next problem you'll face is how to apply data science to business.
I'd like to help.
I’ve spent 100 hours consolidating my learnings into a free 5-day course, How to Solve Business Problems with Data Science. It comes with:
300+ lines of R and Python code
5 bonus trainings
2 systematic frameworks
1 complete roadmap to avoid mistakes and start solving business problems with data science, TODAY.
👉 Here it is for free: https://t.co/YXG4pL97ZN
💵 @eBay has launched ‘Business Cash Advance’, a new revenue-based financing product provided by embedded finance platform @LiberisGlobal#financing#embeddedfinance
https://t.co/AMmPvruLXz
The real power of #AI is not *Artificial*, but Accelerated, Actionable, Adaptable, Amplified, Assisted, Augmented, Automated, Awesome Intelligence. Embrace it in your work!
https://t.co/2Kfgx6oI9h
#GenerativeAI / Extraordinario enfoque en el tema apreciada Sally. Innovación, transformación. Excelente podcast, nuevas tecnologías! Gracias por la etiqueta !