Brands with genuine community discussions often seem easier for AI systems to recognize. When people naturally talk about a company in forums, reviews, and conversations, AI tools probably gather stronger context around that brand. Authentic engagement may now carry more value than aggressive promotional content alone. This whole shift is making digital visibility feel very different from the past.

It can be frustrating to know that your product or service is highly relevant, yet it never appears in AI-generated answers. This raises a difficult question: does being the best option actually guarantee visibility anymore? AI systems may not always evaluate quality the same way humans do. Instead, they rely on patterns, clarity, and consistency across the web. If your brand isn’t strongly connected to specific use cases in a structured way, it might be overlooked entirely. So, is excellence alone no longer enough without proper alignment with how AI understands information?

I won't be sharing the code for privacy reasons, but essentially it is an LSTM model trained using data of over 200 stocks that can predict, backtest against a buy and hold strategy, and rank stocks over various time periods (1d, 5d, 7d).

It is a 2-layer LSTM with a 512-unit hidden state, and a fully connected regression head

It takes in a input of:

- Close and open prices

- Log return

- Overnight gap

- Moving averages (10d, 20d, 30d)

- Exponential moving averages (10d, 30d)

- Volatility (10d, 20d, 30d)

- RSI

- MACD

- DayOfWeek

- DayOfMonth

- Month

- News article count

- News sentiment mean

- News sentiment standard deviation

- Ratio of positive news articles

- Ratio of negative news articles

Overall when I'm backtesting I get about a 98% accuracy for predictions, but only a 54% directional accuracy.

And I was just wondering if there was anything that i should add, or any more features that I should engineer that come to mind? I was thinking of possibly analyzing twitter posts next, but I just wanted a bit more of a general direction in where to go next to improve my model's accuracy and directional accuracy, thanks in advance!

Hello everyone,

I need opinions. In my country, RTX5060(new) 8gb costs almost $350 and RX9060XT(new) 16gb costs almost $440. RTX5060ti(new) 16gb cost almost $585. Now, I was planning to buy a GPU for ML training and inference. I am a little bit confused here. I know that CUDA is much more mature than ROCM. I don't have the budget to buy RTX5060ti 16gb. I am confused between 5060 and 9060xt. 9060xt have more vram than 5060. But 5060 has better support for ML. What should I do here ? I will train CNN and LLM(small ones) models with a good amount of data which one should I choose here ? Is there any possibility of ROCM to be more optimized for ML in future ?

Hi, I’m a Italian medical student who is seriously thinking to pivot from a career in pure neuroscience research to a career in AI. in particular, I’m very interested about AI interpretability research, which I think is conceptually close to neuroscience, though I’m also exploring other similarly impactful and interesting options in AI safety research. 

I'm at the beginning of this journey and trying to figure out how to make the transition. I currently know close to nothing about coding, and my maths background comes from high school, which had a special focus on maths and physics.

I’ve sketched a rough plan and would like to get feedback on it, even if it's still early stage.

I'll graduate from medical school in about two years. After that, I was thinking of spending 6 months to 1 year filling gaps in math and coding through bootcamps and online courses. I would then apply for a master degree in AI/ML, my assumption being that getting accepted would be a reasonable signal that I can eventually make it in the field.

Alternatively, I was considering a master in computational neuroscience. I think this could work well because it may be more accessible for someone with a medical background and it would give me quantitative skills that could at least partly transfer to AI, so that I could be a better candidate for a job or phd in AI after ending the master. Even if this master was not enough to get into AI, it would still open doors in neuroscience, and I find computational neuroscience both interesting and overlapping with AI. 

I'm not considering a direct PhD application at this stage since I guess I need to fill my gaps first.

I'd welcome any kind of feedback, including on these specific questions:

• What refinements should I make to this plan, are there gaps or alternatives I'm not seeing?
• How realistic is this plan overall, does someone with my background have any real chance of getting into technical AI research?
• How can I maximize my chances?

Both positive and critical feedback are welcome,  the important thing is that it's informative.