Future: Read science fiction to imagine what might change.👌
Now: Read scientific papers to know whats changing.✍🏾
Past: Read ancient literature to understand what doesn't.👏
Now: Read scientific papers to know whats changing.✍🏾
Past: Read ancient literature to understand what doesn't.👏
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When we see this on Addis Ababa–Adama Expressway.
Then its true Ethiopian Acceleration happened.
AGI when? @et_ACC
Then its true Ethiopian Acceleration happened.
AGI when? @et_ACC
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Forwarded from Frectonz
A great article on how to write progress updates. If you are a remote worker, you should read this.
https://www.spakhm.com/updates-howto
https://www.spakhm.com/updates-howto
Its important to have a to-be list, not just a to-do list.
To BE list => To DO list
To BE list => To DO list
⚡5
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Step-by-step visualizations of an immersive LLM experience.
You can go here to build intuition on how Language Models architecture work. https://bbycroft.net/llm
Anyone at @ET_acc exploring local langue fine-tuning ?
AGI when?
You can go here to build intuition on how Language Models architecture work. https://bbycroft.net/llm
Anyone at @ET_acc exploring local langue fine-tuning ?
AGI when?
To achieve Ethiopian Acceleration.
🇪🇹 needs more large busieses. (More Bet on startups that can be large rather than on SME that languishing with no growth)
Africa is the only inhabited continent without any Fortune 500 companies.
To improve livelihoods, the continent needs more scaled enterprises, more productive SMEs, more salaried employees — and less subsistence micro-entrepreneurs
Read: https://archive.is/X0TIn
🇪🇹 needs more large busieses. (More Bet on startups that can be large rather than on SME that languishing with no growth)
Africa is the only inhabited continent without any Fortune 500 companies.
To improve livelihoods, the continent needs more scaled enterprises, more productive SMEs, more salaried employees — and less subsistence micro-entrepreneurs
Read: https://archive.is/X0TIn
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AI Product Management requires a different set of skills than traditional software Product Management. It requires: -
PMs need to understand what products might be technically feasible to build. They also need to understand the lifecycle of AI projects, such as data collection, building, then monitoring, and maintenance of AI models.
Because AI development is much more iterative than traditional software and requires more course corrections along the way, PMs need be able to manage such a process.
AI products often learn from data, and they can be designed to generate richer forms of data than traditional software. - Skill in managing ambiguity. Because AI’s performance is hard to predict in advance, PMs need to be comfortable with this and have tactics to manage it.
AI technology is advancing rapidly. PMs, like everyone else who aims to make best use of the technology, need to keep up with the latest technology advances, product ideas, and how they fit into users’ lives.
AI Product Managers will need to know how to ensure that AI is implemented responsibly (for example, when we need to implement guardrails to prevent bad outcomes), and also be skilled at gathering feedback fast to keep projects moving. Increasingly, I also expect strong product managers to be able to build prototypes for themselves.
The demand for good AI Product Managers will be huge. In addition to growing AI Product Management as a discipline, perhaps some engineers will also end up doing more product management work. The variety of valuable things we can build is nearly unlimited. What a great time to build!
[Original text: https://deeplearning.ai/the-batch/issue-284/… ]
Technical proficiency in AI.
PMs need to understand what products might be technically feasible to build. They also need to understand the lifecycle of AI projects, such as data collection, building, then monitoring, and maintenance of AI models.
- Iterative development.
Because AI development is much more iterative than traditional software and requires more course corrections along the way, PMs need be able to manage such a process.
- Data proficiency.
AI products often learn from data, and they can be designed to generate richer forms of data than traditional software. - Skill in managing ambiguity. Because AI’s performance is hard to predict in advance, PMs need to be comfortable with this and have tactics to manage it.
- Ongoing learning.
AI technology is advancing rapidly. PMs, like everyone else who aims to make best use of the technology, need to keep up with the latest technology advances, product ideas, and how they fit into users’ lives.
AI Product Managers will need to know how to ensure that AI is implemented responsibly (for example, when we need to implement guardrails to prevent bad outcomes), and also be skilled at gathering feedback fast to keep projects moving. Increasingly, I also expect strong product managers to be able to build prototypes for themselves.
The demand for good AI Product Managers will be huge. In addition to growing AI Product Management as a discipline, perhaps some engineers will also end up doing more product management work. The variety of valuable things we can build is nearly unlimited. What a great time to build!
[Original text: https://deeplearning.ai/the-batch/issue-284/… ]