Love to chat. Sent u an email (former HFT and kernel guy).
One topic which is feel is neglected is a good GCN (or any GNN) to operate on existing code trees. Most approaches seem to prefer seq or at most tree inputs.
Is this simply not finding yet a good network architecture, or is it a performance issue ?
As a non-US person, is there anyway to bill using Stripe without incorporating? I dont mind withholding tax, etc, i just want to be able to bill and track the initial revenues.
AFAIK, Stripe is not available in all-EU countries. Paypal just works in this case.
IANAVC (not a vc), but i think do this is doubling down on risk.
If you take seed from VC, then:
1. if you are mediocre then things become very difficult for you
2. if you are successful, the VC can discount the risk premium, and push u into an even higher valuation
.. CPC is .. translating a generative modeling problem to a classification problem... uses cross-entropy loss to measure how well the model can classify the “future” representation amongst a set of unrelated “negative” samples...
For the ATARI, the "real world" is the present frame, and a fixed set of 4 buttons and 4 directions. This of course is the game pre-programmed into the ALE ROM.
You can take any action, and get the next frame. but you cant "undo" an action, and you cant restart a game from a fixed state (see the Go-Explore controversy). And you cant explore 4 different actions in an interesting frame.
So now, if you learn a network which predicts the next frame, you can enter the world of model-based learning, where we do a simulated move tree roll-out (i.e. not calling the ATARI), try a gazillions moves, and only then select an action and get the next sample.
In a formally defined synthetic domain such as chess or logic programming, it is not clear whether this is helpful. We are simply trading one cpu time (calling the environment) for other cpu time (running our own learned im-precise model of the environment)
Of course DM has a chess function which does codes the rules of the next move. It can return a LOSS if you try an illegal move. But this function is NOT called for the tree roll out.
> but I am confused by its claim ... without knowing the game rules.
> probably some similar hard-coding of Atari actions
Nope, no hard coding.
Consider trying to MCTS on an Atari game. You have to "learn to predict" the <next frame, action> pairs. Initially this guess is very bad, but eventually your predictions are good enough that rolling out a tree of predictions improves your action selection
For Go, and chess, we twist our self into NOT using the game rules in the simulator e.g. for each move, just indicate if GAME LOSS WIN
Whether this paper worthy of a new Nature hype cycle is a separate debate
Any robo-taxi can be programmed to drive like a grand-ma to improve battery life. The cost of a x2 battery size is easier to amortize. Its life time can be managed and optimized with ML.
And yes - it can get the greatest and latest battery chemistry.
At this stage - we just dont need a "battery miracle" or a Musk-moonshot (trade-mark?) for passenger EVs.
Ireland has ~10GW generation capacity (mostly coal & extra dirty peat).
At around $1/W for new solar and wind generation, the Apple back taxes owned to Ireland can convert the entire Island to renewable energy. It will be an asset that generates income for 30 years.
(yes, ok, more storage needed, and interconnect to the UK, and keep some gas as backup ..)
https://old.reddit.com/r/MachineLearning/comments/khin4c/n_m...