![]() Start with $9999999, all levels unlocked, start with 99999 health, start with 99 lives, infinite ammo for every gun. Start with $9999999, all levels unlocked, start with 99999 health. Start with $9999999, all levels unlocked. Start with $999999999, infinite boost (ignore the meter). Start with 999 of all resources (Player 1 only) Start with lots of money and XP, the cooldown for special moves has been disabled (ignore the cooldown timer), insane difficulty unlocked, infinite population.įailing any mission gives lives instead of losing them, infinite boost, time for all missions increased to 1 hour, losing all cargo will not make you fail.Ĭastlevania: Ninja Gaiden (Alt requested by BorisBeast on Discord): Start with $999999900 XP, the cooldown for special moves has been disabled (note: if you try to spam special moves, it will cancel out the previous special move used so you cannot simply spam them). Start with $999999, infinite nitro, upgrades unlocked. Start with 999999999 souls (money), start with 999999999 ammo, infinite health, reloading the gun is not required (not really sure how to word it, I made it possible to keep shooting even after the gun is out of ammo)Īll unlockables available, all levels unlocked, lots of lives, fast rage regeneration (I cannot make a hack where rage is always available, or else it will softlock) (Note: Getting new weapons is bugged, so never assign a survivor to search for weapons and ammo). Start with all guns, start with 1000000 of all ammo types, start with lots of supplies. Feel free to download these for yourself or reupload for others to play.Īll purchases give coins instead of taking, including buying more bullets. You may take these without crediting me, although it would be appreciated if you did. Reference: /abs/2101.Flash games that I have personally hacked. So it may be some time before the networks associated with mouse, dolphin, or human brains can be hacked in this way. ![]() However, one potential problem is the difficulty neuroscientists have in characterizing the networks in more complex brains, such as mammalian ones. The work also raises the possibility that other biological networks can be commandeered in the same way. But Liang and colleagues do not comment on the specific properties or architecture that make the network of Kenyon cells so efficient. Clearly, evolution will have played a role in selecting better networks in nature. One obvious conundrum is why the biological network is so much more efficient. The work raises a number of fascinating questions. “We view this result as an example of a general statement that biologically inspired algorithms might be more compute efficient compared with their classical (non-biological) counterparts,” says Liang and colleagues. By that they mean it requires a shorter training time while using a smaller memory footprint. And crucially, the biological network uses just a fraction of the computational resources. In their work, they go on to say the fruit fly brain network achieves a comparable performance to existing approaches to natural language processing. “We show that this network can learn semantic representations of words,” says Liang and colleagues. It turns out that the natural network is pretty good at this, even though it evolved for an entirely different purpose. ![]() ![]() So Liang and the team taught the fly brain network to do the same thing. A number of systems, such as BERT, use this approach to generate seemingly natural sentences. In this way, machine learning systems can learn to predict the next word in a sentence, given the ones that already appear. The idea is to start with a corpus of text and then, for each word, to analyze those words that appear before and after it. The task is based on the idea that a word can be characterized by it its context, or the other words that usually appear near it. ![]() The team then trained the network to recognize the correlations between words in the text. The team began by using a computer program to recreate the network that mushroom bodies rely on - a number of projection neurons feeding data to about 2,000 Kenyon cells. The approach is relatively straightforward. Liang and the team says it matches the performance of artificial learning networks while using far fewer computational resources. It's the first time a naturally occurring network has been commandeered in this way.Īnd this biological brain network is no slouch. This team has hacked the fruit fly brain network to perform other tasks, such as natural language processing. Now they get an answer thanks to the work of Yuchan Liang at the Rensselaer Polytechnic Institute, the MIT-IBM Watson AI Lab, and colleagues. But the power and flexibility of this relatively small network has long raised a curious question for neuroscientists: could it be re-programmed to tackle other tasks? ![]()
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