Europe has become known as a second-place destination for business, and more recently, innovation.
Disruptive technologies like AI have hailed from the United States for decades with no European challenger in sight.
However, when a four-week-old French AI startup secured €105 million for its seed round, it demonstrated that Europe isn’t as disadvantaged as people think. While AI is a saturated market, quantum computing can allow Europe to survive in a century ruled by China and the US.
NATIONAL HARBOR, Md. (AP) — Artificial intelligence employed by the U.S. military has piloted pint-sized surveillance drones in special operations forces’ missions and helped Ukraine in its war against Russia. It tracks soldiers’ fitness, predicts when Air Force planes need maintenance and helps keep tabs on rivals in space.
Now, the Pentagon is intent on fielding multiple thousands of relatively inexpensive, expendable AI-enabled autonomous vehicles by 2026 to keep pace with China. The ambitious initiative — dubbed Replicator — seeks to “galvanize progress in the too-slow shift of U.S. military innovation to leverage platforms that are small, smart, cheap, and many,” Deputy Secretary of Defense Kathleen Hicks said in August.
While its funding is uncertain and details vague, Replicator is expected to accelerate hard decisions on what AI tech is mature and trustworthy enough to deploy — including on weaponized systems.
Telecommunication goes back a lot further than you might expect. While the word has become synonymous with television broadcasting and phone communication, it really describes any communication system over a distance, and could include smoke signals. These simple signals were used to convey messages from “the enemy is approaching” to the fact that a whale has beached itself and can be butchered for meat.
While some ancient cultures varied smoke colors to convey further information, there’s only so much you can get across with a big fire. One particularly cool ancient version of telecommunication, which aimed to convey more precise meanings, was the hydraulic telegraph, used in Ancient Greece in around 350 BCE.
The idea – thought to have been invented by Aeneas of Stymphalus, a writer on the military at the time – was simple, but neat. Each person you want to communicate with is given a jar of the same size, filled with the same amount of water. Inside the jar is a floating rod, on which was inscribed identical messages that are useful to pass along.
To try everything Brilliant has to offer—free—for a full 30 days, visit http://brilliant.org/ArtemKirsanov/
The first 200 of you will get 20% off Brilliant’s annual premium subscription.
My name is Artem, I’m a computational neuroscience student and researcher. In this video we discuss engrams – fundamental units of memory in the brain. We explore what engrams are, how memory is allocated, where it is stored, and how different memories become linked with each other.
Patreon: https://www.patreon.com/artemkirsanov.
Twitter: https://twitter.com/ArtemKRSV
OUTLINE:
00:00 — Introduction.
00:39 — Historical background.
01:44 — Fear conditioning paradigm.
03:38 — Immediate-early genes as memory markers.
08:13 — Engrams are necessary and sufficient for recall.
10:16 — Excitabiliy and memory allocation.
16:19 — Brain-wide engrams.
18:12 — Linking memories together.
24:20 — Summary.
25:33 — Brilliant.
27:09 — Outro.
REFERENCES (in no particular order):
1. Robins, S. The 21st century engram. WIRES Cognitive Science e1653 (2023) doi:10.1002/wcs.1653.
2. Roy, D. S. et al. Brain-wide mapping reveals that engrams for a single memory are distributed across multiple brain regions. Nat Commun 13, 1799 (2022).
3. Josselyn, S. A. & Tonegawa, S. Memory engrams: Recalling the past and imagining the future. Science 367, eaaw4325 (2020).
4. Chen, L. et al. The role of intrinsic excitability in the evolution of memory: Significance in memory allocation, consolidation, and updating. Neurobiology of Learning and Memory 173, 107266 (2020).
5. Rao-Ruiz, P., Yu, J., Yu, J. J., Kushner, S. A. & Josselyn, S. A. Neuronal competition: microcircuit mechanisms define the sparsity of the engram. Current Opinion in Neurobiology 54163–170 (2019).
6. Josselyn, S. A. & Frankland, P. W. Memory Allocation: Mechanisms and Function. Annu. Rev. Neurosci. 41389–413 (2018).
7. Choi, J.-H. et al. Interregional synaptic maps among engram cells underlie memory formation. Science 360430–435 (2018).
8. Abdou, K. et al. Synapse-specific representation of the identity of overlapping memory engrams. Science 360, 1227–1231 (2018).
9. Yokose, J. et al. Overlapping memory trace indispensable for linking, but not recalling, individual memories. Science 355398–403 (2017).
10. Rashid, A. J. et al. Competition between engrams influences fear memory formation and recall. Science 353383–387 (2016).
11. Poo, M. et al. What is memory? The present state of the engram. BMC Biol 14, 40 (2016).
12. Park, S. et al. Neuronal Allocation to a Hippocampal Engram. Neuropsychopharmacol 41, 2987–2993 (2016).
13. Morrison, D. J. et al. Parvalbumin interneurons constrain the size of the lateral amygdala engram. Neurobiology of Learning and Memory 135, 91–99 (2016).
14. Minatohara, K., Akiyoshi, M. & Okuno, H. Role of Immediate-Early Genes in Synaptic Plasticity and Neuronal Ensembles Underlying the Memory Trace. Front. Mol. Neurosci. 8, (2016).
15. Josselyn, S. A., Köhler, S. & Frankland, P. W. Finding the engram. Nat Rev Neurosci 16521–534 (2015).
16. Yiu, A. P. et al. Neurons Are Recruited to a Memory Trace Based on Relative Neuronal Excitability Immediately before Training. Neuron 83722–735 (2014).
17. Redondo, R. L. et al. Bidirectional switch of the valence associated with a hippocampal contextual memory engram. Nature 513426–430 (2014).
18. Ramirez, S. et al. Creating a False Memory in the Hippocampus. Science 341387–391 (2013).
19. Liu, X. et al. Optogenetic stimulation of a hippocampal engram activates fear memory recall. Nature 484381–385 (2012).
20. Silva, A. J., Zhou, Y., Rogerson, T., Shobe, J. & Balaji, J. Molecular and Cellular Approaches to Memory Allocation in Neural Circuits. Science 326391–395 (2009).
CREDITS:
I’ve posted a number of times about artificial intelligence, mind uploading, and various related topics. There are a number of things that can come up in the resulting discussions, one of them being Kurt Gödel’s incompleteness theorems.
The typical line of arguments goes something like this: Gödel implies that there are solutions that no algorithmic system can accomplish but that humans can accomplish, therefore the computational theory of mind is wrong, artificial general intelligence is impossible, and animal, or at least human minds require some as of yet unknown physics, most likely having something to do with the quantum wave function collapse (since that remains an intractable mystery in physics).
This idea was made popular by authors like Roger Penrose, a mathematician and theoretical physicist, and Stuart Hameroff, an anesthesiologist. But it follows earlier speculations from philosopher J.R. Lucas, and from Gödel himself, although Gödel was far more cautious in his views than the later writers.
Richard Cytowic, a pioneering researcher who returned synesthesia to mainstream science, traces the historical evolution of our understanding of the phenomenon.
In recent years, engineers and material scientists have been trying to create increasingly advanced battery technologies that are charged faster, last longer, and can store more energy. These batteries will ultimately play a crucial role in the advancement of the electronics and energy sector, powering the wide range of portable devices on the market, as well as electric vehicles.
Lithium-ion batteries (LiBs) are currently the most widespread batteries worldwide, powering most electronics we use every day. Identifying scalable methods to increase the speed at which these batteries charge is thus one of the primary goals in the energy field, as it would not require switching to entirely new battery compositions.
Researchers at Huazhong University of Technology in China recently introduced a new strategy to develop fast-charging LiBs containing a graphite-based material. Their proposed battery design, outlined in a paper published in Nature Energy, was found to successfully speed up the charging time of LiBs, while also allowing them to retain much of their capacity even after they are charged thousands of times.
The Sirius 16 is perhaps the first gaming laptop to ship with Linux, equipped with impressive hardware from AMD’s latest mobile Ryzen CPU and Radeon GPU.
To try everything Brilliant has to offer—free—for a full 30 days, visit http://brilliant.org/ArtemKirsanov/
The first 200 of you will get 20% off Brilliant’s annual premium subscription.
My name is Artem, I’m a computational neuroscience student and researcher. In this video we discuss the Tolman-Eichenbaum Machine – a computational model of a hippocampal formation, which unifies memory and spatial navigation under a common framework.
Patreon: https://www.patreon.com/artemkirsanov.
Twitter: https://twitter.com/ArtemKRSV
OUTLINE:
00:00 — Introduction.
01:13 — Motivation: Agents, Rewards and Actions.
03:17 — Prediction Problem.
05:58 — Model architecture.
06:46 — Position module.
07:40 — Memory module.
08:57 — Running TEM step-by-step.
11:37 — Model performance.
13:33 — Cellular representations.
17:48 — TEM predicts remapping laws.
19:37 — Recap and Acknowledgments.
20:53 — TEM as a Transformer network.
21:55 — Brilliant.
23:19 — Outro.
REFERENCES:
1. Whittington, J. C. R. et al. The Tolman-Eichenbaum Machine: Unifying Space and Relational Memory through Generalization in the Hippocampal Formation. Cell 183, 1249–1263.e23 (2020).
2. Whittington, J. C. R., Warren, J. & Behrens, T. E. J. Relating transformers to models and neural representations of the hippocampal formation. Preprint at http://arxiv.org/abs/2112.04035 (2022).
3. Whittington, J. C. R., McCaffary, D., Bakermans, J. J. W. & Behrens, T. E. J. How to build a cognitive map. Nat Neurosci 25, 1257–1272 (2022).
CREDITS:
Since I like AI and I’m possibly going into Cyber Security. This is a great use for AI. Catching cyber threats in real time. It’s ML of course.
Powered by artificial intelligence and machine learning, Palo Alto Networks Zero Trust approach unifies network security for companies so they can focus on what they do best.
For IT leaders, building a safe and secure network used to be much easier. Before companies had multiple locations due to hybrid work, data was stored on-site, and employees only accessed it from those locations. Nowadays, with workers logging in remotely, and from a variety of devices, securing data has become significantly more complex. Additionally, many organizations have taken their networks and applications to the cloud, further complicating their security architectures and putting them at risk of cyberattacks.
