The iPhone’s locked-down approach to security is spreading, but advanced hackers have found that higher barriers are great for avoiding capture.
You’ve heard of Apple’s famous walled garden, the tightly controlled tech ecosystem that gives the company unique control of features and security. All apps go through a strict Apple approval process, they are confined so sensitive information isn’t gathered on the phone, and developers are locked out of places they’d be able to get into in other systems. The barriers are so high now that it’s probably more accurate to think of it as a castle wall.
Virtually every expert agrees that the locked-down nature of iOS has solved some fundamental security problems, and that with these restrictions in place, the iPhone succeeds spectacularly in keeping almost all the usual bad guys out. But when the most advanced hackers do succeed in breaking in, something strange happens: Apple’s extraordinary defenses end up protecting the attackers themselves. Read More
Daily Archives: March 1, 2021
98 things that can go wrong in an ML project
…This is a long post divided the post into 6 categories. Feel free to read categories that relate best to your role as a data engineer, data scientist, ML engineer, data-business leader:
- ML Problem definition: The formative stage of defining the scope, value definition, timelines, governance, resources associated with the deliverable.
- Dataset Selection: This stage can take a few hours or a few months depending on the overall data platform maturity and hygiene. Data is the lifeblood of ML, so getting the right and reliable datasets is supercritical.
- Data Preparation: Real-world data is messy. Understanding data properties and preparing properly can save endless hours down the line in debugging.
- ML Model Design: This phase involved feature selection, decomposing the problem, and formulating the right model algorithms.
- Model Training: Building the model, evaluating with the hold-out examples, and online experimentation.
- Operationalize in Production: This is the post-deployment phase involving observability of the model and ML pipelines, refresh of the model with new data, and tracking success metrics in the context of the original problem.
New AI ‘Deep Nostalgia’ brings old photos, including very old ones, to life
It seems like a nice idea in theory but it’s a tiny bit creepy as well
An AI-powered service called Deep Nostalgia that animates still photos has become the main character on Twitter this fine Sunday, as people try to create the creepiest fake “video” possible, apparently.
The Deep Nostalgia service, offered by online genealogy company MyHeritage, uses AI licensed from D-ID to create the effect that a still photo is moving. It’s kinda like the iOS Live Photos feature, which adds a few seconds of video to help smartphone photographers find the best shot. Read More
Deterministic multi-qubit entanglement in a quantum network
The generation of high-fidelity distributed multi-qubit entanglement is a challenging task for large-scale quantum communication and computational networks1,2,3,4. The deterministic entanglement of two remote qubits has recently been demonstrated with both photons5,6,7,8,9,10 and phonons11. However, the deterministic generation and transmission of multi-qubit entanglement has not been demonstrated, primarily owing to limited state-transfer fidelities. Here we report a quantum network comprising two superconducting quantum nodes connected by a one-metre-long superconducting coaxial cable, where each node includes three interconnected qubits. By directly connecting the cable to one qubit in each node, we transfer quantum states between the nodes with a process fidelity of 0.911 ± 0.008. We also prepare a three-qubit Greenberger–Horne–Zeilinger (GHZ) state12,13,14 in one node and deterministically transfer this state to the other node, with a transferred-state fidelity of 0.656 ± 0.014. We further use this system to deterministically generate a globally distributed two-node, six-qubit GHZ state with a state fidelity of 0.722 ± 0.021. The GHZ state fidelities are clearly above the threshold of 1/2 for genuine multipartite entanglement15, showing that this architecture can be used to coherently link together multiple superconducting quantum processors, providing a modular approach for building large-scale quantum computers16,17. Read More
Rick's Cafe AI 