Raw LLM Responses

You should dig into the vector within Telecommunications and within our smart devices cellular modems that utilize the frey effect and other microwave methods to spy and track users. One widely know method is called CSI sensing which can also hear conversations and store these conversations onto the cloud for computation as the data will look harmless. Similarly, you can use kismet to capture and analyze the data, compare it to the refraction that create noise, and return. You can also look at the RSSI data and other return items similar to the way check for packet loss/latency issues within the network. The concept is similar and can be leveraged with algorithms to understand the refaction and noise created by objects in the scope of the radio waves. So, your wifi and smart devices are vectors that can basically create a digital twin , observe your movements, and hear you conversations. Makes you wonder what type of data is really collected with the advertising cookies, and domain pings that passively communicate with your smart phone. You could dig deeper an understand the modulation concept of the microwave auditory effect and acoustics within physics to the biological component of the way the auditory nerve is stimulated. However, other crude methods exist from injections into bluetooth devices to transmit. To receive, actors could use lasers and listen to the vibrations from windows of a target. Make you wonder what type of Lidar and other lasers are used on surveillance planes. Just so you know, the Frey effect research was conducted in the 60s. Up to 10 words were transmitted to a target without anyone else hearing them. While the researchers indicated negative effects that basically cook the individuals brain, the tools used are much different than the basic tower and cellular modems used today. Then you have the noise concept and location of a target which is quite easy to explain. Noisey Channel theorem and gradient descent, while basic gps and 5G remove the requirements to implement a error estimation technique to locate a device/target. Anyway, you should try to uncover more information about the way companies manipulate individuals, price and society through the tools used to electronically harass individuals. I did not include anything involving recommender systems or other marketing methods used to influence and manipulate price across geographical locations. But it would be a good way to dig into this topic of corporate/government surveillance or the way unethical doctors and researchers go about conducting their experiments and research. Edit: it would seem multi-behavior sequential recommender systems were used to influence voters through common advertising APIs. However, these are usually nested with other models using logarithmic and logistic regression. However, categorization and recommender system algorithms require the categorization to create a pipe line so the probabilities/weights can help predict behaviour or understand the identities associated with the outcomes, but this varies on the goal. If the categorization and grouping is not finalized, errors and unintended outcomes will result. For example, a recommender system can help with strategic analysis by understanding what caused a customer to seek out a specific product, cancel their subscription (churn), or renewal or re-purchase (retention), or understanding the impact of trends to uncover patterns to increase ROI and profit. A more malicious outcome can be leveraged with these models. A bad actor can spin a narrative around social discourse or subtlety change the opinions, impressions and attitudes of an individual through engagement and exposure to phrases, statements and reoccurring videos or videos with similar identities. Where Identities can be the way a concept is presented, the structure of the statements, and the way the viewer found the video or information. You can learn more about this invasive and ways algorithms were used to elect Trump in the 2016 election from the Canbridge Analytica Scandal. Oh yeah, the car issue is a major one. DEF CON covers the issue with the reason why new age cars are a privacy nightmare. We should have the right to prevent specific information from being shared other way the data required by law from the blackbox. Companies should not have the right to user data that allows them to see the patterns of the driver, or uncover the market share (congestion) of the geographical area. By law, the government should only be the ones that have existing rights to view this data for criminal matters involving car crashes which could be limited in scope, i.e.: time of accident, speed at crash, safety sensors, and data interchange at time of crash. A geofence is likely set up with multiple free wifi spots where users' vehicles pass through to trigger an advertisement, or influence consumer behaviour (spending) or to strategically affect price where surge/dynamic pricing is triggered based on the traffic/congestion of users in an area. Smart devices passively communicate and it is nearly impossible to stop this from occurring. Encryption only protects the companies and their subsidiaries from being caught invading the privacy of users.