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Can Crime-Fighting Robots Live Up To Their Name?



Can Crime-Fighting Robots Live Up To Their Name?

Crime-fighting robots are no longer fiction. Robotics in law enforcement agencies helps defuse potentially dangerous situations working in tandem with police officers and saving lives in the process.

A few years ago, Arnold Schwarzenegger’s Terminator as a crime-fighting robot took the world by storm. Star Wars’ R2D2 was a much-needed funny bone in the genre while there are some iconic AI-powered robots out minding their business. Sophia from Hanson Robotics is a real-life example for the uninitiated. Robots made to do specific tasks are mushrooming too, such as servers, assistants, and even fighting crime. Let’s discuss whether robotics in law enforcement can live up to the name.

What is the Role of Crime-Fighting Robots?


As the name suggests, these robots are made to fight crime in many ways. Some robots act as package carriers in a hostage situation and can be remotely controlled to deliver phones and supplies to the perpetrator. Some robots can infiltrate buildings if the suspects have barricaded them to gain intel on the count, assortment of weapons they are using, etc. Some highly-specialized robotics in law enforcement can launch gas or tear canisters, handle shotguns and use various forms of lethal or non-lethal methods to counter a situation.

Robotics In Law Enforcement Saves Lives

Suspects holding down hostages or trying to set off a bomb is a high-risk situation. In fact, even a traffic stop can turn into a dangerous situation endangering the motorists or the police officer during a pullover. Having a robot can act as a buffer, safeguarding law enforcement officers from putting their lives in harm’s way. During such incidents, remotely controlling a robot and sending it to the perpetrator can aid in saving lives. A few years ago, a man murdered five Dallas police offers threatening to shoot others in a parking garage. An 800-pound robot made by Northrop Grumman with a C4 plastic explosive attached to it was delivered to the suspect and detonated, killing him on the spot. Crime-fighting robots can be used as an extended set of eyes that would roam across a given area 24/7, working in tandem. They can potentially curb break-ins, burglaries and chain snatching, among other crimes. One of the surveillance robots that the folks in Silicon Valley are using is Knightscope K5, a 300-pound roaming robot that looks like R2D2. It comes affixed with a 360° camera system transmitting it to the surveillance center in real-time. Although K5 is non-lethal, it has successfully provided safety, especially with the ability to detect concealed weapons in an area where weapons aren’t allowed, such as schools and hospitals.

Instances Where Robots Went Wrong

As an ever-evolving technology that trains with the datasets provided, there have been instances where these crime-fighting robots have gone wrong. Racial bias and profiling are problems that robots face, thereby putting those with darker skin at higher risks of being held culprits. A Knightscope K5 bashed into a 16-month-old boy at a mall, injuring him in the process. In fact, an algorithm Minority Report-esque by Gawker reportedly encountered issues as it predicted the probability of future crimes by the people of darker complexion. This was because of the data provided that stated a higher likelihood of darker people to commit crimes in the future.

Examples of Crime-Fighting Robots

Northrop Grumman Remotec is a camera-powered robot that can operate in rough terrains, use weapons, climb flights of stairs, etc. Knightscope is actively developing its robotics in law enforcement cavalry with K5 and K7 in the business. The military heavily uses Endeavor Robotics to observe dangerous zones such as a hostage area or a bomb site. It is equipped with surveillance, packbot, mobile operations capability, and night-vision SUGV cameras. Crime-fighting robots are real and they are evolving every day. Perhaps, there’s still a long way to go for these robots to work autonomously without causing any bias and other morale-related issues. They can’t replace humans but work in tandem, diffusing high-tension situations, gathering intel, monitoring areas and observing dangerous zones, among others.  


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The Role of Big Data Analytics in Accounting



The Role of Big Data Analytics in Accounting

Companies generate enormous amounts of data that need to be processed to produce readable insights and outcomes.

Big data analytics in accounting is a game-changer as it’s improving risk identification and real-time access to data and reporting.

More firms are increasingly adopting newer technologies to make them more efficient. This includes blockchain, artificial intelligence, machine learning, robotic process automation, data analytics, etc. The use of traditional accounting has disrupted the world of accounting, but with the onset of big data analytics, it has gone leaps and bounds, tapping into the untapped potential of any business.

Use Cases of Big Data Analytics in Accounting


Businesses accumulate tremendous amounts of data that could go into petabytes and zettabytes. The accounting function in any organization records all types of financial and non-financial transactions, collects them and analyzes them using predictive models to find actionable insights. Data analytics is all about making sense of the data received and thus, it takes away the hassle of traditional accounting. Let’s dive into why you would need to transition your business from using conventional to big data analytics.

1. Real-time Reporting

One of the biggest USPs of using big data analytics in accounting is its real-time reporting functionality. Most of the analytical tools available today are cloud-based, making real-time insights and reporting more accessible than ever. As big data deals with a trove of data, it crunches historical data in terabytes and even petabytes to find actionable insights.

2. Real-time Access

Another characteristic of using data analytics in accounting is real-time access. As it is cloud-based, it has the upper hand in timers of data visibility across different functions in an organization. It can be accessed concurrently, and different users can have different privileges for access.

Apart from that, the data syncs so that the changes made in one node are easily accessible on other nodes. This improved access to information in real-time with transparency makes decision-making easier.


3. Risk Identification and Mitigation

Certain risk factors can prevent a business from outperforming the revenue it hit last quarter or against the rival. Big data can help find risks associated with financial services, such as the supply chain, fraudulent transactions or activities, liquidity, data breach, etc. Businesses can use all the data and add it to various algorithms to anticipate or predict possible outcomes or track fraudulent activities in the books. As accountants can now find errors and risks sooner, the chances of propagating from the point of no return diminish.

4. Data Visualization

Making sense of voluminous data is impossible without using tools such as Tableau. It is a heavily used data visualization tool for big data as it helps find the flow, pattern and irregularities in the dataset. Analyzing the visualized data can assist in making business decisions and strategies needed to adhere to in the future.


Big data analytics in accounting can be a significant driving force toward many use cases. It includes predicting sales performance on food, travel, hospitality and others across different data sources, such as, Yelp, etc. It can reduce downtime and operational costs thanks to monitoring IoT sensor data.

Companies can use data analytics in accounting to zero fraudulent activities. Optimizing labor and staff requirements is another chunk of issue that can be curbed using big data based on prediction analysis.

Organizations worldwide are leveraging the power of big data analytics in accounting over the traditional approach. It is because of the many benefits that it brings to the table, including real-time data access and reporting, data visualization, data audits, and others.

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