From computer-assisted medical diagnoses, to financial trading, to market personalization; () is becoming increasingly relevant, and prevalent, across most major industries.
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As the technology develops and is refined, often via sophisticated algorithms, more innovative applications are discovered to increase efficiency, reduce costs and supplement or replace human capability. Not only is used to engage with and respond to customers in millions of online and voice interactions each day, these computers can learn to anticipate and solve problems, direct enquires, and respond to complex instructions and combinations of scenarios as they arise.
Anyone using , Siri or Google Home is interacting with every day in the form of “Internet of Things” connected appliances, such as gaming consoles and myriad available smart devices that have been programmed or “trained” to respond to verbal or physical triggers or commands with pre-programmed actions or information.
In investigations, internet-connected tools that utilize make information gathering and analysis less expensive and time consuming while proving to be more accurate and often, more covert, reducing risk and increasing solve-rates.
As and continue to accelerate the processing of data at rates of speed and accuracy that are significantly superior to humans, and data retrieval, analysis, and dissemination become increasingly automated, investigators are voicing concerns at the possibility that we are approaching an era whereby human investigators are no longer needed at all.
How and Machine Learning Are Used in Investigations
Timely, accurate and verifiable evidence is critical to the success of any investigation and there can be little argument about the practical application of technology in most cases with any digital or technical elements.
tools allow investigators to spend their time collecting and evaluating evidence that drives the investigation forward, rather than sifting through data. The laborious task of manually extracting data, if it even exists in a format that allows it to be examined or extracted, now takes a fraction of the time, and reduces the risk of critical errors and omissions.
with machine-learning capability can be programmed to not only extract data but to identify, evaluate and classify it while excluding or flagging that which is irrelevant or not based in fact. Deep learning and neural networks represent powerful -based techniques; in forensics, text mining tools can conduct in-depth linguistic analysis of digital documents such as emails, surveys, blogs, and reports.
The Future of in Intelligence Gathering
As recently as a decade ago, voice printing and facial recognition was the stuff of science-fiction and spy films. Today, -assisted surveillance devices regularly scan, record, and analyze live digital footage to identify people in crowds, at borders and in the workplace.
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