Written by : AIDoings Team
Every day with the rising of the sun into the horizon: every working individual arrive at their desks to read the latest news and intelligence reporting that has come in during the past day. The daily rhythm of “reading the morning traffic jam” has remained largely unchanged since the 2000's whether its a developing or a developed country.
Advances in machine learning and computer processing in recent years have triggered a wave of new technologies that aid in processing and interpreting images, video, speech, and text. To date, the technological developments in the realms of computer vision and voice recognition have been most impactful, finding wide-ranging applications from helping drivers avoid accidents to powering digital assistants to recognition of key objects and people for reconnaissance purposes. However, advances in neural networks pioneered in the image processing domain have helped make possible new AI technology capable of comprehending the unstructured language common in everyday life. This technology, known as Natural Language Processing (NLP), has reached a level of maturity that it is now finding far ranging applications across all the spheres of the society.
NLP broadly refers to the set of technologies that enable computers to not only understand, but also generate language in human-readable format. Driven by multi-layer neural networks, machine learning algorithms are now capable of a range of functions long the exclusive domain of humans, including drafting newspaper articles, summarizing large bodies of text data, and identifying a wide range of entities, including people, places, events, and organizations. Moreover, NLP can now understand the relationship among these entities, rapidly extracting and collating key information from thousands of documents such as the number of casualties from a bombing, the political affiliation of an organization, or the type of illness afflicting a political leader. Perhaps most impactful, not only can these algorithms detect when a person is mentioned, but they can then gather all of the relevant information about them across a large set of reporting, thus creating profiles on the fly. These capabilities are changing the paradigm for how national security professionals not only manage daily traffic, but also information flows in times of crisis.
NLP technologies are also eroding the tradeoff analysts historically have had to make between making timely judgments and judgments based on a comprehensive analysis of available intelligence. These technologies are enabling analysts to read-in each morning in a fraction of the time, and interact with all of the reports hitting their in boxes each day, not just those flagged as highest priority or from the most prominent press outlets. The effect of these algorithms goes beyond accelerating the speed and scale that individual analysts can operate, to also mitigating hitherto unavoidable analytic biases associated with source bias. This is lowering the cost analysts face for pursuing hunches, exploring new angles to vexing issues, and creating time for them to learn about new issues.
Perhaps most transformative, emerging NLP technologies are showing promise in powering auto-generating and auto-updating knowledge bases. Although still in their infancy, these self-generating “wikipedias” likely will have the most dramatic impact by eliminating potentially millions of worker-hours of labor manually curating KBs such as spreadsheets, link charts, leadership profiles, and order-of-battle databases. These next-generation KBs will continuously analyze every new piece of intelligence reporting to automatically collate key facts about people, places, and organizations in easily discoverable and editable wiki-style pages. The introduction of this technology will disrupt the daily rhythm of tens of thousands of analysts and operators that spend a significant amount of time each day cataloging facts from intelligence reporting.
To answer the frequently asked but very difficult to answer question: tell me everything we know about person (or place or organization) X? Still today, answering this question is an enormously costly and time-intensive process fraught with pitfalls. Inexperience, imperfect organization, or lack of human resources make it impossible for organizations to fully leverage all of the data they already receive.
In the not-too-distant future, analysts likely will arrive each morning to review profiles created overnight by NLP engines. Rather than spending hours cataloging information in intelligence reports, they'll simply review additions or edits automatically made to "living" databases, freeing them to work on higher-level questions—and minimize the risk of overlooking key intelligence.
AI will able to understand the emotions and our want - Help humans to act smart.