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intelligence analysis are driven by three main factors:
• People. First and most importantly, agencies need to assign clear leaders for AI efforts and provide them with resources and authority. AI prototyping and deployment also require a multidisciplinary team of domain experts, computer scientists and AI specialists. Optimally, the AI team should be physically colocated.
• Process. Solving a specific agency challenge must drive the process of testing and deploying AI capabilities. Agencies should have a concrete challenge (e.g., to speed up annotation of objects in photos), use a specific dataset (the archived photos from August 2019) and identify a desired end state (populating a database with a list of all the objects detected in the photos).
• Technology. To reduce risk and
increase speed to deployment, we recommend starting with proven frameworks — such as TensorFlow, PyTorch or scikit-learn — and focusing on common use cases. Once you have some initial success, then you can take on a harder problem.
Abe Usher and Al Di Leonardo are co-CEOs of Black Cape.
Learn more at Carah.io/FCW-Black-Cape-AI
The bottom line is that machine learning can help agencies examine data that otherwise might never be analyzed by a human.
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