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The the definition adopted by the 

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defense department is that artificial 

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intelligence refers to the ability of 

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machines to accomplish tasks that 

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ordinarily require human intelligence . 

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Well , there's kind of a moving goal 

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post problem embedded in that 

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definition because the things we think 

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of today that require human 

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intelligence are very different from 

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the things we think of maybe 10 years 

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ago that required human intelligence . 

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And so as A I keeps getting better , 

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the definition of what counts as A I 

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keeps changing . We've had A I 

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technology in the dod for decades , but 

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something did happen in this century in 

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the last 15 years or so that caught a 

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lot of people's attention . And that 

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was a transition from deterministic 

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systems to something called deep 

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learning where researchers were able to 

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use artificial neural networks with 

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multiple layers of these artificial 

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neurons to be able to make predictions 

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on real world data . But with deep 

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learning , because we're stacking these 

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artificial neurons into these deep 

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neural networks , oftentimes , when the 

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algorithm produces a result , it might 

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not be the result that the human 

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intended . 

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Our A I systems need to be responsible 

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equitable , traceable , reliable and 

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governable so responsible just means 

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that an appropriate person , that every 

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phase of the life cycle needs to 

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maintain responsibility for the system 

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equitable means that we need to keep 

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one eye on the biases and try to 

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prevent some of those biases on the 

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basis of say race or gender , et cetera 

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that we've seen in industry . Uh 

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traceable is kind of leaning in the 

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direction of what some people call 

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explainable A I . And that principle 

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says that we need to know , users need 

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to know enough about what's happening 

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under the hood to be able to make wise 

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decisions about how to employ it . They 

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need to be reliable uh in the sense 

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that they've been tested and evaluated 

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for the specific use case they're 

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intended for and then they need to be 

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governable , meaning that we can impose 

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the right bounds , the right guard 

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rails . Uh And that we have sort of a 

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kill switch that we can always be able 

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to sort of turn these systems off . 

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The A I ready by 2025 goal comes from 

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the National Security Commission on 

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Artificial Intelligence . And the way 

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that they define that is kind of in two 

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layers . So first , it says that A I 

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ready means that people across the 

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force have access to the uh the 

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workforce development required and the 

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software tools needed to be able to , 

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then this is the second layer to be 

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able to make A I ubiquitous and 

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exercises and training and ultimately 

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in combat . So there's two things that 

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have to happen . First , we have to 

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make sure that we develop the workforce 

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and provide access to the tools so that 

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second , we can have this ubiquitous 

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development of A I across the forest . 

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And so when we think about A I ready 

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within the Department of the Air Force , 

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we're focused on that first , that 

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first level , how do we make sure that 

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people have access to the workforce 

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development tools and to the software 

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tools to be able to start to uh to 

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become proficient at both developing A 

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I and employing A I . One way to look 

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at this is that there are high risk use 

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cases and low risk use cases . And we 

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should allow airmen and guardians to 

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experiment to the max extent possible 

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with low risk use cases . So they can 

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start to get the practice in , so they 

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can start to get reps and sets and 

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identify how these things might fail , 

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what , what a failure mode looks like , 

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uh how to recognize a hallucination , 

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things like that . So eventually , I 

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think the entire workforce will be 

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upskilled in these tools , but only if 

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we give them the ability to experiment 

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at scale . 

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So sometimes people approach these 

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questions as though , you know , the 

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ethics side and the innovation side are 

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at odds with one another . And I don't 

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think that's the right way to think 

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about it , especially within the dod or 

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the Department of the Air Force , our 

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mission is to accomplish the tasks that 

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have been given us by the American 

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people . And we always want to do that 

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as well as we can . And so when we 

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start talking about response A I in the 

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military context responsible A I will 

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make our models perform better , right ? 

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If we adopt these principles , if we 

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make sure that we're keeping one eye on 

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responsibility and on equity and on 

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governability , et cetera , we're , 

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we're going to produce models that 

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perform better than we would if we 

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hadn't adopted these responsible A I 

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approaches . But there's a more 

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profound answer which is that behaving 

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ethically is what the United States 

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military does . That that's who we are . 

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And we've been committed to that for a 

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very long time , the people who are 

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developing these tools want to develop 

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it in such a way that it submits to our 

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professional ethos . And so there's 

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really not the tension there that some 

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people seem to think . And I think 

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generally the , the sort of the ethics 

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side and the innovation side really are , 

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are arm in arm trying to solve the same 

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problems , which is how do we make sure 

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that we're producing models that 

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achieve the effect that they're 

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supposed to achieve ? And how do we 

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make sure that everything that we're 

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doing submits to the norms and ethical 

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principles that we established within 

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the US military