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These outputs. so, that is terrifying. i think you take it another step further and look at the military and the national security and defense applications of artificial intelligence, you hear a lot of talk about killer robots, lethal autonomous weapons. you know, if we don t create guardrails and as i have said before right the rules of the road for these technology then other adversary nations like china are going to develop these battlefield applications and not have humans in the loop. they are going to try to allow these machines to make decisions without that human touch, without retaining that human control. that is extremely problematic. ainsley: what do you think that congress should be looking at or focusing on? yeah. i think the primary thing to focus on is transparence. we have to promote who we call a.i. explainability. that means that these machines, these systems are auditable, we can account for their processes and their behaviors. ....
If you go back to the earlier comments from elon musk, a commitment to open sourcing these algorithms, it might actually create a lot more transparency as to what it is that s fueling information flows on the internet. to some degree, it was a marketing or a separate side matter. i don t know if the poll had much to do with the policy. but the policy says if president trump begins engaging in tweet that is have harmful content, that information would not be amplified. it may put the rest of the social media networks on notice that they should begin thinking about transparency of their algorithms as to how content is distributed across their platforms. i m optimistic, frankly, that we ll move towards more transparency and explainability. fascinating. very good to talk to both of you. a major corporate shake-up at what s supposed to be the happiest place on earth. disney announced it had ....
As national statistical offices (NSOs) modernize, interest in integrating machine learning (ML) into official statisticians' toolbox is growing. Two challenges to such an integration are the potential loss of transparency from using 'black-boxes' and the need to develop a quality framework. In 2019, the High-Level Group for the Modernisation of Official Statistics (HLG-MOS) launched a project on machine learning with one of the objectives being to address these two challenges. One of the outputs of the HLG-MOS project is a Quality Framework for Statistical Algorithms (QF4SA). While many quality frameworks exist, they have been conceived with traditional methods in mind, and they tend to target statistical outputs. Currently, machine learning methods are being looked at for use in processes producing intermediate outputs, which lead to a final statistical output. Therefore, the QF4SA does not replace existing quality frameworks; it complements them. As the QF4SA targets i ....