I continue to be infatuated with the potential of autonomous entities that self-monitor, self-diagnose, self-fix and self-learn without human intervention.\u00a0 The vision of leveraging new digital technologies, growing reams of customer, product and operational data, and advanced analytics to create autonomous entities \u2013 farms, oil fields, factories, airports, theme parks, vehicles, ships, trains, etc. \u2013 seems like science fiction.\u00a0<\/span><\/p>\n Fortunately, the \u201c3 Horizons of Digital Transformation\u201d framework provides a roadmap leveraging for 1) today\u2019s market share grabbing operational excellence initiatives to 2) advance to tomorrow\u2019s IoT-induced smart entities that seek to optimize across interrelated use case decisions that lead to 3) the future world of autonomous entities that optimize their operations across a holistic and comprehensive utility function<\/strong> without human intervention (see Figure 1).<\/span><\/p>\n Figure<\/strong> 1<\/span><\/strong>:<\/strong> 3 Horizons of Digital Transformation<\/strong><\/a><\/p>\n This blog will explore how organizations can build out their digital, data and analytic capabilities today in order to strive towards that Horizon 3 autonomous entity vision of the future.\u00a0 But it won\u2019t be a big bang approach to creating a Horizon 3 autonomous entity.\u00a0 Instead, it\u2019ll require mastering the economic power of data and analytics.<\/em><\/span><\/p>\n I first wrote about autonomous in the blog \u201c<\/span>Transforming from Autonomous to Smart: Reinforcement Learning Basics<\/span><\/a>\u201d.\u00a0 That\u2019s where I talked about the role of reinforcement learning in creating autonomous vehicles.\u00a0<\/span><\/p>\n I next wrote about autonomous in the blog \u201c<\/span>Leverage AI to Create Autonomous Policies that Adapts without Human Intervention<\/span><\/a>\u201d where I talked about recent developments with Deep Reinforcement Learning to create \u201cRational AI Agents.\u201d<\/span><\/p>\n Figure<\/strong> 2<\/strong>:<\/strong> The Agent-Environment Interface<\/strong><\/a><\/p>\n For example, consider all the different subsystems which an autonomous vehicle must orchestrate across in order to achieve its operational objectives (see Figure 3).<\/span><\/p>\n Figure<\/strong> 3<\/span><\/strong>:<\/strong> Automobile Subsystems<\/strong><\/a><\/p>\n The major subsystems of a traditional internal combustion engine automobile against which an autonomous vehicle must orchestrate in order to optimize its utility function could include:<\/p>\n It\u2019s not (very) hard to optimize a system that is only comprised of itself.\u00a0 The braking system by itself could easily optimize its braking decisions if it only had to consider braking.\u00a0 But a car must carefully coordinate the interaction and interrelationships between braking, acceleration and navigational systems in order to successfully navigate a route (where success is defined by the utility function).<\/span><\/p>\n To create an autonomous entity that understands how to orchestrate the appropriate actions across numerous subsystems within a dynamically changing environment without human intervention, the autonomous entity must seek to optimize the utility function across a variety of often-conflicting value dimensions.\u00a0 For example, an autonomous vehicle must seek to reduce travel time while reducing operational costs while ensuring pedestrian and traffic safety while reducing vehicle wear-and-tear while protecting the passengers while reducing its environment impact and carbon footprint while operating within the laws of driving\u2026all without human intervention (see Figure 4).<\/span><\/p>\n Figure<\/strong> 4<\/span><\/strong>:<\/strong> Utility Function Optimizing Across Conflicting Priorities<\/strong><\/a><\/p>\n In order to make a holistic utility determination, collaboration across a diverse set of internal and external stakeholders is required to identify those short-term and long-term metrics and KPI\u2019s against which autonomous success will be measured.\u00a0 The careful weighing of the short-term and long-term metrics associated with the financial\/economic, operational, customer, society, environmental and spiritual dimensions must be taken into consideration if we are to successfully creation autonomous entities for the betterment of society.<\/span><\/p>\n See the blog \u201cWhy Utility Determination Is Critical to Defining AI Success<\/a>\u201d for my thoughts on how best to create the Utility Function<\/strong>.<\/p>\n Google\u2019s AI efforts is losing lots of money, and I mean LOTS:\u00a0 $154 million in 2016, $341 million in 2017, $572 million in 2018.\u00a0 And there were fears expressed in the article \u201c<\/span>DeepMind’s Losses and the Future of Artificial Intelligence<\/span><\/a>\u201d that continued losses could actually discourage other companies to make AI investments.<\/p>\n There aren\u2019t too many organizations around that can afford to lose $1B on a technology bet, so us mere mortal companies need a different approach.\u00a0 That approach is outlined in the 3 Horizons of Digital Transformation and its simple three step process:<\/span><\/p>\n See, it\u2019s not that hard.\u00a0 And we don\u2019t need to lose $1B hoping that something valuable spits out the end.\u00a0 You can make bank today by leveraging the re-usable and refine-able data and analytic assets that accelerate time-to-value and de-risking use cases as the organization towards their autonomous goals.<\/span><\/p>\n \u00a0<\/span><\/p>\n Key blog points:<\/span><\/p>\n \u00a0<\/span><\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Author: Bill Schmarzo I continue to be infatuated with the potential of autonomous entities that self-monitor, self-diagnose, self-fix and self-learn without human intervention.\u00a0 The vision […] Read More<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":457,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[26],"tags":[],"_links":{"self":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/2783"}],"collection":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/comments?post=2783"}],"version-history":[{"count":0,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/2783\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media\/456"}],"wp:attachment":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media?parent=2783"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/categories?post=2783"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/tags?post=2783"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}<\/a><\/span><\/p>\n
Laws of Autonomous Entities<\/strong><\/h1>\n
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Autonomous Law #3: Optimizing Across Interdependent Subsystems<\/strong><\/h1>\n
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Autonomous Law #4:\u00a0<\/span> Carefully Defining the Utility Function<\/strong><\/h1>\n
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Creating Autonomous Summary<\/strong><\/h1>\n
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