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<\/p>\nLast summer, I sped down Chicago\u2019s crowded I-90 highway behind the wheel of a Tesla. A car swerved in front of me and slowed down, and without any effort on my part, the Tesla braked on its own. I was experiencing Tesla\u2019s Autopilot, a feature that allows all Tesla vehicles to function semi-autonomously. Though machine learning hastens product development in Tesla cars, the product development process for Tesla seems to be equally driven by the company\u2019s desire to manage public perception.<\/p>\n
Tesla\u2019s cars embody machine learning at a fundamental level: they are both vehicles for data collection and users of a machine-learning-driven operating system that allows cars to function semi-autonomously. Andrej Karpathy, Tesla\u2019s Director of Artificial Intelligence, details the company\u2019s recent shift toward more robust machine learning, which he dubs \u201cSoftware 2.0.\u201d[1] Software 2.0 is a system of software that, rather than functioning based on a highly engineered architecture, takes data collected by Tesla\u2019s expansive fleet of vehicles and \u201csearch[es] for a program that satisfies, simultaneously, the labels for all the images in its training data set.\u201d[2,3] Due to the highly unpredictable nature of driving, this approach proves superior to simply using a \u201cseries of prewritten computer rules\u201d because it takes into account what could be considered exceptions to the rule (see below for example).[4,5] \u00a0In this way, machine learning drives product development at Tesla: as cars in the fleet \u2013 each of which is equipped with sensors that export data to the cloud \u2013 proliferate, the machine learning algorithm grows more accurate and dispatches updates to all existing cars via a software update so that cars can more accurately perceive and react to their surroundings.[6]<\/p>\n
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Public perception of safety also generates highly publicized product updates. For example, in March of 2018, a driver who was not paying attention while using Tesla\u2019s Autopilot crashed and died, a move which resulted in a $5 billion ding to Tesla\u2019s market value.[7] After this fatality, Tesla responded by releasing enhanced features, such as Navigate on Autopilot and Autosteer, which aim to increase safety and visibility of the navigation system.[8] Machine learning, therefore, can only account for so much product development \u2013 a process which is ultimately driven by subjective human input and fiduciary responsibility to shareholders.<\/p>\n
The 2018 fatality demonstrates a challenge that Tesla faces: where human variability interferes with artificial intelligence, costly accidents can happen. To address this in the short term, Elon Musk is approaching the problem from an education standpoint: he wants consumers to know that Autopilot is much safer than human driving. An official company release stated, \u201cif you are driving a Tesla equipped with Autopilot hardware, you are 3.7 times less likely to be involved in a fatal accident.\u201d[9] The company also has a technical solution that will be implemented in the medium-to-long term: add more data and let machine learning learn. Karpathy explains that the Software 2.0 stack trains itself on additional data, so as the fleet size increases, more data will be added to the algorithm, which will make the product better, which creates a virtuous cycle of more cars and better product: \u201cAnd if this can drive autonomously, then we will sell more of them, and if we sell more of them, then the future is going to be good.\u201d[10]<\/p>\n
Tesla rightly focuses on the potential of its technology and machine learning when it comes to the future of product development within its company. However, Tesla seemingly downplays the role of its technology in accountability for these deaths. As is common in AI, \u201cin systems where machines and humans interact, users often become the \u2018moral crumple zone\u2019 for technological failure.\u201d[11] This being said, I\u2019d recommend that Tesla focus its attention on AI ethical issues so that it can control the future narrative around AI ethics. By doing so, Tesla can acknowledge that its cars must, at times, engage in life-or-death decision-making and should address this issue head-on. By doing so, Tesla can increase the level of trust consumers experience with the company, which will inevitably hasten the cycle of data collection and product improvement. A paper on ethics in AI states: \u201cThe AI research community realizes that machine ethics is a determining factor to the extent autonomous systems are permitted to interact with humans.\u201d[12] By controlling the narrative, Tesla can increase its market share.<\/p>\n
Tesla uses its growing fleet, which is estimated to reach 650,000 cars by the end of 2018, as both a collection tool for and user of machine learning.[13] While machine learning spurs constant, incremental product development, reactions to public perception also move product development forward. But, in a world where AI applications are growing, will humans ever be comfortable ceding decision-making power to machines when it comes to matters of life and death? If not, will Tesla\u2019s autonomous vehicles ever succeed?<\/p>\n
(797 words<\/em>)<\/p>\n <\/p>\n <\/p>\n Footnotes:<\/p>\n [1] Andrej Karpathy, Director of AI, Tesla, remarks made at Train AI Conference, San Francisco, May 9, 2018. From video provided by Figure Eight on Vimeo, https:\/\/vimeo.com\/272696002<\/a>, accessed November 2018.<\/p>\n [2] Ibid.<\/p>\n [3] Elon Musk, CEO, Tesla, transcript of investor call, September 2016. From transcript provided by Electrek, https:\/\/electrek.co\/2016\/09\/11\/transcript-elon-musks-press-conference-tesla-autopilot-under-v8-0-update-part-2\/<\/a>, accessed November 2018.<\/p>\n [4] Harry Surden and Mary-Anne Williams, \u201cTechnological Opacity, Predictability, and Self-Driving Cars,\u201d Colorado Law Scholarly Commons<\/em> (2016): 147.<\/p>\n [5] Karpathy, remarks made at Train AI Conference.<\/p>\n [6] Bernard Marr, \u201cThe Amazing Ways Tesla Is Using Artificial Intelligence and Big Data,\u201d Forbes<\/em>, January 8, 2018, \u00a0https:\/\/www.forbes.com\/sites\/bernardmarr\/2018\/01\/08\/the-amazing-ways-tesla-is-using-artificial-intelligence-and-big-data\/#ac07aaf42704<\/a>, accessed November 2018.<\/p>\n [7] Dana Hull and Tim Smith, \u201cTesla Driver Died Using Autopilot, With Hands Off Steering Wheel,\u201d Bloomberg<\/em>, March 30, 2018, https:\/\/www.bloomberg.com\/news\/articles\/2018-03-31\/tesla-says-driver-s-hands-weren-t-on-wheel-at-time-of-accident<\/a>, accessed November 2018.<\/p>\n [8] Tesla, \u201cDiscover Software Version 9.0,\u201d https:\/\/www.tesla.com\/support\/software-v9<\/a>, accessed November 2018.<\/p>\n