{"id":14079,"date":"2018-01-17T16:42:27","date_gmt":"2018-01-17T15:42:27","guid":{"rendered":"https:\/\/timoelliott.com\/blog\/?p=14079"},"modified":"2018-01-27T11:26:11","modified_gmt":"2018-01-27T10:26:11","slug":"some-top-ai-trends-for-2018-self-driving-everything-algorithm-whisperers-and-more","status":"publish","type":"post","link":"https:\/\/timoelliott.com\/blog\/2018\/01\/some-top-ai-trends-for-2018-self-driving-everything-algorithm-whisperers-and-more.html","title":{"rendered":"Some Top AI Trends for 2018: Self-Driving Everything, Algorithm Whisperers, and More"},"content":{"rendered":"<p><a href=\"https:\/\/i0.wp.com\/timoelliott.com\/blog\/wp-content\/uploads\/2018\/01\/cyber-timo-2.jpg?ssl=1\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" class=\"wp-image-14088 alignright\" style=\"padding-left: 5px;\" src=\"https:\/\/i0.wp.com\/timoelliott.com\/blog\/wp-content\/uploads\/2018\/01\/cyber-timo-2.jpg?resize=204%2C210&#038;ssl=1\" alt=\"\" width=\"204\" height=\"210\" \/><\/a>Here&#8217;s my take on the <em>interesting<\/em> trends in artificial intelligence and machine learning in 2018.<\/p>\n<p><strong>Self-driving everything.<\/strong>\u00a0This year will see the rapid growth of products, services, and business processes that use the power of machine learning algorithms to <em>automatically<\/em> get better as more people use them &#8212; just as self-driving systems get better at navigating roads\u00a0over time by recognizing patterns and learning from any mistakes.<\/p>\n<p>This can and will impact all areas of life, such as:<\/p>\n<ul>\n<li>Self-optimizing cities. Imagine traffic lights that constantly and automatically adapt in real time to improve traffic flow<\/li>\n<li>Self-driving homes, with home automation systems that adapts to your rhythms, such as automatically turning the heat on half an hour before you get home.<\/li>\n<li>\u201cLights-out\u201d finance organizations with processes that learn and improve from\u00a0every exception that requires human interaction,\u00a0getting ever-closer to\u00a0closing the books automatically.<\/li>\n<li>Marketing that proactively adapts to prospects, automatically maximizing exposure to content that interests them while minimizing anything perceived as spam.<\/li>\n<li>Human resources departments that automatically get better at\u00a0shortlisting candidates based on successful hires.<\/li>\n<li>Business intelligence tools that automatically propose answers rather than always waiting for you to ask a question.<\/li>\n<li>And many, many more.<\/li>\n<\/ul>\n<p><strong>Ethics everywhere.<\/strong> Artificial intelligence is designed to maximize\u00a0certain behaviors (&#8220;get to the destination without crashing the car&#8221;), based on the data provided (camera, lidar, traffic rules, etc). But bad data or badly chosen KPIs can lead to unethical and biased results.<\/p>\n<p>For example, if your new, automated HR\u00a0processes are taught using prior hiring data that was full of human bias, the resulting algorithm will\u00a0also be biased. And we often observe sub-optimal behavior from human beings because of\u00a0badly-designed\u00a0incentive plans &#8212; the principle difference with AI is that it will do the bad things much faster and more effectively!<\/p>\n<p>We can\u2019t just outsource our responsibilities to machines. Someone needs to be clearly responsible for any decisions made by algorithms, with the power and resources to make changes when problems arise. To do this effectively there must be transparency, with the ability to monitor and track the resulting effects of any automated tasks.<\/p>\n<p>2018\u00a0is likely to see more high-profile cases of &#8220;algorithm abuse,&#8221; leading to organizations investing in specialized roles around AI adoption.<\/p>\n<p><strong>Algorithm whisperers.<\/strong>\u00a0Because AI is highly dependent on the data it is fed and the patterns we train it to look for, we need people who have the skills to do this right. Call these people \u201calgorithm whisperers\u201d who can make sure that these technology only do what they\u2019re supposed to do..<\/p>\n<p>An algorithm whisperer\u2019s job is to have a deep understanding of the\u00a0<em>context<\/em>\u00a0of algorithm use. It\u2019s about understanding the data and the algorithms that are being used, and interpreting the results. At the end of the day, bad data means bad results \u2013 it\u2019s critical to have someone with the skills to tell what data has been collected, when it doesn\u2019t make sense and why, and who understands the impact this will have on results.<\/p>\n<p><a href=\"https:\/\/i0.wp.com\/timoelliott.com\/blog\/wp-content\/uploads\/2018\/01\/abscombes-quartet.jpg?ssl=1\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" class=\"wp-image-14084 alignright\" style=\"padding-left: 5px;\" src=\"https:\/\/i0.wp.com\/timoelliott.com\/blog\/wp-content\/uploads\/2018\/01\/abscombes-quartet.jpg?resize=261%2C216&#038;ssl=1\" alt=\"\" width=\"261\" height=\"216\" srcset=\"https:\/\/i0.wp.com\/timoelliott.com\/blog\/wp-content\/uploads\/2018\/01\/abscombes-quartet.jpg?resize=608%2C505&amp;ssl=1 608w, https:\/\/i0.wp.com\/timoelliott.com\/blog\/wp-content\/uploads\/2018\/01\/abscombes-quartet.jpg?resize=768%2C638&amp;ssl=1 768w, https:\/\/i0.wp.com\/timoelliott.com\/blog\/wp-content\/uploads\/2018\/01\/abscombes-quartet.jpg?resize=1024%2C851&amp;ssl=1 1024w, https:\/\/i0.wp.com\/timoelliott.com\/blog\/wp-content\/uploads\/2018\/01\/abscombes-quartet.jpg?w=1292&amp;ssl=1 1292w\" sizes=\"auto, (max-width: 261px) 100vw, 261px\" \/><\/a><a href=\"https:\/\/en.wikipedia.org\/wiki\/Anscombe%27s_quartet\" target=\"_blank\" rel=\"noopener noreferrer\">Anscombe&#8217;s Quartet<\/a>\u00a0famously illustrates some of the problems &#8212; these data sets all have the same statistical characteristics (mean, variance, correlation, etc.) but would result from very different types of processes.<\/p>\n<p>This kind of data analysis is what data scientists specialize in, but what really distinguishes an algorithm whisperer is creativity. For example, data scientists working on the 9\/11 memorial in New York <a href=\"https:\/\/www.scientificamerican.com\/article\/september-11-memorial\/\" target=\"_blank\" rel=\"noopener noreferrer\">initially determined<\/a> that it was impossible to achieve the level of adjacency that had been requested to memorialize all of the people impacted. Yet data artist Jer Thorpe <a href=\"http:\/\/blog.blprnt.com\/blog\/blprnt\/all-the-names\" target=\"_blank\" rel=\"noopener noreferrer\">managed it<\/a> by using all resources available, such the physical characteristics of the place, the length of the names, and the choice of font.<\/p>\n<p>Algorithm whisperers can also use their deep expertise to figure out what the results of predictive studies mean. For example, a subway authority wanted to use an algorithm for predictive maintenance, to figure out in advance when a machine might break down. But looking closely at the data, it turned out that every time a single machine broke down, the machine next to it would also break down within a day. It was almost as if they were \u201ccatching\u201d the breakage from each other, like a common cold. It didn\u2019t seem to be logical. Was it a data quality problem with the same machine counted twice? Maybe machines were installed together and tended to break down together after a certain amount of time? The answer\u00a0was that this was a result of repair teams with strict service level agreements. If they missed the window for fixing a machine they were paid less. So, if there was a broken part, they would order a new one, but replace it immediately using a part from the machine next to it. They would go back the next day to fix the \u201cnew\u201d breakage. It took an algorithm whisperer with a full understanding of the context of the data to successfully and correctly interpret what was actually going on.<\/p>\n<p>It&#8217;s unlikely that &#8220;algorithm whispering&#8221; will be a mainstream job title any time soon, but in 2018 today&#8217;s data scientists will get even better at the creative aspects of their role, while business people will adapt the way they work to the new opportunities.<\/p>\n<p><strong>Data sovereignty.<\/strong>\u00a0AI is only as good as the data you have available. But the question of who owns and controls data\u00a0is far from being a neutral question, and the issue of data sovereignty will\u00a0reach new visibility in 2018, with at least\u00a0four different levels of discussion:<\/p>\n<ul>\n<li>There are big rifts between the approaches adopted by various countries, from the buttoned-down <a href=\"https:\/\/discover.sap.com\/gdpr\/en_us\/index.html\" target=\"_blank\" rel=\"noopener noreferrer\">European Global Data Protection Regulation (GDPR),<\/a>\u00a0via the\u00a0more-or-less-anything-goes approach of the US, to China\u2019s government ownership of <a href=\"https:\/\/www.wired.com\/story\/age-of-social-credit\/\" target=\"_blank\" rel=\"noopener noreferrer\">any and all data on its citizens<\/a>. Today&#8217;s computer systems weren&#8217;t designed with national boundaries in mind, and retro-fitting them for today&#8217;s\u00a0<a href=\"http:\/\/www.nyulawglobal.org\/globalex\/Passenger_Data_US_EU1.html\" target=\"_blank\" rel=\"noopener noreferrer\">sometimes-conflicting<\/a> regulatory environments is proving to be complex, confusing, and expensive.<\/li>\n<li>Data is the foundation for the business models of the future, and the biggest opportunities are often where different organizations collaborate and share information across a &#8220;digital supply chain&#8221; and <a href=\"https:\/\/www.sap.com\/documents\/2016\/11\/3409b524-947c-0010-82c7-eda71af511fa.html\" target=\"_blank\" rel=\"noopener noreferrer\">competing as an ecosystem<\/a>. It&#8217;s clear that this can create added value for society as a whole, but it&#8217;s less clear how to deal with corresponding issues of joint ownership of data, the possibility of intellectual property leaks, and more. We&#8217;re seeing these issues play out in the internet of things space, as various organizations experiment with new business models for gathering and sharing data across companies that traditionally compete with each other.<\/li>\n<li>Within companies, the traditional approach of a single &#8220;data warehouse&#8221; that tries to bring together all relevant business data needed for decision-making has been discredited. Instead, there&#8217;s a rise in systems that <a href=\"https:\/\/www.sap.com\/products\/data-hub.html\" target=\"_blank\" rel=\"noopener noreferrer\">&#8220;orchestrate&#8221; data flows<\/a> across different sources inside and outside the organization. This is essentially a &#8220;federation&#8221; approach to data ownership with a compromise between individual silos and the needs of the organization as a whole.<\/li>\n<li>Consumers are increasingly aware of just how much privacy they have been giving up. There may be a backlash about how \u201ctheir\u201d personal information is being collected, controlled, and monetized. There have been advances in systems that allow consumers <a href=\"https:\/\/www.gigya.com\/\" target=\"_blank\" rel=\"noopener noreferrer\">more control over how their information is used<\/a>. And various models have been proposed for \u201cpersonal data sovereignty\u201d that flip the equation\u00a0so that individuals have control over their data, and can choose to provide it to vendors in return for compensation &#8212; but it is hard to see how large-scale adoption of such models would be feasible.<\/li>\n<\/ul>\n<p>We&#8217;re likely to see a lot of talk this year all of these areas &#8212; but, unfortunately, little or no resolution of the complex underlying issues.<\/p>\n<p><strong>Technology is about being human.<\/strong> What&#8217;s the real killer technology to get the most out of AI? It&#8217;s people!<\/p>\n<p>The biggest effect of increased artificial intelligence and automation in 2018 will be the rising importance of human skills. To weather the coming storm, it&#8217;s important to know what AI is capable of, but the key isn&#8217;t to compete with it &#8212; it&#8217;s to double-down on the human skills that got you where you are today.<\/p>\n<p>For example, when\u00a0more of the\u00a0medical diagnoses are being handled by algorithms, the doctors that stand out will be\u00a0those with the best patient skills. And finance teams will reward the people that making sure that the company as a whole actually uses that data to further the needs of the business, rather than those who are good at collecting and processing on data.<\/p>\n<p>For decades, the power of technology has been advancing quicker than our ability to adapt to its use. This is our opportunity to optimize\u00a0our non-technical skills including change management, leadership, and corporate culture.<\/p>\n<p>And finally, we need human judgment more than ever. AI is a very powerful tool, but just because something is now feasible doesn\u2019t mean it\u2019s a good idea.\u00a0We all need to support organizations\u00a0such as the\u00a0<a href=\"https:\/\/www.partnershiponai.org\/\" target=\"_blank\" rel=\"noopener noreferrer\">Partnership on AI<\/a>\u00a0and their mission to\u00a0study and formulate best practices on AI technologies, to advance the public\u2019s understanding of AI, and to serve as an open platform for discussion and engagement about AI and its influences on people and society.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>What are the top trends in AI in 2018? Self-driving everything, algorithm whisperers, data sovereignty, and ethics&#8230; <\/p>\n","protected":false},"author":2,"featured_media":14088,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[14],"tags":[1177,1321,1176,1322,454,1175,1238],"class_list":["post-14079","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-thoughts","tag-ai","tag-algorithm-whisperer","tag-artificial-intelligence","tag-data-sovereignty","tag-ethics","tag-machine-learning","tag-ml"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/timoelliott.com\/blog\/wp-content\/uploads\/2018\/01\/cyber-timo-2.jpg?fit=586%2C615&ssl=1","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p3X9RF-3F5","_links":{"self":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/posts\/14079","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/comments?post=14079"}],"version-history":[{"count":0,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/posts\/14079\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/media\/14088"}],"wp:attachment":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/media?parent=14079"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/categories?post=14079"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/tags?post=14079"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}