{"id":3959,"date":"2020-11-26T01:50:43","date_gmt":"2020-11-26T01:50:43","guid":{"rendered":"https:\/\/techclot.com\/index.php\/2020\/11\/26\/for-artificial-intelligence-most-of-the-work-comes-after-launch\/"},"modified":"2020-11-26T01:50:43","modified_gmt":"2020-11-26T01:50:43","slug":"for-artificial-intelligence-most-of-the-work-comes-after-launch","status":"publish","type":"post","link":"https:\/\/techclot.com\/index.php\/2020\/11\/26\/for-artificial-intelligence-most-of-the-work-comes-after-launch\/","title":{"rendered":"For artificial intelligence, most of the work comes after launch"},"content":{"rendered":"

For artificial intelligence, most of the work comes after launch<\/a><\/p>\n

\n\t\t\t\t Written by<\/p>\n

FedScoop Staff<\/a>
Nov 25, 2020 | FEDSCOOP<\/span><\/p>\n

Successful artificial intelligence applications are difficult to build. But even more difficult than building them is the time spent after launch to manage and monitor a tool to make sure it is doing no harm.<\/p>\n

This was a main thesis of Andrew Moore, vice president of engineering and general manager of cloud, artificial intelligence and industry solutions for Google Cloud<\/a>, during his keynote at FedTalks 2020<\/a>.<\/p>\n

\"artificial
Andrew Moore, VP, Engineering & GM, Cloud AI & Industry Solutions, Google Cloud<\/figcaption><\/figure>\n

Moore shared his lessons learned from building AI or machine learning applications to federal agency leaders at the virtual event, hosted by FedScoop on Nov. 17, 2020.<\/p>\n

He explains that even though the team built a tool accurate enough to solve problems, after implementing it \u201cin every single occasion, I ended up building a much larger team to do all the monitoring, improvement and ability to run new experiments for this machine learning system as it was carrying on through its life.\u201d<\/p>\n

For this reason, Moore said, when building AI applications, most of his time is spent \u201cmaking sure that you\u2019ve got something which is running securely, you\u2019re able to understand and identify a problem if it starts to behave strangely, or if one of your customers is complaining that something really weird has happened.\u201d<\/p>\n

\u201cWhen you\u2019re about to run an AI system, or you\u2019re in charge of an AI project, most of your thinking right from the start should be what you\u2019re going to do after it\u2019s been launched,\u201d Moore said, stressing that the month of operationalizing the machine learning system afterwards is even more important.<\/p>\n

AI for everyone<\/strong><\/p>\n

As AI capabilities becomes more advanced, the necessary skillsets to work with these applications has become less onerous and therefore more accessible for the existing workforce.<\/p>\n

\u201cI believe that now, we\u2019re entering the third decade of the 21st century and we\u2019re at the point where you do not need to be a Ph.D. expert in artificial intelligence in order to build AI,\u201d he said. \u201cWe\u2019ve just hit this threshold where regular folks, people who can play with spreadsheets or can modify Minecraft, that\u2019s the level of technology skill you need in order to be able to build an AI.\u201d<\/p>\n

An auto ML system essentially replaces all of the messy, manual work involved in being a data scientist and democratizes it for the people who aren\u2019t skilled in data science.<\/p>\n

\u201cAnd the idea is that here, someone who\u2019s doing an important thing in an organization who needs to train up a system to do something should be able to if they can\u2019t afford or they don\u2019t have access to their own data scientists \u2014 they should be able to build stuff themselves,\u201d Moore said.<\/p>\n

View the full video of the keynote, \u201cThe Four Factors: How to Lead an AI Enterprise<\/a>,\u201d from Day 1 of FedTalks.<\/em><\/p>\n

This article was produced by FedScoop and underwritten by Google Cloud<\/a>.<\/em><\/p>\n<\/p>\n

Published at Wed, 25 Nov 2020 19:52:30 +0000<\/p>\n

For artificial intelligence, most of the work comes after launch<\/a><\/p>\n

\n\t\t\t\t Written by<\/p>\n

FedScoop Staff<\/a>
Nov 25, 2020 | FEDSCOOP<\/span><\/p>\n

Successful artificial intelligence applications are difficult to build. But even more difficult than building them is the time spent after launch to manage and monitor a tool to make sure it is doing no harm.<\/p>\n

This was a main thesis of Andrew Moore, vice president of engineering and general manager of cloud, artificial intelligence and industry solutions for Google Cloud<\/a>, during his keynote at FedTalks 2020<\/a>.<\/p>\n

\"artificial
Andrew Moore, VP, Engineering & GM, Cloud AI & Industry Solutions, Google Cloud<\/figcaption><\/figure>\n

Moore shared his lessons learned from building AI or machine learning applications to federal agency leaders at the virtual event, hosted by FedScoop on Nov. 17, 2020.<\/p>\n

He explains that even though the team built a tool accurate enough to solve problems, after implementing it \u201cin every single occasion, I ended up building a much larger team to do all the monitoring, improvement and ability to run new experiments for this machine learning system as it was carrying on through its life.\u201d<\/p>\n

For this reason, Moore said, when building AI applications, most of his time is spent \u201cmaking sure that you\u2019ve got something which is running securely, you\u2019re able to understand and identify a problem if it starts to behave strangely, or if one of your customers is complaining that something really weird has happened.\u201d<\/p>\n

\u201cWhen you\u2019re about to run an AI system, or you\u2019re in charge of an AI project, most of your thinking right from the start should be what you\u2019re going to do after it\u2019s been launched,\u201d Moore said, stressing that the month of operationalizing the machine learning system afterwards is even more important.<\/p>\n

AI for everyone<\/strong><\/p>\n

As AI capabilities becomes more advanced, the necessary skillsets to work with these applications has become less onerous and therefore more accessible for the existing workforce.<\/p>\n

\u201cI believe that now, we\u2019re entering the third decade of the 21st century and we\u2019re at the point where you do not need to be a Ph.D. expert in artificial intelligence in order to build AI,\u201d he said. \u201cWe\u2019ve just hit this threshold where regular folks, people who can play with spreadsheets or can modify Minecraft, that\u2019s the level of technology skill you need in order to be able to build an AI.\u201d<\/p>\n

An auto ML system essentially replaces all of the messy, manual work involved in being a data scientist and democratizes it for the people who aren\u2019t skilled in data science.<\/p>\n

\u201cAnd the idea is that here, someone who\u2019s doing an important thing in an organization who needs to train up a system to do something should be able to if they can\u2019t afford or they don\u2019t have access to their own data scientists \u2014 they should be able to build stuff themselves,\u201d Moore said.<\/p>\n

View the full video of the keynote, \u201cThe Four Factors: How to Lead an AI Enterprise<\/a>,\u201d from Day 1 of FedTalks.<\/em><\/p>\n

This article was produced by FedScoop and underwritten by Google Cloud<\/a>.<\/em><\/p>\n<\/p>\n

Published at Wed, 25 Nov 2020 19:52:30 +0000<\/p>\n","protected":false},"excerpt":{"rendered":"

For artificial intelligence, most of the work comes after launch Written by FedScoop Staff Nov…<\/p>\n","protected":false},"author":3,"featured_media":3958,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_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":""},"categories":[3],"tags":[],"class_list":["post-3959","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/techclot.com\/wp-content\/uploads\/2020\/11\/guYPja.png?fit=250%2C250&ssl=1","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p3orZX-11R","jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts\/3959","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/comments?post=3959"}],"version-history":[{"count":0,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts\/3959\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/media\/3958"}],"wp:attachment":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/media?parent=3959"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/categories?post=3959"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/tags?post=3959"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}