投稿時間:2022-08-25 04:22:12 RSSフィード2022-08-25 04:00 分まとめ(28件)

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AWS	AWS Startups Blog	Letter to a future founder: Build community, boost business, and dream big with the AWS Impact Accelerator program	https://aws.amazon.com/blogs/startups/letter-to-a-future-founder-build-community-boost-business-and-dream-big-with-the-aws-impact-accelerator-program/	Letter to a future founder Build community boost business and dream big with the AWS Impact Accelerator programIf you ve been sitting on your application for the AWS Impact Accelerator for Women Founders meet Aireka Harvell founder of Nodat and inaugural member of AWS Impact Accelerator for Black Founders In today s post she shares her best advice for making the most of this unique opportunity and how it is changing the trajectory of her startup Then get your application submitted by August	2022-08-24 18:39:30
AWS	AWS	Welcome to AWS, Switzerland | Amazon Web Services	https://www.youtube.com/watch?v=Dg9m1GOafUs	Welcome to AWS Switzerland Amazon Web ServicesWe are looking for passionate and curious builders in Switzerland who want to join a company where every day is full of challenging and meaningful work We re hiring for a wide variety of roles across Sales Account Management Solutions Architecture Business Development and more as well as in our Zurich Data Centers Learn more Subscribe More AWS videos More AWS events videos ABOUT AWSAmazon Web Services AWS is the world s most comprehensive and broadly adopted cloud platform offering over fully featured services from data centers globally Millions of customers ーincluding the fastest growing startups largest enterprises and leading government agencies ーare using AWS to lower costs become more agile and innovate faster AWSCareers AWS AmazonWebServices CloudComputing	2022-08-24 18:18:56
AWS	lambdaタグが付けられた新着投稿 - Qiita	保存したデータの分析	https://qiita.com/jaken1207/items/9d1f9199710c381da45b	amazon	2022-08-25 03:01:58
python	Pythonタグが付けられた新着投稿 - Qiita	Dictionaryの紐解き方	https://qiita.com/intrajp/items/852fc47736dd3c651cfd	dictionary	2022-08-25 03:26:48
AWS	AWSタグが付けられた新着投稿 - Qiita	Dictionaryの紐解き方	https://qiita.com/intrajp/items/852fc47736dd3c651cfd	dictionary	2022-08-25 03:26:48
AWS	AWSタグが付けられた新着投稿 - Qiita	保存したデータの分析	https://qiita.com/jaken1207/items/9d1f9199710c381da45b	amazon	2022-08-25 03:01:58
海外TECH	DEV Community	How to Build a Machine Learning Recommendation Engine w/ TensorFlow & HarperDB	https://dev.to/logicdudes/how-to-build-a-machine-learning-recommendation-engine-w-tensorflow-harperdb-51jo	How to Build a Machine Learning Recommendation Engine w TensorFlow amp HarperDB This article explains how to create a recommendation system using HarperDB and Custom Functions With HarperDB we are able to deploy a machine learning model to servers located on the edge of the Internet to provide users with content recommendations We have two examples to demonstrate how this can be done a book recommender and a song recommender An Intro to Recommendation SystemsRecommendation engines are the most universally used type of machine learning We interact with them everywhere and it has become the driving force for how we explore new content And while some are less than perfect even the least of them are miles ahead of the previous methods we had available to find new things Take shows and movies for example Do you remember the preview channel…staring at it for hours trying to guess which titles would be worth further exploration The same is true for audio content where we were limited to radio stations to find new music And books were even more difficult I d walk along the bookstore aisles picking random books to read their summary before finally finding one to take a chance on All of these methods did work but they limited us We could only truly explore the content which was made available by the system at hand such as the preview channel radio station and bookstore shelves But there is so much more content out there especially today So instead of having to choose from the limited options presented by the Best Sellers section at the bookstore how does one find new content that s based on their individual interests Recommendation Systems What is a Recommendation System How do they work Recommendation systems are exactly what they sound to be a system that provides recommendations You give it an idea about what you like and it points you in the direction of other things you might like There are many ways to set up such a system For example if we re talking about products a system could be built that finds all of the users who bought the same product as you and then displays the most common products among that group This could be expanded by adding more items to the input Such as moving from a single product to three products that you bought grouping together all of the users who also bought those three items and then displaying the most common other products among that group What if we re including ratings with the items of interest For example if many people are watching one particular movie does that automatically mean it s a good movie to recommend And if not then how would we prevent it from appearing in the recommendations When including ratings and reviews we could set up a filter to remove any content with a bad review to ensure the group of items we re aggregating always contains positive content This is a great starting point It works for many cases where you have a dataset of items and user interactions with those items The challenging part arises from the evolving nature of content How would a song book or product that s new get into the output of the recommendation system Using Machine LearningWhile we could keep adding manual filters to the content by sorting the ratings and weighting interactions with new items there would be areas where we fall short Especially when creating binary gates such as a positive negative rating threshold to determine which items are included in the output This is where machine learning takes over Using libraries such as TensorFlow Recommenders with Keras models it s easy to shape the data in ways that will allow the items and users to be viewed and compared in a multidimensional perspective Qualitative features such as item categories and user profile attributes can be mapped into mathematical concepts that can be quantitatively compared with one another ultimately providing new insights and better recommendations Using a machine learning pipeline like this also allows the models to be continuously trained by taking the results of the previous model the successfulness of those recommendations to drive user interactions and using that data to create better models I recently wrote an article about using TensorFlowJS and HarperDB for Machine Learning if you d like to learn more HarperDB RecommendersOne of the awesome features of living on the edge is the ability to connect users to new insights with low latency and minimal traffic With HarperDB Custom Functions we can deploy a model to multiple nodes in a cluster allowing the closest and most available to return the result to the user Let s look at two example projects to demonstrate how this would look when providing recommendations to a user based on provided content to create a song recommender and a book recommender To ensure the examples are reusable and straightforward these projects are the frontside of the process where we re serving an already trained model In production we would connect a second piece to this puzzle where we track user interactions in a central location train new models and deploy them back out to the edge with HaperDB HarperDB Song RecommenderGithub repo HarperDB song recommenderIn our Song Recommender example a user can find three of their favorite songs in the UI and then the recommendation system returns other songs they re likely to enjoy There s a dataset called The Million Song Dataset that contains very detailed information on over one million songs ranging from audio analysis to the location of the artist There s a nice subset of that data called The Echo Nest Taste Profile Subset which is a list of users songs and play counts Each row in the data contains a userid songid and playcount This is what we used to build the model for this project Training the ModelWe created a Two Tower model which is a design where two latent vectors are created compared during training to shape the embedding layers and compared during inference to find the best match Tower One is the Query Tower This is essentially the input to the equation which in this case is three songs that the user selects Tower Two is the Candidate Tower In this case it s the users in the dataset We find the user in the dataset most similar to the user using the UI and then provide their most played songs as recommendations To look at the training specifics here s the training notebook The basic steps that are taken are as follows Find the most played songs for each userCreate a query candidate pair of three of those songs and the user songs user Build two models and setup TFRS metrics for trainingFeed the query candidate pair into the modelWhen training is complete create a new model that wraps the above twoApply the BruteForce method to extract the best candidate match provided an inputGenerating the Data create a dictionary of inputs and outputsdataset songs user for user songs in users songs items for in range len songs randomly select n songsns in from the user s isbns selected songs np random choice songs n songs in add them to the inputs dataset songs append selected songs add the user to the output dataset user append user Building the Models create the query and candidate modelsn embedding dimensions QUERYsongs in in tf keras Input shape n songs in songs in emb tf keras layers Embedding n songs n embedding dimensions songs in in songs in emb avg tf keras layers AveragePoolingD pool size songs in emb query tf keras layers Flatten songs in emb avg query model tf keras Model inputs songs in in outputs query CANDIDATEuser in tf keras Input shape user emb tf keras layers Embedding n users n embedding dimensions user in candidate tf keras layers Flatten user emb candidate model tf keras Model inputs user in outputs candidate Creating the TensorFlow Recommenders Task TFRS TASK SETUPcandidates dataset batch map lambda x candidate model x user metrics tfrs metrics FactorizedTopK candidates candidates task tfrs tasks Retrieval metrics metrics TFRS MODEL CLASSclass Model tfrs Model def init self query model candidate model super init self query model query model self candidate model candidate model self task task def compute loss self features training False query embedding self query model features songs candidate embedding self candidate model features user return self task query embedding candidate embedding COMPILE AND TRAIN MODELmodel Model query model candidate model load model weights this is to resume training model query model load weights weights dir format query model candidate model load weights weights dir format candidate model compile optimizer tf keras optimizers Adagrad learning rate model fit dataset repeat shuffle batch steps per epoch epochs verbose Deploying the ModelTo deploy this model the final step in the above notebook is converting it to TensorFlowJS From there we add it to the Custom Function s directory along with the logic in recommend js which converts the user s three favorite songs into tensors which are provided to the model The output of the model is a reference to the most similar user to the one using the UI The top songs for that user are then returned and displayed in the UI as our recommendations Creating an input tensor and getting results if this model const modelPath path join dirname tfjs model model json this model await tf loadGraphModel file modelPath const inputTensor tf tensor songIdxs int const results this model execute inputTensor const r await results data HarperDB Book Recommender Github repo HarperDB book recommenderIn our Book Recommender example a user can find three of their favorite books in the UI and then the recommendation system returns other books they re likely to enjoy There s a dataset on Kaggle that includes a list of users books and ratings for about different titles Training the ModelWe again created a Two Tower model which is a design where two latent vectors are created compared during training to shape the embedding layers and compared during inference to find the best match Tower One is the Query Tower This is essentially the input to the equation which in this case is three books that the user rated highly a and above out of Tower Two is the Candidate Tower In this case it s the users in the dataset We find the user in the dataset most similar to the user using the UI and then provide their highest rated books as recommendations To look at the training specifics here s the training notebook The basic steps that are taken are as follows Find the top rated books for each userCreate a query candidate pair of three of those books and the user books user Build two models and setup TFRS metrics for trainingFeed the query candidate pair into the modelWhen training is complete create a new model that wraps the above twoApply the BruteForce method to extract the best candidate match provided an inputGenerating the Data create a dictionary of inputs and outputsdataset isbns user for user id isbns in user isbns items use x the number of isbns gathered for the user this ensures a larger amount of training data for in range len isbns randomly select n isbns in from the user s isbns selected isbns np random choice isbns n isbns in add them to the inputs dataset isbns append selected isbns add the user to the output dataset user append user idxs user id Building the Models create the query and candidate modelsn embedding dimensions QUERYisbns in in tf keras Input shape n isbns in isbns in emb tf keras layers Embedding n isbns n embedding dimensions isbns in in isbns in emb avg tf keras layers AveragePoolingD pool size isbns in emb query tf keras layers Flatten isbns in emb avg query model tf keras Model inputs isbns in in outputs query CANDIDATEisbns out in tf keras Input shape isbns out emb tf keras layers Embedding n users n embedding dimensions isbns out in candidate tf keras layers Flatten isbns out emb candidate model tf keras Model inputs isbns out in outputs candidate Creating the TensorFlow Recommenders Task TFRS TASK SETUPcandidates dataset batch map lambda x candidate model x user metrics tfrs metrics FactorizedTopK candidates candidates task tfrs tasks Retrieval metrics metrics TFRS MODEL CLASSclass Model tfrs Model def init self query model candidate model super init self query model query model self candidate model candidate model self task task def compute loss self features training False query embedding self query model features isbns candidate embedding self candidate model features user return self task query embedding candidate embedding COMPILE AND TRAIN MODELmodel Model query model candidate model load model weights this is to resume training model query model load weights weights dir format query model candidate model load weights weights dir format candidate model compile optimizer tf keras optimizers Adagrad learning rate model fit dataset repeat shuffle batch steps per epoch epochs verbose Create the BruteForce Comparison create the index model to lookup the best candidate match for a queryindex tfrs layers factorized top k BruteForce model query model index index from dataset tf data Dataset zip dataset map lambda x x user batch dataset batch map lambda x model candidate model x user for features in dataset shuffle batch take print isbns features isbns scores users index features isbns print recommended users users Deploying the ModelTo deploy this model the final step in the above notebook is converting it to TensorFlowJS From there we add it to the Custom Function s directory along with the logic in recommend js which converts the user s three favorite books into tensors which are provided to the model The output of the model is a reference to the most similar user to the one using the UI The top rated books for that user are then returned and displayed in the UI as our recommendations RecapAnd there you have it that s how you can create a recommendation system with HarperDB Custom Functions These machine learning models were pre trained for these examples as they do take hours to reach their most accurate state In a production environment there would most likely be a single instance responsible for continuously training the model and distributing it out to the other nodes on the edge Go ahead and launch a HarperDB instance with Custom Functions where you can pull in one of these repos and get recommendations for new songs and books to check out If you get an interesting result that you enjoy please let us know Thanks for reading Kevin	2022-08-24 18:49:20
Apple	AppleInsider - Frontpage News	Tinder parent company files antitrust lawsuit against Apple in India	https://appleinsider.com/articles/22/08/24/tinder-parent-company-files-antitrust-lawsuit-against-apple-in-india?utm_medium=rss	Tinder parent company files antitrust lawsuit against Apple in IndiaMatch Group owner of popular dating app Tinder is the latest company to file an antitrust case against Apple to protest the excessive fee for publishing in the App Store Image by fabrikasimfThe newly discovered case has been filed in India making it the third such case based in the country although Match is the first foreign entity to file against Apple there Read more	2022-08-24 18:06:52
Apple	AppleInsider - Frontpage News	Despite criticism, Self Service Repair still makes devices more repairable	https://appleinsider.com/articles/22/08/24/despite-criticism-self-service-repair-still-makes-devices-more-repairable?utm_medium=rss	Despite criticism Self Service Repair still makes devices more repairableApple recently expanded its Self Service Repair program to include Macs Although the program itself still isn t meant to be the end all solution for Right to Repair advocates it s at least a move in the right direction A Self Service Repair toolkitSince the program was first launched it has attracted criticism from Right to Repair advocates and repair organizations That s to be expected since the program was never meant to be a perfect solution to the points offered by repair advocates and has always been a way to try to head off right to repair and antitrust complaints Read more	2022-08-24 18:01:42
Apple	AppleInsider - Frontpage News	Apple's fall iPhone 14 and Apple Watch Series 8 event is on September 7	https://appleinsider.com/articles/22/08/24/apples-fall-iphone-14-and-apple-watch-series-8-event-is-on-september-7?utm_medium=rss	Apple x s fall iPhone and Apple Watch Series event is on September Apple has confirmed it will be holding its first annual fall special event on September with the Far Out event expected to be the launch venue for the iPhone as well as the Apple Watch Series Continuing the annual cycle once more Apple will be introducing new products during a special presentation in September Announced on TODAYDAY Apple announced the presentation will happen on September at a m Pacific As with previous years it is likely that Apple will use the event to launch a number of products but only part of its fall announcement roster Apple typically holds two fall events with the latter generally happening in October but it has held three in the past Read more	2022-08-24 18:49:02
海外TECH	Engadget	Snap reaches $35 million settlement in Illinois privacy lawsuit over lenses	https://www.engadget.com/snapchat-illinois-bipa-class-action-settlement-184526711.html?src=rss	Snap reaches million settlement in Illinois privacy lawsuit over lensesAnother social media company is paying up due to Illinois Biometric Information Privacy Act Snap Inc the parent company of Snapchat has reached a million settlement in an Illinois class action lawsuit over its use of facial recognition technology The lawsuit alleges that Snapchat violated the BIPA law by collecting and storing the biometric data of users who used its lenses and filters ーwithout their consent Illinois residents who resided in the state after November th and used Snapchat s popular AR features may be eligible for a cut of the settlement Snap Inc is only the latest company to get penalized under BIPA ーwhich requires companies to ask for consent before it can collect biometric data from users The law is unique in that it allows private citizens to sue companies that may have violated the law Earlier this year Facebook reached a settlement over its old photo tagging system and Google agreed on a million settlement over a feature that used facial recognition in Google Photos According to the Chicago Tribune Illinois residents who qualify could be eligible for payouts between and The settlement is still awaiting approval from a district court which will happen in November Illinois residents have until November th of this year to file a claim Snap Inc has not admitted any wrongdoing despite agreeing to the settlement In a statement to the Tribune a company spokesperson wrote that Snapchat issued an in app consent notice to Illinois residents earlier this year out of caution It also denied that the biometric data collected through its app can be used to identify specific people “Lenses do not collect biometric data that can be used to identify a specific person or engage in facial identification Snap said “For example Lenses can be used to identify an eye or a nose as being part of a face but cannot identify an eye or a nose as belonging to any specific person	2022-08-24 18:45:26
海外TECH	Engadget	‘NHL 23’ has a female player on the cover for the first time	https://www.engadget.com/nhl-23-sarah-nurse-cover-star-183041444.html?src=rss	NHL has a female player on the cover for the first timeFor the first time in series history EA s next NHL entry will feature a female player on the cover On Wednesday the publisher announced Team Canada s Sarah Nurse would be one of two athletes to grace the cover of NHL marking another first for the franchise She ll join Anaheim Ducks superstar Trevor Zegras The two are among the most talented young players in the game today Nurse who is biracial was dominant during the Bejing Winter Olympics earlier this year In helping Team Canada reclaim the women s hockey gold after a disappointing silver medal finish in she set two Olympic records for most points and most assists in a single women s tournament She also became the first Black woman to win an Olympic gold medal in hockey As for Zegras even if you re not a hockey fan you ve probably seen his plays in many a highlight reel He s known for his creative decision making often scoring the kind of goals you see in lacrosse not hockey In his first full season with the Anaheim Ducks he netted goals and assists across games That performance earned him second in voting for the NHL s rookie of the year award last season EA will reveal more about NHL when the first trailer drops tomorrow at AM ET View this post on InstagramA post shared by EA SPORTS NHL easportsnhl	2022-08-24 18:30:41
海外TECH	Engadget	Bipartisan bill would push Google and Meta to negotiate fair rates with news orgs	https://www.engadget.com/google-meta-news-organizations-negotiate-legislation-180450508.html?src=rss	Bipartisan bill would push Google and Meta to negotiate fair rates with news orgsA bipartisan group of US senators and members of Congress have released a new version of a bill that aims to make it easier for news organizations to bring the likes of Google and Meta to the negotiating table The lawmakers said in a statement that the Journalism Competition and Preservation Act would remove quot legal obstacles to news organizations ability to negotiate collectively and secure fair terms from gatekeeper platforms that regularly access news content without paying for its value quot The legislation would for instance offer eligible digital publishers quot limited safe harbor from federal and state antitrust laws quot Senate Judiciary Committee members Amy Klobuchar D MN and John Kennedy R LA and House Judiciary Committee members David Cicilline D RI and Ken Buck D CO are all backing the bill Dick Durbin D IL and Jerrold Nadler D NY the chairs of the committees have pledged their support too A previous version of the legislation was introduced last year but it failed to gain enough traction The latest attempt would allow publishers with fewer than full time employees and non network news broadcasters to collectively negotiate with certain platforms over access to their news content The proposed legislation states that publishers would be able to demand arbitration if they reach a stalemate in talks The rules would apply to very few companies specifically ones with more than million US users that have at least a billion monthly active users worldwide or are quot owned or controlled by a person that has either net annual sales or market capitalization greater than billion quot While Google and Facebook meet those benchmarks Twitter does not Google and Meta have siphoned away billions of dollars of ad revenue from news organizations Both companies have voluntarily offered payments to publishers in some regions However Meta said last month it would no longer pay US publishers for news content after its revenue dropped for the first time Other countries have considered ways to make Google and Meta pay publishers for featuring their news Early last year the Parliament of Australia passed a law that forces Google and Meta to pay publishers for using their news Canada s ruling Liberal Party has tabled similar legislation	2022-08-24 18:04:50
海外科学	NYT > Science	Marijuana and Psychedelics Use Soars Among Young Adults, Study Finds	https://www.nytimes.com/2022/08/23/health/marijuana-psychedelics-young-adults.html	compounds	2022-08-24 18:19:19
ニュース	BBC News - Home	Olivia Pratt-Korbel: Former drug dealer held after Liverpool shooting	https://www.bbc.co.uk/news/uk-england-merseyside-62658221?at_medium=RSS&at_campaign=KARANGA	dealer	2022-08-24 18:20:21
ニュース	BBC News - Home	The Hundred: Are the boundaries in the women's competition too small?	https://www.bbc.co.uk/sport/cricket/62637340?at_medium=RSS&at_campaign=KARANGA	hundred	2022-08-24 18:21:07
ビジネス	ダイヤモンド・オンライン - 新着記事	「自分で考えて行動する子」の親がやっている、しかりすぎない注意のコツ - ニュース3面鏡	https://diamond.jp/articles/-/308177	「自分で考えて行動する子」の親がやっている、しかりすぎない注意のコツニュース面鏡夏休みも後半戦になり、宿題の追い込みが始まっているご家庭も多いのではないでしょうか。	2022-08-25 03:55:00
ビジネス	ダイヤモンド・オンライン - 新着記事	部下のやる気を引き出す「仕事の頼み方」7つのコツ、絶対NGな言動とは？ - ニュース3面鏡	https://diamond.jp/articles/-/308282		2022-08-25 03:50:00
ビジネス	ダイヤモンド・オンライン - 新着記事	ロシアとトルコ関係深化、対ロ制裁順守に懸念 - WSJ PickUp	https://diamond.jp/articles/-/308537	wsjpickup	2022-08-25 03:45:00
ビジネス	ダイヤモンド・オンライン - 新着記事	FRB「利上げ継続」でも米国の長期金利が低下し株価が上昇する理由 - マーケットフォーカス	https://diamond.jp/articles/-/308536	国債利回り	2022-08-25 03:40:00
ビジネス	ダイヤモンド・オンライン - 新着記事	人民元の先安観強まる　中国経済に陰り - WSJ PickUp	https://diamond.jp/articles/-/308539	wsjpickup	2022-08-25 03:35:00
ビジネス	ダイヤモンド・オンライン - 新着記事	トランプ邸捜索、沈黙守るバイデン氏 - WSJ PickUp	https://diamond.jp/articles/-/308538	wsjpickupfbi	2022-08-25 03:30:00
ビジネス	ダイヤモンド・オンライン - 新着記事	まだ内定がない23年卒の就活生が、これだけはやるべきこと - 採用のプロが明かす「親必読」最新就活事情	https://diamond.jp/articles/-/308513	まだ内定がない年卒の就活生が、これだけはやるべきこと採用のプロが明かす「親必読」最新就活事情年卒の学生の就活は終盤に入った。	2022-08-25 03:25:00
ビジネス	ダイヤモンド・オンライン - 新着記事	いまとこれから、大学と企業ができる“インクルージョン”は何か？ - キャンパス・インクルージョン	https://diamond.jp/articles/-/307722		2022-08-25 03:20:00
ビジネス	ダイヤモンド・オンライン - 新着記事	リーダーがリスクマネジメントで示さなければならないこと - なぜリスクマネジメントは組織を救うのか	https://diamond.jp/articles/-/307250	リーダーがリスクマネジメントで示さなければならないことなぜリスクマネジメントは組織を救うのか自然災害や戦争、感染症などに加え、近年はサイバーリスクやガバナンスリスク、事業継続リスクなどさまざまなリスクが生まれ、企業や組織はその対応に苦慮している。	2022-08-25 03:15:00
ビジネス	ダイヤモンド・オンライン - 新着記事	【ひろゆきが教える】「そんなことも知らないの？」と上から目線でくる人への「言い返し方」ベスト1 - 1％の努力	https://diamond.jp/articles/-/307996	上から目線	2022-08-25 03:10:00
ビジネス	ダイヤモンド・オンライン - 新着記事	ひとりっ子親「子どもが習い事を始める前に知っておくべき1つのこと」 - ひとりっ子の学力の伸ばし方	https://diamond.jp/articles/-/306698	自己肯定感	2022-08-25 03:05:00
GCP	Cloud Blog	Insights on the future of work and collaboration	https://cloud.google.com/blog/products/workspace/our-articles-series-on-the-future-of-work-and-collaboration/	Insights on the future of work and collaborationBusiness leaders and IT professionals come to Google Workspace to build secure cloud first collaboration solutions that transform how people work together Here s the latest from Google Workspace leaders and partners about the evolving future of work and collaboration all in one place Staying in sync Effective collaboration strategies for distributed workforcesMichael Brenzel Chief Evangelist for Google Workspace discusses how organizations can use the best form of collaboration for the job at hand Read the article Empowering everyday innovation to build a more adaptive businessHear from Google Workspace VP of Engineering Philip Brittan about how organizations can rethink their approach to innovation How organizations can rethink their approach to time management coachingLaura Mae Martin Google s Productivity Expert on helping employees get more from their time without sacrificing their wellbeing Google Workspace and Google Cloud help build the future of work at AirbusSee how Airbus lived up to its mantra of “Any device anytime anywhere with help from Google Workspace and Google Cloud Read the article Shaping the future of work for frontline workers in Asia PacificRead about the new urgency for businesses to take a fresh look at work and technology experiences on the frontlines in APAC Boosting collaboration and participation in the hybrid work worldStrategies and best practices for organizations building people first sustainable hybrid work models The future of work requires a more human approach to securitySecurity is no longer just about protecting information or restricting how that information is accessedーit s about building safe efficient and effective ways to facilitate seamless collaboration and information sharing Read more Insights from our global hybrid work surveyGoogle Workspace commissioned Economist Impact to conduct a global hybrid work survey Here are the resulting insights about employee wellbeing productivity and the need for better technology	2022-08-24 20:00:00