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Sunday, 3 December 2017

Top 1000 Most Popular News Websites In The World

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Friday, 3 November 2017

Contribute your stories to the “On Mornings” project RIGHT HERE.



When I was a teenager, I was on a TV show called 3rd Rock From The Sun. I loved my fellow actors on that show, but I also got quite close with another creative bunch — the writers. They all worked together in this seemingly magical place called the Writers’ Room. Of course there was nothing magical about the room on the surface; I remember the interior decor being aggressively bland. But the work they did there together felt like magic to me.
We’d get a new script every Wednesday, and by the next Tuesday, we were shooting it in front of a live studio audience. So we only had five working days to get it ready. There was a lot of rehearsing to do. But it wasn’t just the actors working on that episode during those five days. When the writers handed us the new script each Wednesday, they weren’t finished with that script, far from it, they were sorta just getting started.
The draft we’d get on Wednesday was always written by only one or two writers. But over the next week, ALL of the writers would dive into it, pitching new jokes, suggesting cuts, even creating entirely new scenes. It was all hands on deck. And usually, it was during this week of mass collaboration that the scripts went from good (or sometimes not so good) to great. And like I said, to me, that felt like magic.
I think a lot of us harbor a certain romantic image of “The Writer” as a lonely soul, sacrificing human contact at the altar of a battle-worn keyboard. There’s something heroic about the idea of accomplishing this herculean task in a shroud of solitude.
And don’t get me wrong, I enjoy writing by myself. It’s fun. But if I’m writing anything very substantial, if it’s something intended for an audience of readers, or especially if it’s a script intended for actors to work with… I prefer to get some input from others.
Getting “fresh eyes” on a piece of writing is invaluable. It’s so hard to be objective about something we’ve written by ourselves. A certain line or paragraph might make perfect sense to us, because we know what we were thinking when we wrote it. But does it make sense to somebody else? The only real way to know is to ask.
Collaborative writing can also serve as an antidote to “Blank Page Syndrome”. I know I’ve experienced this, feeling the urge to write, but not quite knowing what to write about. Oftentimes, I think we get our best ideas when we’re building off of something somebody else has already started.
Now then, if I’ve inspired you at all to give collaborative writing a try, perhaps I might gently inform you (and/or give you the shamelessly hard sell) that we’re starting a collaborative writing project right now on HITRECORD in partnership with Medium. If you’re not familiar with HITRECORD, it’s a community I started years ago where we work together on all kinds of collaborative projects: short films, books, music, TV shows, etc. And, as you now might imagine, the way we write together as a community is largely inspired by my experiences with Writers’ Rooms.
I recently made friends with Ev Williams, who founded Medium on ideals I enthusiastically applaud. I share his concerns that the evolution of online culture is becoming as much a hindrance as a help towards substantial creativity and rational discourse. I think (hope) that HITRECORD and Medium are each trying to address this in their own ways, so I’m excited for our companies to be teaming up.
The project is called “On Mornings”, and it’s gonna be a series of nonfiction pieces collaboratively written on HITRECORD and published on Medium. So yeah, if you wanna jump in, or you know, just check it out from a safe, lurkerly distance, you can find it here. Ev and I also made a video about it you can watch below.
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Advice For New and Junior Data Scientists

Two years ago, I shared my experience on doing data science in the industry. The writing was originally meant to be a private reflection for myself to celebrate my two year twitterversary at Twitter, but I instead published it on Medium because I believe it could be very useful for many aspiring data scientists.
Fast forward to 2017, I have been working at Airbnb for a little bit less than two years and have recently become a senior data scientist — an industry title used to signal that one has acquired a certain level of technical expertise. As I reflect on my journey so far and imagine what’s next to come, I once again wrote down a few lessons that I wish I had known in the earlier days of my career.
If the intended audience of my previous post was for aspiring data scientists and people who are completely new to the field, then this article is for people who are already in the field but are just starting out. My goal is to not only use this post as a reminder to myself about the important things that I have learned, but also to inspire others as they embark onto their DS careers!
Whose Critical Path Are You On?
Philip Guo, an outstanding academic and prolific blogger, reflected on his experience interacting with various mentors throughout his years as a student, intern, and researcher. In his blot post “Whose Critical Path Are You On?”, he made the following observation:
If I was on my mentor’s critical path [for career advancement or fulfillment], then they would fight hard to make sure I got the help that I needed to succeed. Conversely, if I wasn’t on my mentor’s critical path, then I was usually left to fend for myself. […] If you get on someone’s critical path, then you force them to tie your success to theirs, which will motivate them to lift you up as hard as they can.

 Image credit: The Icefields Parkway // Daniel Han
This work dynamic is pretty intuitive, and I wish I had internalized it earlier in my career when choosing projects, selecting teams, or even evaluating which mentors or companies to work for.
As an example, while at Twitter, I had always wanted to learn more about machine learning, but my team, despite being very data driven, largely needed data scientists to focus on experiment design and product analytics. Despite my best efforts, I often found it difficult to marry this intellectual desire with the critical projects of my team.
As a result, when I arrived at Airbnb, I made a conscious decision to focus on joining a project/team where ML is critical to its success. I worked with my manager to identify a few promising opportunities, one of which is to model the lifetime value (LTV) of listings on Airbnb.
This project was not only critical to the success of our business, but also to the development of my career. I learned so much about the workflow of building machine learning model at scale, and there was no better way to learn other than learning in the context of solving a concrete business problem.
Undoubtedly, I was very lucky to find a project that aligned with my aspirations and where I wanted to build my skills. I believe the framework of picking projects on our mentors’ critical paths can make us increasingly “lucky” over time on matching our aspirations with the right projects at work.
Principle I learned: We all have skills that we would like to develop and intellectual interests that we would love to pursue. It’s important to evaluate how well our aspirations align with the critical path of the environment we are in. Find projects, teams, and companies whose critical path best aligned with yours.
Picking the Right Tools For The Problem
Before Airbnb, I had been coding in R and dplyr for most of my professional life. After starting on the LTV project, I soon realized the deliverable was not a piece of analysis code, but rather a production machine learning pipeline. Given that it is much easier to build complex pipelines in Airflow using Python, I was faced with a dilemma — should I switch from R to Python?

 Image source: quickmeme.com (besides R or Python, Excel is also a serious contender 👊)
This turns out to be a very common question among data scientists, since many struggled to decide which language to choose. For me, there is clearly a switching cost once committed to one or the other. I went through the pros and cons to understand the tradeoffs, but the more I thought about it, the more I fell into the trap of decision paralysis. (Here is an entertaining talk that demonstrates this concept). Eventually, I escaped from this paralysis after reading this response on Reddit:
Instead of thinking about which programming language to learn, think about which language offers you the right set of Domain Specific Languages (DSL) that fit your problems.
The appropriateness of a tool is always context dependent and problem specific. It’s not about whether I should learn Python, it’s whether Python is the right tool for the job. To elaborate more on this point, here are a few examples:
If your goal is to apply the most current, cutting-edge statistical methods, R is likely to be the better choice. Why? Because R is built by statisticians and for statisticians. Nowadays, academics publish their research not only in papers but also in R packages. Each week, there are many interesting new R packages made available on CRAN, like this one.
On the other hand, Python is great for building production data pipelines, since it is a general-purpose programming language. For example, one can easily wrap a scikit-learn model using Python UDF to do distributed scoring in Hive, orchestrate Airflow DAGs with complex logic, or write a Flask web app to showcase the output of the model in a browser.
For my particular project, I needed to build a production machine learning pipeline, and my life would be a lot easier if I did it in Python. Eventually, I rolled up my sleeves and embraced this new challenge!
Principle I learned: Instead of fixating on a single technique or programming language, ask yourself, what is the best set of tools or techniques that will help you to solve your problem? Focus on problem solving, and the tools will come naturally.
Building A Learning Project
Even though I have not used Python to do Data Science work before, I did play with the language in a different capacity. However, I never really learned Python fundamentals properly. As a result, I got scared when code was organized into classes, and I always wondered what __init__.py was used for.
To really learn the fundamentals properly this time, I took inspiration from Anders Ericsson’s research on Deliberate Practice:
Deliberate Practice is activities designed, typically by a teacher, for the sole purpose of effectively improving specific aspects of an individual’s performance.
Given that I was my own teacher, insights from Dr. Ericsson were very helpful. For example, I kicked off my “learning project” by curating a set of materials that were most relevant for doing ML in Python. This process took me a few weeks until I settled on a personalized curriculum. I stress tested this curriculum by asking experienced Pythonistas to review my plan. All of this pre-work was meant to ensure I would be on the right learning path.

 Here is a glimpse of my personalized curriculum
Once I had a clearly defined curriculum, I used the following strategies to deliberately practice on the job:
Practice Repeatedly: I forced myself to carry out mundane, non mission-critical analyses in Python instead of in R. This dragged down my productivity initially, but it forced me to get familiar with the basic API of pandas, without the burden of needing to meet an urgent deadline.
Create Feedback Loop: I found opportunities to review other people’s code and fix small bugs when appropriate. For example, I tried to understand how our internal Python libraries were designed before using them. When writing my own code, I also tried to refactor it several times and make it more readable for everyone.
Learn By Chunking and Recalling: By the end of each week, I wrote down my weekly progress, which included the important resources I studied in that week, concepts I learned, and any major takeaways during that week. By recalling the materials I learned, I was able to internalize the concepts better.
Slowly and gradually, I got better each week. It certainly wasn’t easy though: there were times when I had to look up basic syntax in both R and Python because I was switching back and forth between the two languages. That said, I kept in mind that this is a long term investment, and dividends will be paid as I dived into the ML project.
Principle I learned: As supported by many field experiments, before diving into a project, planning ahead helps you to practice more deliberately. Repeating, chunking, recalling, and getting feedback are among the most useful activities to reinforce learning.
Partnering With Experienced Data Scientists
One of the key ingredients of deliberate practice is to receive timely and actionable feedback. No great athletes, musicians, or mathematicians are able to achieve greatness without coaching or targeted feedback.
One common trait I have observed from people who have a strong growth mindset is that they are generally not ashamed of acknowledging what they don’t know and they constantly ask for feedback.
Looking back at my own academic and professional career so far, many times in the past I self-censored my questions because I did not want to appear incapable. However, over time I realized that this attitude was rather detrimental — in the long run, most instances of self-censorship are missed opportunities for learning rather than shame.

 Image source: edutopia — It’s important to have a growth mindset!
Before this project, I had very little experience putting machine learning models into production. Of the many decisions that I made for the project, one of the best decisions was to declare early and shamelessly to my collaborators that I know very little about ML infrastructure, but that I wanted to learn. I promised them, however, as I got more knowledgeable, I would make myself useful for the team.
This turned out to be a pretty good strategy, because people generally love to share their knowledge, especially when they know their mentorship will benefit themselves eventually. Below are a few examples that I would not have learned so quickly without the guidance of my partners:
Scikit-Learn Pipelines: My collaborator suggested to me that I can make my code more modular by adopting Sklearn’s pipeline construct. Essentially, pipelines define a series of data transformation that are consistent across training and scoring. This tool made my code cleaner, more reusable, and more easily compatible with production models.
Model Diagnostics: Given that our prediction problem involves time, my collaborator taught me that typical cross validation will not work, as we could run into the risk of predicting the past using future data. Instead, a better method would be to use time series cross validation. I also learned different diagnostic techniques such as lift chart and various other evaluation metrics such as SMAPE.
Machine Learning Infrastructure: With the help from ML infra engineers, I learned about managing package dependency via virtualenvs, how to serialize models using pickling, and how to make the model available at scoring time using Python UDFs. All these are data engineering skills that I didn’t know before.
As I learned more new concepts, not only was I able to apply them for my own project, I was able to drive engaging discussions with the machine learning infrastructure team so they can build better ML tools for data scientists. This creates a virtuous cycle because the knowledge that was shared with me made me a better partner and collaborator.
Principle I learned: In the long run, most instances of self-censorship are missed opportunities for learning rather than shame. Declare early and shamelessly your desire to learn, and make yourself useful as you become better.
Teaching And Evangelizing
As I got closer to putting my model into production, I noticed that a lot of the skills that I picked up could be very valuable for other data scientists on our team. Having been a graduate student instructor for years, I always knew I had a passion for teaching, and I always learned more about the subject when I became the teacher. Richard Feynman, the late Nobel Laureate in Physics and a phenomenal teacher, spoke about his view on teaching:
Richard Feynman was once asked by a Caltech faculty member to explain why spin one-half particles obey Fermi Dirac statistics. Rising to the challenge, he said, “I’ll prepare a freshman lecture on it.” But a few days later he told the faculty member, “You know, I couldn’t do it. I couldn’t reduce it to the freshman level. That means we really don’t understand it.”
This was really inspiring — if you can’t reduce the subject to its core and make it accessible for others, that means you don’t really understand it. Knowing that teaching these skills can improve my understanding, I seek opportunities to carefully document my model implementations, give learning lunches, and encourage others to try out the tools. This was a win-win because evangelization raises awareness, which in tern helps to drive tool adoption across the team.
As of late September, I have started collaborating with our internal Data University team to prepare a series of classes on our internal ML tools. I am not exactly sure where this will go, but I am very excited about driving more ML education at Airbnb.
Finally, I would end this section with a tweet from Hadley Wickham:
Principle I learned: Teaching is the best way to test your understanding of the subject and the best way to improve your skills. When you learn something valuable, share it with others. You don’t always have to create new software, explaining how existing tools work can also be super valuable.
At Step K, Think About Your Step K+1
From focusing on my own deliverables, to partnering with the ML infrastructure team, to finally teaching and enabling other data scientists to learn more about ML tools, I am really happy that the scope of my original project was much larger than it was a few months ago. Yet, admittedly, I never anticipated this in the first place.
As I reflected on the evolution of this project, one thing that was different from my previous projects was that I always had a slight dissatisfaction with the current state of things, and I always wanted to make it a little bit better. The most eloquent way to characterize this is from Claude Shannon’s essay:

 Image source: Book cover from “A Mind at Play: How Claude Shannon Invented the Information Page” by Jimmy Soni, Rob Goodman
“There’s the idea of dissatisfaction. By this I don’t mean a pessimistic dissatisfaction of the world — we don’t like the way things are — I mean a constructive dissatisfaction. The idea could be expressed in the words, This is OK, but I think things could be done better. I think there is a neater way to do this. I think things could be improved a little. In other words, there is continually a slight irritation when things don’t look quite right; and I think that dissatisfaction in present days is a key driving force in good scientists.”
By no means I am a qualified scientist (even though that is somehow in my job title), but I do think the characterization of slight dissatisfaction is quite telling for whether you will be able to extend the impact of your project. Throughout my project, whenever I am at step K, I naturally would start thinking about what to do for step K+1 and beyond:
From “I don’t know how to build a production model, let me figure out how” to “I think the tools can be improved, here are my pain points, suggestion and feedback for how to make the tools better”, I reframed myself from a customer to a partner with ML infrastructure team.
From “let me learn the tools so I can be good at it” to “let’s make these tools more accessible for all the other Data Scientists interested in ML”, I reframed myself from a partner to an evangelizer.
I think this mindset is extremely helpful — use your good taste and slight dissatisfaction to fuel your progress with persistence. That said, I do think that this dissatisfaction cannot be manufactured, and can only come from working on a problem you care about, which brings to my last point.
Principle I learned: Pay attention to your inner dissatisfaction when working on a project. These are clues to how you can improve and scale your project to the next level.
Parting Thoughts: You And Your Work
Recently, I came across a lecture from Richard Hamming, who is an American Mathematician well known for many of his scientific contributions, including Hamming code and Hamming distance. The lecture was titled You And Your Research, where Dr. Hamming said it can very well be renamed as “You And Your Career”.

As he shared his stories, a few important points stood out for me.
If what you are doing is not important, not likely to be important, why are you doing it? You must work on important problems. I spent Friday afternoon for years thinking about the important problems in my field [that’s 10% of my working time].
Let me warn you about important problems, importance is not the consequence, some problems are not important because you haven’t gotten an attack. The importance of problem, to a great extent, depends on if you got a way of attacking the problem.
This whole course, I am trying to teach you something about style and taste, so you’ll be able to have some hunch on when the problem is right, what problem is right, how to go about it. The right problem at the right time at the right way counts, and nothing else counts. Nothing.
When Dr. Hamming speaks about importance, he means problems that are important to you. For him, it was scientific problems, and for many of us, it might be something different. He also talked about the importance of having a plan of attack. If you don’t have a plan, the problem does not matter, however big the consequences. Lastly, he mentioned doing it with your own unique style and taste.
His bar for doing great work is extremely high, but it’s one worth pursuing. When you find your important problem, you will naturally try to make it better and make it more impactful; you will find ways to teach other about its significance; you will spend time to learn from other great people and build your craft.
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GOD’S TOUCHES

Not long ago, some friends of ours wanted to move to another country to explore new work possibilities and be closer to their family. Though they had countless difficulties throughout their preparations, including a last-minute complication at the airport, with the support of friends and the power of prayer, their move was a success. They reached their goal and now are trying their wings on new horizons. I’ve often told my friends and acquaintances who long to travel or live some other dream: put your desires in God’s hands, because He knows your heart and delights to see you happy.
This summer, we were puzzling as to how to receive our daughter and her family to spend summer vacation with us. Several unexpected inconveniences had arisen in our condo and we had no space to receive them. We committed the problem to God, and suddenly the answer seemed to fall from the sky: a neighboring couple moved and left an empty bungalow just meters away from our home. It was a sign of God’s love in answer to our prayers. We had a marvelous visit and a great summer.
Sometimes God uses the most unexpected means to help and encourage us. We had been praying for a friend, who after several romantic disappointments fell into a 1. 1 John 4:16 NIV deep depression. One day as she was walking close to the university where she works, she found an abandoned dog. She heard an internal voice urging her to pick it up and she decided to adopt it, even though she lived in a cramped apartment. This dog turned out to be a precious unexpected gift from God and has played a decisive role in her emotional healing.
We recently discovered that my wife, Sally, had a breast tumor. As soon as we found out, we began a prayer chain and received tremendous emotional and spiritual support from our friends. From every corner of the world came manifestations of kindness, solidarity, and support in prayer. The tumor was removed and Sally recuperated quickly. Every step of the way we felt the company and loving presence of Jesus who smoothed the path before us.
I believe that it is through the tests of life that we can perceive more clearly the divine love of God, and that He works through many different channels to show His care. “We know and rely on the love God has for us. God is love. Whoever lives in love lives in God, and God in them.”

Gabriel García Valdivieso is the editor of the Spanish edition of Activated and a member of the Family International in Chile.
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New era of learning

Dozens of new libraries and learning centers dedicated to Xi Jinping Thought on Socialism with Chinese Characteristics for a New Era established immediately following 19th CPC National Congress
Xi Jinping Thought has been written into the Party Constitution
Xi’s thought is the latest fruit of Marxism in China
Since last Friday, students at Inner Mongolia University of Science & Technology had one more thing they could do in their spare time: study the thought of Xi Jinping, general secretary of the Communist Party of China ( CPC) Central Committee.
The university’s newly launched “Coordination Learning Center on Xi Jinping Thought on Socialism with Chinese Characteristics for a New Era,” located in the school library, will hold regular discussion panels, debates and lectures to promote Xi’s thought.
“We want to give impetus to a wave of enthusiasm on campus to study Xi’s thought. Students should become its practitioners and disseminators,” Yang Jiaqing, publicity chief and member of the standing committee of Inner Mongolia University of Science & Technology, told the Global Times.
The center is one of nearly 50 new research or learning centers dedicated to Xi Jinping Thought that were established in the week following the conclusion of the 19th CPC National Congress. Some were established by universities and others by provincial governments, including the governments of Hainan, Guizhou and Shandong provinces.
First raised in a report delivered by Xi at the opening of the congress, Xi Jinping Thought on Socialism with Chinese Characteristics for a New Era has been written into the CPC Constitution and builds on and further enriches Marxism- Leninism, Mao Zedong Thought, Deng Xiaoping Theory, the Theory of Three Represents and the Scientifi c Outlook on Development.
Party offi cials and experts have agreed that Xi Jinping Thought represents the latest achievements in adapting Marxism to the Chinese context, which can serve to unite the Party in this new era. It is an important component of the system of theories of socialism with Chinese characteristics.
Booming learning centers
Yang said the decision to establish a learning center on Xi’s thought was completely voluntary.
“We had wanted to establish a learn- ing center for Marxist theories for a long time, and after the 19th CPC congress we saw many universities have built research centers [ on Xi’s thought]. We applied to our university’s Party committee, deciding to follow the latest call and trend and change the name of the learning center to the Center on Xi Jinping Thought on Socialism with Chinese Characteristics for a New Era,” he said.
“From my understanding, Xi’s thought is the latest fruit of Marxism in China and marks that Socialism with Chinese Characteristics has entered a new era. It’s a political declaration and action guide in this era,” he said.
Yang said students at the university have been lacking enthusiasm for Marxist theories as they had been fed such courses since middle school. The learn- ing center was aimed to rekindle their interests.
Wang Haipeng, who’s pursuing a Master’s degree at the university’s School of Marxism Studies, has been participating in the center’s events since its commencement. “Xi Jinping Thought is definitely very important in academia. I believe it will be written into textbooks and be a hot research topic as well,” he told the Global Times.
The trend to establish research centers on Xi’s thought was set by Renmin University of China, which established the fi rst such center on October 25 of this year.
Qin Xuan, professor at Renmin University’s School of Marxism Studies and director of the research center, said at its opening ceremony that the center’s top priority would be to master the essence of Xi Jinping Thought and conduct deeper theoretical research into terms like “new era” and “new contradiction,” Guangming Daily reported.
“The center also has a special mission, which is to help Xi Jinping Thought on Socialism with Chinese Characteristics for a New Era enter textbooks, classrooms and into students’ brains,” Qin was quoted saying by Guangming Daily.
Wu Bo, a research fellow at the Academy for Social Sciences Evaluation under the Chinese Academy of Social Sciences ( CASS), is optimistic about the trend.
“Establishing relevant research organizations shows that these universities are highly proactive in their theoretical awareness,” he told the Global Times, adding that Xi’s thought has important and
“We now have new problems in the new age, so we need a new thought to guide us. And Xi’s thought can serve as a cohesive force for the Party.” Su Wei professor at the Party School of the Chongqing Committee
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white christmas

Christmas, for Lene Samsø, is all about tradition. Her work as an interior stylist means she’s constantly immersed in the latest trends, so she enjoys having a chance to embrace old favourites. “I love the holidays and making the decorations, food and gifts,” she says. “It’s my excuse for going over the top. My work is all about developing new ideas, while Christmas is about returning to the traditions of childhood, and the new traditions you create with your own family.”
The home Lene shares with her two children, Lucas and Filippa (pictured opposite), is strongly inflfluenced by its location in a wooded forest. She mixes modern design favourites like Eames dining chairs and Tom Dixon pendant lights with rustic pieces such as her recycled timber dining table. “I furnish and decorate in a quite intuitive way,” says Lene. “I like to fifind the interaction between the modern and the old, the minimalist and the quirky.”
When the holidays begin, the family heads into the surrounding forest to fifind branches to decorate the house. Some are dressed with ornaments, and some are allowed to stand bare, beautiful as they are. The ornaments themselves are a mix of old and new; Grandma’s fifine Christmas balls, paper ornaments in an origami style, and art photography with festive motifs combined in a seemingly unplanned way, yet somehow they come together.
“I try out combinations, change and add things, and I might even start all over again if it doesn’t work,” says Lene. “One year, I flflirted with acrylic ornaments edged in neon colours, but it just wasn’t for me. I had to take everything down!”
The atmosphere of the house is already cosy, so the decorations complement the existing feel. This year’s table is a reflflection of Lene’s design style, bold in its simplicity. The dining table, homemade from old reclaimed timber planks, is set with Royal Copenhagen porcelain and wooden plates, linen napkins, vintage Holmegaard glasses from Lene’s childhood home, pine cones and candles. Branches and decorations are hung from the ceiling to create a winter wonderland in the mostly white room.
Of course, a stylist’s Christmas is characterised by creative ideas and seasonal vignettes are found throughout. A local spruce tree is left bare apart from its silver star, but the gifts are wrapped in white and black paper and ribbon. Branches are placed in vessels or twisted into wreaths, and candles dot every surface.
“We spend Christmas Day relaxing and preparing our dinner while friends and family stop by,” says Lene. “My favourite thing is the excuse to have a little extra of everything – socialising, eating, decorating, celebrating and loving. Oh, and the snow! We love it when we’re lucky enough to get a white Christmas.”
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LET THERE BE LIGHT

GEELONG has pulled off a major events coup, with the city to host its own White Night.
The popular event, which transforms city streets and buildings into works of art, will be held on October 13 next year, and will pay tribute to the region’s indigenous heritage.
State Major Events Minister and Lara MP John Eren will formally announce the coup today.
It comes on the back of the state’s first regional White Night in Ballarat this year, which attracted more than 40,000 people and provided a $3 million boost for the local economy.
White Night artistic director David Atkins said preparations for the inaugural Geelong White Night would begin immediately.
GEELONG’S newest major event will shine a light on the city’s colourful and creative arts scene.
The State Government will today announce Geelong and Bendigo will have White Nights in 2018, following the success of the event in Melbourne and Ballarat.
State Major Events Minister and Lara MP John Eren will make the announcement at the Geelong Botanic Gardens today, alongside White Night artistic director David Atkins.
“It’s time to shine the light on Geelong and Bendigo,” Mr Eren said.
“The first ever regional White Night was a rousing success and we want more of our regions to experience and benefit from it.”
White Night, first held in Melbourne in 2013, includes citywide projections, art installations, visual arts and music and video performances taking over public spaces throughout an evening.
Geelong’s White Night will be held on October 13, and will pay tribute to the region’s indigenous heritage.
Earlier this year the state’s first regional White Night in Ballarat attracted more than 40,000, bringing in an estimated $3 million boost for the economy.
Atkins said preparation for the first Geelong White Night would start immediately.
“We’re very excited,” he said. “We’re looking forward to starting.
“I’ve got a few of the team coming down and we’ll do the e first step, which is determining the precinct and looking around the buildings, opportunities and parklands.”
Atkins said his team was keen for a Geelong event because it was a distinct city compared with other Victorian an regional centres.
“It sort of bridges the difference between some of the he regional cities and capital cities es in that regard,” he said.
Geelong state Labor MP Christine Couzens welcomed the announcement.
“Geelong has proved time and time again it can put on a show and we can’t wait to add the night of lights,” Ms Couzens said.
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