How to cope with the opportunities and challenges of the artificial intelligence era (with the rapid

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The development of artificial intelligence is changing rapidly, and how to keep oneself at the forefront of technological development is the key to the success of every relevant technology worker. This article comes from compilation and hopes to be enlightening to you.

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Image by Werner Du plessis on Unslash The development of data science is very rapid. Five years ago, if you said you were interested in artificial intelligence, you might have a doctor's degree in quantum mathematics. Now, even my mother often discusses ChatGPT.

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Artificial intelligence is no longer something ethereal, but has made tangible progress. Now, you must understand some real materials. So, how can you keep up with the latest developments in the fields of artificial intelligence and data science? Although there are already many good suggestions online, I have found that many strategies recommended by others do not work for me.

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As a data scientist working in the industry, my time spent on research is very limited, and spending all of it on academic papers and technical publication documents is meaningless. What I need is short and practical resources that can help me understand how to utilize new technologies in my daily work.

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In this guide, I will summarize some useful resources for me. My goal is to highlight materials that are helpful from an application perspective (not just a business or research perspective). If you are a data scientist, I hope these resources can help you stay at the forefront of technology in your daily work and identify innovation opportunities Read blogs from other data science teams

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Many data science teams have blogs, and my personal favorites are team blogs from companies such as Netflix, Tripadvisor, Duolingo, Meta, and Spotify. One thing I like about these blogs is that they are super practical. Although many academic research papers focus on theoretical aspects of data science and machine learning, company blogs tend to emphasize "how to use data science in practice to solve real-world problems.

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As a data scientist working in the industry, I have found that these are indeed very helpful. I do not need to screen a large number of theoretical terms or decipher hundreds of grayscale flowcharts, but can directly enter the core of the problem and understand how to use data science to solve various problems that I face as a data scientist in my daily work.

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For example, a few days ago, I was searching for some information about A/B testing, a common framework in data science, and I personally have little experience with it. I found many good articles online and then stumbled upon a wonderful series on Netflix's technology blog. This series not only answered my questions about A/B testing, but also, There are many practical examples of how to use A/B testing on Netflix.

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This is of great help in understanding both theory and practice Start writing online

The process of artificial intelligence film reading is reflected in

One of the main reasons why I started writing on Medium was to help myself learn, not for teaching or marketing, but to learn what people often say: you can remember 10% of what you read, 20% of what you see, and 95% of what you teach.

If you completely rely on reading others' blogs, it is unlikely to gain the full value of your knowledge or remember all the information you are reading. Writing a data science blog on your own is an excellent way to consolidate this knowledge and strengthen the content you have learned. This forces you to think step by step and helps you gain a deeper understanding of the topic.

If the idea of writing online sounds a bit beyond your comfort zone, don't worry. I also feel the same way, but ask yourself: what is the worst-case scenario that could happen? As far as I am concerned, I have found that the worst outcome may be being slightly mocked by my colleagues. Overall, I believe these consequences can be tolerated, and if you consider the experience you can gain from writing, you will find that writing is very worthwhile.

If you don't know what to write, you can take a look at some suggestions on the FAQ page of Towards Data Science Subscribing to TLDR AI Newsletters for reading and writing blogs is a great way to learn about the latest developments in data science and artificial intelligence. However, if you want to keep up with business developments, I suggest subscribing to AI focused newsletters that allow you to track startups, acquisitions, and research trends.

My personal favorite is TLDR AI Newsletter. They send an email every day summarizing key news stories in the industry, and this is more targeted than their more mature technical communication. Similar products include MIT Technology Review's The Download and DeepLearning. AI's The Batch.

I like newsletters like this because they usually only take me 2 minutes to read, but they can help me quickly understand things in the AI world Follow "ML Papers of the week" GitHub repoDAIR.AI to maintain a great GitHub repo, updating 10 new machine learning papers every week.

For each paper, DAIR will provide a brief summary and link to a Twitter post to explain more about the findings of the paper. If you don't want to watch GitHub repos, you can also follow DAIR.AI on Twitter

Image source: Medium Personally, I have found that these brief summaries are more operational than the knowledge you receive from services such as Google School or arXiv. On Google School and arXiv, you will soon be overwhelmed by a large amount of knowledge and end up with nothing due to "information overload".

The greatness of DAIR.AI is that they have done a lot of work for you, screening out the most interesting and cutting-edge papers, and allowing you to choose to continue reading, or give up and move forward decisively Subscribing to the Data Science channel Two Minute Papers on YouTube is a YouTube channel that uploads two new videos every week, each aimed at extracting the findings of a recent research paper, many of which are related to artificial intelligence.

At the time of writing this article, they already had nearly 500 videos on their artificial intelligence and deep learning playlists. Subscribing to this channel is a great way to learn about the latest developments in artificial intelligence research. I particularly like their classic video "OpenAIPlays Hide and Seek", but to be honest, there are too many exciting videos to choose from.

The other two channels that I particularly like are Josh Starmer's StatQuest and 3Blue1Brown. I like these channels because they explain the concepts of statistics and machine learning in a very intuitive and easy to understand way, without requiring a lot of basic knowledge.

Although these channels are most famous for their introductory courses, they have also released many videos on cutting-edge topics in machine learning, such as 3Blue1Brown's recent introduction to convolution Many organizations hold free webinars to discuss the latest innovations in the fields of data science and artificial intelligence.

Personally, I am a loyal fan of these webinars because scheduling webinars forces me to free up time to learn and improve, which is very helpful in ensuring learning time. For example, if you use cloud database systems such as Google BigQuery or AWS RDS in your daily work, you may benefit from attending webinars hosted by Google or AWS, Discuss how to maximize the use of these tools.

I recently attended a great BigQuery webinar on optimizing SQL code 7 Follow some AI leaders on Twitter. If you are like me, it is easy to get lost in the technical details of many AI news releases and technical documentation, then Twitter is a good place. You can read many frank opinions and suggestions from experts here.

I particularly recommend following people like Yann LeCun, Timnit Gebru, Geoffrey Hinton, Andrew Ng, and Christopher Manning for AI researchers to use Twitter to share their work progress and some useful "side studies" that may not necessarily be included in scientific papers.

Image source: Medium However, the main reason I like Twitter is that it is a good way to focus on data scientists and artificial intelligence practitioners working in the industry (rather than just researching). For example, people like Chris Alban, Jay Alammar, and Cassie Kozyrkof study issues that are closely related to the daily lives of data scientists working in the industry, Paying attention to these big shots is a good way to understand the work of other data science organizations.

Another point is that Twitter has a better sense of humor than arXiv.

AI Memes8 on Twitter If you want to establish your career in this field of data science, it is absolutely crucial to understand the latest trends in artificial intelligence and data science. However, based on experience, I find it easy to be attracted to the details of specific tasks and overlook the overall development trends of the industry.

In this article, my purpose is to emphasize the strategies that can help me keep up with the latest trends and discover innovation opportunities in my daily work. If you think I have missed any essence, please tell me in the comments and look forward to hearing your suggestions Translator: Jane Editor: Source of subjective perspective: Shenyi Bureau