Industry News

Industry News

Machine learning AI News covering topics of Blurred Lines between Traders and Programmers. Why you should not be so scared of AI. AI blood cell classification using Google Co-Lab. AI getting better at cancer detection. Forecasting GDP – can you trade this? Democracy in the age of big data. Data Science for Good project utilizing Automated Feature Engineering.

AI & Machine Learning News. 27, August 2018

Image result for blurred lines robin thicke

Lines Blurred Between Traders and Programmers

This week’s Wall Street Journal weekend edition came with a great piece entitled “Trading Places.” The tag line under the title was this, “Wall Street used to have a strict hierarchy: Traders made money and won glory while programmers wrote code and stayed out of sight. Now, the line between the jocks and the nerds is disappearing.” The article focused upon details from Adam Korn, a 16-year veteran at Goldman. He stated that success today depends less on trusting one’s gut, rather much of a trader’s job is embedded in the computer code or algorithms, which do much of the work now.
2018-08-23 11:30:32-04:00 Read the full story.

CloudQuant Thoughts… This article and the article on which it is based (Trading Places from the WSJ) references new job titles for 200 or so Goldman Sachs employees who they originally called “Straders” and now title “Traders who Code”. At CloudQuant we beleive there are three groups: Traders who Code, Coders who Trade and Data Scientists who Trade. Right now you may be only one skill away from joining these elite groups. Register with CloudQuant, learn how to Trade, Code or Analyse. Ask any questions you have (we are here to help) and you could become a member of this very exclusive club!


Don’t be so scared of AI and new Tech

I read a lot of debates happening on the web and at coffee breaks and even at the company senior stakeholder meetings about applied AI is around the corner and the revolution will sweep away the way we work and major workforce redundancies are about to happen. The more you get involved in those discussions the magnitude grows and this leads for many of us to get anxious about theirs’ and their kids’ future.
2018-08-26 00:35:27 Read the full story.
CloudQuant Thoughts… The Luddites thought all their work would be lost to the machines so they threw their clogs into them hence “clogging the machine”. This article brings the disruption closer to home with the upheaval around the birth of tech and its effect on the clerical sector (it destroyed it!), yet unemployment rates are, if anything, lower than before the birth of the tech age.

Building a blood cell classification model using Keras, tfjs and Google Co-Lab

AI is really a major game changer. Applications of AI are huge and it’s scope in the field of healthcare is vast. Advanced AI tools can assist doctors and lab technicians to diagnose diseases with better accuracy, for example a doctor in Nigeria can use this tool to identify a disease from a sample of blood which he is not at all aware of, this helps him to better understand the disease and thus cures can be developed in a faster way, this is one such advantage of democratizing AI, because AI models and tools are accessible world wide , a doctor in Nigeria can use the same tools and technologies that are being used by research scholars in MIT or any other great universities in the world.
Google co-lab provides a cloud based python notebook with a virtual instance tied to a GPU runtime, the GPU runtime of google colab is powered by NVIDIA k-80, a powerful GPU and expensive too. But co-lab allows us to use the GPU for free without having to pay for it. Maximum time of an instance is 12 hours, after 12 hours the instance will be destroyed and new one will be created, so we can perform only those computations that doesn’t last longer than 12 hours.
2018-08-26 04:02:13.840000+00:00 Read the full story.
CloudQuant Thoughts… The article is interesting but the Google co-lab is fascinating. It would be cool if someone could create something like that for trading.. wait what? Someone over my shoulder is shouting “CloudQuant AI“. Watch this space!

Artificial intelligence system detects often-missed cancer tumors

Medical scientists and engineers have come together to develop artificial intelligence system designed to detect often-missed cancer tumors, thereby helping to boots patient survival rates. Researchers based at University of Central Florida developed the system by teaching a computer platform the optimal way to detect small specks of lung cancer in computerized tomography (CT) scans. These are of the type, through size and appearance, that radiologists sometimes have difficultly in identifying. In trials, the healthcare artificial intelligence system was found to be 95 percent accurate in total. Moreover, this was ahead of the typical scores achieved by human medics, which typically fall within the range of 65 percent when accuracy.
2018-08-26 00:00:00 Read the full story.
CloudQuant Thoughts… Scanning slide after slide of cell biopsies is tedious and obviously prone to error. Even an ineffective AI can cull the job load down to just those that appear “different” if not “abnormal”. In time AI will be able to massively outstrip human performance in this role to the benefit of us all.

Forecasting GDP in the eurozone using Eurostat data & ARIMA modelling

This analysis uses public Eurostat datasets, to forecast future total quarterly GDP of all eurozone countries. Eurostat is the statistical office of the European Union situated in Luxembourg. Its mission is to provide high quality statistics for Europe. Eurostat offers a whole range of important and interesting data …
2018-08-26 12:05:18.706000+00:00 Read the full story.
CloudQuant Thoughts…  A very interesting article, using the EU’s own data to predict the GDP of individual countries. You could adapt this R to Python, put it into CloudQuant and assign country ETFs to trade these shifts in GDP! What are you waiting for? Head over to CloudQuant now and write that model!

Is democracy safe in the age of big data?

“I saw the beginnings of creating a private, parallel intelligence-gathering operation that reported solely to the president and his political advisors without any kinds of oversight or control.” So says Christopher Wylie, a self-described queer, Canadian vegan with pink hair and facial piercings. He built up the data team at Cambridge Analytica and then resigned when he saw the destructive ends to which his talents were being put. He later blew the whistle when he realised the extent of the monster he helped create.
Cambridge Analytica’s psychographic modeling techniques are able to infer the hot-button issues for individuals based on their personality traits. The system was used in both the American presidential election in 2016 and the Brexit vote that same year.
2018-08-25 00:00:00 Read the full story.
CloudQuant Thoughts… The real problem here is that the cat is out of the bag! Cambridge Analytica were not the only company doing this. The cat, in this case, is your personality. Most people do not change their fundamental beliefs from year to year, or even decade to decade. So if Cambridge and their ilk have already identified “who you are”, then they know you better than Google!

A “Data Science for Good“ Machine Learning Project Walk-Through in Python

Solving a complete machine learning problem for societal benefit. Data science is an immensely powerful tool in our data-driven world. Call me idealistic, but I believe this tool should be used for more than getting people to click on ads or spend more time consumed by social media.
In this article and the sequel, we’ll walk through a complete machine learning project on a “Data Science for Good” problem: predicting household poverty in Costa Rica. Not only do we get to improve our data science skills in the most effective manner — through practice on real-world data — but we also get the reward of working on a problem with social benefits.
2018-08-23 11:30:32-04:00 Read the full story.
CloudQuant Thoughts… This article contains my Quote of the week: “The best approach is to spend some time creating a few features by hand using domain knowledge, and then hand off the process to automated feature engineering to generate hundreds or thousands more.” (Automated Feature Engineering)

Kaggle Competition : Analyzing The Cure discography

Hi! In this kernel we are going to use the spotifyr package, which allows us to enter an artist’s name and retrieve their entire discography from Spotify’s Web API, along with audio features and track/album popularity metrics. Since The Cure are one of my favourite groups, we are going to analyze some metrics and audio features from their songs! The Cure are an English rock band formed in Crawley, West Sussex, in 1976. By the way, Friday I’m In Love is my favourite song.
2018-08-23 11:30:32-04:00 Read the full story.
CloudQuant Thoughts… We started with Robin Thicke, let’s end with The Cure. Who knew there was a Spotify Package – spotifyr is a wrapper for pulling track audio features and other information from Spotify’s Web API in bulk. Available fields… acousticness, analysis_url, danceability, duration_ms, energy, id, instrumentalness, key, liveness, loudness, mode, speechiness, tempo, time_signature, track_href, type, uri, valence.

Below the Fold

New Kaggle Data Sources : Drought and the War in Syria

  1.  ???? Did Drought Cause the War in Syria? (link)
  2. ???? Time Series for Beginners with ARIMA (link)
  3. ???? Understand ARIMA and Tune P, D, Q (link)
  4. ???? A Hitchhiker’s Guide to Lending Club Loan Data (link)
  5. ???? Yellowbrick — Regression Visualizer Examples (link)
  6. ???? (Bio) statistics in R: Part #3 (link)
  7. ???? EDA – The Cure Discography (link)
  8. ???? Dataset: Example Brain Mapping Data (link)
  9. ???? Dataset: Face Dataset with Age, Emotion, Ethnicity (link)
  10. ⚽ Dataset: FIFA World Cup 2018 Tweets (link)

2018-08-23 00:00:00 Read the full story.

AI took center stage at VentureBeat’s inaugural Transform event

If one theme defined VentureBeat’s inaugural Transform conference on artificial intelligence (AI), it’s metamorphosis. Luminaries from Samsung, Google, Gogo, Uber, Intel, Pinterest, and others spoke about AI‘s increasing ability to handle tasks no human could perform at scale, like creating onboarding guides for tens of thousands of ridesharing drivers and predicting hundreds of millions of users’ taste in fashion.
“It’s about enabling companies to [innovate] faster,” said Faizan Buzdar, senior director and platform manager at cloud storage provider Box. “Think about data entry. When you replace it with machine learning, the validation process looks [the same], but you [as a business] saved a lot of money.”
An air of optimism pervaded panel discussions, product showcases, and fireside chats about AI in apparel, travel, food delivery, retail, and countless other markets. The consensus? Predictive systems not only have the potential to boost bottom lines and optimize workflows, they are set to improve user experiences.
2018-08-24 00:00:00 Read the full story.

12 Dimensionality Reduction Techniques in Python

Have you ever worked on a dataset with more than a thousand features? How about over 50,000 features? I have, and let me tell you it’s a very challenging task, especially if you don’t know where to start! Having a high number of variables is both a boon and a curse. It’s great that we have loads of data for analysis, but it is challenging due to size.
It’s not feasible to analyze each and every variable at a microscopic level. It might take us days or months to perform any meaningful analysis and we’ll lose a ton of time and money for our business! Not to mention the amount of computational power this will take. We need a better way to deal with high dimensional data so that we can quickly extract patterns and insights from it. So how do we approach such a dataset?
2018-08-27 02:02:43+05:30 Read the full story.

KAGGLE : Getting Started with Kaggle Competitions

In the field of data science, there are almost too many resources available: from Datacamp to Udacity to KDnuggets, there are thousands of places online to learn about data science. However, if you are someone who likes to jump in and learn by doing, Kaggle might be the single best location for expanding your skills through hands-on data science projects.
While it originally was known as a place for machine learning competitions, Kaggle — which bills itself as “Your Home for Data Science” — now offers an array of data science resources. Although this series of articles will focus on a competition, it’s worth pointing out the main aspects of Kaggle:

  • Datasets: Tens of thousands of datasets of all different types and sizes that you can download and use for free. This is a great place to go if you are looking for interesting data to explore or to test your modeling skills.
  • Machine Learning Competitions: once the heart of Kaggle, these tests of modeling skill are a great way to learn cutting edge machine learning techniques and hone your abilities on interesting problems using real data.
  • Learn: A series of data science learning tracks covering SQL to Deep Learning taught in Jupyter Notebooks.
  • Discussion: A place to ask questions and get advice from the thousands of data scientists in the Kaggle community.
  • Kernels: Online programming environments running on Kaggle’s servers where you can write Python/R scripts, or Jupyter Notebooks. These kernels are entirely free to run (you can even add a GPU) and are a great resource because you don’t have to worry about setting up a data science environment on your own computer. The kernels can be used to analyze any dataset, compete in machine learning competitions, or complete the learning tracks. You can copy and build on existing kernels from other users and share your kernels with the community for feedback.

2018-08-22 00:00:00 Read the full story.

The Formula Of Using Artificial Intelligence In F1 Races

Formula One sport is known to use the best of technologies and has never hesitated to spend on the safety of their drivers. It is not just a car race, but also a race of the technology and is popularly crowned as the Pinnacle of Motorsport. Now the F1 teams are gearing up to introduce artificial intelligence in the races. They are set to use cloud-based real-time analysis and machine learning techniques to enhance race metrics.
2018-08-27 10:39:03+00:00 Read the full story.

Researcher Steven Riddiough on Volume and Alternative Data in FX

It’s an industry at the dawn of its life and it’s undoubtedly going to evolve considerably over the next few years. I think we’re still trying to understand the best way to implement AI and machine learning in practice. It’s natural that many people at this stage are being employed to get the data aspect of the problem right. Focusing on making sure that datasets are cleaned and in a usable format for analysis is crucial and at the heart of the industry. But the follow-on stage of understanding whether the data is actually valuable is equally important and likely requires a different set of skills to those possessed by the people cleaning and organizing the data. It’s here where individuals with skills at the intersection of data science and economic science become really important.
2018-08-24 06:36:30+00:00 Read the full story.

How to select the Right Evaluation Metric for Machine Learning Models: Part 1 Regression Metrics

Each machine learning model is trying to solve a problem with a different objective using a different dataset and hence, it is important to understand the context before choosing a metric. Usually, the answers to the following question help us choose the appropriate metric:
Type of task (Regression/Classification)? Business goal? What is the distribution of the target variable?
Well, in this post, I will be discussing the usefulness of each error metric depending on the objective and the problem we are trying to solve.
Part1 focuses only to the regression evaluation metrics.
2018-08-26 19:29:04.560000+00:00 Read the full story.

A Hands on Guide to Automated Feature Engineering using Featuretools in Python

Anyone who has participated in machine learning hackathons and competitions can attest to how crucial feature engineering can be. It is often the difference between getting into the top 10 of the leaderboard and finishing outside the top 50!
I have been a huge advocate of feature engineering ever since I realized it’s immense potential. But it can be a slow and arduous process when done manually. I have to spend time brainstorming over what features to come up, and analyze their usability them from different angles. Now, this entire FE process can be automated and I’m going to show you how in this article.
We will be using the Python feature engineering library called Featuretools to do this. But before we get into that, we will first look at the basic building blocks of FE, understand them with intuitive examples, and then finally dive into the awesome world of automated feature engineering using the BigMart Sales dataset.
2018-08-22 16:44:53+05:30 Read the full story.

Intel Eyes Deep Learning with Vertex.AI Acquisition for its Movidius Unit

Intel is going strong over acquisitions, with a $1 billion spending spree on AI tech companies, including Mighty AI, DataRobot, Lumiata, AEye and others; Intel is building its AI capabilities. Additionally, Intel has also acquired Israel-based autotech player Mobileye for $15.3 billion and Movidius which is into specialised low-power processor chips development for computer vision. Intel acquired Movidius in September 2016 for an undisclosed sum, rumoured to around $300 million.
Vertex.AI the newest acquired tech platform will join the Movidius group, and assist to strong Intel’s AI capabilities to build powerful processors and deploying them to build AI into apps.
2018-08-26 13:34:32+05:30 Read the full story.

Academia to data science: a work in progress – Towards Data Science

Leaving academia for data science in a way seems like a natural next (albeit alternative) step. I spent most of my time since starting college being interested in the external and biological factors that make people and other animals do the things they do. More recently I began to become interested in the idea of measuring human behavior on a larger scale and actually using that insight to influence decisions. While this seems like a tough ask in the world of academia it is essentially the job description of many data science positions.
Since making the decision to leave academia, there are paths and pieces of advice that that I have so far found useful and others that I could have done without. There are things that I think I did well and things I am not particularly proud of. There have been resources that took me a long time to find that I wish I had found earlier. This post is a collection of ramblings about some of it with the purpose of passing that on to anyone that is interested.
2018-08-26 03:41:52.388000+00:00 Read the full story.

These engineers see the patterns driving social media trends

Keeping track of the latest news on social media is daunting enough, so imagine if your job were to dissect and analyze every single post, tweet and Instagram story on the internet. Enter the data science and analytics team at Networked Insights. The team develops technology to make sense of billions of daily posts, categorizing the text using more than 30,000 classifications to help companies better understand their customers.
Four members of the company’s data team gave us the lowdown on how they extract insights from social media — and the machine learning tools they use to get the job done.
2018-08-23 00:00:00 Read the full story.

Machine learning environment setup within 10min

One of the main problems with getting started learning AI and ML is installing software because we need to choose the algorithm, framework and software library for our application. Installing new software & libraries takes time. We may also face some issues during the setup process. To overcome these problems we use a docker environment.
2018-08-27 10:51:23.700000+00:00 Read the full story.

How Intel’s recent move will affect Deep Learning – Towards Data Science

Who hasn’t heard of Intel, the tech giant setting the pace with its processors? While it used to lead the computing devices industry, its reputation was slowly being eclipsed lately due to competitors sprouting up, with processors for mobile and other next-generation devices. Fortunately, this tech leader does not plan on getting submerged anytime soon. To quote Ingrid Lunden in her TechCrunch article, “The company has set its sights on being at the centre of the next wave of computing, and that is the wider context for its focus on R&D and other investments in AI.”
2018-08-20 15:32:08.038000+00:00 Read the full story.

Consider Predictive Analytics a Necessity, Not an Alternative

As a customer, you receive an email or an SMS notification stating that the groceries are about to expire, and asking if you would want to re-order them. How does the supermarket know? Well, they are not mind readers ; instead, it is predictive analytics being leveraged across huge datasets. The retail industry is known for being the front runners for utilizing and identifying data. They initiated the use of prescriptive analytics and now have entered the arena of predictive analytics.
Facebook leveraged predictive analytics techniques to become one of the largest data collecting companies identifying trends, and them to their users. They have more than 2 billion active users to date. You don’t need to be as massive as Facebook, but you definitely need to use predictive analytics model to experience the positives of considering it as a necessity, and not an alternative.
2018-08-24 12:02:52+00:00 Read the full story.

AI creating big winners in finance, but risks emerge

Artificial intelligence is changing the finance industry, with some early big movers already monetising their investments in back-office AI applications. But as this trend widens, new systemic and security risks may be introduced in the financial system, warns a new report from the World Economic Forum and Deloitte.
The report, based on more than 200 interviews with industry players as well as a host of workshops, concludes that AI is “fundamentally changing the physics of financial services”.
2018-08-24 00:01:00 Read the full story.

The Importance of AI for a Web Developer

When it comes to artificial intelligence (AI), it is no longer limited to sci-fi but is expected to grow into a market of $153 billion in the years to come. This has spurred the need for artificial intelligence courses, thus readying students and professionals to take part in the next wave of change for web development. In website development, an efficient user interface is at the forefront of the service. Artificial intelligence provides a sophisticated customer experience through reply predictions, voice optimisation, and some unique value-adds:


2018-08-23 16:21:51+00:00 Read the full story.

This Is The Right Time To Standardise Big Data Technologies

Is it time to standardise big data technologies? The once siloed, inaccessible, and mostly underutilised data has now become crucial to enterprises for success. And experts say that there is still room to promote interoperability between the available tools. But how does one define ‘standardisation’? Northeastern University’s College of Computer and Information Science defines “standard” as a formal agreement of meaning of a collection of concepts among groups — in this case, tech companies and enterprises.
2018-08-26 07:46:01+00:00 Read the full story.

Big data, big responsibility: The ethical question guiding Networked Insights’ CTO

Integrity is doing the right thing even when no one is looking.
Networked Insights CTO Brad Burke knows a thing or two about that — it’s his guidepost in the evolving world of marketing analytics.
Burke’s team sorts through billions of social media posts every day and turns them into consumer insights for companies. It’s a lofty task that comes with a lot of responsibility. While recent news has shown how easily that kind of information can be misused, Burke has adopted a simple guiding philosophy to ensure he can be proud of the work his team does.
2018-08-23 00:00:00 Read the full story.

Hype kills value, and other hard lessons from veteran voice app developers

Three industry veterans offer tried-and-true advice for successful voice computing. Perhaps more than any other portion of the tech industry, bots and artificial intelligence have made great strides in the past few years while simultaneously suffering from overhyped and even false claims. After a while, it can become tough to tell truth from fiction.
2018-08-26 00:00:00 Read the full story.

ICICI Lombard Launches India’s First AI To Automate Health Insurance Claims

ICICI Lombard General Insurance has used artificial intelligence to provide instant health insurance claim approval. Reportedly, the system can scan the documents sent by the hospital and match them with the medical coverage. In the past, this was a cumbersome process involving many people:

  • Doctors looked at the cases
  • Executives entered the data and balance
  • Checking the balance sum insured
  • Going through the room rent limits for the insured

This entire process would take over 60 minutes. Now, because of this new AI-powered system aided by OCR, the process can be carried out in just one minute.
2018-08-22 11:54:05+00:00 Read the full story.

Waymo sets up subsidiary in Shanghai as Google plans China push

Alphabet’s self-driving unit Waymo has set up a subsidiary in Shanghai, according to a business registration filing, the latest sign that the U.S. internet giant is attempting to make new inroads into China.
Waymo established a wholly-owned company called Huimo Business Consulting on May 22 in Shanghai’s free trade zone with registered capital of 3.5 million yuan ($509,165), according to China’s National Enterprise Information Publicity System.
2018-08-23 00:00:00 Read the full story.

Artificial Intelligence Is Now a Pentagon Priority. Will Silicon Valley Help?

In a May memo to President Trump, Defense Secretary Jim Mattis implored him to create a national strategy for artificial intelligence.
Mr. Mattis argued that the United States was not keeping pace with the ambitious plans of China and other countries. With a final flourish, he quoted a recent magazine article by Henry A. Kissinger, the former secretary of state, and called for a presidential commission capable of “inspiring a whole of country effort that will ensure the U.S. is a leader not just in matters of defense but in the broader ‘transformation of the human condition.’” Mr. Mattis included a copy of Mr. Kissinger’s article with his four-paragraph note.
2018-08-26 00:00:00 Read the full story.

IBM researchers propose ‘factsheets’ for AI transparency

We’re at a pivotal moment in the path to mass adoption of artificial intelligence (AI). Google subsidiary DeepMind is leveraging AI to determine how to refer optometry patients. Haven Life is using AI to extend life insurance policies to people who wouldn’t traditionally be eligible, such as people with chronic illnesses and non-U.S. citizens. And Google self-driving car spinoff Waymo is tapping it to provide mobility to elderly and disabled people. But despite the good AI is clearly capable of doing, doubts abound over its safety, transparency, and bias.
IBM thinks part of the problem is a lack of standard practices.
2018-08-22 00:00:00 Read the full story.

Weekly Selection — Aug 24, 2018 – Towards Data Science

  • Deep Dive into Math Behind Deep Networks
  • Recent Advances for a Better Understanding of Deep Learning
  • Use Kaggle to start (and guide) your ML/ Data Science journey – Why and How
  • A “Data Science for Good” Machine Learning Project Walk-Through in Python
  • Everything you need to know about AutoML and Neural Architecture Search
  • Generative Adversarial Nets and Variational Autoencoders at ICML 2018
  • Using Bidirectional Generative Adversarial Networks to estimate Value-at-Risk for Market Risk Management
  • Measuring Model Goodness

2018-08-24 16:12:35.125000+00:00 Read the full story.

Behind a paywall…

Why the artificial intelligence bubble looks like it has already burst

In 1964, an American computer scientist named John McCarthy set up a research centre at California’s Stanford University to explore an exciting new discipline: artificial intelligence. McCarthy had helped coin the term several years earlier, and interest in the field was growing fast. By then, the first computer programs that could beat humans at chess had been developed, and thanks to plentiful government grants at the height of the Cold War, AI researchers were making rapid progress in other areas such as algebra and language translation.
2018-08-25 00:00:00 Read the full story.

Google’s plans for a return to China has many up in arms

When Google announced in 2010 that it would no longer censor results on its Chinese service, a prerequisite for operating in the communist state, internet users laid flowers at the search giant’s Beijing headquarters to mourn its exit. People outside China praised Google’s decision, as it revealed attempts to spy on Chinese dissidents in Gmail, an assault on liberties that the company thought went too far.
2018-08-25 00:00:00 Read the full story.

Computer-created portrait to be sold by Christie’s in sale which marks ‘the arrival of AI art on the world auction stage’

A portrait created by a computer is to be sold at Christie’s in New York, marking the first time a leading auction house has dealt in art made by artificial intelligence.
The “painting” of a fictional man, who has been named Edmond Belamy, is the work of a Paris-based trio of 25-year-olds, who are making a name for themselves with pioneering computer-generated art.
Gauthier Vernier, one of the three co-founders of the Obvious art collective, laughed when asked if they intended to put human artists out of business.
2018-08-22 00:00:00 Read the full story.

The challenges of a no-deal Brexit are as nothing compared with those of Artificial Intelligence

Britain, it would seem, is in a state of total unpreparedness for the coming storm. The Government pays lip service to the necessary precautionary work, but in practice does nothing beyond warning of the potential dangers and trying to ensure continuation of as much of the status quo as possible. I’m not talking here of the possibility of a “no-deal” Brexit. I’ll come back to that. Rather, I’m referring to the advent of Artificial Intelligence.
2018-08-24 00:00:00 Read the full story.

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