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Check out the top 6 machine learning GitHub repositories created in June
There’s a heavy focus on NLP again, with XLNet outperforming Google’s BERT on several state-of-the-art benchmarks
All machine learning GitHub repositories are open source; download the code and start experimenting!Introduction
Do you sometimes feel that machine learning is too broad and vast to keep up? I certainly feel that way. Just check out the list of major developments in Natural Language Processing (NLP) in the last year:
It can become overwhelming as a data scientist to simply keep track of all that’s happening in machine learning. My aim of running this GitHub series since January 2023 has been to take that pain away for our community.
We trawl through every open source machine learning release each month and pick out the top developments we feel you should absolutely know. This is an ever-evolving field – and data scientists should always be on top of these breakthroughs. Otherwise, we risk being left behind.
This month’s machine learning GitHub collection is quite broad in its scope. I’ve covered one of the biggest NLP releases in recent times (XLNet), a unique approach to reinforcement learning by Google, understanding actions in videos, among other repositories.
Fun times ahead so let’s get rolling!
You can also go through the GitHub repositories and Reddit discussions we’ve covered so far this year:Top Machine Learning GitHub Repositories
Of course we are starting with NLP. It is the hottest field in machine learning right now. If you thought 2023 was a big year (and it was), 2023 has taken up the mantle now.
The latest state-of-the-art NLP framework is XLNet. It has taken the NLP (and machine learning) community by storm. XLNet uses Transformer-XL at its core. The developers have released a pretrained model as well to help you get started with XLNet.
XLNet has so far outperformed Google’s BERT on 20 NLP tasks and achieved state-of-the-art performance on 18 such tasks. Here are a few results on popular NLP benchmarks for reading comprehensions:
Model RACE accuracy SQuAD1.1 EM SQuAD2.0 EM
BERT 72.0 84.1 78.98
XLNet 81.75 88.95 86.12
Want more? Here are the results for text classification:
Model IMDB Yelp-2 Yelp-5 DBpedia Amazon-2 Amazon-5
BERT 4.51 1.89 29.32 0.64 2.63 34.17
XLNet 3.79 1.55 27.80 0.62 2.40 32.26
XLNet is, to put it mildly, very impressive. You can read the full research paper here.
Wait – were you wondering how you can implement XLNet on your machine? Look no further – this repository will get you started in no time.
If you’re well versed with NLP features this will be pretty simple to understand. But if you’re new to this field, take a few moments to go through the documentation I mentioned above and then try this out.
The developer(s) has also provided the entire code in Google Colab so you can leverage GPU power for free! This is a framework you DON’T want to miss out on.
I’m a huge football fan so the title of the repository instantly had my attention. Google Research and football – what in the world do these two have to do with each other?
Well, this “repository contains a reinforcement learning environment based on the open-source game Gameplay Football”. This environment was created exclusively for research purposes by the Google Research team. Here are a few scenarios produced within the environment:
The research paper makes for interesting reading, especially if you’re a football or reinforcement learning enthusiast (or both!). Check it out here.
This is a fascinating concept. CRAFT stands for Character Region Awareness for Text Detection. This should be on your to-read list if you’re interested in computer vision. Just check out this GIF:
Can you figure out how the algorithm is working? CRAFT detects the text area by exploring each character region present in the image. And the bounding box of the text? That is obtained by simply finding minimum bounding rectangles on a binary map.
You’ll grasp CRAFT in a jiffy if you’re familiar with the concept of object detection. This repository includes a pretrained model so you don’t have to code this algorithm from scratch!
You can find more details and an in-depth explanation of CRAFT in this paper.
Ever worked with video data before? It’s a really challenging but rewarding experience. Just imagine the sheer amount of things we can do and extract from a video.
How about understanding the action being performed in a particular video frame? That’s what the MMAction repository does. It is an “open source toolbox for action understanding based on PyTorch”. MMAction can perform the below tasks, as per the repository:
Action recognition from trimmed videos
Temporal action detection (also known as action localization) in untrimmed videos
Spatial-temporal action detection in untrimmed videos
MMAction’s developers have also provided tools to deal with different kinds of video datasets. The repository contains a healthy number of steps to at least get you up and running.
Here is the getting started guide for MMAction.
One of the most crucial, and yet overlooked, aspects of a data scientist’s skillset – software engineering. It is an intrinsic part of the job. Knowing how to build models is great, but it’s equally important to understand the software side of your project.
The best part about TRAINS (and there are many) is that it’s free and open source. You only need to write two lines of code to fully integrate TRAINS into your environment. It currently integrates with PyTorch, TensorFlow, and Keras and also supports Jupyter notebooks.
The developers have set up a demo server here. Go ahead and try out TRAINS using whatever code you want to test.End Notes
My pick for this month is surely XLNet. It has opened up endless opportunities for NLP scientists. There’s only one caveat though – it requires strong computational power. Will Google Colab come to the rescue? Let me know if you’ve tried it out yet.
On a relevant note, NLP is THE field to get into right now. Developments are happening at breakneck speed and I can easily predict there’s a lot more coming this year. If you haven’t already, start delving into this as soon as you can.
You're reading 6 Powerful Open Source Machine Learning Github Repositories For Data Scientists
Coding is among one of the best things about being a data scientist. There are often days when I find myself immersed in programming something from scratch. That exhilarating feeling you get when you see your hard work culminate in a successful model? Exhilarating and unparalleled!
But as a data scientist (or a programmer), its equally important to create checkpoints of your code at various intervals. It’s incredibly helpful to know where you started off from last time so if you have to rollback your code or simply branch out to a different path, there’s always a fallback option. And that’s why GitHub is such an excellent platform.
The previous posts in this monthly series have expounded on why every data scientist should have an active GitHub account. Whether it’s for collaboration, resume/portfolio, or educational purposes, it’s simply the best place to enhance your coding skills and knowledge.
And now let’s get to the core of our article – machine learning code! I have picked out some really interesting repositories which I feel every data scientist should try out on their own.
To make things easier for you, here’s the entire collection so far of the top GitHub repositories and Reddit discussions (from April onwards) we have covered each month:GitHub Repositories
Keeping our run going of including reinforcement learning resources in this series, here’s one of the best so far – OpenAI’s Spinning Up! This is an educational resource open sourced with the aim of making it easier to learn deep RL. Given how complex it can appear to most folks, this is quite a welcome repository.
The repo contains a few handy resources:
An introduction to RL terminology, kinds of algorithms, and basic theory
An essay about how to grow into an RL research role
A curated list of important papers organized by topic
A code repo of short, standalone implementations of key algorithms
A few exercises to get your hands dirty
This one is for all the audio/speech processing people out there. WaveGlow is a flow-based generative network for speech synthesis. In other words, it’s a network (yes, a single network!) that can generate impressive high quality speech from mel-spectrograms.
This repo contains the PyTorch implementation of WaveGlow and a pre-trained model to get you started. It’s a really cool framework, and you can check out the below links as well if you wish to delve deeper:
We covered the PyTorch implmentation of BERT in last month’s article, and here’s a different take on it. For those who are new to BERT, it stands for Bidirectional Encoder Representations from Transformers. It’s basically a method for pre-training language representations.
BERT has set the NLP world ablaze with it’s results, and the folks at Google have been kind enough to release quite a few pre-trained models to get you on your way.
This repository “uses BERT as the sentence encoder and hosts it as a service via ZeroMQ, allowing you to map sentences into fixed-length representations in just two lines of code”. It’s easy to use, extremely quick, and scales smoothly. Try it out!
Quick, Draw is a popular online game developed by Google where a neural network tries to guess what you’re drawing. The neural network learns from each drawing, hence increasing it’s already impressive ability to correctly guess the doodle. The developers have built up a HUGE dataset from the amount of drawings users have made previously. It’s an open-source dataset which you can check out here.
And now you can build your own Quick, Draw game in Python with this repository. There is a step-by-step explanation of how to do this. Using this code, you can run an app to either draw in front of the computer’s webcam, or on a canvas.
GAN Dissection, pioneered by researchers at MIT’s Computer Science & Artificial Intelligence Laboratory, is a unique way of visualizing and understanding the neurons of Generative Adversarial Networks (GANs). But it isn’t just limited to that – the researchers have also created GANPaint to showcase how GAN Dissection works.
This helps you explore what a particular GAN model has learned by inspecting and manipulating it’s internal neurons. Check out the research paper here and the below video demo, and then head straight to the GitHub repository to dive straight into the code!Reddit Discussions
Has this question ever crossed your mind while learning basic machine learning concepts? This is one of the fundamental algorithms we come across in our initial learning days and has proven to be quite effective in ML competitions as well. But once you start going through this thread, prepare to seriously question what you’ve studied previously.
What do you do when the developer of a complex and massive neural network vanishes without leaving behind the documentation needed to understand it? This isn’t a fictional plot, but a rather common situation the original poster of the thread found himself in.
It’s a situation that happens regularly with developers but takes on a whole new level of intrigue when it comes to deep learning. This thread explores the different ways a data scientist can go about examining how a deep neural network model was initially designed. The responses range from practical to absurd, but each adds a layer of perspective which could help you one day if you ever face this predicament.
It took around a year of total experience to beat the Montezuma’s Revenge game at a super human level – pretty impressive!
This one is for all the aspiring data scientists reading the article. The author of the thread expounds on how he landed the coveted job, his background, where he studied data science from, etc. After answering these standard questions, he has actually written a very nice post on what others in a similar position can do to further their ambitions.End Notes
Quite a collection this month. I found the GAN Dissection repository quite absorbing. I’m currently in the process of trying to replicate it on my own machine – should be quite the ride. I’m also keeping an eye on the ‘Reverse Engineering a Massive Neural Network’ thread as the ideas spawning there could be really helpful in case I ever find myself in that situation.
Introduction Artificial Intelligence Creates Synthetic Data for Machine Learning
Generative Adversarial Networks are one of the main tools that artificial intelligence is used to produce synthetic data (GANs). A generator plus a Bayesian classifier make up a GAN, a particular kind of neural network. The generator oversees producing fake data, while the discriminator determines if the data is real or fake. Together, the two networks are trained, with the generator attempting to produce data that the discriminator finds difficult to separate from actual data and the discriminator working to become more adept at recognizing artificial information.Synthetic Data
Two sources exist for synthetic data −
Real World Data
Although personally identifying information (PII) and personal health information (PHI) can be removed from real-world data, this does not completely protect privacy since the data records can still be matched to other sources that can be used to identify individuals. Like the COVID-19 example, the anonymized data must be mixed again in a way that keeps all the data set’s statistical characteristics for the machine learning algorithms to make accurate inferences and develop accurate rules.
In some cases, a lack of real-world data is a challenge for machine learning. Sometimes it would be impractical or too expensive to acquire data from the real world. Simulated data may occasionally be close enough to real-world instances for machine learning algorithms to recognise it. The self-driving car industry, for instance, blends real sensor data from moving vehicles with simulated data from driving simulations (even video games like Grand Theft Auto).
Unfortunately, adopting synthetic data comes with several difficulties that must be overcome. The requirement that the synthetic data be representational of the real-world data presents one of the key obstacles. The machine learning model may not function well if the synthetic data does not precisely match the real-world data. Another challenge is that the synthetic data must be sufficiently varied to account for every scenario that the model might face in the actual world.
Another challenge is that biassed models might be produced using fake data. Biased models are models that have learned to produce inaccurate predictions for certain groups of people. For example, a model that is trained on synthetic data that is biased towards a particular race or gender may produce inaccurate predictions for people who are not in that group. To avoid this, it is important to ensure that the synthetic data is diverse and representative of the real-world data.Synthetic Data Applications
Software Testing that is automated for DevOps. Test data has always been necessary for software development, but today’s quick Agile development cycles of DevOps demand more test data than ever.
Robots and Automation in manufacturing. Synthetic data can speed up the training of AI systems in robotics and manufacturing applications because real-world data collecting can be sluggish and expensive, like automobile data collection.
Monetary services. Personal financial data is subject to strict confidentiality restrictions, just like healthcare data, and synthetic data provides developers and business users with access to larger datasets without invading privacy.
Consumer Behavior Simulations in Marketing. Since the GDPR and other restrictions apply to actual consumer online behavior, marketing AI can be trained more broadly and thoroughly using a synthetic dataset.
Clinical Medical Investigation. Since PHI is heavily regulated, artificial intelligence (AI) and machine learning are made viable in situations where datasets might otherwise be too limited to be helpful.
Facial Identification to avoid privacy violations and biases from underrepresented types of faces, synthetic facial data can be used instead of real-world pictures to train facial recognition.Conclusion
In conclusion, AI is being used to create synthetic data that can be used to train machine learning models. Synthetic data can be used to augment limited real-world data sets, as well as to create data for tasks that are difficult or impossible to collect real-world data for. However, it is important to ensure that the synthetic data is representative of the real-world
Looking for machine learning projects to do right now? Here are 7 wide-ranging GitHub projects to try out
These projects cover multiple machine learning domains, including NLP, computer vision and Big Data
Add these to your machine learning skillset and expand your knowledgeIntroduction
I have conducted tons of interviews for data science positions in the last couple of years. One thing has stood out – aspiring machine learning professionals don’t focus enough on projects that will make them stand out.
And no, I don’t mean online competitions and hackathons (though that is always a plus point to showcase). I’m talking about off-the-cuff experiments you should do using libraries and frameworks that have just been released. This shows the interviewer two broad things:
You have an unquenchable curiosity for machine learning. This is a vital aspect of being a successful data scientist
You are not afraid to experiment with new algorithms and techniques
And guess which platform has the latest machine learning developments and code? That’s right – GitHub!
So let’s look at the top seven machine learning GitHub projects that were released last month. These projects span the length and breadth of machine learning, including projects related to Natural Language Processing (NLP), Computer Vision, Big Data and more.Top Machine Learning GitHub Projects
I’ll be honest – the power of Natural Language Processing (NLP) blows my mind. I started working in data science a few years back and the sheer scale at which NLP has grown and transformed the way we work with text – it almost defies description.
PyTorch-Transformers is the latest in a long line of state-of-the-art NLP libraries. It has beaten all previous benchmarks in various NLP tasks. What I really like about PyTorch Transformers is that is contains PyTorch implementations, pretrained models weights and other important components to get you started quickly.
You might have been frustrated previously at the ridiculous amount of computation power required to run state-of-the-art models. I know I was (not everyone has Google’s resources!). PyTorch-Transformers eradicates the issue to a large degree and enables folks like us to build state-of-the-art NLP models.
Here are a few in-depth articles to get you started with PyTorch-Transformers (and the concept of pre-trained models in NLP):
Multi-label classification on text data is quite a challenge in the real world. We typically work on single label tasks when we’re dealing with early stage NLP problems. The level goes up several notches on real-world data.
In a multi-label classification problem, an instance/record can have multiple labels and the number of labels per instance is not fixed.
NeuralClassifier enables us to quickly implement neural models for hierarchical multi-label classification tasks. What I personally like about NeuralClassifier is that it provides a wide variety of text encoders we are familiar with, such as FastText, RCNN, Transformer encoder and so on.
We can perform the below classification tasks using NeuralClassifier:
Binary-class text classification
Multi-class text classification
Multi-label text classification
Hierarchical (multi-label) text classification
Here are two excellent articles to read up on what exactly multi-label classification is and how to perform it in Python:
This TDEngine repository received the most stars of any new project on GitHub last month. Close to 10,000 stars in less than a month. Let that sink in for a second.
TDEngine is an open-source Big Data platform designed for:
Internet of Things (IoT)
IT Infrastructure, and much more.
TDEngine essentially provides a whole suit of tasks that we associate with data engineering. And we get to do all this at super quick speed (10x speed on processing queries and 1/5th computational usage).
There’s a caveat (for now) – TDEngine only supports execution on Linux. This GitHub repository includes the full documentation and starter’s guide with code.
I suggest checking out our comprehensive resource guide for data engineers:
What about videos, though? The difficult level goes up several notches when we’re asked to simply draw bounding boxes around objects in videos. The dynamic aspect of objects makes the entire concept more complex.
So, imagine my delight when I came across this GitHub repository. We just need to draw a bounding box around the object in the video to remove it. It really is that easy! Here are a couple of examples of how this project works:
If you’re new to the world of computer vision, here are a few resources to get you up and running:
You’ll love this machine learning GitHub project. As data scientists, our entire role revolves around experimenting with algorithms (well, most of us). This project is about how a simple LSTM model can autocomplete Python code.
The code highlighted in grey below is what the LSTM model filled in (and the results are at the bottom of the image):
As the developers put it:
If you’ve ever spent (wasted) time on writing out mundane Python lines, this might be exactly what you’re looking for. It’s still in the very early stages so be open to a few issues.
And if you’re wondering what in the world LSTM is, you should read this introductory article:
TensorFlow and PyTorch both have strong user communities. But the incredible adoption rate of PyTorch should see it leapfrog TensorFlow in the next year or two. Note: This isn’t a knock on TensorFlow which is pretty solid.
So if you have written any code in TensorFlow and a separate one in PyTorch and want to combine the two to train a model – the tfpyth framework is for you. The best part about tfpyth is that we don’t need to rewrite the earlier code.
This GitHub repository includes a well structured example of how you can use tfpyth. It’s definitely a refreshing look at the TensorFlow vs. PyTorch debate, isn’t it?
Installing tfpyth is this easy:pip install tfpyth
Here are a couple of in-depth articles to learn how TensorFlow and PyTorch work:
I associate transfer learning with NLP. That’s my fault – I am so absorbed with the new developments that I did not imagine where else transfer learning could be applied. So I was thrilled when I came across this wonderful MedicalNet project.
This GitHub repository contains a PyTorch implementation of the ‘Med3D: Transfer Learning for 3D Medical Image Analysis‘ paper. This machine learning project aggregates the medical dataset with diverse modalities, target organs, and pathologies to build relatively large datasets.
And as we well know, our deep learning models do (usually) require a large amount of training data. So MedicalNet, released by TenCent, is a brilliant open source project I hope a lot of folks work on.
The developers behind MedicalNet have released four pretrained models based on 23 datasets. And here is an intuitive introduction to transfer learning if you needed one:End Notes
Quite a mix of machine learning projects we have here. I have provided tutorials, guides and resources after each GitHub project.
I have one ask – pick the project that interests you, go through the tutorial, and then apply that particular library to solve the problem. For example, you could take up the NeuralClassifier repository and use that to solve a multi-label classification problem.
Introduction to Penetration Testing Open Source Tools
Web development, programming languages, Software testing & othersList of Various Open-Source Tools
So, here is a list of various open-source tools.1. Netsparker
Netsparker is an efficient vulnerability scanner for web applications that automatically detect XSS, SQL Injection, and other vulnerabilities in web applications and web services. It is available as an on-site solution and as a SAAS solution.
Features of Netsparker:
The scanner automatically detects custom 404 error pages and URL rewrite rules.
REST API for smooth integration with the SDLC, systems for monitoring bugs, etc.
It is a highly configurable system that Scans 1,000 web applications in 1 day.2. Acunetix
Features of Acunetix:
It scans all variants of XSS, SQL Injection, and 5000+ additional vulnerabilities.
It can detect over 1400 WordPress cores, plugins, and other vulnerabilities.
It is Scalable and fast as it crawls thousands of pages without interruptions in less time.
It provides Integration with popular WAFs.
It is Available Onsite as well as a Cloud solution.3. Indusface
To detect and monitor SANS top 25 and OWASP top 10-based vulnerabilities, Indusface WAS provides manual penetration testing and automated scanning.
Features of Indusface:
Its Crawler scans single-page applications.
It has a Pause and Resumes functionality.
Automated Scanning and manual Penetration testing Reports can be seen on the same dashboard.
It provides Unlimited proof of concept requests as evidence of vulnerabilities identified.
Optional WAF integration to provide Zero False-positive instant virtual patchings.4. Aircrack
Features of Aircrack:
Aircrack supports more cards or drivers.
It is available on all OS.
It provides Support for Fragmentation attacks as well as WEP dictionary attacks.
Improved tracking speed.5. Nexpose Rapid 7
Nexpose Rapid 7 is a widely used and popular vulnerability management tool. It scans and detects vulnerabilities in real time.
Features of Nexpose Rapid 7:
It offers a Real-Time View of the Risk.
It brings progressive and innovative approaches which help the user to secure from attacks.6. Nessus
Nessus is a scanner that is the most robust software vulnerability identifier. It provides a wide range of website scanning, sensitive data searches, compliance checks, IP scans, etc., and helps to find the system’s “weak spots”.
It provides an easy-to-use and interactive GUI.
It is an effective scanning engine.
It helps in Generating vulnerability status reports in different formats.
It has Fast activated and deactivated attack modules.
It provides a pause and resumes a scan or an attack for the pen test.7. W3af
W3af is a popular Web Application Attack and Audit tool. It helps detect and exploit over 200 vulnerabilities in web applications such as XSS, SQL injection, DoS, DDoS, etc.
Features of W3af:
It has a user-friendly console and graphical interface.
It provides security from Cross-Site Scripting (XSS), CRLF Injection, SEL Injection, and Xpath Injection.
It also provides Command execution detection.8. Wapiti
Wapiti is another widely used penetration testing tool. It provides auditing of the security of web applications. Wapiti supports importing cookies, GET, and POST HTTP methods for vulnerability checks.
Features of Wapiti:
It helps in Generating vulnerability reports in different formats.
It can activate and deactivate attack modules quickly.
It Supports HTTP as well as HTTPS proxies.
It provides Automatic deletion of a parameter in URLs.
It offers activation and deactivation of SSL certificate verification.
Users can Extract URLs from Flash SWF files with the help of Wapiti.Conclusion
In this article, we have seen various open-source tools for penetration testing. You can choose any of them based on your requirements. We hope you will find this article helpful.Recommended Articles
This is a guide to Penetration Testing Open Source Tools. Here we discuss the introduction and various Open Source Tools, respectively. You may also have a look at the following articles to learn more –
All of us who have worked for big companies know that executives don’t like bad information and have a tendency to shoot the messenger. Often problems that cripple a company are known, but covered up, for years before the result is so evident it can no longer be covered up.
I wrote one of the postmortems for IBM’s fall in the ‘80s. The problems I reported were largely mirrored by Microsoft in the ‘90s and I think we are seeing the beginning of these same problems with Google. Given people migrated many of these problems as they left IBM and moved to Microsoft in the ’90s, and that people are moving from Microsoft to Google, and that Google is moving very fast, I think we’ll see this business cancer likely to progress at record speed but, perhaps, not peak until Google is vastly more powerful than any other technology company has ever been.
The problems I’m talking about relate to the need for company executives to only want to hear that which is consistent with their existing views and to attack anything that is inconsistent.A better example would be with Iraq and the U.S. government; you may recall that early on the chief military officer testified there weren’t enough troops to protect that country after it the U.S. took it chúng tôi was fired after being widely criticized disrespectfully by the administration for these views, which are clearly now known to be correct.
But Does the Same Trend Apply Broadly to Open Source?
Open Source is about sharing, but is it about candor?I’ve often compared Open to Transparent and I wonder if when we talk about the first we forget that it is the second that is the more important.Microsoft’s issues surrounded trust, and that speaks to transparent more than it does to whether or not you could see their source code. (And, coincidentally, you have to admit given their recent financials, they appear to be recovering nicely.)
People being people, why wouldn’t the same kind of problems that plague companies who have a tendency to cover up and conceal problems also apply in the Open Source community?
So what are the two topics with Open Source that should concern us but, because the discussion would trigger the famous Open Source FUD response, are being avoided?
1.What does Google’s extreme future dominance mean and, given Google’s success is significantly enabled by Open Source, will the outcome actually be better or worse, in the sense of “Freedom,” than it was during either IBM or Microsoft dominance?
The Rise of the Uber-Monopoly: Google
With SCO in the headlines and Microsoft on the offensive, Open Source was getting a massive amount of publicity, and vendors who wanted the related visibility appeared to embrace the underlying chúng tôi that is marketing, and for way too many people, marketing and reality have very little connection to each other.
During the upswing companies like Red Hat were the poster child for the industry, but Red Hat has never been that profitable, at least not when compared to Google, who appears to be the primary beneficiary of Open Source, and companies like Novell have found profit elusive.
The true poster child for Open Source is Google, which makes its money not by sharing technology but by using it effectively to reduce technology costs to incredibly low chúng tôi everyone were to follow Google’s example much of the existing technology industry, from Sun to Microsoft, would simply cease to exist.Google’s long term plan would appear to be to become more powerful than AT&T, IBM, and Microsoft chúng tôi even though they are having a little trouble controlling costs, the company is executing on this plan at an impressive speed.
Will a world dominated by Google – with more power than the combined power of the firms they displace – be better or worse than it is now? That will depend on Google, but clearly companies that achieved a fraction of that kind of power in the past (do no evil policies aside) have not handled it well, and I doubt Google will either. Because inside the search giant are people, many of whom came from the same companies that had issues when they dominated their respective segments.
But, and here is the kicker, if Google wins as they intend, Open Source effectively is dead in much of the market as is Free (as in Free Speech) Software. In other words Google will define what you get and don’t get and they likely will define much of what you see as well. Granted much will be Free, as in Free Beer, but I wonder if the cost of this “Free” will be more than any of us now intend to chúng tôi could call this collateral damage.
In addition, those that have adopted Open Source generally find line managers focused like a laser, not on getting down hardware or software cost (which is already as low as it can go, and you can’t get blood out of a stone), but on getting down labor cost, resulting in off-shoring or foreign labor being brought in at discount rates.There really is nothing to support the compensation for OSS developers like there generally is in the proprietary world.
While this may seem inconsistent it is, however, consistent with what large companies do when they are told information they don’t want to hear. They ignore the information even if, personally, they may be planning to react to it. (I can recall a report I put out years ago at IBM talking about turning around a problem business unit. Executives vocally disagreed, but then generally left the company a few weeks after reading the negative report).
For those of us that covered the dotcom years the problem came down to one big thing: a complete avoidance of financial fundamentals. People were building products and services that either didn’t have defined customers or revenues that ever could exceed costs, often chúng tôi was in, everyone was running around saying they could provide the next big thing and Netscape was the example, a company that largely gave away their product and still was successful, for awhile anyway.
Of course, in Netscape’s case, they actually were trying to sell something and collapsed when they actually shifted to Free and belatedly learned that the right model was closer to what Yahoo and Google adopted, with minimal focus on the browser and a lot of focus on what the browser was connected to.
Open Source grew up during this time, and many who support it undoubtedly benefited from the rise, but the concept of Free, as in Free Beer, should likely have been abandoned or at least enhanced to ensure that people earned a fair return for their contribution to the chúng tôi wasn’t done, and with regard to the people actually building the Open Source stuff there are still a lot of very strong contributors who help make companies like Google successful but don’t share in that chúng tôi I doubt will continue to do so indefinitely.
Look At the Outcomes
If you look at what appears to be the outcome of all of this OSS focus, Microsoft is still reporting record revenues but is just as clearly not the power player they once were. That spot has been taken by Google, a company that makes massive amounts of money from Open Source software but doesn’t seem to contribute back any more than Microsoft does (and no I don’t think Free Search and Google Apps count).
With North America apparently bleeding jobs along with much of the developed world, I wonder if there should be less focus on creating really cheap software and more focus on ensuring programmers, who provide the kind of value companies like Google are clearly getting, are compensated for that value.
In the end I don’t think Free is just killing OSS, I think it is killing one of the primary incentives to create great software in the developed chúng tôi should be Free as in Freedom. Free as in Free Beer works for some things but applied globally it substantially appears to reduce the value of the people creating software.
I don’t argue it is nice to hear good news, and you’ll note I’m actually not suggesting any change in buying behavior. I’m just suggesting there are likely things you too don’t want to hear that you need to listen to that probably go well beyond this chúng tôi there are always people who want to dictate what you can and can’t hear. Keeping others from controlling your information sources and closing that information gap could do a great deal to prevent the kinds of problems I’ve pointed out above.
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