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Developing a Naive Bayes Text Classifier in JAVA




NaiveBayes-JAVAIn previous articles we have discussed the theoretical background of Naive Bayes Text Classifier and the importance of using Feature Selection techniques in Text Classification. In this article, we are going to put everything together and build a simple implementation of the Naive Bayes text classification algorithm in JAVA. The code of the classifier is open-sourced (under GPL v3 license) and you can download it from Github.

Update: The Datumbox Machine Learning Framework is now open-source and free to download. Check out the package com.datumbox.framework.machinelearning.classification to see the implementation of Naive Bayes Classifier in Java.

Naive Bayes Java Implementation

The code is written in JAVA and can be downloaded directly from Github. It is licensed under GPLv3 so feel free to use it, modify it and redistribute it freely.

The Text Classifier implements the Multinomial Naive Bayes model along with the Chisquare Feature Selection algorithm. All the theoretical details of how both techniques work are covered in previous articles and detailed javadoc comments can be found on the source code describing the implementation. Thus in this segment I will focus on a high level description of the architecture of the classifier.

1. NaiveBayes Class

This is the main part of the Text Classifier. It implements methods such as train() and predict() which are responsible for training a classifier and using it for predictions. It should be noted that this class is also responsible for calling the appropriate external methods to preprocess and tokenize the document before training/prediction.

2. NaiveBayesKnowledgeBase Object

The output of training is a NaiveBayesKnowledgeBase Object which stores all the necessary information and probabilities that are used by the Naive Bayes Classifier.

3. Document Object

Both the training and the prediction texts in the implementation are internally stored as Document Objects. The Document Object stores all the tokens (words) of the document, their statistics and the target classification of the document.

4. FeatureStats Object

The FeatureStats Object stores several statistics that are generated during the Feature Extraction phase. Such statistics are the Joint counts of Features and Class (from which the joint probabilities and likelihoods are estimated), the Class counts (from which the priors are evaluated if none are given as input) and the total number of observations used for training.

5. FeatureExtraction Class

This is the class which is responsible for performing feature extraction. It should be noted that since this class calculates internally several of the statistics that are actually required by the classification algorithm in the later stage, all these stats are cached and returned in a FeatureStats Object to avoid their recalculation.

6. TextTokenizer Class

This is a simple text tokenization class, responsible for preprocessing, clearing and tokenizing the original texts and converting them into Document objects.

Using the NaiveBayes JAVA Class

In the NaiveBayesExample class you can find examples of using the NaiveBayes Class. The target of the sample code is to present an example which trains a simple Naive Bayes Classifier in order to detect the Language of a text. To train the classifier, initially we provide the paths of the training datasets in a HashMap and then we load their contents.

 //map of dataset files Map<String, URL> trainingFiles = new HashMap<>(); trainingFiles.put("English", NaiveBayesExample.class.getResource("/datasets/training.language.en.txt")); trainingFiles.put("French", NaiveBayesExample.class.getResource("/datasets/")); trainingFiles.put("German", NaiveBayesExample.class.getResource("/datasets/")); //loading examples in memory Map<String, String[]> trainingExamples = new HashMap<>(); for(Map.Entry<String, URL> entry : trainingFiles.entrySet()) { trainingExamples.put(entry.getKey(), readLines(entry.getValue())); }

The NaiveBayes classifier is trained by passing to it the data. Once the training is completed the NaiveBayesKnowledgeBase Object is stored for later use.

 //train classifier NaiveBayes nb = new NaiveBayes(); nb.setChisquareCriticalValue(6.63); //0.01 pvalue nb.train(trainingExamples); //get trained classifier NaiveBayesKnowledgeBase knowledgeBase = nb.getKnowledgeBase();

Finally to use the classifier and predict the classes of new examples all you need to do is initialize a new classifier by passing the NaiveBayesKnowledgeBase Object which you acquired earlier by training. Then by calling simply the predict() method you get the predicted class of the document.

 //Test classifier nb = new NaiveBayes(knowledgeBase); String exampleEn = "I am English"; String outputEn = nb.predict(exampleEn); System.out.format("The sentense "%s" was classified as "%s".%n", exampleEn, outputEn); 

Necessary Expansions

The particular JAVA implementation should not be considered a complete ready to use solution for sophisticated text classification problems. Here are some of the important expansions that could be done:

1. Keyword Extraction:

Even though using single keywords can be sufficient for simple problems such as Language Detection, other more complicated problems require the extraction of n-grams. Thus one can either implement a more sophisticated text extraction algorithm by updating the TextTokenizer.extractKeywords() method or use Datumbox’s Keyword Extraction API function to get all the n-grams (keyword combinations) of the document.

2. Text Preprocessing:

Before using a classifier usually it is necessary to preprocess the document in order to remove unnecessary characters/parts. Even though the current implementation performs limited preprocessing by using the TextTokenizer.preprocess() method, when it comes to analyzing HTML pages things become trickier. One can simply trim out the HTML tags and keep only the plain text of the document or resort to more sophisticate Machine Learning techniques that detect the main text of the page and remove content which belongs to footer, headers, menus etc. For the later you can use Datumbox’s Text Extraction API function.

3. Additional Naive Bayes Models:

The current classifier implements the Multinomial Naive Bayes classifier, nevertheless as we discussed in a previous article about Sentiment Analysis, different classification problems require different models. In some a Binarized version of the algorithm would be more appropriate, while in others the Bernoulli Model will provide much better results. Use this implementation as a starting point and follow the instructions of the Naive Bayes Tutorial to expand the model.

4. Additional Feature Selection Methods:

This implementation uses the Chisquare feature selection algorithm to select the most appropriate features for the classification. As we saw in a previous article, the Chisquare feature selection method is a good technique which relays on statistics to select the appropriate features, nevertheless it tends to give higher scores on rare features that only appear in one of the categories. Improvements can be made removing noisy/rare features before proceeding to feature selection or by implementing additional methods such as the Mutual Information that we discussed on the aforementioned article.

5. Performance Optimization:

In the particular implementation it was important to improve the readability of the code rather than performing micro-optimizations on the code. Despite the fact that such optimizations make the code uglier and harder to read/maintain, they are often necessary since many loops in this algorithm are executed millions of times during training and testing. This implementation can be a great starting point for developing your own tuned version.

Almost there… Final Notes!

I-heard-hes-good-at-coding-lTo get a good understanding of how this implementation works you are strongly advised to read the two previous articles about Naive Bayes Classifier and Feature Selection. You will get insights on the theoretical background of the methods and it will make parts of the algorithm/code clearer.

We should note that Naive Bayes despite being an easy, fast and most of the times “quite accurate”, it is also “Naive” because it makes the assumption of conditional independence of the features. Since this assumption is almost never met in Text Classification problems, the Naive Bayes is almost never the best performing classifier. In Datumbox API, some expansions of the standard Naive Bayes classifier are used only for simple problems such as Language Detection. For more complicated text classification problems more advanced techniques such as the Max Entropy classifier are necessary.

If you use the implementation in an interesting project drop us a line and we will feature your project on our blog. Also if you like the article please take a moment and share it on Twitter or Facebook. 🙂

About Vasilis Vryniotis

My name is Vasilis Vryniotis. I’m a Data Scientist, a Software Engineer, author of Datumbox Machine Learning Framework and a proud geek. Learn more



Natural language processing: A cheat sheet




Learn the basics about natural language processing, a cross-discipline approach to making computers hear, process, understand, and duplicate human speech.


Image: Visual Generation, Getty Images/iStockphoto

It wasn’t too long ago that talking to a computer and having it not only understand, but speak back, was confined to the realm of science fiction, like that of the shipboard computers of Star Trek. The technology of the 24th century’s Starship Enterprise is reality in the 21st century thanks to natural language processing (NLP), a machine learning-driven discipline that gives computers the ability to understand, process, and respond to spoken words and written text.

Make no mistake: NLP is a complicated field that one can spend years studying. This guide contains the basics about NLP, details how it can benefit businesses, and explains where to get started with its implementation.

SEE: Managing AI and ML in the enterprise 2020: Tech leaders increase project development and implementation (TechRepublic Premium)

What is natural language processing?

Natural language processing (NLP) is a cross-discipline approach to making computers hear, process, understand, and duplicate human language. Fields including linguistics, computer science, and machine learning are all a part of the process of NLP, the results of which can be seen in things like digital assistants, chatbots, real-time translation apps, and other language-using software.

The concept of computers learning to understand and use language isn’t a new one—it can arguably be traced all the way back to Alan Turing’s Computing Machinery and Intelligence paper published in 1950, which was where the idea of the Turing Test comes from. 

In brief, Turing attempted to determine whether machines could behave in a way indistinguishable from a human, which fundamentally requires the ability to process language and respond in a sensible way. 

SEE: All of TechRepublic’s cheat sheets and smart person’s guides

Since Turing wrote his paper, a number of approaches to natural language processing have emerged. First came rules-based systems, like ELIZA, which were limited in what they could do to a set of instructions. Systems like ELIZA were easy to distinguish from a human because of their formulaic, non-specific responses that quickly become repetitive and feel unnatural: It lacked understanding, which is a fundamental part of modern NLP.

With the advent of machine learning, which allows computers to algorithmically develop their own rules based on sample data, natural language processing exploded in ways Turing never could have predicted. 

Natural language processing has reached a state where it’s now better at understanding human speech than real humans. Even this impressive milestone still falls short of truly complete NLP, though, because the machine performing the work was simply transcribing language, not being asked to comprehend it. 

Modern NLP platforms are also capable of visually processing speech. Facebook’s Rosetta, for example, is able to “extract text in different languages from more than a billion images and video frames in real time,” TechRepublic sister site CNET said.

Additional resources

What are the challenges of natural language processing?

Computers don’t need to understand human speech to speak a language–the machines operate on a kind of linguistic structure that allows them to accept input, process data, and respond to commands.

Languages like Swift, Python, JavaScript, and others all have something in common that natural language lacks: Precision.

Human speech isn’t precise by any stretch of the definition: It’s contextual, metaphorical, ambiguous, and spoken imperfectly all the time, and understanding language requires a lot of background and interpretive ability that computers lack.

Computational linguist Ekaterina Kochmar, in a talk about natural language processing, explained that words exist in a sort of imaginary semantic space. In our minds, Kochmar said, we have representations of words, and words with related or similar meanings live close together in a web of semantic understanding.

Thinking of language in that manner allows machine learning tools to be built that let computers algorithmically create their own semantic space, which lets them infer relations between words and better understand natural speech.

SEE: Robotic process automation: A cheat sheet (free PDF) (TechRepublic)

That doesn’t mean challenges are overcome, though. Going from understanding simple, precise statements like those given to digital assistants to producing sensible speech on their own is still difficult for NLP programs. Candy hearts produced by artificial intelligence (AI) taught to understand romantic language are predictably absurd, and 1 the Road, a novel written entirely by an artificial neural network, is generally nonsensical with only the most occasional glimpse of semantic understanding, which could be entirely chalked up to chance.

As advanced as natural language processing is in its ability to analyze speech, turn it into data, understand it, and use an algorithm to generate an appropriate response, still generally lacks the ability to speak on its own or grasp the ambiguity and metaphor that is fundamental to natural language. 

We’ve mastered the first part: Understanding. It’s the second part, generating natural speech or human language, that we’re still a bit stuck on. And we might be stuck there for a while, if pioneering mathematician and computer scientist Ada Lovelace is correct: She posited that computers were only able to do what we told them to, and were incapable of originality. Known as Lady Lovelace’s Objection, it’s become a common part of criticism of the Turing Test and thus a criticism of natural language processing: If machines can’t have original thoughts, then is there any way to teach them to use language that isn’t ultimately repetitive?

Additional resources:

How is natural language processing used?

Natural language processing has a lot of practical applications for a variety of business uses. 

Google Duplex is perhaps the most remarkable use of natural language processing available as an example today. The digital assistant, introduced in 2018, is not only able to understand complex statements, but it also speaks on the phone in a way that’s practically indistinguishable from a human—vocal tics and all. Duplex’s goal is to carry out real-world tasks over the phone, saving Google users time spent making appointments, booking services, placing orders, and more. 

Ninety-eight percent of Fortune 500 companies are now using natural language processing software to filter candidates for job searches with products known as applicant tracking systems. These products pick through resumes to look for appropriate keywords and other linguistic elements.

SEE: Robotics in the enterprise (free PDF) (TechRepublic)

Chatbots are quickly becoming the first line of online customer service, with 68% of consumers saying they had a positive experience speaking with one. These bots use natural language processing to address basic requests and problems, while also being able to elevate requests to humans as needed.

Uses of NLP in healthcare settings are numerous: Physician dictation, processing hand-written records, compiling unstructured healthcare data into usable formats, and connecting natural language to complicated medical billing codes are all potential uses. NLP has also been used recently to screen COVID-19 patients.

NLP can be used to gauge customer attitudes in call center environments, perform “sentiment analysis” on social media posts, can be used as part of business intelligence analysis, and can supplement predictive analytics.

Natural language processing has a potentially endless variety of applications: Anything involving language can, with the right approach, be a use case for NLP, especially if it involves dealing with a large volume of data that would take a human too long to work with. 

Additional resources:

How can developers learn about natural language processing?

NLP is a complicated topic that a computer scientist could easily spend years learning the ins and outs of. If your objective is being at the cutting edge of NLP research, it’s probably best to think about attending a university known for having a good computational linguistics program.

Developers who want to learn to make use of current NLP technology don’t need to dive that far into the deep end. Text analytics firm MonkeyLearn has an excellent rundown of resources and steps to get started with natural language processing; here are a few key points from its guide.

MonkeyLearn’s guide also has a variety of links in it to articles, research, and journals that any budding NLP developer should be aware of. 

Additional resources: 

What is the best way for businesses to get started with natural language processing?

Every business uses language, so there’s a good chance you can come up with at least one or two uses for natural language processing in your organization—but how do you go from thinking about what NLP could do for you to actually doing it? There are a lot of steps to consider.

For starters, you need to know what your objectives are for NLP in your business. Do you want to use it to aggregate data as an analytics tool, or do you want to build a chatbot that can interact with customers via text on your support portal? Maybe you want to use NLP as the backbone of an e-mail filter, understand customer sentiment, or use it for real-time translation. 

No matter what you want NLP to do for your business you need to know your goal before even starting to think about achieving it.

SEE: Top cloud providers in 2020: AWS, Microsoft Azure, and Google Cloud, hybrid, SaaS players (TechRepublic)

Once you know what you want to do with natural language processing, it’s time to find the right talent to build the system you want. You may already have developers in-house who are familiar with Python and some of the NLP frameworks mentioned above. If that’s the case, get them involved in the planning stages from the very beginning. 

If you don’t have anyone in-house who can develop natural language processing software, you’re faced with a choice: Hire new people or bring in a third-party that specializes in NLP solutions.

If you choose to go about your NLP objectives in-house, you’ll need to find the right software solutions or providers for hosting your NLP platform, and there are plenty of recognizable names to choose from. 

IBM Watson has options, AWS offers Amazon Comprehend and other NLP services, Microsoft Azure has NLP services as well, as does Google Cloud. Choosing the proper platform will require input from your developers because they’re the ones who will be working with the software every day, and your NLP initiative’s success may hinge on how well they can use the platform.

Additional resources:


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The path to real-world artificial intelligence




Experts from MIT and IBM held a webinar this week to discuss where AI technologies are today and advances that will help make their usage more practical and widespread.

Artificial Intelligence project creating. Abstract concept of cyber technology, machine learning.Brain of AI. Futuristic Innovative technology in science concept

Image: Sompong Rattanakunchon / Getty Images

More about artificial intelligence

Artificial intelligence has made significant strides in recent years, but modern AI techniques remain limited, a panel of MIT professors and IBM’s director of the Watson AI Lab said during a webinar this week.

Neural networks can perform specific, well-defined tasks but they struggle in real-world situations that go beyond pattern recognition and present obstacles like limited data, reliance on self-training, and answering questions like “why” and “how” versus “what,” the panel said.

The future of AI depends on enabling AI systems to do something once considered impossible: Learn by demonstrating flexibility, some semblance of reasoning, and/or by transferring knowledge from one set of tasks to another, the group said. 

SEE: Robotic process automation: A cheat sheet (free PDF) (TechRepublic)

The panel discussion was moderated by David Schubmehl, a research director at IDC, and it began with a question he posed asking about the current limitations of AI and machine learning.

“The striking success right now in particular, in machine learning, is in problems that require interpretation of signals—images, speech and language,” said panelist Leslie Kaelbling, a computer science and engineering professor at MIT. 

For years, people have tried to solve problems like detecting faces and images and directly engineering solutions that didn’t work, she said.

We have become good at engineering algorithms that take data and use that to derive a solution, she said. “That’s been an amazing success.” But it takes a lot of data and a lot of computation so for some problems formulations aren’t available yet that would let us learn from the amount of data available, Kaelbling said.

SEE: 9 super-smart problem solvers take on bias in AI, microplastics, and language lessons for chatbots (TechRepublic)

One of her areas of focus is in robotics, and it’s harder to get training examples there because robots are expensive and parts break, “so we really have to be able to learn from smaller amounts of data,” Kaelbling said.

Neural networks and deep learning are the “latest and greatest way to frame those sorts of problems and the successes are many,” added Josh Tenenbaum, a professor of cognitive science and computation at MIT. 

But when talking about general intelligence and how to get machines to understand the world there is still a huge gap, he said.

“But on the research side … really exciting things are starting to happen to try to capture some steps to more general forms of intelligence [in] machines,” he said. In his work, “we’re seeing ways in which we can draw insights from how humans understand the world and taking small steps to put them in machines.”

Although people think of AI as being synonymous with automation, it is incredibly labor intensive in a way that doesn’t work for most of the problems we want to solve, noted David Cox, IBM director of the MIT-IBM Watson AI Lab.

Echoing Kaelbling, Cox said that leveraging tools today like deep learning requires huge amounts of “carefully curated, bias-balanced data,” to be able to use them well. Additionally, for most problems we are trying to solve, we don’t have those “giant rivers of data” to build a dam in front of to extract some value from that river, Cox said.

Today, companies are more focused on solving some type of one-off problem and even when they have big data, it’s rarely curated, he said. “So most of the problems we love to solve with AI—we don’t have the right tools for that.”

That’s because we have problems with bias and interpretability with humans using these tools and they have to understand why they are making these decisions, Cox said. “They’re all barriers.” 

However, he said, there’s enormous opportunity looking at all these different fields to chart a path forward. 

That includes using deep learning, which is good for pattern recognition, to help solve difficult search problems, Tenenbaum said.
To develop intelligent agents, scientists need to use all the available tools, said Kaelbling. For example, neural networks are needed for perception as well as higher level and more abstract types of reasoning to decide, for example, what to make for dinner or to decide how to disperse supplies.

“The critical thing technologically is to realize the sweet spot for each piece and figure out what it is good at and not good at. Scientists need to understand the role each piece plays,” she said.

The MIT and IBM AI experts also discussed a new foundational method known as neurosymbolic AI, which is the ability to combine statistical, data-driven learning of neural networks with the powerful knowledge representation and reasoning of symbolic approaches.

Moderator Schubmehl commented that having a combination of neurosymbolic AI and deep learning “might really be the holy grail” for advancing real-world AI.

Kaelbling agreed, adding that it may be not just those two techniques but include others as well.

One of the themes that emerged from the webinar is that there is a very helpful confluence of all types of AI that are now being used, said Cox. The next evolution of very practical AI is going to be understanding the science of finding things and building a system we can reason with and grow and learn from, and determine what is going to happen. “That will be when AI hits its stride,” he said.

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Role of Artificial Intelligence in Social Media




Considering the fact that there are 3.81 billion (continue to grow) active social media population worldwide, it would no wrong to say that we live in an era of social media. Different online studies reveal that every smartphone users uses at least one social media application (Instagram, Facebook, Twitter, Tumblr, LinkedIn, Snapchat, etc.).

But, social media is not just about connecting to your friends or family, rather, it has become a perfect place for businesses to find new clients or nurture their relationships with the existing ones. By sharing their thoughts, photos, or videos on these platforms, both the businesses and individuals are adding an unimaginable amount of data that is increasing exponentially with each passing year.

And, if you are wondering how these platforms are managing the same, then the answer is through AI and various other technologies. Yes, AI or artificial intelligence is contributing massively to manage this sea of human data coming to these platforms.

This branch of computer science makes machines to act, think, and behave like human beings. AI and machine learning (a subset of AI) in social media are helping the giant social networking companies to make sense of the user-generated data to manage various activities. This article is all about the impact of artificial intelligence on social media.

How is AI used in social media?

Managing social media platforms (flooded with innumerable users) is not a child’s play; it requires a lot of things to look upon. With artificial intelligence, social networking companies are analyzing voluminous data to find out what’s trending, different hashtags, and patterns. This analysis helps in understanding users’ behavior.

With the help of various algorithms, artificial intelligence can keep an eye on the unstructured user comments to offer a personalized experience and to recognize crisis. The technology can also assist in providing content analyzing different activities as well as demographics.

Most of the top social networking companies have already adopted AI to scale up their processes and take their business to the next level.


Top social networking platform uses machine learning and AI for serving you the content of your interest, recognizing your face in photos, recommending you tag options, identifying visuals, and for various other tasks.


The platform uses AI to detect a face, from a complete image, to create a thumbnail. It utilizes neural networks to decipher- which section of an image the user would like. Twitter also uses this technology to suggest replies while commenting on a tweet or answering a comment.


This social media platform relies on machine learning and artificial intelligence to predict suitable candidates for a particular job role. Using AI, Linkedin also highlights the candidates who are actively looking out for a new opportunity or are most likely to respond.


There are more than 200 billion users who Pinterest pins on this platform and 80% of them make a purchase the personalized content. The platform uses neural networking to show its users the content of their interest. It means images available on Pinterest are linked to a neural network based on a particular theme.

Apart from these four, other social networking websites are also leveraging AI and machine learning to streamline their processes and deliver an unmatchable user experience.


Benefits of using AI in social media

To recognize images

AI-powered image recognition software and tools help in recognizing various images to understand the change in users’ behavior or pattern. Through complex algorithms, it can go through millions of images to bring out valuable information.

AI-powered chatbots

Businesses running over social media can use AI-powered chatbots to answer their customers’ queries in no time. AI-enabled chatbots can efficiently conduct conversations with the consumers and provide them the required answers by understanding the intent of a query. With this, businesses can improve customer experience to a significant level.

Analyzing sentiments

Since AI can analyze the nature or intent of a query or comment or something posted by a user, it can help brands to identify sentiments to know how you feel. For this, AI uses another subset known as natural language processing. NLP also helps in finding out positive and negative words in a post or comment.

Increased security

AI can help social media platforms to protect the user data and increase the privacy of their information. Through user authentication, pattern detection, fraud prevention, and other features, this technology can help users to improve the security of their social media accounts.


Future of AI in social media

The amazing benefits artificial intelligence is rendering to social media platforms depict that the technology is here to stay for long. Keeping in view the growing number of social media users, it would be no wonder to consider it as the biggest marketplace in the future. The technology will help social networking companies to deliver better customer experience and help marketers to target the right customers that will increase the conversion rate and ROI. They will use images to boost the engagement of their targeted audiences and look into their behavior.

How AI is used in social media ads or social media marketing?

AI-powered tools help you to look into your brand’s social media profiles and visitors. Using those tools, you can understand users’ behavior and what they say or post about your brand. This data can further help you to know your global brand equity, recognize new trends, target new audiences based on their interest, and identify new ways of social media promotion.

While social media platform gives individuals and businesses to connect to people and targeted audience, it also allows brands to run paid advertisements based on behavioral targeting and demographics. Having funds is not the only criterion to run a brand advertisement successfully; you need to be creative to make your ad get the desirable clicks.

There are several AI-based tools that can help to know how to optimize your ad for conversions and clicks. These tools can also provide you other valuable information, such as which language will deliver better results or what words are popular among customers looking out for the products or services like yours.

AI-enabled tools can also help you to measure the performance of your ad campaign to know whether it is going in the right direction or delivering the desired output or not. AI can also help you to predict market growth and make your ad strategies accordingly. You can increase ROI and get more organic customers for your brand.

Final Thoughts

Artificial intelligence is helping social media platforms to manage the pool of data and make sense of it to know the latest trends, user behavior and their interests, find out and block abusive content, and for various other purposes. It has a bright future in this industry as it improves user experiences and help brands to serve them better. AI also plays a major role in social media marketing by letting the brands measure the performance of the company and identify users that can be converted into potential customers. To integrate artificial intelligence in your existing social media application or to build a new AI-powered social media app, reach out to a reliable AI development company, or hire AI/ML developers.

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