Category Archives: Machine Learning

The most crucial aspect of every business is its cybersecurity. It aids in ensuring the security and safety of their data. Artificial intelligence and machine learning are in high demand, changing the cybersecurity industry as a whole. Cybersecurity may benefit greatly from machine learning, which can be used to better available antivirus software, identify cyber dangers, and battle online crime. With the increasing sophistication of cyber threats, companies are constantly looking for innovative ways to protect their systems and data. Machine learning is one emerging technology that is making waves in cybersecurity. Cybersecurity professionals can now detect and mitigate cyber threats more effectively by leveraging artificial intelligence and machine learning algorithms. This article will delve into key areas where machine learning is transforming the security landscape.

One of the biggest challenges in cybersecurity is accurately identifying legitimate connection requests and suspicious activities within a companys systems. With thousands of requests pouring in constantly, human analysis can fall short. This is where machine learning can play a crucial role. AI-powered cyber threat identification systems can monitor incoming and outgoing calls and requests to the system to detect suspicious activity. For instance, there are many companies that offer cybersecurity software that utilizes AI to analyze and flag potentially harmful activities, helping security professionals stay ahead of cyber threats.

Traditional antivirus software relies on known virus and malware signatures to detect threats, requiring frequent updates to keep up with new strains. However, machine learning can revolutionize this approach. ML-integrated antivirus software can identify viruses and malware based on their abnormal behavior rather than relying solely on signatures. This enables the software to detect not only known threats but also newly created ones. For example, companies like Cylance have developed smart antivirus software that uses ML to learn how to detect viruses and malware from scratch, reducing the dependence on signature-based detection.

Cyber threats can often infiltrate a companys network by stealing user credentials and logging in with legitimate credentials. It can be challenging to detect with traditional methods. However, machine learning algorithms can analyze user behavior patterns to identify anomalies. By training the algorithm to recognize each users standard login and logout patterns, any deviation from these patterns can trigger an alert for further investigation. For instance, Darktrace offers cybersecurity software that uses ML to analyze network traffic information and identify abnormal user behavior patterns.

Machine learning offers several advantages in the field of cyber security. First and foremost, it enhances accuracy by analyzing vast amounts of data in real time, helping to identify potential threats promptly. ML-powered systems can also adapt and evolve as new threats emerge, making them more resilient against rapidly growing cyber-attacks. Moreover, ML can provide valuable insights and recommendations to cybersecurity professionals, helping them make informed decisions and take proactive measures to prevent cyber threats.

As cyber threats continue to evolve, companies must embrace innovative technologies like machine learning to strengthen their cybersecurity defenses. Machine learning is transforming the cybersecurity landscape with its ability to analyze large volumes of data, adapt to new threats, and detect anomalies in user behavior. By leveraging the power of AI and ML, companies can stay ahead of cyber threats and safeguard their systems and data. Embrace the future of cybersecurity with machine learning and ensure the protection of your companys digital assets.

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Application od Machine Learning in Cybersecurity - Read IT Quik

Available today, customers can automatically generate tailored COIs in under a minute, furthering NEXT's commitment to provide a simple and efficient insurance experience

PALO ALTO, Calif., April 26, 2023 /PRNewswire/ --NEXT Insurance, a leading digital insurtech company transforming small business insurance, today announced the launch and availability of the Certificate of Insurance (COI) Analyzer an innovative, new offering for small business owners to generate free, instant, custom-made COIsto show valid insurance coverage to potential employers in under a minute. This new offering is the latest iteration of NEXT's commitment to advancing innovation in the small business insurance space, fulfilling its promise to provide a simple and streamlined insurance experience.

A COI is often required and may make the difference between being hired or not for a job. NEXT's COI Analyzer enables customers to upload a sample certificate and receive an automatically generated COI within seconds,via the 24/7 self-service portal on desktop or mobile app. Advanced machine learning models read the sample document using Optical Character Recognition (OCR) and an Object Detector Network, to accurately extract and understand the certificate holder details, as well as any special requirements that may be included in the sample certificate.

"Insurance shouldn't stall a small business owner from thriving, it should empower them to build, launch, grow and expand. This new innovation will only speed up the owners' mission to meet the next job opportunity, challenge and goal, and we're excited to be part of that success story," said Effi Fuks-Leichtag CPO at NEXT. "Leveraging the latest machine learning models, we're able to remove the guesswork, likelihood of human error and ensure that the COI is right the first time so that the individual can get back to their passion of running a business."

For businesses including those in construction, retail, cleaning professionals, sports and fitness and more, a new and personalized COI is often required for each and every job. In fact, NEXT has confirmed that some construction business owners may need to share a COI nearly 200 times a year.In 2022, NEXT's customers on average created 16,314 COIs per month, with 9,215 coming from construction businesses 1,204 from retail and 984 from cleaning professionals. With that much documentation from differing customers and businesses, comes countless potential inputs and needs for completing a COI. This also benefits insurance agents who regularly receive COI examples from customers reviewing new job opportunities. They are required to both verify that their clients have the correct coverage, and also create their COI for them. This new feature can now save agents time on both fronts. Now,in less than a minute from start to finish, the COI Analyzer speeds up the process, eliminates errors and ensures a modern experience.

"As a fitness, nutrition and wellness coach, COIs are critical for me to quickly secure jobs and maintain my work with clients," said Laura Jean, founder and CEO of Fit by LJ, Inc. "Every six months I may need to create up to four different COIs, so efficiency and accuracy for each request are crucial. NEXT's COI Analyzer eliminated several tedious steps from the process, saving me an average of 10 minutes. Just recently, I used the COI Analyzer to complete the process and after NEXT automatically sent the proof to my potential employer, I was hired within 20 minutes."

Visit us to learn more about the advantage of NEXT's free digital Certificates of Insurance and how to generate free, instant, custom-made Certificates of Insurance with the COI Analyzer.

About NEXT InsuranceNEXT Insurance is transforming small business insurance with simple, digital and affordable coverage tailored to the self-employed. Trusted by over 450,000 business owners, NEXT offers policies that are easy to buy and provides 24/7 access to Live Certificates of Insurance, Additional Insured, and more, with no extra fees. Revolutionizing a historically complicated insurance industry, NEXT utilizes AI and machine learning to simplify the purchasing process and provide more affordable coverage. Founded in 2016, the company is headquartered in Palo Alto, has received a total of $881 million in venture capital funding, is rated "A- Excellent" by AM Best and has been recognized by CNBC Disruptor 50, Forbes Fintech 50,Inc.'s Best-Led Companies, and Forbes Best Startup Employers. For more information visit NEXTInsurance.com. To learn more about partnering with NEXT and the value of embedded insurance please visit NEXT's partner page. Stay up to date on the latest with NEXT on Twitter, LinkedIn, Facebook and our blog.

SOURCE NEXT Insurance

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AI paraphrasing has become so popular nowadays that writers use them every day for improving their own write-ups.

It is no secret that content writing is immensely popular and that content writers have tight deadlines due to huge workloads.

They use content optimization tools to improve their own productivity and spend less time on editing and post-processing. One of the most used types of content optimization tools is paraphrasing tools.

Today, we are going to explore a little bit of what happens behind the scenes in an AI paraphrasing tool.

More specifically, we are going to see what machine learning technologies are applied and how they drive these paraphrasing tools.

We are going to check out what is the process that happens when an AI paraphraser receives some input.

We are also going to look at which kind of ML technology is used during each step of the process. So, lets start.

This is the first step in the paraphrasing process. The software/online tool has to detect the text provided to it and analyze it.

During this analysis, the individual words are recognized, and the meaning of phrases and sentences is extracted.

Depending on the tool being used, tone detection also occurs during this phase.

So, how does all of this happen? Well, in this phase a subfield of machine learning called Natural Language Processing (NLP) is used.

NLP basically combines linguistics, computer science, and artificial intelligence.

With NLP, computer systems are able to understand and interact with natural language in a way similar to humans.

Understanding text with NLP involves the following steps:

This is where it ends if the purpose is just understanding. There are more steps involved if the task requires paraphrasing. So, lets move on.

Now, paraphrasing text can be done in a fair few ways. But lets see what are the two basic ways in which paraphrasing is done with AI tools. There are two steps involved in that.

After understanding the text is over, an AI paraphraser will use machine learning to find out whether the important words and phrases have synonyms or not.

For that purpose, it will run those words/phrases through its own catalog of known words and pick out the ones that have the same meaning.

This is done via machine learning and more specifically it is a machine learning classification task. The tool classifies words according to their meaning. In machine learning, the system learns to find patterns among the given data.

Once it has learned to find these patterns, it can identify them in new and unknown datasets as well.

This is basically what happens during paraphrase identificationpatterns where words having similar meanings are identified.

Then these synonyms are used for paraphrasing the input sentences and changing them syntactically, but not semantically.

Example of a Paraphrasing Tool Using This Technique

You can find a lot of paraphrase tools online that utilize this technique. We will show you an example in which we utilize an AI paraphrasing tool. You can see it below.

In this example, we can see that the different words have been replaced with synonyms that have the same meaning.

Another thing that we can see is that the new words are bold. Clicking on the bold words opens a drop-down list that contains even more synonyms.

This is possible because this paraphrase tool utilizes a machine-learning classification technique.

In paraphrase generation, the classification approach is ditched in favor of the generation approach.

Basically, instead of finding words and phrases that have the same meaning and using them, it generates new sentences and phrases themselves.

There are multiple ways in which this can be done. A popular technique is to use a large language model (LLM) like GPT-4.

This is a pre-trained transformer that can create human-like text from prompts.

Naturally, it is very good at paraphrasing texts too. It is available as an API and many AI paraphrasers use it.

Other approaches that work are using syntactic trees, reinforcement learning, deep learning, and even the combination of several of these techniques.

These approaches are generally more time-consuming compared to using LLMs and pre-trained models.

Example of a Paraphrasing Tool Using This Technique

Nowadays you dont have to find and use GPT-4 raw, instead, you can simply utilize some tools that have GTP-4.

Fortunately, the tool we discussed in our previous example also utilizes GPT-4 in some of its modes. To see an example of this, check out the image below.

You can see that entire phrases are completely changed. Thats possible because of the generation of semantically identical text with the help of large language models.

So, these are some of the machine learning technologies and techniques that are used in AI paraphrasing. Since there are different technologies and not all tools use the same technologies, differences in paraphrasing arise.

Hopefully, this article helped you to understand a little bit more about machine learning technologies used in AI paraphrasing. If you want to learn more about AI, then you can head to our blog and find more information.

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Data augmentation is the process of applying various transformations to the training data. It helps increase the diversity of the dataset and prevent overfitting. Overfitting mostly occurs when you have limited data to train your model.

Here, you will learn how to use TensorFlow's data augmentation module to diversify your dataset. This will prevent overfitting by generating new data points that are slightly different from the original data.

You will use the cats and dogs dataset from Kaggle. This dataset contains approximately 3,000 images of cats and dogs. These images are split into training, testing, and validation sets.

The label 1.0 represents a dog while the label 0.0 represents a cat.

The full source code implementing data augmentation techniques and the one that does not are available in a GitHub repository.

To follow through, you should have a basic understanding of Python. You should also have basic knowledge of machine learning. If you require a refresher, you may want to consider following some tutorials on machine learning.

Open Google Colab. Change the runtime type to GPU. Then, execute the following magic command on the first code cell to install TensorFlow into your environment.

Import TensorFlow and its relevant modules and classes.

The tensorflow.keras.preprocessing.image will enable you to perform data augmentation on your dataset.

Create an instance of the ImageDataGenerator class for the train data. You will use this object for preprocessing the training data. It will generate batches of augmented image data in real time during model training.

In the task of classifying whether an image is a cat or a dog, you can use the flipping, random width, random height, random brightness, and zooming data augmentation techniques. These techniques will generate new data which contains variations of the original data representing real-world scenarios.

Create another instance of the ImageDataGenerator class for the test data. You will need the rescale parameter. It will normalize the pixel values of the test images to match the format used during training.

Create a final instance of the ImageDataGenerator class for the validation data. Rescale the validation data the same way as the test data.

You do not need to apply the other augmentation techniques to the test and validation data. This is because the model uses the test and validation data for evaluation purposes only. They should reflect the original data distribution.

Create a DirectoryIterator object from the training directory. It will generate batches of augmented images. Then specify the directory that stores the training data. Resize the images to a fixed size of 64x64 pixels. Specify the number of images that each batch will use. Lastly, specify the type of label to be binary (i.e., cat or dog).

Create another DirectoryIterator object from the testing directory. Set the parameters to the same values as those of the training data.

Create a final DirectoryIterator object from the validation directory. The parameters remain the same as those of the training and testing data.

The directory iterators do not augment the validation and test datasets.

Define the architecture of your neural network. Use a Convolutional Neural Network (CNN). CNNs are designed to recognize patterns and features in images.

model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3), activation='relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())

model.add(Dense(128, activation='relu'))

model.add(Dropout(0.5))

model.add(Dense(1, activation='sigmoid'))

Compile the model by using the binary cross-entropy loss function. Binary classification problems commonly use It. For the optimizer, use the Adam optimizer. It is an adaptive learning rate optimization algorithm. Finally, evaluate the model in terms of accuracy.

Print a summary of the model's architecture to the console.

The following screenshot shows the visualization of the model architecture.

This gives you an overview of how your model design looks.

Train the model using the fit() method. Set the number of steps per epoch to be the number of training samples divided by the batch_size. Also, set the validation data and the number of validation steps.

The ImageDataGenerator class applies data augmentation to the training data in real time. This makes the training process of the model slower.

Evaluate the performance of your model on the test data using the evaluate() method. Also, print the test loss and accuracy to the console.

The following screenshot shows the model's performance.

The model performs reasonably well on never seen data.

When you run code that does not implement the data augmentation techniques, the model training accuracy is 1. Which means it overfits. It also performs poorly on data it has never seen before. This is because it learns the peculiarities of the dataset.

TensorFlow is a diverse and powerful library. It is capable of training complex deep learning models and can run on a range of devices from smartphones to clusters of servers. It has helped power edge computing devices that utilize machine learning.

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Artificial intelligence has transitioned from being justinterestingto deliveringimpact for businesses and consumers

This is an exclusive article series conducted by the Editor Team of CIO News with Abhishek Dwivedi,Vice President of Technology atVista

Introduction

Machine learning and artificial intelligence are rapidly growing fields that have had a significant impact on various industries. Predictions show that the AI market will reach $500 billion by 2023 and an estimated $1,597.1 billion by 2030, highlighting the continued demand for machine-learning technologies in the coming years.

In 2023, we can expect to see increased adoption of ML in several technical segments, including creative AI, autonomous systems, enterprise management, and cybersecurity. ML will continue to play a crucial role in improving efficiency and enhancing work security across a broader range of business fields.

Generative AI

Generative AI allows enterprises to generate a range of content, such as images, videos, and written material, thereby reducing turnaround time. These artificial intelligence networks utilise transfer-style learning or general adversarial networks to create content from various sources. Not only does this technology have obvious applications in marketing, but it could also have a major impact on the media industry. The filmmaking process could be transformed with the ability to restore old films in high definition and enhance special effects. Additionally, building avatars in the metaverse is just one of many limitless possibilities.

Large language models, such as GPT-3, will also play a key role in creating compelling content across various genres, including fiction, non-fiction, and academic articles. However, its important to be aware of potentially malicious applications, including the creation of deep fakes and the spread of fake news and propaganda. To address these emerging threats, GPTZero is already being developed to distinguish between AI-generated content and text written by humans.

Adaptive AI

Artificial intelligence holds the potential for organisations to make rapid progress by continually learning and generating new data insights. Adaptive AI, which can modify its own code to accommodate unforeseen changes, enables design adaptability and resilience. This allows the artificial intelligence system to continuously learn and react to changes in real time, bypassing the traditional learning phase. The operationalization of AI is crucial, as it facilitates the rapid development, deployment, adaptation, and maintenance of artificial intelligence across various enterprise environments. Self-adaptive artificial intelligence models are capable of faster and more accurate development, leading to improved user experiences that adapt to changing real-world situations. The future will belong to a continuous learning approach, adapting to incoming signals and making personalised experiences ubiquitous in any shopping format.

Edge AI

The rise of mobile computing and IoT has led to a massive increase in the number of connected devices, generating a large amount of data at the network edge. This has caused high latency and network bandwidth usage when collecting data in cloud data centres. To address this issue, edge artificial intelligence (Edge AI) has emerged as a solution that balances the use of centralised data centres (the cloud) and devices closer to humans and physical objects (the edge). With advancements in technology such as 5G, low-power, high-performance hardware, and faster networks, edge AI has become more accessible.

Lower computing costs due to reduced data requirements are creating a market for smart and responsive devices, especially in industries such as healthcare and finance, where data management is regulated. With edge AI, models are tailored to the specific edge environment, and critical data is kept within the edge network. Edge AI will see widespread adoption in industries such as smart warehouses, manufacturing, and utilities as organisations aim to reduce the carbon footprint of artificial intelligence and meet sustainability goals.

Explainable AI

Explainable Artificial Intelligence (XAI) is a crucial aspect of artificial intelligence development that enables human users to understand and trust the results generated by machine learning algorithms. XAI helps to describe the workings of an artificial intelligence model, its expected impact, and any potential biases that may be present. This helps to increase the transparency, fairness, and accuracy of artificial intelligence-powered decision-making, building trust and confidence among stakeholders.

There are various techniques that can be used to increase the interpretability of AI models, such as LIME and SHAP. LIME perturbs the inputs and assesses the impact on the output, while SHAP uses a game theory-based approach to analyse the combined effects of various features on the resulting delta. This creates explainability scores that highlight which aspects of the input had the greatest impact on the output. For example, in image-based predictions, the dominant area or pixels contributing to the output can be identified.

As the impact of artificial intelligence continues to increase in business and society, it is crucial to consider the potential ethical issues that may arise from these complex use cases. This includes implementing proper data governance frameworks, tools to detect bias, and factors for transparency to ensure compliance with legal and social structures. Models will need to be thoroughly tested for drifts, humility, and bias, and proper model validation and audit mechanisms with built-in explainability and reproducibility checks will become standard practise to prevent ethical lapses.

Conclusion

In 2023, machine learning will continue to be a promising and rapidly growing field that will present many interesting innovations. Artificial intelligence has transitioned from being justinterestingto deliveringimpact for businesses and consumers. Many core AI technologies like large language models, multimodal machine learning, transformers, and TinyML will gain considerable importance in the near and mid-term future, leading to standardised software and devices that organisations use daily that will become smarter with the infusion of AI.

Also read:AI and ML, two rapidly growing fields in the realm of computer science, Aravind Raghunathan

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CFA Institute Launches Data Science for Investment Professionals Certificate

Certificate will allow participants to learn about the use of AI and machine learning in the investment process and develop in-demand skills for jobs at the intersection of data science and investment management

CFA Institute, the global association of investment professionals, has launched its Data Science for Investment Professionals Certificate designed to provide current or aspiring investment professionals with practical knowledge of the fundamentals of artificial intelligence and machine learning techniques and how they are used in the investment process.

The Data Science for Investment Professionals Certificate is suitable for individuals from a variety of backgrounds and requires no prior data science knowledge. Among those most likely to benefit are current or aspiring investment professionals in roles including, but not limited to, investment analyst, portfolio manager, relationship manager, and trader.

What does studying for the Certificate involve?

The Certificate comprises five interactive courses totalling approximately 100 hours, which participants can study in their own time, followed by a final 90-minute assessment. The content is hands-on application-oriented and includes instructional videos, coding labs, and case studies from industry practitioners.

Participants will learn how to:

The five courses are:

Richard Fernand, Head of Certificate Management at CFA Institute comments:

Data science is sweeping the investment industry, but currently only about one in four investment professionals interested in acquiring the necessary knowledge is actively doing so. As asset managers continue to adapt to the fast-changing dynamics of the AI, big data, and machine learning environment, everyone in an investment role will need to understand how they can utilize data science techniques.

The Data Science for Investment Professionals Certificate seeks to address this skills gap by providing a strong foundational learning and practical content for anyone working in any investment-related job. It equips learners with the knowledge to understand the application of data science in the investment process, as well as the language to be able to explain and translate machine learning concepts and their application to real-world investment problems. These skills will be key for professionals wishing to position themselves for the growing number of jobs found at the intersection of data science and investment management.

According to a CFA Institute report The Future of Work in Investment Management: The Future of Skills and Learning, almost two thirds (64 percent) of surveyed investment professionals report an interest in learning more about AI and machine learning. In the same survey, just three percent of investment professionals say they are already proficient in AI and machine learning concepts.

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