But with so many options available on the market, how do you choose the best AI writing generator for your needs? In this article, we’ll look at the top AI writing tools that can take your content creation to the next level.

Discovering the best AI writing tools 

Let’s dive into the features and capabilities of some of the most popular AI writing generators currently available:

1. Jasper (formerly Jarvis)

Jasper is a widely recognized name in the AI writing space. It boasts an impressive array of features, including:

• Over 50 content templates
  Jasper offers a vast library of templates for various content types, including social media captions, blog post outlines, website copy, and video scripts. This makes it a versatile tool for various content creation needs.
• Boss mode
  This unique feature allows Jasper to generate long-form content, like blog posts and articles, by following your chosen outline and providing relevant information.
• SEO optimization
  Jasper integrates with SEO tools to help you optimize your content for search engines, increasing its visibility and organic traffic potential.
• Multiple brand voice options
  Train Jasper on your specific brand voice and tone to ensure consistent messaging across your content.
• Content improver
  This feature helps refine existing content by suggesting improvements in grammar, clarity, and engagement.
• Jasper extension for Chrome or Edge
  The Jasper extension for Chrome or Edge browsers integrates artificial intelligence into your content workflow and helps you write better anywhere online.

Pros: User-friendly interface, extensive template library, Jasper extension for Chrome or Edge, Boss Mode for long-form content, SEO optimization, and brand voice customization.

Cons: Pricing can be a barrier for some users, with limited control over the creative direction of AI-generated content.

2. Rytr

Rytr is a budget-friendly option with a good balance of features and affordability. Here’s what it brings to the table:

• Multiple content tones
  Choose from various tones for your content, from formal and informative to casual and conversational.
• Use cases
  Rytr caters to specific use cases, such as writing product descriptions, marketing copy, emails, and even letters.
• Long-form content creation
  While not as robust as Jasper’s Boss Mode, Rytr allows you to generate longer content pieces with its “Long-form Content” feature.
• Chrome extension
  The Rytr Chrome extension integrates seamlessly with your workflow, allowing you to generate content directly within other platforms.
• Freemium model
  Rytr offers a generous free plan with limited features. Paid plans unlock additional features and higher character limits.

Pros: Affordable pricing, user-friendly interface, multiple content tones, Chrome extension for integration, freemium model for testing the waters.

Cons: Limited functionalities compared to higher-tier tools, may require more editing for longer content pieces.

3. Writesonic

Writesonic positions itself as an AI writing assistant designed for businesses and marketers. Let’s see what makes it stand out:

• Landing page copywriting
  Writesonic excels at crafting compelling landing page copy that converts visitors into leads or customers.
• AI article writing
  Writesonic generates articles based on keywords and outlines you provide, saving you time on research and writing.
• Product description generator
  This feature helps create engaging and informative product descriptions that entice customers.
• Multilingual support
  Writesonic offers content generation in multiple languages, opening doors to international markets.
• Chrome extension
  Similar to Rytr, Writesonic integrates with your workflow through a Chrome extension.

Pros: Business and marketing-oriented features, landing page copywriting tools, AI article writing, multilingual support, Chrome extension for easy access.

Cons: Limited free plan, may not be ideal for creative writing or personal content creation.

4. Scalenut

• SEO content assistant
  Scalenut provides data-driven insights on search intent and relevant keywords to optimize your content for search engines.
• Content scheduler
  Create a content calendar and schedule your AI-generated content for future publication directly within Scalenut.
• AI content repurposing
  This feature allows you to repurpose existing content into different formats, maximizing its reach and value.
• Team Collaboration Features: Scalenut facilitates collaboration on content creation projects within teams, streamlining the workflow.
• Free Plan with Limited Features: Like other tools, Scalenut offers a free plan to test the waters before committing to a paid subscription.

Pros: SEO-focused features, content scheduler, AI content repurposing, team collaboration tools, free plan for basic use.

Cons: It lacks the breadth of competitors’ templates and may have a steeper learning curve for non-marketing professionals.

5. Simplified

Simplified takes a unique approach to AI writing, focusing on workflow automation. Let’s explore its key offerings:

• Workflow automation
  Simplified integrates with various marketing platforms and allows you to automate repetitive content creation tasks.
• Content calendar and scheduler
  Like Scalenut, Simplified helps you plan and schedule your content creation process.
• AI-powered drafts
  Simplified uses AI to generate draft content based on your chosen format and topic.
• Content insights
  Gain insights into content performance to understand what resonates most with your audience.
• Free trial
  Simplified offers a free trial period to get acquainted with the platform’s functionalities.

Pros: Workflow automation features, content calendar and scheduler, AI-powered drafts, content insights, free trial for testing.

Cons: Limited template library compared to some tools, may not be the best fit for individual content creators.

6. Paragraph AI

Standing out for its mobile app accessibility, Paragraph AI offers a convenient way to leverage AI writing on the go. Let’s see what it brings to the table:

• Mobile app
  Generate and edit content directly from your smartphone or tablet with Paragraph AI’s mobile app.
• Multiple content formats
  Paragraph AI allows you to generate content in various formats, including social media posts, emails, and even blog intros.
• AI-powered editing
  Refine your writing with AI suggestions on grammar, clarity, and style.
• Multiple content tones
  Choose from different content tones to ensure your message aligns with your desired audience and purpose.
• Free plan with limitations
  Paragraph AI offers a limited free plan, with paid options unlocking additional features and content generation limits.

Pros: Mobile app for on-the-go content creation, AI-powered editing, multiple content tones, free plan for basic use.

Cons: Limited functionalities compared to desktop-based AI writing tools, the free plan has significant limitations.

Choosing the right AI writing tool for you

With a diverse range of AI content generators for writing available, selecting the perfect one hinges on your specific needs and budget. Here are some factors to consider when making your choice:

• Content types: Which content formats do you need help creating the most? Consider tools that offer relevant templates or use cases for your content needs.
• Features: Do you need SEO optimization, content scheduling, or team collaboration features? Prioritize tools with functionalities that align with your workflow.
• Budget: AI writing generators offer different pricing structures. Free plans can be a great starting point, but paid plans offer more features and higher generation limits.
• Ease of use: Some tools have more intuitive interfaces than others. Consider your technical comfort level when making your decision.

The future of AI in the writing industry

As AI tools continue to advance, AI content writers and AI chatbots are bound to become even more sophisticated and capable of producing human-like content. The key will be finding the right balance between automation and creative input from writers to maintain authenticity and relevance in content creation.

The field of AI writing is constantly evolving. Here’s a glimpse into what the future holds:

• Enhanced accuracy and control. As AI algorithms become more sophisticated, AI writing will generate even more accurate and factually correct content while offering users greater control over the creative direction of the generated text.
• Seamless integration. AI writing tools will integrate seamlessly with other content creation platforms and marketing automation tools, streamlining the content creation workflow.
• Focus on personalization. AI will personalize content creation to cater to individual user preferences and writing styles.

Bonus tip: AI writing best practices

To maximize your success with AI content generators, here are some valuable tips:

• Start with a clear outline. The more precise your outline or topic, the more focused and relevant the AI-generated content will be.
• Provide ample input. Feed the AI with relevant keywords, data points, and brand guidelines to ensure the generated content aligns with your goals.
• Fact-check and edit. AI writing could be better. Always fact-check the generated content, refine the language, and ensure it conveys your message effectively.
• Maintain creative control. Feel free to experiment and guide the AI in the direction you want the content to go.
• Use AI as a springboard. AI-generated content can be a fantastic starting point. Use it to spark ideas, overcome writer’s block, and refine it with your unique voice and expertise.

The final word

AI writing generators are not magic bullets but powerful tools that can significantly enhance your content creation process. By leveraging the strengths of AI while retaining your human creativity and editorial oversight, you can unlock a new level of efficiency and productivity in your content creation endeavors. Explore the tools mentioned above, select the one that best fits your needs, and begin your journey of empowered content creation with the aid of AI!

FAQ

The post What’s the best AI writing generator in 2024? Power up your content creation appeared first on airSlate Blog | Business automation.

In this blog, we’ll explore what DCNNs are, how they work, and how you can design one of your own in conjunction with your AI. Let’s begin with a brief recap of the different types of neural networks you might come across and what each of them specializes in

What are deep convolutional neural networks?

Neural networks have been around for a long time. In fact, the invention of the first one is generally credited to psychologist Frank Rosenblatt, who built the Perceptron in 1958 (after its conception in 1943) to model how the human brain processes visual data.

Since then, the understanding of machine learning has undergone a revolution. Generally speaking, we can categorize modern neural networks into three distinct types:

• Feedforward neural networks (FNNs) consist of an input layer, one or more hidden layers, and an output layer. Information flows in one direction, from the input to the output layer, without loops. This kind of network is excellent for simple classification and regression jobs.
• Recurrent neural networks (RNNs), on the other hand, have connections that loop back on themselves. This gives RNNs memory that can retain previous inputs in its hidden state. For this reason, they’re particularly well suited to tasks such as time series forecasting and natural language processing.
• Convolutional neural networks (CNNs) are used for pattern recognition. They achieve this by adaptively learning spatial hierarchies of features they extract from input images using many layers.

It’s this last type that we’ll be focusing on in this article. When we add the word “deep” to the term “convolutional neural network,” we’re just flagging that we’re talking about deep learning, a machine learning technique used to build artificial intelligence systems. 

When harnessing AI for business purposes, it’s often helpful to dip a toe into the technical waters. We’ll start by looking at what you can use DCNNs for and then ‘under the hood’ at the architecture of a deep convolutional neural network.

Applications of convolutional neural networks

So, what kind of business technology solutions use deep convolutional neural networks? Unsurprisingly, CNNs are terrific for any solution that needs reliable, automatic optical recognition. These include:

Image recognition

Any application that relies on image recognition is a good candidate for the use of a CNN:

• Security systems: Automated surveillance systems use CNNs to detect and recognize suspicious activities in real-time.
• Automated checkouts: Some modern retail checkout solutions use CNNs to speed up self-serve checkout times.

E-commerce

• Visual search: CNNs enable customers to do visual searches for products rather than typing in the names in a search bar.
• Recommendation software: Can suggest potential purchases to customers that are visually similar to previous purchases.

Quality control

• Detecting defects: Automated systems can inspect products for defects using visual data.
• Automated sorting tasks: Robots can use visual data to complete tasks like sorting or assembling.

How convolutional neural networks work – the architecture

Now that we know what they’re for, let’s look at a typical CNN architecture. It might seem slightly complex at first glance – but don’t worry. We’ll go through it step by step.

What we have here is a representation of the CNN process. As you can see, the input image is a handwritten figure “2”. Here’s how CNN transforms that input into the correct output.

Convolutional layer

The first stage is the convolution layer. (The word “convolution” is just the name of the mathematical operation used here – essentially, a particular way of combining elements.) This step aims to detect patterns in the input data, such as spotting edges, textures, and shapes.

The convolution layer uses a set of filters (also known as kernels), each of which is small in spatial terms but extends through the full depth of the input volume.

As the filter slides (or convolves) around the input image, it multiplies its values by the original pixel values in the image. These products are summed, resulting in a single pixel in the output array. After that, this process repeats across the entire image. Multiple filters are used in each convolutional layer, each detecting different features. 

You end up with the first raw representation of the image in pixel form.

ReLU activation layer

But wait! We need to eliminate any negative numbers cropped up because they’re not particularly useful in this context. The Rectified Linear Unit (ReLU) Activation Layer accomplishes this for us by replacing any negative values with zero using the function:

f(x) = max (0,x)

Now, we need to simplify the image data, which the CNN does using a pooling layer.

Pooling layer

With many complex tech systems, such as containers networking, simplifying design is a primary goal because it can improve overall system performance. This is exactly the thinking behind the use of pooling layers in CNNs.

These layers reduce the spatial dimensions of the output handed on to the following layer, which cuts down on the number of parameters and computations needed overall. In turn, this makes the network more robust – and faster.

There are two different approaches commonly used here:

• Max pooling: for each 2×2 array, the maximum value is retained and becomes the sole output
• Average pooling: for each 2×2 array, the average of all four values is calculated and becomes the sole output

Opinions vary as to which is better, but it depends on the data you’re dealing with. Think of it as reducing the resolution of an image by squashing each 2×2 group of pixels together into a single value. In essence, that’s exactly what’s happening.

Fully connected layer

After the series of convolutional and pooling layers have done their bit, CNNs often have one or more fully connected layers where every neuron is connected to every neuron in the previous layer, playing spot the difference. In other words, the purpose of this last layer is to take the information generated by the earlier layers and categorize the features of the image.

The connections between the neurons have what are known as weights. These weights are used to determine the importance of different features extracted by the convolutional layers. They’re adjusted during training to minimize prediction errors.

The fully connected layer uses the softmax function to create a probability distribution around which class the image will likely belong. At the end of which, you have your final output.

How to design your own deep convolutional neural network

Let’s get to grips with how to design your own deep convolutional neural network. First thing first: what are you aiming to use it for?

For basic tasks

If you want your CNN to carry out simple tasks – like recognizing the number “2” in the earlier example– you won’t need an overly complex build. Start with the basics and work your way up. To begin with, have just one hidden layer with around ten kernels and one max pooling layer.

That’s plenty to get you off the ground. Remember that you can always add more layers as you go.

For more complex jobs

Clever leaders know that using tried and trusted solutions improves employee morale. So, if you want to use a CNN for a complex purpose, do yourself a favor and use pre-trained networks.

Luckily, there is something called transfer learning – using a network previously trained on other data. Yes, it’s possible. You’re basically using the existing network as a template, swapping in the layers you want, and adding your dataset. It speeds things up considerably.

Using GitOps principles to develop a CNN for your AI

Using a GitOps model can be helpful when you’re developing your CNN. Here are a few tips for how to approach the design and training aspects:

Version control for model definitions

First, store the architecture of your CNN, hyperparameters, and any other relevant configuration in a Git repository. It would be best if you made any changes to the design or structure of your model through a Git commit.

Automated training pipelines

Remember to set up Continuous Integration (CI) pipelines that trigger model training whenever a commit to the repository exists. You’ll also need to use Continuous Deployment (CD) to automatically deploy trained models to a staging or production environment.

Monitoring the training procedure

Monitor the training process as it goes along. Once the model is deployed, monitor its performance in real-time. If you find that the model’s performance degrades or feel the need to retrain your network with new data, you can use a new Git commit for this.

Reproducibility

The great thing about storing everything in Git is that every state of your model is versioned, from the basic architecture to any trained weights. This means experiments are reproducible – and you can roll back to previous states if you have to.

Infrastructure as code

If you’re using cloud resources like Magento cloud hosting or specific hardware for training and deployment, you can also version the infrastructure setup using tools like Terraform or Ansible. This will mean that the infrastructure setup remains consistent and reproducible.

Getting started with deep convolutional neural networks

As with anything else in tech, the best way to learn is by doing. Luckily, there’s a lot of information to help guide you and plenty of options for simplifying the process, such as using a transfer learning approach.

Deep convolutional neural networks are turning out to be so incredibly useful for optical recognition applications that you’ll likely discover all kinds of handy uses for them in your work. So it’s worth spending a little time and effort to begin getting to grips with developing one for your AI.

The post How to design deep convolutional neural networks for your AI appeared first on airSlate Blog | Business automation.

A third of companies already use artificial intelligence in some form, which is a sign that this technology is here to stay. Businesses have started using AI everywhere, from hiring processes to writing code and developing websites. Of course, the quality of this AI-supported work can vary, but it’s there, nevertheless. 

Artificial intelligence will undoubtedly become one of the main tools for conducting business analysis. With its ability to analyze vast amounts of data, AI will help you and your business with strategic planning and decision-making. 

However, embracing AI solely because it’s a current trend can lead to many setbacks. You need to understand how to properly utilize AI in your business while being aware of the challenges that come with it.

Document Workflow Automation for Business Analysts

• Data collection
• Data management
• Business process efficiency

Enroll now

Understanding AI and its relevance in business

Understanding how AI intersects with business analysis and why that would matter to you is only possible by grasping the basics of artificial intelligence and defining the buzzwords. 

Computer science is a broad area that encompasses various theoretical and applied disciplines. Artificial intelligence is just one of many branches of CS, and the experts working in it aim to replicate human intelligence through machines or software.

AI includes the development of algorithms that can learn, solve problems, recognize patterns, and make decisions. There are two main types of artificial intelligence, Narrow, which specializes in specific tasks such as voice recognition, and general, which is self-explanatory. 

General AI is mainly theoretical, and while many projects are being developed worldwide based on it, nothing can be truly classified as AGI. Instead, we will focus on narrow AI. 

Machine learning, deep learning, and natural language processing are important subfields of narrow AI. When it comes to business analysis, machine learning uses predictive analytics to prepare for future trends based on historical data. In decision-making, multiple types of AI have proven useful in helping companies make better choices. 

One of the most popular AI projects to date is ChatGPT which utilizes natural language processing. This  branch of artificial intelligence works on creating software and machines that can  read, understand and generate textual and audio data. 

NLP allows businesses to analyze vast amounts of textual data from product reviews, comments on social media, or other mentions on the internet. 

Even before ChatGPT was launched, many companies used automated chatbots for  customer service. While the majority of them weren’t precisely  AI, they had some resemblance to modern chatbots. Implementing an AI chatbot for your website, be it an online store or a SaaS website, can improve the experience of new customers.

The shift in business analysis

In the past, business analysis has been a time-consuming process that depended on the effectiveness of the person in charge.

Traditional business analysis involves: 

• identifying all the needs of the company 
• understanding the impact that decisions might have
• gathering and interpreting data
• recommending solutions

The goal of modern business analysis remains the same, but the tools that decision-makers have at their disposal are remarkably different and more advanced. However, it’s worth noting that many corporations started in a garage, and while the instruments we can utilize to make better decisions have advanced, the success of a business has numerous diverse factors. 

Compared  to traditional, modern business analysis utilizes AI to collect and classify data in real-time and present the decision-makers with insightful results. Overall, it automates and streamlines processes related to business analysis, whether essential or optional. 

The essential benefit of combining AI and business analysis is a lower chance of human error and bias. Insights delivered by AI are assembled from quantities of data that a human couldn’t have analyzed for such a short amount of time.  

While these advanced decision-making methods make the executives’ jobs much more accessible, they still need additional analysis and consideration. It’s important not to overlook the results delivered by the software of your choice.

The change that artificial intelligence brings in various fields is that it decreases the amount of meaningless and time-consuming work so that employees can focus on more critical tasks. For example, the role of AI in human resources is to help HR specialists focus more on connecting with current and future employees while they automate tasks such as job posting or resume screening. 

Harnessing AI for strategic business advantage

Throughout the article, we’ve already mentioned some significant advantages of utilizing AI for business. If they don’t appear as if they can provide your company with the proper benefits, let us get this clear – you will get ahead of your competitors.

No matter the size and budget of your company, better management and decision-making will help your business take over the market. A better understanding of the economy, your competitors, and  current and potential customers will help you position your business in the market and prepare for future changes. 

While it won’t magically make your profits skyrocket, AI will help you optimize various  business processes. As mentioned, there are some general advantages that AI can bring, but there are important examples of industry-specific applications.

Of course, you would need effective team collaboration to ensure that the conclusions received from the business analysis are implemented. Just because C-level management knows how to do that right doesn’t mean the frontline workers won’t need extra help understanding the assignment. 

For example, many online stores implement  fraud detection software to minimize fraudulent behaviors and account takeovers. Even one of the largest credit card companies in the world, American Express, has started using artificial intelligence to detect fraudulent transactions.

Other great examples of effective implementation of artificial intelligence are Netflix and Starbucks. Both companies use AI for their software to provide  users with recommendations tailored  to their previous behavior and preferences. This helps companies increase customer engagement and retention. 

You can use artificial intelligence to analyze your audience, separate it into segments, and then use it again to personalize content for each type of customer appropriately.

Challenges in implementing AI for business analysis

Just like with any, especially unexplored, technology, there are many challenges in implementing artificial intelligence for your business processes. One of the most significant concerns people have is their personal information and data safety.

Since the adoption of GDPR, businesses have started embracing a privacy-first approach. AI-powered business analysis requires large databases to deliver good results and insights to managers and decision-makers. There are many concerns about how this data is collected and stored. 

Storing large amounts of user data requires additional cybersecurity efforts even if you completely comply with international and regional privacy regulations.

Some artificial intelligence algorithms can inadvertently lead to biased results and insights depending on the data type   they’ve employed. If not handled properly, this can lead to discriminatory outcomes and policies. Mitigating these outcomes is essential since they can lead to severe blows to reputation and disappointed customers.

The real problems that AI faces are often technical. One of the most critical issues that people who develop and use artificial intelligence have is poor-quality data. For AI-powered business analysis to be effective, you will need high-quality, relevant, and diverse data. However, businesses often need help with complete, accurate, and consistent data, which can severely limit the effectiveness of AI.

Overcoming this problem can be time-consuming and expensive, and you can’t turn anyone into a data scientist. You’ll need to hire experts to supervise this process and  might even have to invest additional money into resources.

Business analysis can be more effective depending on the size of your business and the amount of data you can collect without breaking any regulations.

Both stakeholders and decision-makers in the company must have insights into how AI delivers its results. This is necessary to identify and correct potential mistakes or biases. While some excellent AI software can help you with your business analysis, its current state needs improvement regarding transparency and accuracy.

The future of business analysis with AI

While it’s too early to go all-in with the latest business analysis software that utilizes AI, you should look toward a bright future. Predictive and prescriptive analytics are becoming increasingly sophisticated, providing more profound and actionable insights.

Even if you have yet to invest in artificial intelligence software for your company, it’s advisable to pay attention to the market and observe whether there are services that can suit your business model and industry. 

You can only do your best to invest in the necessary technology infrastructure once the adoption of such software becomes commonplace. Businesses should also invest in upskilling their workforce to work effectively with AI. 

The additional training needed to handle AI properly includes areas like data science, machine learning, and AI ethics, as well as understanding the connection between privacy regulations and AI. 

Conclusion

If you stumble across any form of content that discusses the implementation of AI without mentioning all aspects of the process, then they aren’t honest about it. With all the benefits of combining AI and business analysis, there isn’t a one size fits all solution. 

Depending on your business goals and employee values, you can find many benefits that implementing such a way of decision-making can bring. However, companies have been successfully functioning without using super-smart computers for a long time. 

If you give up on a slower yet more considerate approach to traditional business analysis for a shiny new piece of software, you might experience a setback. AI business analysis software has been around for some time, and the progress accelerated in the past decade. 

On the other hand, it’s amazing what types of benefits artificial intelligence can bring to businesses in any industry and significantly improve decision-making processes through software that utilizes deep learning and NLP.

The post Modern business analysis: Harnessing AI for strategic advantage appeared first on airSlate Blog | Business automation.