• Neural networks are a subset of machine learning, which is a technique used to help computers learn using training that is modeled on results gleaned from large data sets.  As such, neural networks are an attempt mimic human thinking, specifically how biological neurons are thought to signal to one another. The Google search engine, with its many interrelated nodes, is a good best example of a neural network. It is probably the largest in existence as it has the task of providing the instant and accurate results that users demand.
• Deep learning could be defined as a form of machine learning that is firmly based on AI neural networks. In some ways, deep learning can be viewed as advanced neural networks as it takes the basic capabilities of neural networks to a whole new level.

Also see: The Pros and Cons of Deep Learning

Neural Networks vs. Deep Learning

Neural networks and deep learning are often confused; the terms are sometimes used interchangeably in general AI discussion.

A good way to differentiate them: deep learning goes deeper than standard neural networks – hence its name. How? By implementing more layers within a neural network. This depth of analysis, then, involves more time, training, and investment.

Neural networks requires less time than deep learning

Neural networks, while powerful in synthesizing AI algorithms, typically require less resources. In contrast, as deep learning platforms take time to get trained on complex data sets to be able to analyze them and provide rapid results, they typically take far longer to develop, set up and get to the point where they yield accurate results.

Deep learning goes deeper

Many applications don’t need deep learning, with its ability to plumb the depths of AI’s capacity. Neural networks also offer powerhouse performance, though less so; yet  increasingly, even basic AI tools are harnessing relatively simple neural networks as part of their operations. But as complexity rises, deep learning must be introduced to provide the expected level of performance and accuracy.

Neural networks need less investment

Basic neural networks require less financial outlay than deep learning, which needs far more processing power (such as Graphics Processing Units, often supplied by Nvidia), more expensive hardware and more advanced software.

Neural networks represent a major advance in AI technology. They are increasingly used in almost all the latest AI applications. Deep learning steps it up further but has a more limited set of applications. It typically requires more time and resources to set up and analyze but provides deeper and better conclusions, whereas neural networks solutions can often be arrived at faster as they are more narrowly defined and apply to a smaller data set.

Now let’s take a deeper look at neural networks and deep learning: 

Also see: Best Artificial Intelligence Software 2023

What is a Neural Network?

Neural networks are software constructs that are comprised of various layers such as input layers, hidden layers and output layers. Each node functions like an artificial neuron. It connects to other nodes and sends data to other layers of the network. They must be trained, must have their accuracy tuned, but offer the ability to classify data rapidly.

Training

Neural networks are trained on data as a way of learning and improving their conclusions over time. As with all AI deployments, the more data it’s trained on the better. Neural networks must be fine-tuned for accuracy over and over as part of the learning process to transform them into powerful artificial intelligence tools. Fortunately for many businesses, plenty of neural networks have been trained for years – far before the current craze inspired by ChatGPT – and are now powerful business tools.

Data classification

Once trained and tuned, neural networks can classify data and cluster it at incredible velocities. As a result, neural networks can take on complex AI tasks such as in speech and image recognition and complete them in minutes. Significantly, this rapid speed is still increasing, suggesting this already brief time period will fall to seconds.

Also see: Top Generative AI Apps and Tools

Neural Network Use Cases

Neural networks are now involved in more and more types of AI. This includes speech and image recognition, advanced search, and generative AI. In particular, the generative AI use cases are being adopted at a rapid pace by businesses.

Speech recognition

There are so many accents, languages, idioms and dialects that the area of speech recognition has been a problematic area of technology for many years. AI backed by neural networks provides the processing and differentiating capabilities needed to minimize these challenges. What’s required is training, across time and many dialects; you’ll notice that more voice-based chatbots are now able to recognize more idioms and dialects.

Image recognition

Similarly, image recognition poses real difficulties due to the myriad of objects potentially on display. Neural networks help to increase accuracy and speed of recognition. Neural networks in combination with computer vision is fast becoming one of the most common uses of AI, as monitoring/safety devices are used in many public places.

Advanced search

Google has been using neural networks in search for ages. Others are adopting it due to its ability to provide answers rapidly and predict requests as a person is typing or speaking. The advantage here is that each node of a neural network is involved with the creation of a response, which sums the power of all the nodes to create a more powerful AI application.

Generative AI

When it comes to generating content, neural networks provide the support needed to provide articles, documentation and papers that will provide a good starting point for a human to then create a valuable piece of content. Neural networks are included in an overall generative AI architecture that enables remarkably fast and very powerful use of a large language model – this LLM is the source, but it’s the neural network that does a key part of the heavy lifting to create generative AI’s output. Specifically, neural networks identify the logical structures within a vast storehouse of data.

As an additional note about neural network use cases, realize that there are many different types of neural networks, of varying capability and scope and focus. But regardless of this enormous variety, all neural networks are applied to solving user problems and making the predictions needed by a wide range of uses cases.

Also see: 100+ Top AI Companies 2023

What is Deep Learning?

Deep learning systems use multiple processing layers to extract progressively better and more high-level insights from data. The key point is the “multiple processing layers,” which enables deep learning software architecture to provide far more robust compute capability.

Deep learning applications can be viewed as a more sophisticated deployment of basic neural networks that make heavy use of machine learning algorithms, are inspired by the human mind, can keep learning from their mistakes and solve highly complex problems.

Machine learning algorithms

Deep learning systems make use of complex machine learning techniques and can be considered a subset of machine learning. But in keeping with the multi-layered architecture of deep learning, these machine learning instances can be of various types and various strategies throughout a single deep learning application.

Human-inspired

The mathematical structures that comprise deep learning have been loosely inspired by the structure and function of the brain. Meaning that humans employ a complex array of variables to make decisions, instead of the usual “on or off” nature of machine computing. The layered mathematical structures in a typical deep learning deployment are an attempt to build a system that can mimic the complexity and nuance of human decision making.

Continuous learning

As they can learn by example and correct their actions based on errors detected, they keep learning and improving their level of accuracy. In the world of artificial intelligence, this is not unique to deep learning; by its very nature, AI is trained and “learns” as it is fed more data, and/or is in active use across time. Consequently, a deep learning application will be much higher performing six months from now than it is now.

High complexity

Deep learning allows machines to tackle problems of similar complexity to those humans can solve.

Thus, deep learning has enabled researchers to scale up the AI models they use in a way that goes well beyond traditional neural networks. By utilizing multiple forms of machine learning systems, models, neural networks and algorithms, deep learning opens many new doors for analysis and problem solving.

“Deep learning can be leveraged to analyze the exceptions when a human intervenes with AI decisions,” said Rick Wagner, Senior Director, Product Management, SailPoint. “Those exceptions can ultimately be analyzed for patterns which ultimately will improve the effectiveness of AI.”

Also see: Generative AI Examples 

Deep learning Use Cases

Deep learning use cases go way beyond those of machine learning and simple neural networks. Machine learning is broadly applicable to a huge range of tasks. As the name implies, deep learning is harnessed to solve problems at a deeper and more complex level. Deep learning is being used to generate text, automatically deliver meeting transcripts, capture data from documents and generate video content from text.

Generate text

Deep learning-based, large language models can generate legible text on various topics or generate realistic images from text prompts. The use of LLM and generative AI application involving text creation is now being widely adopted and therefore is a key driver of deep learning adoption. Deep learning is also being used to provide high-accuracy text transcripts from audio recordings of business meetings and phone calls.

Automatic data capture

Deep learning can be deployed to automatically capture data from business documents with high accuracy. This can be an important part of how deep learning boosts the performance of data analytics, which is happening with increasing frequency in the enterprise. Indeed, while deep learning in data analytics is still on the forward edge, it will certainly gain full saturation as AI gains more adoption.

“Deep learning is being used to automatically capture data from business documents with high accuracy,” said Petr Baudis, CTO and chief AI architect at Rossum. “This can save businesses a lot of time and money, as it eliminates the need for manual data entry.”

Self-driving

Deep learning models are being employed to solve the many challenges inherent in autonomous operation of vehicles. Whether it is self-driving vehicles, vehicle driver assist, obstacle avoidance or equipment that moves around industrial and commercial operations, deep learning is being deployed to ensure safety and improve accuracy.

In sum, deep learning use cases provide multi-faceted answers to complex situations and problems. It elevates traditional machine learning and basic neural networks in terms of scale and depth of analysis.

For more information: AI vs. ML

Bottom Line: Neural Networks vs. Deep learning

There are many similarities between neural networks and deep learning. They each comprise algorithms that are addressed to decode complex challenges.

Deep learning, though, utilizes more sophisticated models than do neural networks and takes longer to set up. Deep learning requires more time to crunch through the much larger data sets and more nuanced problems they typically analyze or address.

As such, deep learning is deployed among a much smaller user base due to the time and cost required to build and run its systems.

In sum, neural networks are now applied across the spectrum of AI applications while deep learning is reserved for more specialized or advanced use cases.

With generative AI very much in the spotlight, it should be pointed out that new applications like ChatGPT and others make heavy use of neural networks. In an increasing number of cases, this involves the very advanced neural networks that can be classified as deep learning. To create content that passes muster, after all, a vast amount of compute resources are required to develop something that is even vaguely comparable to the work of a skilled human.

“When it comes to AI in general, neural networks and deep learning go together, the deeper the learning, the more layers of neurons, the more trained a model can be to enable deployment for different purposes,” said Greg Schulz, an analyst at StorageIO Group. “Think of it as a hierarchy of cognitive computing: basic AI involves relatively simple rules and reasoning; more advanced machine learning adds to the knowledge basis and deep learning takes you to the creation, training and testing of new or enhanced models.”

Also see: Generative AI Startups 

The post Neural Networks vs. Deep Learning appeared first on eWEEK.

• Generative AI is a specific form of AI that is designed to generate content. This content could be text, images, video and music. It uses AI algorithms to analyze patterns in datasets to mimic style or structure to replicate different types of content. It is used to create deepfake videos and voice messages.

• Artificial Intelligence (AI) is a technology that has the ability perform tasks that typically require human intelligence. AI is often used to build systems that have the cognitive capacity to mine data, and so continuously boost performance – to learn – from repeated events.

Let’s more deeply examine generative AI and AI, lay out their respective use cases, and compare these two rapidly growing emerging technologies.

Generative AI vs. AI

Both generative AI and artificial intelligence use machine learning algorithms to obtain their results. However, they have different goals and purposes.

Generative AI is intended to create new content, while AI goes much broader and deeper – in essence to wherever the algorithm coder wants to take it. These possible AI deployments might be better decision making, removing the tedium from repetitive tasks, or spotting anomalies and issuing alerts for cybersecurity.

In contrast, generative AI finds a home in creative fields like art, music and product design, though it is also gaining major role in business. AI itself has found a very solid home in business, particularly in improving business processes and boosting data analytics performance.

To summarize the differences between generative AI and AI, briefly:

• Creativity Generative AI is creative and produces things that have never existed before. Traditional AI is more about analysis, decision making and being able to get more done in less time.
• Predicting the future: Generative AI spots patterns and combines them into unique new forms. AI has a predictive element whereby it utilizes historical and current data to spot patterns and extrapolate potential futures in very powerful ways.
• Broad vs. Narrow: Generative AI uses complex algorithms and deep learning and large language models to generate new content based on the data it is trained on. It is a specific and narrow application of AI to very creative use cases. Traditional AI can accomplish far more based on how the algorithms are designed to analyze data, make predictions and automate actions – AI is the foundation of automation.

Also see: Top Generative AI Apps and Tools

Now, let’s go deeper into generative AI and artificial intelligence:

Understanding Generative AI

Generative AI is AI technology geared for creating content. Generative AI combines algorithms, large language models and neural network techniques to generate content that is based on the patterns it observes in other content.

Although the output of a generative AI system is classified – loosely – as original material, in reality it uses machine learning and other AI techniques to create content based on the earlier creativity of others. It taps into massive repositories of content and uses that information to mimic human creativity; most generative AI systems have digested large portions of the Internet.

Machine learning algorithms

Generative AI systems use advanced machine learning techniques as part of the creative process. These techniques acquire and then process, again and again, reshaping earlier content into a malleable data source that can create “new” content based on user prompts.

Using earlier creativity

As noted above, the content provided by generative AI is inspired by earlier human-generated content. This ranges from articles to scholarly documents to artistic images to popular music. The music of pop singer Drake and the band The Weekend was famously used by a generative AI program to create a “new” song that received considerable positive attention from listeners (the song was soon removed from major platform in response to the musicians’ record label).

Vast datasets

Generative AI can accomplish tasks like analyze the entire database of an insurance company, or the entire record keeping system of a trucking company to produce an original set of data and/or business process that provides a major competitive boost.

Thus, generative AI goes far beyond traditional machine learning. By utilizing multiple forms of machine learning systems, models, algorithms and neural networks, generative AI provides a completely new form of human creativity.

Also see: Generative AI Companies: Top 12 Leaders

Generative AI Use Cases

Generative AI is being used to augment but not replace the work of writers, graphic designers, artists and musicians by producing fresh material. It is particularly useful in the business realm in areas like product descriptions, suggesting variations to existing designs or helping an artist explore different concepts.

Generate text

Generative AI can generate legible text on various topics. It can compose business letters, provide rough drafts of articles and compose annual reports. Some journalistic organizations have experimented with having generative AI programs create news articles. Indeed, many journalists feel the threat from generative AI.

Generate images

Generative AI can generate realistic or surreal images from text prompts, create new scenes and simulate a new painting. Note, however, that the fact that these images are originally based the images fed into the generative AI system is prompting lawsuits by creative artists. (And not only graphic artists, but writers and musicians as well.)

Generate video

It can compile video content from text automatically and put together short videos using existing images. The company Synthesia, for instance, allows users to create text prompts that will create “video avatars,” which are talking heads that appear to be human.

Generate music

It can compile new musical content by analyzing a music catalog and rendering a similar composition in that style. While this has caused copyright issues (as noted in the Drake and The Weekend example above), generative AI can also be used in collaboration with human musicians to produce fresh and arguably interesting new music.

Product design

Generative AI can be fed inputs from previous versions of a product and produce several possible changes that can be considered in a new version. Given that these iterations can be produced in a very short amount of time – with great variety – generative AI is fast becoming an indispensable tool for product design, at least in the early creative stages.

Personalization

Generative AI can personalize experiences for users such as product recommendations, tailored experiences and unique material that closely matches their preferences. The advantage is that generative AI benefits from the hyper-speed of AI – producing personalization for many consumers in mere minutes – but also the creativity it has displayed in art and music to generative fresh, individualized personalizations.

“Generative AI is an indispensable ally for individuals who are newly entering the workforce,” said Iterate.ai Co-Founder Brian Sathianathan. “It can serve as an invisible mentor, assisting with everything from crafting compelling resumes and mastering interview strategies to generating professional correspondence and formulating career plans. By providing personalized advice, learning opportunities, and productivity tools, it can help new professionals navigate their career paths more confidently.”

Also see: AI Detector Tools

Understanding AI

Artificial intelligence is a technology used to approximate – often to transcend – human intelligence  and ingenuity through the use of software and systems. Computers using AI are programmed to carry out highly complex tasks and analyze vast amounts of data in a very short time. An AI system can sift through historical data to detect patterns, improve the decision-making process, eliminate manually intensive task and heighten business outcomes.

Also see: 100+ Top AI Companies 2023

Isolating patterns

AI can spot patterns among vast amounts of data. It does this using specialized GPU processors (Nvidia is a leader in the GPU market) that enable super fast computing speed. Some systems are “smart enough” to predict how those patterns might impact the future – this is called predictive analytics and is a particular strength of AI.

Better business decisions

AI can be used to provide management with possible opportunities for expansion as well as detecting potential threats that need to be addressed. It helps in ways such as product recommendations, more responsive customer service and tighter management of inventory levels. Some executives use AI as an “additional advisor,” meaning they incorporate recommendations from both their colleagues and AI systems, and weigh them accordingly.

Heightened data analytics

AI adds another dimension to data analytics. It offers greater accuracy and speed to the processes of using data analytics. Used correctly, AI increases the chance of success and achieving positive outcomes by basing data analytics decisions on a much wider volume of data – and ideally higher quality data – whether historical or in real time.

Through the rapid detection of data analytics patterns, business processes can be improved to bring about better business outcomes and thereby assist organizations in gaining competitive advantage.

AI Use Cases

AI has almost limitless use cases – and more seem to crop up every week. Some of the top AI use cases include automation, speed of analysis and execution, chat and enhanced security. Be aware the additional vertical use cases are launching in education, healthcare, finance and other industry sectors.

Automation

AI can automate complex, multi-step tasks to help people get more done in a shorter span of time. For instance, IT teams can use it to configure networks, provision devices, and monitor networks far more efficiently than humans. AI is the driver behind robotic process automation, which helps office workers automate many mundane tasks, freeing up humans for higher value tasks.

Speed

AI finishes tasks with extraordinary speed. It uses technologies like machine learning, neural networks and deep learning to find and manipulate data in a very short time frame. This helps organizations to detect and respond to trends and opportunities in as close to real time as possible. The amount of data AI can analyze lies far outside the range of rapid inspection by a person.

Chat

AI-based chat, and the chatbots it powers, appears to be the app that has finally taken AI into the mainstream. Systems such as ChatGPT and others are introducing chat into untold numbers of applications. Done well, these applications improve customer service, search and querying, to name a few. And the advantage of AI is that, over time, the system improves, meaning that the AI chatbot is capable of ever more human conversation.

Enhanced Security

AI harnesses machine learning algorithms to analyze, detect, and alert managers about anomalies within the network infrastructure. Some of these algorithms attempt to mimic human intuition in applications that support the prevention and mitigation of cyber threats. This can help to alleviate the work burden on understaffed or overworked cybersecurity teams. In some cases, AI systems can be programmed to automatically take remediation steps following a breach.

AI, therefore, is finding innumerable use cases across a wide range of industries. It provides managers with data and conclusions they can use to improve business outcomes. Moreover, AI technology in all of its forms is still in its infancy, so expect the application of AI to uses cases to both broaden and deepen.

Also see: Best Artificial Intelligence Software 2023

Bottom Line: Generative AI vs. AI

Algorithms can be regarded as some of the essential building blocks that make up artificial intelligence. AI uses various algorithms that act in tandem to find a signal among the noise of a mountain of data and find paths to solutions that humans would not be capable of. AI makes use of computer algorithms to impart autonomy to the data model and emulate human cognition and understanding.

Generative AI is a specific use case for AI that is used for sophisticated modeling with a creative goal. It takes existing patterns and combines them to be able to generate something that hasn’t ever existed before. Because of its creativity, generative AI is seen as the most disruptive form of AI.

“Mainline AI applications based around learning, training and rules are fairly common in support of autonomous operations (vehicles, drones, control systems) as well as diagnostics, fraud and security detection, among other uses,” said Greg Schulz, an analyst at StorageIO Group. “Generative AI has the ability to ingest large amounts of data from various sources that gets processed by large language models (LLMs) influenced by various parameters to create content (articles, blogs, recommendations, news, etc.) with a human-like tone and style.”

Also see: ChatGPT vs. Google Bard: Generative AI Comparison

The post Generative AI vs. AI appeared first on eWEEK.

Predictive AI is also a type of artificial intelligence. In contrast with generative AI, predictive AI uses statistical algorithms to analyze data and make predictions about future events. It is sometimes also called predictive analytics and may sometimes be loosely termed as machine learning.

Let’s examine generative AI and predictive AI, lay out their use cases, and compare these two powerful forms of artificial intelligence.

Also see: Top Generative AI Apps and Tools

Generative AI vs. Predictive AI

At their foundation, both generative AI and and predictive AI use machine learning. However, generative AI turns machine learning inputs into content whereas predictive AI uses machine learning in an attempt to determine the future and prevent bad outcomes by using data to identify early warning signs.

Among the key differences between generative AI and predictive AI:

Creativity – generative AI is creative and produces things that have never existed before. Predictive AI lacks the element of content creation.

Inferring the future – predictive AI is all about using historical and current data to spot patterns and extrapolate potential futures. Generative AI also spots patterns but combines them into unique new forms.

Different algorithms – generative AI uses complex algorithms and deep learning to generate new content based on the data it is trained on. Predictive AI generally relies on statistical algorithms and machine learning to analyze data and make predictions.

Both generative AI and predictive AI use artificial intelligence algorithms to obtain their results. You can see this difference shown in how they are used. Generative AI generally finds a home in creative fields like art, music and fashion. Predictive AI is more commonly found in finance, healthcare and marketing – although there is plenty of overlap.

Now let’s take a deeper look at both generative AI and predictive AI.

Also see: Generative AI Companies: Top 12 Leader

What is Generative AI?

Generative AI functionality is all about creating content. It combines algorithms and deep learning neural network techniques to generate content that is based on the patterns it observes in other content.

Although the output of generative AI is classified as original material, in reality it uses machine learning and other AI techniques based on the earlier creativity of others – this is a major criticism of generative AI. This emerging AI technology taps into massive repositories of content and uses that information to mimic human creativity.

Generative AI systems use standard machine learning techniques as part of the creative process. Generative AI can do things like analyze the entire works of Dickens or Rollins or Hemingway and produce an original novel that seeks to simulate their style and writing patterns.

Thus, generative AI goes a stage beyond traditional machine learning. By utilizing multiple forms of machine learning systems, models, algorithms and neural networks, generative AI offers a tech-based foray into the world of creativity.

Also see: Generative AI Examples

Generative AI Use Cases

By producing fresh content, generative AI is being used to augment but not replace the work of writers, graphic designers, artists and musicians. It is particularly useful in the business realm in areas like product descriptions, variations to existing designs or helping an artist explore different concepts. Among its most common use cases:

Text – generative AI can generate credible text on various topics. It can compose business letters, provide rough drafts of articles and compose annual reports.

Images – generative AI can also output realistic images from text prompts, create new scenes and simulate a new painting.

Video – generative can compile video content from text automatically and put together short videos using existing images.

Music – generative AI can compile new musical content by analyzing a music catalog and rendering a new composition.

Product design – generative AI can be fed inputs from previous versions of a product and produce several possible changes that can be considered in a new version.

Personalization – generative AI can personalize experiences for users such as product recommendations, tailored experiences and feeding material that closely matches their preferences.

Also see: 100+ Top AI Companies 2023

What is Predictive AI?

Predictive AI studies historical data, identifies patterns and makes predictions about the future that can better inform business decisions. Predictive AI’s value is shown in the ways it can detect data flow anomalies and extrapolate how they will play out in the future in terms of results or behavior; enhance business decisions by identifying a customer’s purchasing propensity as well as upsell potential; and improve business outcomes.

Significantly, predictive AI can enlighten management on future trends, opportunities and threats. It can be used to recommend products, upsell, improve customer service and fine-tune inventory levels.

Predictive AI adds another dimension and greater accuracy to the processes of management. Used correctly, it increases the chance of success and achieving positive business and outcomes, particularly in the area of inventory management.

Through accurate predictions and improved decision-making, predictive AI can help organizations glean far more value from the data they collect and use it to their competitive business advantage.

Also see: AI Detector Tools

Predictive AI Use Cases

Predictive AI has a great many use cases. Some of the top ones include financial forecasting, fraud detection, healthcare and marketing.

Financial Services – predictive AI enhances financial forecasts. By pulling data from a wider data set and correlating financial information with other forward-looking business data, forecasting accuracy can be greatly improved.

Fraud Detection – predictive AI can be used to spot potential fraud by sensing anomalous behavior. In banking and e-commerce, there might be an unusual device, location or request that doesn’t fit with the normal behavior of a specific user. A login from a suspicious IP address, for example, is an obvious red flag.

Healthcare – predictive AI is already in use in healthcare. It is finding use cases such as predicting disease outbreaks, identifying higher-risk patients and spotting the most successful treatments.

Marketing – predictive AI can more closely define the most appropriate channels and messages to use in marketing. It can provide marketing strategists with the data they need to write impactful campaigns and thereby bring about greater success.

Predictive AI, therefore, is finding innumerable use cases across a wide range of industries. If managers knew the future, they would always take appropriate steps to capitalize on how things are going to turn out. Anything that improves the likelihood of knowing the future has high value in business.

Also see: The Benefits of Generative AI 

Bottom Line: Generative AI vs. Predictive AI

Both generative AI and predictive AI use algorithms to address complex business and logistical challenges.

Generative AI tends to utilize more sophisticated modeling and algorithms than predictive AI to add a creative element. In contrast to the role of predictive AI in recognizing patterns – where it draws inferences and suggests outcomes and forecasts – generative AI takes existing patterns and combines them to generate new content.

As AI evolves, the distinction between generative AI and predictive AI is likely to fade. AI systems are emerging that seamlessly merge generative AI and predictive AI. Instead of using one set of algorithms to predict and another to create, advanced AI systems combine both and can deliver both types of result. By combining the algorithms that identify trends and forward-looking correlations with those that recombine those patterns into new creations, the value of AI will be improved even further.

While there are certainly differences between generative AI and predictive AI, these distinctions are far from rigid. Each contains similar elements to the other. It is how they are put to use that brings about the different outcomes. As AI evolves, both generative AI and predictive AI will play a role in reshaping the future.

Also see: ChatGPT vs. Google Bard: Generative AI Comparison

The post Generative AI vs. Predictive AI appeared first on eWEEK.

Machine learning (ML) is a technique used to help computers learn tasks and actions using training that is modeled on results gleaned from large data sets. It is a key component of artificial intelligence (AI) systems.

Let’s compare generative AI and machine learning, dig deep into each, and lay out their respective use cases.

Also see: Top Generative AI Apps and Tools

Generative AI vs. Machine Learning

Generative AI builds on the foundation of machine learning, which is a powerful sub- category of artificial intelligence. ML can crunch through vast amounts of data, gleaning patterns from it and providing key insights. In contrast, generative AI turns ML inputs into content and is bi-directional rather than unidirectional. Meaning that generative AI can both learn to generate data and then turn around to critique and refine its outputs.

Generative AI and machine learning are both invaluable tools in assisting humans in addressing problems and lessening the burden of repetitive manual labor. Both will play a role in the development of a more intelligent future and each has specific use cases.

Two more key points:

• Machine learning algorithms unearth patterns and generative AI transforms them into something actionable.
• Machine learning algorithms can be viewed as the heavy lifter of the AI world. Its efforts make it possible for generative AI to add creativity via fresh content.

Let’s take a deeper look at both generative AI and machine learning.

Also see: Generative AI Companies: Top 12 Leaders

What is Generative AI?

Generative AI is an emerging technology that uses artificial intelligence, algorithms and large language models to generate content. Machine learning makes uses of deep learning and neural network techniques to generate content that is based on the patterns it observes in a wide array of other content.

Although this content is classified as original, in reality generative AI uses machine learning and AI models to analyze and then replicate the earlier creativity of others. It taps into massive repositories of content and uses that information to mimic human creativity.

Generative AI can perform tasks like analyze the entire works of Charles Dickens, JK Rollins or Ernest Hemingway and produce an original novel that seeks to simulate these authors’ style and writing patterns.

Thus, generative AI ventures well beyond traditional machine learning. By utilizing multiple forms of machine learning systems, models, algorithms and neural networks, generative AI offers a new foray into the world of creativity.

For more information: What is Generative AI?

Generative AI Use Cases

Generative AI is used to augment but not replace the work of writers, graphic designers, artists and musicians by producing fresh material. It is particularly useful in the business realm in areas like product descriptions, creating variations to existing designs or helping an artist explore novel concepts.

Among the media it creates:

Text – Generative AI can generate credible text on various topics. It can compose business letters, provide rough drafts of articles and compose annual reports. It can also compose novels – although the results may not be entirely satisfactory.

Images – Generative AI can generate realistic and vivid images from text prompts, create new scenes and simulate a new painting.

Video – Generative Ai can compile video content from text automatically and put together short videos using existing images.

Music – Generative Ai can compile new musical content by analyzing a music catalog and rendering a similar composition in terms of style. Famously, musicians used generative AI to create a sound-alike tune that resembled a Drake song that generated considerable buzz.

Product design – Generative AI can be fed inputs from previous versions of a product and produce several possible changes that can be considered in a “new version.”

Personalization – Generative AI can personalize experiences for users such as product recommendations, tailoring design to experiences and feeding material that closely matches user preferences.

Generative AI in its current form can certainly assist people in creating content. But beyond basic business functions that stick to a rigid format and message, its main use is likely to be to help creators come up with ideas which they then take and turn into something truly original and authentic.

And: ChatGPT: Understanding the ChatGPT ChatBot

What is Machine Learning?

Machine learning uses artificial intelligence to learn and adapt automatically without the need for continual instruction. Machine learning is based on algorithms and statistical AI models that analyze and draw inferences from patterns discovered within data.

Once set up, the ML system applies itself to a dataset or problem, spots situations and solves problems. It utilizes algorithms to parse data, learn and make decisions. Machine learning models train on large amounts of data, gradually learning and improving their accuracy rates over time.

The ML models used can be supervised, unsupervised, semi-supervised or reinforcement learning. Regardless of the way the model operates, it is all about recognizing patterns and making predictions and drawing inferences, addressing complex problems and solving them automatically.

Algorithms are procedures designed to automatically solve well-defined computational or mathematical problems or to complete computer processes. Consequently, ML algorithms go beyond computer programming as they require understanding of the various possibilities available when solving a problem.

Machine learning algorithms, then, can be regarded as the essential building blocks of modern AI. Machine learning finds a pattern or anomaly amongst the noise of data and finds paths to solutions within a time frame that humans would not be capable of. They also help impart autonomy to the data model and emulate human cognition and understanding.

Also see: Generative AI Startups

Machine Learning Use Cases

Machine learning has a great many use cases – and the use cases are continually expanding. In fact, machine learning has crept into just about every conceivable area where computers are used. Machine learning is found in data analytics, rapid processing, calculations, facial recognition, cybersecurity, and human resources, among other areas.

Machine learning use cases include:

Analytics – Data analytics systems are made faster and smarter by harnessing machine learning.

Data processing – ML is used in the rapid processing of vast quantities of data.

Calculation – Just as pocket calculators largely replaced manual addition and multiplication, machine learning takes care of mathematical calculations of almost infinite proportions.

Facial recognition – Machine learning algorithms can find an identity among millions of candidates as part of facial recognition systems.

Cybersecurity – Machine learning is now part and parcel of network monitoring, threat detection and cybersecurity remediation technology.

Human resources – When incorporated into recruitment tools, machine learning brings about more efficient tracking of applicants, analysis of employee sentiment, monitoring of overall productivity and acceleration of the hiring process.

Machine learning, therefore, is employed to find needles in haystacks consisting of massive quantities of data. It ties into big data in that these algorithms can be utilized to scan structured and unstructured data, social media feeds, and other essential key data in large repositories.

For more information: AI vs. ML

Bottom Line: Generative AI vs. Machine Learning

Generative AI and machine learning are closely related and are often used in tandem. Both generative AI and machine learning use algorithms created to address complex challenges, but generative AI uses more sophisticated modeling and more advanced algorithms to add the creative element.

Generative AI in some ways might be viewed as representing the next level of machine learning, as it offers far more value than merely recognizing patterns and drawing inferences. Generative AI takes those patterns and combines them to be able to generate something that hasn’t ever existed before.

That said, neither generative AI nor machine learning will ever completely replace humans. Just think about all the bad product recommendations you get on websites or streaming services, or all the dumb answers and robotic responses you receive from chatbots.

Within the creative sphere, generative AI may assist the creators of content but can never supplant them. Perhaps Dan Brown or James Patterson will ask AI to write their next books. The writers must come up with the plot, the characters, and so on. AI can then perhaps churn out the stories. But the authors will still have to go through it, take out various sections of nonsense and provide something that might satisfy their fans. However, if that becomes art, then don’t hold your breath waiting for a modern renaissance.

Still, both AI technologies are major disruptors. So even if generative AI and machine learning don’t usher in a new era of creativity, they are destined to bring fundamental change across a great many industries.

Also see: 100+ Top AI Companies 2023

The post Generative AI vs. Machine Learning appeared first on eWEEK.

• GPT4 is the latest version of ChatGPT, the popular AI-based chatbot that can also be used to generate basic computer code. It was developed by OpenAI and is now incorporated into Bing and other Microsoft products.
• ELMAR is a large language model (LLM) that enables businesses to create sophisticated chatbots. Developed by Got It AI, it emphasizes secure, on-premises AI and is carving out a specific niche where it looks to be competitive with ChatGPT and others. Yet ELMAR is even newer than ChatGPT in the marketplace.

Let’s examine the similarities and the differences between GPT4 and ELMAR across a range of different criteria.

Also see: Top Generative AI Apps and Tools

And: 100+ Top AI Companies 

Quick Comparison: GPT vs. ELMAR

GPT4 vs. ELMAR: Feature Comparison

GPT is an abbreviation for Generative Pre-trained Transformer, a form of advanced artificial intelligence (AI). GPT4 simulates thought by using a neural network machine learning model trained based upon a vast trove of data gathered from the internet.

The ChatGPT AI-powered language model was developed by OpenAI. It was trained to be able to generate human-like text responses to a given prompt. It answers questions, can converse with users on a variety of topics, and even generate creative writing pieces.

As such, GPT4 goes far beyond being a chatbot. It can create documents and articles and solve problems. GPT4 can also do image interpretation using multimodal language AI models. This enables it to build websites based on sketches, and suggest recipes based on a photo.

GPT4 can perform complex tasks. It has achieved some success with basic computer programming duties, but it ventures well beyond that into territory such as drawing up simple lawsuits, creating elementary computer games, passing exams, checking for plagiarism, generating written content, summarizing documentation, highlighting key passages within text and translating dozens of languages.

ELMAR is short for Enterprise Language Model Architecture. It styles itself as an enterprise-ready language model that enables businesses to create sophisticated chatbots that can communicate with customers or internal stakeholders in a natural and engaging way.

It is designed to be fine-tuned for specific use cases. The latest iteration includes a TruthChecker which is a post-processor that helps to compare responses generated by other language models to flag potentially incorrect, misleading or incomplete answers and AI hallucinations.

There are also preprocessors that add more security and can filter out unwanted data or mask personal data. ELMAR is aimed at specific and narrowly defined data sets. It has been used successfully with the knowledge bases of Zendesk and Confluence. Hence, it doesn’t try to compete with GPT4 as a way to “analyze the internet.”

GPT4 wins on breadth of features and capabilities, as it is far more than a chatbot.

On a related topic: What is Generative AI?

GPT4 vs. ELMAR: Chatbot Functions

GPT4 is used a lot in chatbot applications to automate customer service, answer FAQs, and engage in conversation with users. It can respond conversationally by tapping into a comprehensive set of online text, as well as news items, novels, websites, and more.

Overall, GPT4 does a good job of analyzing information, evaluating online behavior, and even making product recommendations as part of the online sales and upselling process. Automation features extend to appointment scheduling, reservations, payment processing, queries about shipping schedules, order progress, product returns, product and service availability, and more, with a good level of accuracy.

ELMAR can be looked upon as a much more focused chatbot than GPT4. It can be fine-tuned to produce task-specific custom models that reside on-premises for security or data privacy purposes. Further, it doesn’t need to use third-party APIs, which provides additional enhancement to the security posture and eliminates the possibility of a surge in inference costs, which can happen on other AI models. ELMARs’ pre-processing and post-processing features catch and filter out wrong answers and hallucinations.

GPT4 wins as a broad chatbot. ELMAR wins for on-premises and highly security-conscious use cases.

Also see: Generative AI Companies: Top 12 Leaders

GPT4 vs. ELMAR: Accuracy of Response

GPT4 can be prone to error based on assumptions made on data that may not be current. But most of the time it is accurate. It got into hot water with a few strange responses to queries and one or two completely wrong answers.

Fortunately, each new version gets better. GPT4 added a greater degree of accuracy over ChatGPT. OpenAI stated that GPT4 is 82% less likely to respond to requests for content that OpenAI does not allow than its predecessor, and 60% less likely to invent answers. But don’t expect it to be perfect. That includes coding. Its programming output should always be verified by human eyes.

Accuracy is a big advantage of ELMAR. Many large language models hallucinate and cannot be trusted for mission-critical applications. ELMAR surrounds the core LLM model with pre-processing and post-processing features to minimize unpredictability and inaccuracy. Its TruthChecker, for example, can spot problematic or incorrect responses. When used, it takes the hallucination rate of ELMAR from 14% to 1.4%. Per numbers from Got It AI, GPT4 has a hallucination rate of 8.4%. These accuracy features enhance the reliability of chatbot interactions.

ELMAR wins on accuracy.

Also see: ChatGPT vs GitHub Copilot

GPT4 vs. ELMAR: Integration

GPT4 comes out of the open source community. It can be plugged into other applications to generate responses via an API. Plugins are becoming available, including those for the likes of Kayak, Expedia, OpenTable, Slack and Shopify, with more on the way. It is also integrated with a lot of different programming languages.

The ELMAR LLM can be integrated with any knowledge base for dialog-based chatbot Q&A applications but is not integrated with that vast database known as the internet.

Both integrate well, but perhaps GPT4 has much broader integration capabilities across the cloud and the web. ELMAR wins for localized integration on-premises.

Also see: Generative AI Startups

GPT4 vs. ELMAR: Security

GPT4 hasn’t really had much attention on security to date. Developers using it are expected to build in their own security features.

ELMAR, on the other hand, is all about security and data privacy. Those deploying it can take advantage of many features to secure their language model architecture against attacks. Its models are deployed within the enterprise security envelope and are under the full control of the business. There is no going to the cloud or the web for answers and potentially exposing applications to attack.

Users can also set policies to remove data such as personally identifiable information (PII) so that unauthorized internal users only get to see the information they need. For those that need AI chat but need to avoid information exposure by linking to third-party applications, ELMAR is a good solution.

ELMAR is the clear winner on security and data privacy.

For more information, also see: Top AI Startups

GPT4 vs. ELMAR: Pricing

GPT4 has a basic version available for free but the main ChatGPT Plus version costs roughly $20/month. Those subscribing gain access to ChatGPT at peak times, faster responses, and priority access to new features and improvements.

On top of the basic subscription, there is a pricing scale per 1,000 tokens (chunks of words). 1,000 tokens comes out to about 750 words of material. Costs range from 3 cents to 6 cents per 1,000 tokens for prompts, and another 6 to 12 cents per thousand once finished. The higher rate provides access to a larger set of contextual data.

Unfortunately, ELMAR pricing isn’t publicly available. The company does say that it runs using commodity hardware. Not only does this ensure that sensitive data does not leave the premises, it is also said to keep costs low. But there are no specifics available. In any case, ELMAR remains in the testing phase and so an announcement about pricing should come once it is broadly released.

GPT4 wins on pricing until such times as ELMAR releases its rates.

On a related topic: Top Natural Language Processing Companies

GPT4 vs. ELMAR: Bottom Line

GPT4 uses a transformer-based architecture as part of a neural network that handles sequential data. Although the data it draws from may be a little dated at times, GPT4 seems to perform okay in coding, and does a very good job on chat, language translation, answering questions, and understanding images. It can even determine why a joke is funny.

Overall, ELMAR offers an attractive value proposition for businesses looking to leverage an AI chatbot that is secure and keeps data private. As it runs on commodity hardware internally and doesn’t use third-party APIs, it looks like a no-brainer to organizations with highly sensitive data and strict security needs. But remember that ELMAR is still in the testing phase. Got It AI is running enterprise pilots, which are being used to improve ELMAR’s speed, accuracy and cost-effectiveness. So, it has a way to go before it can be considered a viable alternative to GPT4.

Also see: What is Artificial Intelligence?

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• Machine learning is a technique used to help computers learn using training that is modeled on results gleaned from large data sets.
• Deep learning is a form of machine learning based on artificial neural networks that are modeled after the native capabilities of the human brain. It can be viewed as “machine learning on steroids” as it takes the basic capabilities of machine learning to a higher level.

For more information, also see: Best Machine Learning Platforms

How Machine Learning Compares to Deep Learning

Machine learning is highly advanced technology; some of the tasks that can be done with it seem miraculous. Deep learning is still more complex but has a more limited set of applications. It typically requires more time and resources to set up and analyze but provides deeper and better conclusions. In contrast, machine learning solutions can often be arrived at faster as they are more narrowly defined and apply to a smaller data set.

Deep Learning Takes More Time

As deep learning platforms take time to analyze data sets, it typically take far longer to set up and longer to reveal its results. More compute and processing power is usually involved.

Machine Learning Can be More Specific and Faster

Machine learning algorithms can be unleashed on a specific issue to solve or improve it rapidly. Because of this, machine learning has become a very common enterprise use of artificial intelligence.

Deep Learning Goes Deeper

Machine learning can sift through data to spot patterns while deep learning can analyze a much larger data set and detect more subtle patterns of anomalies.

Deep Learning is “Smarter”

Deep learning is better able to learn from mistakes and adapt to do better next time.

Machine learning and deep learning are both incredibly valuable tools in assisting humans in addressing problems and in removing the burden of repetitive manual labor. Both will play a role in the development of a more intelligent future applications.

Also see: Generative AI Companies: Top 12 Leaders 

How Does Machine Learning Work?

Machine learning users computerized systems that can learn and adapt automatically without the need for continual instruction. Once set up, the system applies itself to a dataset or problem, spots situations or solves problems. Machine learning can draw inferences, address complex problems and solve them automatically.

Draws Inferences

Machine learning is based on algorithms and statistical models that analyze and draw inferences from patterns discovered within data.

Creates Automatic Solutions

Algorithms are procedures designed to automatically solve well-defined computational or mathematical problems or to complete computer processes.

Solves Complex Problems

Algorithms go beyond computer programming as they require understanding of the various possibilities available when solving a problem.

Machine learning algorithms, then, can be regarded as the essential building blocks of modern AI. Machine learning finds a pattern or anomaly amongst the noise of data and finds paths to solutions within a time frame that humans would not be capable of. They also help impart autonomy to the data model and emulate human cognition and understanding.

Also see: Top Generative AI Apps and Tools

How Does Deep Learning Work?

Deep learning systems use multiple processing layers to extract progressively better and more high-level insights from data. It can be viewed as a more sophisticated application of machine learning that makes heavy use of machine learning algorithms, are inspired by the human mind, can keep learning from their mistakes and solve highly complex problems.

Use Machine Learning Algorithms

Deep learning systems use standard machine learning techniques and can be considered a subset of machine learning. Yet it is almost always more sophisticated than machine learning, in terms of its problem solving ability.

Is Human-Inspired

The mathematical structures that comprise deep learning have been loosely inspired by the structure and function of the brain. Meaning it can handle more nuance and come closer to what humans think of as creativity.

Enables Continuous Learning

Since deep learning applications can learn by example and correct their actions based on errors detected, they keep learning and improving their level of accuracy.

Contains High Complexity

Deep learning allows machines to tackle problems of similar complexity to those humans can solve.

Thus, deep learning has enabled researchers to scale up the models they use in a way that goes well beyond traditional machine learning. By utilizing multiple forms of machine learning systems, models and algorithms, deep learning opens new doors for analysis and problem solving.

Also see: What is Artificial Intelligence?

Machine Learning Use Cases

Machine learning has a great many use cases. In fact, machine learning has crept into just about every conceivable area where computers are used. For example, it is used in analytics, rapid processing, calculations, facial recognition, cybersecurity, and human resources.

Drives Analytics

Data analytics systems are made faster and smarter by harnessing machine learning. At this point, most all enterprise data analytics applications incorporate machine learning.

Performs Calculations

Just as pocket calculators largely replaced manual addition and multiplication, machine learning takes care of mathematical calculations of almost infinite proportions.

Enables Facial Recognition

Machine learning algorithms can find an identity among millions of candidates as part of facial recognition systems.

Assists Cybersecurity

Machine learning is now part and parcel of network monitoring, threat detection and cybersecurity remediation technology.

Supports Human Resources (HR)

When incorporated into recruitment tools, machine learning brings about more efficient tracking of applicants, analysis of employee sentiment, overall productivity and can speed the hiring process.

Machine learning, therefore, is employed to find needles in haystacks consisting of massive quantities of data. It ties into big data in that these algorithms can be utilized to scan structured and unstructured data, and social media feeds.

For more information, also see: Top AI Software 

Deep Learning Use Cases

Deep learning use cases go beyond those of machine learning. Machine learning is broadly applicable to a huge range of tasks. As the name implies, deep learning is harnessed to solve problems at a deeper and more complex level. Deep learning is used to generate text, automatically deliver meeting transcripts, capture data from documents and generate video content from text.

Generate Text

Deep learning-based, large language models can generate credible and in-depth text on various topics or generate realistic images from text prompts.

Create Transcripts

Deep learning is being used to provide high-accuracy text transcripts from audio recordings of business meetings and phone calls.

 Automatically Capture Data

Deep learning can be deployed to automatically capture data from business documents with high accuracy.

Produce Video Content

Another use case that is emerging is to generate video content from text automatically. In this case, the video often uses virtual avatars for the onscreen speakers.

Deep learning use cases provide multi-faceted answers to complex situations and problems. It elevates machine learning in terms of scale and depth of analysis.

On a related topic: Top Natural Language Processing Companies

Bottom Line: Machine Learning vs. Deep Learning

In many ways, machine learning and deep learning can be viewed as cousins, if not siblings. They each comprise algorithms that are addressed to complex challenges. Deep learning, though, utilizes more sophisticated models that take longer to set up and require more time to crunch through the larger data sets they typically analyze.

As such, deep learning is in use among a much smaller user base due to the time and cost required to build and run its systems.

But as time goes on, the necessary investment is diminishing. Perhaps within a year or two, the separation between machine learning and deep learning will become a moot point. The technology could advance to the point where deep learning techniques become so accessible that they begin to be applied broadly to problems that are currently the province of more limited machine learning algorithms.

On a related subject: Algorithms and AI

The post Machine Learning vs. Deep Learning appeared first on eWEEK.

• ChatGPT is a popular generative AI solution that can be used to generate basic computer code, along with text, images and other content.
• GitHub Copilot is a cloud-based artificial intelligence tool developed by GitHub and OpenAI specifically to assist users to develop code.

To understand which generative AI solution is best for your purposes, we’ll dig into the similarities and the differences across a range of different criteria.

Also see: Top Generative AI Apps and Tools 

And: Generative AI Companies: Top 12 Leaders

Quick Comparison: ChatGPT vs. GitHub Copilot

ChatGPT vs. GitHub Copilot: Overall

GPT is an abbreviation for Generative Pre-trained Transformer, a form of advanced artificial intelligence (AI). ChatGPT simulates human thought by using a neural network machine learning model trained based upon a vast trove of data gathered from the internet.

The ChatGPT AI-powered language model was developed by OpenAI. It was trained on a massive amount of text data from the internet to be able to generate human-like text responses to a given prompt. It answers questions, can converse with users on a variety of topics, and even generate creative writing pieces. As such, ChatGPT goes far beyond being a chatbot to being able to create documents and articles, and solve problems.

GitHub Copilot is supported by GitHub, whose user base counts more than 100 million developers, with more than 400 million open-source contributions using about 500 languages to build software (JavaScript is the most popular). It has tens of millions of visitors every month from all over the globe. GitHub is a convenient place to store, track and collaborate on software projects while also providing social networking opportunities.

GitHub Copilot is a cloud-based artificial intelligence tool developed by GitHub and OpenAI to assist users of Visual Studio Code, Visual Studio, Neovim, and JetBrains integrated development environments (IDEs). This enables it to write code faster with less work.

Copilot draws context from comments and code to suggest individual lines and whole functions instantly. The OpenAI Codex it uses is a generative pre-trained language model that is trained on natural language text and source code from publicly available sources, including code in public repositories on GitHub.

Winner:

ChatGPT wins on breadth of features and capabilities, while GitHub Copilot wins on the strength and depth of its programming capabilities.

Also see: Generative AI Startups 

ChatGPT vs. GitHub Copilot: Chatbot Functions

ChatGPT is used a lot in chatbot applications to automate customer service, answer FAQs, and engage in conversation with users. It can respond conversationally by tapping into a comprehensive set of online text written by actual people, as well as news items, novels, websites, and more.

ChatGPT does a good job of analyzing information, evaluating online behavior, and even making product recommendations as part of the online sales and upselling process. Automation features extend to appointment scheduling, reservations, payment processing, queries about shipping schedules, order progress, product returns, product and service availability, and more with a high level of accuracy.

ChatGPT can also do image interpretation using multimodal language AI models. This enables it to build websites based on sketches, and suggest recipes based on a photo of what is in the fridge or sitting on a countertop. Further ChatGPT can perform complex tasks. It has achieved some success with basic computer programming duties, but it ventures well beyond that into territory such as drawing up simple lawsuits, creating elementary computer games, passing exams, checking for plagiarism, generating written content, summarizing documentation, highlighting key passages within text and translating into dozens of languages.

GitHub Copilot doesn’t do any of this, but it isn’t designed to. Rather than trying to be everything ChatGPT attempts to be, GitHub Copilot focuses – deeply and effectively – on its role as an AI-assistant for software coding.

Winner: 

ChatGPT wins as a chatbot.

On a related topic: What is Generative AI?

ChatGPT vs. GitHub Copilot: Accuracy of Response

ChatGPT can be prone to error, based on assumptions made on data that may not be current. But most of the time it is accurate.

ChatGPT got into hot water with a few strange responses to queries and a number of completely wrong answers. Fortunately, each new version gets better. GPT-4 added a greater degree of accuracy.

OpenAI stated that GPT-4 is 82% less likely to respond to requests for content that OpenAI does not allow than its predecessor, and 60% less likely to invent answers. But don’t expect it to be perfect. That includes coding. Its programming output should always be verified by human eyes.

GitHub Copilot similarly has some accuracy issues. Users accept on average 26% of all completions shown by GitHub Copilot. In certain languages like Python that goes up to 40%.

In other words, GitHub Copilot does not generate perfect code. Instead, it creates the best code possible given the context it has access to. Thus, code may not always work. At times Copilot may rely on old or deprecated libraries and languages.

Overall, languages like Python, JavaScript, TypeScript, and Go tend to perform better compared to other programming languages. GitHub Copilot is said to increase in accuracy when code is split into smaller functions, when meaningful names are used for function parameters, and good instructions are given. Any code suggested by GitHub Copilot should be carefully tested, reviewed, and vetted. That said, it is better than the code generated by ChatGPT.

Winner: 

GitHub Copilot wins on software coding.

And: ChatGPT4 vs. ChatGPT 

ChatGPT vs. GitHub Copilot: Integration

ChatGPT comes out of the open-source community. It can be plugged into other applications to generate responses via an API. Plugins are becoming available, including those for the likes of Kayak, Expedia, OpenTable, Slack and Shopify with more on the way. It is also integrated with a lot of different programming languages.

GitHub Copilot is well integrated with the Visual Studio Code, Visual Studio, Neovim, and the JetBrains IDEs. It can analyze and pull from billions of lines of code in multiple languages.

Note, though, that it is trained on publicly available code. Brand new libraries, frameworks and APIs are less integrated (and coding accuracy will be lower) as less public code is available for the model to learn from. It takes time for the GitHub Copilot codebase to build up enough examples to provide accurate code.

Winner: 

There is no clear winner when it comes to integration.

For more information, also see: Top Robotics Startup

ChatGPT vs. GitHub Copilot: Security

ChatGPT hasn’t really had much attention on security to date. Developers using it are expected to build in their own security features.

GitHub Copilot benefits from the many security features that have been added to the GitHub platform in recent years. In addition, Copilot for business users provides plenty of coding privacy and other protection measures. An AI-based vulnerability prevention system blocks insecure coding patterns in real-time to make suggestions more secure.

Copilot targets the most common vulnerable coding patterns, including hardcoded credentials, SQL injections and path injections. This helps to detect vulnerable patterns in incomplete fragments of code.

Winner: 

GitHub Copilot wins on security.

On a related topic: Top Natural Language Processing Companies

ChatGPT vs. GitHub Copilot: Pricing

ChatGPT has a basic version available for free but the main ChatGPT Plus version costs roughly $20/month. Those subscribing gain access to ChatGPT at peak times, faster responses, and priority access to new features and improvements.

On top of the basic subscription, there is a pricing scale per 1,000 tokens (chunks of words). 1,000 tokens comes out to about 750 words of material. Costs range from 3 cents to 6 cents per 1,000 tokens for prompts, and another 6 to 12 cents per thousand once finished. The higher rate provides access to a larger set of contextual data.

GitHub Copilot pricing is much simpler. It is $10 a month for individuals and $19 a month per user for business. The business version comes with management, policy and privacy tools for added security and collaboration.

Winner: 

GitHub Copilot wins on pricing.

For more information, also see: Top AI Startups

ChatGPT vs. GitHub Copilot: Bottom Line

ChatGPT uses a transformer-based architecture as part of a neural network that handles sequential data. Although the data it draws from may be a little dated at times, ChatGPT seems to perform OK in coding, and does a very good job on chat, language translation, answering questions, understanding images, and can even determine why a joke is funny.

But ChatGPT is not so much an automatic coding platform as an AI-based system that can be incorporated into development functions.

GitHub Copilot was designed 100% with code generation in mind. As such its output is in code snippets as opposed to the natural language responses of ChatGPT. GitHub is the obvious tool for professional coders as well as for amateurs with plenty of experience.

Winner: 

ChatGPT may be the better option for beginning coders and those who rarely code and aren’t part of the Github community. Copilot definitely aids developers in writing code faster, while ChatGPT can play a role in streamlining the development process but is better as a broader AI system that incorporates chat and some coding.

In short, ChatGPT is more versatile as an overall generative AI to create content of all kinds, including software code. But GitHub is a far more powerful platform for developing software code.

In either case, though, think of them as assistants to developers and content creators and not a replacement. Human minds remain an essential element of solid creation.

Also see: What is Artificial Intelligence?

The post ChatGPT vs. GitHub Copilot: Comparing Generative AI Apps appeared first on eWEEK.

Advanced AI Chatbots – in essence, generative AI platforms – are applications designed to address online chat functions using text or speech. By adding advanced artificial intelligence (AI) and machine learning algorithms to online chat, text and image creation, accuracy and responsiveness are enhanced.

When comparing ChatGPT and Watson Assistant, which is best? Let’s examine the similarities and the differences between these generative AI bots across a range of different criteria.

Also see: Generative AI Companies: Top 12 Leaders 

And: ChatGPT vs. Google Bard: Generative AI Comparison

ChatGPT vs. Watson Assistant: Quick Comparison

On a related topic: What is Generative AI?

ChatGPT vs. Watson Assistant: Feature Comparison

GPT is an abbreviation for Generative Pre-trained Transformer, a form of advanced artificial intelligence (AI). Using a neural network, ChatGPT simulates thought using a machine learning model trained based upon a vast trove of data gathered from the internet.

The ChatGPT is a generative AI language model was developed by OpenAI. It was trained on a massive amount of text data from the internet to generate human-like text responses to a given prompt. It answers questions, can converse with users on a variety of topics, and even generate creative writing pieces.

As such, ChatGPT goes far beyond being a chatbot to being able to create documents, and articles, and solve problems. ChatGPT is a far broader offering than Watson.

Watson Assistant is much more focused as an offering. Using artificial intelligence, this AI-chatbot platform developed by IBM enables businesses to build, train, and deploy conversational interactions across web, mobile, messaging platforms, and other channels. It offers personalized customer experiences and is designed specifically for customer service, technical support, and as a virtual assistant.

Watson Assistant, then, is best for those building custom chatbots for businesses. ChatGPT should be looked upon as more of a language-based AI model that can enhance the conversational capabilities of chatbots.

In reality, then, they are not really direct competitors.

Also see: Generative AI Startups

ChatGPT vs. Watson Assistant: Chatbot Functions

ChatGPT is used a lot in chatbot applications to automate customer service, answer FAQs, and engage in conversation with users. It can respond conversationally by tapping into a comprehensive set of online text written by actual people, as well news items, novels, websites, and more.

Watson is a narrower chatbot app that is highly customizable to specific verticals and use cases. IBM provides pre-built components to use in areas such as flight and other reservations and answering FAQs.

As a chatbot, Watson rigorously sticks to what it is programmed to respond to and never strays from that. If it doesn’t know, it will refer to an agent.

Due to this, Watson is a more reliable chatbot application than ChatGPT, though not nearly as capable in a broader sense. IBM has been integrating more conversational AI features into Watson Assistant, but it is far behind ChatGPT in that regard.

ChatGPT vs. Watson Assistant: Marketing and Sales Support

ChatGPT does a good job of analyzing information, evaluating online behavior, and making product recommendations as part of the online sales and upselling process. Automation features extend to appointment scheduling, reservations, payment processing, queries about shipping schedules, order progress, product returns, product and service availability, and more with a high level of accuracy.

Watson Assistant has long years of experience in providing customer service to verticals such as insurance, finance, and healthcare where it is programmed to understand industry-specific language. It also does a good job of tech support.

For specific products and services, Watson harnesses a database all about those tools and services, and ably lowers the call burden on call centers teams while providing users with the information they need. It is also set up to guide customers through a sales funnel, offer product recommendations, and pass on leads to sales.

Watson Assistant wins here, too.

On a related topic: The AI Market: An Overview

ChatGPT vs. Watson Assistant: Image Interpretation

It is the image interpretation category that really sets ChatGPT apart. The latest version, known as GPT-4, is a multimodal language AI model that interprets images. As a result, it can build websites based on sketches, and suggest recipes based on a photo of what is in the fridge or sitting on a countertop.

Watson Assistant can’t do any of that. ChatGPT is the clear winner in this category. Expect IBM to add such features in the future, though.

ChatGPT vs. Watson Assistant: Accuracy of Response

ChatGPT can be prone to error, based on assumptions made on data that may not be current. But most of the time it is accurate.

ChatGPT got into hot water with a few strange responses to queries and one or two completely wrong answers. Fortunately, each new version gets better. GPT-4 added a greater degree of accuracy. OpenAI stated that GPT-4 is 82% less likely to respond to requests for content that OpenAI does not allow than its predecessor, and 60% less likely to invent answers. But don’t expect it to be perfect.

Watson Assistant will make even fewer goofs as it addresses a tiny subset of the total data that ChatGPT covers. ChatGPT might do better at attempting to explain the meaning of life, but Watson Assistant is more likely to recommend the right product upgrade or technical support action.

On a related topic: The Future of Artificial Intelligence 

ChatGPT vs. Watson Assistant: Integration

Watson Assistant can integrate with back-end systems, and a great many CRM, voice assistants, knowledge management, and other enterprise systems. IBM’s long years of experience in the IT community mean that it has the deep relationships needed for far reaching integration.

ChatGPT comes out of the open-source community and lacks the commercial relationships of IBM. As such, it is not nearly as advanced on the integration front. But it can be plugged into them to generate responses via an API. Plugins are becoming available. So far, there plugins are ready for applications such as Kayak, Expedia, OpenTable, Slack and Shopify, with more on the way.

ChatGPT vs. Watson Assistant: Complex Tasks

The greater the complexity of the task, the more ChatGPT comes into its own. It has achieved success with basic computer programming, in drawing up simple lawsuits, in creating elementary computer games, and in passing exams.

In the Biology Olympiad test, for example, ChatGPT scored in the 99th percentile. Further capabilities include an AI Text Classifier, which is a plagiarism checker. It has gotten quite good at distinguishing between AI-written and human-written text, and in the detection of automated misinformation campaigns that take advantage of AI tools.

Users are warned, however, that the general limitations of both AI chatbots include a higher likelihood of inaccuracy with texts below 1,000 characters, and that plagiarism results are better with English than other languages.

Content generation is another of the complex tasks that ChatGPT can accomplish. It can produce text that can use the same style and grammar as an original piece of material, summarize long texts and reports to reflect the primary ideas with some accuracy, highlight key passages within text, and translate into dozens of languages.

Watson Assistant can address complexity within the narrow bounds of product information about a certain number of products for a specific vendor or market niche. It doesn’t stray from those parameters. ChatGPT wins as the complexity of the required tasks are magnified.

Also see: ChatGPT: Understanding the ChatGPT ChatBot 

ChatGPT vs. Watson Assistant: Security

Watson Assistant, as would be expected due to its use in large enterprises, provides a robust set of security features. It has integrated user authentication and access controls, and offers comprehensive encryption. It complyies with a wide array of standards and regulations.

ChatGPT, however, hasn’t really had much attention on security to date. Developers using it are expected to build in their own security features.

ChatGPT vs. Watson Assistant: Pricing

ChatGPT has a basic version available for free but the main ChatGPT Plus version costs roughly $20/month. Those subscribing gain access to ChatGPT at peak times, faster responses, and priority access to new features and improvements.

On top of the basic subscription, there is a pricing scale per 1,000 tokens (chunks of words). 1,000 tokens comes out to about 750 words of material. Costs range from 3 cents to 6 cents per 1,000 tokens for prompts, and another 6 to 12 cents per thousand once finished. The higher rate provides access to a larger set of contextual data.

Watson Assistant has a lite version available free, which might work for home or mobile users. For $140 a month, you gain phone and SMS integration, and must pay extra for heavy usage beyond a particular threshold.

For more information, also see: Top AI Startups

ChatGPT vs. Watson Assistant: Bottom Line

ChatGPT uses a transformer-based architecture as part of a neural network that handles sequential data. Although the data it draws from may be a little dated at times, ChatGPT seems to perform respectably in chatbots, and does a very good job on language translation, answering questions, understanding images, and can even determine why a joke is funny. But ChatGPT is not so much a chatbot as an AI-based system that can be incorporated into other chatbot applications.

Watson Assistant, on the other hand, can be regarded as a narrower but more targeted version of ChatGPT that is precisely aimed at the chatbot market. It lacks the contextual understanding that ChatGPT can bring to the table but is a lot more focused and generally better as a chatbot.

Watson Assistant is also not influenced by hate speech or misinformation, unlike ChatGPT at times. And it does better at knowing when it needs to turn things over to a human agent. In addition, Watson Assistant is good at remembering the data it is trained on and thus, in not straying far from what it is supposed to do.

In sum, Watson Assistant is best for purely chatbot actions whereas ChatGPT should be regarded as a more general purpose AI technology that can enhance the chatbot function.

However, the added cost of Watson Assistant means that it is never going to achieve the widespread use that ChatGPT is likely to have. It will remain a niche product used where the economics of customer service and sales make sense. ChatGPT is destined for much broader usage.

On a related topic: Top Natural Language Processing Companies

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GPT is an abbreviation for Generative Pre-trained Transformer, a form of advanced artificial intelligence. It simulates thought by using a neural network machine learning model trained on a vast trove of data gathered from the internet. Advanced AI chatbots use AI models to generate human-like text responses to questions, create documents, and solve problems.

Both GPT-4 and ChatGPT have earned plaudits as excellent AI-based tools. There are obvious similarities between them – GPT-4 is essentially an upgrade to ChatGPT, which is based on GPT-3.5.

Hence, GPT-4 is more advanced, and beats ChatGPT in just about every category. This article compares ChatGPT and GPT-4 across a range of criteria, including text-based queries, image recognition, detection of plagiarism, pricing, and dealing with complex tasks.

On a related topic: What is Generative AI?

And: ChatGPT vs. Google Bard: Generative AI Comparison

GPT-4 vs. ChatGPT: Text-Based Queries

ChatGPT and GPT-4 are both AI-powered generative AI language models developed by OpenAI. They have been trained on a massive amount of text data from the internet to be able to generate human-like text responses to a given prompt.

Both were built using a deep learning architecture called the Transformer, which enables them to learn patterns in language and generate text that is coherent and human-like. As GPT-4 can access more recent data with more AI resources, and use more parameters, its responses have a higher degree of accuracy than ChatGPT’s.

Also see: ChatGPT: Understanding the ChatGPT ChatBot 

GPT-4 vs. ChatGPT: Chatbot Applications

ChatGPT is well proven among the chatbot applications that are often used to automate customer service, answer FAQs, and engage in conversation with users. This AI-powered chatbot takes advantage of machine learning to respond conversationally, and does it much better than the far more basic chatbots in current use on websites.

GPT-4 can do the same, but it has access to a more comprehensive set of online text written by actual people, as well news items, novels, websites, and more.

Both GPT-4 and ChatGPT are good at this chatbot function but GPT-4 does a better job addressing queries about shipping schedules, progress, product returns, product and service availability and options – with a higher level of accuracy.

On a related topic: The AI Market: An Overview

GPT-4 vs. ChatGPT: Marketing and Sales Support

Both ChatGPT and GPT-4 do a good job in analyzing information, evaluating online behavior, and making product recommendations as part of the online sales and upselling process.

For both AI chatbots, automation features extend to appointment scheduling, reservations, payment processing, and more.

Also see: Generative AI Startups 

GPT-4 vs. ChatGPT: Image Interpretation

It is the image interpretation category that really sets GPT-4 apart from ChatGPT.

GPT-4 can be considered to be far more of a multimodal language AI model than ChatGPT. As such, it interprets images quite well.

As a result, GPT-4 can build websites based on sketches, and suggest recipes based on a photo of what is in the fridge or sitting on a countertop. ChatGPT can’t really do any of these things.

GPT-4 vs. ChatGPT: Number of Parameters Analyzed

ChatGPT ranges from more than 100 million parameters to as many as six billion to churn out real-time answers. That was a really impressive number when it came out.

But GPT-4 is rumored to have up to 100 trillion parameters. That may be an exaggeration, but the truth is likely to still lie somewhere in the range of 1 trillion to 10 trillion.

OpenAI remains tight-lipped about the actual number of parameters.

On a related topic: The Future of Artificial Intelligence 

GPT-4 vs. ChatGPT: Dealing with Current Data

Both GPT-4 and ChatGPT have the limitation that they draw from data that may be dated.

Both AI chatbots miss out on current data, though GPT-4 includes information that is a few months closer to present time than ChatGPT.

As such, both are prone to error due to recent changes, ChatGPT more so.

GPT-4 vs. ChatGPT: Accuracy of Response

ChatGPT got into hot water with a few strange responses to queries and one or two completely wrong answers.

Fortunately, GPT-4 is more accurate than ChatGPT. OpenAI stated that GPT-4 is 82% less likely to respond to requests for content that OpenAI does not allow, and 60% less likely to invent answers. But don’t expect the chatbot to be perfect.

Also see: Cloud and AI Combined: Revolutionizing Tech 

GPT-4 vs. ChatGPT: Plagiarism Detection

ChatGPT includes AI Text Classifier, which is a plagiarism checker. It is good at indicating potential cases of plagiarism but these should never be acted upon as it may get it wrong.

OpenAI recommends that once ChatGPT spots possible candidates, humans should become involved to look at the data and determine the truth.

Meanwhile, GPT-4 spots plagiarism examples with more certainty (though it is far from 100%). It also does a better job at distinguishing between AI-written and human-written text, and in the detection of automated misinformation campaigns that take advantage of AI tools.

Users are warned, however, that the general limitations of both include a higher likelihood of inaccuracy with texts below 1,000 characters and that plagiarism results are better with English than other languages.

For more information, also see: Top AI Startups

GPT-4 vs. ChatGPT: Content Generation

It is said to be possible to train ChatGPT to produce text that can use the same style and grammar as an original piece of material. This is probably good for consistency in social media posts and email marketing, but is unlikely to work well in creative writing or poetry.

ChatGPT can also summarize long texts, articles, and reports to reflect the primary ideas with some accuracy, as well as highlighting key passages within text.

GPT-4 can perform these tasks, too, but with better even results.

On a related topic: Top Natural Language Processing Companies

GPT-4 vs. ChatGPT: Complex Tasks

The greater the complexity of the task, the more GPT-4 comes into its own. Above a particular threshold, its reliability and creativity compared to ChatGPT become more and more apparent.

For example, GPT-4 is superior at basic computer programming, in drawing up simple lawsuits, in creating elementary computer games, and in passing exams. In the Biology Olympiad test, for example, it scored in the 99th percentile (ChatGPT only made the 31st percentile).

For more information, also see: Top AI Software

GPT-4 vs. ChatGPT: Translations

ChatGPT pushed the field of automated translation of English into other languages to a whole new level.

Yet GPT-4 takes things a stage further. It beat ChatGPT in translation accuracy in 24 out of 26 languages tested.

GPT-4 vs. ChatGPT: Pricing

ChatGPT has a basic version available for free but the main ChatGPT Plus version costs $20/month. Those subscribing gain access to ChatGPT at peak times, faster responses, and priority access to new features and improvements.

GPT-4 is only available by subscription. Its pricing is a little complex. Rates are available per 1,000 tokens (chunks of words). 1,000 tokens comes out to about 750 words of material. Costs range from 3 cents to 6 cents per 1,000 tokens for prompts, and another 6 to 12 cents per thousand once finished. The higher rate provides access to a larger set of contextual data.

For more information, also see: History of AI

Bottom Line: GPT-4 vs. ChatGPT

ChatGPT and GPT-4 both use a transformer-based architecture as part of a neural network that handles sequential data. ChatGPT is based on GPT-3.5 so it is less advanced, has a smaller number of potential parameters included, and its data may be a little more dated. Thus, it is less accurate and lags GPT-4 in results to a greater degree, especially as the complexity of the problem or challenge rises.

ChatGPT in its current form seems to perform well in chatbots, language translation, and answering simple questions. But GPT-4 is smarter, can understand images, and process eight times as many words as its predecessor.

Reporters will no doubt continue to manage to fool both of them in various ways, but GPT-4 is far less prone to errors and hallucinatory responses. It can even explain why jokes are humorous. Thus, users should go straight to GPT-4 as it contains everything that is in ChatGPT (based on GPT-3.5) and far more besides.

Also see: What is Artificial Intelligence?

The post GPT-4 vs. ChatGPT: AI Chatbot Comparison appeared first on eWEEK.

However, there are as many differences as similarities between these two solutions. In sum, they’re very different products that will appeal to buyers with different goals in mind. Here’s a look at both, how they compare, and their ideal use cases.

For more information, also see: Best Data Analytics Tools 

Datadog vs. Splunk: Key Feature Comparison

The Splunk platform enables searching, network monitoring, and analyzing a vast amount of IT data to identify data patterns, provide metrics, diagnose problems and aid in business and IT decision making.

To understand the scope of Splunk: Security Information & Event Management (SIEM) can be considered just one small part of its feature arsenal. Beyond security, it takes in APM, compliance, automation, orchestration, forensics, as well as plenty of features related to IT service management (ITSM) and IT operations management (ITOM).

Splunk’s wide range of products and features are aggregated within the Splunk Observability Suite. The platform can be used to analyze, ingest, and store data for later use, as well as detect issues impacting customers. Overall, it offers a breadth of management that spans all of IT and security. Those wishing to manage SIEM, ITOM and ITSM in an integrated fashion will find Splunk to be a fine tool that can do the job. It offers plenty of real-time visualization and analysis features, as well as management and monitoring.

Datadog stops short of calling itself a complete SIEM, ITSM or ITOM platform. It is more focused on cloud monitoring and security. It offers the ability to see inside any stack or application at any scale and anywhere. Infrastructure monitoring, APM, log management, device monitoring, cloud workload monitoring, server monitoring, and database monitoring fall within its feature set.

Datadog is particularly astute at dealing with the performance and visibility of multiple clouds operating on the network and in managing cloud services. Datadog helps IT to drill down into performance data. It generates alerts about potential problems and helps IT to discover any underlying issues.

Datadog can assemble data from logs and other metrics to provide context that is helpful in minimizing incident response time. The user interface centralizes performance monitoring, alert management, and data analysis in one place. Recent additions to its platforms include network monitoring, security analysis, AIOps, business analytics, a mobile app, and an incident management interface.

Delving deeper into both tools, the best way to differentiate them is how they operate. The Splunk application takes more of a log management approach, which makes it ideal for managing and monitoring the large amount of data generated from the devices running on the network. Datadog, on the other hand, takes more of a monitoring approach geared toward analytics. Thus, Datadog tends to be favored by DevOps and IT teams to address cloud and infrastructure performance.

Splunk wins on breadth of features while Datadog wins slightly in terms of APM depth.

For more information, also see: Top Data Mining Tools

Datadog vs. Splunk: New Features

Both companies have been active with new features and updates, with Datadog being by far the most frequent when it comes to product announcements. These include integration with Amazon Security Lake to make it easy for Amazon Security Lake users to send cloud security logs to Datadog in a standard format. This eliminates the need to build data pipelines to aggregate and route security logs to various security analytics solutions.

Datadog makes this possible via minimal configuration requirements. Once security logs are ingested, users can analyze and identify threats through out-of-the-box detection rules or by writing custom security rules.

In addition, Datadog has released Universal Service Monitoring, which automatically detects all microservices across an organization’s environment and provides visibility into their health and dependencies without any code changes. This complements Datadog’s existing infrastructure monitoring and application monitoring capabilities.

Finally, Datadog has released Cloud Cost Management to show an organization’s cloud spend in the context of observability data. This allows engineering and FinOps teams to automatically attribute spend to applications, services, and teams, track any changes in spend, understand why those changes occurred and include costs as a key performance indicator of application health.

Splunk’s announcements have tended to focus on financials, highlighting its position as an established player in the market that is well ahead of Datadog in the revenue stakes. But there have been a few recent product and service updates.

Splunk extended its collaboration with Amazon Web Services (AWS), with whom it is named the ISV Partner of the Year in North America. It, too, has released the Splunk Add-on for Amazon Security Lake to the Splunkbase content marketplace. This enables the creation of a security data lake from integrated cloud and on-premises data sources as well as from private applications. Joint Splunk and AWS customers can benefit via simplified sharing and analyzing of disparate security data by eliminating the step of normalizing the data first.

Datadog wins on new features and innovation.

To learn more, also see: Top Business Intelligence Software 

Datadog vs. Splunk: Management, Support, and Ease of Use

Splunk’s wide range of products and features are aggregated within the Splunk Observability Suite. The platform can be used to analyze, ingest, and store data for later use, as well as detect issues impacting customers.

Overall, Splunk offers a breadth of management that Datadog doesn’t attempt to rival. Those wishing to manage all security information and events (SIEM), all IT operations (ITOM), or all IT services (ITSM) will find Splunk far more complete by far than Datadog. There is no question that Splunk spans a lot more of the IT landscape than Datadog.

Thus, there are advantages for those that choose Splunk. For example, Splunk offers a wealth of real-time visualization and analysis features that Datadog cannot compete with. If real-time management and monitoring are vital, then this one is a no contest.

Splunk, however, isn’t easy to implement, according to user reports. Initial deployment can be accomplished via the cloud. Due to the size and complexity of Splunk, it isn’t for beginners. It requires a higher level of skilled internal resources as well as vendor support to deploy and operate. Users report that the sophistication of Splunk is mirrored in ease of use. Those very familiar with the platform will find it relatively easy to run. Everyone else has a steep learning curve.

Datadog installation, in contrast, is said to be straightforward, courtesy of the deployment of agents. But some command line scripting is required. It is relatively easy to customize dashboards and interfaces to the way you want them. The main interface covers a lot of ground. Great for experienced users, but it might be tough for new users who may be overwhelmed by the number of options.

Whereas Splunk wins hands down on breadth of management, Datadog comes out ahead on depth – at least across a limited feature set. Purely within APM and cloud services, Datadog offers better drill down and general management capabilities. Further, it is better at managing itself. Whereas Splunk relies on IT to notice and troubleshoot issues related to Splunk, Datadog generates alerts about potential or actual problems within itself and helps IT to identify the underlying issues.

This one is a split decision.

Also see: What is Data Visualization

Datadog vs. Splunk: Pricing

It is well known that Splunk isn’t a low-cost option. Once it ascended to become the darling of SIEM and ITSM a few years ago, it set its prices accordingly. The various modules within Splunk also have a reputation for being expensive.

Further, upselling can send the budget much higher i.e., if you want the SIEM module. If you need performance monitoring, that adds in an APM module, and slowly other modules creep in and the price tag rises. This is normal enough in IT. But when you are already dealing with a pricey platform, it is important to determine what you really need and what you can dispense with.

For example, Splunk offers a wealth of real-time visualization and analysis features that Datadog does not. If real-time management and monitoring are vital, then Splunk is the clear choice. But it does come at a price.

Real-time monitoring sounds great, but not everyone needs it enough for to pay this price premium. Datadog skips real-time and is quite a bit cheaper than its big rival. As for deployment, and support, Datadog also comes out well ahead in terms of keeping costs down. Splunk implementation and support costs can escalate as the software is rolled out.

Also see: Real Time Data Management Trends

Datadog vs. Splunk: Bottom Line

Splunk and Datadog are both excellent tools designed to solve a great many challenges related to security and performance monitoring. You can’t go wrong too far wrong with either one. Both are strong in APM. In fact, both are regarded as leaders in the latest Gartner APM Magic Quadrant. Both also offer a lot of advanced features for your money that go far beyond APM. And both are trailblazers when it comes to innovation and future roadmaps.

In reality, though, it isn’t a case of one versus the other so much as it is a case of determining what you really need. Datadog is all about performance measurement for cloud services and is particularly adept at measuring the performance of databases and servers and measuring performance in a multi-cloud world. It doesn’t attempt to embrace the entire SIEM, ITOM, ITSM spectrum. Rather it takes one slice and does that portion really well. Those that have already deployed plenty of tools for security and IT management, therefore, may gravitate more toward Datadog to supplement ongoing efforts.

Splunk, however, is a much broader platform and toolset geared for a heavy duty large enterprise. Its log management approach often proves invaluable in rapidly analyzing log files and making sense of mountains of data so that IT knows what is going on. Whether it’s a performance slowdown or a security incursion, Splunk is a good way to stay one step ahead of trouble. Those needing an all-encompassing security and IT management platform, therefore, will find Splunk closer to their needs. Additionally, those with aging applications that are ready for a major management makeover will find Splunk a good fit. It covers a large amount of ground – if you have the budget for it.

For more information, also see: What is Data Governance

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