Evaluate

Evaluate

Weigh the pros and cons of technologies, products and projects you are considering.

• Claude AI vs. ChatGPT: How do they compare?

  Wondering whether to use Anthropic's Claude or OpenAI's ChatGPT for your project? Explore how the two stack up against each other in terms of cost, performance and features. Continue Reading

• Everything you need to know about the new EU AI Act

  The European Union's new AI Act defines AI regulations based on risk and outlines hefty fines for noncompliance. Explore the details of the AI Act and how it could apply to you. Continue Reading

• Compare enterprise generative AI deployment options

  To pick the best generative AI deployment model for your organization, examine how cloud and on-premises approaches fit into your security, cost, infrastructure and network needs. Continue Reading

• How AI is transforming project management

  As the project management field increasingly embraces AI-powered software, the benefits can help organizations thrive -- but only if the risks are properly considered too. Continue Reading

• Compare large language models vs. generative AI

  While large language models like ChatGPT grab headlines, the generative AI landscape is far more diverse, spanning models that are changing how we create images, audio and video. Continue Reading

• Gemini vs. ChatGPT: What's the difference?

  ChatGPT took early lead among AI-generated chatbots before Google answered with Gemini, formerly Bard. While ChatGPT and Gemini perform similar tasks, there are differences.Continue Reading

• GPT-3.5 vs. GPT-4: Biggest differences to consider

  GPT-3.5 or GPT-4? With multiple OpenAI language models to choose from, picking the right option for your organization's needs comes down to the details.Continue Reading

• AI and compliance: Which rules exist today, and what's next?

  The AI regulatory landscape is still racing to catch up with the fast pace of industry and technological developments, but a few key themes are starting to emerge for businesses.Continue Reading

• AI news roundup: OpenAI video model, Nvidia chatbot and more

  Explore last week's AI news highlights with analyst Mike Leone's roundup of top developments, including OpenAI's launch of video model Sora and Nvidia's locally running chatbot.Continue Reading

• AI news highlights: Gemini Ultra, Cisco-Nvidia partnership

  Explore last week's AI highlights with analyst Mike Leone's roundup of top news headlines, including updates to Google's Gemini Ultra and strategic acquisitions and partnerships.Continue Reading

• Explore real-world use cases for multimodal generative AI

  Multimodal generative AI can integrate and interpret multiple data types within a single model, offering enterprises a new way to improve everyday business processes.Continue Reading

• The importance and limitations of open source AI models

  Despite rising interest in more transparent, accessible AI, a scarcity of public training data and compute infrastructure presents significant hurdles for open source AI projects.Continue Reading

• FTC inquiry, Claypot acquisition and more recent AI news

  Enterprise Strategy Group analyst Mike Leone breaks down five of the most significant news developments in generative AI from the past week.Continue Reading

• What does generative AI mean for the legal sector?

  Generative AI tools such as ChatGPT are entering law practices, promising more efficiency and less time spent on rote tasks. But risks remain around accuracy, ethics and privacy.Continue Reading

• A guide to artificial intelligence in the enterprise

  AI in the enterprise is changing how work is done, but companies must overcome various challenges to derive value from this powerful and rapidly evolving technology.Continue Reading

• Generative AI vs. machine learning: How are they different?

  Generative AI differs from simpler forms of machine learning in several ways, but both can enhance efficiency, personalize customer experiences and drive revenue growth.Continue Reading

• What is generative AI? Everything you need to know

  Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data.Continue Reading

• Democratization of AI creates benefits and challenges

  What happens when you expand the use of AI beyond a circle of experts? To prevent business challenges, leaders must make smart investments in AI tools and training for workers.Continue Reading

• Compare 3 top AI coding tools

  AI-powered coding tools GitHub Copilot, Amazon CodeWhisperer and Tabnine take an innovative approach to software development -- but don't count the human developer out just yet.Continue Reading

• 10 top AI and machine learning trends for 2024

  Custom enterprise models, open source AI, multimodal -- learn about the top AI and machine learning trends for 2024 and how they promise to transform the industry.Continue Reading

• Compare GPUs vs. CPUs for AI workloads

  GPUs are often presented as the vehicle of choice to run AI workloads, but the push is on to expand the number and types of algorithms that can run efficiently on CPUs.Continue Reading

• The top 5 benefits of AI in banking and finance

  The strategic deployment of AI in banking and finance can bring substantial benefits. Learn about how AI tools are transforming financial services and the risks to be mindful of.Continue Reading

• 6 generative AI predictions for 2024

  Analyst Mike Leone predicts what's next for generative AI -- from open source to regulatory shifts -- offering a comprehensive view of where the industry is headed in 2024.Continue Reading

• Amazon's innovative generative AI moves at re:Invent 2023

  Analyst Mike Leone takes a comprehensive look at Amazon's generative AI announcements at this year's re:Invent, including the Q assistant, Bedrock enhancements and chip updates.Continue Reading

• How generative AI could change healthcare

  Generative AI has joined the ranks of healthcare professionals in early use cases from medical research to patient communications. AI at scale isn't far behind.Continue Reading

• The role of precision in crafting generative AI prompts

  Enterprise Strategy Group analyst Stephen Catanzano explains how and why to craft precise queries that get actionable, relevant results from generative AI models.Continue Reading

• Transformer neural networks are shaking up AI

  Introduced in 2017, transformers were a breakthrough in modeling language that enabled generative AI tools such as ChatGPT. Learn how they work and their uses in enterprise settings.Continue Reading

• Compare 8 prompt engineering tools

  To get the most out of large language models, developers and other users rely on prompt engineering techniques to achieve their desired output. Review 8 tools that can help.Continue Reading

• Microsoft Ignite updates pave the way for GenAI innovation

  Microsoft's latest products and updates to existing platforms center on using artificial intelligence to improve productivity.Continue Reading

• AI vs. machine learning vs. deep learning: Key differences

  AI terms are often used interchangeably, but they are not the same. Understand the difference between artificial intelligence, machine learning and deep learning.Continue Reading

• What are the risks and limitations of generative AI?

  As enterprise adoption grows, it's crucial for organizations to build frameworks that address generative AI's limitations and risks, such as model drift, hallucinations and bias.Continue Reading

• How generative AI is changing creative work

  Text, image and audio generators offer new content creation capabilities, but they raise concerns about originality, ethics and the impact of automation on creative jobs.Continue Reading

• How do LLMs like ChatGPT work?

  AI expert Ronald Kneusel explains how transformer neural networks and extensive pretraining enable large language models like GPT-4 to develop versatile text generation abilities.Continue Reading

• Tips for planning a machine learning architecture

  When planning a machine learning architecture, organizations must consider factors such as performance, cost and scalability. Review necessary components and best practices.Continue Reading

• Generative AI ethics: 8 biggest concerns and risks

  Under the radar for decades, generative AI is upending business models and forcing ethical issues like customer privacy, brand integrity and worker displacement to the forefront.Continue Reading

• Successful generative AI examples and tools worth noting

  Industries are using generative AI in various ways to generate new content. Learn about successful examples of this technology and notable tools in use.Continue Reading

• The data privacy risks of third-party enterprise AI services

  Using off-the-shelf enterprise AI can both increase productivity and expose internal data to third parties. Learn best practices for assessing and mitigating data privacy risk.Continue Reading

• 10 realistic business use cases for ChatGPT

  Many business use cases for ChatGPT are emerging, but organizations must decide which best fit their specific needs. Consider 10 pragmatic example applications.Continue Reading

• Explore 14 real-world use cases for adaptive AI

  Adaptive AI's ability to alter its code in response to changing circumstances is useful in dynamic, complex environments. Discover potential business use cases for this technology.Continue Reading

• The future of generative AI: How will it impact the enterprise?

  Learn how generative AI will affect organizations in terms of capabilities, enterprise workflows and ethics, and how the technology will shape enterprise use cases.Continue Reading

• Evaluate model options for enterprise AI use cases

  To successfully implement AI initiatives, enterprises must understand which AI models will best fit their business use cases. Unpack common forms of AI and best practices.Continue Reading

• Generative AI vs. predictive AI: Understanding the differences

  Generative AI and predictive AI vary in how they handle use cases and unstructured and structured data, respectively. Explore the benefits and limitations of each.Continue Reading

• What is machine learning and how does it work? In-depth guide

  Machine learning (ML) is a type of artificial intelligence (AI) focused on building computer systems that learn from data. The broad range of techniques ML encompasses enables software applications to improve their performance over time.Continue Reading

• Assessing the environmental impact of large language models

  Large language models like ChatGPT consume massive amounts of energy and water during training and after deployment. Learn how to understand and reduce their environmental impact.Continue Reading

• The readiness of AI and LLM technology

  Generative AI technology has already disrupted how enterprises work. While many companies are uneasy with the fast-evolving technology, that's expected to change.Continue Reading

• Google extends generative AI leadership at Google Cloud Next

  Industry analyst Mike Leone unpacks the wave of generative AI announcements at the recent Google Cloud Next conference, including updates to Vertex and Duet.Continue Reading

• Supervised vs. unsupervised learning: Experts define the gap

  Learn the characteristics of supervised learning, unsupervised learning and semisupervised learning and how they're applied in machine learning projects.Continue Reading

• Generative AI in business: Fast uptake, earmarked funding

  More than half of IT and business decision-makers said they have generative AI on the near-term adoption track, according to a report from TechTarget's Enterprise Strategy Group.Continue Reading

• 8 machine learning benefits for businesses

  For business leaders, machine learning's predictive capabilities can forecast product demand, reduce equipment downtime and retain customers.Continue Reading

• Data science vs. machine learning: How are they different?

  Data science and machine learning both play crucial roles in AI, but they have some key differences. Compare the two disciplines' goals, required skills and job responsibilities.Continue Reading

• Top 12 machine learning use cases and business applications

  Machine learning applications are increasing the efficiency and improving the accuracy of business functions ranging from decision-making to maintenance to service delivery.Continue Reading

• 8 areas for creating and refining generative AI metrics

  When gauging the success of generative AI initiatives, metrics should be agreed upon upfront and focus on the performance of the model and the value it delivers.Continue Reading

• 10 top resources to build an ethical AI framework

  Several standards, tools and techniques are available to help navigate the nuances and complexities in establishing a generative AI ethics framework that supports responsible AI.Continue Reading

• Prompt engineering vs. fine-tuning: What's the difference?

  Prompt engineering and fine-tuning are both practices used to optimize AI output. But the two use different techniques and have distinct roles in model training.Continue Reading

• Compare 3 AI writing tools for enterprise use cases

  AI writing tools target enterprise use cases, but aren't ready to replace a human writer just yet. Explore three popular options for content creation: Writer, Jasper and ChatGPT.Continue Reading

• Evaluate the risks and benefits of AI in cybersecurity

  Incorporating AI in cybersecurity can bolster organizations' defenses, but it's essential to consider risks such as cost, strain on resources and model bias before implementation.Continue Reading

• New skills in demand as generative AI reshapes tech roles

  With generative AI adoption on the rise, employers are prioritizing creativity and problem-solving alongside technical skills for roles in software development and data science.Continue Reading

• Choosing between a rule-based vs. machine learning system

  Deciding between a rule-based vs. machine learning system comes down to complexity and organizational needs. Compare the advantages, drawbacks and use cases for each AI approach.Continue Reading

• Pros and cons of ChatGPT for finance and banking

  While LLMs show promise in the financial industry, responsible implementation requires proceeding with caution. Explore potential use cases and considerations to keep in mind.Continue Reading

• IT observability tool proliferation fuels AIOps deployments

  Enterprise Strategy Group's Jon Brown discusses the latest findings in his newly released report on observability in IT and application infrastructures and integrating AIOps.Continue Reading

• Tracking recent generative AI news from 9 big tech companies

  Analyst Mike Leone breaks down the latest generative AI announcements and products from big tech, spanning conversational AI, developer tools, industry partnerships and more.Continue Reading

• AI existential risk: Is AI a threat to humanity?

  What should enterprises make of the recent warnings about AI's threat to humanity? AI experts and ethicists offer opinions and practical advice for managing AI risk.Continue Reading

• HPE goes all-in on supercomputing in the cloud

  Given the popularity of large language model technology, tech vendors such as HPE are now looking to give customers offerings to help them train these models.Continue Reading

• Pros and cons of conversational AI in healthcare

  Conversational AI platforms have well-documented drawbacks, but if they are regulated and used correctly, they can benefit industries such as healthcare.Continue Reading

• How different industries benefit from edge AI

  From manufacturing to energy and healthcare, edge AI is promising to various industries. It brings data processing and analysis closer to data sources.Continue Reading

• How AI changes quality assurance in tech

  AI and automation have become more commonplace across business processes. In the tech industry, for example, the use of both can enhance quality assurance.Continue Reading

• Top advantages and disadvantages of AI

  Is AI good or bad? Many experts worry about unchecked use of the technology, while others believe AI could benefit society with the correct guidelines in place.Continue Reading

• AI needs guardrails as generative AI runs rampant

  Generative AI hype has businesses eager to adopt it, but they should slow down. Frameworks and guardrails must first be put in place to mitigate generative AI's risks.Continue Reading

• 15 AI risks businesses must confront and how to address them

  These risks associated with implementing AI systems must be acknowledged by organizations that want to use the technology ethically and with as little liability as possible.Continue Reading

• ChatGPT in the current manufacturing landscape

  Industry leaders in manufacturing must understand the challenges posed by ChatGPT and other generative AI technologies to overcome them and reap AI's benefits.Continue Reading

• How businesses can measure AI success with KPIs

  Organizations can measure the success of AI systems and projects using a few key metrics. The most important AI KPIs are quantitative, yet others are qualitative.Continue Reading

• ChatGPT vs. GPT: How are they different?

  Although the terms ChatGPT and GPT are both used to talk about generative pre-trained transformers, there are significant technical differences to consider.Continue Reading

• Generative AI landscape: Potential future trends

  Learn more about the growth of generative AI, its impact on other technologies, use cases and 10 trends that will contribute to the technology's development.Continue Reading

• Assessing different types of generative AI applications

  Learn how industries use generative AI models in content creation and alongside discriminative models to identify, for example, instances of real vs. fake.Continue Reading

• GAN vs. transformer models: Comparing architectures and uses

  Discover the differences between generative adversarial networks and transformers, as well as how the two techniques might combine in the future to provide users with better results.Continue Reading

• How construction is an Industry 4.0 application for AI

  Industry 4.0 is best known for enhancing the manufacturing sector, but the construction industry is another good use case for AI and related tools.Continue Reading

• The 'iPhone moment' for generative AI

  Tech companies are now redirecting their attention and resources to develop generative AI. Like the invention of the iPhone, generative AI is now disrupting the tech industry.Continue Reading

• No- and low-code AI's role in the enterprise

  Low-code tools enable noncoders to build and deploy AI applications. The growing list of these tools ensures that AI development is no longer confined to experts.Continue Reading

• GANs vs. VAEs: What is the best generative AI approach?

  The use of generative AI is taking off across industries. Two popular approaches are GANs, which are used to generate multimedia, and VAEs, used more for signal analysis.Continue Reading

• The rise of automation and governance in MLOps

  MLOps can make many of an organization's operations more efficient, but only when its automation capabilities are paired with effective governance strategies.Continue Reading

• AI use cases in banking create opportunities, improve systems

  AI has become increasingly more common in the banking industry and has found a home sifting through data, improving back-end systems and assisting with customer service.Continue Reading

• 10 steps to achieve AI implementation in your business

  AI technologies can enable and support essential business functions. But organizations must have a solid foundation in place to bring value to their business strategy and planning.Continue Reading

• Data, analytics and AI predictions for 2023

  The expansion of big data, analytics and artificial intelligence we saw in 2022 will continue into the new year and present both new opportunities and challenges for organizations.Continue Reading

• AI examples that can be used effectively in agriculture

  AI technologies can be utilized in agriculture for increased visibility into factors affecting crops, increased efficiency and minimized risk.Continue Reading

• Unlocking the potential of white box machine learning algorithms

  Transparent, explainable machine learning algorithms have demonstrated benefits and use cases. Although white box AI is nascent and largely unknown, it's worth exploring further.Continue Reading

• Evaluating multimodal AI applications for industries

  Various industries, including healthcare and media, are currently making use of multimodal AI applications and have determined that the benefits outweigh drawbacks.Continue Reading

• Augmented analytics, decision intelligence power modern BI

  Business intelligence is rapidly evolving, and businesses would be best equipped to handle today's data analytics challenges with both augmented analytics and decision intelligence.Continue Reading

• How businesses can benefit from conversational AI applications

  Conversational AI tools have traditionally been limited in scope, but as they become more humanlike, businesses have realized their potential and applied them to more use cases.Continue Reading

• Why banks need MLOps for digital transformation

  Financial institutions should look to MLOps to ease the development, deployment and management of machine learning models. MLOps is often ignored, yet banking will benefit from it.Continue Reading

• The future of data science: Career outlook and industry trends

  The future of data science as a profession is unclear, as new technologies change the responsibilities of data scientists. It may also soon change the nature of the job.Continue Reading

• Hybrid AI examples demonstrate its business value

  As businesses weigh the potential benefits of implementing AI systems, hybrid AI examples demonstrate the technology's practical value for businesses.Continue Reading

• Stochastic processes have various real-world uses

  The breadth of stochastic point process applications now includes cellular networks, sensor networks and data science education. Data scientist Vincent Granville explains how.Continue Reading

• Interpretability and explainability can lead to more reliable ML

  Interpretability and explainability as machine learning concepts make algorithms more trustworthy and reliable. Author Serg Masís assesses their practical value in this Q&A.Continue Reading

• How cloud RPA is key to automation's future

  Companies have traditionally used robotic process automation (RPA) as on-premises software but are now embracing cloud RPA as its business benefits are outweighing the drawbacks.Continue Reading

• Why TinyML use cases are taking off

  TinyML technology can successfully collect and analyze data in real scenarios, as demonstrated in various use cases.Continue Reading

• How warehouse automation robotics transformed the supply chain

  To maximize efficiency in warehouses and ameliorate supply chain issues, companies are turning to automation technology, leading them to embrace warehouse automation robotics.Continue Reading

• Will autonomous vehicles transform the supply chain?

  Autonomous vehicles are being road tested and companies are predicting added value if these vehicles become integrated in supply chains, but certain obstacles must be overcome.Continue Reading

• How neural network training methods are modeled after the human brain

  Training neural nets to mirror the human brain enables deep learning models to apply learning to data they've never seen before.Continue Reading

• ML model optimization with ensemble learning, retraining

  Making ML models better post-deployment can be accomplished. Learn the ins and outs of two key techniques: ensemble learning and frequent model retraining.Continue Reading

• What is data science? The ultimate guide

  Data science is the process of using advanced analytics techniques and scientific principles to analyze data and extract valuable information for business decision-making, strategic planning and other uses.Continue Reading