Evaluate

Evaluate

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

• 8 metrics to measure GenAI’s performance and business value

  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

• 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

• AI inference vs. training: Key differences and tradeoffs

  AI inference and training are both critical phases of model development. Learn how to balance their demands to optimize performance, manage costs and scale models effectively. Continue Reading

• Hyperparameter tuning techniques to optimize ML models

  Hyperparameters play a key role in shaping how machine learning models learn from data. Learn how adjusting these settings can improve model accuracy and performance. Continue Reading

• Privacy and security risks surrounding Microsoft Recall

  Microsoft's Recall feature promises AI-powered convenience, but it raises significant security and privacy concerns that the company must address before a public release. Continue Reading

• A state-by-state guide to AI laws in the U.S.

  In lieu of federal regulation, U.S. states are proposing and enacting AI laws of their own. This up-to-date breakdown by state can help the C-suite keep tabs on developments.Continue Reading

• Compare 9 prompt engineering tools

  To get the most out of generative AI models, developers and other users rely on prompt engineering techniques to achieve their desired output. Explore nine tools that can help.Continue Reading

• What is enterprise AI? A complete guide for businesses

  Enterprise AI tools are transforming how work is done, but companies must overcome various challenges to derive value from this powerful and rapidly evolving technology.Continue Reading

• Is getting an AI certification worth it? Factors to consider

  Some say AI certification is essential, while others worry it's a waste of time. Learn the benefits and downsides of AI certifications and how to find the right one for your needs.Continue Reading

• How to choose the right LLM for your needs

  Selecting the best large language model for your use case requires balancing performance, cost and infrastructure considerations. Learn what to keep in mind when comparing LLMs.Continue Reading

• Interpretability vs. explainability in AI and machine learning

  Understanding how AI models make decisions is challenging, but two concepts -- interpretability and explainability -- can shed light on model outputs.Continue Reading

• How to effectively manage AI projects in 12 steps

  AI is a high priority for companies but results often fall short of expectations. These 12 steps will help you successfully manage AI projects and deliver business value.Continue Reading

• How small businesses can take advantage of AI

  Small business owners see AI's potential for automation and transformation, yet concerns remain. This seven-step program will help AI implementations get off on the right track.Continue Reading

• Microsoft Copilot vs. Google Gemini: How do they compare?

  Microsoft Copilot and Google Gemini are frontrunners in the generative AI productivity tools space. Explore how they compare in terms of key features, capabilities and pricing.Continue Reading

• What is Gen AI? Generative AI explained

  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

• Compare 7 top AutoML tools for machine learning workflows

  From cloud-based platforms to open source options, compare the pros and cons of leading AutoML tools, which automate key machine learning tasks to accelerate workflows.Continue Reading

• 24 advantages and disadvantages of AI

  Is AI good or bad? AI will benefit society, according to experts, but only with the correct guidelines in place and a solid understanding of what AI systems can and cannot do.Continue Reading

• AI in banking: Benefits, risks, what's next

  AI technologies bring operational efficiency and customer benefits to banking. Learn how GenAI and other AI tools are transforming financial services and risks to watch out for.Continue Reading

• Explore the benefits and risks of AI in fintech

  Financial technology can benefit greatly from AI tools and strategies. But financial services companies looking to adopt AI need to understand the risks too.Continue Reading

• 13 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

• What's the best programming language for machine learning?

  While no one language is perfect for AI and machine learning, considering factors like efficiency, readability and community support can help developers make the best choice.Continue Reading

• What businesses should know about OpenAI's GPT-4o model

  Enterprise use cases are driving the need for faster AI response times, better data handling and cost optimization. OpenAI attempts to meet that need with GPT-4o.Continue Reading

• How to choose the best GPUs for AI projects

  GPUs are not all built the same. Factors like total core count, memory clock speed, hardware optimizations and cost can influence which GPU is right for a specific AI project.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

• 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

• Will AI replace technical writers? One user's experience

  As with many jobs, how AI advancements will affect the future of technical writing is up for debate. Hear from one expert on why AI is here to stay -- but so are technical writers.Continue Reading

• How businesses are using AI in the construction industry

  As businesses in the construction industry integrate AI and machine learning into their processes, the sector's approach to design, safety and project management is changing.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

• What is machine learning? Guide, definition and examples

  Machine learning is a branch of AI focused on building computer systems that learn from data.Continue Reading

• How to use and run Jupyter Notebook: A beginner's guide

  Learn how to create your first project with Jupyter Notebook, a popular platform for presenting data science and machine learning work with interactive code, text and visuals.Continue Reading

• How and why to run machine learning workloads on Kubernetes

  Running ML model development and deployment on Kubernetes is an absolute must in a world where decoupling workloads can optimize resources and cut costs.Continue Reading

• How and why to create an AI bill of materials

  AIBOMs help developers and security teams by providing a transparent view of AI system components, improving supply chain security and compliance. Use this guide to get started.Continue Reading

• Tips to prevent machine learning scalability problems

  Addressing ML scalability challenges involves selecting the right models, planning resource usage and managing network connectivity to support expanding applications and data load.Continue Reading

• GPT-4o vs. GPT-4: How do they compare?

  OpenAI's GPT-4o promises improved multimodal capabilities and increased efficiency. Explore the differences between GPT-4o and its predecessor, GPT-4.Continue Reading

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

  Compare Anthropic's Claude vs. OpenAI's ChatGPT in terms of features, model options, costs, performance and privacy to decide which generative AI tool better suits your needs.Continue Reading

• Generative AI ethics: 8 biggest concerns and risks

  As its adoption grows, generative AI is upending business models and forcing ethical issues like customer privacy, brand integrity and worker displacement to the forefront.Continue Reading

• How to build the business case for AI initiatives

  Building a compelling business case for AI requires attention to business pain points, financial and risk considerations, and collaboration with the CFO.Continue Reading

• Data science vs. machine learning: What's the difference?

  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

• 10 popular libraries to use for machine learning projects

  Machine learning libraries expedite the development process by providing optimized algorithms, prebuilt models and other support. Learn about 10 widely used ML libraries.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

• What are the benefits of an MLOps framework?

  While maintaining machine learning models throughout their lifecycles can be challenging, implementing an MLOps framework can enhance collaboration, efficiency and model quality.Continue Reading

• Will AI replace cybersecurity jobs?

  Although AI can enhance cybersecurity practices like threat detection and vulnerability management, the technology's limitations ensure a continued need for human security pros.Continue Reading

• How AI is changing the real estate market

  AI tools and systems are becoming an asset for many real estate endeavors. Explore seven top use cases for AI in the real estate industry and challenges to adoption.Continue Reading

• Supervised vs. unsupervised learning explained by experts

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

• 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

• 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

• 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

• Compare 6 top MLOps platforms

  Choosing the right MLOps platform means considering features, pricing and ease of integration into your current machine learning environment. Evaluate six leading options.Continue Reading

• Planning for GenAI disillusionment

  Hype around GenAI will inevitably be followed by generative AI disillusionment. Experts ruminate on how to shorten the trough and prepare for the future.Continue Reading

• Dell AI Factory takes the spotlight at Dell Technologies World

  Dell has aligned its entire portfolio behind AI and has expanded critical partnerships to support customers adopting artificial intelligence technologies.Continue Reading

• Examining the future of AI and open source software

  As AI coding tools gain traction in the enterprise, it remains unclear whether AI-generated code violates open source software licenses -- but legal claims indicate possible risk.Continue Reading

• At Red Hat Summit 2024, generative AI goes open source

  Analyst Scott Sinclair unpacks this year's Red Hat Summit, where the company launched new AI integration tools and features as part of its open source generative AI strategy.Continue Reading

• Beyond algorithms: The rise of data-centric AI

  Prioritizing data curation, preparation and engineering -- rather than tweaking model architecture -- could significantly improve AI systems' reliability and trustworthiness.Continue Reading

• Best practices for getting started with MLOps

  As AI and machine learning become increasingly popular in enterprises, organizations need to learn how to set their initiatives up for success. These MLOps best practices can help.Continue Reading

• How to use Perplexity AI: Tutorial, pros and cons

  AI-powered search engine Perplexity offers a conversational tone and much-needed source citations -- but it's not perfect. Learn how the tool works and how to start using it.Continue Reading

• How to measure the ROI of enterprise AI initiatives

  Interest in AI tools and systems has skyrocketed across industries. To ensure their endeavors are worthwhile, businesses are increasingly emphasizing return on investment.Continue Reading

• Beyond AI doomerism: Navigating hype vs. reality in AI risk

  As AI becomes increasingly widespread, viewpoints featuring both sensationalism and real concern are shaping discussions about the technology and its implications for the future.Continue Reading

• Compare proprietary vs. open source for enterprise AI

  Unsure whether to choose proprietary or open source AI for your enterprise deployment? Compare the pros and cons of both software models, including how each can benefit businesses.Continue Reading

• Google Cloud Next 24 recap: GenAI tools for enterprises

  New products introduced at Google Cloud Next stand to provide enterprises with GenAI tools for analytics, data management, AI model building and more.Continue Reading

• Evaluate whether your organization needs a chief AI officer

  As more businesses start developing comprehensive AI strategies, the new role of chief AI officer, or CAIO, might become the next addition to your organization's executive suite.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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

  The terms artificial intelligence, machine learning and deep learning are often used interchangeably, but they aren't the same. Understand the differences and how they're used.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

• 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

• 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

• 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

• 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