Tips

Tips

• How to build an enterprise generative AI tech stack

  Generative AI tech stacks consist of key components like LLMs, vector databases and fine-tuning tools. The right tech stack can help enterprises maximize their generative AI ROI.  Continue Reading

• How to get started with machine learning

  Machine learning roles are rapidly evolving and require a diverse range of skills. Looking to join the field? Start by exploring job responsibilities and required experience.  Continue Reading

• How organizations should handle AI in the workplace

  When implementing AI in the workplace, organizations must build a comprehensive strategy aligned with business values. Failing to do so could lead to the emergence of shadow AI.  Continue Reading

• Clean data is the foundation of machine learning

  Clean data is crucial to achieving accurate, consistent and thorough machine learning models. With the right prep techniques, teams can improve data quality and model outcomes.  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

• Explore mitigation strategies for 10 LLM vulnerabilities

  As large language models enter more enterprise environments, it's essential for organizations to understand the associated security risks and how to mitigate them.  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

• Prompt engineering tips for ChatGPT and other LLMs

  Master the art of prompt engineering -- from basic best practices to advanced strategies -- with practical tips to get more precise, relevant output from large language models.  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

• Improve AI security by red teaming large language models

  Cyberattacks such as prompt injection pose significant security risks to LLMs, but implementing red teaming strategies can test models' resistance to various cyberthreats.  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

• The role of trusted data in building reliable, effective AI

  Without quality data, creating and managing AI systems is an uphill battle. Methods such as zero-copy integration and primary key consistency can ensure trusted data for better AI.  Continue Reading

• 8 top generative AI tool categories for 2024

  Need a generative AI-specific tool for your organization's development project? Explore the major categories these tools fall into and their capabilities.  Continue Reading

• AI model optimization: How to do it and why it matters

  Challenges like model drift and operational inefficiency can plague AI models. These model optimization strategies can help engineers improve performance and mitigate issues.  Continue Reading

• The future of AI: What to expect in the next 5 years

  AI's impact in the next five years? Human life will speed up, behaviors will change and industries will be transformed -- and that's what can be predicted with certainty.  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

• Artificial intelligence vs. human intelligence: Differences explained

  Artificial intelligence is humanlike. There are differences, however, between natural and artificial intelligence. Here are three ways AI and human cognition diverge.  Continue Reading

• How AI is advancing assistive technology

  Recent advances in generative AI could revolutionize assistive technology. For people relying on assistive tools, AI-powered devices could usher in a new era of accessibility.  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

• Learn how to create a machine learning pipeline

  Well-considered machine learning pipelines provide a structured approach to AI development in modern IT environments, ensuring uniformity, speed and business alignment.  Continue Reading

• Explore the impact of data science in business workflows

  Data science and machine learning are reshaping business workflows and customer experiences, ushering in an era of highly tailored services and predictive strategies.  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

• How do big data and AI work together?

  Enterprises are leaning on big data to train AI algorithms and, in turn, are using AI to understand big data. The results are pushing operations forward.  Continue Reading

• How an AI governance framework can strengthen security

  Learn how AI governance frameworks promote security and compliance in enterprise AI deployments with essential components such as risk analysis, access control and incident response.  Continue Reading

• How to become an MLOps engineer

  Explore the key responsibilities and skills needed for a career in MLOps, which focuses on managing ML workflows throughout the model lifecycle.  Continue Reading

• A guide to ChatGPT Enterprise use cases and implementation

  ChatGPT Enterprise promises powerful generative AI capabilities for business use cases, but successful implementation requires careful planning for security, costs and integration.  Continue Reading

• How to build a winning AI strategy, explained by experts

  Executives are aware of the value artificial intelligence in its many forms can bring to enterprises yet devising a viable AI strategy can be as complex as the technology itself.  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

• 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

• Adversarial machine learning: Threats and countermeasures

  As machine learning becomes widespread, threat actors are developing clever attacks to manipulate and exploit ML applications. Review potential threats and how to combat them.  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

• How to source AI infrastructure components

  Rent, buy or repurpose AI infrastructure? The right choice depends on an organization's planned AI projects, budget, data privacy needs and technical personnel resources.  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

• GitHub Copilot vs. ChatGPT: How do they compare?

  Copilot and ChatGPT are generative AI tools that can help coders be more productive. Learn about their strengths and weaknesses, as well as alternative coding assistants.  Continue Reading

• 10 prompt engineering tips and best practices

  Asking the right questions is key to using generative AI effectively. Learn 10 tips for writing clear, useful prompts, including mistakes to avoid and advice for image generation.  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

• Top 10 generative AI courses and training resources

  These wide-ranging resources help users of all skill levels learn the ins and outs of generative AI, offer advice and training on tools, and help users keep up with trends.  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

• What are machine learning models? Types and examples

  Training data and algorithms are key, but there are many learning techniques, processes and practices that influence the selection, care and feeding of machine learning models.  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

• 6 machine learning challenges facing businesses

  Machine learning challenges cover the spectrum from ethical and cybersecurity issues to data quality and user acceptance concerns. Read on to learn about six common obstacles.  Continue Reading

• How to build and organize a machine learning team

  Explore the process of building an ML team, including reasons to build one and descriptions of the core roles of project manager, data engineer, data scientist and ML engineer.  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

• The history of artificial intelligence: Complete AI timeline

  From the Turing test's introduction to ChatGPT's celebrated launch, AI's historical milestones have forever altered the lifestyles of consumers and operations of businesses.  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

• 4 types of learning in machine learning explained

  Factoring performance, accuracy, reliability and explainability, data scientists consider supervised, unsupervised, semi-supervised and reinforcement models to reach best outcomes.  Continue Reading

• 4 main types of artificial intelligence: Explained

  How close are we to creating an artificial superintelligence that surpasses the human mind? Though we aren't close, the pace is quickening as we develop more advanced types of AI.  Continue Reading

• How to manage generative AI security risks in the enterprise

  Despite its benefits, generative AI poses numerous -- and potentially costly -- security challenges for companies. Review possible threats and best practices to mitigate risks.  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

• How and why businesses should develop a ChatGPT policy

  ChatGPT-like tools have enterprise potential, but also pose risks such as data leaks and costly errors. Establish guardrails to prevent inappropriate use while maximizing benefits.  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 AI can help businesses circumvent inflation

  AI can potentially help businesses avoid -- and even counteract -- inflation. Machine learning may be better at this than large language models.  Continue Reading

• Understand key MLOps governance strategies

  Machine learning developers can speed up production of ML applications -- while avoiding risks to their organizations -- with an MLOps governance framework.  Continue Reading

• 15 top applications of artificial intelligence in business

  The use of AI in business applications and operations is expanding. Learn about where enterprises are applying AI and the benefits AI applications are driving.  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

• The role of AI parameters in the enterprise

  What is the correlation between the number of parameters and an AI model's performance? It's not as straightforward as the parameter-rich generative AI apps would have us believe.  Continue Reading

• What is trustworthy AI and why is it important?

  What are the tenets of trustworthy AI and how do the funders and developers of AI ensure they're upheld?  Continue Reading

• Use cases show the combined potential of AI and blockchain

  AI and blockchain are both hot topics in IT, yet used for different purposes. However, enterprises across various sectors can now combine both technologies to their advantage.  Continue Reading

• History of generative AI innovations spans 9 decades

  ChatGPT's debut has prompted widespread publicity and controversy surrounding generative AI, a subset of artificial intelligence that's deep-rooted in historic milestones.  Continue Reading

• Types of AI algorithms and how they work

  AI algorithms can help businesses gain a competitive advantage. Learn the main types of AI algorithms, how they work and why companies must thoroughly evaluate benefits and risks.  Continue Reading

• How AI can transform industrial safety

  AI tools can help ensure workplace safety, from injury detection to VR training. To prevent hazards, business leaders should lean into AI adoption and understand its 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

• 7 generative AI challenges that businesses should consider

  The promise of revolutionary, content-generating AI models has a flip side: the perils of misuse, algorithmic bias, technical complexity and workforce restructuring.  Continue Reading

• 7 top generative AI benefits for business

  This rapidly evolving artificial intelligence field has the potential to help organizations quickly generate content, improve customer service and develop new products.  Continue Reading

• Generative models: VAEs, GANs, diffusion, transformers, NeRFs

  Choosing the right GenAI model for the task requires understanding the techniques each uses and their specific talents. Learn about VAEs, GANs, diffusion, transformers and NerFs.  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

• What do large language models do in AI?

  To capitalize on generative AI, business IT leaders must understand the features of large language models.  Continue Reading

• Why continuous training is essential in MLOps

  Organizations with machine learning strategies must consider when evolving data needs require continuous training of ML models.  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

• Inside the MLOps lifecycle stages

  Developers tasked to train machine learning models are turning to the MLOps lifecycle. The different stages are meant to increase operational speed and efficiency.  Continue Reading

• How industries use AI to ensure sustainability

  How can e-commerce companies optimize shipping routes to reduce emissions? How can data centers lower energy use? The answer is a sustainability strategy driven by AI technologies.  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

• Recent developments show us the future of chatbots

  Experts in conversational AI are optimistic about what recent advancements in chatbot technology mean for the future. Despite challenges, these advancements can point the way forward.  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

• How significant is AI's role in Industry 4.0?

  Many examples exist to demonstrate the effectiveness of AI in Industry 4.0. It entails the newest revolution in manufacturing, so naturally advanced tech like AI will play a crucial role.  Continue Reading

• How financial institutions can streamline compliance with AI

  AI systems help make compliance processes more efficient and effective for financial institutions. Automation can reduce problems like human error and regulatory breaches.  Continue Reading

• How AI governance and data privacy go hand in hand

  Given instances where AI compromise data privacy and security, it's imperative that organizations understand both AI and data privacy can coexist in their AI governance frameworks.  Continue Reading

• Real-world hyperautomation examples show AI's business value

  Hyperautomation examples in the real world help businesses automate as many of their processes as possible and achieve their strategic goals. AI is instrumental in these efforts.  Continue Reading

• Weighing quantum AI's business potential

  Quantum AI has the potential to revolutionize business computing, but logistic complexities create sizeable obstacles for near-term adoption and success.  Continue Reading

• Use an AI governance framework to surmount challenges

  As AI governance adapts to the rapidly expanding field of AI, businesses need a holistic framework to surmount challenges with clearly defined roles and responsibilities.  Continue Reading

• How AI ethics is the cornerstone of governance

  The concept of AI ethics ensures that AI systems provide accuracy and reliability. Businesses will benefit from adopting AI ethics strategies of their own.  Continue Reading

• How companies can achieve AI ROI

  Companies realize AI for security is crucial to mitigate today's threats and think ROI from such investments is achievable. The investment community is also bullish on the future of AI ROI.  Continue Reading

• AI's growing cybersecurity role

  Artificial intelligence capabilities are increasingly used to detect cybersecurity threats. As threats proliferate, AI cybersecurity capabilities will likely be the norm.  Continue Reading

• Securing AI during the development process

  AI systems can have their data corrupted or 'poisoned' by bad actors. Luckily, there are protective measures developers can take to ensure their systems remain secure.  Continue Reading

• 11 data science skills for machine learning and AI

  As companies realize the power of data, they're tasked with finding data science practitioners with AI and ML skill sets to help them use the data to make better business decisions.  Continue Reading

• Data science's ongoing battle to quell bias in machine learning

  Machine learning expert Ben Cox of H2O.ai discusses the problem of bias in predictive models that confronts data scientists daily and his techniques to identify and neutralize it.  Continue Reading

• How to create NLP metrics to improve your enterprise model

  As standardized NLP framework evaluations become popular, experts urge users to focus on individualized metrics for enterprise success.  Continue Reading

• AI in the construction industry refurbishes trade procedures

  From design to reducing workplace injury, AI in the construction industry is changing manual labor jobs. Deploying cobots and AI systems is creating visible business value.  Continue Reading

• Perfect AI-defined infrastructure by analyzing your data center

  Before implementing AI, evaluate your IT team and data storage center. Experts explain the fundamental elements of data storage required to tailor an AI-defined infrastructure.  Continue Reading