Tips

Tips

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• Artificial intelligence data storage planning best practices

  AI storage planning is similar to the storage planning you're used to: Consider capacity, IOPS and reliability requirements for source data and the application's database.  Continue Reading

• How to use machine learning to build a predictive algorithm

  Machine learning is an invaluable tool for solving business problems, but don't jump into it for predictive analytics without understanding these important factors.  Continue Reading

• Synthetic data could ease the burden of training data for AI models

  Sometimes it's better to manufacture training data for machine learning models than it is to collect it.  Continue Reading

• Dun & Bradstreet's chief data scientist: Don't ignore these eight AI topics

  Anthony Scriffignano's list of AI topics to watch in 2018 highlights the benefits and complications the widespread application of artificial intelligence technology will have on the enterprise in the coming year.  Continue Reading

• Why machine learning models require a failover plan

  Flawed machine learning models lead to failures and user interruptions. Expert Judith Myerson explains the causes for failures and how a failover plan can improve user experience.  Continue Reading