Tips

Tips

• 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

• What is AI governance and why do you need it?

  AI governance is a new discipline given the recent expansion of AI. It's different from standard IT governance practices in that it's concerned with the responsible use of AI.  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

• 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

• 10 top AI and machine learning trends for 2022

  Tiny ML, multi-modal learning, responsible AI -- learn about the top trends in AI for 2022 and how they promise to transform how business gets done.  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

• 9 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

• 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

• 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

• How to formulate a winning AI strategy

  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

• 10 steps to achieve AI implementation in your business

  Maximizing the value of insights into your business, industry and competition requires a thoughtful, creative, experimental, incremental and team approach to deploying AI.  Continue Reading

• 4 main types of artificial intelligence: Explained

  AI technology can exceed human performance in many areas, but it is still no match for the human brain. Learn about the four main types of AI.  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

• 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