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• How businesses can measure AI success with KPIs

  AI KPIs should include direct and indirect metrics. Learn about the standard and generative AI-specific KPIs that will help you gauge the success of your AI projects. 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

• 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

• How to manage proprietary enterprise data in AI deployments

  Explore strategies for managing sensitive data in enterprise AI deployments, from establishing clear data governance to securing tools and building a responsible AI culture. 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 business leaders should know about EU AI Act compliance

  AI compliance expert Arnoud Engelfriet shares key takeaways from his book 'AI and Algorithms,' describing the EU AI Act's effects on innovation, risk management and ethical AI.Continue Reading

• How can AI drive revenue? Here are 10 approaches

  Artificial intelligence has captured the imagination of many a boardroom. Now, the emphasis has shifted to capturing revenue through AI-driven use cases.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

• 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

• AI regulation: What businesses need to know in 2024

  The rapid evolution and adoption of AI tools has policymakers scrambling to craft effective AI regulation and laws. Law professor Michael Bennett analyzes what's afoot in 2024.Continue Reading

• Industry experts share top tactics for AI-powered analytics

  Unlock the power of AI in data analytics with expert guidance. Learn how to implement AI tools that drive strategic success and future-proof your business.Continue Reading

• 12 key benefits of AI for business

  AI experts expound on these top areas where artificial intelligence technologies can improve enterprise operations and services.Continue Reading

• How to ensure interpretability in machine learning models

  When building ML models, developers can use several techniques to make models easier for humans to interpret, leading to improved transparency, troubleshooting and user acceptance.Continue Reading

• What is the future of machine learning?

  Machine learning is changing how we write code, diagnose illnesses and create content, but implementation requires careful consideration to maximize benefits and mitigate risks.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

• Best practices for integrating AI and ESG strategies

  Integrating environmental, social and governance goals into AI models can help organizations achieve more ethical, responsible and sustainable business outcomes.Continue Reading

• Is your business ready for the EU AI Act?

  The EU AI Act provides businesses with a rubric for AI compliance. Businesses must understand the landmark act to align their practices with upcoming AI regulations.Continue Reading

• 7 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 seven common obstacles.Continue Reading

• What companies are getting wrong about AI

  Incorporating AI successfully means reimagining the organization. Learn how a phased approach can combat structural challenges and foster cross-team collaboration and innovation.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

• How to become a natural language processing engineer

  Explore the education, experience and skills needed to excel in the demanding yet rewarding field of NLP engineering, including expertise in linguistics, math and programming.Continue Reading

• AI, copyright and fair use: What you need to know

  As AI technology advances, U.S. and international copyright laws are struggling to keep pace, raising legal and ethical questions about ownership and AI-generated content.Continue Reading

• How to craft a responsible generative AI strategy

  Generative AI's potential in the enterprise must be balanced with responsible use. Without a clear strategy in place, the technology's risks could outweigh its rewards.Continue Reading

• Compare natural language processing vs. machine learning

  Both natural language processing and machine learning identify patterns in data. What sets them apart is NLP's language focus vs. ML's broader applicability to many AI processes.Continue Reading

• Explore the evolving role of AI in the insurance industry

  AI is transforming the insurance industry by automating processes and improving risk assessment, but it also poses challenges in data transparency and algorithmic decision-making.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

• 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

• How to build an MLOps pipeline

  Machine learning initiatives involve multiple complex workflows and tasks. A standardized pipeline can streamline this process and maximize the benefits of an MLOps approach.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 train an LLM on your own data

  Retraining or fine-tuning an LLM on organization-specific data offers many benefits. Learn how to start enhancing your LLM's performance for specialized business use cases.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

• 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

• 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

• The need for common sense in AI systems

  Building explainable and trustworthy AI systems is paramount. To get there, computer scientists Ron Brachman and Hector Levesque suggest infusing common sense into AI development.Continue Reading

• Autonomous AI agents: A progress report

  Now in the early stages of development, AI agents using LLMs might one day number in the billions, operate networks of interconnected ecosystems and alter the commercial landscape.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

• 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

• 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

• 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

• Explore real-world examples of AI implementation success

  In 'All-In on AI,' authors Davenport and Mittal explore AI implementation examples from organizations that already made the AI leap with success. Read this book excerpt to learn more.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

• Custom generative AI models an emerging path for enterprises

  Custom enterprise generative AI promises security and performance benefits, but successfully developing models requires overcoming data, infrastructure and skills challenges.Continue Reading

• Designing systems that reduce the environmental impact of AI

  Understanding AI's full climate impact means looking past model training to real-world usage, but developers can take tangible steps to improve efficiency and monitor emissions.Continue Reading

• Former Google exec on how AI affects internet safety

  Longtime trust and safety leader Tom Siegel offers an insider's view on moderating AI-generated content, the limits of self-regulation and concrete steps to curb emerging risks.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

• Q&A: Expert tips for running machine learning in production

  In this interview, 'Designing Machine Learning Systems' author Chip Huyen shares advice and best practices for building and maintaining ML systems in real-world contexts.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

• The implications of generative AI for trust and safety

  Leaving generative AI unchecked risks flooding platforms with disinformation, fraud and toxic content. But proactive steps by companies and policymakers could stem the tide.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

• 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

• 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

• Businesses benefit from AI-infused Industry 4.0 practices

  It's daunting for a business to adopt Industry 4.0 technologies at scale. However, given the added value of automation and process optimization, the benefits can outweigh risks.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

• 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

• Defining requirements key to manage machine learning projects

  Machine learning projects are likely to fail without proper planning. 'Managing Machine Learning Projects' provides guidance on how to plan by defining ML project requirements.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

• How AI and automation play a role in ITOps

  Tech professionals agree that AI, intelligent automation and cybersecurity play important roles in the enterprise and can revolutionize ITOps when implemented and used correctly.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 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

• Enterprise hybrid AI use is poised to grow

  Hybrid AI is an approach for businesses that combines human insight with machine learning and deep learning networks. Despite certain challenges, experts believe it shows promise.Continue Reading

• Automated machine learning improves project efficiency

  Until recently, machine learning projects had a small chance of success given the amount of time they require. Automated machine learning software speeds up the process.Continue Reading

• AutoML platforms push data science projects to the finish line

  Data science projects often have trouble reaching the production phase, but automated machine learning platforms are accelerating data scientists' work to help them come to fruition.Continue Reading

• Piloting machine learning projects through harsh headwinds

  To get machine learning projects off the ground and speed deployments, data science teams need to ask questions on a host of issues ranging from data quality to product selection.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

• TinyML at the very edge of IoT shows signs of promise

  TinyML can enable machine learning on small devices that exist within IoT systems and experts are currently debating the breadth of its practical real-world uses.Continue Reading

• Machine learning on microcontrollers enables AI

  Using today's advanced AI systems to run machine learning on smaller devices like microprocessors offers benefits, but also limits, which experts are working to surmount.Continue Reading

• Capitalizing on the many artificial neural network uses

  Neural networks have many use cases. Businesses interested in using AI should consider both the challenges and potential gains of deploying neural nets.Continue Reading

• AI carbon footprint: Helping and hurting the environment

  Companies can use AI to help the environment, including by using it to prevent forest fires and reduce factory waste. At the same time, AI has its own carbon footprint.Continue Reading

• AI and climate change: The mixed impact of machine learning

  AI can both help and hurt the environment. While companies use artificial intelligence to increase factory efficiency and lower energy costs, training AI demands a lot of energy.Continue Reading

• Energy consumption of AI poses environmental problems

  Data centers and large AI models use massive amounts of energy and are harmful to the environment. Businesses can take action to lower their environmental impact.Continue Reading

• AI accountability: Who's responsible when AI goes wrong?

  Who should be held accountable when AI misbehaves? The users, the creators, the vendors? It's not clear, but experts have some ideas.Continue Reading

• Building trustworthy AI is key for enterprises

  Organizations need to focus on transparency in models, ethical procedures and responsible AI in order to best comply with guidelines for developing trustworthy AI systems.Continue Reading

• 5 top chatbot features to boost your AI plan

  By infusing their chatbots with natural language understanding, contextual messaging and other AI features, enterprises can build and deploy more powerful chatbots.Continue Reading

• Advanced SQL skills boost data scientists' value

  Learning advanced SQL skills can help data scientists effectively query their databases and unlock new insights into data relationships, resulting in more useful information.Continue Reading

• How emotion analytics will impact the future of NLP

  Conversational agents and chatbots struggle to understand complex human speech, including sarcasm. But that could change as NLP increasingly incorporates emotional understanding.Continue Reading

• 4 AI career path trajectories for IT professionals

  As the desire for AI and machine learning in-house skills skyrocket, those looking to break into the market have a variety of career path options, including AI architect and BI developer.Continue Reading

• How to hire data scientists

  Enterprises tend to want data scientists who have a drive to continue their training, through peer training or online platforms, to keep up with ongoing changes in the field.Continue Reading

• How to detect bias in existing AI algorithms

  While enterprises can't eliminate bias from their data, they can significantly reduce bias by establishing a governance framework and employing more diverse employees.Continue Reading

• Unsupervised machine learning: Dealing with unknown data

  Learn how machine learning works when dealing with unclassified, unlabeled data sets and how, using certain algorithms and other practices, the system can learn on its own.Continue Reading

• Cutting through the fear of how AI will affect jobs through automation

  Dive into Steven Shwartz's recent book, 'Evil Robots, Killer Computers, and Other Myths,' with a chapter excerpt on employment and the future of work.Continue Reading

• Free machine learning course: Using ML algorithms, practices and patterns

  This 13-lesson series offers an overview of machine learning and its applications for those hoping to break into the machine learning job market or learn more about this technology.Continue Reading

• Tackling the AI bias problem at the origin: Training data

  Though data bias may seem like a back-end issue, the enterprise implications of an AI software using biased data can derail model implementation.Continue Reading

• Data democratization strategy for machine learning enterprise

  In the enterprise, data democratization works to break down data silos by opening access to an organization's data across teams in an effort to improve workflows.Continue Reading

• Finding the balance between edge AI vs. cloud AI

  Centralized cloud resources allow AI to continuously improve while edge AI allows for real-time decision-making and larger models. The best approach combines them.Continue Reading

• Speech to text for deaf users aids in accessibility

  For the millions of people who are hard of hearing, speech-to-text advancements have improved their ability to complete daily tasks -- but the tech still has a long way to go.Continue Reading

• Why AI literacy is critical, even for non-technical employees

  To successfully deploy and manage AI projects and build a vision of a digital workplace, businesses need to ensure a basic level of AI competency across all employees.Continue Reading

• How to avoid overfitting in machine learning models

  Overfitting remains a common model error, but data scientists can combat the problem through automated machine learning, improving AI literacy and creating test data sets.Continue Reading

• 8 examples of AI personalization across industries

  Through AI content personalization, organizations can build unique profiles of users and customers and tailor their products, advertisements and services to better fit them.Continue Reading