Features

Features

• 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

• How to detect AI-generated content

  AI- or human-generated? To test their reliability, six popular generative AI detectors were asked to judge three pieces of content. The one they got wrong may surprise you.  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

• Survey: Enterprise generative AI adoption ramped up in 2024

  Nearly a third of organizations are now using generative AI in production. But data quality, the AI skills gap and an evolving regulatory landscape continue to present hurdles.  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

• 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

• Lessons on AI-driven marketing at EmTech MIT 2024

  In addition to generating personalized advertising content, marketing professionals are using large language models as new personas to shape brand perception and strategy.  Continue Reading

• Responsible AI vs. ethical AI: What's the difference?

  Ethical AI establishes principles for AI development and use, while responsible AI ensures they're implemented in practice. Learn how the two differ and complement each other.  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

• 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

• 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

• 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

• 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

• 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

• 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

• The dangers of voice deepfakes in the November election

  The growth of generative AI has led to more audio cloning technology. This could affect the U.S. election. Recent incidents show that existing safeguards are not effective.  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

• Few-shot learning explained: What you should know

  Training data quality and availability aren't always a given in machine learning projects. When data is limited, costly or nonexistent, few-shot learning can help.  Continue Reading

• What is regression in machine learning?

  Regression in machine learning helps organizations forecast and make better decisions by revealing the relationships between variables. Learn how it's applied across industries.  Continue Reading

• 6 ways to reduce different types of bias in machine learning

  As adoption of machine learning grows, companies must become data experts or risk results that are inaccurate, unfair or even dangerous. Here's how to combat machine learning bias.  Continue Reading

• CNN vs. RNN: How are they different?

  Convolutional and recurrent neural networks have distinct but complementary capabilities and use cases. Compare each model architecture's strengths and weaknesses in this primer.  Continue Reading

• Machine learning regularization explained with examples

  Regularization in machine learning refers to a set of techniques used by data scientists to prevent overfitting. Learn how it improves ML models and prevents costly errors.  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

• What is boosting in machine learning?

  Boosting is a technique used in machine learning that trains an ensemble of so-called weak learners to produce an accurate model, or strong learner. Learn how it works.  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

• 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

• 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

• Attributes of open vs. closed AI explained

  What's the difference between open vs. closed AI, and why are these approaches sparking heated debate? Here's a look at their respective benefits and limitations.  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

• How to build a machine learning model in 7 steps

  Building a machine learning model is a multistep process involving data collection and preparation, training, evaluation, and ongoing iteration. Follow these steps to get started.  Continue Reading

• LinkedIn unveils AI updates for business users, job seekers

  New features for job applicants, recruiters and businesses include personalized writing suggestions, more advanced search capabilities and AI conversations within LinkedIn Learning.  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

• 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

• 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

• 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

• Experts: AI digital humans come with benefits -- and risks

  Businesses using AI digital humans in sales and marketing can cut costs through efficiency. However, experts warn of the legal risks and public distrust of the technology.  Continue Reading

• Balancing generative AI cybersecurity risks and rewards

  At the MIT Sloan CIO Symposium, enterprise leaders grappled with AI's benefits and risks, emphasizing the need for cross-team collaboration, security controls and responsible AI.  Continue Reading

• Insights on generative AI for automation vs. augmentation

  At the MIT Sloan CIO Symposium, industry leaders shared experiences with generative AI's benefits and challenges, highlighting the technology's ability to assist human workers.  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

• 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

• Mixture-of-experts models explained: What you need to know

  By combining specialized models to handle complex tasks, mixture-of-experts architectures can improve efficiency and accuracy for large language models and other AI systems.  Continue Reading

• AI hardware vendors band together to challenge Nvidia

  An industry group including Arm and Intel seeks to increase the number of options in the AI market and decrease developers' dependence on GPUs.  Continue Reading

• Deriving value from generative AI with the right use case

  The technology will be valuable to tech vendors. For users, a return on investment will depend on the applications as well as whether enterprises choose to build or buy their models.  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

• 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

• 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

• 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

• Manufacturing group uses AI for EHS safety compliance

  To pinpoint risky and dangerous incidents in workplace environments without having to sift through thousands of data points, a manufacturing group turned to Benchmark Gensuite.  Continue Reading

• AI, the 2024 U.S. election and the spread of disinformation

  Generative technology-fueled deepfakes could interfere with the November election due to ease of use and power of the technology. The outlook for regulation seems dim.  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

• Challenges the fintech industry faces with generative AI

  As the new technology has exploded in other industries, financial organizations are also exploring how they can apply it. However, regulatory requirements hinder fast adoption.  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

• Catch up on the top AI news of 2023

  Look back on a hectic year in AI and get up to speed for 2024 by catching up on some of TechTarget Editorial's top AI news stories from the past year.  Continue Reading

• Five generative AI trends to look for in 2024

  The boom will persist as enterprises become acclimated to the technology. More enterprises will start using genAI systems and organizations will incorporate governance measures.  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

• Big money investments, not acquisitions, fuel GenAI startups

  With the generative AI explosion comes a new trend for the tech giants. Instead of buying smaller companies, big cloud vendors are partnering with the startups.  Continue Reading

• Generative AI as a copilot for finance and other sectors

  While many fear that the popularity of large language models could lead to job loss and replacement, some industries such as finance and education are using AI to augment workers.  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

• Enterprises weigh open source generative AI risks, benefits

  At EmTech MIT, experts explored the challenges and benefits of adopting generative AI in the enterprise, including the pros and cons of open source generative AI models.  Continue Reading

• Enterprise IT shops more likely to buy GenAI than build it

  GenAI power requirements, the cost of computing and storage, and the high salaries demanded by AI specialists make it unlikely enterprises will take a do-it-yourself approach.  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

• 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

• Demystifying AI with a machine learning expert

  In this interview, author Ronald Kneusel discusses his new book 'How AI Works,' the recent generative AI boom and tips for those looking to enter the AI field.  Continue Reading

• A customer experience provider's take on Amazon Bedrock

  Alida gained early access to the foundation model service in June. It found value using Anthropic's Claude summarization capability within the service.  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

• 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

• Lessons on integrating generative AI into the enterprise

  At Generative AI World 2023, various industries convened to explore existing and potential generative AI use cases. Review insights from one company's implementation experience.  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

• 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

• Build a natural language processing chatbot from scratch

  In this excerpt from the book 'Natural Language Processing in Action,' you'll walk through the steps of creating a simple chatbot to understand how to start building NLP pipelines.  Continue Reading

• Q&A: How to start learning natural language processing

  In this Q&A, 'Natural Language Processing in Action' co-author Hobson Lane discusses how to start learning NLP, including benefits and challenges of building your own pipelines.  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

• 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

• 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

• Compare machine learning vs. software engineering

  Although machine learning has a lot in common with traditional programming, the two disciplines have several key differences, author and computer scientist Chip Huyen explains.  Continue Reading

• What vendors must know about the AI assistant craze

  More vendors are introducing products to assist enterprises and consumers complete mundane tasks. But there's a need to be strategic and transparent with these products.  Continue Reading

• New skills in demand as generative AI reshapes tech roles

  With generative AI adoption on the rise, employers are prioritizing creativity and problem-solving alongside technical skills for roles in software development and data science.  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

• A look at open source AI models

  Open source AI models have advantages over generative AI services offered by major cloud providers. But enterprises have to weigh the benefits against the costs.  Continue Reading

• IT observability tool proliferation fuels AIOps deployments

  Enterprise Strategy Group's Jon Brown discusses the latest findings in his newly released report on observability in IT and application infrastructures and integrating AIOps.  Continue Reading

• AI existential risk: Is AI a threat to humanity?

  What should enterprises make of the recent warnings about AI's threat to humanity? AI experts and ethicists offer opinions and practical advice for managing AI risk.  Continue Reading

• How data quality shapes machine learning and AI outcomes

  Data quality directly influences the success of machine learning models and AI initiatives. But a comprehensive approach requires considering real-world outcomes and data privacy.  Continue Reading

• How different industries benefit from edge AI

  From manufacturing to energy and healthcare, edge AI is promising to various industries. It brings data processing and analysis closer to data sources.  Continue Reading

• How AI changes quality assurance in tech

  AI and automation have become more commonplace across business processes. In the tech industry, for example, the use of both can enhance quality assurance.  Continue Reading

• Tribe 9 Foods uses digital twin technology from AI startup

  The food manufacturer saves time and money by using the startup's technology to gain insight into what consumers think about products it has released or is considering releasing.  Continue Reading

• Sport teams drive sales leads with an AI digital assistant

  American soccer club Louisville City and the NBA's Milwaukee Bucks use Conversica to target the most promising leads for their sales teams and drive profit for their organizations.  Continue Reading

• ChatGPT in the current manufacturing landscape

  Industry leaders in manufacturing must understand the challenges posed by ChatGPT and other generative AI technologies to overcome them and reap AI's benefits.  Continue Reading

• CNN vs. GAN: How are they different?

  Convolutional neural networks and generative adversarial networks are both deep learning models but differ in how they work and are used. Learn the ins and outs of CNNs and GANs.  Continue Reading

• How construction is an Industry 4.0 application for AI

  Industry 4.0 is best known for enhancing the manufacturing sector, but the construction industry is another good use case for AI and related tools.  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

• How AI has cemented its role in telemedicine

  Many healthcare clinicians rely on AI when performing daily tasks and see benefits that outweigh the drawbacks.  Continue Reading

• Exploring GPT-3 architecture

  With 175 billion parameters, GPT-3 is one of the largest and most well-known neural networks available for natural language applications. Learn why people are so pumped about it.  Continue Reading