Evaluate

Evaluate

Weigh the pros and cons of technologies, products and projects you are considering.

• 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

• Microsoft Ignite updates pave the way for GenAI innovation

  Microsoft's latest products and updates to existing platforms center on using artificial intelligence to improve productivity. 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

• 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

• 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

• 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

• 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

• What is generative AI? Everything you need to know

  Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data.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

• 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

• 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

• What is machine learning and how does it work? In-depth guide

  Machine learning (ML) is a type of artificial intelligence (AI) focused on building computer systems that learn from data. The broad range of techniques ML encompasses enables software applications to improve their performance over time.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

• The readiness of AI and LLM technology

  Generative AI technology has already disrupted how enterprises work. While many companies are uneasy with the fast-evolving technology, that's expected to change.Continue Reading

• A guide to artificial intelligence in the enterprise

  AI in the enterprise is changing how work is done, but companies must overcome various challenges to derive value from this powerful and rapidly evolving technology.Continue Reading

• Google extends generative AI leadership at Google Cloud Next

  Industry analyst Mike Leone unpacks the wave of generative AI announcements at the recent Google Cloud Next conference, including updates to Vertex and Duet.Continue Reading

• Supervised vs. unsupervised learning: Experts define the gap

  Learn the characteristics of supervised learning, unsupervised learning and semisupervised learning and how they're applied in machine learning projects.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

• 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

• Data science vs. machine learning: How are they different?

  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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• Tracking recent generative AI news from 9 big tech companies

  Analyst Mike Leone breaks down the latest generative AI announcements and products from big tech, spanning conversational AI, developer tools, industry partnerships and more.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

• HPE goes all-in on supercomputing in the cloud

  Given the popularity of large language model technology, tech vendors such as HPE are now looking to give customers offerings to help them train these models.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 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

• 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

• AI needs guardrails as generative AI runs rampant

  Generative AI hype has businesses eager to adopt it, but they should slow down. Frameworks and guardrails must first be put in place to mitigate generative AI's risks.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

• 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

• 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

• 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

• 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

• 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

• The 'iPhone moment' for generative AI

  Tech companies are now redirecting their attention and resources to develop generative AI. Like the invention of the iPhone, generative AI is now disrupting the tech industry.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

• 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

• GANs vs. VAEs: What is the best generative AI approach?

  The use of generative AI is taking off across industries. Two popular approaches are GANs, which are used to generate multimedia, and VAEs, used more for signal analysis.Continue Reading

• 10 top AI and machine learning trends for 2023

  Multi-modal learning, ChatGPT, the industrial metaverse -- learn about the top trends in AI for 2023 and how they promise to transform how business gets done.Continue Reading

• The rise of automation and governance in MLOps

  MLOps can make many of an organization's operations more efficient, but only when its automation capabilities are paired with effective governance 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

• AI use cases in banking create opportunities, improve systems

  AI has become increasingly more common in the banking industry and has found a home sifting through data, improving back-end systems and assisting with customer service.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

• Data, analytics and AI predictions for 2023

  The expansion of big data, analytics and artificial intelligence we saw in 2022 will continue into the new year and present both new opportunities and challenges for organizations.Continue Reading

• AI examples that can be used effectively in agriculture

  AI technologies can be utilized in agriculture for increased visibility into factors affecting crops, increased efficiency and minimized risk.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

• Evaluating multimodal AI applications for industries

  Various industries, including healthcare and media, are currently making use of multimodal AI applications and have determined that the benefits outweigh drawbacks.Continue Reading

• Augmented analytics, decision intelligence power modern BI

  Business intelligence is rapidly evolving, and businesses would be best equipped to handle today's data analytics challenges with both augmented analytics and decision intelligence.Continue Reading

• How businesses can benefit from conversational AI applications

  Conversational AI tools have traditionally been limited in scope, but as they become more humanlike, businesses have realized their potential and applied them to more use cases.Continue Reading

• Why banks need MLOps for digital transformation

  Financial institutions should look to MLOps to ease the development, deployment and management of machine learning models. MLOps is often ignored, yet banking will benefit from it.Continue Reading

• The future of data science: Career outlook and industry trends

  The future of data science as a profession is unclear, as new technologies change the responsibilities of data scientists. It may also soon change the nature of the job.Continue Reading

• Hybrid AI examples demonstrate its business value

  As businesses weigh the potential benefits of implementing AI systems, hybrid AI examples demonstrate the technology's practical value for businesses.Continue Reading

• Stochastic processes have various real-world uses

  The breadth of stochastic point process applications now includes cellular networks, sensor networks and data science education. Data scientist Vincent Granville explains how.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

• Interpretability and explainability can lead to more reliable ML

  Interpretability and explainability as machine learning concepts make algorithms more trustworthy and reliable. Author Serg Masís assesses their practical value in this Q&A.Continue Reading

• How cloud RPA is key to automation's future

  Companies have traditionally used robotic process automation (RPA) as on-premises software but are now embracing cloud RPA as its business benefits are outweighing the drawbacks.Continue Reading

• Why TinyML use cases are taking off

  TinyML technology can successfully collect and analyze data in real scenarios, as demonstrated in various use cases.Continue Reading

• How warehouse automation robotics transformed the supply chain

  To maximize efficiency in warehouses and ameliorate supply chain issues, companies are turning to automation technology, leading them to embrace warehouse automation robotics.Continue Reading

• Will autonomous vehicles transform the supply chain?

  Autonomous vehicles are being road tested and companies are predicting added value if these vehicles become integrated in supply chains, but certain obstacles must be overcome.Continue Reading

• How neural network training methods are modeled after the human brain

  Training neural nets to mirror the human brain enables deep learning models to apply learning to data they've never seen before.Continue Reading

• ML model optimization with ensemble learning, retraining

  Making ML models better post-deployment can be accomplished. Learn the ins and outs of two key techniques: ensemble learning and frequent model retraining.Continue Reading

• What is data science? The ultimate guide

  Data science is the process of using advanced analytics techniques and scientific principles to analyze data and extract valuable information for business decision-making, strategic planning and other uses.Continue Reading

• Solving the AI black box problem through transparency

  Ethical AI black box problems complicate user trust in the decision-making of algorithms. As AI looks to the future, experts urge developers to take a glass box approach.Continue Reading

• 3 ways to evaluate and improve machine learning models

  Training performance evaluation, prediction performance evaluation and baseline modeling can refine machine learning models. Learn how they work together to improve predictions.Continue Reading

• 5 ways AI bias hurts your business

  A biased AI system can lead businesses to produce skewed, harmful and even racist predictions. It's important for enterprises to understand the power and risks of AI bias.Continue Reading

• Wrangling data with feature discretization, standardization

  A variety of techniques help make data useful in machine learning algorithms. This article looks into two such data-wrangling techniques: discretization and standardization.Continue Reading

• Designing and building artificial intelligence infrastructure

  Building an artificial intelligence infrastructure requires a serious look at storage, networking and AI data needs, combined with deliberate and strategic planning.Continue Reading

• 8 considerations for buying versus building AI

  Business leaders should consider their employees' technical expertise, technology budgets and regulatory needs, among other factors, when deciding to build or buy AI.Continue Reading

• Data scientists vs. machine learning engineers

  The positions of data scientist and machine learning engineer are in high demand and are important for enterprises that want to make use of their data and use AI.Continue Reading

• Moving beyond NLP to make chatbots smarter

  Machine reasoning could help chatbots better understand context, which is crucial to understanding human emotions and formulating emotionally relevant responses.Continue Reading

• 5 reasons NLP for chatbots improves performance

  Experts say chatbots need some level of natural language processing capability in order to become truly conversational. Without language capabilities, bots are simple order takers.Continue Reading

• Synthetic data for machine learning combats privacy, bias issues

  Synthetic data generation for machine learning can combat bias and privacy concerns while democratizing AI for smaller companies with data set issues.Continue Reading

• Businesses pivot back to AI adoption after year of slow growth

  AI adoption has taken a step back when it comes to enterprise IT spending priority, but it remains a steady investment for enterprises across industries.Continue Reading

• Artificial general intelligence in business holds promise

  While AGI in business remains unattainable today, truly intelligent systems, chatbots and predictive analytics are potential use cases enterprises should keep their eyes on.Continue Reading

• Training GANs relies on calibrating 2 unstable neural networks

  Understanding the complexities and theory of dueling neural networks can carve out a path to successful GAN training.Continue Reading

• Artificial general intelligence examples remain out of reach

  Artificial general intelligence remains largely an aspiration goal of researchers, but as technologies advance, so too does the dream become more realistic.Continue Reading

• Defining enterprise AI: From ETL to modern AI infrastructure

  The promise of enterprise AI is built on old ETL technologies, and it relies on an AI infrastructure effectively integrating and processing loads of data.Continue Reading

• KDD in data mining assists data prep for machine learning

  While data scientists are often familiar with data mining, the deeper knowledge discovery in databases (KDD) procedure can help prepare data to train machine learning algorithms.Continue Reading

• AI trends in 2020 marked by expectation shift and GPT-3

  In the past year, AI hyperscalers got serious about their machine learning platforms, expectations were reset and transformer networks empowered the GPT-3 language model.Continue Reading

• AI ROI questions to ask and the hidden costs of AI

  While ROI can be difficult to show with AI projects, it is crucial for AI teams to anticipate costs and prove each investment is worth the enterprise's time.Continue Reading

• How AI adoption by industry is being impacted by COVID-19

  While COVID-19 has impacted budgets and businesses plans, some industries are seeing improved processes and consumer relationships due to new investments in AI and automation.Continue Reading

• Reality check: Analysts check in on the AI hype cycle

  AI applications still come with significant hype, but with a targeted approach, organizations can get the most out of their applications.Continue Reading