Features
Features
AI business strategies
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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How AI serves as a cornerstone of Industry 4.0
For manufacturing environments to be included in Industry 4.0, they must adopt up-to-date technologies to improve operations. AI should be foremost among them. Continue Reading
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Snapchat's My AI uses ChatGPT, but not all enterprises can
The social media app's new AI chatbot uses the latest OpenAI technology. However, OpenAI's privacy policy might make it difficult for enterprises to use the large language model. Continue Reading
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Federal report focuses on AI diversity and ethics
A national group formed to advance the research and development of AI in the U.S. proposes ways to add more variety among students, educators and researchers studying AI. Continue Reading
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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
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Four AI trends to look for in 2023
From a new algorithm law to combat hiring bias in New York City to the growth of generative tools and technologies, AI will keep growing and be used by more enterprises next year. Continue Reading
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How AI can assist industries in environmental protection efforts
While technology for environmental protection isn't a new concept, AI advancements empower businesses to achieve sustainable operations. Continue Reading
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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
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Augmentation a better approach than automation for AI
Fears that robots will replace human workers grow as technologists create new tools that imitate what humans do. Instead, industries should focus on using AI to complement humans. Continue Reading
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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
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Why and when to consider a feature store in machine learning
Feature stores exist to make data for training machine learning models reusable. Explore both the benefits and challenges of feature stores that organizations can experience. Continue Reading
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The long-term answer to fixing bias in AI systems
The technology is exploding with new developments daily. However, problems with training data can lead to bias. Fixing it requires retraining the data and educating users. Continue Reading
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Industries leading the way in conversational AI
Learn how companies in vertical markets are using conversational AI and even partnering with AI developers for software that's tailored to their unique business needs. Continue Reading
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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
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New AI ethics advisory board will deal with challenges
Created by the Institute for Experiential AI at Northeastern University, the board will help organizations without internal audit boards but will face some challenges. Continue Reading
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The white-box model approach aims for interpretable AI
The white-box model approach to machine learning makes AI interpretable since algorithms are easy to understand. Ajay Thampi, author of 'Interpretable AI,' explains this approach. Continue Reading
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How the economic downturn is affecting the AI sector
Enterprise budget cutting is slowing AI projects. Vendors may not feel the impact now but likely won't be spared. Meanwhile, venture capitalists have cut back investments. Continue Reading
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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
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Green AI tackles effects of AI, ML on climate change
AI and ML are making a significant contribution to climate change. Developers can help reverse the trend with best practices and tools to measure carbon efficiency. Continue Reading
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How hybrid chatbots improve customer experience
Hybrid chatbots combine human intelligence with AI used in standard chatbots to improve customer experience. Learn how industries are using them to engage with customers. Continue Reading
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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
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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
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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
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Stochastic point processes and their practical value
Data scientists learn and utilize stochastic point processes for myriad pragmatic uses. Data scientist Vincent Granville explains this in his new book. Continue Reading
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Spotify personalizes audio experiences with machine learning
The streaming platform builds models using analytics, data from users and content to create a personalized audio experience for users and try to keep them as long-term customers. Continue Reading
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Using digital twins simulation to reduce risks in industry
Whether it's fighting wildfires or working in a warehouse, people often encounter dangerous working conditions. Researchers are using digital twin simulation to counter those risks. Continue Reading
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Swiss retailer uses open source Ray tool to scale AI models
Ricardo uses Anyscale's Ray for scaling its product classification models. Ray helps enterprises scale their applications from a laptop to the cloud. Continue Reading
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Finance industry giants disclose AI challenges
Education, explainability, privacy and integration are some of the problems institutions face when implementing machine learning tools and technology. Continue Reading
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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
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Learn the benefits of interpretable machine learning
In this excerpt from 'Interpretable Machine Learning with Python,' read how machine learning models and algorithms add value when they are both interpretable and explainable. Continue Reading
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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
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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
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Q&A: How retail AI tools can help combat inflation
Prices for food, gas and more have risen during the past year. Revionics' senior director of retail innovation discusses how retail AI tools can help companies navigate inflation. Continue Reading
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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
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Language models and the metaverse top AI stories of 2021
From moves toward government regulation to the metaverse, language models getting bigger and autonomous vehicle tech slowing, these are some of the biggest stories of the year. Continue Reading
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A look at Honeywell's digital transformation strategy
The century-old, multinational conglomerate is going through internal and external changes. The survival of its brand will depend on maintaining its trust and reputation. Continue Reading
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Why optimizing machine learning models is important
A look at why AI needs optimization and how it speeds up inferencing, helps deploy models on small devices and reduces memory footprint. Continue Reading
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Predictive analytics vs. machine learning
Machine learning lends itself to various applications, while predictive analytics focuses on forecasting specific variables and scenarios. Learn what they can do when combined. Continue Reading
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Strategies to successfully deploy AI in the enterprise
Deloitte executive director Beena Ammanath talks about ways businesses can see successful return on their investment and deployment of artificial intelligence. Continue Reading
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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
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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
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Expanding explainable AI examples key for the industry
Improving AI explainability and interpretability are keys to building consumer trust and furthering the technology's success. Continue Reading
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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
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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
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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
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Why transparency in AI matters for businesses
To ensure model accuracy, businesses need to understand why their machine learning models make their decisions. Certain tools and techniques can help with that. Continue Reading
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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
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The benefits of an AI-first strategy
Enterprises should put AI first in their business strategies by constantly collecting and using new data to power AI models, argues startup investor Ash Fontana. Continue Reading
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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
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Combating racial bias in AI
By employing a diverse team to work on AI models, using large, diverse training sets, and keeping a sharp eye out, enterprises can root out bias in their AI models. Continue Reading
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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
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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
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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
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Biden sets stage for national AI strategy
Biden's focus on AI includes funding research and development, manufacturing chips in the U.S. and preparing a workforce to use AI tools. Continue Reading
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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
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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
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New DataRobot CEO sees bright AI future for the vendor
New CEO Dan Wright discusses how DataRobot can stay competitive in a crowded AI marketplace, new markets for the vendor, and how DataRobot has tackled the pandemic. Continue Reading
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Mastercard senior VP talks about AI and fraud prevention
Mastercard uses and sells AI-powered technology to prevent fraud and has found that AI-powered services can inspire customer loyalty. Continue Reading
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5 examples of effective NLP in customer service
Through use cases such as chatbots, recommendation systems and customer relationship management, NLP and AI are playing an important role in enterprise customer service. Continue Reading
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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
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CTO on the need for AI ethics and diversity
A CTO talks about the importance of diverse data sets when creating AI models and how a lack of diversity can create bias in systems. Continue Reading
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AI vendors may have to prove systems don't discriminate
Washington state is considering a bill that would require vendors to prove their AI algorithms aren't biased. If enacted, the AI regulation could have far-reaching implications. Continue Reading
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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
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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