Features

Features

Machine learning platforms

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• ElevenLabs and the risks of voice-generating AI

  The startup's technology is popular among content creators and also bad actors who use it maliciously. But the AI voice platform also raises the issue of what's real and fake.  Continue Reading

• How a digital retail firm uses enterprise search

  Glean trains language models based on a customer's documents and other stored content. Its platform sits on users' technology stack to provide for smooth integration.  Continue Reading

• Making avatars and metaverse technologies more mature

  While a digital human is likely to capture the attention of enterprises, those interacting with the avatar see ways the virtual being can be improved to help humans.  Continue Reading

• How AI in weather prediction can aid human intelligence

  AI and machine learning models are becoming more widely used in climate prediction and disaster preparedness to aid experts without replacing them.  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

• 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

• 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

• 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

• AI for video editing: How one startup is doing it

  The vendor's Magnifi platform enables enterprises to generate clips from live or prerecorded videos. The platform uses AI and computer vision to create short clips.  Continue Reading

• Combating AI bias in the financial sector

  Companies must use explainable AI to avoid making unfair and biased decisions about consumers. Some use machine learning tools; others avoid personally identifying information.  Continue Reading

• 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

• 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

• 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

• How a soccer club uses facial recognition access control

  The Los Angeles Football Club began using the Rock, an autonomous access platform, in 2021. Players and staff use the Rock to access facilities without a key system.  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

• 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

• 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

• 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

• 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

• Tips and tricks for deploying TinyML

  A typical TinyML deployment has many software and hardware requirements, and there are best practices that developers should be aware of to help simplify this complicated process.  Continue Reading

• Cryptocurrency broker uses Ada AI platform for better CX

  LiteBit partnered with the customer service vendor in 2017 when the cryptocurrency market was booming. Since then, it has been using the vendor's AI-powered chatbot.  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

• 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

• 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

• Autonomous vehicle technology advancing amid big challenges

  Self-driving vehicles won't be widely viable commercially until their AI guidance systems are better than human drivers and can adjust to unpredictable road circumstances.  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

• 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

• 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

• 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

• 10 AI tech trends data scientists should know

  The rising environmental and monetary costs of deep learning are catching enterprises' attention, as are new AI techniques like graph neural networks and contrastive learning.  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

• 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

• 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

• AWS SageMaker training, making machine learning accessible

  Making machine learning more accessible and helping developers with AWS SageMaker training is at the core of Julien Simon's book, 'Learn Amazon SageMaker.'  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

• 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

• 9 data quality issues that can sideline AI projects

  The quality of your data affects how well your AI and machine learning models will operate. Getting ahead of these nine data issues will poise organizations for successful AI models.  Continue Reading

• How to troubleshoot 8 common autoencoder limitations

  Autoencoders' ability for automated feature extraction, data preparation, and denoising are complicated by their common problems and limitations in usage.  Continue Reading

• Responsible AI champions human-centric machine learning

  Encompassing ethics, transparency and human centricity, responsible AI is an effective approach to deploying machine learning models and achieving actionable insights.  Continue Reading

• Bias in machine learning examples: Policing, banking, COVID-19

  Human bias, missing data, data selection, data confirmation, hidden variables and unexpected crises can contribute to distorted machine learning models, outcomes and insights.  Continue Reading

• 14 best machine learning platforms for 2020

  Turn ever-growing volumes of data into enterprise insights with the right platform for machine learning. Learn more about the vendors and products in this cutting-edge market.  Continue Reading

• AI in operations management relieves pressure on IT teams

  AI, when combined with IT operations and DevOps teams, forms AIOps that can greatly improve how IT assets are developed, produced and managed.  Continue Reading

• Care provider uses automated machine learning in healthcare

  An assisted living and transitional care provider uses DataRobot to automate the process of building and deploying machine learning models, enabling it to deploy models quickly.  Continue Reading

• Learn the business value of AI's various techniques

  To drive business value from AI, business managers need to distinguish between the various AI techniques, starting with the many flavors of machine learning.  Continue Reading

• Deep learning's role in the evolution of machine learning

  Machine learning has continued to evolve since its beginnings some seven decades ago. Learn how deep learning has catalyzed a new phase in the evolution of machine learning.  Continue Reading

• Cloud computing for machine learning offers on-demand tools

  Automated machine learning and MLaaS tools are now being developed for the cloud, and enterprises need better workflows and infrastructure to successfully integrate the technology.  Continue Reading

• MLOps tools hope to boost enterprise model implementation

  Taking notes from DevOps lifecycle management, machine learning operations tools and platforms seek to improve accuracy, ease integration problems and keep models trained.  Continue Reading

• 4 explainable AI techniques for machine learning models

  At its core, AI is a complex modeling process with layers of information. In order to be able to explain the algorithm's decision-making process, start with its input data.  Continue Reading

• 5 deep learning model training tips

  Deep learning model training requires not only the right amount of data, but the right type of data. Enterprises must be inventive and careful when training their models.  Continue Reading

• Comparing MLaaS providers by cost, UX and ease of use

  MLaaS allows companies to add machine learning capabilities without software development. There are still some barriers to entry, however, and providers are not one-size-fits-all.  Continue Reading

• Common sense in AI remains elusive

  While AI and machine learning have made major improvements and advancements to computers, common sense in AI has proven to be a significant challenge.  Continue Reading

• How to optimize hyperparameter tuning for machine learning models

  Adding hyperparameters tuning to your organization's research and design modelling process enables use case, region or data-specific model specifications.  Continue Reading

• Where are we with machine translation in AI?

  Machine translation has received a boost from cutting-edge technology like deep learning but continues to struggle with the complexities and nuances of human languages.  Continue Reading

• Comparing semi-supervised machine learning vs. one-shot learning

  Machine learning models require massive amounts of data -- labeled or unlabeled. Two new approaches are hoping to curtail the need for large data sets and overarching human interference.  Continue Reading

• AI in hiring gets companies more talent, faster

  Through processes like automated candidate sourcing and candidate rediscovery, AI sifts through many resumes to find those most suited to a position.  Continue Reading

• The peaks and pitfalls of hyper-personalization marketing

  As consumers begin to revolt against unlimited personal data collection and usage, the longevity of hyper-personalized communication may be cut short.  Continue Reading

• UX defines chasm between explainable vs. interpretable AI

  From deep learning to simple code, all algorithms should be transparent. The frameworks of AI interpretability and explainability aim to make machine learning understandable to humans.  Continue Reading

• Data visualization in machine learning boosts data scientist analytics

  Data scientists offer practical insights into the role of visualization tools in building, exploring, deploying and monitoring their machine learning models.  Continue Reading

• Using small data sets for machine learning models sees growth

  While massive data sets allow for easy training, developers are using new techniques to mine and transfer data that allows for training on limited labeled information.  Continue Reading

• The importance of AI for fraud prevention

  As fraudsters become increasingly more professional and technologically advanced, financial organizations need to rely on products that use AI for to prevent fraud.  Continue Reading

• AI for retailers is progressing

  AI in retail adoption has been relatively slow, but it's starting to pick up as retailers see the benefits of AI technologies and the realities of e-commerce competition.  Continue Reading

• AI in law enforcement is growing, but needs work

  AI for police includes numerous different analytics, machine learning and natural language processing technologies, including facial recognition and automated transcription tools.  Continue Reading

• More to machine learning platforms than meets the AI

  To reach full analytics potential, machine learning platforms powered by AI must provide scalability, handle multiple models, integrate with data sources and be cloud-friendly.  Continue Reading

• GPU analytics speeds up deep learning, other data insights

  GPU-based systems have become a popular platform for deep learning applications, and they're now also being used to accelerate analysis of IoT and geospatial data.  Continue Reading

• AI network security tool autonomously does microsegmentation

  To ensure network security, a U.S. law firm has turned to automated network microsegmentation vendor Edgewise. The startup uses machine learning to deploy microsegmentation.  Continue Reading

• AI in professional services revolutionizes white-collar jobs

  Professional services and consulting firms are adopting AI at a rapid rate, even though these types of jobs, which mainly focus on interpersonal interaction, may not seem like strong targets for automation.  Continue Reading

• AI in pharma: Pfizer team tries Vyasa deep learning platform

  To help automatically categorize drug particle shapes, a Pfizer research team is experimenting with Vyasa, a deep learning platform for the life sciences.  Continue Reading

• How pattern matching in machine learning powers AI

  Pattern matching may sound like a simple idea, but it's being used to create some highly advanced AI tools, powering everything from image recognition to natural language applications.  Continue Reading

• Reinforcement learning applications provide focused models

  Goal-driven AI uses trial-and-error learning methods to find optimal solutions to enterprise problems, while distancing themselves from requiring human maintenance.  Continue Reading

• Use of AI in payments industry is set to explode

  Payment processors are making wider use of AI technologies as part of an effort to make better use of their vast troves of data and connect more directly with customers.  Continue Reading

• March Madness analytics, AI help data scientist fill bracket

  To create his March Madness bracket predictions, the head of data science at DataRobot uses a host of machine learning algorithms and some predictive analytics.  Continue Reading

• Data preparation for machine learning still requires humans

  Looking to AI to automate more of your processes? Don't overlook the labor that's still needed to prepare data for training machine learning and AI algorithms.  Continue Reading

• Automated machine learning streamlines model building

  Automated machine learning leads to faster model building while democratizing use and increasing implementation. Expert Mike Gualtieri answers major questions about the rising tech.  Continue Reading

• New uses for GAN technology focus on optimizing existing tech

  GAN technology has emerged as the latest facet of AI that can be applied to existing technology to stack learning. What else can it do? Here are emerging use cases for GANs.  Continue Reading

• The new AI frontier: Hyperpersonalized automated advertising

  AI-powered automated advertising is being utilized to connect consumer to products, leading to more sales. Simply put: Hyperpersonalized content is taking over the ad space.  Continue Reading

• AI in 2019 will be all about bots and pre-trained models

  2019 promises to be a big year for AI, as we're likely to see some trends -- such as adoption of virtual assistants and strong venture capital funding -- continue and others emerge.  Continue Reading

• Knowledge graph applications in the enterprise gain steam

  As the maturity of knowledge graphs improves, enterprises are finding new ways to incorporate them into business operations, though stumbling blocks remain.  Continue Reading

• Convert unstructured data to structured data with machine learning

  With access to powerful compute power and advances in machine learning, unstructured data is becoming easier and cheaper for businesses to turn into usable sources of insight.  Continue Reading

• How to make a wise machine learning platforms comparison

  What data sources does it support? Is it easy to use? Does it have automation features? These are just a few questions to ask when making a machine learning platforms comparison.  Continue Reading

• What do businesses do with the top machine learning platforms?

  Take a deep dive into machine learning, including decision trees, clustering, reinforced learning, neural networks, as well as supervised and unsupervised machine learning.  Continue Reading

• How to find the best machine learning frameworks for you

  There's no shortage of open source deep learning tools today, and evaluating them can be a challenge. But there are some primary considerations to keep in mind.  Continue Reading

• At Intuit, machine learning enhances products, CTO says

  Intuit's machine learning and AI technology are transforming its many financial software products. CTO Tayloe Stansbury discusses what that means for Intuit.  Continue Reading

• Labeled data brings machine learning applications to life

  The types of data being collected for analytics use are increasing, but traditional structured data is a good match for machine learning. Gartner's Svetlana Sicular explains why.  Continue Reading

• GPU cloud tools take complexity out of machine learning infrastructure

  While talk of AI on GPUs is abuzz, actually building a machine learning infrastructure remains a dark art. A startup's PaaS is looking to automate parts of the process.  Continue Reading

• Getting to machine learning in production takes focus

  Bridging the gap between training and production is one of the biggest machine learning development hurdles enterprises face, but some are finding ways to streamline the process.  Continue Reading

• Big data throws bias in machine learning data sets

  AI holds massive potential for good, but it also amplifies negative outcomes if data scientists don't recognize data biases and correct them in machine learning data sets.  Continue Reading