Features

Features

• 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

• Addressing 3 infrastructure issues that challenge AI adoption

  One of the biggest problems enterprises run into when adopting AI infrastructure is using a development lifecycle that doesn't work when building and deploying AI models.  Continue Reading

• 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

• How to hire data scientists

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

• How to detect bias in existing AI algorithms

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

• 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

• 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

• 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

• Automatic speech recognition may be better than you think

  Even as more enterprises turn to voice recognition systems to process unstructured audio and build virtual assistants, many organizations don't have confidence in the high accuracy of these systems.  Continue Reading

• AI voice technology has benefits and limitations

  The quality of an automated transcription depends on high-quality recording equipment as well as modern AI-powered transcription software, according to one CTO.  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

• Broad use of EHR voice assistants still years away

  EHR voice assistants aren't much more than a Siri-type interface to the patient's healthcare record right now. But vendors and clinicians see big things for the tech.  Continue Reading

• 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

• Edge AI brings new uses to IoT devices

  A Lenovo executive describes AI at the edge, highlighting how this rapidly advancing technology unlocks new automations and capabilities within IoT devices.  Continue Reading

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

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

• 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

• Steel producer reduces costs using AI in manufacturing

  The largest long steel producer in Latin America used data and machine learning predictions to save money, while maintaining the same level of production quality.  Continue Reading

• 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

• 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

• Nvidia acquisition of Arm faces industry, regulatory hurdles

  Nvidia's acquisition of Arm Ltd. could change the chipmaker landscape and is reportedly raising industry and regulatory eyebrows.  Continue Reading

• 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

• 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

• Automated speech recognition gives CX vendor an edge

  An automated transcription service can help users train their sales staff and stop robocalls. A contact center software vendor included one for free in its platform.  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

• 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

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

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

• Data democratization strategy for machine learning enterprise

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

• Diverse talent pools and data sets can help solve bias in AI

  Bringing historically underrepresented employees into critical parts of the design process while creating an AI model can reduce or eliminate bias in that model.  Continue Reading

• The power and limitations of enterprise AI

  A panel at CES 2021, held virtually this year, discusses the areas in which modern-day AI and automation shine, and where they still struggle.  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

• Conversation intelligence helps ChowNow meet online order demand

  Using RingDNA, online food ordering platform ChowNow can automatically surface insights about what works in sales calls to better coach its sales staff.  Continue Reading

• Finding the balance between edge AI vs. cloud AI

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

• 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

• Emerging AI startups to look at in 2021

  AI startups in the legal, MLOps, NLP and data training markets make this year's list of emerging AI vendors to look out for.  Continue Reading

• Nuance CTO: Conversational AI is the 'next big step'

  Conversational AI has steadily grown more advanced over the past several years. Nuance CTO Joe Petro explains why the vendor is refocusing on the technology.  Continue Reading

• Speech to text for deaf users aids in accessibility

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

• 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

• 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

• 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

• Google AI art tool turns 2D creatures into 3D models

  These 3D chimera creatures are brought to you by Google's Chimera Painter, an AI-powered tool that turns 2D images into 3D creature models.  Continue Reading

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

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

• How to avoid overfitting in machine learning models

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

• 8 examples of AI personalization across industries

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

• AI might not have rights, but it could pay taxes

  Tax, liability and patent laws can't handle AI systems, which have grown steadily smarter. As AI becomes ubiquitous, the legal system may need to change to accommodate it.  Continue Reading

• Bayesian networks applications are fueling enterprise support

  Cloud-based infrastructure has opened the door for enterprises to take advantage of the versatile predictive capability of Bayesian networks technology.  Continue Reading

• How AI can be used in agriculture: Applications and benefits

  The use of agricultural AI optimizes the farming industry by decreasing workloads, analyzing harvesting data and improving accuracy through seasonal forecasting.  Continue Reading

• AI fraud detection tools can help fight rising e-commerce fraud

  As the coronavirus pandemic has forced more businesses online, e-commerce fraud has spiked. AI fraud detection and prevention tools, however, can help merchants.  Continue Reading

• How 5G and artificial intelligence may influence each other

  5G and AI can be combined to improve the network speed, responsiveness and efficiencies of organizations in the enterprise, but the former needs more time to mature.  Continue Reading

• Insurance provider uses AI for legal to manage contracts

  The legal team at Asurion, a major insurance provider, kept its documents spread out across filing cabinets, hard drives and the cloud. It turned to an AI platform to better manage them.  Continue Reading

• General AI vs. narrow AI comes down to adaptability

  AI today has limited and specific applications, but the continual growth of the technology may just lead to the replication of human intelligence through general AI.  Continue Reading

• Understanding motion analytics, where it is and where it's going

  Machine learning is helping make motion analysis more usable for the average enterprise, creating new use cases and applications that can drive value.  Continue Reading

• GPT-3 AI language model sharpens complex text generation

  GPT-3 is the latest natural language generation model, but its acquisition by Microsoft leaves developers wondering when, and how, they'll be able to use the model.  Continue Reading

• COVID-19 to drive more AI in retail for small retailers

  Retailers were hit hard by the COVID-19 economic fallout, especially small, local businesses. Amid a shift online, AI could help some retailers adjust to the new reality.  Continue Reading

• Establishing AI governance in a business

  It's hard to ethically manage data for AI models, but AI governance, as well as a strong ethics framework, can help enterprises effectively manage data and models.  Continue Reading

• IBM chatbot gives voting information to Idaho residents

  By turning to IBM Watson Assistant, the state of Idaho deployed a chatbot to help voters get information about the primary elections, which were mail-in only, back in May.  Continue Reading

• AI vs. predictive analytics debate shows powerful combination

  Artificial intelligence and machine learning, when combined with predictive analytics, allow companies and organizations to get the most out of their data.  Continue Reading

• Enterprise and home find use for intelligent virtual assistants

  Intelligent virtual assistants have the capacity to augment employees, as well as improve convenience in homes, but only time will see their limitations resolved.  Continue Reading

• Advantages of AI in agriculture include increased efficiency

  Artificial intelligence has the capacity to improve the supply chain and agricultural industry by improving demand forecasting and increasing productivity.  Continue Reading

• Application of AI in robotics boosts enterprise potential

  The combination of AI and robotics has allowed companies to move past automation and tackle more complex and high-level tasks with their robots.  Continue Reading

• The deepfake 2020 election threat is real, but containable

  Disinformation could harm the 2020 presidential election, and technology simply isn't advanced enough to detect manipulated content, especially deepfakes.  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

• AI in transportation reduces employee workloads

  By using intelligent automation, a shipping company trimmed employee workloads, enabling employees to shift from document processing to customer service.  Continue Reading

• Explore the foundations of artificial neural network modeling

  Dive into Giuseppe Bonaccorso's recent book 'Mastering Machine Learning Algorithms' with a chapter excerpt on modeling neural networks.  Continue Reading

• 3 keys for the implementation of AI in the enterprise

  Organizations need to focus on diversity, proper scaling and augmentation capabilities when implementing artificial intelligence in order to keep the process pain-free.  Continue Reading

• Combining AI and predictive analytics crucial for the enterprise

  Predictive analytics, when combined with artificial intelligence, can assist organizations with their risk management, as well as their planning and optimization.  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

• It's been a slow road for AI in oil and gas industries

  Analytics and AI can help oil and gas companies better predict supply and demand, involuntary flaring and where to drill. But the companies struggle to get talent.  Continue Reading

• It's time to address AI ethics

  Enterprises need to focus on creating and adopting AI ethical guidelines, especially for emerging technologies such as facial recognition and home assistants.  Continue Reading

• Future of autonomous vehicles depends on driver attitudes

  Getting the public behind the idea of an autonomous vehicle means peeling back the black box nature of AI and proving the safety of self-driving technology.  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

• AI in retail has helped retailers during COVID-19

  During the Ai4 2020 virtual conference, a panel of retail experts discusses how AI and analytics have helped retailers deal with the economic fallout of the COVID-19 pandemic.  Continue Reading

• Automated document processing helps PPP applications

  Using its own AI models, as well as technology from Abbyy, PwC can rapidly and correctly file a large volume of PPP loan applications.  Continue Reading

• AI, robotics help businesses pivot supply chain during COVID-19

  Big enterprises such as Wayfair, UPS, Unilever and Siemens move to automate more of their supply chains with AI as the coronavirus pandemic disrupts business operations.  Continue Reading

• Machine learning limitations marked by data demands

  Machine learning has impressive capabilities in the enterprise, but with high-data requirements and struggles with explainability, it remains unable to reach widespread use.  Continue Reading

• 4 ways AI and digital transformation enable deeper automation

  Organizations that are going beyond the enterprise adoption of digitization are entering a new wave of AI-enabled digital transformation.  Continue Reading

• Reimagining creativity and AI to boost enterprise adoption

  AI has yet to reach the point of creativity but continues to advance, while assisting humans in the production of their own creative works and improvement of their organizations.  Continue Reading

• Process automation in education helps school during COVID-19

  Using process automation, a school in Sydney helped keep classes going online by automating the daily roll call process. Organizations have increasingly turned to automation and AI during COVID-19.  Continue Reading

• Autoencoders' example uses augment data for machine learning

  Autoencoders are neural networks that serve machine learning models -- from denoising to dimensionality reduction. Seven use cases explore the practical application of autoencoder technology.  Continue Reading

• Future of AI in video games focuses on the human connection

  The future of gameplay is reliant on the usage and perfection of Emotional AI and its ability to create and emulate realistic and human relationships.  Continue Reading

• Applications of generative adversarial networks hold promise

  Generative adversarial networks are tied to fake online content known as 'deepfakes,' but GANs can help data-poor enterprises supplement their data needs.  Continue Reading

• AIoT applications prove the technology's adaptability

  The combination of artificial intelligence and IoT has led to better predictive capabilities for devices, informed data storage, and enterprise machine optimization.  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

• 5 major benefits of machine learning in the enterprise

  Businesses are inserting machine learning into processes wherever possible. Here are a few of the ways machine learning users are benefiting from machine learning.  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

• The state of AI in 2020 likely sees more adoption

  AI adoption has appeared to grow this year, as organizations deploy automation to augment dwindling workforces and help deal with growing demand during the COVID-19 pandemic.  Continue Reading

• Accounts payable automation eliminates invoice backlog

  Purple, a mattress company, struggled each week to manually process its myriad invoices and bills. It found relief in automated accounts payable software from a startup vendor.  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

• AI in tax preparation gets a boost from classification tech

  Tax filers and tax collectors are using AI tools to make the process of paying and collecting taxes simpler. The data-rich, complex processes of tax collection are an ideal use case for AI.  Continue Reading

• Warehouse AI and robotics help train employees

  Workers at a logistics and transportation company learn on the job with an autonomous cart, Chuck. The robot teaches employees the layout of the warehouses and where items are.  Continue Reading

• Artificial intelligence content writing ramps up publishing

  To ease the burden that is associated with content production, AI in content production has been deployed to augment writers' work and to help monitor and measure post engagement.  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

• Accelerating digital transformation through remote work

  As working remotely became a near-global mandate, companies have been thrust headfirst into a digital transformation. AI is helping to smooth the journey.  Continue Reading

• Understanding how deep learning black box training creates bias

  Bias in AI is a systematic issue that derails many projects. Dismantling the black box of deep learning algorithms is crucial to the advancement and deployment of the technology.  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

• AI web scraping augments data collection

  Web scraping automates the data gathering process and refines the data pipeline, but it requires careful attention to choosing the right tools, languages and programs.  Continue Reading

• Machine learning for fraud prevention keeps TrafficGuard agile

  TrafficGuard uses machine learning to prevent ad fraud but has faced the challenges that come along with it. Full-scale commitment and investment have eased those obstacles.  Continue Reading

• NLP, chatbot development reignite conversational commerce AI

  The world of e-commerce has been focused on the adoption of conversational commerce for years to boost online sales. COVID-19 might just be the catalyst for widespread use.  Continue Reading