Features

Features

• How AI has cemented its role in telemedicine

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

• Exploring GPT-3 architecture

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

• 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

• 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

• 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

• 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

• VR platform aims to give retailers entry into the metaverse

  Through a partnership with SAP, Obsess integrated the e-commerce platform within its virtual stores to create an interface that engages young consumers.  Continue Reading

• 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

• Implications of AI art lawsuits for copyright laws

  Lawsuits filed by Getty Images and several artists could determine whether generative AI developers can use copyrighted materials as part of their training data sets.  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

• 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

• 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

• 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

• 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

• 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

• 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

• Enterprise applications of the metaverse slow but coming

  From holographic avatars to training surgeons, metaverse applications for enterprises are many. But the technology for organizations is still in its infancy.  Continue Reading

• 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

• 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

• James Earl Jones, AI and the growing voice cloning market

  The growing text-to-speech and speech-to-speech market is sparking new enterprise applications. However, the technologies also raise concerns about privacy and misuse of the tools.  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

• 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

• The creative thief: AI tools creating generated art

  AI systems such as OpenAI's Dall-E, Midjourney and Stable Diffusion are used to create striking images. But it can be unclear if the images are inspired by others or stolen.  Continue Reading

• Digital twin consortium accelerates growth of the technology

  Through its working groups, the consortium helps organizations deal with challenges in managing systems and data related to the technology. The subgroups publish their findings.  Continue Reading

• 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

• National Grid saves $2 million by partnering with AI startup

  The gas and electric company used AiDash to realize efficiencies and improve reliability and vegetation management in Massachusetts after a challenging year.  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

• Facing labor shortage, pizza franchise turns to AI phone bot

  Jet's Pizza says OrderAI Talk frees up employees, keeps consumers from enduring long wait times and is an option for those who prefer using the phone to text ordering.  Continue Reading

• 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

• 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

• 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

• A closer look at what makes the AI tool Dall-E powerful

  The language processing tool differs from most chatbots because it has access to specialized data sets. This makes it powerful, but also potentially dangerous.  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

• 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

• How enterprises can establish an AI-first data strategy

  Enterprises looking to mature in their use of AI must focus on the information they're putting into their models. Their models should create trust in their business.  Continue Reading

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• AI and disinformation in the Russia-Ukraine war

  From false videos circulating on TikTok to AI-generated humans and deepfakes, the Russia-Ukraine war is playing out both in the physical world and virtually.  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

• 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

• 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

• Differentiating between good and bad AI bias

  As lawmakers and regulators look at ways to make machine learning models fair, some tech vendors are creating tools that aim to enable enterprises to achieve that purpose.  Continue Reading

• How enterprises will use the still-undefined metaverse

  Some metaverse systems will affect the future of work and how enterprises operate. However, their impact will be fully seen only after the full meaning of the metaverse is known.  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

• 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

• 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

• Efforts to craft AI regulations will continue in 2022

  Regulating AI can be challenging for many reasons, including varying definitions of fairness and explainability. However, AI regulations will be a top focus for lawmakers in 2022.  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

• 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

• 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

• 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

• 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

• Using machine learning to address COVID-19 vaccine hesitancy

  A look at how Final Mile is trying to fix vaccine hesitancy with its AI model and behavioral science and design, along with the impact of models developed during the pandemic.  Continue Reading

• 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

• 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

• 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

• How to build scalable edge AI systems

  A look at the different challenges enterprises and vendors face in this new arena, and some of the different ways the merging technology can be applied, including in healthcare.  Continue Reading

• 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

• Is AI a boon or bane for cybersecurity?

  Like other developing technologies, AI has its pros and cons. However, it has proven an indispensable cybersecurity tool, with many scenarios where AI is helping protect data.  Continue Reading

• How RPA and machine learning work together in the enterprise

  Use cases demonstrate how using RPA and machine learning with other AI techniques achieves 'intelligent automation,' but the best automation solution depends on a company's needs.  Continue Reading

• 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

• 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

• FTC pursues AI regulation, bans biased algorithms

  The agency tries to regulate how businesses use AI algorithms by enforcing the Fair Credit Reporting Act, Equal Opportunity Credit Act and FTC Act. Critics want more regulation.  Continue Reading

• Google Cloud, Hologic use AI tools to fight cervical cancer

  The tech giant and medical technology vendor are working together to make cervical cancer screening technology widely accessible to women everywhere and fight the disease.  Continue Reading

• 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

• SambaNova makes a mark in the AI hardware realm

  The startup says it is innovating AI hardware systems with its data flow architecture that enterprises can use to be more efficient when processing large AI data sets.  Continue Reading

• A look at AI trends and bias in AI algorithms

  In the past few years, more and more organizations have focused on AI. However, just as the use of AI and machine learning has expanded, concern about AI bias is also growing.  Continue Reading

• 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

• 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

• 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

• 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

• RPA market booms as enterprises automate with bots

  The RPA sector is expected to reach nearly $3 billion this year as new vendors compete with larger established RPA specialists and tech giants for enterprise automation business.  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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• Advanced SQL skills boost data scientists' value

  Learning advanced SQL skills can help data scientists effectively query their databases and unlock new insights into data relationships, resulting in more useful information.  Continue Reading

• 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

• 4 AI career path trajectories for IT professionals

  As the desire for AI and machine learning in-house skills skyrocket, those looking to break into the market have a variety of career path options, including AI architect and BI developer.  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