Features

Features

• 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

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

  Generative AI is gaining steam in the tech sector. Two popular approaches are GANs, which are used more for multimedia, and VAEs, which are used more for signal analysis.  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

• TinyML at the very edge of IoT shows signs of promise

  TinyML can enable machine learning on small devices that exist within IoT systems and experts are currently debating the breadth of its practical real-world uses.  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

• Facebook facial recognition technology ban: Will it hold?

  The social media network's use of the technology led to criticism. Some think the technology may soon be rebranded or used on Facebook parent company Meta's other platforms.  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

• Exploring GPT-3 architecture

  GPT-3 is one of the largest and most well-known neural networks for natural language applications available. With 175 billion parameters, the model easily outpaces similar models.  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

• A guide to artificial intelligence in the enterprise

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

• 7 key benefits of AI for business

  Leading AI experts expound on seven areas where artificial intelligence technologies can improve business operations and services.  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

• Top 9 types of machine learning algorithms, with cheat sheet

  Machine learning can assist enterprises by quickly modeling large data sets. Choosing the right algorithm depends on the desired outcome and the makeup of your data science team.  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

• 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

• How to build a machine learning model in 7 steps

  Building a viable, reliable and agile machine learning model that streamlines operations and bolsters business planning takes patience, preparation and perseverance.  Continue Reading

• Transformer neural networks are shaking up AI

  Transformers are revolutionizing the field of natural language processing with an approach known as attention. That's just the beginning for this new type of neural network.  Continue Reading

• In-depth guide to machine learning in the enterprise

  Enterprises are adopting machine learning technologies at rapid rates. In this machine learning guide, we break down what you need to know about this transformative technology.  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

• CNN vs. RNN: How are they different?

  Convolutional neural networks and recurrent neural nets underlie many of the AI applications that drive business value. Learn about CNNs vs. RNNs in this primer.  Continue Reading

• Supervised vs. unsupervised learning: Use in business

  Learn how LinkedIn, Zillow and others choose between supervised learning, unsupervised learning and semi-supervised learning for their machine learning projects.  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