Features

Features

• 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

• 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

• 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

• 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

• 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