Evaluate

Evaluate

Weigh the pros and cons of technologies, products and projects you are considering.

• 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

• Why banks need MLOps for digital transformation

  Financial institutions should look to MLOps to ease the development, deployment and management of machine learning models. MLOps is often ignored, yet banking will benefit from it. 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

• 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

• 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

• What is AI governance and why do you need it?

  AI governance is a new discipline given the recent expansion of AI. It's different from standard IT governance practices in that it's concerned with the responsible use of AI.Continue Reading

• How businesses can measure AI success with KPIs

  Organizations can measure the success of AI systems and projects using a few key metrics. The most important AI KPIs are quantitative, yet others are qualitative.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

• 10 top AI and machine learning trends for 2022

  Tiny ML, multi-modal learning, responsible AI -- learn about the top trends in AI for 2022 and how they promise to transform how business gets done.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

• 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

• 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

• 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

• 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

• ML model optimization with ensemble learning, retraining

  Making ML models better post-deployment can be accomplished. Learn the ins and outs of two key techniques: ensemble learning and frequent model retraining.Continue Reading

• What is data science? The ultimate guide

  Data science is the process of using advanced analytics techniques and scientific principles to analyze data and extract valuable information for business decision-making, strategic planning and other uses.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

• 3 ways to evaluate and improve machine learning models

  Training performance evaluation, prediction performance evaluation and baseline modeling can refine machine learning models. Learn how they work together to improve predictions.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

• AI vs. machine learning vs. deep learning: Key differences

  AI terms are often used interchangeably, but they are not the same. Understand the difference between artificial intelligence, machine learning and deep learning.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

• 4 main types of artificial intelligence: Explained

  AI technology can exceed human performance in many areas, but it is still no match for the human brain. Learn about the four main types of AI.Continue Reading

• The top 5 benefits of AI in banking and finance

  The strategic deployment of AI in banking and finance can bring substantial benefits. Learn about how AI tools are transforming financial services and the risks to be mindful of.Continue Reading

• Wrangling data with feature discretization, standardization

  A variety of techniques help make data useful in machine learning algorithms. This article looks into two such data-wrangling techniques: discretization and standardization.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

• 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

• 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

• Moving beyond NLP to make chatbots smarter

  Machine reasoning could help chatbots better understand context, which is crucial to understanding human emotions and formulating emotionally relevant responses.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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• Why AI adoption in the enterprise continues to lag

  In this episode of 'Today I Learned About Data,' we discuss AI adoption in the enterprise, and it's been slower than many have predicted.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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• How to choose between a rules-based vs. machine learning system

  Debating rules-based systems over machine learning comes down to the complexity of the task at hand. Machine learning dominates complex tasks, but requires more long-term expertise.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

• 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

• 10 common uses for machine learning applications in business

  Machine learning applications are unlocking value across business functions. Here are 10 examples of how machine learning applications are being used in business.Continue Reading

• Hospital IoT highlighted by AI use cases and wearable devices

  AI in healthcare can provide patients and doctors with better care and support through prescriptive analytics, wearable devices and illness detection.Continue Reading

• 5 AI technologies in business that are making a big impact

  Learn how image recognition, speech recognition, chatbots, natural language generation and sentiment analysis are changing how businesses operate.Continue Reading

• Cloud computing for machine learning offers on-demand tools

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

• AI document processing remains a subtle but powerful use case

  Artificial intelligence has found strong use cases in content summarization and document categorization within the medical, marketing and legal fields.Continue Reading

• AI COVID-19 tech bolsters social distancing, supply chains

  As the world waits for staggered reopenings and a return to normal life, AI-based technology is assisting scientists with diagnosis, detection, research and remote-based workforces.Continue Reading

• AI and augmented reality blur lines between virtual and reality

  Using AI with augmented reality and virtual reality in industries as varied as gaming and conversational systems can enhance the already impressive technologies.Continue Reading

• Are giant AI chips the future of AI hardware?

  Giant AI chips like the Cerebras WSE are dazzlingly fast and could transform AI models, but how soon is the question for CIOs. Experts mull the merits of small vs. big AI chips.Continue Reading

• CPUs vs. GPUs for AI workloads

  GPUs are often presented as the vehicle of choice for running AI workloads, but the push is on to expand the number and types of algorithms that can run efficiently on CPUs.Continue Reading

• AI and edge computing security

  While boasting benefits such as reduced latency, edge computing can increase a company's vulnerability. Ensuring security is crucial to getting the most out of the technology.Continue Reading

• 5 AI risks businesses must confront and how to address them

  Some risks associated with implementing AI systems are familiar to enterprise leaders; these five are unique to AI. Here's how to address them.Continue Reading

• Can the future of voice assistants include enterprise use?

  Voice assistants haven't had the success that companies originally expected. AI and machine learning could go a long way to boost this technology.Continue Reading

• Value of AI in capitalizing on big data, expanding automation

  The strategic value of AI in business is growing as companies learn how to use AI technologies to capitalize on big data, drive automation and better serve customers.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

• How far are we from artificial general intelligence?

  Developers and researchers are currently debating the extent to which artificial general intelligence needs to mimic the human brain. Explore the two schools of thought.Continue Reading

• AI and RPA are here to stay

  The coronavirus pandemic has caused enterprises to rely more on AI technologies as they are forced to lay off employees or temporarily close their offices.Continue Reading

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

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

• Common sense in AI remains elusive

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

• How to optimize hyperparameter tuning for machine learning models

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

• Data center energy usage combated by AI efficiency

  Though often forgotten by the general public, data centers account for 1% of the world's energy consumption. Explore what brought about data centers and how AI can be used to help mitigate their presence.Continue Reading

• RPA adoption trends point to broadening use cases

  RPA is frequently discussed, but here we sort out the potential of this technology -- and the limiting factors that prevent it from living up to the hype.Continue Reading

• The state of AI defined by global adoption and regulation

  Cognilytica reports on AI adoption by both countries and companies across the globe, as well as the former's overall strategies and regulation frameworks.Continue Reading

• The AI accelerator chip can make AI accessible to all

  Specialized AI chips released by companies like Amazon, Intel and Google tackle model training efficiently and generally make AI solutions more accessible.Continue Reading

• Edge computing use cases led by autonomous cars and coffee bars

  Edge computing can decrease latency times dramatically and has found its place in autonomous vehicles, manufacturing plants and retail.Continue Reading

• How stock market prediction using AI impacts the trading floor

  Robo-advisors and stock monitoring bots are changing the way traders and investors take on the stock market.Continue Reading

• Standards for data sharing should guide AI government regulation

  The White House has taken a deregulatory approach to AI and aims to inspire innovation. An expert weighs in on the role of government in AI and where the industry stands.Continue Reading

• Where are we with machine translation in AI?

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

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

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

• What do NLP benchmarks like GLUE and SQuAD mean for developers?

  AI models for various language understanding tasks have been dramatically improved due to the rise in scale and scope of NLP data sets and have set the benchmark for other models.Continue Reading

• Patience is pivotal for the autonomous vehicle future

  The fatal collision between an Uber ATG vehicle and a pedestrian was a reminder that autonomous vehicles are not ready and that a difficult technological hill remains.Continue Reading