Artificial intelligence platforms

Whether you've only just begun to evaluate artificial intelligence platforms or have already invested in AI tools such as machine learning, it's important to be aware of what's available in the market. Find valuable insights into how to make enterprise AI software and device decisions and how to integrate those AI tools into your existing IT infrastructure.

January 19, 2022 19 Jan'22
Nvidia releases updates to AI enterprise suite

The AI suite includes a full integration with Tanzu, giving enterprises the flexibility to run workloads on containers and VMs. The vendor also introduced two new servers.
January 13, 2022 13 Jan'22
Lucidworks releases AI-powered search platform

The vendor's SaaS platform includes a newly released application, Connected Search. It's the first of several releases expected over the next year.
January 13, 2022 13 Jan'22
Deloitte launches new metaverse services and studio

The consulting giant's new metaverse service and studio use Nvidia's Omniverse tools. They help creators looking to learn more about and build virtual and augmented reality worlds.
January 12, 2022 12 Jan'22
Mostly AI plans to use $25M funding for responsible AI efforts

The synthetic data vendor works with customers in Europe and North America. Experts say more than half of AI projects will be created using synthetic models by 2024.

Bring yourself up to speed with our introductory content

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
ambient intelligence (AmI)

Ambient intelligence (AmI) is the element of a pervasive computing environment that enables it to interact with and respond appropriately to the humans in that environment. 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

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

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
Benefits and drawbacks of AI in cloud computing

AI and cloud computing work hand in hand to improve upon various processes, but the combination of these technologies comes with a price. 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

Learn to apply best practices and optimize your operations.

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
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
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

We’ve gathered up expert advice and tips from professionals like you so that the answers you need are always available.

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
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
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