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Explainable AI (XAI): Guide to enterprise-ready AI
As AI tools become more advanced, more computations are done in a “black box” that humans can hardly comprehend. This approach is problematic since it prevents transparency, trust and model understanding. After all, people don’t easily trust a machine’s recommendations that they don’t thoroughly understand.
AI Chips: A Guide to Cost-efficient AI Training & Inference
In the past decade, machine learning, particularly deep neural networks, has been pivotal in the rise of commercial AI applications. Significant advancements in the computational power of modern hardware enabled the successful implementation of deep neural networks in the early 2010s.
40 IoT Applications & Use Cases with Real-Life Examples
Worldwide annual revenue on IoT in 2033 is expected to be $934B. IoT enables a myriad of different business applications. Knowing those IoT examples and use cases can help businesses integrate IoT technologies into their future investment decisions.
Top 7 AI Avatar Generation Tools
When choosing the right AI avatar generation tool, businesses can take into account the following components: Top 7 AI avatar generation tools The table above is sorted based on the number of reviews. Sources: For more on prices, check the pricing comparison.
Top 20 Analytics Case Studies
Although the potential of Big Data and business intelligence are recognized by organizations, Gartner analyst Nick Heudecker says that the failure rate of analytics projects is close to 85%. Uncovering the power of analytics improves business operations, reduces costs, enhances decision-making, and enables the launching of more personalized products.
Insight Engines: Top 3 Real-Life Use Cases & 19 Tools
Enterprise search has been a game-changing technology in terms of increasing the productivity of organizations. Enterprise search engines combined with the power of a modern search engine with the company’s internal data.
Handwriting Recognition Benchmark: LLMs vs OCRs
Today, OCR technology provides higher than 99% accuracy with typed characters in high-quality images. However, the diversity in human writing types, spacing differences, and handwriting irregularities causes less accurate character recognition, as shown in the featured image. Thus, tools that read handwriting cannot provide the same accuracy that OCR systems offer on typed characters.
State of OCR: Is it dead or a solved problem?
Optical Character Recognition (OCR) is one of the earliest areas of artificial intelligence research. Today OCR is a relatively mature technology and it is not even called AI anymore which is a good example of Pulitzer Prize winner Douglas Hofstadter’s quote: AI is whatever hasn’t been done yet.
Compare Google Dialogflow and Its Competitors
Tech giants such as Google, IBM, Microsoft, Amazon, and Facebook are investing in conversational AI to enable developers to build chatbots easily. These AI-powered chatbots can automate various routine tasks such as sending emails, searching for information on search engines, etc.
Top companies in AI-powered medical imaging
Around 75% of all medical malpractice claims against radiologists are related to diagnostic errors. Many radiology errors can be traced back to breakdowns in communication during the imaging or reporting process. AI algorithms can be trained to analyze medical images and identify patterns and abnormalities that may be missed by human eyes.