Startups face a number of unique challenges, but selecting technology doesn't have to be one of them. Whether building, launching, or scaling your product to meet demand, NVIDIA has technologies to accelerate your startup journey. In this evergreen overview, we'll cover key frameworks and services …

Obtaining datasets that meet the demands of state-of-the-art machine learning and deep learning applications — such as sufficiently large, high-quality, representative, labeled, and compliant — is a ubiquitous challenge for companies large and small across industries. We'll explore how NVIDIA …

Technology is critical in addressing climate change, and it's no surprise that startups play a unique role. Given that nearly every human activity contributes to our carbon footprint, from how we grow our food to our transportation and our energy grid, there will be no single solution to this complex …

Thousands of startups have joined the NVIDIA Inception program over the years and have received numerous benefits that helped them get to market faster, expand their reach, grow their technical expertise, and ultimately scale their business. Hear from a variety of tech startups that can share …

Hear a panel of leading lights from our Inception startup program that are implementing innovative solutions based on the Omniverse platform. We’ll get an understanding how Omniverse helped accelerate time to market, scale solutions. From world scale accurate searchable digital 3d map of the…

Among AI technologies that have recently been in the spotlight, digital human services based on interactive conversational AI are widely used in fields such as retail, finance, education, and media. At a time when digital human non-face-to-face services have become commonplace due to COVID-19, we'…

Drone operations have spurred innovation in the global aviation industry. In Kenya, NVIDIA Inception member Swift Lab designs, manufactures, and develops drones for humanitarian causes. The lab is partnering with the Postal Corporation of Kenya to roll out drone-based last-mile delivery services …

Greenhouse gases (GHG), including water vapor, carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3), are the primary drivers of climate change. The Intergovernmental Panel on Climate Change report, released in August 2021, assessed the scientific basis, impacts, and risk of …

When humans see segmentations — that is, when we get to see the segmentation mask, or some visual effect on top of it — we're actually much more sensitive to temporal consistency than to accuracy (beyond a certain threshold). I'll explain how we approach this problem — in data creation, …

AI technology is growing exponentially and affecting every sector and livelihood. As Africa is home to the youngest and fastest-growing population, capacity development paves the road to AI democratization. AI research is fundamental to capacity building. In Africa, we've been fostering …

Human intelligence has always found ways to connect machines with nature and humanity. As we begin to harness the power of artificial intelligence, machine learning, and data science in our everyday lives, we also raise complex ethical and social questions associated with bias, fairness, and …

Around the world, developers, researchers, startups, enterprises are coming together to drive the AI future through collective community efforts. Along with the capacity building, professional networking, and career development, AI communities deliver other unique values. For example, crowdsourcing in …

As a machine learning (ML) practitioner, have you ever built an amazing model but couldn’t reproduce it for a colleague? Maybe you've questioned why your model is making strange predictions downstream? Is it difficult to explain to others the impact of what you’re doing? In traditional …

If there are biases in the real world, they're often magnified in the virtual world. These same challenges apply to training machine learning (ML) models that increasingly control critical decisions in our lives such as whether we get hired for a job, or in ML algorithms to detect heart disease. We’ll run a …

The current machine learning application development process is broken. Teams spend months, if not years, just collecting labeled training data. Then, they focus solely on iterating on the models, which has diminishing returns on model performance. Iterating on the data itself is the arbiter of success or…