As the vision foundation models like the Segment Anything Model (SAM) demonstrate potent universality, they also present challenges in giving ambiguous and uncertain predictions. Significant variations in the model output and granularity can occur with simply subtle changes in the prompt, contradicting the consensus requirement for the robustness of a model. While some established works have been dedicated to stabilizing and fortifying the prediction of SAM, this paper takes a unique path to explore how this flaw can be inverted into an advantage when modeling inherently ambiguous data distributions. We introduce an optimization framework based on a conditional variational autoencoder, which jointly models the prompt and the granularity of the object with a latent probability distribution. This approach enables the model to adaptively perceive and represent the real ambiguous label distribution, taming SAM to produce a series of diverse, convincing, and reasonable segmentation outputs controllably.

This is an edition of The Atlantic Daily, a newsletter that guides you through the biggest stories of the day, helps you discover new ideas, and recommends the best in culture. The tech world's most attention-grabbing man had a very busy week. Elon Musk launched a rocket, dealt with bad news at Tesla, stoked fear that AI could end humankind, and rolled out another controversial change on Twitter. Through it all, Musk exemplifies the danger of what happens when technology and ego collide. Earlier today, a SpaceX rocket exploded in the skies over the Gulf of Mexico, detonating itself after the booster failed to separate from the upper portion of the vehicle after launch.

Companies of all sizes and across all verticals continue to embrace artificial intelligence (AI) and machine learning (ML) for myriad reasons. They're eager to leverage AI for big data analytics to identify business trends and become more innovative, while also improving services and products. Companies are also using AI to automate sales processes, marketing programs and customer service initiatives with the common goal of increasing revenue. But the unfortunate reality is that 85% of AI and machine learning projects fail to deliver, and only 53% of projects make it from the prototype to production. Nevertheless, according to a recent IDC Spending Guide, spending on artificial intelligence in the United States will grow to $120 billion by 2025, representing growth of 20% or more.

The time is right for investing in the global natural language processing (NLP) market, projected to grow from $20.98 billion in 2021 to $127.26 billion in 2028 at a CAGR of 29.4% in that forecast period. To get a sense on NLP user perspectives, this past February, Applause surveyed its global crowdtesting community to gain insight into perceptions around the use of artificial intelligence (AI) voice applications such as chatbots, interactive voice response (IVR), and other conversational assistants. Check out our summary infographic for some highlights. We had over 6,600 responses from around the world. I want to share our findings and call out a few interesting points.

In light of the April 2021 announcement of the world's first legislative framework for regulating Artificial Intelligence (AI), the European Artificial Intelligence Act (EU AIA), now is an opportune time for developers to revisit their strategies for testing AI applications. Incoming regulations mean that the group of stakeholders who care about your testing results just got bigger and more involved. The stakes are high, not least because companies that violate the terms of the legislation could face fines higher than those levied under the General Data Protection Act (GDPR). For the purpose of transparency, certain types of AI also have to make their accuracy metrics available to users, which adds to the pressure to get functional testing right. Following on from Applause's step-by-step guide to training and testing your AI algorithm, this article summarizes how developers should be testing AI applications in anticipation of the new era of AI regulations.

With artificial intelligence advancing at a rapid pace, the importance of safeguarding humans from automated decision-making becomes inevitable. Despite the many benefits AI technology can provide – for instance, AI models can detect breast cancer more accurately than radiologists – we also need to be aware of the potential negative consequences of AI, including deepfakes and nefarious uses of facial recognition. In fact, the regulation of artificial intelligence is emerging as a key disagreement among the world's biggest tech companies. Most recently, Sundar Pichai, CEO of Google and parent company Alphabet, added to the heated debate with an op-ed, calling for greater regulation of AI technologies. Especially with AI permeating into areas of our lives that used to be based on human decision-making such as healthcare, recruiting, and criminal justice, we need to ensure we're still placing people at the centre of these modern technologies.

Despite the many benefits AI technology can provide – for instance, AI models can detect breast cancer more accurately than radiologists – we also need to be aware of the potential negative consequences of AI, including deepfakes and nefarious uses of facial recognition. In fact, the regulation of artificial intelligence is emerging as a key disagreement among the world's biggest tech companies. Most recently, Sundar Pichai, CEO of Google and parent company Alphabet, added to the heated debate with an op-ed, calling for greater regulation of AI technologies. Especially with AI permeating into areas of our lives that used to be based on human decision-making such as healthcare, recruiting, and criminal justice, we need to ensure we're still placing people at the centre of these modern technologies. Most often the lack of limited training data can be the root cause.

The relationship between humans and machines is a hot topic of discussion, with news segments often pitting them against each other. Bringing humans and machines together – in true partnership – is the only way to realize the incredible potential of AI and the efficiencies and cost savings it can deliver. On Wednesday, December 11 at 1:00 PM ET, join Kristin Simonini, VP of Product at Applause, to find out how the human element is critical to ensuring AI works correctly. You'll also learn about Applause's survey from the 2019 AI & Big Data Expo that showcases the importance of diverse training data to avoid AI bias.

Boston–November 6, 2019 – Applause, the worldwide leader in digital quality and crowdsourced testing, today announced its new solution for AI training and testing. The scalable solution trains algorithms to learn quickly and tests the output to ensure those algorithms are processing and responding appropriately. The solution leverages Applause's global community of vetted testers to deliver the widest possible range of training inputs. The results are then tested across every possible device, location, and circumstance to identify issues and provide actionable user feedback in real time. This enables today's leading brands to identify issues of quality or bias earlier in the development process so that they are ultimately delivering top-quality AI experiences for their customers.

Testing specialists Applause have debuted an AI solution promising to help tackle algorithmic bias while providing the scale of data needed for robust training. Applause has built a vast global community of testers for its app testing solution which is trusted by brands including Google, Uber, PayPal, and more. The company is leveraging this relatively unique asset to help overcome some of the biggest hurdles facing AI development. AI News spoke with Kristin Simonini, VP of Product at Applause, about the company's new solution and what it means for the industry ahead of her keynote at AI Expo North America later this month. "Our customers have been needing additional support from us in the area of data collection to support their AI developments, train their system, and then test the functionality," explains Simonini.