At the International Joint Conference on Artificial Intelligence (IJCAI) 2023, I had the opportunity to interview Jerry Tan from Lattel Robotics, a company dedicated to promoting AI-focused robotics education and training. They work closely with the RoboCup@Home Education initiative, supporting schools and institutions in introducing AI and service robot development to students. Their goal is to equip learners with practical AI application skills in computer vision, autonomous navigation, object manipulation and speech interactions. Through their AI robotics and AI applications workshops, Lattel Robotics offers an introduction to robot operating system (ROS)-based AI applications development in service robotics. As a hardware partner for the RoboCup@Home Education initiative, they assist schools and institutions in competing in AI robotic challenges by developing applications that address real-world problems.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Deci, a deep-learning software maker that uses AI templates designed to create AI-based applications, today launched v2.0 of its development platform, which it claims speeds the way for developers to build, optimize and deploy computer vision models. The term "speed" and AI application development are rarely used in the same sentence, but by using this platform, resulting AI models can be more swiftly prepared to run on any hardware and environment, including cloud, edge and mobile – with accuracy and high runtime performance, Deci CEO and co-founder Yonatan Geifman said in a media advisory. This is because much of the grunge work has been eliminated by the Deci series of DeciNet templates made available in the v2.0 platform. Using Deci, the company says, AI developers can achieve improved inference performance and efficiency to enable effective deployments on resource-constrained edge devices, maximize hardware use and reduce training and inference cost, Geifman said.

Today I'm excited to announce Snorkel AI's launch out of stealth! Snorkel AI, which spun out of the Stanford AI Lab in 2019, was founded on two simple premises: first, that the labeled training data machine learning models learn from is increasingly what determines the success or failure of AI applications. And second, that we can do much better than labeling this data entirely by hand. At the Stanford AI lab, the Snorkel AI founding team spent over four years developing new programmatic approaches to labeling, augmenting, structuring, and managing this training data. We were fortunate to develop and deploy early versions of our technology with some of the world's leading organizations like Google, Intel, Apple, Stanford Medicine, resulting in over thirty-six peer-reviewed publications on our findings; innovations in weak supervision modeling, data augmentation, multi-task learning, and more; inclusion in university computer science curriculums; and deployments in popular products and systems that you've likely interacted with in the last few hours.

With the impending confluence of 5G, AI, and AR/VR, text, voice, photos, images, and video streaming will act as seemingly magical channels that connect the physical and the digital worlds. But, the barriers to entry for AI are high – as is cost. In Making Up the Mind, the neuroscientist Chris Frith describes how our perception of the world is not direct, but instead relies on "unconscious reasoning". Before we can perceive an object, the brain must infer what the object is based on the information that reaches our senses. And this constitutes humans' most important ability – the ability to predict and handle unexpected events.