Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries. As the demand for more capable AI models continues to grow, we are now entering an era of Trillion Parameter Models (TPM), or models with more than a trillion parameters -- such as Huawei's PanGu-$\Sigma$. We describe a vision for the ecosystem of TPM users and providers that caters to the specific needs of the scientific community. We then outline the significant technical challenges and open problems in system design for serving TPMs to enable scientific research and discovery. Specifically, we describe the requirements of a comprehensive software stack and interfaces to support the diverse and flexible requirements of researchers.

Visual reasoning is a long-term goal of vision research. In the last decade, several works have attempted to apply deep neural networks (DNNs) to the task of learning visual relations from images, with modest results in terms of the generalization of the relations learned. In recent years, several innovations in DNNs have been developed in order to enable learning abstract relation from images. In this work, we systematically evaluate a series of DNNs that integrate mechanism such as slot attention, recurrently guided attention, and external memory, in the simplest possible visual reasoning task: deciding whether two objects are the same or different. We found that, although some models performed better than others in generalizing the same-different relation to specific types of images, no model was able to generalize this relation across the board. We conclude that abstract visual reasoning remains largely an unresolved challenge for DNNs.

"Mark my words; AI is far more dangerous than nukes" Elon Musk AI has a massive impact on our social thinking process. The impact is positive as well as negative. We use mobiles phones, robots, self-driving cars, etc., excessively. Majority of us come into contact with Artificial Intelligence in some capacity or the other virtually daily. AI has fast contracted into our lives.

Technology, human behavior, Customer Experience are intersecting through the use of Artificial Intelligence (AI) and taking marketing in a new direction. To understand how it requires a better understanding of what the inventor of the hashtag Chris Messina's calls Conversational Commerce. We spoke to Messina on a recent podcast about this concept and how it applies to customer-driven growth. Messina coined the phrase Conversational Commerce in 2016 to describe all the changes happening in the way we interact with customers in the consumer marketplace. Specifically, it relates to how brands and consumers are going to communicate through messaging and social media. Messina is an expert on this subject.

Last March, at the South by Southwest tech conference in Austin, Texas, Tesla and SpaceX founder Elon Musk issued a friendly warning: "Mark my words," he said, billionaire casual in a furry-collared bomber jacket and days' old scruff, "AI is far more dangerous than nukes." No shrinking violet, especially when it comes to opining about technology, the outspoken Musk has repeated a version of these artificial intelligence premonitions in other settings as well. "I am really quite close… to the cutting edge in AI, and it scares the hell out of me," he told his SXSW audience. "It's capable of vastly more than almost anyone knows, and the rate of improvement is exponential." Musk, though, is far from alone in his exceedingly skeptical (some might say bleakly alarmist) views. A year prior, the late physicist Stephen Hawking was similarly forthright when he told an audience in Portugal that AI's impact could be cataclysmic unless its rapid development is strictly and ethically controlled.

Artificial intelligence and conversational computing platforms are no longer the sole domain of big businesses. Voice-computing technology can help a small or medium-sized business owner make interactions with customers more valuable. For one thing, voice computing technology can help improve customer service and efficiency by allowing customers to quickly and easily have a conversational experience with a business without the need for in-depth technical expertise. In the simplest terms, conversational computing is when you talk to a device and it talks back to you. Chris Messina, product designer and the inventor of the hashtag, has a bird eye's view of changes in the way we have conversations. We can use a variety of interfaces (voice, screen, messaging, etc.) and then carry the conversation to other interfaces without losing a beat or starting over.

Hashtag creator Chris Messina today launched Molly, a service that allows people to ask questions about you and glean information from your various social media profiles. Molly skims your posts on platforms like Instagram, Twitter, and Medium to learn about you and formulate natural language questions. When someone asks something Molly can't answer, that question is sent to the Molly app for you to answer yourself. In addition to following your social media activity, the Molly app asks you to answer questions about yourself, like "Do you own an Amazon Echo?" or "Do you have a sweet tooth or a savory tooth?" The more you swipe through the questions, the more Molly learns about you, and the more you learn about how your friends have answered similar questions.

Facebook wants developers everywhere to build bots for 900 million people on the Messenger Platform, Quartz's first news app is built on a bot-like conversational paradigm, and a UX designer just built his personal website as a chat bot. Why are bots so hot, and will all apps go the messaging route? We asked Uber's developer experience lead -- and the inventor of the social-media hashtag -- Chris Messina: The idea of mobile, Messina says, is that you are connected wherever you go. But mobile app user interfaces, even those designed as mobile-first from the bottom up, still tend to rely on metaphors that we developed for desktop computing 30 years ago. A big part of that desktop metaphor is an assumption: some degree of immersion over time in a task.