Neural language models are gaining popularity in real-life creative tasks such as text-adventure games, collaborative slogan writing, and even sports journalism, poetry and novel generation. Most such language models however provide limited interaction support for users, as control that goes beyond simple left-to-right text generation requires explicit training. To address this limitation, a team from Google Research has proposed Wordcraft, a text editor with a built-in AI-powered creative writing assistant. Wordcraft leverages few-shot learning and the natural affordances of conversation to support a variety of user interactions; and can help with story planning, writing and editing. The Wordcraft web interface comprises a traditional text editor augmented with a number of key commands for triggering requests to the AI assistant.

Artificial Intelligence applies algorithms to leverage deep learning and other techniques to solve actual problems. The sub-field of Vision Intelligence impacts the physical security industry directly when you consider that surveillance cameras are the ultimate end-point device, the "all-seeing eyes" of the Internet. Could "intelligence" be applied to video to create "human context" to eliminate the false alarms and tailgating pain points that have long plagued the physical security industry? As a security leader, Philip Jang, Sr. Manager Physical Systems & Technology at VMWare, has been exploring how AI technologies and digital transformation processes impact the physical security profession for several years. Jang has global enterprise experience in the planning, execution, delivery and automation of advanced technologies such as AI, Robotics, Drones, advanced analytics and IoT.

Machine learning algorithms can be used to improve the authentication process based on the results supplied by AI systems. FREMONT, CA: Artificial intelligence-based authentication systems and machine learning-based predictive analytics are already making our lives easier. Soon, these solutions will extend beyond basic sign-in methods to include other areas where user data security is critical. Machine learning algorithms can be used to improve the authentication process based on the results supplied by AI systems. Biometric verification is one of the most common and user-friendly ways of authentication.

Model Predictive Control (MPC) is a versatile and a widely used for model-based control approaches, which involves an online optimization of the control strategy over a pre-determined predictive receding horizon. A central limitation of the traditional MPC online optimization is that it requires a relatively inexpensive models. As a result, linear and non-linear (quadratic) approximations to the plant-models are considered - unless, of-course an explicit model in the form of a differential equation is readily available. The non-linear modeling presents a computation challenge, that requires one to solve nonlinear programming problems online. This works fine for relatively low-dimensional systems.

Today, Tractable is worth $1 billion. Our AI is used by millions of people across the world to recover faster from road accidents, and it also helps recycle as many cars as Tesla puts on the road. And yet six years ago, Tractable was just me and Raz (Razvan Ranca, CTO), two college grads coding in a basement. Here's how we did it, and what we learned along the way. In 2013, I was fortunate to get into artificial intelligence (more specifically, deep learning) six months before it blew up internationally.

In this post, we summarise the first two invited talks from the International Conference on Machine Learning (ICML). These presentations covered the fascinating topics of drug discovery, and the cryosphere. In Daphne's talk, she outlined some of the work she has been doing on transforming drug discovery using digital biology. To introduce the topic, Daphne described drug discovery as an interesting space that one can view as glass half-full or glass half-empty. The half-full version is demonstrated by the amazing advances in new medicines, such as vaccines, cell therapies, genetically targeted therapies, and cancer immunotherapies.

"Andrew is famous for his ability to teach complex topics that blend mathematics and algorithms, and this work I think is his best yet." Andrew Glassner is a research scientist specializing in computer graphics and deep learning. He is currently a Senior Research Scientist at Weta Digital, where he works on integrating deep learning with the production of world-class visual effects for films and television. He has previously worked as a researcher at labs such as the IBM Watson Lab, Xerox PARC, and Microsoft Research. He was Editor in Chief of ACM TOG, the premier research journal in graphics, and Technical Papers Chair for SIGGRAPH, the premier conference in graphics.

GitHub Copilot is a new service from GitHub and OpenAI, described as "Your AI pair programmer". It is a plugin to Visual Studio Code which auto-generates code for you based on the contents of the current file, and your current cursor location. It really feels quite magical to use. For example, here I've typed the name and docstring of a function which should "Write text to file fname": The grey body of the function has been entirely written for me by Copilot! I just hit Tab on my keyboard, and the suggestion gets accepted and inserted into my code. This is certainly not the first "AI powered" program synthesis tool.

Protein design researchers used artificial intelligence to generate hundreds of new protein structures, including this 3D view of human interleukin-12 bound to its receptor. Scientists have waited months for access to highly accurate protein structure prediction since DeepMind presented remarkable progress in this area at the 2020 Critical Assessment of Structure Prediction, or CASP14, conference. The wait is now over. Researchers at the Institute for Protein Design at the University of Washington School of Medicine in Seattle have largely recreated the performance achieved by DeepMind on this important task. These results were published online by the journal Science on July 15, 2021.

Last month, OpenAI cofounder Wojciech Zaremba said the company has disbanded its robotics team in a Weights & Biases podcast. "I was actually working for several years on robotics. Recently, we changed the focus at OpenAI. I disbanded the robotics team. There are actually plenty of domains that are very rich with data. Ultimately that was holding us back, in the case of robotics," said Zaremba.