As the promise of artificial intelligence (AI) captivates biomedicine, few people are riding the wave like Priscilla Chan--because few people have her resources. Trained as a pediatrician, Chan and her husband, Facebook creator Mark Zuckerberg, co-run a philanthropy that launched in 2015 with the wildly ambitious--some would say quixotic--goal of curing, preventing, or managing every disease by the end of the century. The couple pledged nearly their entire fortune-- 45 billion then and more than 200 billion today--to the Chan Zuckerberg Initiative (CZI), which would also support their education and progressive causes. Recently, however, the foundation has wound down support for almost everything but science. And this week, CZI announced it is increasing its research spending, doubling down on AI, and vowing to meet Chan and Zuckerberg's biomedical goal even earlier--although CZI won't set a specific target.

The in-context learning ability of large language models (LLMs) enables them to generalize to novel downstream tasks with relatively few labeled examples. However, they require enormous computational resources to be deployed. Alternatively, smaller models can solve specific tasks if fine-tuned with enough labeled examples. These examples, however, are expensive to obtain. In pursuit of the best of both worlds, we study synthetic data generation of fine-tuning training data via fine-tuned teacher LLMs to improve the downstream performance of much smaller models. In four text classification and two text generation tasks, we find that both data generation and annotation dramatically improve the respective downstream model's performance, occasionally necessitating only a minor fraction of the original training dataset.

Alation, Inc. is hiring for Full Time Senior Product Manager - Data Management & Lineage - Redwood City, California - a Senior-level AI/ML/Data Science role offering benefits such as Career development, Competitive pay, Equity, Flex hours, Flex vacation, Health care, Startup environment

C3.ai, Inc. (NYSE:AI) is a leading provider of Enterprise AI software for accelerating digital transformation. The proven C3 AI Suite provides comprehensive services to build enterprise-scale AI applications more efficiently and cost-effectively than alternative approaches. The core of the C3 AI offering is an open, data-driven AI architecture that dramatically simplifies data science and application development. C3 AI is looking for experienced data and analytics professionals to join our AI Solution Architecture team (presales). You will be applying your analytics platform experience to design, recommend, and validate solution designs to help our prospective customers deliver high-value, high-impact projects driven by AI/ML.

PubMatic is a digital advertising technology company for premium content creators. The PubMatic platform empowers independent app developers and publishers to control and maximize their digital advertising businesses. PubMatic's publisher-centric approach enables advertisers to maximize ROI by reaching and engaging their target audiences in brand-safe, premium environments across ad formats and devices. Since 2006, PubMatic has created an efficient, global infrastructure and remains at the forefront of programmatic innovation. Headquartered in Redwood City, California, PubMatic operates 13 offices and nine data centers worldwide.

Johnson & Johnson (J&J) is an American multinational corporation founded in 1886 that develops medical devices, pharmaceuticals, and consumer packaged goods. Its common stock is a component of the Dow Jones Industrial Average and the company is ranked No. 36 on the 2021 Fortune 500 list of the largest United States corporations by total revenue.

Analog AI processor company Mythic launched its M1076 Analog Matrix Processor today to provide low-power AI processing. The company uses analog circuits rather than digital to create its processor, making it easier to integrate memory into the processor and operate its device with 10 times less power than a typical system-on-chip or graphics processing unit (GPU). The M1076 AMP can support up to 25 trillion operations per second (TOPS) of AI compute in a 3-watt power envelope. It is targeted at AI at the edge applications, but the company said it can scale from the edge to server applications, addressing multiple vertical markets including smart cities, industrial applications, enterprise applications, and consumer devices. To address a wider range of designs, the M1076 AMP comes in several form factors: a standalone processor, an ultra-compact PCIe M.2 card, and a PCIe card with up to 16 AMPs.

WIRE)--Citrine Informatics has been named an Enterprise of the Year Gold winner in the Best in Biz Awards, the only independent business awards program judged by prominent editors and reporters from top-tier publications in North America. Citrine Informatics' artificial intelligence technology is used by the world's largest materials and chemicals companies to accelerate the product development cycle. Since 2011, Best in Biz Awards' entrants have spanned the spectrum, from the most innovative local companies and start-ups to some of the most recognizable global brands. With more than 700 entries, the 9th annual program attracted a record number of entries from an impressive array of public and private companies of all sizes and spanning all geographic regions and industries in the U.S. and Canada. Best in Biz Awards 2019 honors were conferred in 80 different categories, including Company of the Year, Fastest-Growing Company, Most Innovative Company, Best Place to Work, Customer Service Department, Executive of the Year, Most Innovative Product, Enterprise Product, Best New Service, CSR Program, Event and Blog of the Year.

Abstract: Attention mechanisms have been around since at least 2014, and they have been driving the state of the art in NLP (natural language processing) ever since. Now we are at the end of 2018, and yet it is still very uncommon to hear about their use in enterprise environments. This talk will explain attention mechanisms and how they fit into the deep learning landscape. It will show you the different types of attention mechanisms and examples of them being applied to several different problems. Finally, you will see how you might apply them yourself in models build with Deeplearning4J. Speaker: Paul Dubs (Paul Dubs IT Consulting) Paul holds a Master of Science in Computer Science from TU Darmstadt and has over a decade of professional software engineering experience.

Before it started scampering after the machine learning chip market in 2016, but after it was founded at the University of Michigan in 2012, Mythic was trying to build embedded chips that would let surveillance drones run software modeled after the human brain. Part of the funding for the company, then known as Isocline, came from the Department of Defense. But after relaunching two years ago, Mythic refocused on embedded devices like autonomous cars and security cameras. Now the company is only a few months from sampling chips based on an aggressively ambitious architecture, which uses analog computing inside flash memory cells to accelerate machine learning tasks like facial recognition. Helping it over the finish line is $40 million raised last month from new and existing investors, including SoftBank Ventures, Draper Fisher Jurvetson and Lux Capital.