WHEN STEVE, A thirtysomething engineer, launched a software company in San Francisco a few years ago, he and his cofounder faced the daunting task of hiring a team, from low-level engineers to a new VP. Novices, they nonetheless had an advantage: access to Option Impact, an exclusive database of tech salaries that has become a go-to reference for Silicon Valley startups. That insider information, Steve realized, has immense value. “You get into the venture capitalist club and get all the magical data, which puts you in a power position,” he says. He recalls walking away from negotiations with a newly hired employee thinking, “Man, this guy is underpaid—we got a good deal.”
Unlike popular self-reported salary sites like Glassdoor or Stack Overflow, Option Impact is reserved for elite users—VCs and the executives at the startups they back (plus legal and consulting firms). And while salary surveys are routine across industries, Option Impact is unique in its specificity and accuracy. Companies share their employees’ anonymized salaries in exchange for access to the vault, which is searchable by job title, location, company size, revenue, and funding stage. Executives are required to update their entries every six months, and Option Impact vets its data regularly.
It’s now the world’s largest salary data set for startups.
Facebook says it is making advertising on the social network more “transparent” by publishing more information about the adverts firms are running on the site.
The social network has added a new feature which enables users to see the adverts any firm with a Facebook page is running on the site at that time and to report any adverts they believe are suspicious.
Facebook said it would also start displaying more information about pages, even if they do not advertise, to give users a better understanding of their history.
Replacing a cluster installed in 2012, Princeton’s new flagship computer, TIGER, arrived quietly on campus in March and was put through months of routine testing and debugging before being officially unveiled in late May. Several tours and a reception were held at the High-Performance Computing Research Center (HPCRC) on the Forrestal Campus during Reunions.
VentureBeat decided to take a look at what kinds of AI startups are forming in an area that venture capitalists have traditionally overlooked — the Midwest — and what problems they’re trying to tackle. Using data from research firms CB Insights and Crunchbase, VentureBeat looked for Midwestern startups with unique machine learning or artificial intelligence platforms that have raised significant amounts of venture capital. We also spoke with two Midwestern venture capitalists about which startups are worth watching.
Chris Olsen, a partner at Columbus, Ohio venture capital firm Drive Capital is bullish on the Midwest’s ability to produce game-changing AI startups.
Baidu today unveiled a new chip for AI, joining the ranks of Google, Nvidia, Intel, and many other tech companies making processors especially for artificial intelligence.
Kunlun is made to handle AI models for edge computing on devices and in the cloud via datacenters. The Kunlun 818-300 model will be used for training AI, and the 818-100 for inference.
Earlier this week, NASA’s Kepler team received an indication that the spacecraft fuel tank is running very low. NASA has placed the spacecraft in a hibernation-like state in preparation to download the science data collected in its latest observation campaign. Once the data has been downloaded, the expectation is to start observations for the next campaign with any remaining fuel.
Since May 12, Kepler has been on its 18th observation campaign, staring at a patch of sky towards the constellation of Cancer it previously studied in 2015. The data from this second look will provide astronomers with an opportunity to confirm previous exoplanet candidates and discover new ones. Returning the data back to Earth is the highest priority for the remaining fuel.
The Intercept, Ryan Gallagher and Henrik Moltke (h/t Bruce Schneier)
from
The secrets are hidden behind fortified walls in cities across the United States, inside towering, windowless skyscrapers and fortress-like concrete structures that were built to withstand earthquakes and even nuclear attack. Thousands of people pass by the buildings each day and rarely give them a second glance, because their function is not publicly known. They are an integral part of one of the world’s largest telecommunications networks – and they are also linked to a controversial National Security Agency surveillance program.
Atlanta, Chicago, Dallas, Los Angeles, New York City, San Francisco, Seattle, and Washington, D.C. In each of these cities, The Intercept has identified an AT&T facility containing networking equipment that transports large quantities of internet traffic across the United States and the world. A body of evidence – including classified NSA documents, public records, and interviews with several former AT&T employees – indicates that the buildings are central to an NSA spying initiative that has for years monitored billions of emails, phone calls, and online chats passing across U.S. territory.
Peblio is an instructional tool that helps high school computer science teachers save time, build confidence, and teach 21st century skills to prepare students to succeed in the digital age. … NextGen Bootcamp offers in person computer science education tailored to high school students to prepare them for the AP test and help them place out of college courses.
In my second year of medical school, I decided to open an unfamiliar email entitled “UBC Centennial Symposium on Health Informatics”. What was Health Informatics? I had no idea, but I intended to find out. The symposium featured the UK’s National Health Service (NHS) and showcased their use of large population datasets. I was impressed with how the NHS uses data to improve care. I went into medicine because of a personal desire to help people one-on-one; applying data science to healthcare seemed to provide the opportunity to positively impact millions at a time, in addition to one at a time.
When I first heard about the program, I saw UBC’s Master of Data Science (MDS) as an entry point into the world of data-driven healthcare. The MDS is a 10-month intensive program focused on developing statistics, computing and machine learning expertise, working with multiple real-life data sets. It’s topped off by an industry capstone project, where students get to apply all that they’ve learned in a 2-month project. My capstone team worked with health tech startup QxMD on building a recommendation system for medical research papers. Our recommender outperforms the current system and will be deployed to production in July 2018 to 500,000 users.
Something I’ve been thinking about a lot lately is how much information we really convey with emoji. I was recently at the 1st International Workshop on Emoji Understanding and Applications in Social Media and one theme that stood out to me from the papers was that emoji tend to be used more to communicate social meaning (things like tone and when a conversation is over) than semantics (content stuff like “this is a dog” or “an icecream truck”).
I’ve been itching to apply an information theoretic approach to emoji use for a while, and this seemed like the perfect opportunity. Information theory is the study of storing, transmitting and, most importantly for this project, quantifying information. In other words, using an information theoretic approach we can actually look at two input texts and figure out which one has more information in it. And that’s just what we’re going to do: we’re going to use a measure called “entropy” to directly compare the amount of information in text and emoji.
The race to become the global leader in artificial intelligence (AI) has officially begun. In the past fifteen months, Canada, Japan, Singapore, China, the UAE, Finland, Denmark, France, the UK, the EU Commission, South Korea, and India have all released strategies to promote the use and development of AI. No two strategies are alike, with each focusing on different aspects of AI policy: scientific research, talent development, skills and education, public and private sector adoption, ethics and inclusion, standards and regulations, and data and digital infrastructure.
This article summarizes the key policies and goals of each national strategy. It also highlights relevant policies and initiatives that the countries have announced since the release of their initial strategies.