The AI system, from Primer, a company focused on the intelligence industry, identified key themes in the information campaign by analyzing thousands of public news sources. In practice, Primer’s system can analyze classified information too.
Read More
If the U.S. government wants to win the information wars, Cold War-era tactics won’t cut it anymore. Facebook and Twitter made the decision to remove a dubious New York Post story from their platforms—provoking heated debate in the internet’s various echo chambers.
Read More
For all the U.S. military’s technical advantages over adversaries, it still struggles to counter disinformation. A new software tool to be developed for the U.S. Air Force and Special Operations Command, or SOCOM, may help change that.
Read More
SAN FRANCISCO, Oct. 1, 2020 /PRNewswire/ -- Primer, a machine intelligence company, announced today the additions of General Raymond Anthony "Tony" Thomas III (ret) and Lieutenant General VeraLinn "Dash" Jamieson (ret) to its Advisory Board.
Read More
SAN FRANCISCO, Oct. 1, 2020 /PRNewswire/ -- Primer, a machine intelligence company, announced today winning a multi-million dollar Small Business Innovation Research (SBIR) contract from the United States Air Force (USAF) and Special Operations Command (USSOCOM).
Read More
Expected to read upwards of 200,000 words daily from hundreds, if not thousands, of documents, financial analysts are asked to perform the impossible. Primer is using AI to apply the equivalent of compression technology to this mountain of data to help make work easier for them as well as analysts across a range of other industries.
Read More
Language processing is now entering a kind of golden age, in which once impossible tasks are increasingly within reach. These new systems are already starting to transform how businesses operate—and they stand poised to do so in a much bigger way in the coming years.
Read More
Developers hope that tools for processing natural language will help biomedical researchers and clinicians to find the COVID-19 papers that they need. Driven by a combination of factors — including the availability of a large collection of relevant papers, advances in natural-language processing (NLP) technology and the urgency of the pandemic itself.
Read More
Primer has added federal service veterans Brett McGurk and Sue Gordon to the San Francisco-based data analysis automation company’s board as an independent director and a strategic adviser, respectively.
Read More
In the latest edition of our State Secrets podcast, Cipher Brief COO Brad Christian talks with Brian Raymond who works for Primer, one of our partners at The Cipher Brief’s Open Source Collection, featured in our M-F daily newsletter.
Read More
Here's the latest news on Primer. For press inquiries, contact us at [email protected]
The AI system, from Primer, a company focused on the intelligence industry, identified key themes in the information campaign by analyzing thousands of public news sources. In practice, Primer’s system can analyze classified information too.
If the U.S. government wants to win the information wars, Cold War-era tactics won’t cut it anymore. Facebook and Twitter made the decision to remove a dubious New York Post story from their platforms—provoking heated debate in the internet’s various echo chambers.
For all the U.S. military’s technical advantages over adversaries, it still struggles to counter disinformation. A new software tool to be developed for the U.S. Air Force and Special Operations Command, or SOCOM, may help change that.
SAN FRANCISCO, Oct. 1, 2020 /PRNewswire/ -- Primer, a machine intelligence company, announced today the additions of General Raymond Anthony "Tony" Thomas III (ret) and Lieutenant General VeraLinn "Dash" Jamieson (ret) to its Advisory Board.
SAN FRANCISCO, Oct. 1, 2020 /PRNewswire/ -- Primer, a machine intelligence company, announced today winning a multi-million dollar Small Business Innovation Research (SBIR) contract from the United States Air Force (USAF) and Special Operations Command (USSOCOM).
Expected to read upwards of 200,000 words daily from hundreds, if not thousands, of documents, financial analysts are asked to perform the impossible. Primer is using AI to apply the equivalent of compression technology to this mountain of data to help make work easier for them as well as analysts across a range of other industries.
Language processing is now entering a kind of golden age, in which once impossible tasks are increasingly within reach. These new systems are already starting to transform how businesses operate—and they stand poised to do so in a much bigger way in the coming years.
Developers hope that tools for processing natural language will help biomedical researchers and clinicians to find the COVID-19 papers that they need. Driven by a combination of factors — including the availability of a large collection of relevant papers, advances in natural-language processing (NLP) technology and the urgency of the pandemic itself.
Primer has added federal service veterans Brett McGurk and Sue Gordon to the San Francisco-based data analysis automation company’s board as an independent director and a strategic adviser, respectively.
In the latest edition of our State Secrets podcast, Cipher Brief COO Brad Christian talks with Brian Raymond who works for Primer, one of our partners at The Cipher Brief’s Open Source Collection, featured in our M-F daily newsletter.
In the race between science and SARS-CoV-2, the new coronavirus is still winning. Research published on the new coronavirus is doubling every two weeks. “I don’t think I can ever think of a scientific field where we’ve had a doubling time of 14 days,” says Sean Gourley, the founder of Primer.ai, a machine intelligence company.
“That is literally the moment that changed this company,” John Bohannon, director of science at San Francisco technology startup Primer, says of BERT’s publication. Difficult problems Primer once had—such as teaching a system how to determine whom the pronouns “he” and “she” refer to in a sentence when the primary noun wasn’t present—BERT can now handle with only a modicum of additional training.
Computational warfare and disinformation campaigns will, in 2020, become a more serious threat than physical war, and we will have to rethink the weapons we deploy to fight them.
In today’s episode we discuss: The future of the US-China relationship, and possible war. What differentiates AI creativity from humans. The automation law that ALWAYS leads to occasional epic consequences, and more.
As these new technologies proliferate, biases can appear almost anywhere. At Primer, Dr. Bohannon and his engineers recently used BERT to build a system that lets businesses automatically judge the sentiment of headlines, tweets, and other streams of online media. But after training his tool, Dr. Bohannon noticed a consistent bias.
These are transformational times for business. That was the message shared with attendees at the ANZ Finance and Treasury Forum 2019 in Singapore. The event saw ANZ voices and globally recognised thought leaders address more than 200 CFOs and Treasurers from the region.
"You can kind of think of this like zero-day attacks in the cybersecurity space," says Gourley. "The zero-day attack is one that no one's seen before, and thus has no defenses against." As is often the case with cybersecurity, it can be difficult for those trying to solve issues and patch bugs to remain one step ahead of malicious actors.
One of the most frustrating parts of journalism is writing headlines — they need to be pithy and smart, drawing in readers but not infuriating them with cheap clickbait. Perhaps the simplest solution is to summarize an article as efficiently as possible. And because machines are getting increasingly good at that, AI headline writers can now nearly instantly generate titles that outshine even some human-made ones.
In 2015, Sean Gourley penned an article called “Robot Propaganda” for Wired magazine. It contained this then-bold prediction: “We are likely to see versions of these bots deployed on U.S. audiences as part of the 2016 presidential election campaigns.”
Weiner’s Law: Automation will occasionally tidy up ordinary messes. But will occasionally create extraordinary messes.
Sean Gourley, CEO of machine intelligence company Primer, spoke with Newshub Nation about the dangers of our ever-increasing access to information and how emerging artificial intelligence technology could transform society.
In 2019, many of the first drafts of history will be written by artificial intelligence. Rather than spending tens or hundreds of hours synthesising information from thousands of sources, analysts will have a personalised AI that generates written briefings for them in minutes, auto-updating as data inputs change. AI will become a core layer of the stack.
Technology from Primer, a San Francisco artificial intelligence start-up, is already used by unspecified intelligence services to read through written material in an effort to identify trends and significant events. The results help guide human analysts to focus on what is important. The same software is used by retailer Walmart, where analysts constantly monitor a large number of product markets to identify opportunities and risks in the company’s supply chain.
Identifying overlooked scientists wasn’t all Quicksilver could do. It could also automatically draft Wikipedia-style entries on those scientists using all the reference information at its fingertips (so to speak). The company published 100 of these entries online in the hopes a human would pick up where Quicksilver left off by actually adding the entries to Wikipedia.
The research has been carried out by an AI startup named Primer as a demonstration of the company’s expertise in natural language processing (NLP). This is a challenging but lively subfield of AI that’s all about understanding and generating digital text. Wikipedia is often used as a source to train these sorts of programs, but Primer wants to give back to the site.
Quicksilver found 40,000 people missing from Wikipedia that it believed deserved pages, including a good number of women scientists. It did this by analyzing 30,000 English Wikipedia articles about boffins, their corresponding Wikidata entries – a free knowledge base used for Wikimedia projects.
“Our goal is definitely not to have a ‘bot write Wikipedia,” Bohannon says. Instead, it’s a launchpad for people who do want to write new pages or update old ones. If you’re curious to see a sample of what Quicksilver’s output looks like, head on over to this page—it has 100 examples of AI-generated Wikipedia-style blurbs.
Quicksilver uses machine learning algorithms to scour news articles and scientific citations to find notable scientists missing from Wikipedia, and then write fully sourced draft entries for them.
The Technology Pioneers cohort of 2018 brings together 61 early-stage companies from around the world that are pioneering new technologies and innovations ranging from the use of artificial intelligence in drug discovery, the development of autonomous vehicles, advancing cybersecurity and reducing food waste, to applying blockchain to a decentralized engagement platform.
Sean Gourley, founder and CEO of Primer, a San Francisco-based machine intelligence company, points out that Xinhua could join the large newswires in the ranks quickly—and gain a lot of influence.
Connecting organisations with a global, on-demand network of AI experts
Computer algorithms are being used with increasing frequency to make decisions about humans - from whether a job applicant makes it through a selection process or if a prison inmate gets released on parole. But how are the algorithms making their decisions? And what if they make a mistake? In this special episode of Babbage, we explore the complex work of algorithmic decision-making.
Top AI thinkers including Future Today Institute founder Amy Webb, Primer CEO and founder Sean Gourley, and former world chess champion Garry Kasparov, share their thoughts on the future of machine and human interaction, at WSJ's Future of Everything Festival.
In our conversation, John and I discuss his work on Primer Science, a tool that harvests content uploaded to arxiv, sorts it into natural topics using unsupervised learning, then gives relevant summaries of the activity happening in different innovation areas.
In our conversation, John and I discuss his work on Primer Science, a tool that harvests content uploaded to arxiv, sorts it into natural topics using unsupervised learning, then gives relevant summaries of the activity happening in different innovation areas. We spend a good amount of time on the inner workings of Primer Science, including their data pipeline and some of the tools they use, how they determine “ground truth” for training their models, and the use of heuristics to supplement NLP in their processing.
Beijing plans to be the world leader in the technology by 2030. The contest will come down to who can better manipulate the data.
There’s a quest to understand the world we live in. There is a deeper truth in the universe. The physicist's journey is to come up with reasons, explanations, and theories as to why the world is as it is. And now it’s time to build tools to help understand the world as we see it.
Artificial intelligence will automate and optimize fake news, warns a technology supplier to US intelligence agencies.
Primer is targeting fields like finance and intelligence that rely on large numbers of human analysts to glean information expressed in different languages. Customers include Walmart, whose analysts use Primer’s work to distill information about many different commodities and products.
For the CIA, hedge funds and the largest retail enterprises, the confounding problem is the same: too much data. The world and its actors have never seemed more complex, and with no way to absorb a meaningful part of the information out there, events appear harder than ever to understand.
Using a mixture of supervised and unsupervised machine learning models, Primer can ingest unstructured data and produce insights — think scouring the web for news related to a specific company and then organizing it into key themes.
Primer’s tool spins up what looks like a well-researched Wikipedia entry on any topic: Bitcoin, terrorism, oil, organic beef, regional liquor sales—you name it. Structured and unstructured data goes in, proper analyses come out.
Today, Primer is coming out of stealth. The 35-person startup, which has raised $14.7 million to date and recently closed a Series A round of funding, has developed a machine learning system that is able to quickly search through tens of millions of data sources–news articles, academic papers, social media posts, and so on–to surface the kinds of information that is essential to both intelligence analysts and corporate analysts alike.
Primer is a super-useful tool for parsing out the never-ending information flow that is the modern internet — it takes a human, or a whole team of humans, just to make sense of everything. That doesn't really scale.
A startup emerging out of stealth today wants to help companies understand massive stores of text data using AI. The company is called Primer, and it uses machine learning techniques to help parse and collate a large number of documents across several languages in order to facilitate further investigation.
Primer has developed an AI system that’s in part intended to augment the job of an intelligence analyst at a spy agency. Intelligence isn’t the only field they’re working in—their partners include Walmart and a sovereign wealth fund in Singapore—but it’s perhaps the most intriguing.