When it comes to gaining that elusive trading edge, data is fast becoming the new frontier whether it comes from Fitbits, Rokus and Teslas or employment websites like Glassdoor.com.
That’s why some of the world’s biggest hedge funds, from Steve Cohen’s Point72 Asset Management to Ken Griffin’s Citadel, have been snapping up large swaths of alternative data. Many are paying big money for it.
“There is not one major hedge fund or asset manager that doesn’t have data initiatives underway or that are not using alternative data in some way,” said Michael Marrale, chief executive officer of M Science, a firm that provides data and analytics to hedge funds.
Millions are spent each year concocting the latest tastes and flavors to tantalize consumers, with companies experimenting with various AI-based technologies to help come up with interesting combinations. New research from the Monell Center suggests a big data analysis of reviews left by Amazon consumers may be a handy short-cut towards a better understanding of the changing tastes of the market.
In total they crawled through around 400,000 food reviews left by consumers, with the general consensus being that many of the foods on the market today are excessively sweet.
“This is the first study of this scale to study food choice beyond the artificial constraints of the laboratory,” the researchers say. “Sweet was the most frequently mentioned taste quality and the reviewers definitively told us that human food is over-sweetened.”
Scientists in China are concerned about what they see as growing anti-Chinese sentiment in the United States. They dismiss claims of a vast conspiracy to steal U.S. intellectual property and worry that new visa restrictions, scrutiny of export of scientific devices, and U.S. investigations of Chinese and Chinese American scientists will hinder international collaborations. That could harm both countries’ research efforts as well as global scientific progress, many say.
Increasingly, “Chinese scholars will hesitate to work with collaborators in the U.S.,” warns Cao Cong, a China science policy specialist at the University of Nottingham Ningbo China. The Chinese government may also steer funding away from U.S.-based projects, he adds. Indeed, visa issues are threatening additional Chinese funding for the Thirty Meter Telescope (TMT), an international project planned for a site on Mauna Kea in Hawaii.
The twisted tale of the United Kingdom’s planned withdrawal from the European Union has taken a perilous turn. Boris Johnson, a charismatic and incautious politician with scant public views on science, became U.K. prime minister last week. He immediately packed his Cabinet with ministers pledging to exit the European Union by a 31 October deadline, even without a deal in place for an amicable divorce—the “no-deal Brexit” that economists predict would cause a recession and scientists say would cause additional hardships for research. Although no-deal now seems more likely than before, Johnson has touted the benefits of science and may be open to post-Brexit immigration reforms that U.K. scientists want. “This is a moment of both opportunity and risk,” says Beth Thompson, the EU policy director for the Wellcome Trust, a biomedical charity in London.
A few weeks after the clock was supposed to run out, HPE has swooped in to become MapR’s white knight. With some of the pieces to transform MapR into a hybrid cloud platform, will this be a match made in heaven?
With a large photo in the background declaring that already more than one in three supercomputers around the world are powered by Lenovo, the tech giant is working now with Intel to drive improvements in high-performance computing and artificial intelligence.
The tech giants on Monday disclosed a “multiyear collaboration” that will have their firms looking to enable computers “accelerate solutions for the world’s most challenging problems.”
[Igor Jablokov is] moving his new company and his big idea out of “stealth mode” and into the public spotlight.
Augmented decision-making
The big idea, this time, is to build a reliable and trustworthy voice recognition platform that business leaders could use to become more efficient with their time and in making decisions. While Alexa aims for a mass audience, this technology would be made specifically for business uses, with the promise of being faster, more accurate and more secure than other voice-activated AIs.
Big artificial intelligence companies are promising an automated future, but many of their products rely on the labeled training data coming from Scale AI, a startup that highlights machine learning’s intimate bond between human contractors and algorithms.
The three-year-old startup announced Monday that it had closed a $100 million Series C round of financing led by Founders Fund with participation from Accel, Coatue Management, Index Ventures, Spark Capital, Thrive Capital, Instagram founders Kevin Systrom and Mike Krieger and Quora CEO Adam d’Angelo. A report in Bloomberg details that this funding will bring Scale’s valuation past $1 billion.
“In general, AI and machine learning is just growing so quickly as a field, that it’s appropriate to raise this amount that will allow us to capitalize on our ambitions,” the company’s 22-year-old chief executive Alexandr Wang told TechCrunch in an interview. “We don’t want to be in the business of constantly needing to raise capital, so ideally this is the last fundraise for us.”
Scientists have topped a survey of trusted professions, with adults in the US more confident that they act in the public’s best interests than employees from any other line of work studied.
The survey found that confidence in scientists has risen markedly since 2016 and more than half of American adults believe the specialists should be actively involved in policy decisions surrounding scientific matters.
The upswing in public trust, a rise of 10 percentage points since 2016, led to 86% of US adults expressing at least a “fair amount” of confidence that scientists put the public interest first. The trust rating placed scientists above politicians, the military, business leaders, school principals and journalists.
In a paper being presented at the Machine Learning for Healthcare conference this week, MIT researchers demonstrate a model that automatically learns features predictive of vocal cord disorders. The features come from a dataset of about 100 subjects, each with about a week’s worth of voice-monitoring data and several billion samples — in other words, a small number of subjects and a large amount of data per subject. The dataset contain signals captured from a little accelerometer sensor mounted on subjects’ necks.
In experiments, the model used features automatically extracted from these data to classify, with high accuracy, patients with and without vocal cord nodules. These are lesions that develop in the larynx, often because of patterns of voice misuse such as belting out songs or yelling. Importantly, the model accomplished this task without a large set of hand-labeled data.
… It’s possible to imagine a new organization of doctors that has nothing to do with the business of medicine and everything to do with promoting the health of patients and adroitly confronting the transformational challenges that lie ahead for the medical profession. Such an organization wouldn’t be a trade guild protecting the interests of doctors. It would be a doctors’ organization devoted to patients. Its top priority might be restoring the human factor—the essence of medicine—which has slipped away, taking with it the patient-doctor relationship. It might oppose anti-vaxxers; challenge drug pricing and direct-to-consumer advertisements; denounce predatory, unregulated stem-cell clinics; promote awareness of the health hazards of climate change; and call out the false health claims for products advocated by celebrities such as Gwyneth Paltrow and Mehmet Oz. This partial list provides a sense of how many momentous matters have been left unaddressed by the medical profession as a whole. Tackling any one of them would be hard; perhaps patient-advocacy groups could join in common cause.
Such an organization could also address the profound changes that are on the horizon for the medical profession.
Carnegie Endowment for International Peace; Charlotte Stanton, Vivien Lung, Nancy (Hanzhuo) Zhang, Minori Ito, Steve Weber, Katherine Charlet
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So far, China and the United States are outspending everyone else while simultaneously taking steps to protect their investments from foreign competition. In 2017, China passed legislation requiring foreign companies to store data from Chinese customers within China’s borders, effectively hamstringing outsiders from using Chinese data to offer services to non-Chinese parties. For its part, the U.S. Committee on Foreign Investment blocked a Chinese investor from acquiring a leading U.S. producer of semiconductors, which are essential components for computing. While this was officially a national security action, it could also benefit U.S competitiveness by protecting its stake in semiconductor production.2
Both data and certain classes of semiconductors are core elements of the AI value chain. Given AI’s economic and geopolitical significance, they’re also increasingly being considered strategic assets. The extent to which countries can participate in this value chain will determine how they fare in the emerging global economic order and the stability of the broader international system. Indeed, if the gains from AI are distributed in highly variable ways, extreme divergence in national outcomes could drive widespread instability.
So what does the AI value chain look like? And where in the physical world are the key nodes of value creation and control emerging? This article addresses these questions, introducing the idea of a machine learning value chain and offering insights on the geopolitical implications for countries searching for competitive advantage in the age of AI.
UTSA Provost and Senior Vice President for Academic Affairs Kimberly Andrews Espy announced the appointment of Jianwei Niu as interim academic director of the university’s new School of Data Science (SDS), effective immediately.
In this new role, Niu will be responsible for providing leadership to propel UTSA to become the leading institution to further advance the state of knowledge in data science. This will include developing innovative transdisciplinary curriculum in data science at the undergraduate and graduate level across all fields and sectors, and laying the groundwork for the operational and administrative functions of the new school.
If you’re in Canada and identify as an underrepresented student or know people who are, please send me a DM. All backgrounds are welcome! No experience in coding or analytics is necessary. Thank you!
“ML4H 2019 invites submissions describing innovative machine learning research focused on relevant problems in health and biomedicine. For the first time, ML4H 2019 will accept papers for a formal proceedings as well as accepting traditional, non-archival extended abstract submissions.” Workshop at NeurIPS 2019. Deadline for submissions is September 9.
For the first three years of OpenAI, I dreamed of becoming a machine learning expert but made little progress towards that goal. Over the past nine months, I’ve finally made the transition to being a machine learning practitioner. It was hard but not impossible, and I think most people who are good programmers and know (or are willing to learn) the math can do it too. There are many online courses to self-study the technical side, and what turned out to be my biggest blocker was a mental barrier — getting ok with being a beginner again.
Whether you call it ‘content analysis’, ‘textual data labeling’, ‘hand-coding’, or ‘tagging’, a lot more researchers and data science teams are starting up annotation projects these days. Many want human judgment labeled onto text to train AI (via supervised machine learning approaches). Others have tried automated text analysis and found it wanting. Now they’re looking for ways to label text that aren’t so hard to interpret and explain. Some just want what social scientists have always wanted: a way to analyze massive archives of human behavior (like the Supreme Court’s transcripts or diplomatic correspondence) at high scales. With so much digitized textual data now available, and so many patterns and insights to be discovered, it’s no wonder people are excited about annotation.
We always encourage researchers to dream big and tackle the most intricate and impactful questions in their fields. But annotation projects are not exactly easy (I say that as someone who has consulted a hundred or more annotation projects since my days at UC Berkeley when I was teaching research methods, and founding and leading text analysis organizations at the D-Lab and Berkeley Institute for Data Science). Here, I outline the five most common ways things go wrong, and offer some advice to keep you in the clear.