IBM recently developed three artificial intelligence tools that could help medical researchers fight cancer.
Now, the company has decided to make all three tools open-source, meaning scientists will be able to use them in their research whenever they please, according to ZDNet. The tools are designed to streamline the cancer drug development process and help scientists stay on top of newly-published research — so, if they prove useful, it could mean more cancer treatments coming through the pipeline more rapidly than before.
First programmable memristor computer aims to bring AI processing down from the cloud
The memristor array chip plugs into the custom computer chip, forming the first programmable memristor computer. The team demonstrated that it could run three standard types of machine learning algorithms. Credit: Robert Coelius, Michigan Engineering
The first programmable memristor computer—not just a memristor array operated through an external computer—has been developed at the University of Michigan.
It could lead to the processing of artificial intelligence directly on small, energy-constrained devices such as smartphones and sensors. A smartphone AI processor would mean that voice commands would no longer have to be sent to the cloud for interpretation, speeding up response time.
By identifying patterns in successful rehousing, a research team in L.A. is working to make the housing system more efficient
In Hollywood, nestled between a strip mall and a recording studio where bands like the Rolling Stones have recorded, the residents of a small homeless encampment greet passers by with a friendly “Hi, hello, how are you doing?”
Some people respond in kind; others seem nervous and terse. But according to one of the most outgoing people here, Cedric — who didn’t want to give his last name — they simply hope that if their neighbors see them as friendly and nonthreatening, they won’t call the cops and have their tents removed. L.A. police and the Bureau of Sanitation have become increasingly strict about the “cleanup” of homeless encampments, even though most residents here have nowhere to move to.
Los Angeles has the second largest homeless population in the U.S. after New York, with an estimated 52,765 homeless individuals in 2018. The numbers are compiled by the Los Angeles Homeless Services Authority (LAHSA), a city agency that helps get people off the streets — and LAHSA says the number of people experiencing homelessness for the first time is increasing.
In an initiative started in January 2018, LAHSA is now sharing data from the Homeless Management Information System (HMIS) with researchers at the Center for Artificial Intelligence in Society (CAIS) at the University of Southern California. The researchers are using the data to build a system that can identify behaviors and outcomes, and allocate the type of housing with the greatest statistical chance of long-term success, while also reducing racial discrimination in the system. The project — Housing Allocation for Homeless Persons: Fairness, Transparency, and Efficiency in Algorithmic Design — brings together researchers from both the engineering and social work schools.
How to deal with a breakdown in theoretical support in machine learning? Researchers from Carnegie Mellon and Facebook describe winning many hands against the world’s top poker players by inventing smart search strategies to counter a lack of theoretical math used in most game-playing AI.
Among the many achievements of machine learning in recent years, some of the most striking are the victories of the machine against human players in games, such as Google’s DeepMind group’s conquest of Go in 2016. In such milestones, researchers are often guided by theoretical math that says there can be an optimal strategy to be found, given a good algorithm and enough compute.
The spread of intelligence machines will worsen geographic inequality, unless we take proactive measures
Historically, the worst times for labor have been those characterized by both worker-replacing technological change and slow productivity growth. If A.I. technologies turn out to be as brilliant as some of us think, we can expect some workers to see their incomes vanish in the process — even as new jobs are created elsewhere in the economy. That is what has happened in recent years, and it is also what happened during the most tumultuous years of industrialization.
If current trends continue in the coming years, the divide between the automation winners and losers will become even wider. And there are good reasons to think that it will. Looking at the automatability of existing jobs, we have seen that most occupations that require a college degree remain hard to automate, while many unskilled jobs — like those of cashiers, food preparers, call center agents, and truck drivers — seem set to vanish, though how soon is highly uncertain. But there are also unskilled jobs that remain outside the realms of A.I. Many in-person service jobs that center on complex social interactions — like those of fitness trainers, hairstylists, concierges, and massage therapists — will remain safe from automation.
“It’s like teaching image recognition software with lots of pictures of cats and dogs, but then it’s able to recognize elephants.”
Since we can’t travel billions of years back in time — not yet, anyways — one of the best ways to understand how our universe evolved is to create computer simulations of the process using what we do know about it.
Most of those simulations fall into one of two categories: slow and more accurate, or fast and less accurate. But now, an international team of researchers has built an AI that can quickly generate highly-accurate, three-dimensional simulations of the universe — even when they tweak parameters the system wasn’t trained on.
The World Economic Forum today announced its list of 56 companies selected as Technology Pioneers, and this year’s class demonstrates the growing embrace of artificial intelligence and machine learning across a broad range of sectors.
Of those selected, at least 20 companies say they are using AI or machine learning in some fashion to tackle challenges in fields such as advertising technology, smart cities, cleantech, supply chain, manufacturing, cybersecurity, autonomous vehicles, and drones.
Some languages that have never been deciphered could be the next ones to get the machine translation treatment.
In 1886, the British archaeologist Arthur Evans came across an ancient stone bearing a curious set of inscriptions in an unknown language. The stone came from the Mediterranean island of Crete, and Evans immediately traveled there to hunt for more evidence. He quickly found numerous stones and tablets bearing similar scripts and dated them from around 1400 BCE.
‘Being smart is about working in a smarter way with different partners and empowering citizens’
Stockholm is one of the world’s most connected cities, and a beacon for innovators and international talent. We are also a forward-looking city, leading the environmental and smart city agendas. By 2040, we have the ambition to be both carbon neutral and the smartest city in the world.
Endel highlights how AI could change the way music is both created and experienced.
Artificial Intelligence and Machine Learning are slowly but surely infiltrating multiple industries, slowly weaving their way into our daily lives. Medical professionals are using deep learning models to identify cancer, weak AI to construct better buildings and machine learning to drive the world of robotics.
Navigator, the new project from the creators of Mailbox, launches with $12M
Years ago, a mobile app for email launched to immediate fanfare. Simply called Mailbox, its life was woefully cut short — we’ll get to that. Today, its founders are back with their second act: An AI-enabled assistant called Navigator meant to help teams work and communicate more efficiently.
With the support of $12 million in Series A funding from CRV, #Angels, Designer Fund, SV Angel, Dropbox’s Drew Houston and other angel investors, Aspen, the San Francisco and Seattle-based startup behind Navigator, has quietly been beta testing its tool within 50 organizations across the U.S.
Another day, another deepfake: but this time they can sing.
Finally, technology that can make Rasputin sing like Beyoncé
New research from Imperial College in London and Samsung’s AI research center in the UK shows how a single photo and audio file can be used to generate a singing or talking video portrait. Like previous deepfake programs we’ve seen, the researchers uses machine learning to generate their output. And although the fakes are far from 100 percent realistic, the results are amazing considering how little data is needed.
