The merging of machine capability and human consciousness is already happening. Writing exclusively for WIRED, DARPA director Arati Prabhkar outlines the potential rewards we face in the future - and the risks we face
Peter Sorger and Ben Gyori are brainstorming with a computer in a laboratory at Harvard Medical School. Their goal is to figure out why a powerful melanoma drug stops helping patients after a few months. But if their approach to human-computer collaboration is successful, it could generate a new approach to fundamentally understanding complexities that may change not only how cancer patients are treated, but also how innovation and discovery are pursued in countless other domains.
At the heart of their challenge is the crazily complicated hairball of activity going on inside a cancer cell - or in any cell. Untold thousands of interacting biochemical processes, constantly morphing, depending on which genes are most active and what's going on around them. Sorger and Gyori know from studies of cells taken from treated patients that the melanoma drug's loss of efficacy over time correlates with increased activity of two genes. But with so many factors directly or indirectly affecting those genes, and only a relatively crude model of those global interactions available, it's impossible to determine which actors in the cell they might want to target with additional drugs.
That's where the team's novel computer system comes in. All Sorger and Gyori have to do is type in a new idea they have about the interactions among three proteins, based on a mix of clinical evidence, their deep scientific expertise, and good old human intuition. The system instantly considers the team's thinking and generates hundreds of new differential equations, enriching and improving its previous analytical model of the myriad activities inside drug-treated cells. And then it spits out new results.
These don't predict all the relevant observations from tumour cells, but it gives the researchers another idea involving two more proteins - which they shoot back on their keyboard. The computer churns and responds with a new round of analysis, producing a model that, it turns out, predicts exactly what happens in patients and offers new clues about how to prevent some cases of melanoma recurrence.
Human beings are in danger of being eclipsed by artificial intelligence and need to evolve the ability to communicate directly with machines or risk irrelevance, Elon Musk said in a typically heartwarming speech from everyone’s favorite billionaire technologist.
“Over time I think we will probably see a closer merger of biological intelligence and digital intelligence," Musk told an audience at the World Government Summit in Dubai, where he also launched Tesla in the United Arab Emirates, according to CNBC. "It's mostly about the bandwidth, the speed of the connection between your brain and the digital version of yourself, particularly output."
Poised to seriously disrupt the world, will the impacts of artificial intelligence be for the good of humanity, or destroy it? The question sounds like the basis of a sci-fi flick, but with the speed that AI is advancing, hundreds of AI and robotics researchers have converged to compile the Asilomar AI Principles, a list of 23 principles, priorities and precautions that should guide the development of artificial intelligence to ensure it's safe, ethical and beneficial.
The list is the brainchild of the Future of Life Institute, an organization that aims to help humanity steer a safe course through the risks that might arise from new technology. Prominent members include the likes of Stephen Hawking and Elon Musk, and the group focuses on the potential threats to our species posed by technologies and issues like artificial intelligence, biotechnology, nuclear weapons and climate change.
At the Beneficial Artificial Intelligence (BAI) 2017 conference in January, the group gathered AI researchers from universities and companies to discuss the future of artificial intelligence and how it should be regulated. Before the meeting, the institute quizzed attendees on how they thought AI development needed to be prioritized and managed in the coming years, and used those responses to create a list of potential points. The revised version was studied at the conference, and only when 90 percent of the scientists agreed on a point would it be included in the final list.
The full list of the Asilomar AI Principles reads like an extended version of Isaac Asimov's famous Three Laws of Robotics. The 23 points are grouped into three areas: Research Issues, Ethics and Values, and Longer-Term Issues.
The Wall Street Journal did an interesting piece looking at Google, privacy and the extent of their knowledge (as well as other entities like Facebook). I suggest you read it for the details. The following image gives some insight into just how much Google has on one WSJ reporter.
ABSTRACT: Transcranial electrical stimulation (tES) is a technique that is increasingly used to modulate cortical excitability and induce neural plasticity in the human brain. Two prominent types of tES are transcranial direct current stimulation (tDCS) and transcranial variable frequency stimulation (tVFS). Over a period of two years, 1010 human subjects received tES directed to motor and/or non-motor cortical areas using the Halo Neurostimulation System, a novel neurostimulation device. This paper summarizes safety and describes the adverse event profile of tES, specifically tDCS and tVFS, observed in this series. Each of the 1010 subjects was assessed post-stimulation to identify adverse events. In addition to general assessment, subjects were specifically queried and results tabulated for any scalp burning (i.e., lesion), headache, scalp pain, and seizure. Mild sensation due to stimulation (e.g., tingling or itching) was not tabulated unless reported as scalp pain or causing withdrawal from the study. A total of 557 subjects received active stimulation, while 453 subjects received sham stimulation. The most commonly reported adverse event was headache (2.0% in active stimulation group and 3.8% in sham group). Scalp pain was also reported in 1.1% of subjects in the stimulation group and in 0.67% of the sham group. Withdrawal due to unpleasant sensation occurred in 0.54% of subjects receiving active stimulation. There were no reports of burns or seizure. Our results suggest that tDCS and tVFS can be safely applied to motor and non-motor cortical areas using the Halo Neurostimulation System in healthy humans.
It looks like 2,000 citizens in Finland will welcome the new year with outstretched arms.
These Finns are the lucky recipients of a guaranteed income beginning this year, as the country’s government finally rolls out its universal basic income (UBI) trial run.
UBI is a potential source of income that could one day be available to all adult citizens, regardless of income, wealth, or employment status.
This pioneering UBI program was launched by the federal social security institution, Kela. It will give out €560 (US$587) a month, tax free, to 2,000 Finns that were randomly selected.
The only requirement was that they had to be already receiving unemployment benefits or an income subsidy.
The program allows unemployed Finns to not lose their benefits, even when they try out odd jobs.
"Incidental earnings do not reduce the basic income, so working and … self-employment are worthwhile no matter what," says Marjukka Turunen, legal unit head at Kela.
If successful, the program could be extended to include all adult Finns.
"Its purpose is to reduce the work involved in applying for subsidies, as well as free up time and resources for other activities, such as making or applying for work," according to a press release by Kela.
Furthermore, the Finnish government, as well as UBI advocates, may see how this program can end up saving more money for Finland in the long run - as it is less costly than maintaining social welfare services for the unemployed.
Are you lying? Do you have a racial bias? Is your moral compass intact? To find out what you think or feel, we usually have to take your word for it. But questionnaires and other explicit measures to reveal what’s on your mind are imperfect: you may choose to hide your true beliefs or you may not even be aware of them.
But now there is a technology that enables us to “read the mind” with growing accuracy: functional magnetic resonance imaging (fMRI). It measures brain activity indirectly by tracking changes in blood flow – making it possible for neuroscientists to observe the brain in action. Because the technology is safe and effective, fMRI has revolutionised our understanding of the human brain. It has shed light on areas important for speech, movement, memory and many other processes.
More recently, researchers have used fMRI for more elaborate purposes. One of the most remarkable studies comes from Jack Gallant’s lab at the University of California. His team showed movie trailers to their volunteers and managed to reconstruct these video clips based on the subjects’ brain activity, using a machine learning algorithm.
In this approach, the computer developed a model based on the subject’s brain activity rather than being fed a pre-programmed solution by the researchers. The model improved with practice and after having access to enough data, it was able to decode brain activity. The reconstructed clips were blurry and the experiment involved extended training periods. But for the first time, brain activity was decoded well enough to reconstruct such complex stimuli with impressive detail.
“Forgiving does not erase the bitter past. A healed memory is not a deleted memory. Instead, forgiving what we cannot forget creates a new way to remember. We change the memory of our past into a hope for our future.” ― Lewis B. Smedes
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