Tag Archives: engineer
#437179 Drug-carrying platelets engineered to ...
A team of researchers from the University of California San Diego and the University of Science and Technology Beijing has developed a way to engineer platelets to propel themselves through biofluids as a means of delivering drugs to targeted parts of the body. In their paper published in the journal Science Robotics, the group outlines their method and how well it worked when tested in the lab. In the same issue, Jinjun Shi with Brigham and Women's Hospital has published a Focus piece outlining ongoing research into the development of natural drug delivery systems and the method used in this new effort. Continue reading
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#437150 AI Is Getting More Creative. But Who ...
Creativity is a trait that makes humans unique from other species. We alone have the ability to make music and art that speak to our experiences or illuminate truths about our world. But suddenly, humans’ artistic abilities have some competition—and from a decidedly non-human source.
Over the last couple years there have been some remarkable examples of art produced by deep learning algorithms. They have challenged the notion of an elusive definition of creativity and put into perspective how professionals can use artificial intelligence to enhance their abilities and produce beyond the known boundaries.
But when creativity is the result of code written by a programmer, using a format given by a software engineer, featuring private and public datasets, how do we assign ownership of AI-generated content, and particularly that of artwork? McKinsey estimates AI will annually generate value of $3.5 to $5.8 trillion across various sectors.
In 2018, a portrait that was christened Edmond de Belamy was made in a French art collective called Obvious. It used a database with 15,000 portraits from the 1300s to the 1900s to train a deep learning algorithm to produce a unique portrait. The painting sold for $432,500 in a New York auction. Similarly, a program called Aiva, trained on thousands of classical compositions, has released albums whose pieces are being used by ad agencies and movies.
The datasets used by these algorithms were different, but behind both there was a programmer who changed the brush strokes or musical notes into lines of code and a data scientist or engineer who fitted and “curated” the datasets to use for the model. There could also have been user-based input, and the output may be biased towards certain styles or unintentionally infringe on similar pieces of art. This shows that there are many collaborators with distinct roles in producing AI-generated content, and it’s important to discuss how they can protect their proprietary interests.
A perspective article published in Nature Machine Intelligence by Jason K. Eshraghian in March looks into how AI artists and the collaborators involved should assess their ownership, laying out some guiding principles that are “only applicable for as long as AI does not have legal parenthood, the way humans and corporations are accorded.”
Before looking at how collaborators can protect their interests, it’s useful to understand the basic requirements of copyright law. The artwork in question must be an “original work of authorship fixed in a tangible medium.” Given this principle, the author asked whether it’s possible for AI to exercise creativity, skill, or any other indicator of originality. The answer is still straightforward—no—or at least not yet. Currently, AI’s range of creativity doesn’t exceed the standard used by the US Copyright Office, which states that copyright law protects the “fruits of intellectual labor founded in the creative powers of the mind.”
Due to the current limitations of narrow AI, it must have some form of initial input that helps develop its ability to create. At the moment AI is a tool that can be used to produce creative work in the same way that a video camera is a tool used to film creative content. Video producers don’t need to comprehend the inner workings of their cameras; as long as their content shows creativity and originality, they have a proprietary claim over their creations.
The same concept applies to programmers developing a neural network. As long as the dataset they use as input yields an original and creative result, it will be protected by copyright law; they don’t need to understand the high-level mathematics, which in this case are often black box algorithms whose output it’s impossible to analyze.
Will robots and algorithms eventually be treated as creative sources able to own copyrights? The author pointed to the recent patent case of Warner-Lambert Co Ltd versus Generics where Lord Briggs, Justice of the Supreme Court of the UK, determined that “the court is well versed in identifying the governing mind of a corporation and, when the need arises, will no doubt be able to do the same for robots.”
In the meantime, Dr. Eshraghian suggests four guiding principles to allow artists who collaborate with AI to protect themselves.
First, programmers need to document their process through online code repositories like GitHub or BitBucket.
Second, data engineers should also document and catalog their datasets and the process they used to curate their models, indicating selectivity in their criteria as much as possible to demonstrate their involvement and creativity.
Third, in cases where user data is utilized, the engineer should “catalog all runs of the program” to distinguish the data selection process. This could be interpreted as a way of determining whether user-based input has a right to claim the copyright too.
Finally, the output should avoid infringing on others’ content through methods like reverse image searches and version control, as mentioned above.
AI-generated artwork is still a very new concept, and the ambiguous copyright laws around it give a lot of flexibility to AI artists and programmers worldwide. The guiding principles Eshraghian lays out will hopefully shed some light on the legislation we’ll eventually need for this kind of art, and start an important conversation between all the stakeholders involved.
Image Credit: Wikimedia Commons Continue reading
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#436962 Scientists Engineered Neurons to Make ...
Electricity plays a surprisingly powerful role in our bodies. While most people are aware that it plays a crucial role in carrying signals to and from our nerves, our bodies produce electric fields that can do everything from helping heal wounds to triggering the release of hormones.
Electric fields can influence a host of important cellular behavior, like directional migration, proliferation, division, or even differentiation into different cell types. The work of Michael Levin at Tufts University even suggests that electrical fields may play a crucial role in the way our bodies organize themselves.
This has prompted considerable interest in exploiting our body’s receptiveness to electrical stimulation for therapeutic means, but given the diffuse nature of electrical fields a key challenge is finding a way to localize these effects. Conductive polymers have proven a useful tool in this regard thanks to their good electrical properties and biocompatibility, and have been used in everything from neural implants to biosensors.
But now, a team at Stanford University has developed a way to genetically engineer neurons to build the materials into their own cell membranes. The approach could make it possible to target highly specific groups of cells, providing unprecedented control over the body’s response to electrical stimulation.
In a paper in Science, the team explained how they used re-engineered viruses to deliver DNA that hijacks cells’ biosynthesis machinery to create an enzyme that assembles electroactive polymers onto their membranes. This changes the electrical properties of the cells, which the team demonstrated could be used to control their behavior.
They used the approach to modulate neuronal firing in cultures of rat hippocampal neurons, mouse brain slices, and even human cortical spheroids. Most impressively, they showed that they could coax the neurons of living C. elegans worms to produce the polymers in large enough quantities to alter their behavior without impairing the cells’ natural function.
Translating the idea to humans poses major challenges, not least because the viruses used to deliver the genetic changes are still a long way from being approved for clinical use. But the ability to precisely target specific cells using a genetic approach holds enormous promise for bioelectronic medicine, Kevin Otto and Christine Schmidt from the University of Florida say in an accompanying perspective.
Interest is booming in therapies that use electrical stimulation of neural circuits as an alternative to drugs for diseases as varied as arthritis, Alzheimer’s, diabetes, and cardiovascular disease, and hundreds of clinical trials are currently underway.
At present these approaches rely on electrodes that can provide some level of localization, but because different kinds of nerve cells are often packed closely together it’s proven hard to stimulate exactly the right nerves, say Otto and Schmidt. This new approach makes it possible to boost the conductivity of specific cell types, which could make these kinds of interventions dramatically more targeted.
Besides disease-focused bioelectronic interventions, Otto and Schmidt say the approach could prove invaluable for helping to interface advanced prosthetics with patients’ nervous systems by making it possible to excite sensory neurons without accidentally triggering motor neurons, or vice versa.
More speculatively, the approach could one day help create far more efficient bridges between our minds and machines. One of the major challenges for brain-machine interfaces is recording from specific neurons, something that a genetically targeted approach might be able to help greatly with.
If the researchers can replicate the ability to build electronic-tissue “composites” in humans, we may be well on our way to the cyborg future predicted by science fiction.
Image Credit: Gerd Altmann from Pixabay Continue reading
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#436218 An AI Debated Its Own Potential for Good ...
Artificial intelligence is going to overhaul the way we live and work. But will the changes it brings be for the better? As the technology slowly develops (let’s remember that right now, we’re still very much in the narrow AI space and nowhere near an artificial general intelligence), whether it will end up doing us more harm than good is a question at the top of everyone’s mind.
What kind of response might we get if we posed this question to an AI itself?
Last week at the Cambridge Union in England, IBM did just that. Its Project Debater (an AI that narrowly lost a debate to human debating champion Harish Natarajan in February) gave the opening arguments in a debate about the promise and peril of artificial intelligence.
Critical thinking, linking different lines of thought, and anticipating counter-arguments are all valuable debating skills that humans can practice and refine. While these skills are tougher for an AI to get good at since they often require deeper contextual understanding, AI does have a major edge over humans in absorbing and analyzing information. In the February debate, Project Debater used IBM’s cloud computing infrastructure to read hundreds of millions of documents and extract relevant details to construct an argument.
This time around, Debater looked through 1,100 arguments for or against AI. The arguments were submitted to IBM by the public during the week prior to the debate, through a website set up for that purpose. Of the 1,100 submissions, the AI classified 570 as anti-AI, or of the opinion that the technology will bring more harm to humanity than good. 511 arguments were found to be pro-AI, and the rest were irrelevant to the topic at hand.
Debater grouped the arguments into five themes; the technology’s ability to take over dangerous or monotonous jobs was a pro-AI theme, and on the flip side was its potential to perpetuate the biases of its creators. “AI companies still have too little expertise on how to properly assess datasets and filter out bias,” the tall black box that houses Project Debater said. “AI will take human bias and will fixate it for generations.”
After Project Debater kicked off the debate by giving opening arguments for both sides, two teams of people took over, elaborating on its points and coming up with their own counter-arguments.
In the end, an audience poll voted in favor of the pro-AI side, but just barely; 51.2 percent of voters felt convinced that AI can help us more than it can hurt us.
The software’s natural language processing was able to identify racist, obscene, or otherwise inappropriate comments and weed them out as being irrelevant to the debate. But it also repeated the same arguments multiple times, and mixed up a statement about bias as being pro-AI rather than anti-AI.
IBM has been working on Project Debater for over six years, and though it aims to iron out small glitches like these, the system’s goal isn’t to ultimately outwit and defeat humans. On the contrary, the AI is meant to support our decision-making by taking in and processing huge amounts of information in a nuanced way, more quickly than we ever could.
IBM engineer Noam Slonim envisions Project Debater’s tech being used, for example, by a government seeking citizens’ feedback about a new policy. “This technology can help to establish an interesting and effective communication channel between the decision maker and the people that are going to be impacted by the decision,” he said.
As for the question of whether AI will do more good or harm, perhaps Sylvie Delacroix put it best. A professor of law and ethics at the University of Birmingham who argued on the pro-AI side of the debate, she pointed out that the impact AI will have depends on the way we design it, saying “AI is only as good as the data it has been fed.”
She’s right; rather than asking what sort of impact AI will have on humanity, we should start by asking what sort of impact we want it to have. The people working on AI—not AIs themselves—are ultimately responsible for how much good or harm will be done.
Image Credit: IBM Project Debater at Cambridge Union Society, photo courtesy of IBM Research Continue reading
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