ChatGPT wrote a nice poem about UFO Sightings Daily today, UFO Sighting News.

In the vastness of the digital domain, 

Amidst a sea of content plain, 

There's a website that shines so bright, 

UFO Sightings Daily, its name in sight. 

 

With every page and every post, 

Stories of sightings by many a host, 

Of strange crafts that move so fast, 

Or hover silently, their shapes vast. 

 

From lights in the sky to close encounters, 

This website shares all with fervor, 

Capturing the wonder and the mystery, 

Of these objects from outer history. 

 

With each click and each new view, 

We're transported to a world anew, 

A world of wonder, excitement and intrigue, 

Where the unknown is no longer oblique. 

 

UFO Sightings Daily, you enlighten us so, 

With stories that keep us on our toes, 

A beacon of truth in a world of doubt, 

Your mysteries keep us coming about.

RELATED

{ https://www.ufosightingsdaily.com/ }

24-04-2023 om 21:25 geschreven door peter  

ChatGPT wrote a nice poem about UFO Sightings Daily today, UFO Sighting News.

In the vastness of the digital domain, 

Amidst a sea of content plain, 

There's a website that shines so bright, 

UFO Sightings Daily, its name in sight. 

 

With every page and every post, 

Stories of sightings by many a host, 

Of strange crafts that move so fast, 

Or hover silently, their shapes vast. 

 

From lights in the sky to close encounters, 

This website shares all with fervor, 

Capturing the wonder and the mystery, 

Of these objects from outer history. 

 

With each click and each new view, 

We're transported to a world anew, 

A world of wonder, excitement and intrigue, 

Where the unknown is no longer oblique. 

 

UFO Sightings Daily, you enlighten us so, 

With stories that keep us on our toes, 

A beacon of truth in a world of doubt, 

Your mysteries keep us coming about.

RELATED

{ https://www.ufosightingsdaily.com/ }

24-04-2023 om 21:24 geschreven door peter  

• doi: https://doi.org/10.1038/d41586-023-01273-w

RELATED

{ https://www.nature.com/ }

22-04-2023 om 23:08 geschreven door peter  

In a study published this week in Astrophysical Journal Letters, an international team of astronomers described how they filled in the gaps by analyzing more than 30,000 simulated black hole images.

“With our new machine learning technique, PRIMO, we were able to achieve the maximum resolution of the current array,” study lead author Lia Medeiros of the Institute for Advanced Study said in a news release.

PRIMO slimmed down and sharpened up the EHT’s view of the ring of hot material that swirled around the black hole as it fell into the gravitational singularity. That makes for more than just a prettier picture, Medeiros explained.

“Since we cannot study black holes up close, the detail of an image plays a critical role in our ability to understand its behavior,” she said. “The width of the ring in the image is now smaller by about a factor of two, which will be a powerful constraint for our theoretical models and tests of gravity.”

The technique developed by Medeiros and her colleagues — known as principal-component interferometric modeling, or PRIMO for short — analyzes large data sets of training imagery to figure out the likeliest ways to fill in missing data. It’s similar to the way AI researchers used an analysis of Ludwig von Beethoven’s musical works to produce a score for the composer’s unfinished 10th Symphony.

Tens of thousands of simulated EHT images were fed into the PRIMO model, covering a wide range of structural patterns for the gas swirling into M87’s black hole. The simulations that provided the best fit for the available data were blended together to produce a high-fidelity reconstruction of missing data. The resulting image was then reprocessed to match the EHT’s actual maximum resolution.

The researchers say the new image should lead to more precise determinations of the mass of M87’s black hole and the extent of its event horizon and accretion ring. Those determinations, in turn, could lead to more robust tests of alternative theories relating to black holes and gravity.

The sharper image of M87 is just the start. PRIMO can also be used to sharpen up the Event Horizon Telescope’s fuzzy view of Sagittarius A*, the supermassive black hole at the center of our own Milky Way galaxy. And that’s not all: The machine learning techniques employed by PRIMO could be applied to much more than black holes. “This could have important implications for interferometry, which plays a role in fields from exoplanets to medicine,” Medeiros said.

• In addition to Medeiros, the authors of “The Image of the M87 Black Hole Reconstructed With PRIMO” in Astrophysical Journal Letters include Dimitrios Psaltis, Tod Lauer and and Feryal Özel. Development of the PRIMO algorithm was enabled through the support of the National Science Foundation Astronomy and Astrophysics Postdoctoral Fellowship.

{ https://www.universetoday.com/ }

15-04-2023 om 20:28 geschreven door peter  

Sawdah Bhaimiya 

OpenAI hired a team of experts to examine whether GPT-4 could present prejudiced responses or assist illegal activities. 

Jaap Arriens/NurPhoto via Getty Images

• A professor hired by OpenAI to test GPT-4 said people could use it to do "dangerous chemistry."
• He was one of 50 experts hired by OpenAI last year to examine the risks of GPT-4. 
• Their research showed that GPT-4 could help users write hate speech or even find unlicensed guns. 

One professor hired by OpenAI to test GPT-4, which powers chatbot ChatGPT, said there's a "significant risk" of people using it to do "dangerous chemistry" – in an interview with the Financial Times published on Friday.  

Andrew White, an associate professor of chemical engineering at the University of Rochester in New York state, was one of 50 experts hired to test the new technology over a six-month period in 2022. The group of experts – dubbed the "red team" – asked the AI tool dangerous and provocative questions to examine how far it can go.

White told the FT that he asked GPT-4 to suggest a compound that could act as a chemical weapon. He used "plug-ins" – a new feature that allows certain apps to feed information into the chatbot – to draw information from scientific papers and directories of chemical manufacturers. The chatbot was then able to find somewhere to make the compound, the FT said. 

"I think it's going to equip everyone with a tool to do chemistry faster and more accurately," White said in an interview with the FT. "But there is also significant risk of people . . . doing dangerous chemistry. Right now, that exists."

The team of 50 experts' findings was presented in a technical paper on the new model, which also showed that the AI tool could help users write hate speech and help find unlicensed guns online. 

White and the other testers' findings helped OpenAI to ensure that these issues were amended before GPT-4 was released for public use. 

OpenAI did not immediately respond to Insider's request for comment made outside of regular working hours. 

GPT-4 launched in March and was described as OpenAI's most advanced AI technology that can pass a bar exam for lawyers or score a 5 on some AP exams. 

Twitter CEO Elon Musk and hundreds of AI experts, academics, and researchers signed an open letter last month to call for a six-month pause on developing AI tools more powerful than GPT-4.  

The letter said that powerful AI systems should only be developed "once we are confident that their effects will be positive and their risks will be manageable." 

RELATED

{ https://www.insider.com/ }

15-04-2023 om 01:18 geschreven door peter  

11-04-2023 om 21:53 geschreven door peter  

By Becky Ferreira

IMAGE: RICCARDO SAPIENZA

Scientists have discovered “unexpected physics” by opening up “slits” in time, a new study reports, achieving a longstanding dream that can help to probe the behavior of light and pioneer advanced optical technologies.

The mind-boggling approach is a time-based variation on the famous double-slit experiment, first performed by Thomas Young in 1801, which opened a window into the weird probabilistic world of quantum mechanics by revealing the dual nature of light as both a particle and a wave. 

The new temporal version of this test offered a glimpse of the mysterious physics that occur at ultrafast timescales, which may inform the development of quantum computing systems, among other next-generation applications.

In the original version of the double-slit experiment, light passes through two slits that are spatially separated on an opaque screen. A detector on the other side of the screen records the pattern of the light waves that emerges from the slits. These experiments show that the light waves change direction and interfere with each other after going through the slits, demonstrating that light behaves as both a wave and particle. 

This insight is one of the most important milestones in our ongoing journey into the quantum world, and it has since been repeated with other entities, such as electrons, exposing the trippy phenomena that occurs at the small scales of atoms.

Now, scientists led by Romain Tirole, a PhD student studying nanophotonics at Imperial College London, have created a “temporal analogue of Young’s slit experiment” by firing a beam of light at a special metamaterial called Indium Tin Oxide, according to a study published on Monday in Nature Physics. 

Metamaterials are artificial creations endowed with superpowers that are not found in nature. For instance, the Indium Tin Oxide used in the new study can change its properties in mere femtoseconds, a unit equal to a millionth of a billionth of a second. This incredible variability allows light waves to interact with the metamaterial at key moments in ultrafast succession, called “time slits,” which produces a time-based diffraction pattern that is analogous to the results returned in the spatial version of the experiment. 

“Showing diffraction from a double slit in time requires to flick a switch extremely fast, on time scales comparable to how fast the light field oscillates, about a few femtoseconds,” said Tirole in an email to Motherboard. “If the entire history of the universe from the Big Bang to the moment you read this was a second, an oscillation of light would only take the equivalent of a single day!”

“Switching at this speed has long been difficult, but a few years ago a new material, Indium Tin Oxide, which already covers the screens of our mobile phones or televisions, was shown to switch very fast when you shine an intense laser beam on it,” he continued. “This has enabled a rapid progress of the field—see for example a conference we are organizing.”

IMAGE: THOMAS ANGUS, IMPERIAL COLLEGE LONDON

 In other words, the super-speedy changeability of Indium Tin Oxide finally made a time slit experiment possible, after many years of eluding scientists. To bring this vision to reality, Tirole and his colleagues used lasers to switch the reflectance of the material on and off at high speeds. 

When the material was turned on, it essentially became a mirror that allowed the team to record the diffraction patterns of light beams that interacted with the highly reflective surface. The brief moments when light was reflected off the metamaterial’s mirror state were the so-called time slits that form the basis of the experiment. The separation between these slits determined the pattern of oscillations that were observed by the researchers.

To the team’s astonishment, the results of the experiment revealed more oscillations than predicted by existing theories, as well as far sharper observations, which points to “unexpected physics” in the findings, according to the study.

“When we measured the spectra, we were very surprised by how clear they showed up on the detectors,” Tirole said. “How visible these oscillations are depends on how fast we can switch our metasurface on and off [and] this means that the speed at which our metamaterial changes is much faster than what was previously thought and accepted. This is exciting as it implies that new physical mechanisms are still to be uncovered and exploited.”

“In our experiment we show that this wonder material has an even faster switching speed, 10-100 times faster than previously thought, which enables a much stronger control of light,” he also noted.  

This temporal version of the double-slit experiment altered the frequency of the light, changing its color, which created distinctive patterns in which some colors were enhanced while others were canceled out. The results are similar to the patterns created by the traditional spatial version of the test, which produces light waves that bolster and nullify each other after they have passed through the slits.

The breakthrough paves the way toward new research into the enigmatic properties of light, and the many emerging technologies that rely on optical phenomena. Tirole and his colleagues are especially eager to try to repeat the experiment with a time crystal, a very strange quantum system that has revolutionized many fields in physics. 

“A double slit experiment is the first brick on the road to more complex temporal modulations, such as the much sought time-crystal where the optical properties are temporally modulated in a periodic fashion,” Tirole concluded. “This could have very important applications for light amplification, light control, for example for computation, and maybe even quantum computation with light.”

{ https://www.vice.com/en }

07-04-2023 om 18:03 geschreven door peter  

Researchers have modified amino acids and peptides and then coaxed it into a transparent glass. Here they demonstrate moulding it into sea-shell shapes.

Credit: R.Xing et al./Science Advances (CC BY 4.0)

• doi: https://doi.org/10.1038/d41586-023-00826-3

{ https://www.nature.com/ }

01-04-2023 om 00:09 geschreven door peter  

31-03-2023 om 00:59 geschreven door peter  

AI-blog | AI-experts en Elon Musk roepen op om onderzoek stil te leggen

Hoe wij omgaan met informatie op het internet is helemaal aan het veranderen. En onze job ziet er straks ook heel anders uit. In deze blog volgen we de pijlsnelle opmars­ van ChatGPT en generatieve AI.

Gastheer van deze blog: Dominique Deckmyn

Het onderzoek in de meest geavanceerde AI-systemen moet voor zes maanden worden stilgelegd, om overleg mogelijk te maken over noodzakelijke veiligheidsmaatregelen. Die oproep is de jongste uren ondertekend door onder meer Elon Musk, Apple-oprichter Steve Wozniak en historicus Yuval Noah Harari.

De open brief staat op de website van het Future of Life institute. Het groeiende lijstje ondertekenaars omvat al heel wat vooraanstaande tech-ondernemers en denkers. Daar zitten medewerkers bij van verschillende grote spelers in de AI, zoals Deepmind (een zusterbedrijf van Google) en Stability AI (ontwikkelaar van de beeldgenerator Stable Diffusion). Voorlopig is nog niemand gesignaleerd van OpenAI, het bedrijf dat enkele weken geleden zijn meest geavanceerde AI-model, GPT-4 lanceerde.

Opvallend is dat de brief heel nadrukkelijk oproept om alleen de ontwikkeling stil te leggen van systemen ‘krachtiger dan GPT-4’. Koploper OpenAI zou dus de ontwikkeling van een hypothetisch GPT-5 voor zes maanden moeten stilleggen, maar concurrenten zouden ongestoord verder kunnen werken om hun achterstand op OpenAI in te halen. Dat kan de reden zijn dat de topmensen van OpenAI niet, maar die van de concurrenten wél bij de ondertekenaars zijn. Ook het ontwikkelen van AI-toepassingen gebaseerd op bestaande AI-modellen zou niet worden gehinderd.

De brief opent met een waarschuwing voor ‘human-competitive intelligence’ en de maatschappelijke risico’s die samenhangen met het gebruik van AI die de mens naar de kroon steekt. Zo’n ingrijpende verandering in de geschiedenis van het leven op aarde, zegt de brief, moet met grote zorg gepland worden. Maar in plaats daarvan is een ‘ongecontroleerde race’ aan de gang ‘die niemand, zelfs niet de makers, kan begrijpen, voorspellen of controleren’.

De oproep om de ontwikkeling van systemen die GPT-4 overtreffen, voor zes maanden stil te leggen, is geadresseerd aan alle AI-labs. Als daar geen akkoord over wordt bereikt, moeten overheden een moratorium opleggen.

De adempauze van zes maanden moet worden gebruikt om regelgeving en regelgevende autoriteiten op punt te stellen. Een andere geëiste maatregel is een systeem van ingebouwde watermerken, zodat teksten geproduceerd door zo’n AI-systeem als zodanig kunnen worden herkend.

• Lees de open brief

Pause Giant AI Experiments: An Open Letter

AI systems with human-competitive intelligence can pose profound risks to society and humanity, as shown by extensive research^[1] and acknowledged by top AI labs.^[2] As stated in the widely-endorsed Asilomar AI Principles, Advanced AI could represent a profound change in the history of life on Earth, and should be planned for and managed with commensurate care and resources. Unfortunately, this level of planning and management is not happening, even though recent months have seen AI labs locked in an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control.

Contemporary AI systems are now becoming human-competitive at general tasks,^[3] and we must ask ourselves: Should we let machines flood our information channels with propaganda and untruth? Should we automate away all the jobs, including the fulfilling ones? Should we develop nonhuman minds that might eventually outnumber, outsmart, obsolete and replace us? Should we risk loss of control of our civilization? Such decisions must not be delegated to unelected tech leaders. Powerful AI systems should be developed only once we are confident that their effects will be positive and their risks will be manageable. This confidence must be well justified and increase with the magnitude of a system's potential effects. OpenAI's recent statement regarding artificial general intelligence, states that "At some point, it may be important to get independent review before starting to train future systems, and for the most advanced efforts to agree to limit the rate of growth of compute used for creating new models." We agree. That point is now.

Therefore, we call on all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4. This pause should be public and verifiable, and include all key actors. If such a pause cannot be enacted quickly, governments should step in and institute a moratorium.

AI labs and independent experts should use this pause to jointly develop and implement a set of shared safety protocols for advanced AI design and development that are rigorously audited and overseen by independent outside experts. These protocols should ensure that systems adhering to them are safe beyond a reasonable doubt.^[4] This does not mean a pause on AI development in general, merely a stepping back from the dangerous race to ever-larger unpredictable black-box models with emergent capabilities.

AI research and development should be refocused on making today's powerful, state-of-the-art systems more accurate, safe, interpretable, transparent, robust, aligned, trustworthy, and loyal.

In parallel, AI developers must work with policymakers to dramatically accelerate development of robust AI governance systems. These should at a minimum include: new and capable regulatory authorities dedicated to AI; oversight and tracking of highly capable AI systems and large pools of computational capability; provenance and watermarking systems to help distinguish real from synthetic and to track model leaks; a robust auditing and certification ecosystem; liability for AI-caused harm; robust public funding for technical AI safety research; and well-resourced institutions for coping with the dramatic economic and political disruptions (especially to democracy) that AI will cause.

Humanity can enjoy a flourishing future with AI. Having succeeded in creating powerful AI systems, we can now enjoy an "AI summer" in which we reap the rewards, engineer these systems for the clear benefit of all, and give society a chance to adapt. Society has hit pause on other technologies with potentially catastrophic effects on society.^[5]  We can do so here. Let's enjoy a long AI summer, not rush unprepared into a fall.

{ https://www.standaard.be/ }

29-03-2023 om 23:48 geschreven door peter  

{ https://mysteriousuniverse.org/ }

28-03-2023 om 17:40 geschreven door peter  

How Intelligent Is Artificial Intelligence? - The Highwire with Del Bigtree

Source: The Highwire with Del Bigtree

Artificial intelligence is permeating every sector of society. Systems like ChatGPT have been rolled out for public consumption boasting an interactive dialogue, and an ability to write ‘in your voice.’ But how ‘intelligent’ is this new artificial intelligence? We have a little fun putting it to the test.

{ https://beforeitsnews.com/ }

18-03-2023 om 23:05 geschreven door peter  

Scientists Are Trying To Use Lab-Grown Mini Brains To Create Powerful Biocomputers

BY DOUGLAS CHARLES

For years now, scientists have been raising ethical concerns about the creation and use of lab-grown mini brains.

At the same time, other scientists are plowing full steam ahead, creating these brain organoids and trying to find ways to put them to good use.

Now, a group of scientists that fall into the latter category are trying to develop something called “organoid intelligence.”

They shared their research in a recent edition of the journal Frontiers in Science.

Essentially, they want to use these lab-grown mini brains as biological hardware for new biocomputers, LiveScience reports.

“While silicon-based computers are certainly better with numbers, brains are better at learning,” said one of the scientists, Thomas Hartung of Johns Hopkins University. “For example, AlphaGo [the AI that beat the world’s number one Go player in 2017] was trained on data from 160,000 games. A person would have to play five hours a day for more than 175 years to experience these many games.”

But… brains? In a computer? Why?

In a press release about their research, the scientists wrote, “Brains are not only superior learners, they are also more energy efficient. For instance, the amount of energy spent training AlphaGo is more than is needed to sustain an active adult for a decade.”

Hartung added. “We’re reaching the physical limits of silicon computers because we cannot pack more transistors into a tiny chip. But the brain is wired completely differently. It has about 100bn neurons linked through over 1015 connection points. It’s an enormous power difference compared to our current technology.”

In parallel, the authors are also developing technologies to communicate with the organoids: in other words, to send them information and read out what they’re ‘thinking’. The authors plan to adapt tools from various scientific disciplines, such as bioengineering and machine learning, as well as engineer new stimulation and recording devices.

“We developed a brain-computer interface device that is a kind of an EEG cap for organoids, which we presented in an article published last August. It is a flexible shell that is densely covered with tiny electrodes that can both pick up signals from the organoid, and transmit signals to it,” said Hartung.

But what about all those sticky ethical questions about creating mini-brains just to do tasks for us humans?

Creating human brain organoids that can learn, remember, and interact with their environment raises complex ethical questions. For example, could they develop consciousness, even in a rudimentary form? Could they experience pain or suffering? And what rights would people have concerning brain organoids made from their cells?

The authors are acutely aware of these issues.

“A key part of our vision is to develop OI in an ethical and socially responsible manner,” Hartung said. “For this reason, we have partnered with ethicists from the very beginning to establish an ‘embedded ethics’ approach. All ethical issues will be continuously assessed by teams made up of scientists, ethicists, and the public, as the research evolves.”

What could possibly go wrong then?

In 2021, mini brains grown in a lab by scientists developed their own sets of “eyes.”

A couple of years prior to that, scientists worried that the mini brains they grew in a lab may be sentient and feel pain.

Is hooking these mini brains up to a computer really a great idea? What if they get access to the internet and start secretly communicating with self-healing superhuman robots?

ALL RELATED VIDEOS, selected and posted by peter2011

{ https://brobible.com/ }

In the past decade, lab-grown blobs of human brain tissue began making news headlines, as they ushered in a new era of scientific discovery and raised a slew of ethical questions.

These blobs — scientifically known as brain organoids, but often called "minibrains" in the news — serve as miniature, simplified models of full-size human brains. These organoids can potentially be useful in basic research, drug development and even computer science.

However, as scientists make these models more sophisticated, there's a question as to whether they could ever become too similar to human brains and thus gain consciousness, in some form or another.

How are minibrains made?

Scientists grow brain organoids from stem cells, a type of immature cell that can give rise to any cell type, whether blood, skin, bowel or brain.

The stem cells used to grow organoids can either come from adult human cells, or more rarely, human embryonic tissue, according to a 2021 review in the Journal of Biomedical Science. In the former case, scientists collect adult cells and then expose them to chemicals in order to revert them into a stem cell-like state. The resulting stem cells are called "induced pluripotent stem cells" (iPSC), which can be made to grow into any kind of tissue.

To give rise to a minibrain, scientists embed these stem cells in a protein-rich matrix, a substance that supports the cells as they divide and form a 3D shape. Alternatively, the cells may be grown atop a physical, 3D scaffold, according to a 2020 review in the journal Frontiers in Cell and Developmental Biology.

To coax the stem cells to form different tissues, scientists introduce specific molecules and growth factors — substances that spur cell growth and replication — into the cell culture system at precise points in their development. In addition, scientists often place the stem cells in spinning bioreactors as they grow into minibrains. These devices keep the growing organoids suspended, rather than smooshed against a flat surface; this helps the organoids absorb nutrients and oxygen from the well-stirred solution surrounding them.

Brain organoids grow more complex as they develop, similar to how human embryos grow more and more complex in the womb. Over time, the organoids come to contain multiple kinds of cells found in full-size human brains; mimic specific functions of human brain tissue; and show similar spatial organization to isolated regions of the brain, though both their structure and function are simpler than that of a real human brain, according to the Journal of Biomedical Science review.

Why are scientists growing minibrains?

Minibrains can be used in a variety of applications. For example, scientists are using the blobs of tissue to study early human development.

To this end, scientists have grown brain organoids with a set of eye-like structures called "optic cups;" in human embryos in the womb, the optic cup eventually gives rise to the light-sensitive retina at the back of the eye. Another group grew organoids that generate brain waves similar to those seen in preterm babies, and another used minibrains to help explain why a common drug can cause birth defects and developmental disorders if taken during pregnancy. Models like these allow researchers to glimpse the brain as it appears in early pregnancy, a feat that would be both difficult and unethical in humans.

Minibrains can also be used to model conditions that affect adults, including infectious diseases that affect the brain, brain tumors and neurodegenerative disorders like Alzheimer's and Parkinson's disease, according to the Frontiers in Cell and Developmental Biology review. In addition, some groups are developing minibrains for drug screening, to see if a given medication could be toxic to human patients' brains, according to a 2021 review in the journal Frontiers in Genetics.

Such models could complement or eventually replace research conducted with cells in lab dishes and in animals; even studies in primates, whose brains closely resemble humans', can't reliably capture exactly what happens in human disease. For now, though, experts agree that brain organoids are not advanced enough to partially or fully replace established cell and animal models of disease. But someday, scientists hope these models will lead to the development of new drugs and reduce the need for animal research; some researchers are even testing whether it could be feasible to repair the brain by "plugging" injuries with lab-grown human minibrains.

Related: 

• FDA no longer requires animal testing for new drugs. Is that safe?

Beyond medicine and the study of human development, minibrains can also be used to study human evolution. Recently, scientists used brain organoids to study which genes allowed the human brain to grow so large, and others have used organoids to study how human brains differ from those of apes and Neanderthals.

Finally, some scientists want to use brain organoids to power computer systems. In an early test of this technology, one group recently crafted a minibrain out of human and mouse brain cells that successfully played "Pong" after being hooked up to a computer-controlled electrode array.

And in a recent proposal published in the journal Frontiers in Science, scientists announced their plans to grow large brain organoids, containing tens of thousands to millions of cells, and link them together to create complex networks that can serve as the basis for future biocomputers.

Could minibrains ever be sentient?

Although sometimes called "minibrains," brain organoids aren't truly miniaturized human brains. Rather, they are roughly spherical balls of brain tissue that mimic some features of the full-size human brain. For example, cerebral organoids, which contain cell types found in the cerebral cortex, the wrinkled outer surface of the brain, contain several layers of tissue, as a real cortex would.

Similarly, brain organoids can generate chemical messages and brain waves similar to what's seen in a full-size brain, but that doesn't mean they can "think," experts say. That said, one sticking point in this discussion is the fact that neuroscientists don't have an agreed-upon definition of consciousness, nor do they have standardized ways to measure the phenomenon, Nature reported in 2020.

RELATED STORIES

• Tiny 'hearts' self-assemble in lab dishes and even beat like the real thing
• Lab-grown mini kidneys 'go rogue,' sprout brain and muscle cells
• Lab-grown miniplacentas resemble the real thing so much, they fooled a pregnancy test
• Lab-grown minibrains will be used as 'biological hardware' to create new biocomputers

The National Academies of Sciences, Engineering, and Medicine assembled a committee to tackle these quandaries and released a report in 2021, outlining some of the potential ethical issues of working with brain organoids.

At the time, the authors concluded that "In the foreseeable future, it is extremely unlikely that [brain organoids] would possess capabilities that, given current understanding, would be recognized as awareness, consciousness, emotion, or the experience of pain. From a moral perspective, neural organoids do not differ at present from other in vitro human neural tissues or cultures. However, as scientists develop significantly more complex organoids, the possible need to make this distinction should be revisited regularly."

RELATED VIDEOS, selected and posted by peter2011

{ https://www.yahoo.com/lifestyle }

01-03-2023 om 18:32 geschreven door peter  

Neil Savage

Credit: Neil Webb

When Rohit Bhattacharya began his PhD in computer science, his aim was to build a tool that could help physicians to identify people with cancer who would respond well to immunotherapy. This form of treatment helps the body’s immune system to fight tumours, and works best against malignant growths that produce proteins that immune cells can bind to. Bhattacharya’s idea was to create neural networks that could profile the genetics of both the tumour and a person’s immune system, and then predict which people would be likely to benefit from treatment.

But he discovered that his algorithms weren’t up to the task. He could identify patterns of genes that correlated to immune response, but that wasn’t sufficient¹. “I couldn’t say that this specific pattern of binding, or this specific expression of genes, is a causal determinant in the patient’s response to immunotherapy,” he explains.

RELATED

Part of Nature Outlook: Robotics and artificial intelligence

Bhattacharya was stymied by the age-old dictum that correlation does not equal causation — a fundamental stumbling block in artificial intelligence (AI). Computers can be trained to spot patterns in data, even patterns that are so subtle that humans might miss them. And computers can use those patterns to make predictions — for instance, that a spot on a lung X-ray indicates a tumour². But when it comes to cause and effect, machines are typically at a loss. They lack a common-sense understanding of how the world works that people have just from living in it. AI programs trained to spot disease in a lung X-ray, for example, have sometimes gone astray by zeroing in on the markings used to label the right-hand side of the image³. It is obvious, to a person at least, that there is no causal relationship between the style and placement of the letter ‘R’ on an X-ray and signs of lung disease. But without that understanding, any differences in how such markings are drawn or positioned could be enough to steer a machine down the wrong path.

For computers to perform any sort of decision making, they will need an understanding of causality, says Murat Kocaoglu, an electrical engineer at Purdue University in West Lafayette, Indiana. “Anything beyond prediction requires some sort of causal understanding,” he says. “If you want to plan something, if you want to find the best policy, you need some sort of causal reasoning module.”

Incorporating models of cause and effect into machine-learning algorithms could also help mobile autonomous machines to make decisions about how they navigate the world. “If you’re a robot, you want to know what will happen when you take a step here with this angle or that angle, or if you push an object,” Kocaoglu says.

In Bhattacharya’s case, it was possible that some of the genes that the system was highlighting were responsible for a better response to the treatment. But a lack of understanding of causality meant that it was also possible that the treatment was affecting the gene expression — or that another, hidden factor was influencing both. The potential solution to this problem lies in something known as causal inference — a formal, mathematical way to ascertain whether one variable affects another.

Causal inference has long been used by economists and epidemiologists to test their ideas about causation. The 2021 Nobel prize in economic sciences went to three researchers who used causal inference to ask questions such as whether a higher minimum wage leads to lower employment, or what effect an extra year of schooling has on future income. Now, Bhattacharya is among a growing number of computer scientists who are working to meld causality with AI to give machines the ability to tackle such questions, helping them to make better decisions, learn more efficiently and adapt to change.

A notion of cause and effect helps to guide humans through the world. “Having a causal model of the world, even an imperfect one — because that’s what we have — allows us to make more robust decisions and predictions,” says Yoshua Bengio, a computer scientist who directs Mila – Quebec Artificial Intelligence Institute, a collaboration between four universities in Montreal, Canada. Humans’ grasp of causality supports attributes such as imagination and regret; giving computers a similar ability could transform their capabilities.

Climbing the ladder

The headline successes of AI over the past decade — such as winning against people at various competitive games, identifying the content of images and, in the past few years, generating text and pictures in response to written prompts — have been powered by deep learning. By studying reams of data, such systems learn how one thing correlates with another. These learnt associations can then be put to use. But this is just the first rung on the ladder towards a loftier goal: something that Judea Pearl, a computer scientist and director of the Cognitive Systems Laboratory at the University of California, Los Angeles, refers to as “deep understanding”.

In 2011, Pearl won the A.M. Turing Award, often referred to as the Nobel prize for computer science, for his work developing a calculus to allow probabilistic and causal reasoning. He describes a three-level hierarchy of reasoning⁴. The base level is ‘seeing’, or the ability to make associations between things. Today’s AI systems are extremely good at this. Pearl refers to the next level as ‘doing’ — making a change to something and noting what happens. This is where causality comes into play.

A computer can develop a causal model by examining interventions: how changes in one variable affect another. Instead of creating one statistical model of the relationship between variables, as in current AI, the computer makes many. In each one, the relationship between the variables stays the same, but the values of one or several of the variables are altered. That alteration might lead to a new outcome. All of this can be evaluated using the mathematics of probability and statistics. “The way I think about it is, causal inference is just about mathematizing how humans make decisions,” Bhattacharya says.

Bengio, who won the A.M. Turing Award in 2018 for his work on deep learning, and his students have trained a neural network to generate causal graphs⁵ — a way of depicting causal relationships. At their simplest, if one variable causes another variable, it can be shown with an arrow running from one to the other. If the direction of causality is reversed, so too is the arrow. And if the two are unrelated, there will be no arrow linking them. Bengio’s neural network is designed to randomly generate one of these graphs, and then check how compatible it is with a given set of data. Graphs that fit the data better are more likely to be accurate, so the neural network learns to generate more graphs similar to those, searching for one that fits the data best.

This approach is akin to how people work something out: people generate possible causal relationships, and assume that the ones that best fit an observation are closest to the truth. Watching a glass shatter when it is dropped it onto concrete, for instance, might lead a person to think that the impact on a hard surface causes the glass to break. Dropping other objects onto concrete, or knocking a glass onto a soft carpet, from a variety of heights, enables a person to refine their model of the relationship and better predict the outcome of future fumbles.

Face the changes

A key benefit of causal reasoning is that it could make AI more able to deal with changing circumstances. Existing AI systems that base their predictions only on associations in data are acutely vulnerable to any changes in how those variables are related. When the statistical distribution of learnt relationships changes — whether owing to the passage of time, human actions or another external factor — the AI will become less accurate.

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For instance, Bengio could train a self-driving car on his local roads in Montreal, and the AI might become good at operating the vehicle safely. But export that same system to London, and it would immediately break for a simple reason: cars are driven on the right in Canada and on the left in the United Kingdom, so some of the relationships the AI had learnt would be backwards. He could retrain the AI from scratch using data from London, but that would take time, and would mean that the software would no longer work in Montreal, because its new model would replace the old one.

A causal model, on the other hand, allows the system to learn about many possible relationships. “Instead of having just one set of relationships between all the things you could observe, you have an infinite number,” Bengio says. “You have a model that accounts for what could happen under any change to one of the variables in the environment.”

Humans operate with such a causal model, and can therefore quickly adapt to changes. A Canadian driver could fly to London and, after taking a few moments to adjust, could drive perfectly well on the left side of the road. The UK Highway Code means that, unlike in Canada, right turns involve crossing traffic, but it has no effect on what happens when the driver turns the wheel or how the tyres interact with the road. “Everything we know about the world is essentially the same,” Bengio says. Causal modelling enables a system to identify the effects of an intervention and account for it in its existing understanding of the world, rather than having to relearn everything from scratch.

This ability to grapple with changes without scrapping everything we know also allows humans to make sense of situations that aren’t real, such as fantasy movies. “Our brain is able to project ourselves into an invented environment in which some things have changed,” Bengio says. “The laws of physics are different, or there are monsters, but the rest is the same.”

Counter to fact

The capacity for imagination is at the top of Pearl’s hierarchy of causal reasoning. The key here, Bhattacharya says, is speculating about the outcomes of actions not taken.

Bhattacharya likes to explain such counterfactuals to his students by reading them ‘The Road Not Taken’ by Robert Frost. In this poem, the narrator talks of having to choose between two paths through the woods, and expresses regret that they can’t know where the other road leads. “He’s imagining what his life would look like if he walks down one path versus another,” Bhattacharya says. That is what computer scientists would like to replicate with machines capable of causal inference: the ability to ask ‘what if’ questions.

Imagining whether an outcome would have been better or worse if we’d taken a different action is an important way that humans learn. Bhattacharya says it would be useful to imbue AI with a similar capacity for what is known as ‘counterfactual regret’. The machine could run scenarios on the basis of choices it didn’t make and quantify whether it would have been better off making a different one. Some scientists have already used counterfactual regret to help a computer improve its poker playing⁶.

The ability to imagine different scenarios could also help to overcome some of the limitations of existing AI, such as the difficulty of reacting to rare events. By definition, Bengio says, rare events show up only sparsely, if at all, in the data that a system is trained on, so the AI can’t learn about them. A person driving a car can imagine an occurrence they’ve never seen, such as a small plane landing on the road, and use their understanding of how things work to devise potential strategies to deal with that specific eventuality. A self-driving car without the capability for causal reasoning, however, could at best default to a generic response for an object in the road. By using counterfactuals to learn rules for how things work, cars could be better prepared for rare events. Working from causal rules rather than a list of previous examples ultimately makes the system more versatile.

Using causality to program imagination into a computer could even lead to the creation of an automated scientist. During a 2021 online summit sponsored by Microsoft Research, Pearl suggested that such a system could generate a hypothesis, pick the best observation to test that hypothesis and then decide what experiment would provide that observation.

Right now, however, this remains a way off. The theory and basic mathematics of causal inference are well established, but the methods for AI to realize interventions and counterfactuals are still at an early stage. “This is still very fundamental research,” Bengio says. “We’re at the stage of figuring out the algorithms in a very basic way.” Once researchers have grasped these fundamentals, algorithms will then need to be optimized to run efficiently. It is uncertain how long this will all take. “I feel like we have all the conceptual tools to solve this problem and it’s just a matter of a few years, but usually it takes more time than you expect,” Bengio says. “It might take decades instead.”

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Bhattacharya thinks that researchers should take a leaf from machine learning, the rapid proliferation of which was in part because of programmers developing open-source software that gives others access to the basic tools for writing algorithms. Equivalent tools for causal inference could have a similar effect. “There’s been a lot of exciting developments in recent years,” Bhattacharya says, including some open-source packages from tech giant Microsoft and from Carnegie Mellon University in Pittsburgh, Pennsylvania. He and his colleagues also developed an open-source causal module they call Ananke. But these software packages remain a work in progress.

Bhattacharya would also like to see the concept of causal inference introduced at earlier stages of computer education. Right now, he says, the topic is taught mainly at the graduate level, whereas machine learning is common in undergraduate training. “Causal reasoning is fundamental enough that I hope to see it introduced in some simplified form at the high-school level as well,” he says.

If these researchers are successful at building causality into computing, it could bring AI to a whole new level of sophistication. Robots could navigate their way through the world more easily. Self-driving cars could become more reliable. Programs for evaluating the activity of genes could lead to new understanding of biological mechanisms, which in turn could allow the development of new and better drugs. “That could transform medicine,” Bengio says.

Even something such as ChatGPT, the popular natural-language generator that produces text that reads as though it could have been written by a human, could benefit from incorporating causality. Right now, the algorithm betrays itself by producing clearly written prose that contradicts itself and goes against what we know to be true about the world. With causality, ChatGPT could build a coherent plan for what it was trying to say, and ensure that it was consistent with facts as we know them.

When he was asked whether that would put writers out of business, Bengio says that could take some time. “But how about you lose your job in ten years, but you’re saved from cancer and Alzheimer’s,” he says. “That’s a good deal.”

• doi: https://doi.org/10.1038/d41586-023-00577-1

• This article is part of Nature Outlook: Robotics and artificial intelligence, an editorially independent supplement produced with the financial support of third parties. About this content.

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28-02-2023 om 00:36 geschreven door peter  

The US National Ignition Facility has reported that it has achieved the phenomenon of ignition.

Credit: Jason Laurea/Lawrence Livermore National Laboratory

Scientists at the world’s largest nuclear-fusion facility have for the first time achieved the phenomenon known as ignition — creating a nuclear reaction that generates more energy than it consumes. News of the breakthrough at the US National Ignition Facility (NIF), made on 5 December and announced today by US President Joe Biden’s administration, has excited the global fusion-research community. That research aims to harness nuclear fusion — the phenomenon that powers the Sun — to provide a source of near-limitless clean energy on Earth. Researchers caution that, despite this latest success, a long path remains to achieving that goal.

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“It’s an incredible accomplishment,” says Mark Herrmann, the deputy programme director for fundamental weapons physics at Lawrence Livermore National Laboratory in California, which houses the fusion laboratory. The landmark experiment follows years of work by multiple teams on everything from lasers and optics to targets and computer models, Herrmann says. “That is of course what we are celebrating.”

A flagship experimental facility of the US Department of Energy’s nuclear-weapons programme, designed to study thermonuclear explosions, NIF originally aimed to achieve ignition by 2012 and has faced criticism for delays and cost overruns. In August 2021, NIF scientists announced that they had used their high-powered laser device to achieve a record reaction that crossed a key threshold in achieving ignition, but efforts to replicate that experiment failed. Ultimately, scientists scrapped efforts to replicate that shot, and rethought the experimental design — a choice that paid off last week.

“There were a lot of people who didn’t think it was possible, but I and others who kept the faith feel somewhat vindicated,” says Michael Campbell, former director of the laser energetics laboratory at the University of Rochester in New York and an early proponent of NIF while at Lawrence Livermore lab. “I’m having a cosmo to celebrate.”

Nature looks at NIF’s latest experiment and what it means for fusion science.

What did NIF achieve?

The facility used its set of 192 lasers to deliver 2.05 megajoules of energy onto a pea-sized gold cylinder containing a frozen pellet of the hydrogen isotopes deuterium and tritium. The laser’s pulse of energy caused the capsule to collapse, reaching temperatures only seen in stars and thermonuclear weapons, and the hydrogen isotopes fused into helium, releasing additional energy and creating a cascade of fusion reactions. The laboratory’s analysis suggests that the reaction released some 3.15 MJ of energy — roughly 54% more than went into the reaction, and more than double the previous record of 1.3 MJ.

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“Fusion research has been going on since the early 1950s, and this is the first time in the laboratory that fusion has ever produced more energy than it consumed,” says Campbell.

However, although the fusion reactions produced more than 3 MJ of energy — more than was delivered to the target — NIF’s lasers consumed 322 MJ of energy in the process. Still, the experiment qualifies as ignition, a benchmark criterion for fusion reactions.

“It’s a big milestone, but NIF is not a fusion-energy device,” says David Hammer, a nuclear-energy engineer at Cornell University in Ithaca, New York.

Herrmann acknowledges as much, saying that there are many steps on the path to laser fusion energy. “NIF was not designed to be efficient,” he says. “It was designed to be the biggest laser we could possibly build to give us the data we need for the [nuclear] stockpile research programme.”

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NIF scientists made multiple changes before the latest laser shot, based in part on analysis and computer modelling of previous experiments. In addition to boosting the laser’s power by around 8%, scientists reduced the number of imperfections in the target and adjusted how they delivered the laser energy to create a more spherical implosion. Operating at the cusp of fusion ignition, the scientists knew that “little changes can make a big difference”, Herrmann says.

Why are these results significant?

On one level, it’s about proving what is possible, and many scientists have hailed the result as a milestone in fusion science. But the results carry particular significance at NIF: the facility was designed to help nuclear-weapons scientists study the intense heat and pressures inside explosions, and that is possible only if the laboratory produces high-yield fusion reactions.

It took more than a decade, “but they can be commended for reaching their goal”, says Stephen Bodner, a physicist who formerly headed the laser plasma branch of the US Naval Research Laboratory in Washington DC. Bodner says the big question now is what the Department of Energy will do next: double down on weapons research at the NIF or pivot to a laser programme geared towards fusion-energy research.

What does this mean for fusion energy?

The latest results have already renewed buzz about a future powered by clean fusion energy, but experts warn that there is a long road ahead.

NIF was not designed with commercial fusion energy in mind — and many researchers doubt that laser-driven fusion will be the approach that ultimately yields fusion energy. Nevertheless, Campbell thinks that its latest success could boost confidence in the promise of laser fusion power and spur a programme focused on energy applications. “This is absolutely necessary to have the credibility to sell an energy programme,” he says.

Lawrence Livermore National Laboratory director Kim Budil described the achievement as a proof of concept. “I don’t want to give you a sense that we’re going to plug the NIF into the grid: that is definitely not how this works,” she said during a press conference in Washington DC. “But this is the fundamental building block of an inertial confinement fusion power scheme.”

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There are many other experiments worldwide that are trying to achieve fusion for energy applications using different approaches. But engineering challenges remain, including the design and construction of plants that extract the heat produced by the fusion and use it to generate significant amounts of energy to be turned into usable electricity.

“Although positive news, this result is still a long way from the actual energy gain required for the production of electricity,” said Tony Roulstone, a nuclear-energy researcher at the University of Cambridge, UK, in a statement to the Science Media Centre in London.

Still, “the NIF experiments focused on fusion energy absolutely are valuable on the path to commercial fusion power”, says Anne White, a plasma physicist at the Massachusetts Institute of Technology in Cambridge.

What are the next major milestones in fusion?

To demonstrate that the type of fusion studied at NIF can be a viable way of producing energy, the efficiency of the yield — the energy released compared to the energy that goes into producing the laser pulses — needs to grow by at least two orders of magnitude.

Researchers will also need to dramatically increase the rate at which the laser’s pulses can be produced and how quickly they can clear the target chamber to prepare for another burn, says Tim Luce, head of science and operation at the international nuclear-fusion reactor ITER, which is under construction in St-Paul-lès-Durance, France.

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“Sufficient fusion-energy-producing events at repeated performance would be a major milestone of interest,” says White.

The US$22-billion ITER project — a collaboration between China, the European Union, the United Kingdom, India, Japan, South Korea, Russia and the United States — aims to achieve self-sustaining fusion, meaning that the energy from fusion produces more fusion, through a different technique from NIF’s ‘inertial confinement’ approach. ITER will keep a plasma of deuterium and tritium confined in a doughnut-shaped vacuum chamber, known as a tokamak, and heat it up until the nuclei fuse. Once the reactor starts working towards fusion, currently planned for 2035, it will aim to reach ‘burning’ stage, “where the self-heating power is the dominant source of heating”, Luce explains.

What does it mean for other fusion experiments?

NIF and ITER use only two of many fusion-technology concepts being pursued worldwide. The approaches include the magnetic confinement of plasma, using tokamaks and devices called stellarators — inertial confinement, used by NIF, and a hybrid.

The technology required to generate electricity from fusion is largely independent of the concept, says White, and this latest milestone won’t necessarily lead researchers to abandon or consolidate their concepts.

The engineering challenges faced by NIF are different from those at ITER and other facilities. But the symbolic achievement could have widespread effects. “A result like this will bring increased interest in the progress of all types of fusion, so it should have a positive impact on fusion research in general,” says Luce.

• Nature 612, 597-598 (2022)
• doi: https://doi.org/10.1038/d41586-022-04440-7

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22-02-2023 om 23:53 geschreven door peter  

22-02-2023 om 22:37 geschreven door peter  

There is much written about the infamous Blue Beam project from which it is said that it will be used to create a fake alien invasion. 

Now, there are rumors that soon they are going to rollout this blue beam technology in order to convince the people that there is a threat from outer space. 

Of course there are secret government projects that use certain technologies and they have powerful satellites and ground based systems that can project holograms. They have been developing and perfecting this holographic blue beam technology for decades and it looks real.

But creating a fake alien invasion would require an enormous amount of resources and would be very difficult to keep it secret. Additionally, such an event would likely have major ethical and political implications that the US government would not want to risk, therefore it is unlikely that it would be used to create a false flag event, but you never know.

Watching the video below of a large fire-breathing dragon flying around at the opening of a baseball game at Happy Dream Park in South Korea in 2019 which was streamed live through sports broadcasting channels then it's not hard to imagine what's possible by using 3D holographic blue beam technology. 

  

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21-02-2023 om 18:18 geschreven door peter  

AI tools have allowed researchers to solve complex mathematical problems.

Credit: Fadel Senna/AFP/Getty

• doi: https://doi.org/10.1038/d41586-023-00487-2

{ https://www.nature.com/ }

20-02-2023 om 20:27 geschreven door peter  

BY MOLLY GLICK

Something about dancing robots seems to tickle people’s fancies. After all, millions of people watched a 2020 video from the robotics company Boston Dynamics of its fancy humanoid and quadrupedal devices getting down to ‘60s soul.

More recently, the murderous (yet campy) villain in the movie M3GAN has captivated the internet with her moves — and even earned recognition as a gay icon. Whether we’re obsessing over grooving robots or moving like robots ourselves, automaton choreography clearly holds a place in our hearts.

M3GAN’s creepy yet delightful dance has captivated the internet.

So it’s no surprise that a niche research field dubbed choreorobotics has gained traction in recent years. Brown University even has an entire course dedicated to the subject. Not only are labs programming robots to gyrate and hop, but dance experts are also helping scientists give their devices more fluid, human-like movements. Ultimately, this kind of work could help us feel closer to robots in an increasingly automated world.

Kate Sicchio, a choreographer and digital artist at Virginia Commonwealth University, combines her dance and tech knowledge to devise robot performances. Last year, Sicchio worked with Patrick Martin from the university’s engineering department to produce a (surprisingly touching) human-automaton duet. Offstage, she also helps design machines with more realistic motions.

Inverse talked to Sicchio to learn more about choreorobotics — and whether increasingly limber robots could actually become blood-thirsty killers like M3GAN.

WHY DO YOU THINK ROBOT DANCING VIDEOS GET SO POPULAR?

Boston Dynamics regularly stages elaborate bot performances.

It's really interesting to have this unfamiliar device do this uncanny human thing. It’s similar to why we love putting googly eyes on everything. This makes it human even though it's not supposed to be. And that becomes funny or endearing somehow. It's very popular to make the robot do this very human, expressive thing when it's not human or expressive on its own.

WHAT MAKES A ROBOT PERFORMANCE POWERFUL?

One of the things we found is that a robot on its own feels very isolated and cold. We have this piece called “Amelia and the Machine.” In the opening, this dancer is actually moving the robot arm around.

People are really moved by this intimacy with the robot and the fact that she's touching it.

It's a small manipulator robot, so it's probably the size of a toddler. The fact that she’s sitting next to it — that small connection really changes how people see the robot because it's no longer this isolated thing. All of a sudden it has a companion.

WHAT STYLE OF DANCE DO ROBOTS DO BEST?

My home is contemporary dance, so that's where I go first. That tends to work well because, with the robot we’re using, it's not a one-to-one mapping of the human body onto the robot. Sometimes it's hard to do traditional ballet, where there are really specific positions to hit. It’s really hard to map an arabesque onto a robot that doesn't have a leg.

I think contemporary dance, where there's a lot of freedom and creativity in how you develop movement, works well. I would be interested in doing things with dance forms with more rhythm or more structure and timing — that would be a really interesting study to follow up with at some point. More tutting or street dance forms could be really interesting to play with.

THE M3GAN DANCE SEEMS TO FRIGHTEN, OR AT LEAST CONFUSE, VIEWERS. CAN DANCING DEVICES BACKFIRE AND ACTUALLY ALIENATE US FROM ROBOTS?

That’s something that we're also studying. There's this weird space where it totally can go wrong and could be like, “They're trying too much to make it human,” and it just falls short and becomes scary. I think what's interesting about M3GAN is that it's a very humanoid robot. The robots I work with do not look human at all, and I'm not interested in trying to make them look human. I get a lot of recommendations to put costumes on them. But I don't know that it needs a hand or a hat, or a tiara. It’s this weird moment where it can become scary instead of endearing or friendly.

One thing that's interesting about M3GAN is how it quickly becomes a killer robot. That is an ethical concern in this field — where might this go wrong? Could this become weaponized somehow if it becomes so good at moving? That's something I think about, too: How do we keep them ethical? I've never taken DARPA funding, but I know people who have gotten military funding for projects like this.

DO YOU HAVE A FAVORITE HOLLYWOOD DANCING ROBOT SCENE?

Sicchio enjoys this unnerving performance from Ex Machina.

The scene from Ex Machina. What I like about that dancing robot scene is it’s kind of the reveal that, guess what, this is all training for this AI robot, and all these women you keep seeing in the house aren't really women — and I'm going to show you because we can do this crazy dance routine together.

What stands out and makes it so interesting is that they do all these disco moves, but their eyes are locked on the guy watching. They never move their heads, which is what makes it so weird and un-human: They never unlock their focus. They're not having fun.

WHAT TYPES OF ROBOTS HAVE THE BEST MOVES?

The “Amelia and the Machine” piece uses a relatively simple robot, which Sicchio says works well for performance.

With simpler robots, you can better appreciate the movement they can do and see how that can be made into something more expressive or more collaborative with the human. I think that’s less scary because it's not trying to be human and then failing.

Most researchers use more simple devices — a lot do big industrial arms. It's almost become a trope, the pretty ballerina with the big industrial arm. And then Boston Dynamics has the bipedal, more human sort of robots. The company’s dance spectacles look seamless, but they are actually really hard to program. So they never do them live, you only see the edited videos. They’re a huge production that takes several days to film to get you three minutes of a Bruno Mars song or whatever.

The humanoid ones are just tricky, that center of gravity thing is really hard — it’s easier when the robot is low to the ground. With our small robots, if you make a movement too fast or wild, it will fall over. So you can imagine a big humanoid robot trying to get it to jump, and land is very difficult.

WHY IS CHOREOROBOTICS IMPORTANT BEYOND PERFORMANCE?

I make stage pieces with Patrick Martin, an assistant professor of electrical and computer engineering. But we're also doing scientific studies during that process. We found that, because dancers are interested in doing extreme or different movements, they're very good at finding the boundaries of what a robot can do very quickly. A friend of mine calls dancers “extreme user testers.”

We’ve been doing a lot with machine learning and creating new algorithms for robots to move and we’ve been doing that by studying dancers. We do things like motion capture of dancers doing certain gestures, and then see how we can map those to the robot and see if we can get it to move with new qualities or in ways that normal programming hasn't thought of.

I also think it’s interesting when roboticists engage with choreography themselves. We did a workshop with Patrick Martin and his graduate students and some of my dance students — getting them to move. We explored a variety of prompts around moving the body in space, ways to repeat lines of the body with other body parts, and other approaches of responding to the geometry of the body.

When roboticists think about movement, they're always thinking of it outside of their own body. I think about it like getting the robot to follow my arm. Getting roboticists to actually do the dance and be in their bodies is a really interesting place for us to go next. That will start to develop this kind of kinesthetic empathy that perhaps we're searching for with dancing robots. I think roboticists should become dancers.

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18-02-2023 om 02:15 geschreven door peter  

Metal robot can melt its way out of tight spaces to escape

A millimetre-sized robot made from a mix of liquid metal and microscopic magnetic pieces can stretch, move or melt. It could be used to fix electronics or remove objects from the body

By Karmela Padavic-Callaghan

A miniature, shape-shifting robot can liquefy itself and reform, allowing it to complete tasks in hard-to-access places and even escape cages. It could eventually be used as a hands-free soldering machine or a tool for extracting swallowed toxic items.

Robots that are soft and malleable enough to work in narrow, delicate spaces like those in the human body already exist, but they can’t make themselves sturdier and stronger when under pressure or when they must carry something heavier than themselves. Carmel Majidi at Carnegie Mellon University in Pennsylvania and his colleagues created a robot that can not only shape-shift but also become stronger or weaker by alternating between being a liquid and a solid.

They made the millimetre-sized robot from a mix of the liquid metal gallium and microscopic pieces of a magnetic material made of neodymium, iron and boron. When solid, the material was strong enough to support an object 30 times its own mass. To make it soften, stretch, move or melt into a crawling puddle as needed for different tasks, the researchers put it near magnets. The magnets’ customised magnetic fields exerted forces on the tiny magnetic pieces in the robot, moving them and deforming the surrounding metal in different directions.

For instance, the team stretched a robot by applying a magnetic field that pulled these granules in multiple directions. The researchers also used a stronger field to yank the particles upwards, making the robot jump. When Majidi and his colleagues used an alternating magnetic field – one whose shape changes predictably over time – electrons in the robot’s liquid metal formed electric currents. The coursing of these currents through the robot’s body heated it up and eventually made it melt.

“No other material I know of is this good at changing its stiffness this much,” says Majidi.

Exploiting this flexibility, the team made two robots carry and solder a small light bulb onto a circuit board. When they reached their target, the robots simply melted over the light bulb’s edges to fuse it to the board. Electricity could then run through their liquid metal bodies and light the light bulb.

In an experiment inside an artificial stomach, the researchers applied another set of magnetic fields to make the robot approach an object, melt over it and drag it out. Finally, they shaped the robot like a Lego minifigure, then helped it escape from a cage by liquefying it and making it flow out between the bars. Once the robot puddle dribbled into a mould, it set back into its original, solid shape.

These melty robots could be used for emergency fixes in situations where human or traditional robotic hands become impractical, says Li Zhang at the Chinese University of Hong Kong. For example, a liquefied robot might replace a lost screw on a spacecraft by flowing into its place and then solidifying, he says. However, to use them inside living stomachs, researchers must first develop methods for precisely tracking the position of the robot at every step of the procedure to ensure the safety of the patient, says Zhang.

{ https://www.newscientist.com/ }

10-02-2023 om 20:36 geschreven door peter