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Monday, April 17, 2023

Google Project Magi: The Future of Search


 

  • Google's New AI Search Engine Will Change the Way You Search

Magi is designed to be more personalized and helpful than ever before, using artificial intelligence to anticipate your needs and provide you with the information you need, when you need it.

Some of the features that Magi will offer include:

  • Personalized search results: Magi will learn your preferences and interests over time, and use that information to deliver more relevant results.
  • Natural language processing: Magi will be able to understand your natural language queries, even if they are incomplete or ambiguous.
  • Smart answers: Magi will be able to provide you with smart answers to your questions, even if they are open ended or challenging.
  • Transactional search: Magi will allow you to complete transactions directly from the search results, such as booking flights or buying products.

Magi is still in development, but it has the potential to revolutionize the way we search the web. Stay tuned for more information as it becomes available!

Monday, April 3, 2023

Artificial Brain: The Future of Intelligence?


The future of artificial brains is uncertain. It is possible that artificial brains could one day become as intelligent as humans, or even surpass human intelligence. If this happens, it would have a profound impact on society. Artificial brains could be used to solve some of the world's most pressing problems, such as climate change and poverty. However, they could also be used for malicious purposes, such as developing autonomous weapons.

Artificial intelligence (AI) is rapidly evolving, and with it, the possibility of creating artificial brains. While this may seem like something out of a science fiction movie, it is actually a very real possibility. In fact, researchers at Indiana University (IU) are already working on developing artificial brains that could one day rival the capabilities of the human brain.

The IU team is led by Professor of Computer Science David B. Hardcastle, who is an expert in artificial intelligence and machine learning. Hardcastle and his team are working on developing artificial brains that can learn and adapt in the same way that human brains do. They believe that this type of artificial intelligence could have a profound impact on many different areas of our lives, from healthcare to education to transportation.

One of the main goals of the IU team is to develop artificial brains that can be used to improve healthcare. They believe that artificial brains could be used to diagnose diseases, develop new treatments, and even provide personalized care to patients. For example, artificial brains could be used to analyze medical images and identify potential problems that human doctors might miss. They could also be used to develop new drugs and treatments that are tailored to the specific needs of each patient.

The IU team is also working on developing artificial brains that can be used to improve education. They believe that artificial brains could be used to create personalized learning experiences for students. For example, artificial brains could be used to identify each student's strengths and weaknesses and then provide them with the appropriate level of challenge. They could also be used to provide feedback to students in a way that is both timely and helpful.

In addition to healthcare and education, the IU team is also working on developing artificial brains that can be used to improve transportation. They believe that artificial brains could be used to develop self-driving cars and other autonomous vehicles. For example, artificial brains could be used to navigate complex traffic conditions and avoid accidents. They could also be used to provide passengers with a more comfortable and enjoyable travel experience.

The work being done by the IU team is just one example of the many ways that artificial intelligence is being used to develop new technologies. Artificial intelligence has the potential to revolutionize many different areas of our lives, and the IU team is at the forefront of this research. It will be interesting to see what the future holds for artificial intelligence and artificial brains.

The Benefits of Artificial Brains

Artificial brains could have a number of benefits, including:

  • Improved healthcare: Artificial brains could be used to diagnose diseases, develop new treatments, and even provide personalized care to patients.
  • Improved education: Artificial brains could be used to create personalized learning experiences for students.
  • Improved transportation: Artificial brains could be used to develop self-driving cars and other autonomous vehicles.
  • Increased productivity: Artificial brains could be used to automate tasks and increase productivity in a variety of industries.
  • Enhanced creativity: Artificial brains could be used to generate new ideas and solve problems in innovative ways.

The Challenges of Artificial Brains

While there are many potential benefits to artificial brains, there are also a number of challenges that need to be addressed. These challenges include:

  • Safety: Artificial brains need to be designed in a way that ensures they are safe and do not pose a threat to humans.
  • Ethics: The development of artificial brains raises a number of ethical concerns, such as the potential for job displacement and the impact on human autonomy.
  • Control: It is important to ensure that artificial brains are under human control and do not become uncontrollable.
  • Bias: Artificial brains are trained on data, and if that data is biased, the artificial brain will also be biased. This is a major challenge that needs to be addressed in order to ensure that artificial brains are fair and unbiased.

The Future of Artificial Brains

The future of artificial brains is uncertain. It is possible that artificial brains could one day become as intelligent as humans, or even surpass human intelligence. If this happens, it would have a profound impact on society. Artificial brains could be used to solve some of the world's most pressing problems, such as climate change and poverty. However, they could also be used for malicious purposes, such as developing autonomous weapons.

It is important to start thinking about the implications of artificial brains now, so that we can be prepared for whatever the future holds.

Tuesday, February 14, 2023

EctoLife: World's First Artificial Womb Facility Can Grow 30,000 Babies a Year Based on Groundbreaking Research.



In a groundbreaking move, scientists have announced the opening of the world's first artificial womb facility - EctoLife. This facility has the potential to grow up to 30,000 babies every year and is based on over 50 years of cutting-edge scientific research from across the globe.

Artificial wombs have been a subject of fascination and research for decades, and this development marks a significant milestone in reproductive science. These womb-like devices provide a nurturing environment for developing embryos, giving them the support and resources they need to grow and thrive.

EctoLife is a highly advanced facility that has been designed to simulate the natural environment of a womb. It is equipped with state-of-the-art technology that can monitor and adjust the conditions inside the womb to ensure optimal growth and development of the fetus.

One of the biggest benefits of artificial wombs is that they can potentially provide a safer and more controlled environment for gestating embryos. In traditional pregnancies, a range of factors can impact the health and wellbeing of the fetus, including infections, lifestyle choices, and other environmental factors. In an artificial womb, many of these risks can be mitigated or eliminated entirely, resulting in a safer and healthier pregnancy.

Moreover, this technology has the potential to revolutionize fertility treatment and help couples struggling with infertility. Currently, many couples have to undergo invasive and expensive treatments like IVF to conceive a child. With the advent of artificial wombs, it may become possible to grow embryos outside the body, eliminating the need for these invasive procedures.

Of course, the use of artificial wombs is not without controversy. Critics argue that this technology could lead to a devaluation of traditional pregnancies and further separate humans from the natural world. There are also concerns about the ethical implications of creating and disposing of large numbers of embryos in a lab setting.

Despite these concerns, it's clear that artificial womb technology has the potential to bring about significant advancements in reproductive science. EctoLife represents a huge step forward in this field, and it will be fascinating to see how this technology evolves in the years to come.

Saturday, February 11, 2023

The Rise of Artificial Intelligence: Understanding its Applications and Impacts


Artificial Intelligence (AI) is a branch of computer science that deals with the creation of intelligent machines that work and react like human beings. It has become a critical tool in a wide range of industries, including healthcare, finance, manufacturing, retail, and many others. AI technology has made significant progress in recent years, and it is changing the way businesses and individuals interact with technology.

AI is based on the idea that machines can learn from experience, recognize patterns in data, and make decisions. There are two main types of AI: narrow or weak AI, which is designed to perform a specific task, and general or strong AI, which has the ability to perform any intellectual task that a human can. The most common forms of AI include machine learning, natural language processing (NLP), computer vision, and robotics.

Machine learning is a type of AI that enables computers to learn from data, identify patterns, and make predictions. It is used in a variety of applications, including recommendation systems, image and speech recognition, and fraud detection. NLP is a branch of AI that focuses on the interaction between computers and human language. It is used in applications such as chatbots, language translation, and sentiment analysis.

Computer vision, another form of AI, is the ability of computers to interpret and understand visual information from the world, such as images and videos. This technology is used in a wide range of applications, including object recognition, facial recognition, and autonomous vehicles. Robotics is the field of AI that deals with the design, construction, operation, and use of robots. It is used in manufacturing, healthcare, and other industries to automate tasks and increase efficiency.

AI has the potential to revolutionize the way we live and work, and it has already begun to do so. It is helping businesses to make better decisions, improve customer experiences, and increase efficiency. It is also being used to solve complex problems in healthcare, such as disease diagnosis and drug discovery. However, as with any new technology, there are also concerns about the potential consequences of AI, including job loss and privacy issues.

In conclusion, AI is a rapidly evolving technology that has the potential to bring about significant changes in the way we live and work. While there are certainly challenges to be addressed, the benefits of AI are undeniable, and its impact on society and the global economy will only continue to grow in the years to come. 

Wednesday, January 3, 2018

New technique allows rapid screening for new types of solar cells


Approach could bypass the time-consuming steps currently needed to test new photovoltaic materials.
This experimental setup was used by the team to measure the electrical output of a sample of solar cell material, under controlled conditions of varying temperature and illumination. The data from those tests was then used as the basis for computer modeling using statistical methods to predict the overall performance of the material in real-world operating conditions.
Image: Riley Brandt
  

The worldwide quest by researchers to find better, more efficient materials for tomorrow’s solar panels is usually slow and painstaking. Researchers typically must produce lab samples — which are often composed of multiple layers of different materials bonded together — for extensive testing.


Now, a team at MIT and other institutions has come up with a way to bypass such expensive and time-consuming fabrication and testing, allowing for a rapid screening of far more variations than would be practical through the traditional approach.

The new process could not only speed up the search for new formulations, but also do a more accurate job of predicting their performance, explains Rachel Kurchin, an MIT graduate student and co-author of a paper describing the new process that appears this week in the journal Joule. Traditional methods “often require you to make a specialized sample, but that differs from an actual cell and may not be fully representative” of a real solar cell’s performance, she says.

For example, typical testing methods show the behavior of the “majority carriers,” the predominant particles or vacancies whose movement produces an electric current through a material. But in the case of photovoltaic (PV) materials, Kurchin explains, it is actually the minority carriers — those that are far less abundant in the material — that are the limiting factor in a device’s overall efficiency, and those are much more difficult to measure. In addition, typical procedures only measure the flow of current in one set of directions — within the plane of a thin-film material — whereas it’s up-down flow that is actually harnessed in a working solar cell. In many materials, that flow can be “drastically different,” making it critical to understand in order to properly characterize the material, she says.

“Historically, the rate of new materials development is slow — typically 10 to 25 years,” says Tonio Buonassisi, an associate professor of mechanical engineering at MIT and senior author of the paper. “One of the things that makes the process slow is the long time it takes to troubleshoot early-stage prototype devices,” he says. “Performing characterization takes time — sometimes weeks or months — and the measurements do not always have the necessary sensitivity to determine the root cause of any problems.”

So, Buonassisi says, “the bottom line is, if we want to accelerate the pace of new materials development, it is imperative that we figure out faster and more accurate ways to troubleshoot our early-stage materials and prototype devices.” And that’s what the team has now accomplished. They have developed a set of tools that can be used to make accurate, rapid assessments of proposed materials, using a series of relatively simple lab tests combined with computer modeling of the physical properties of the material itself, as well as additional modeling based on a statistical method known as Bayesian inference.

The system involves making a simple test device, then measuring its current output under different levels of illumination and different voltages, to quantify exactly how the performance varies under these changing conditions. These values are then used to refine the statistical model.

“After we acquire many current-voltage measurements [of the sample] at different temperatures and illumination intensities, we need to figure out what combination of materials and interface variables make the best fit with our set of measurements,” Buonassisi explains. “Representing each parameter as a probability distribution allows us to account for experimental uncertainty, and it also allows us to suss out which parameters are covarying.”

The Bayesian inference process allows the estimates of each parameter to be updated based on each new measurement, gradually refining the estimates and homing in ever closer to the precise answer, he says.

In seeking a combination of materials for a particular kind of application, Kurchin says, “we put in all these materials properties and interface properties, and it will tell you what the output will look like.”

The system is simple enough that, even for materials that have been less well-characterized in the lab, “we’re still able to run this without tremendous computer overhead.” And, Kurchin says, making use of the computational tools to screen possible materials will be increasingly useful because “lab equipment has gotten more expensive, and computers have gotten cheaper. This method allows you to minimize your use of complicated lab equipment.”

The basic methodology, Buonassisi says, could be applied to a wide variety of different materials evaluations, not just solar cells — in fact, it may apply to any system that involves a computer model for the output of an experimental measurement. “For example, this approach excels in figuring out which material or interface property might be limiting performance, even for complex stacks of materials like batteries, thermoelectric devices, or composites used in tennis shoes or airplane wings.” And, he adds, “It is especially useful for early-stage research, where many things might be going wrong at once.”

Going forward, he says, “our vision is to link up this fast characterization method with the faster materials and device synthesis methods we’ve developed in our lab.” Ultimately, he says, “I’m very hopeful the combination of high-throughput computing, automation, and machine learning will help us accelerate the rate of novel materials development by more than a factor of five. This could be transformative, bringing the timelines for new materials-science discoveries down from 20 years to about three to five years.”

The research team also included Riley Brandt '11, SM '13, PhD '16; former postdoc Vera Steinmann; MIT graduate student Daniil Kitchaev and visiting professor Gerbrand Ceder, Chris Roat at Google Inc.; and Sergiu Levcenco and Thomas Unold at Hemholz Zentrum in Berlin. The work was supported by a Google Faculty Research Award, the U.S. Department of Energy, and a Total research grant through the MIT Energy Initiative.
 
Credit : https://news.mit.edu/2017/new-technique-allows-rapid-screening-new-types-solar-cells-1220

Friday, December 8, 2017

Facebook to introduce live streaming, video chats to Messenger games



More than a year after launching "Instant Games" -- a new platform for gaming with friends on the Messenger chat app, Facebook has announced support for live streaming via Facebook Live and video chatting with fellow gamers.

"First, we're launching live streaming, which will start to roll out today, to gamers who love to share their playthroughs and engage in a little smack talk," Facebook wrote in a blog post late Thursday.
The users will be able to record these live streams so that they can be posted to the profile afterwards.
"Over 245 million people video chat every month on Messenger. We're excited to begin a test soon that will enable people to play games with each other while video chatting," the company added.
Meanwhile, the social media giant also announced additions to "Instant Games" with a handful of big-name mobile titles that will be "re-imagined" for the platform.

"Launching globally in early 2018 is none other than Angry Birds, a new game built for Messenger that will feature classic gameplay with an exciting new way to challenge friends," Facebook said.
The immensely popular game will join the recently launched Tetris which includes beloved features like marathon mode and the ability to play with friends in Messenger group chats.

Sunday, November 26, 2017

High-speed encryption to secure future internet


In a bid to fight against the future cyber threats, scientists have developed a new system with high-speed encryption properties that drives quantum computers to create theoretically hack-proof forms of data encryption. The novel system is capable of creating and distributing encryption codes at megabit-per-second rates, which is five to 10 times faster than existing methods and on par with current internet speeds when running several systems in parallel. The technique is secure from common attacks, even in the face of equipment flaws that could open up leaks.

"We are now likely to have a functioning quantum computer that might be able to start breaking the existing cryptographic codes in the near future," said Daniel Gauthier, Professor at The Ohio State University. "We really need to be thinking hard now of different techniques that we could use for trying to secure the internet," Gauthier added, in the paper appearing in the journal Science Advances. For the new system to work, both the hacker as well as the sender must have access to the same key, and it must be kept secret. The novel system uses a weakened laser to encode information or transmit keys on individual photons of light, but also packs more information onto each photon, making the technique faster. By adjusting the time at which the photon is released, and a property of the photon called the phase, the new system can encode two bits of information per photon instead of one.

This trick, paired with high-speed detectors powers the system to transmit keys five to 10 times faster than other methods. "It was changing these additional properties of the photon that allowed us to almost double the secure key rate that we were able to obtain if we hadn't done that," Gauthier said.

Thursday, October 22, 2015

How Filmmakers Manipulate Our Emotions With Color



Most of us don’t think about the color schemes of the films we watch. But for a long time now, movie studios have followed a special formula for each genre. Red tones for romance, blue for horror, and so on. 

The Verge explains how filmmakers manipulate our emotions using color in this trending video.

Friday, October 10, 2014

Manipulating memory with light: Scientists erase specific memories in mice


Just look into the light: not quite, but researchers at the UC Davis Center for Neuroscience and Department of Psychology have used light to erase specific memories in mice, and proved a basic theory of how different parts of the brain work together to retrieve episodic memories.
During memory retrieval, cells in the hippocampus
connect to cells in the brain cortex.
Credit: Photo illustration by Kazumasa Tanaka and 
Brian Wiltgen/UC Davis
Optogenetics, pioneered by Karl Diesseroth at Stanford University, is a new technique for manipulating and studying nerve cells using light. The techniques of optogenetics are rapidly becoming the standard method for investigating brain function.

Kazumasa Tanaka, Brian Wiltgen and colleagues at UC Davis applied the technique to test a long-standing idea about memory retrieval. For about 40 years, Wiltgen said, neuroscientists have theorized that retrieving episodic memories -- memories about specific places and events -- involves coordinated activity between the cerebral cortex and the hippocampus, a small structure deep in the brain.

"The theory is that learning involves processing in the cortex, and the hippocampus reproduces this pattern of activity during retrieval, allowing you to re-experience the event," Wiltgen said. If the hippocampus is damaged, patients can lose decades of memories.

But this model has been difficult to test directly, until the arrival of optogenetics.

Wiltgen and Tanaka used mice genetically modified so that when nerve cells are activated, they both fluoresce green and express a protein that allows the cells to be switched off by light. They were therefore able both to follow exactly which nerve cells in the cortex and hippocampus were activated in learning and memory retrieval, and switch them off with light directed through a fiber-optic cable.

They trained the mice by placing them in a cage where they got a mild electric shock. Normally, mice placed in a new environment will nose around and explore. But when placed in a cage where they have previously received a shock, they freeze in place in a "fear response."

Tanaka and Wiltgen first showed that they could label the cells involved in learning and demonstrate that they were reactivated during memory recall. Then they were able to switch off the specific nerve cells in the hippocampus, and show that the mice lost their memories of the unpleasant event. They were also able to show that turning off other cells in the hippocampus did not affect retrieval of that memory, and to follow fibers from the hippocampus to specific cells in the cortex.

"The cortex can't do it alone, it needs input from the hippocampus," Wiltgen said. "This has been a fundamental assumption in our field for a long time and Kazu’s data provides the first direct evidence that it is true."

They could also see how the specific cells in the cortex were connected to the amygdala, a structure in the brain that is involved in emotion and in generating the freezing response.

Co-authors are Aleksandr Pevzner, Anahita B. Hamidi, Yuki Nakazawa and Jalina Graham, all at the Center for Neuroscience. The work was funded by grants from the Whitehall Foundation, McKnight Foundation, Nakajima Foundation and the National Science Foundation.

Story Source:
The above story is based on materials provided by University of California - Davis. Note: Materials may be edited for content and length.

Journal Reference:
Kazumasa Z. Tanaka, Aleksandr Pevzner, Anahita B. Hamidi, Yuki Nakazawa, Jalina Graham, Brian J. Wiltgen. Cortical Representations Are Reinstated by the Hippocampus during Memory Retrieval. Neuron, 2014 DOI: 10.1016/j.neuron.2014.09.037

Saturday, September 13, 2014

Incredibly light, strong materials recover original shape after being smashed


Materials scientists have developed a method for creating new structural materials by taking advantage of the unusual properties that solids can have at the nanometer scale. They have used the method to produce a ceramic (e.g., a piece of chalk or a brick) that contains about 99.9 percent air yet is incredibly strong and can recover its original shape after being smashed by more than 50 percent.

This sequence shows how the Greer Lab's three-dimensional,
ceramic nanolattices can recover after being compressed by
more than 50 percent. Clockwise, from left to right, an alumina
nanolattice before compression, during compression, fully
compressed, and recovered following compression.
Credit: Lucas Meza/Caltech

Imagine a balloon that could float without using any lighter-than-air gas. Instead, it could simply have all of its air sucked out while maintaining its filled shape. Such a vacuum balloon, which could help ease the world's current shortage of helium, can only be made if a new material existed that was strong enough to sustain the pressure generated by forcing out all that air while still being lightweight and flexible. 

Caltech materials scientist Julia Greer and her colleagues are on the path to developing such a material and many others that possess unheard-of combinations of properties. For example, they might create a material that is thermally insulating but also extremely lightweight, or one that is simultaneously strong, lightweight, and nonbreakable -- properties that are generally thought to be mutually exclusive.

Greer's team has developed a method for constructing new structural materials by taking advantage of the unusual properties that solids can have at the nanometer scale, where features are measured in billionths of meters. In a paper published in the September 12 issue of the journal Science, the Caltech researchers explain how they used the method to produce a ceramic (e.g., a piece of chalk or a brick) that contains about 99.9 percent air yet is incredibly strong, and that can recover its original shape after being smashed by more than 50 percent.

"Ceramics have always been thought to be heavy and brittle," says Greer, a professor of materials science and mechanics in the Division of Engineering and Applied Science at Caltech. "We're showing that in fact, they don't have to be either. This very clearly demonstrates that if you use the concept of the nanoscale to create structures and then use those nanostructures like LEGO to construct larger materials, you can obtain nearly any set of properties you want. You can create materials by design."

The researchers use a direct laser writing method called two-photon lithography to "write" a three-dimensional pattern in a polymer by allowing a laser beam to crosslink and harden the polymer wherever it is focused. The parts of the polymer that were exposed to the laser remain intact while the rest is dissolved away, revealing a three-dimensional scaffold. That structure can then be coated with a thin layer of just about any kind of material -- a metal, an alloy, a glass, a semiconductor, etc. Then the researchers use another method to etch out the polymer from within the structure, leaving a hollow architecture.

The applications of this technique are practically limitless, Greer says. Since pretty much any material can be deposited on the scaffolds, the method could be particularly useful for applications in optics, energy efficiency, and biomedicine. For example, it could be used to reproduce complex structures such as bone, producing a scaffold out of biocompatible materials on which cells could proliferate.

In the latest work, Greer and her students used the technique to produce what they call three-dimensional nanolattices that are formed by a repeating nanoscale pattern. After the patterning step, they coated the polymer scaffold with a ceramic called alumina (i.e., aluminum oxide), producing hollow-tube alumina structures with walls ranging in thickness from 5 to 60 nanometers and tubes from 450 to 1,380 nanometers in diameter.

Greer's team next wanted to test the mechanical properties of the various nanolattices they created. Using two different devices for poking and prodding materials on the nanoscale, they squished, stretched, and otherwise tried to deform the samples to see how they held up.

They found that the alumina structures with a wall thickness of 50 nanometers and a tube diameter of about 1 micron shattered when compressed. That was not surprising given that ceramics, especially those that are porous, are brittle. However, compressing lattices with a lower ratio of wall thickness to tube diameter -- where the wall thickness was only 10 nanometers -- produced a very different result.

"You deform it, and all of a sudden, it springs back," Greer says. "In some cases, we were able to deform these samples by as much as 85 percent, and they could still recover."

To understand why, consider that most brittle materials such as ceramics, silicon, and glass shatter because they are filled with flaws -- imperfections such as small voids and inclusions. The more perfect the material, the less likely you are to find a weak spot where it will fail. Therefore, the researchers hypothesize, when you reduce these structures down to the point where individual walls are only 10 nanometers thick, both the number of flaws and the size of any flaws are kept to a minimum, making the whole structure much less likely to fail.

"One of the benefits of using nanolattices is that you significantly improve the quality of the material because you're using such small dimensions," Greer says. "It's basically as close to an ideal material as you can get, and you get the added benefit of needing only a very small amount of material in making them."

The Greer lab is now aggressively pursuing various ways of scaling up the production of these so-called meta-materials.

Story Source: http://www.sciencedaily.com/releases/2014/09/140911135450.htm