Archive for the ‘Artificial Intelligence’ Category

AI Tool Created to Study the Universe, Unlock the Mysteries of Dark Energy – Newsweek

An artificial intelligence tool has been developed to help predict the structure of the universe and aid research into the mysteries of dark energy and dark matter.

Researchers in Japan used two of the world's fastest astrophysical simulation supercomputers, known as ATERUI and ATERUI II, to create an aptly-named "Dark Emulator" tool, which is able to ingest vast quantities of data and produce analysis of the universe in seconds.

The AI could play a role in studying the nature of dark energy, which seems to make up a large amount of the universe but remains an enigma.

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When observed from a distance, the team noted how the universe appears to consist of clusters of galaxies and massive voids that appear to be empty.

But as noted by NASA, leading models of the universe indicate it is made of entities that cannot be seen. Dark matter is suspected of helping to hold galaxy clusters in place gravitationally, while dark energy is believed to play a role in how the universe is expanding.

According to the researchers responsible for Dark Emulator, the AI tool is able to study possibilities about the "origin of cosmic structures" and how dark matter distribution may have changed over time, using data from some of the top observational surveys conducted about space.

"We built an extraordinarily large database using a supercomputer, which took us three years to finish, but now we can recreate it on a laptop in a matter of seconds," said Associate Prof. Takahiro Nishimichi, of the Yukawa Institute for Theoretical Physics.

"Using this result, I hope we can work our way towards uncovering the greatest mystery of modern physics, which is to uncover what dark energy is. I also think this method we've developed will be useful in other fields such as natural sciences or social sciences."

Nishimichi added: "I feel like there is great potential in data science."

The teams, which included experts from the Kavli Institute for the Physics and Mathematics of the Universe and the National Astronomical Observatory of Japan, said in a media release this week that Dark Emulator had already shown promising results during extensive tests.

In seconds, the tool predicted some of effects and patterns found in previous research projects, including the Hyper Suprime-Cam Survey and Sloan Digital Sky Survey. The emulator "learns" from huge quantities of data and "guesses outcomes for new sets of characteristics."

As with all AI tools, data is key. The scientists said the supercomputers have essentially created "hundreds of virtual universes" to play with, and Dark Emulator predicts the outcome of new characteristics based on data, without having to start new simulations every time.

Running simulations through a supercomputer without the AI would take days, researchers noted. Details of the initial study were published in The Astrophysical Journal last October. The team said they hope to input data from upcoming space surveys throughout the next decade.

While work on this one study remains ongoing, there is little argument within the scientific community that understanding dark energy remains a key objective.

"Determining the nature of dark energy [and] its possible history over cosmic time is perhaps the most important quest of astronomy for the next decade and lies at the intersection of cosmology, astrophysics, and fundamental physics," NASA says in a fact-sheet on its website.

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AI Tool Created to Study the Universe, Unlock the Mysteries of Dark Energy - Newsweek

‘More than human’: How neural implants, robotics and artificial intelligence are redefining who we are – Genetic Literacy Project

When you hear the word cyborg, scenes from the 1980s films RoboCop or The Terminator might spring to mind. But the futuristic characters made famous in those films may no longer be mere science fiction. We are at the advent of an era where digital technology and artificial intelligence are moving more deeply into our human biological sphere. Humans are already able to control a robotic arm with their minds. Cyborgshumans whose skills and abilities exceed those of others because of electrical or mechanical elements built into the bodyare already among us.

But innovators are pushing the human-machine boundary even further. While prosthetic limbs are tied in with a persons nervous system, future blends of biology and technology may be seen in computers that are wired into our brains.

Our ability to technologically enhance our physical capabilitiesthe hardware of our human systems, you could saywill likely reshape our social world. Will these changes bring new forms of dominance and exploitation? Will unaltered humans be subjected to a permanent underclass or left behind altogether? And what will it mean to be humanor will some of us be more than human?

Initial answers may be closer than we think.

Physicist Max Tegmark, MIT professor and president of the Future of Life Institute, considers the recent advances in artificial intelligence and technology through an evolutionary lens to imagine us as more than human. He categorizes all life into three levels. In his view, the vast majority of lifefrom bacteria to mice, iguanas to lobstersfalls into what he calls Life 1.0. These creatures survive and replicate, but they cannot redesign themselves within their lifetime. They evolve and learn over many generations.

Moving up, somewhere between Life 1.0 and 2.0, Tegmark classifies animals such as some primates, cetaceans, and corvids that have the ability to intermesh biology and culture. These animals are able to learn complex new skills, like how to use tools. Humans take this to an extreme, and Tegmark categorizes humans as Life 2.0. Through extensive language, social intelligence, and culture, Life 2.0 individuals can jump into new environments independently of genetic constraints. (If you missed it, we wrote about how body modification, as one example, makes us more socially human in part I, Your Body as a Map, of this pair of posts.)

Just think about how our ability to learn a new language within our lifetime is a bit like adding a software package to a computer. We can add an infinite number of self upgrades during our lifetime and pass our knowledge on to future generations. We also can manipulate other life forms to our own ends on a grand scalefrom cattle farming to harnessing bacteria in the preparation of fermented foods like cheese.

But with the leaps were seeing in artificial intelligence, neuroscience, and biotechnology, our concept of animal and human could compete with the most imaginative Hollywood film. Life 3.0 doesnt yet exist on Earth, but Tegmark argues that in the future, we will see a technological life-form that can design both its hardware (which neither 1.0 or 2.0 can do) and its software (which currently only 2.0 can do).

Even in the near future, humans may be somewhere in between life-forms 2.0 and 3.0. In 2016, Elon Musk, CEO of Tesla and SpaceX, co-founded Neuralink, a company that aims to develop a braincomputer interface. Musk says his goal is to help human beings merge with software and be in sync with advances in artificial intelligence.

Whether people will volunteer to have a robot insert wires into their brain that are attached to a tiny chip implant remains to be seen. But humans across cultures have embraced a variety of technologies in surprising ways.

Today over 5 billion people have access to mobile phones. By 2025, around 71 percent of the worlds population is expected to be connected. The thought that virtually every aspect of a persons day might be influenced by a smartphone or something like it once seemed like science fiction. But as the number of digital natives grows, our relationship with technology does too.

Some of us readily anthropomorphize our gadgets and give our apps and devices names such as Siri or Alexa. We talk to them, allow them to control our surroundings, finances, shopping, and schedules. Yet many hesitate when it comes to embedding technology in our bodies if we are otherwise physically healthy.

Take, for example, microchips inserted under the skin, which can be used to pay for your shopping as well as a bus ride home. This is little different from a credit card in your back pocket, save for the convenience of not having to remember to take it with you.

Our resistance may be influenced by the yuck factor of new or different technologies or cultural shifts. But over time, what we think of as disgusting or offensive may become normalized. Lab-grown meat, for example, has gone from being a scientific and economic fantasy to something that might well be in stores by 2022. Similarly, eating insects, for those unused to the idea in the West, has become more accepted as a sustainable source of protein.

Even if more of us grow to accept the idea of implants, is Life 3.0 a genuine possibility? For now, mindcontrolled prosthetics are the closest innovation that hints at a Neuralink-type future. Such prosthetics are still in relatively early stages of development and not universally available. Nonetheless, as far as Musk is concerned, many of us are already cyborgs, with an indepth digital version of ourselves in the form of social media, email, and much more. His team, or others, may well inch us toward a version of Life 3.0.

Other early signs of how technologically integrated lives might function and impact our individual lives and societies are visible in places such as Scandinavia, where checks and cash are on their way out. In Denmark, for example, the majority of citizens make payments using their mobile phones. The absence of cash has had a direct effect on homeless people. Without smartphones of their own, homeless individuals were unable to receive payments for the newspapers they sold to earn money.

The solution was to provide homeless people with smartphones (and thus mobile payment methods). No longer a luxury, mobile phones became a basic tool vital for anyone engaging in modern society in Denmark.

As soon as we move into the idea of integrated technology as a social essential, we recognize a thorny possibility: a world where a new path to social or class dominance emergesperhaps a division between those who can and those who cannot afford to interface with technology. It begins to sound like the plot of the 20th-century dystopian novel Brave New World.

In that new world, would the Life 2.0 human without enhancements be relegated to a servile underclass? Perhaps this reflects a false dichotomy. After all, millions of people living in relatively remote regions around the planet have been able to fast-track to mobile technology, effectively skipping over earlier versions of the telephone and other communication technologies.

Nonetheless, developers of integrated technologies involving invasive surgery would be wise to consider the social ramifications of their work. Today we can accurately reconstruct the wealth distribution of an entire nation based on individual phone records. Can we predict the negative social impacts of a future Life 3.0? If contemporary clues are any answer, yes, we can. But whether we choose to ameliorate those impacts or not still lies within our control.

Matthew Gwynfryn Thomas is a data scientist and anthropologist working in the nonprofit sector in London, U.K. His current work combines machine learning and social science to address the needs of people in crisis. He has also written popular science articles for a variety of outlets, includingBioNews, SciDev.Net, and the Wellcome Trust Blog. Follow him on Twitter@matthewgthomas

Djuke Veldhuis is an anthropologist and science writer based at Monash University in Australia, where she is a course director in the B.Sc. advancedglobal challenges degree program. Her Ph.D. research examined the effects of rapid socioeconomic change on the health and well-being of people in Papua New Guinea. She has written for a series of popular science outlets, including SciDev.Net,Asia Research News, andNew Scientist. Follow her on Twitter@DjukeVeldhuis

A version of this article was originally published at the Conversation and has been republished here with permission.

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'More than human': How neural implants, robotics and artificial intelligence are redefining who we are - Genetic Literacy Project

Global LegalTech Artificial Intelligence Market is Expected to Grow at a CAGR of More Than 37.7% Over the Forecast Period Owing to Digitalization…

PUNE, India, Feb. 4, 2020 /PRNewswire/ -- The digital reforms in the legal industry have transformed the traditional courtrooms and law practices, thus strengthening the prevalence of Artificial Intelligence (AI) in legal technology or legaltech. The increasing burden of legal activities, carried out around the globe, over a limited number of law practioners has pushed the digitization of legal practices such as Document Management System, e-Discovery, Practice and Case Management, e-Billing, Contract Management and many others. Major law firms are adopting legaltech solutions featuring AI capabilities to tackle the growing competition and reduce the turn-around time of legal cases. For instance, CMS Legal, a global law firm, has deployed AI-based software for quick and efficient analysis of contracts and other legal documents. Data analytics in law industry can be a complex and time consuming task owing to the huge amount of paperwork. Artificial Intelligence has been recognized for its analytical capabilities and legaltech has harnessed that capability in recent years. Companies such as Luminance Technologies Ltd. are offering AI based platform for locating patterns from the loaded document and identifying deviations from standard clauses. These factors have thus catalyzed the growth of global legaltech artificial intelligence market.

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The digitalization trend has also impacted the judicial system of numerous governments. Countries worldwide are transforming their conventional judicial practices along with their courtrooms. For instance, countries such as China and Australia have implemented digital courts to reduce the net cost of legal services to government. China introduced Judicial Big Data Service Network platform in 2017 to improve the judicial system of country using big data and artificial intelligence. This initiative has led to introduction of three online courts with plans to expand further. These courts are limited to civil and administrative claims form e-commerce and other online activities. These courts employ virtual judges based on artificial intelligence and the entire hearing takes place online. Moreover, the state of New South Wales, Australia introduced online courts in 2016 to conduct preliminary hearings. These factors have pushed the law firms and clients to adopt digital methods owing to the ease of use and reduced turn-around time. Artificial intelligence has improved the efficiency of legaltech thus increasing its adoption in government agencies as well as private law firms and is thus, fueling the growth of global legaltech artificial intelligence market.

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Global LegalTech Artificial Intelligence Market is Expected to Grow at a CAGR of More Than 37.7% Over the Forecast Period Owing to Digitalization...

Why Artificial Intelligence is Both a Risk and a Way to Manage Risk – AiThority

Spending on Artificial Intelligence (AI) is expected to more than double from $35 billion in 2019 to $79 billion in 2022, according to IDC forecasts. But as we enter the fourth industrial revolution powered by AI, technologists have divided themselves into utopian and alarmists camps. Thats a false and dangerous dichotomy. We need to adopt a pragmatic mindset that sees AI as both a risk and a way to manage risk.

From killer robots to racism, todays headlines provide AI alarmists with ample fodder. The risks associated with AI grow as technology improves and proliferates. But unlike other paradigm-shifting technologies like the printing press, mass production, or digital commerce, its the invisible aspects of AI that we most need to worry about: algorithms that learn from patterns and can trigger costly errors and, left unchecked, can pull projects and organizations in entirely wrong directions with catastrophic consequences.

For the first time in history, a single person can customize a message for billions and share it with them within a matter of days. A software engineer can create an army of AI-powered bots, each pretending to be a different person, promoting biased content on behalf of political or commercial interests or worse, attack vulnerable systems.

Read More: How CMOs Succeed with AI-Powered CX

The doomsday scenarios arent a fait accompli, but they do underscore the need for AI systems that engage with humans in transparent ways. Every time a new technology is introduced, it creates new challenges, safety issues, and potential hazards. For example, when pharmaceuticals were first introduced, there were no safety tests, quality standards, childproof caps or tamper-resistant packages. AI is a new technology and will undergo a similar evolution.

To trust an AI system, we must have confidence in its decisions. Increasingly, bankers are asking important questions about how AI will affect consumers. The Defense Department has signaled that it understands the importance of empowering ethicists to guide AI technologies.

Meanwhile, were beginning to include AI in our long-overdue conversations about criminal justice. These are all good signs, but we need to rapidly scale our ethical inquiries by using supervisory AI systems to provide visibility and control over production AI systems.

Read More: Shaped by AI, the Future of Work Sees Soft Skills and Creativity as Essential

AI systems must reflect our values. We can do this through investment, education, and policy. But first, we must dispense with the utopian and alarmist positions. Utopians assume that every AI solution will automatically be an improvement over what came before it, and therefore miss the opportunity to address critical questions about values before deployment.

At the opposite end of the spectrum, alarmists assume the worst and therefore fail to show up to the debate. A pragmatic approach that sees AI as both a risk and a way to manage risk by pairing AI with other AI is the prerequisite mental model for grappling with the issues raised by the fourth industrial revolution.

Read More: Efficient Ways the AI Will Boost Your E-Commerce Sales

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Why Artificial Intelligence is Both a Risk and a Way to Manage Risk - AiThority

Intel drops work on one of its AI-chip lines in favor of an other – Network World

Well, that was short.

Intel is ending work on its Nervana neural network processors (NNP) in favor of an artificial intelligence line it gained in the recent $2 billion acquisition of Habana Labs.

Intel acquired Nervana in 2016 and issued its first NNP chip one year later. After the $408 million acquisition by Intel, Nervana co-founder Naveen Rao was placed in charge of the AI platforms group, which is part of Intel's data platforms group. The Nervana chips were meant to compete with Nvidia GPUs in the AI inference training space, and Facebook worked with Intel in close collaboration, sharing its technical insights, according to former Intel CEO Brian Krzanich.

For now, Intel has ended development of its Nervana NNP-T training chips and will deliver on current customer commitments for its Nervana NNP-I inference chips; Intel will move forward with Habana Labs' Gaudi and Goya processors in their place.

There are two parts to neural networks: training, where the computer learns a process, such as image recognition; and inference, where the system puts what it was trained to do to work. Training is far more compute-intensive than inference, and its where Nvidia has excelled.

Intel said the decision was made after input from customers, and that this decision is part of strategic updates to its data-center AI acceleration roadmap. "We will leverage our combined AI talent and technology to build leadership AI products," the company said in a statement to me.

The Habana product line offers the strong, strategic advantage of a unified, highly-programmable architecture for both inference and training. By moving to a single hardware architecture and software stack for data-center AI acceleration, our engineering teams can join forces and focus on delivering more innovation, faster to our customers, Intel said.

This outcome from the Habana acquisition wasn't entirely unexpected. "We had thought that they might keep one for training and one for inference. However, Habana's execution has been much better and the architecture scales better. And, Intel still gained the IP and expertise of both companies, said Jim McGregor, president of Tirias Research.

The good news is that whatever developers created for Nervana wont have to be thrown out. The frameworks work on either architecture, McGregor said. "While there will be some loss going from one architecture to another, there is still value in the learning, and I'm sure Intel will work with customers to help them with the migration.

This is the second AI/machine learning effort Intel has shut down, the first being Xeon Phi. Xeon Phi itself was a bit of a problem child, dating back to Intels failed Larrabee experiment to build a GPU based on x86 instructions. Larrabee never made it out of the gate, while Xeon Phi lasted a few generations as a co-processor but was ultimately axed in August 2018.

Intel still has a lot of products targeting various AI: Mobileye, Movidius, Agilex FPGA, and its upcoming Xe architecture. Habana Labs has been shipping its Goya Inference Processor since late 2018, and samples of its Gaudi AI Training Processor were sent to select customers in the second half of 2019.

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Intel drops work on one of its AI-chip lines in favor of an other - Network World