Category Archives: Singularity

Ashes of the Singularity: Escalation, Stardocks gargantuan RTS, has a new update out today. Ashes of the Singularity: Escalation v2.3 fattens up the game with a new campaign, new ships and tweaks to improve game balance.

The update chucks a pair of Juggernaut-class bruisers into the fray, both of which can level up indefinitely, getting bonuses to health and damage every time that they do. The Leonidas is a PHC ship that boasts an incredibly powerful weapon that can tear Dreadnoughts apart. The Substrate get The Nest of the Queen, a vessel capable of summoning a fleet of frigates to lend it a hand.

We're really excited to introduce the Juggernauts," writes Stardock CEO Brad Wardell. "Fans of the game have been asking about them since we teased them several months ago, and we know these won't disappoint them. These warships are pricey to buildyou won't see them in quick matchesbut the cost is worth it. It takes some serious firepower to stop one of these guys once they hit the field."

The new campaign, Genesis, continues the Ashes story and comes with six missions. Stardock says that its increased the challenge of the higher difficulty level to please more advanced players, so that probably doesnt include me.

Stardocks also tweaked the previous campaigns. Ships and orbitals that previously werent available have been added to missions, while the maps have been tweaked and AI improved.

The update is free and available to all owners of Ashes of the Singularity: Escalation today.

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Ashes of the Singularity: Escalation 2.3 update adds a new campaign today - PC Gamer

A prime example of the strong collaboration between Deloitte and SU is this week's Exponential Finance Conference taking place in New York City. Over 700 global executives, entrepreneurs, financial advisors, insurance and banking executives, and venture capitalists are convening over three days in New York City to explore and create the future of the financial services industry. Participants will learn how disruptive technologies such as artificial intelligence, Blockchain, robotics, nanotechnologies and crowdfunding, among others, can be leveraged for exponential growth, and to help address the global challenges the world is facing.

Deloitte and SU continue to expand their alliance to empower a global community with the mindset, skillset and network to embrace exponential opportunities, including topics of global importance such as Smart Cities and the Future of Work.

"We live in a time where unprecedented change is disrupting nearly every way we work and live. Our personal and professional lives are shifting in response to new technologies and business models that are changing what's possible and calling for us to respond and adapt, or fall behind. Since 2014, over 1,300 Deloitte clients have participated in SU programs around the world, empowering them to envision and realize significant exponential growth and efficiency opportunities," said Andrew Vaz, Deloitte Touche Tohmatsu Limited's Global Chief Innovation Officer. "The Deloitte/SU alliance is critical to our joint mission of helping organizations recognize and embrace opportunities to grow and differentiate themselves during an era of significant disruptions, while making the world a better place."

"We are gratified that our long-term alliance with Deloitte is being renewed and welcome their expanded involvement in new global events and programs," said Rob Nail, CEO and Associate Founder of Singularity University. "Together we bring the technical expertise, global networks, business acumen, and future vision to help organizations of all sizes innovate and grow exponentially."

To learn more about any of the SU programs and events and Deloitte innovation resources, go to http://www.su.org and https://www2.deloitte.com/us/en/pages/strategy/topics/innovation-consulting.html.

ABOUT DELOITTE Deloitte provides industry-leading audit, consulting, tax and advisory services to many of the world's most admired brands, including 80 percent of the Fortune 500 and more than 6,000 private and middle market companies.Our people work across more than 20 industry sectors to deliver measurable and lasting results that help reinforce public trust in our capital markets, inspire clients to make their most challenging business decisions with confidence, and help lead the way toward a stronger economy and a healthy society.

Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee ("DTTL"), its network of member firms, and their related entities. DTTL and each of its member firms are legally separate and independent entities. DTTL (also referred to as "Deloitte Global") does not provide services to clients. In the United States, Deloitte refers to one or more of the US member firms of DTTL, their related entities that operate using the "Deloitte" name in the United States and their respective affiliates. Certain services may not be available to attest clients under the rules and regulations of public accounting. Please see http://www.deloitte.com/about to learn more about our global network of member firms.

ABOUT SINGULARITY UNIVERSITY (SU)Singularity University is a global learning and innovation community using exponential technologies to tackle the world's biggest challenges and build an abundant future for all. SU's collaborative platform empowers individuals and organizations across the globe to learn, connect, and innovate breakthrough solutions using accelerating technologies like artificial intelligence, robotics, and digital biology. SU was founded in 2008 by renowned innovators Ray Kurzweil and Peter H. Diamandis and has partnered with leading organizations including Google, Deloitte, Genentech, and UNICEF. To learn more, visitSU.org, join us onFacebook, follow us on Twitter @SingularityU, and download our SingularityU Hub mobile app from theApp Store.

MEDIA CONTACTS Anna Roubos, singularityu@ogilvy.com 774-232-2460 Jodie Stern, jodiestern@deloitte.com 414-702-0167

To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/deloitte-and-singularity-university-extend-their-relationship-to-foster-innovation-worldwide-300471082.html

SOURCE Singularity University; Deloitte

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Deloitte and Singularity University Extend Their Relationship To ... - PR Newswire (press release)

In the movie Office Space, Peter Gibbons has a stroke of genius. Confronted with the utter mundanity of a life slaving away at his office park software company, he convinces his friends to make a computer virus to skim a fraction of a cent off transactions into a shared bank account.

This, of course, goes horribly wrong. But the concept is actually pretty solid.

In the real world, where there are literally billions of transactions crisscrossing the globe every day, you can make a big profit buying and selling securities whose prices barely differ.

But heres the key. You have to be fast. Inhumanly so. Enter physics and computers.

Computerized high-frequency trading was born from a collision of rapidly growing computing power and an influx of math and physics PhDs into finance. These wonks worked out complex quantitative buy-sell strategies, built them into algorithms, and set their software loose.

While the practice is nothing if not controversialand there are quantitative strategies that work over longer time frames tooits impact on the market is undeniable. In any given year, high-frequency trading is responsible for up to half or more of all trades. And of course, notoriously, such algorithmic trading was also involved in 2010s infamous Flash Crash.

But all this is only the beginning of how physics and computers can flip finance upside down.

At Singularity Universitys Exponential Finance Summit this week, Andrew Fursman said quantum computers, which harness natures most basic laws, are coming sooner than you think. And while digital computing was an evolution, quantum computing will be a revolution.

Fursman is CEO and cofounder of 1Qbit, a quantum computing software startup focused on making quantum computing applications practical for industry.

Quantum computing, he said, is just in its earliest stages, more akin to the hulking special-purpose computers of the 40s and 50s instead of the sleeker personal digital machines of recent decades. But he thinks its about to get practical, and itll pay dividends to those paying attention.

In finance, computing power is really a bit of an arms race, Fursman said. And as you all know, in many of these situations, it's winner takes all.

The next revolution has been a long time coming. It began with physicist Richard Feynman.

When modern digital computers were just gaining momentum, Feynman looked far down the roadhe was a genius theorist after alland noted the most powerful computers would not be digital, theyd be quantum. That is, theyd harness the laws of nature to compute.

Its counterintuitive to think of the world as a computer, said Fursman, but its an instructive analogy if you want to grasp the speed and simultaneity of quantum computers.

Complexity is nothing to nature. Just imagine how quickly and effortlessly glass breaks, he said.

In far less time than it takes to blink your eye, the laws of nature instruct the atoms in the glass to fracture into a massively complex spider web. Not unlike a computer, the laws of physics are the underlying logic allowing the glass to compute its complex demise in an instant.

Quantum computers similarly harness natures power to compute. Instead of using 1s and 0s to calculate things, they use the rules of quantum mechanics to compute with 1s, 0s, and both simultaneously. This means they can rapidly solve massively complex problems.

[Go here to learn more about how quantum computers work.]

But todays machines, like D-Waves adiabatic quantum computers, arent like your laptop, which is whats called a universal computer due to its ability to do many tasks. Instead, quantum computers today are specialized, complicated, difficult to program, and expensive.

Fursman thinks well get universal quantum computers in future, but well before then, in something like three to five years, he thinks early quantum computers will get practical. And because they can do things no other computer can, theyll be powerful.

In finance, its often about optimization. And todays quantum machines excel at optimization.

Consider building a portfolio out of all the stocks in the S&P 500, Fursman said. Given expected risk and return at various points in time, your choice is to include a stock, or not. The sheer number of possible portfolio combinations over time is mindboggling.

In fact, the possibilities dwarf the number of atoms in the observable universe.

To date, portfolio theory has necessarily cut corners and depended on approximations. But what if you could, in fact, get precise? Quantum computers will be able to solve problems like this in a finite amount of time, whereas traditional computers would take pretty much forever.

The work is already underway to make this possible.

Fursman noted a paper written by Gili Rosenberg, Poya Haghnegahdar, Phil Goddard, Peter Carr, Kesheng Wu, and Marcos Lpez de Prado in which they outline a new way to solve for an optimal portfolio. Instead of finding the best portfolio at discrete times in the future, they outline a way to find the best portfolio overall through time. Such a portfolio would reduce the transaction costs of rebalancing portfolios and potentially save the industry billions.

To be clear, this isnt ready for prime time yet. But Fursman thinks it will be shortly. The key? Their proposed portfolio optimization method is compatible with existing quantum computers. Specifically, they looked at D-Waves adiabatic machines, and according to the paper, they believe it can scale up in complexity as the underlying technology improves.

It's something that has real ability to impact what's possible within your industry and to make money doing all the things you already dobut in completely new ways, Fursman said.

Exponential Finance, according to Fursman, is a bit ahead of the curve. The event has focused on the possibility of quantum computing in finance for the last several years.

But now, its poised to make an impact. Google recently announced they expect to achieve quantum supremacy by the end of this year. That means theyll have shown a quantum computer capable of solving a problem no conventional computer can.

Fursman thinks the slowing of Moores Law may be lulling some into complacency. Whereas at one point you could barely keep pace, even if you bought a new computer every year; these days, the computer you bought four years ago is basically stillable run whatever you want today.

But for businesses, the pace of progress is about to speed up again.

The quantum computing industry [today] is just [the] spark. Its just the very, very beginning of whats going to be possible, Fursman said. Those sparks are going to turn into a huge explosion, and all of a sudden, youre going to be faced with incredible amounts of computing capabilities that directly tackle the types of problems most relevant to what youre doing.

This isnt going to take 20 years, he said, or even ten years. Itll be here in three to five years. So, now is the time to start thinking about what quantum will do for you.

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Quantum Computers Will Analyze Every Financial Model at Once - Singularity Hub

In BriefNine experts weighed in on the future of artificialintelligence and machine learning recently for IEEE Spectrum. Theiranswers provide a glimpse into what's coming in the world of AI andwhat to expect from the Singularity. AI Visionaries

Artificial intelligence (AI) is progressing so fast that there are new developments in the field almost every week. The tendrils of AI grow further into human life and continue to rapidly intertwine with our reality, and this process will only accelerate. Some worry about the consequences of a future in which AIs have more capabilities than humans, while some relish this prospect. IEEE Spectrum has just published a special issue for June 2017, which reports on the views of nine visionaries, technologists, and futurists on whats coming in AI.

Each expert was asked, When will we have computers as capable as the brain? Ray Kurzweil thinks this will happen in 2029, while Jrgen Schmidhuber simply agrees that it will be soon, and Gary Marcus estimates that it will happen 20 to 50 years from now. Nick Bostrom predicts within a small number of decades. Rodney Brooks is a little more conservative, estimating 50 to 100 years, while both Robin Hanson and Martine Rothblatt think that it will happen within the 21st century.

Ruchir Puris answer to this question was perhaps the most interesting: A human brain is fundamentally different than being a champion chess, Jeopardy!, or Go player. It is something that entails essential traits like caring, empathy, sharing, ingenuity, and innovation. These human brain traits might prove to be elusive to machines for a long time. . .. Although AIs impact on society will accelerate further. . .it will be a while before we will be able to holistically answer [that] question.

So, How will brainlike computers change the world? Robin Hanson thinks that humans will get rich from robot labor, while Gary Marcus anticipates major advancements in science and medicine and Martine Rothblatt agrees with Kurzweil that we will essentially eventually become downloadable and therefore immortal. Ray Kurzweil sees AI as a massive brain extender, and therefore a problem solver, making every aspect of our lives better. Rodney Brooks thinks making realistic predictions about this isnt possible since its too far off, and instead posits that in 20 years, baby boomers including Kurzweil will be assisted by in-home computers, but wont be immortal. Jrgen Schmidhuber thinks that AIs will be fascinated by the possibilities of space as they become self-motivated and pursue their own goals.

Finally, Do you have any qualms about a future in which computers have human-level (or greater) intelligence? Carver Mead points out that people always fear new technologies, even though history shows that we have continually benefitted from them. Robin Hanson thinks anyone who doesnt have qualms about a change this momentous isnt paying attention, but Martine Rothblatt doesnt have qualms, because she thinks human needs will shape a Darwinian market for robots. Ray Kurzweil thinks we will avoid peril and gain optimally by merging with AI. Nick Bostrom is concerned by the problem of scalable control of AI, while Rodney Brooks says he has no qualms at all, and that qualming is not useful, even for Nick Bostrom. Gary Marcus doesnt see clear solutions to potential problems yet, but thinks that future technologies will provide them.

The experts had different ideas about many things, but there was no dispute about the most important point: the singularity is coming, and its closer than we think.

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Experts Weigh in on AI and the Singularity - Futurism

Singularity Universitys Exponential Finance Summit begins today and runs through June 9 in New York, the finance industrys bustling capital. You can tune into the summit as it happens from anywhere with this livestream.

Singularity Hub is also covering the event as it brings together financial and technology leaders from across the industry. From exciting startups like Lemonade and HyperScience to established financial institutions such as BlackRock and Bank of America, well be learning about how emerging technologies are changing the workings of the finance industry and how financial services companies do business.

At the summit, experts will dive into:

Ric Edelman, founder of Edelman Financial Services, and Sharon Sputz, director of Columbia Universitys Data Science Institute, will discuss the future of financial advice and investing. Angela Strange, partner at Andreesen Horowitz, will break down exponential technology and insurance, and BlackRocks chief talent officer, Matthew Breitfelder, will take a look at the future of work.

Of course, as usual, well also keep an eye on talks and question-and-answer sessions with Ray Kurzweil, Singularity University cofounder and chancellor, and Peter Diamandis, Singularity University cofounder and chairman.

Be sure to join the conversation on the future of finance in real-time on Twitter with@SingularityHuband@xfinanceor using the hashtag#xfin.

Much of the latest technology driving fintech is still new, and its impact has yet to be fully fleshed outwhich should make for an interesting summit.

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Tune Into the Future of Fintech at Exponential Finance This Week - Singularity Hub

Singularity University is dismayed by the Trump administrations choice to withdraw from the Paris Accord. Climate change is one of the greatest risks to humankind, and the decisions we make over the next few decades will impact life on earth for thousands of years.

At SU were proud to support the responsible development of exponential technologies, such as AI, robotics, nanotechnology, and digital biology, that may provide solutions to climate change. These exponential technologies should be nurtured in enabling policy environments, but independent of the decisions made by politicians, SU will move forward with our plans to address climate change.

Were proud to see an increase in breakthroughs that greatly improve our stewardship of the planet and global abundance such as in vitro meat production, carbon capture techniques, genetic engineering of climate resilient crops, advances in atmospheric water extraction, and countless others.

While this is a disappointing decision, there are more powerful forces at work. The global response to the federal governments decision has renewed our faith in the common goodness of humankind. Innovation will continue. We will move forward.

We at SU provide access to a deep and broad innovation ecosystem that includes forward thinking corporations (e.g., Deloitte, Google, Lowes), development organizations (e.g., Stockholm Resilience Center, Unicef, World Wide Fund for Nature), and governments around the world. We will continue to work across industries and disciplines to bring abundance to all.

We welcome you to join our bold march into the future.

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Beyond Politics: Innovating for a Sustainable Future - Singularity Hub

Humankind lost another important battle with artificial intelligence (AI) last month when AlphaGo beat the worlds leading Go player Kie Je by three games to zero.

AlphaGo is an AI program developed by DeepMind, part of Googles parent company Alphabet. Last year it beat another leading player, Lee Se-dol, by four games to one, but since then AlphaGo has substantially improved.

Kie Je described AlphaGos skill as like a god of Go.

AlphaGo will now retire from playing Go, leaving behind a legacy of games played against itself. Theyve been described by one Go expert as like games from far in the future, which humans will study for years to improve their own play.

Go is an ancient game that essentially pits two playersone playing black pieces the other whitefor dominance on board usually marked with 19 horizontal and 19 vertical lines.

Go is a far more difficult game for computers to play than chess, because the number of possible moves in each position is much larger. This makes searching many moves aheadfeasible for computers in chessvery difficult in Go.

DeepMinds breakthrough was the development of general-purpose learning algorithms that can, in principle, be trained in more societal-relevant domains than Go.

DeepMind says the research team behind AlphaGo is looking to pursue other complex problems, such as finding new cures for diseases, dramatically reducing energy consumption or inventing revolutionary new materials. It adds:

"If AI systems prove they are able to unearth significant new knowledge and strategies in these domains too, the breakthroughs could be truly remarkable. We cant wait to see what comes next."

This does open up many opportunities for the future, but challenges still remain.

AlphaGo combines the two most powerful ideas about learning to emerge from the past few decades: deep learning and reinforcement learning. Remarkably, both were originally inspired by how biological brains learn from experience.

In the human brain, sensory information is processed in a series of layers. For instance, visual information is first transformed in the retina, then in the midbrain, and then through many different areas of the cerebral cortex.

This creates a hierarchy of representations where simple, local features are extracted first, and then more complex, global features are built from these.

The AI equivalent is called deep learning; deep because it involves many layers of processing in simple neuron-like computing units.

But to survive in the world, animals need to not only recognize sensory information, but also act on it. Generations of scientists and psychologists have studied how animals learn to take a series of actions that maximize their reward.

This has led to mathematical theories of reinforcement learning that can now be implemented in AI systems. The most powerful of these is temporal difference learning, which improves actions by maximizing expectation of future reward.

By combining deep learning and reinforcement learning in a series of artificial neural networks, AlphaGo first learned human expert-level play in Go from 30 million moves from human games.

But then it started playing against itself, using the outcome of each game to relentlessly refine its decisions about the best move in each board position. A value network learned to predict the likely outcome given any position, while a policy network learned the best action to take in each situation.

Although it couldnt sample every possible board position, AlphaGos neural networks extracted key ideas about strategies that work well in any position. It is these countless hours of self-play that led to AlphaGos improvement over the past year.

Unfortunately, as yet there is no known way to interrogate the network to directly read out what these key ideas are. Instead, we can only study its games and hope to learn from these.

This is one of the problems with using such neural network algorithms to help make decisions in, for instance, the legal system: they cant explain their reasoning.

We still understand relatively little about how biological brains actually learn, and neuroscience will continue to provide new inspiration for improvements in AI.

Humans can learn to become expert Go players based on far less experience than AlphaGo needed to reach that level, so there is clearly room for further developing the algorithms.

Also, much of AlphaGos power is based on a technique called back-propagation learning that helps it correct errors. But the relationship between this and learning in real brains is still unclear.

The game of Go provided a nicely constrained development platform for optimizing these learning algorithms. But many real-world problems are messier than this and have less opportunity for the equivalent of self-play (for instance self-driving cars).

So, are there problems to which the current algorithms can be fairly immediately applied?

One example may be optimization in controlled industrial settings. Here the goal is often to complete a complex series of tasks while satisfying multiple constraints and minimizing cost.

As long as the possibilities can be accurately simulated, these algorithms can explore and learn from a vastly larger space of outcomes than will ever be possible for humans. Thus DeepMinds bold claims seem likely to be realized, and as the company says, we cant wait to see what comes next.

This article was originally published on The Conversation. Read the original article.

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No More Playing Games: AlphaGo AI to Tackle Some Real World Challenges - Singularity Hub

The age of the cyborg may be closer than we think. Rapidly improving medical robotics, wearables, and implants means many humans are already part machine, and this trend is only likely to continue.

It is most noticeable in the field of medical prosthetics where high-performance titanium and carbon fiber replacements for limbs have become commonplace. The use of blades by Paralympians has even raised questions over whether they actually offer an advantage over biological limbs.

For decades, myoelectric prostheticspowered artificial limbs that read electrical signals from the muscles to allow the user to control the devicehave provided patients with mechanical replacements for lost hands.

Now, advances in robotics are resulting in prosthetic hands that are getting close to matching the originals in terms of dexterity. The Michelangelo prosthetic hand is fully articulated and precise enough to carry out tasks like cooking and ironing.

Researchers have even demonstrated robotic hands that have a sense of touch and can be controlled using the mind. And just last month another group showed that fitting a standard myoelectric arm with a camera and a computer vision system allowed it to see and grab objects without the user having to move a muscle.

Medical exoskeletons are already commercially availablemost notably, ReWalk and Ekso Bionics devices designed to help those with spinal cord injuries stand and walk. Elsewhere, this technology is being used to rehabilitate people after strokes or other traumatic injuries by guiding their limbs through their full range of motion.

At present, these technologies are aimed solely at those who have been injured or incapacitated, but an editorial in Science Robotics last week warned that may not always be the case.

There needs to be a debate on the future evolution of technologies as the pace of robotics and AI is accelerating, the authors wrote.

It seems certain that future assistive technologies will not only compensate for human disability but also drive human capacities beyond our innate physiological levels. The associated transformative influence will bring on broad social, political, and economic issues.

This can already be seen with the development of military exoskeletons designed to boost soldiers endurance. More bizarrely, Japanese researchers have recently floated the idea of adding to our limbs rather than replacing them. The MetaLimbs project gives users two extra robotic arms that can be controlled using sensors on their legs and feet.

Last weeks issue of Science Robotics actually included a study demonstrating that a soft robotic exosuit was actually more effective at lightening the load on a runner when it didnt follow a humans natural running pattern and instead used computer simulations to decide what forces to apply.

This suggests there is considerable room for machines to not only augment the power of our muscles but even optimize the biomechanics of our movement. And as the authors of the editorial note, biomechanics is only one strand of research where scientists are trying to replicate and ultimately improve our abilities.

Devices like cochlear implants have been used to restore hearing in the deaf for decades and there are a number of experimental efforts to create bionic eyes to help the blind see again. Efforts to augment our intelligence with neural implants have been widely reported on in recent months.

Admittedly, there is still a long way to go before people start demanding to amputate their arm so they can get a shiny, new robotic one. And its likely the companies driving for consumer-grade neural interfaces are overestimating how many people will voluntarily undergo brain surgery.

But weve already taken the first steps towards merging our biological selves with machines.

You can argue smartphones are already essentially a prosthetic designed to boost communication and memory. And more overtly cyborg-like augmentations are likely to appear in many of our lifetimes.

What then does that mean for humankind? Natural evolution has long relied on mutation conferring minute but significant advantages to individuals that gradually spread throughout populations. If new prosthetic technologies start to confer these advantages overnight the effects could be very patchy.

The worry is that the latest augmentations are only available to the few who can afford them and in just a few generations you could end up with an elite who not only dwarf the rest of humanity financially but also physically and cognitively.

At the same time, these technologies hold huge promise to restore a decent standard of living to the countless people incapacitated by injury or disease. And if applied equitably, devices aimed at augmenting our abilities could better equip us to face the many challenges society faces.

But as the authors of the editorial note, the conversation on how best to guide us through this next stage of our evolution needs to start now. Because these devices have so far been focused on restoring functions that have been lost, we have largely missed the fact that they are now reaching the point where they can improve those functions or even enable new ones.

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What Happens When Cyborg Tech Goes Beyond Medicine? - Singularity Hub

Originally a concept that was conceived by David Tucker, and Richard Platt on a napkin in 1993, the first IP PBX in the world changed the IP network platform. Soon, convergence emerged as a crucial trend, as connecting people through real-time voice became essential for many businesses. As convergence became more popular, companies like Cisco responded accordingly, integrating voicemail, call centre solutions, IM, conferencing, video, and more into a singular platform for enterprise communication.

One of the key elements of Ciscos success has been the use of a teeming system of collaboration solutions and developers. Rich APIs for call management, automation, interoperability, and compliance mean in-house developers and ISVs can mainline business intelligence straight to the communications infrastructure, connecting the IoT, processes, systems, and people.

During its most recent attempt to bring the business world into its network, Cisco Spark has taken the IP collaboration stack out of the server and into the cloud, bringing together consistent chat, WebEx video conferencing, screen sharing, HD video and voice, and a range of unique hardware endpoints into a unique user experience. Cisco Sparks end-to-end encryption solution blurs the lines between WAN and LAN with UC infrastructure interoperability, adaptive bandwidth usage, and reliability-at-scale solutions.

Perhaps two of the most innovative solutions for developers and end-users have been the launch of the Cisco Spark video SDK, and the Cisco Spark Board room-conferencing system. Bringing in fantastic reviews in the world of business equipment, Ciscos Spark Board has changed the environment for many enterprises, as a hugely capable solution for UC.

Using the Cisco Spark Board, customers can connect to the Spark cloud effortless by simply plugging into their network. They then have access to the complete enterprise conference room, with stunning touchscreen collaboration and video conferencing that connects seamlessly to both PC and mobile devices.

Additionally, the Cisco SparkVideo SDK complements the Cisco Spark ability to offer omnipresent video collaboration, giving developers more power to embed their Spark collaboration needs into existing applications. The Spark SDK currently supports Swift/Ios browser apps through WebRTC and JavaScript, but Android will follow soon. The system offers self-contained widgets and frameworks that let coders transform mobile apps and web pages into high-performance, secure tools for collaboration.

When you combine Ciscos existing messaging APIs to Spark to these new solutions, business IM can begin to encounter all the possibilities of chatbots that can be linked to automation and IT systems, delivering a fully pervasive cloud collaboration solution for any agile enterprise.

In a world thats increasingly focused on bringing all of its solutions for unified communications into the same space, Ciscos latest developments, including the acquisition of the MindMeld AI company, shows its devotion to collaboration singularity. Although there are few details on what the future might look like for Cisco, the term cognitive collaboration has been mentioned. Its thrilling to think about convergence, integration, and networks will continue to transform communications in businesses all over again.

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Approaching the World of Collaboration Singularity - CommsTrader

Reaching the technological singularity is almost certainly going to involve AI that is able to improve itself. Google may have now taken a small step along this path by creating AI that can build AI.

Speaking at the companys annual I/O developer conference, CEO Sundar Pichai announced a project called AutoML that can automate one of the hardest parts of designing deep learning software: choosing the right architecture for a neural network.

The Google researchers created a machine learning system that used reinforcement learningthe trial and error approach at the heart of many of Googles most notable AI exploitsto figure out the best architectures to solve language and image recognition tasks.

Not only did the results rival or beat the performance of the best human-designed architectures, but the system made some unconventional choices that researchers had previously considered inappropriate for those kinds of tasks.

The approach is still a long way from being practical, the researchers told MIT Tech Review, as it tied up 800 powerful graphics processors for weeks. But Google is betting that automating the process of building machine learning systems could help get around the shortage of human-machine learning and data science talent that is slowing the technologys adoption.

Its not the only one. Facebook engineers have built what they like to call an automated machine learning engineer, according to Wired. Its also called AutoML and can choose algorithms and parameters that are most likely to solve the problem at hand.

Last summer, the AutoML challenge saw teams go head-to-head to build machine learning black boxes that can select models and tune parameters without any human intervention. Even game designers are in on the actthe team behind the hit game Space Engineers has used some of their profits to set up a team of experts to design AI able to optimize its own hardware and software.

While this kind of automation could make it easier for non-experts to design and deploy AI systems, it also seems to be laying the foundation for machines that can take control of their own destiny.

The concept of "recursive self-improvement" is at the heart of most theories on how we could rapidly go from moderately smart machines to AI superintelligence. The idea is that as AI gets more powerful, it can start modifying itself to boost its capabilities. As it makes itself smarter it gets better at making itself smarter, so this quickly leads to exponential growth in its intelligence.

Generally, the so-called seed AI is envisaged as an artificial general intelligence (AGI), a machine that is able to carry out any intellectual task a human could rather than being a specialist in a very specific area, like most of todays algorithms are.

The systems being worked on today are clearly a long way from AGI, and they are directed at building and improving other machine learning systems rather than themselves. Outside of machine learning, self-modifying code has been around for a while, but it would likely be far more complex to deploy this technique to edit neural networks.

But creating algorithms able to work on machine learning code is clearly afirst step towards the kind of self-improving AI envisaged by futurists.

Other recent developments could also feed in this direction. Many AI researchers are trying to encode curiosity and creativity into machine learning systems, both traits likely to be necessary for a machine to redesign itself in performance-boosting ways. Others are working on allowing robots to share the lessons theyve learned, effectively turning them into a kind of hive mind.

Doubtless, it will be a long time before any of these capabilities reach the stage where they can be usefully employed to create a self-improving AI. But we can already see the technological foundations being laid.

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Google's AI-Building AI Is a Step Toward Self-Improving AI - Singularity Hub