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5 Things To Watch In AI And Machine Learning

Five Things To Watch In AI And Machine Learning In 2017

Without a doubt, 2016 was an amazing year for Machine Learning (ML) and Artificial Intelligence (AI). During the year, we saw nearly every high tech CEO claim the mantel of becoming an “AI Company”. However, only a few companies were actually able to monetize their significant investments in AI, notably Amazon AMZN +0.71%, Baidu , Facebook FB +1.75%, Google GOOGL +3.77%, IBM IBM -0.02%, Microsoft MSFT +0.29%, Tesla Motors TSLA +1.75% and NVIDIA NVDA -1.29%. But 2016 was nonetheless a year of many firsts. As a posterchild for the potential for ML, Google Deep Mind mastered the subtle and infinitely complex game of GO, soundly beating the reigning world champion. And more than a few cool products were introduced that incorporated Machine Learning, from the first autonomous vehicles to new “intelligent” household assistants such as Google Home and Amazon Echo. But will 2017 finally usher in the long-promised age of Artificial Intelligence?

NVIDIA's Saturn V supercomputer for Machine Learning is the 28th fastest computer in the world, and is the #1 in the Green 500 list of the most power efficient. (Source: NVIDIA)

NVIDIA’s Saturn V supercomputer for Machine Learning is the 28th fastest computer in the world, and is the #1 in the Green 500 list of the most power efficient. (Source: NVIDIA)

Two domains: AI and Machine Learning. These terms are not interchangeable. Machine learning, a completely different way to program a computer by training it with a massive ocean of sample data, is real and is here to stay. General Artificial Intelligence remains a distant goal and is perhaps 5-20 years away depending on the specific domain of the “intelligence” being learned. To be sure, computers trained using Machine Learning hold tremendous promise, as well as the potential for massive disruption in the workplace. But these systems remain a far cry from genuine intelligence. Just ask Apple AAPL -0.14% Siri, and you will see what I mean. The hype around AI, and confusion over what the term actually means, will inevitably lead to some disillusionment as the limitations of this technology become apparent.

With that context in mind, here’s what I expect for the coming year for Machine Learning and AI.

1. Hardware accelerators for Machine Learning will proliferate.

Today, nearly all training of deep neural networks (DNNs) is performed using NVIDIA GPUs. Conversely, DNN inference, or the actual use of a trained network can be done efficiently on CPUs, GPUs, FPGAs, or even specialized ASICs such as the Google TPU, depending on the type of data being analyzed. Both training and inference markets will be hotly contested in 2017, as Advanced Micro Devices GPUs, Intel’s newly acquired Nervana chips, NVIDIA, Xilinx and several startups all launch accelerators specifically targeting this lucrative market. If you would like a deeper dive into the various semiconductor alternatives for AI, please see my companion article on this subject here.

2. Select application domains will leverage Machine Learning to improve efficiency of mission-critical processes.

If you are trying to find the killer AI app, the increasingly pervasive nature of the technology will make it difficult to identify. However, Machine Learning has begun to deliver spectacular results in very specific niches where the pattern recognition capabilities can be exploited, and this trend will continue to expand into new markets in 2017.

All organisations are AI services

A years from currently, will certainly we be stating that

all organisations are AI companies

.


Self-replicating Machine

 

 

Eleven Need-to-Know Facts About the Self-replicating Machine

machine
Self-replicating machines | Isaac Arthur | YouTube.com
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In an eye-opening video about the potential of self-replicating machines, Isaac Arthur describes what they are and how they will soon have a huge effect on our future. 

From it, we gleaned 11 need-to-know facts about self-replicating machines:

1. The Concept of the Self-replicating Machine Goes Back 400 Years

René Descartes | wikipedia.org

It was Descartes who first described humans as machines in the 1650s. Sam Butler claimed that the body is a self replicating machine, and centuries later, Eric Drexel further defined and popularized this and other nanotechnology theories in his 1986 book, Engines of Creation. In the book, he describes the universalassembler, whic is a machine that is able to place atoms or molecules in specific places, thus being able to create any given object.

2. Technically, They Are Alive

What is life?

Life is usually defined as the ability to eat, grow, excrete, replicate, adapt and react to the environment.

At a minimum, self-replicating machines must be able to be able to take in and use matter to create a copy of itself and form a pattern, much like our DNA. They must be able to adapt to and interact with their environments. They do not need to be able to grow, or repair themselves per se, as long as they are able create copies of themselves before they deteriorate.

Most SRMs go beyond meeting the bare minimum requirements of qualifying as life.

3. Technically, We Use Them Right Now

A 3D printer that is able to print itself is a self-replicating machine. Though it is possible, self-replicating machines do not need to be able to produce their own building material. This is the same as in humans, who use individual life forms to keep ourselves alive such as bacteria and mitochondria.

4. Mutation is Not Likely

If they are so life-like, doesn’t this mean they will eventually mutate?

Self-replicating machines are only able to mutate by design. Even if mutation did occur, say due to an adaptation in regards to ingredients used to self-replicate, it would be extremely improbable that enough machines would mutate in the same way to create a problem for their programmed directive.

5. A Range of Sizes

The machines can be microscopic or large. Because of this, practical use of self-replicating machines will most likely exist off-planet or inside of human begins as their amazing ability to build and repair could lead to prolonged life.

It is possible that in humans, nano-robots will be able to repair tissue or failing organs without invasive surgery. They will also be able to monitor systems from within, repairing and rebuilding themselves as time goes on.

With SRMs being used for space travel, space probes will be able to repair themselves for thousands of years during exploration. This allows for humanity’s evolution into extrasolar travel.

6. Future Possibilities for Space Travel

There are countless different kinds of interstellar self-replicators that will be possible In the future. The most basic and all-encompassing is the Von Neumann probe, which is an interstellar probe that self-repairs and makes copies of itself periodically while exploring space. The idea of these probes stopping to repair periodically is a way to reduce the amount of time that is lost in the repair phase, and it is an important factor to consider with all of the interstellar space probe theories.

Stanley Kubrick’s 2001: A Space Odyssey | inktank.fi

A Bracewell probe is designed to communicate with other forms of life. In his video on self-replicating machines, Isaac Arthur gives the example of the black rectangular column in Stanley Kubrick’s 2001: A Space Oddysey.

The probe is designed to monitor life and then figure out how to communicate with it, which means that these probes have human level intelligence or greater. Although bracewll probes are not necessarily Von Neumann machines, it would make more sense for them to be that say so that they can unpack, and build upon arrival to a new planet.

7. Hints of Doom

There a few doomsday theories about what could go wrong when dispatching out these living robots into the solar system. But as long as we are aware of the possibilities, we should be able to avoid dire consequences.

A terraforming swarm is defined by sending probes out to begin inhabiting life-sustaining planets. After the probes find a planet that is suitable for human life, they begin to terraform, which has some moral and ethical constraints, especially if there is life already on the planet.

Berserker swarms happen when probes seek out new life and destroy it. And a graygoo swarm is the concept of self-replicating machines seeking out life and eating it. Both of these ideas for SRMs play into doomsday theories about all of the negative possibilities of using self-replicating machines for space exploration, but it is important to note that these SRMs would only come about from malicious intent.

10. Destroyers of the Universe?

Despite concerns about possible malevolent robots, it is impossible for robots to gray goo a planet and destroy it all at once. Self-replicating machines cannot realistically replicate faster than organisms of the same size. However, they can reproduce faster than biological life, they are constrained by the bottleneck effect that heat has on speed and production. Exponential growth has its limits.

Further, the more complicated the machine is, the slower reproduction will be. SRMs can be microscopic or larger than a person, and with added bulk and intelligence to the machine also comes the addition of the amount of time it takes to build itself.

Excuse me, sir.

11. Get Ready

Isaac Arthur argues that we will see self-replicating machines being used for space exploration and in medicine in our lifetime. The technology could totally transform the way that we go about our exploration of the universe and could be a cheaper solution and learning tool for the future. Self-replicating robots can be used in space mining, colonization, and manufacturing.

Edgy Labs Readers: What did we miss? What else should we know about SRMs?

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Points You Needed to know Concerning Deep Knowing 101 as well as Were Too Embarrassed to Ask

Deep learning aims to move in this direction by recording a ‘great’ depiction of input information using make-ups of non-linear makeovers. A ‘good’ representation could be specified as one that disentangles hidden variables of variation for input data. It ends up that deep understanding methods could discover valuable abstract depictions of data across several domains: it has had great commercial success powering the majority of Google and Microsoft’s current speech acknowledgment, photo category, all-natural language handling, item acknowledgment, and so on. Facebook is likewise intending on making use of deep learning approaches to recognize its user’s Deep understanding has been so impactful in sector that MIT Innovation Review named it as a top-10 development innovation of

Deep Knowing for Photo Editing and enhancing

Deep knowing, a subfield of machine learning, has become one of the most known areas in the ongoing AI buzz. Having caused several essential magazines as well as impressive results, it is put on loads of different situations and also has currently generated fascinating results like human-like speech generation, high accuracy things detection, advanced machine translation, super-resolution and also a lot more.

Exactly what is deep learning (deep neural networking)? – Meaning from WhatIs.com

Deep learning is an aspect of artificial intelligence (AI) that is interested in replicating the learning strategy that humans make use of to obtain particular kinds of understanding. At its simplest, deep understanding could be considered a method to automate predictive analytics

What counts as unnaturally smart? AI and also deep knowing explained

Deep understanding techniques are now being utilized for all sorts of day-to-day jobs. A lot of the huge tech firms have their very own AI departments, as well as both Facebook as well as Google have actually introduced initiatives to open up their study by open-sourcing several of their software program. Google even introduced a totally free three-month online course in deep knowing last month. And while academic scientists might work in loved one obscurity, these business organizations are churning out unique applications for this technology weekly: every little thing from Microsoft’s” emotional recognition ” internet app to Google’s unique Deep Dream photos. This is another reason why we’re hearing a whole lot concerning deep learning lately: big, consumer-facing business are playing with it, as well as they’re sharing a few of the weirder stuff they’re making.

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Satisfy the researchers constructing electronic ‘minds’ for your phone The future of AI is neuromorphic. The factor they depend on cloud-based computer is that today’s electronic devices do not come with adequate computer power to run the processing-heavy formulas required for maker knowing. What is Moore’s Law?Also as these brain-like chips were being established, constructing formulas for them has actually stayed a difficulty. Exactly what makes Nengo helpful is its usage of the acquainted Python shows language– recognized for it’s user-friendly phrase structure– as well as its capacity to place the formulas on several various equipment systems, consisting of neuromorphic chips. Rise of the equipments: are formulas stretching out of our control?, a task that in 2012 gained global appreciation for being the most intricate mind design ever before substitute on a computer system. Lately, by utilizing neuromorphics, many of Spaun has actually been run 9000x quicker, making use of much less power compared to it would certainly on traditional CPUs– as well as by the end of 2017, all of Spaun will certainly be running on Neuromorphic equipment.Eliasmith won NSERC’s John C. Polyani honor for that job– Canada’s highest possible acknowledgment for an innovation clinical accomplishment– as well as soon as Suma discovered the research study, both signed up with pressures to advertise these devices.” Imagine a SIRI that pays attention and also sees all of your communications as well as discussions.,” he claims.“The most basic distinction in between the majority of readily available AI systems of today and also the organic smart systems we are made use of to, is the truth that the last constantly run in real-time. Brains as well as bodies are developed to function with the physics of the globe,” he states.Currently, significant initiatives throughout the IT sector are warming up to obtain their AI solutions right into the hands of individuals., are creating conversational aides they really hope will certainly one day end up being electronic assistants.The future of AI is neuromorphic. The factor they count on cloud-based computer is that today’s electronic devices do not come with adequate computer power to run the processing-heavy formulas required for equipment understanding. What is Moore’s Law? Rise of the devices: are formulas stretching out of our control?” Imagine a SIRI that pays attention and also sees all of your communications as well as discussions.

SIGGRAPH 2018

For more than four decades, SIGGRAPH conferences have been at the center of innovation in computer graphics and interactive techniques. Immerse yourself in a new generation of technology, trends and techniques at SIGGRAPH 2018.

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LEARN

The most accomplished minds in research, design and development gather at SIGGRAPH to share their discoveries and innovations. From VFX and animation techniques to VR and game design, SIGGRAPH 2018 features five days of courses, talks, sessions and panels that will blow you away.

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Roll up your sleeves for hands-on exploration in the Studio. Demo the latest in mixed reality in the Immersive Pavilion. Go behind the VFX of the latest blockbuster game at one of our Production Sessions. Let SIGGRAPH reignite your imagination and then take your inspiration back to your workplace.

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Our community is diverse, curious, and passionate. We are artists and researchers, students and pioneers. We come from around the globe, from different disciplines, with various levels of experience and points-of-view. We gather at SIGGRAPH to create, discover and learn from one another.

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