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Disturbing video depicts near-future ubiquitous lethal autonomous weapons


Campaign to Stop Killer Robots | Slaughterbots

In response to growing concerns about autonomous weapons, the Campaign to Stop Killer Robots, a coalition of AI researchers and advocacy organizations, has released a fictional video that depicts a disturbing future in which lethal autonomous weapons have become cheap and ubiquitous worldwide.

UC Berkeley AI researcher Stuart Russell presented the video at the United Nations Convention on Certain Conventional Weapons in Geneva, hosted by the Campaign to Stop Killer Robots earlier this week. Russell, in an appearance at the end of the video, warns that the technology described in the film already exists* and that the window to act is closing fast.

Support for a ban against autonomous weapons has been mounting. On Nov. 2, more than 200 Canadian scientists and more than 100 Australian scientists in academia and industry penned open letters to Prime Minister Justin Trudeau and Malcolm Turnbull urging them to support the ban.

Earlier this summer, more than 130 leaders of AI companies signed a letter in support of this week’s discussions. These letters follow a 2015 open letter released by the Future of Life Institute and signed by more than 20,000 AI/robotics researchers and others, including Elon Musk and Stephen Hawking.

“Many of the world’s leading AI researchers worry that if these autonomous weapons are ever developed, they could dramatically lower the threshold for armed conflict, ease and cheapen the taking of human life, empower terrorists, and create global instability,” according to an article published by the Future of Life Institute, which funded the video. “The U.S. and other nations have used drones and semi-automated systems to carry out attacks for several years now, but fully removing a human from the loop is at odds with international humanitarian and human rights law.”

“The Campaign to Stop Killer Robots is not trying to stifle innovation in artificial intelligence and robotics and it does not wish to ban autonomous systems in the civilian or military world,” explained Noel Sharkey of the International Committee for Robot Arms Control. Rather we see an urgent need to prevent automation of the critical functions for selecting targets and applying violent force without human deliberation and to ensure meaningful human control for every attack.”

For more information about autonomous weapons:

* As suggested in this U.S. Department of Defense video:


Perdix Drone Swarm – Fighters Release Hive-mind-controlled Weapon UAVs in Air | U.S. Naval Air Systems Command

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A tool to debug ‘black box’ deep-learning neural networks

Oops! A new debugging tool called DeepXplore generates real-world test images meant to expose logic errors in deep neural networks. The darkened photo at right tricked one set of neurons into telling the car to turn into the guardrail. After catching the mistake, the tool retrains the network to fix the bug. (credit: Columbia Engineering)

Researchers at Columbia and Lehigh universities have developed a method for error-checking the reasoning of the thousands or millions of neurons in unsupervised (self-taught) deep-learning neural networks, such as those used in self-driving cars.

Their tool, DeepXplore, feeds confusing, real-world inputs into the network to expose rare instances of flawed reasoning, such as the incident last year when Tesla’s autonomous car collided with a truck it mistook for a cloud, killing its passenger. Deep learning systems don’t explain how they make their decisions, which makes them hard to trust.

Modeled after the human brain, deep learning uses layers of artificial neurons that process and consolidate information. This results in a set of rules to solve complex problems, from recognizing friends’ faces online to translating email written in Chinese. The technology has achieved impressive feats of intelligence, but as more tasks become automated this way, concerns about safety, security, and ethics are growing.

Finding bugs by generating test images

Debugging the neural networks in self-driving cars is an especially slow and tedious process, with no way to measure how thoroughly logic within the network has been checked for errors. Current limited approaches include randomly feeding manually generated test images into the network until one triggers a wrong decision (telling the car to veer into the guardrail, for example); and “adversarial testing,” which automatically generates test images that it alters incrementally until one image tricks the system.

The new DeepXplore solution — presented Oct. 29, 2017 in an open-access paper at ACM’s Symposium on Operating Systems Principles in Shanghai — can find a wider variety of bugs than random or adversarial testing by using the network itself to generate test images likely to cause neuron clusters to make conflicting decisions, according to the researchers.

To simulate real-world conditions, photos are lightened and darkened, and made to mimic the effect of dust on a camera lens, or a person or object blocking the camera’s view. A photo of the road may be darkened just enough, for example, to cause one set of neurons to tell the car to turn left, and two other sets of neurons to tell it to go right.

After inferring that the first set misclassified the photo, DeepXplore automatically retrains the network to recognize the darker image and fix the bug. Using optimization techniques, researchers have designed DeepXplore to trigger as many conflicting decisions with its test images as it can while maximizing the number of neurons activated.

“You can think of our testing process as reverse-engineering the learning process to understand its logic,” said co-developer Suman Jana, a computer scientist at Columbia Engineering and a member of the Data Science Institute. “This gives you some visibility into what the system is doing and where it’s going wrong.”

Testing their software on 15 state-of-the-art neural networks, including Nvidia’s Dave 2 network for self-driving cars, the researchers uncovered thousands of bugs missed by previous techniques. They report activating up to 100 percent of network neurons — 30 percent more on average than either random or adversarial testing — and bringing overall accuracy up to 99 percent in some networks, a 3 percent improvement on average.*

The ultimate goal: certifying a neural network is bug-free

Still, a high level of assurance is needed before regulators and the public are ready to embrace robot cars and other safety-critical technology like autonomous air-traffic control systems. One limitation of DeepXplore is that it can’t certify that a neural network is bug-free. That requires isolating and testing the exact rules the network has learned.

A new tool developed at Stanford University, called ReluPlex, uses the power of mathematical proofs to do this for small networks. Costly in computing time, but offering strong guarantees, this small-scale verification technique complements DeepXplore’s full-scale testing approach, said ReluPlex co-developer Clark Barrett, a computer scientist at Stanford.

“Testing techniques use efficient and clever heuristics to find problems in a system, and it seems that the techniques in this paper are particularly good,” he said. “However, a testing technique can never guarantee that all the bugs have been found, or similarly, if it can’t find any bugs, that there are, in fact, no bugs.”

DeepXplore has applications beyond self-driving cars. It can find malware disguised as benign code in anti-virus software, and uncover discriminatory assumptions baked into predictive policing and criminal sentencing software, for example.

The team has made their open-source software public for other researchers to use, and launched a website to let people upload their own data to see how the testing process works.

* The team evaluated DeepXplore on real-world datasets including Udacity self-driving car challenge data, image data from ImageNet and MNIST, Android malware data from Drebin, PDF malware data from Contagio/VirusTotal, and production-quality deep neural networks trained on these datasets, such as these ranked top in Udacity self-driving car challenge. Their results show that DeepXplore found thousands of incorrect corner case behaviors (e.g., self-driving cars crashing into guard rails) in 15 state-of-the-art deep learning models with a total of 132,057 neurons trained on five popular datasets containing around 162 GB of data.

Abstract of DeepXplore: Automated Whitebox Testing of Deep Learning Systems

Deep learning (DL) systems are increasingly deployed in safety- and security-critical domains including self-driving cars and malware detection, where the correctness and predictability of a system’s behavior for corner case inputs are of great importance. Existing DL testing depends heavily on manually labeled data and therefore often fails to expose erroneous behaviors for rare inputs.

We design, implement, and evaluate DeepXplore, the first whitebox framework for systematically testing real-world DL systems. First, we introduce neuron coverage for systematically measuring the parts of a DL system exercised by test inputs. Next, we leverage multiple DL systems with similar functionality as cross-referencing oracles to avoid manual checking. Finally, we demonstrate how finding inputs for DL systems that both trigger many differential behaviors and achieve high neuron coverage can be represented as a joint optimization problem and solved efficiently using gradient-based search techniques.

DeepXplore efficiently finds thousands of incorrect corner case behaviors (e.g., self-driving cars crashing into guard rails and malware masquerading as benign software) in state-of-the-art DL models with thousands of neurons trained on five popular datasets including ImageNet and Udacity self-driving challenge data. For all tested DL models, on average, DeepXplore generated one test input demonstrating incorrect behavior within one second while running only on a commodity laptop. We further show that the test inputs generated by DeepXplore can also be used to retrain the corresponding DL model to improve the model’s accuracy by up to 3%.

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Researchers watch video images people are seeing, decoded from their fMRI brain scans in near-real-time

Purdue Engineering researchers have developed a system that can show what people are seeing in real-world videos, decoded from their fMRI brain scans — an advanced new form of  “mind-reading” technology that could lead to new insights in brain function and to advanced AI systems.

The research builds on previous pioneering research at UC Berkeley’s Gallant Lab, which created a computer program in 2011 that translated fMRI brain-wave patterns into images that loosely mirrored a series of images being viewed.

The new system also decodes moving images that subjects see in videos and does it in near-real-time. But the researchers were also able to determine the subjects’ interpretations of the images they saw — for example, interpreting an image as a person or thing — and could even reconstruct a version of the original images that the subjects saw.

Deep-learning AI system for watching what the brain sees

Watching in near-real-time what the brain sees. Visual information generated by a video (a) is processed in a cascade from the retina through the thalamus (LGN area) to several levels of the visual cortex (b), detected from fMRI activity patterns (c) and recorded. A powerful deep-learning technique (d) then models this detected cortical visual processing. Called a convolutional neural network (CNN), this model transforms every video frame into multiple layers of features, ranging from orientations and colors (the first visual layer) to high-level object categories (face, bird, etc.) in semantic (meaning) space (the eighth layer). The trained CNN model can then be used to reverse this process, reconstructing the original videos — even creating new videos that the CNN model had never watched. (credit: Haiguang Wen et al./Cerebral Cortex)

The researchers acquired 11.5 hours of fMRI data from each of three women subjects watching 972 video clips, including clips showing people or animals in action and nature scenes.

To decode the  fMRI images, the research pioneered the use of a deep-learning technique called a convolutional neural network (CNN). The trained CNN model was able to accurately decode the fMRI blood-flow data to identify specific image categories (such as the face, bird, ship, and scene examples in the above figure). The researchers could compare (in near-real-time) these viewed video images side-by-side with the computer’s visual interpretation of what the person’s brain saw.

Reconstruction of a dynamic visual experience in the experiment. The top row shows the example movie frames seen by one subject; the bottom row shows the reconstruction of those frames based on the subject’s cortical fMRI responses to the movie. (credit: Haiguang Wen et al./ Cerebral Cortex)

The researchers were also able to figure out how certain locations in the visual cortex were associated with specific information a person was seeing.

Decoding how the visual cortex works

CNNs have been used to recognize faces and objects, and to study how the brain processes static images and other visual stimuli. But the new findings represent the first time CNNs have been used to see how the brain processes videos of natural scenes. This is “a step toward decoding the brain while people are trying to make sense of complex and dynamic visual surroundings,” said doctoral student Haiguang Wen.

Wen was first author of a paper describing the research, appearing online Oct. 20 in the journal Cerebral Cortex.

“Neuroscience is trying to map which parts of the brain are responsible for specific functionality,” Wen explained. “This is a landmark goal of neuroscience. I think what we report in this paper moves us closer to achieving that goal. Using our technique, you may visualize the specific information represented by any brain location, and screen through all the locations in the brain’s visual cortex. By doing that, you can see how the brain divides a visual scene into pieces, and re-assembles the pieces into a full understanding of the visual scene.”

The researchers also were able to use models trained with data from one human subject to predict and decode the brain activity of a different human subject, a process called “cross-subject encoding and decoding.” This finding is important because it demonstrates the potential for broad applications of such models to study brain function, including people with visual deficits.

The research has been funded by the National Institute of Mental Health. The work is affiliated with the Purdue Institute for Integrative Neuroscience. Data reported in this paper are also publicly available at the Laboratory of Integrated Brain Imaging website.

UPDATE Oct. 28, 2017 — Additional figure added, comparing the original images and those reconstructed from the subject’s cortical fMRI responses to the movie; subhead revised to clarify the CNN function. Two references also added.

Abstract of Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision

Convolutional neural network (CNN) driven by image recognition has been shown to be able to explain cortical responses to static pictures at ventral-stream areas. Here, we further showed that such CNN could reliably predict and decode functional magnetic resonance imaging data from humans watching natural movies, despite its lack of any mechanism to account for temporal dynamics or feedback processing. Using separate data, encoding and decoding models were developed and evaluated for describing the bi-directional relationships between the CNN and the brain. Through the encoding models, the CNN-predicted areas covered not only the ventral stream, but also the dorsal stream, albeit to a lesser degree; single-voxel response was visualized as the specific pixel pattern that drove the response, revealing the distinct representation of individual cortical location; cortical activation was synthesized from natural images with high-throughput to map category representation, contrast, and selectivity. Through the decoding models, fMRI signals were directly decoded to estimate the feature representations in both visual and semantic spaces, for direct visual reconstruction and semantic categorization, respectively. These results corroborate, generalize, and extend previous findings, and highlight the value of using deep learning, as an all-in-one model of the visual cortex, to understand and decode natural vision.

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IBM scientists say radical new ‘in-memory’ computing architecture will speed up computers by 200 times

(Left) Schematic of conventional von Neumann computer architecture, where the memory and computing units are physically separated. To perform a computational operation and to store the result in the same memory location, data is shuttled back and forth between the memory and the processing unit. (Right) An alternative architecture where the computational operation is performed in the same memory location. (credit: IBM Research)

IBM Research announced Tuesday (Oct. 24, 2017) that its scientists have developed the first “in-memory computing” or “computational memory” computer system architecture, which is expected to yield 200x improvements in computer speed and energy efficiency — enabling ultra-dense, low-power, massively parallel computing systems.

Their concept is to use one device (such as phase change memory or PCM*) for both storing and processing information. That design would replace the conventional “von Neumann” computer architecture, used in standard desktop computers, laptops, and cellphones, which splits computation and memory into two different devices. That requires moving data back and forth between memory and the computing unit, making them slower and less energy-efficient.

The researchers used PCM devices made from a germanium antimony telluride alloy, which is stacked and sandwiched between two electrodes. When the scientists apply a tiny electric current to the material, they heat it, which alters its state from amorphous (with a disordered atomic arrangement) to crystalline (with an ordered atomic configuration). The IBM researchers have used the crystallization dynamics to perform computation in memory. (credit: IBM Research)

Especially useful in AI applications

The researchers believe this new prototype technology will enable ultra-dense, low-power, and massively parallel computing systems that are especially useful for AI applications. The researchers tested the new architecture using an unsupervised machine-learning algorithm running on one million phase change memory (PCM) devices, successfully finding temporal correlations in unknown data streams.

“This is an important step forward in our research of the physics of AI, which explores new hardware materials, devices and architectures,” says Evangelos Eleftheriou, PhD, an IBM Fellow and co-author of an open-access paper in the peer-reviewed journal Nature Communications. “As the CMOS scaling laws break down because of technological limits, a radical departure from the processor-memory dichotomy is needed to circumvent the limitations of today’s computers.”

“Memory has so far been viewed as a place where we merely store information, said Abu Sebastian, PhD. exploratory memory and cognitive technologies scientist, IBM Research and lead author of the paper. But in this work, we conclusively show how we can exploit the physics of these memory devices to also perform a rather high-level computational primitive. The result of the computation is also stored in the memory devices, and in this sense the concept is loosely inspired by how the brain computes.” Sebastian also leads a European Research Council funded project on this topic.

* To demonstrate the technology, the authors chose two time-based examples and compared their results with traditional machine-learning methods such as k-means clustering:

  • Simulated Data: one million binary (0 or 1) random processes organized on a 2D grid based on a 1000 x 1000 pixel, black and white, profile drawing of famed British mathematician Alan Turing. The IBM scientists then made the pixels blink on and off with the same rate, but the black pixels turned on and off in a weakly correlated manner. This means that when a black pixel blinks, there is a slightly higher probability that another black pixel will also blink. The random processes were assigned to a million PCM devices, and a simple learning algorithm was implemented. With each blink, the PCM array learned, and the PCM devices corresponding to the correlated processes went to a high conductance state. In this way, the conductance map of the PCM devices recreates the drawing of Alan Turing.
  • Real-World Data: actual rainfall data, collected over a period of six months from 270 weather stations across the USA in one hour intervals. If rained within the hour, it was labelled “1” and if it didn’t “0”. Classical k-means clustering and the in-memory computing approach agreed on the classification of 245 out of the 270 weather stations. In-memory computing classified 12 stations as uncorrelated that had been marked correlated by the k-means clustering approach. Similarly, the in-memory computing approach classified 13 stations as correlated that had been marked uncorrelated by k-means clustering. 

Abstract of Temporal correlation detection using computational phase-change memory

Conventional computers based on the von Neumann architecture perform computation by repeatedly transferring data between their physically separated processing and memory units. As computation becomes increasingly data centric and the scalability limits in terms of performance and power are being reached, alternative computing paradigms with collocated computation and storage are actively being sought. A fascinating such approach is that of computational memory where the physics of nanoscale memory devices are used to perform certain computational tasks within the memory unit in a non-von Neumann manner. We present an experimental demonstration using one million phase change memory devices organized to perform a high-level computational primitive by exploiting the crystallization dynamics. Its result is imprinted in the conductance states of the memory devices. The results of using such a computational memory for processing real-world data sets show that this co-existence of computation and storage at the nanometer scale could enable ultra-dense, low-power, and massively-parallel computing systems.

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AlphaGo Zero trains itself to be most powerful Go player in the world

(credit: DeepMind)

Deep Mind has just announced AlphaGo Zero, an evolution of AlphaGo, the first computer program to defeat a world champion at the ancient Chinese game of Go. Zero is even more powerful and is now arguably the strongest Go player in history, according to the company.

While previous versions of AlphaGo initially trained on thousands of human amateur and professional games to learn how to play Go, AlphaGo Zero skips this step. It learns to play from scratch, simply by playing games against itself, starting from completely random play.

(credit: DeepMind)

It surpassed Alpha Lee in 3 days, then surpassed human level of play, defeating the previously published champion-defeating version of AlphaGo by 100 games to 0 in just 40 days.

The achievement is described in the journal Nature today (Oct. 18, 2017)


DeepMind | AlphaGo Zero: Starting from scratch

Abstract of Mastering the game of Go without human knowledge

A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo.

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Using ‘cooperative perception’ between intelligent vehicles to reduce risks

Networked intelligent vehicles (credit: EPFL)

Researchers at École polytechnique fédérale de Lausanne (EPFL) have combined data from two autonomous cars to create a wider field of view, extended situational awareness, and greater safety.

Autonomous vehicles get their intelligence from cameras, radar, light detection and ranging (LIDAR) sensors, and navigation and mapping systems. But there are ways to make them even smarter. Researchers at EPFL are working to improve the reliability and fault tolerance of these systems by sharing data between vehicles. For example, this can extend the field of view of a car that is behind another car.

Using simulators and road tests, the team has developed a flexible software framework for networking intelligent vehicles so that they can interact.

Cooperative perception

“Today, intelligent vehicle development is focused on two main issues: the level of autonomy and the level of cooperation,” says Alcherio Martinoli, who heads EPFL’s Distributed Intelligent Systems and Algorithms Laboratory (DISAL). As part of his PhD thesis, Milos Vasic has developed cooperative perception algorithms, which extend an intelligent vehicle’s situational awareness by fusing data from onboard sensors with data provided by cooperative vehicles nearby.

Milos Vasic, PhD, and Alcherio Martinoli made two regular cars intelligent using off-the-shelf equipment. (credit: Alain Herzog/EPFL)

The researchers used  cooperative perception algorithms as the basis for the software framework. Cooperative perception means that an intelligent vehicle can combine its own data with that of another vehicle to help make driving decisions.

They developed an assistance system that assesses the risk of passing, for example. The risk assessment factors in the probability of an oncoming car in the opposite lane as well as kinematic conditions such as driving speeds, the distance required to overtake, and the distance to the oncoming car.

Difficulties in fusing data

The team retrofitted two Citroen C-Zero electric cars with a Mobileye camera, an accurate localization system, a router to enable Wi-Fi communication, a computer to run the software and an external battery to power everything. “These were not autonomous vehicles,” says Martinoli, “but we made them intelligent using off-the-shelf equipment.”

One of the difficulties in fusing data from the two vehicles involved relative localization. The cars needed to be able to know precisely where they are in relation to each other as well to objects in the vicinity.

For example, if a single pedestrian does not appear to both cars to be in the same exact spot, there is a risk that, together, they will see two figures instead of one. By using other signals, particularly those provided by the LIDAR sensors and cameras, the researchers were able to correct flaws in the navigation system and adjust their algorithms accordingly. This exercise was even more challenging because the data had to be processed in real time while the vehicles were in motion.

Although the tests involved only two vehicles, the longer-term goal is to create a network between multiple vehicles as well with the roadway infrastructure.

In addition to driving safety and comfort, cooperative networks of this sort could eventually be used to optimize a vehicle’s trajectory, save energy, and improve traffic flows.

Of course, determining liability in case of an accident becomes more complicated when vehicles cooperate. “The answers to these issues will play a key role in determining whether autonomous vehicles are accepted,” says Martinoli.


École polytechnique fédérale de Lausanne (EPFL) | Networked intelligent vehicles

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Ray Kurzweil on The Age of Spiritual Machines: A 1999 TV interview

Dear readers,

For your interest, this 1999 interview with me, which I recently re-watched, describes some interesting predictions that are still coming true today. It’s intriguing to look back at the last 18 years to see what actually unfolded. This video is a compelling glimpse into the future, as we’re living it today.

Enjoy!

— Ray

Dear readers,

This interview by Harold Hudson Channer was recorded on Jan. 14, 1999 and aired February 1, 1999 on a Manhattan Neighborhood Network cable-access show, Conversations with Harold Hudson Channer.

In the discussion, Ray explains many of the ahead-of-their-time ideas presented in The Age of Spiritual Machines*, such as the “law of accelerating returns” (how technological change is exponential, contrary to the common-sense “intuitive linear” view); the forthcoming revolutionary impacts of AI; nanotech brain and body implants for increased intelligence, improved health, and life extension; and technological impacts on economic growth.

I was personally inspired by the book in 1999 and by Ray’s prophetic, uplifting vision of the future. I hope you also enjoy this blast from the past.

— Amara D. Angelica, Editor

* First published in hardcover January 1, 1999 by Viking. The series also includes The Age of Intelligent Machines (The MIT Press, 1992) and The Singularity Is Near (Penquin Books, 2006).

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Intel’s new ‘Loihi’ chip mimics neurons and synapses in the human brain

Loihi chip (credit: Intel Corporation)

Intel announced this week a self-learning, energy-efficient neuromorphic (brain-like) research chip codenamed “Loihi”* that mimics how the human brain functions. Under development for six years, the chip uses 130,000 “neurons” and 130 million “synapses” and learns in real time, based on feedback from the environment.**

Neuromorphic chip models are inspired by how neurons communicate and learn, using spikes (brain pulses) and synapses capable of learning.

The idea is to help computers self-organize and make decisions based on patterns and associations,” Michael Mayberry, PhD, corporate vice president and managing director of Intel Labs at Intel Corporation, explained in a blog post.

He said the chip automatically gets smarter over time and doesn’t need to be trained in the traditional way. He sees applications in areas that would benefit from autonomous operation and continuous learning in an unstructured environment, such as automotive, industrial, and personal-robotics areas.

For example, a cybersecurity system could identify a breach or a hack based on an abnormality or difference in data streams. Or the chip could learn a person’s heartbeat reading under various conditions — after jogging, following a meal or before going to bed — to determine a “normal” heartbeat. The system could then continuously monitor incoming heart data to flag patterns that don’t match the “normal” pattern, and could be personalized for any user.

“Machine learning models such as deep learning have made tremendous recent advancements by using extensive training datasets to recognize objects and events. However, unless their training sets have specifically accounted for a particular element, situation or circumstance, these machine learning systems do not generalize well,” Mayberry notes.

The Loihi test chip

Loihi currently exists as a research test chip that offers flexible on-chip learning and combines training and inference. Researchers have demonstrated it learning at a rate that is a 1 million times improvement compared with other typical spiking neural nets, as measured by total operations to achieve a given accuracy when solving MNIST digit recognition problems, Mayberry said. “Compared to technologies such as convolutional neural networks and deep learning neural networks, the Loihi test chip uses many fewer resources on the same task.”

Fabricated on Intel’s 14 nm process technology, the chip is also up to 1,000 times more energy-efficient than general-purpose computing required for typical training systems, he added.

In the first half of 2018, Intel plans to share the Loihi test chip with leading university and research institutions with a focus on advancing AI. The goal is to develop and test several algorithms with high efficiency for problems including path planning, constraint satisfaction, sparse coding, dictionary learning, and dynamic pattern learning and adaptation.

“Looking to the future, Intel believes that neuromorphic computing offers a way to provide exascale performance in a construct inspired by how the brain works,” Mayberry said.

* “Loihi seamount, sometimes known as the ‘youngest volcano’ in the Hawaiian chain, is an undersea mountain rising more than 3000 meters above the floor of the Pacific Ocean … submerged in the Pacific off of the south-eastern coast of the Big Island of Hawaii.” — Hawaii Center for Volcanology

** For comparison, IBM’s TrueNorth neuromorphic chip currently has 1 million neurons and 256 million synapses.

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Why futurist Ray Kurzweil isn’t worried about technology stealing your job — Fortune

1985: Ray Kurzweil looks on as Stevie Wonder experiences the Kurzweil 250, the first synthesizer to accurately reproduce the sounds of the piano — replacing piano-maker jobs but adding many more jobs for musicians (credit: Kurzweil Music Systems)

Last week, Fortune magazine asked Ray Kurzweil to comment on some often-expressed questions about the future.

Does AI pose an existential threat to humanity?

Kurzweil sees the future as nuanced, notes writer Michal Lev-Ram. “A lot of people think things are getting worse, partly because that’s actually an evolutionary adaptation,” Kurzweil said. “It’s very important for your survival to be sensitive to bad news. … I think if you look at history, though, we’re being helped [by new technology] more than we’re being hurt.”

How will artificial intelligence and other technologies impact jobs?

“We have already eliminated all jobs several times in human history,” said Kurzweil, pointing out that “for every job we eliminate, we’re going to create more jobs at the top of the skill ladder. … You can’t describe the new jobs, because they’re in industries and concepts that don’t exist yet.”

Why are we so bad at predicting certain things? For example, Donald Trump winning the presidency?

Kurzweil: “He’s not technology.”

Read Fortune article here.

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Why futurist Ray Kurzweil isn’t worried about technology stealing your job — Fortune

1985: Ray Kurzweil looks on as Stevie Wonder experiences the Kurzweil 250, the first synthesizer to accurately reproduce the sounds of the piano — replacing piano-maker jobs but adding many more jobs for musicians (credit: Kurzweil Music Systems)

Last week, Fortune magazine asked Ray Kurzweil to comment on some often-expressed questions about the future.

Does AI pose an existential threat to humanity?

Kurzweil sees the future as nuanced, notes writer Michal Lev-Ram. “A lot of people think things are getting worse, partly because that’s actually an evolutionary adaptation,” Kurzweil said. “It’s very important for your survival to be sensitive to bad news. … I think if you look at history, though, we’re being helped [by new technology] more than we’re being hurt.”

How will artificial intelligence and other technologies impact jobs?

“We have already eliminated all jobs several times in human history,” said Kurzweil, pointing out that “for every job we eliminate, we’re going to create more jobs at the top of the skill ladder. … You can’t describe the new jobs, because they’re in industries and concepts that don’t exist yet.”

Why are we so bad at predicting certain things? For example, Donald Trump winning the presidency?

Kurzweil: “He’s not technology.”

Read Fortune article here.