The Remako HD Graphics Mod is a mod that completely revamps the pre-rendered backgrounds of the classic JRPG Final Fantasy VII. All of the backgrounds now have 4 times the resolution of the original.
Using state of the art AI neural networks, this upscaling tries to emulate the detail the original renders would have had. This helps the new visuals to come as close to a higher resolution re-rendering of the original as possible with current technology.
What does it look like?
Bbelow are two trailers. One is a comparison of the raw images, while the other shows off the mod in action.
To advance the state-of-the-art in speech neuroprosthesis, we combined the recent advances in deep learning with the latest innovations in speech synthesis technologies to reconstruct closed-set intelligible speech from the human auditory cortex. We investigated the dependence of reconstruction accuracy on linear and nonlinear (deep neural network) regression methods and the acoustic representation that is used as the target of reconstruction, including auditory spectrogram and speech synthesis parameters. In addition, we compared the reconstruction accuracy from low and high neural frequency ranges. Our results show that a deep neural network model that directly estimates the parameters of a speech synthesizer from all neural frequencies achieves the highest subjective and objective scores on a digit recognition task, improving the intelligibility by 65% over the baseline method which used linear regression to reconstruct the auditory spectrogram
Now, we introduce our StarCraft II program AlphaStar, the first Artificial Intelligence to defeat a top professional player. In a series of test matches held on 19 December, AlphaStar decisively beat Team Liquid’s Grzegorz “MaNa” Komincz, one of the world’s strongest professional StarCraft players, 5-0, following a successful benchmark match against his team-mate Dario “TLO” Wünsch. The matches took place under professional match conditions on a competitive ladder map and without any game restrictions.
Although there have been significant successes in video games such as Atari, Mario, Quake III Arena Capture the Flag, and Dota 2, until now, AI techniques have struggled to cope with the complexity of StarCraft. The best results were made possible by hand-crafting major elements of the system, imposing significant restrictions on the game rules, giving systems superhuman capabilities, or by playing on simplified maps. Even with these modifications, no system has come anywhere close to rivalling the skill of professional players. In contrast, AlphaStar plays the full game of StarCraft II, using a deep neural network that is trained directly from raw game data by supervised learning and reinforcement learning.
The Victoria Police are the primary law enforcement agency of Victoria, Australia. With over 16,000 vehicles stolen in Victoria this past year — at a cost of about $170 million — the police department is experimenting with a variety of technology-driven solutions to crackdown on car theft. They call this system BlueNet.
To help prevent fraudulent sales of stolen vehicles, there is already a VicRoads web-based service for checking the status of vehicle registrations. The department has also invested in a stationary license plate scanner — a fixed tripod camera which scans passing traffic to automatically identify stolen vehicles.
Don’t ask me why, but one afternoon I had the desire to prototype a vehicle-mounted license plate scanner that would automatically notify you if a vehicle had been stolen or was unregistered. Understanding that these individual components existed, I wondered how difficult it would be to wire them together.
But it was after a bit of googling that I discovered the Victoria Police had recently undergone a trial of a similar device, and the estimated cost of roll out was somewhere in the vicinity of $86,000,000. One astute commenter pointed out that the $86M cost to fit out 220 vehicles comes in at a rather thirsty $390,909 per vehicle.
Surely we can do a bit better than that.
Existing stationary license plate recognition systems
The Success Criteria
Before getting started, I outlined a few key requirements for product design.
Requirement #1: The image processing must be performed locally
Streaming live video to a central processing warehouse seemed the least efficient approach to solving this problem. Besides the whopping bill for data traffic, you’re also introducing network latency into a process which may already be quite slow.
Although a centralized machine learning algorithm is only going to get more accurate over time, I wanted to learn if an local on-device implementation would be “good enough”.
Requirement #2: It must work with low quality images
Since I don’t have a Raspberry Pi camera or USB webcam, so I’ll be using dashcam footage — it’s readily available and an ideal source of sample data. As an added bonus, dashcam video represents the overall quality of footage you’d expect from vehicle mounted cameras.
Requirement #3: It needs to be built using open source technology
Relying upon a proprietary software means you’ll get stung every time you request a change or enhancement — and the stinging will continue for every request made thereafter. Using open source technology is a no-brainer.
My solution
At a high level, my solution takes an image from a dashcam video, pumps it through an open source license plate recognition system installed locally on the device, queries the registration check service, and then returns the results for display.
The data returned to the device installed in the law enforcement vehicle includes the vehicle’s make and model (which it only uses to verify whether the plates have been stolen), the registration status, and any notifications of the vehicle being reported stolen.
If that sounds rather simple, it’s because it really is. For example, the image processing can all be handled by the openalpr library.
This is really all that’s involved to recognize the characters on a license plate:
A Minor Caveat
Public access to the VicRoads APIs is not available, so license plate checks occur via web scraping for this prototype. While generally frowned upon — this is a proof of concept and I’m not slamming anyone’s servers.
Here’s what the dirtiness of my proof-of-concept scraping looks like:
Results
I must say I was pleasantly surprised.
I expected the open source license plate recognition to be pretty rubbish. Additionally, the image recognition algorithms are probably not optimised for Australian license plates.
The solution was able to recognise license plates in a wide field of view.
Annotations added for effect. Number plate identified despite reflections and lens distortion.
Although, the solution would occasionally have issues with particular letters.
Incorrect reading of plate, mistook the M for an H
But … the solution would eventually get them correct.
A few frames later, the M is correctly identified and at a higher confidence rating
As you can see in the above two images, processing the image a couple of frames later jumped from a confidence rating of 87% to a hair over 91%.
I’m confident, pardon the pun, that the accuracy could be improved by increasing the sample rate, and then sorting by the highest confidence rating. Alternatively a threshold could be set that only accepts a confidence of greater than 90% before going on to validate the registration number.
Those are very straight forward code-first fixes, and don’t preclude the training of the license plate recognition software with a local data set.
The $86,000,000 Question
To be fair, I have absolutely no clue what the $86M figure includes — nor can I speak to the accuracy of my open source tool with no localized training vs. the pilot BlueNet system.
I would expect part of that budget includes the replacement of several legacy databases and software applications to support the high frequency, low latency querying of license plates several times per second, per vehicle.
On the other hand, the cost of ~$391k per vehicle seems pretty rich — especially if the BlueNet isn’t particularly accurate and there are no large scale IT projects to decommission or upgrade dependent systems.
Future Applications
While it’s easy to get caught up in the Orwellian nature of an “always on” network of license plate snitchers, there are many positive applications of this technology. Imagine a passive system scanning fellow motorists for an abductors car that automatically alerts authorities and family members to their current location and direction.
Teslas vehicles are already brimming with cameras and sensors with the ability to receive OTA updates — imagine turning these into a fleet of virtual good samaritans. Ubers and Lyft drivers could also be outfitted with these devices to dramatically increase the coverage area.
Using open source technology and existing components, it seems possible to offer a solution that provides a much higher rate of return — for an investment much less than $86M.
TAUS, the language data network, is an independent and neutral industry organization. We develop communities through a program of events and online user groups and by sharing knowledge, metrics and data that help all stakeholders in the translation industry develop a better service. We provide data services to buyers and providers of language and translation services.
The shared knowledge and data help TAUS members decide on effective localization strategies. The metrics support more efficient processes and the normalization of quality evaluation. The data lead to improved translation automation.
TAUS develops APIs that give members access to services like DQF, the DQF Dashboard and the TAUS Data Market through their own translation platforms and tools. TAUS metrics and data are already built in to most of the major translation technologies.
Robots normally need to be programmed in order to get them to perform a particular task, but they can be coaxed into writing the instructions themselves with the help of machine learning, according to research published in Science.
Engineers at Vicarious AI, a robotics startup based in California, USA, have built what they call a “visual cognitive computer” (VCC), a software platform connected to a camera system and a robot gripper. Given a set of visual clues, the VCC writes a short program of instructions to be followed by the robot so it knows how to move its gripper to do simple tasks.
“Humans are good at inferring the concepts conveyed in a pair of images and then applying them in a completely different setting,” the paper states.
“The human-inferred concepts are at a sufficiently high level to be effortlessly applied in situations that look very different, a capacity so natural that it is used by IKEA and LEGO to make language-independent assembly instructions.”
Don’t get your hopes up, however, these robots can’t put your flat-pack table or chair together for you quite yet. But it can do very basic jobs, like moving a block backwards and forwards.
It works like this. First, an input and output image are given to the system. The input image is a jumble of colored objects of various shapes and sizes, and the output image is an ordered arrangement of the objects. For example, the input image could be a number of red blocks and the output image is all the red blocks ordered to form a circle. Think of it a bit like a before and after image.
The VCC works out what commands need to be performed by the robot in order to organise the range of objects before it, based on the ‘before’ to the ‘after’ image. The system is trained to learn what action corresponds to what command using supervised learning.
Dileep George, cofounder of Vicarious, explained to The Register, “up to ten pairs [of images are used] for training, and ten pairs for testing. Most concepts are learned with only about five examples.”
Here’s a diagram of how it works:
A: A graph describing the robot’s components. B: The list of commands the VCC can use. Image credit: Vicarious AI
The left hand side is a schematic of all the different parts that control the robot. The visual hierarchy looks at the objects in front of the camera and categorizes them by object shape and colour. The attention controller decides what objects to focus on, whilst the fixation controller directs the robot’s gaze to the objects before the hand controller operates the robot’s arms to move the objects about.
The robot doesn’t need too many training examples to work because there are only 24 commands, listed on the right hand of the diagram, for the VCC controller.
AI systems excel in pattern recognition, so much so that they can stalk individual zebrafish and fruit flies even when the animals are in groups of up to a hundred.
To demonstrate this, a group of researchers from the Champalimaud Foundation, a private biomedical research lab in Portugal, trained two convolutional neural networks to identify and track individual animals within a group. The aim is not so much to match or exceed humans’ ability to spot and follow stuff, but rather to automate the process of studying the behavior of animals in their communities.
“The ultimate goal of our team is understanding group behavior,” said Gonzalo de Polavieja. “We want to understand how animals in a group decide together and learn together.”
The resulting machine-learning software, known as idtracker.ai, is described as “a species-agnostic system.” It’s “able to track all individuals in both small and large collectives (up to 100 individuals) with high identification accuracy—often greater than 99.9 per cent,” according to a paper published in Nature Methods on Monday.
The idtracker.ai software is split into a crossing-detector network and an identification network. First, it was fed video footage of the animals interacting in their enclosures. For example in the zebrafish experiment, the system pre-processes the fish as coloured blobs and learns to identify the animals as individuals or which ones are touching one another or crossing past each other in groups. The identification network is then used to identify the individual animals during each crossing event.
Surprisingly, it reached an accuracy rate of up to 99.96 per cent for groups of 60 zebrafish and increased to 99.99 per cent for 100 zebrafish. Recognizing fruit flies is harder. Idtracker.ai was accurate to 99.99 per cent for 38 fruit flies, but decreased slightly to 99.95 per cent for 72 fruit flies.
As good as they are at causing mischief, researchers from the MIT-IBM Watson AI Lab realized GANs are also a powerful tool: because they paint what they’re “thinking,” they could give humans insight into how neural networks learn and reason. This has been something the broader research community has sought for a long time—and it’s become more important with our increasing reliance on algorithms.
“There’s a chance for us to learn what a network knows from trying to re-create the visual world,” says David Bau, an MIT PhD student who worked on the project.
So the researchers began probing a GAN’s learning mechanics by feeding it various photos of scenery—trees, grass, buildings, and sky. They wanted to see whether it would learn to organize the pixels into sensible groups without being explicitly told how.
Stunningly, over time, it did. By turning “on” and “off” various “neurons” and asking the GAN to paint what it thought, the researchers found distinct neuron clusters that had learned to represent a tree, for example. Other clusters represented grass, while still others represented walls or doors. In other words, it had managed to group tree pixels with tree pixels and door pixels with door pixels regardless of how these objects changed color from photo to photo in the training set.
The GAN knows not to paint any doors in the sky.
MIT Computer Science & Artificial Intelligence Laboratory
“These GANs are learning concepts very closely reminiscent of concepts that humans have given words to,” says Bau.
Not only that, but the GAN seemed to know what kind of door to paint depending on the type of wall pictured in an image. It would paint a Georgian-style door on a brick building with Georgian architecture, or a stone door on a Gothic building. It also refused to paint any doors on a piece of sky. Without being told, the GAN had somehow grasped certain unspoken truths about the world.
This was a big revelation for the research team. “There are certain aspects of common sense that are emerging,” says Bau. “It’s been unclear before now whether there was any way of learning this kind of thing [through deep learning].” That it is possible suggests that deep learning can get us closer to how our brains work than we previously thought—though that’s still nowhere near any form of human-level intelligence.
Other research groups have begun to find similar learning behaviors in networks handling other types of data, according to Bau. In language research, for example, people have found neuron clusters for plural words and gender pronouns.
Being able to identify which clusters correspond to which concepts makes it possible to control the neural network’s output. Bau’s group can turn on just the tree neurons, for example, to make the GAN paint trees, or turn on just the door neurons to make it paint doors. Language networks, similarly, can be manipulated to change their output—say, to swap the gender of the pronouns while translating from one language to another. “We’re starting to enable the ability for a person to do interventions to cause different outputs,” Bau says.
The team has now released an app called GANpaint that turns this newfound ability into an artistic tool. It allows you to turn on specific neuron clusters to paint scenes of buildings in grassy fields with lots of doors. Beyond its silliness as a playful outlet, it also speaks to the greater potential of this research.
“The problem with AI is that in asking it to do a task for you, you’re giving it an enormous amount of trust,” says Bau. “You give it your input, it does it’s ‘genius’ thinking, and it gives you some output. Even if you had a human expert who is super smart, that’s not how you’d want to work with them either.”
With GANpaint, you begin to peel back the lid on the black box and establish some kind of relationship. “You can figure out what happens if you do this, or what happens if you do that,” says Hendrik Strobelt, the creator of the app. “As soon as you can play with this stuff, you gain more trust in its capabilities and also its boundaries.”
A video software firm has come up with a way to prevent people from sharing their account details for Netflix and other streaming services with friends and family members.
UK-based Synamedia unveiled the artificial intelligence software at the CES 2019 technology trade show in Las Vegas, claiming it could save the streaming industry billions of dollars over the next few years.
Casual password sharing is practised by more than a quarter of millennials, according to figures from market research company Magid.
Separate figures from research firm Parks Associates predicts that by $9.9 billion (£7.7bn) of pay-TV revenues and $1.2 billion of revenue from subscription-based streaming services will be lost to credential sharing each year.
The AI system developed by Synamedia uses machine learning to analyse account activity and recognise unusual patterns, such as account details being used in two locations within similar time periods.
The idea is to spot instances of customers sharing their account credentials illegally and offering them a premium shared account service that will authorise a limited level of password sharing.
“Casual credentials sharing is becoming too expensive to ignore. Our new solution gives operators the ability to take action,” said Jean Marc Racine, Synamedia’s chief product officer.
“Many casual users will be happy to pay an additional fee for a premium, shared service with a greater number of concurrent users. It’s a great way to keep honest people honest while benefiting from an incremental revenue stream.”
Artificial intelligence can potentially identify someone’s genetic disorders by inspecting a picture of their face, according to a paper published in Nature Medicine this week.
The tech relies on the fact some genetic conditions impact not just a person’s health, mental function, and behaviour, but sometimes are accompanied with distinct facial characteristics. For example, people with Down Syndrome are more likely to have angled eyes, a flatter nose and head, or abnormally shaped teeth. Other disorders like Noonan Syndrome are distinguished by having a wide forehead, a large gap between the eyes, or a small jaw. You get the idea.
An international group of researchers, led by US-based FDNA, turned to machine-learning software to study genetic mutations, and believe that machines can help doctors diagnose patients with genetic disorders using their headshots.
The team used 17,106 faces to train a convolutional neural network (CNN), commonly used in computer vision tasks, to screen for 216 genetic syndromes. The images were obtained from two sources: publicly available medical reference libraries, and snaps submitted by users of a smartphone app called Face2Gene, developed by FDNA.
Given an image, the system, dubbed DeepGestalt, studies a person’s face to make a note of the size and shape of their eyes, nose, and mouth. Next, the face is split into regions, and each piece is fed into the CNN. The pixels in each region of the face are represented as vectors and mapped to a set of features that are commonly associated with the genetic disorders learned by the neural network during its training process.
DeepGestalt then assigns a score per syndrome for each region, and collects these results to compile a list of its top 10 genetic disorder guesses from that submitted face.
An example of how DeepGestalt works. First, the input image is analysed using landmarks and sectioned into different regions before the system spits out its top 10 predictions. Image credit: Nature and Gurovich et al.
The first answer is the genetic disorder DeepGestalt believes the patient is most likely affected by, all the way down to its tenth answer, which is the tenth most likely disorder.
When it was tested on two independent datasets, the system accurately guessed the correct genetic disorder among its top 10 suggestions around 90 per cent of the time. At first glance, the results seem promising. The paper also mentions DeepGestalt “outperformed clinicians in three initial experiments, two with the goal of distinguishing subjects with a target syndrome from other syndromes, and one of separating different genetic subtypes in Noonan Syndrome.”
There’s always a but
A closer look, though, reveals that the lofty claims involve training and testing the system on limited datasets – in other words, if you stray outside the software’s comfort zone, and show it unfamiliar faces, it probably won’t perform that well. The authors admit previous similar studies “have used small-scale data for training, typically up to 200 images, which are small for deep-learning models.” Although they use a total of more than 17,000 training images, when spread across 216 genetic syndromes, the training dataset for each one ends up being pretty small.
For example, the model that examined Noonan Syndrome was only trained on 278 images. The datasets DeepGestalt were tested against were similarly small. One only contained 502 patient images, and the other 392.
Learning in environments with large state and action spaces, and sparse rewards, can hinder a Reinforcement Learning (RL) agent’s learning through trial-and-error. For instance, following natural language instructions on the Web (such as booking a flight ticket) leads to RL settings where input vocabulary and number of actionable elements on a page can grow very large. Even though recent approaches improve the success rate on relatively simple environments with the help of human demonstrations to guide the exploration, they still fail in environments where the set of possible instructions can reach millions. We approach the aforementioned problems from a different perspective and propose guided RL approaches that can generate unbounded amount of experience for an agent to learn from. Instead of learning from a complicated instruction with a large vocabulary, we decompose it into multiple sub-instructions and schedule a curriculum in which an agent is tasked with a gradually increasing subset of these relatively easier sub-instructions. In addition, when the expert demonstrations are not available, we propose a novel meta-learning framework that generates new instruction following tasks and trains the agent more effectively. We train DQN, deep reinforcement learning agent, with Q-value function approximated with a novel QWeb neural network architecture on these smaller, synthetic instructions. We evaluate the ability of our agent to generalize to new instructions on World of Bits benchmark, on forms with up to 100 elements, supporting 14 million possible instructions. The QWeb agent outperforms the baseline without using any human demonstration achieving 100% success rate on several difficult environments.
A team of researchers in Japan have devised an artificial intelligence (AI) system that can identify different types of cancer cells using microscopy images. Their method can also be used to determine whether the cancer cells are sensitive to radiotherapy. The researchers reported their findings in the journal Cancer Research. In cancer patients, there can be tremendous variation in the types of cancer cells in a single tumor. Identifying the specific cell types present in tumors can be very useful when choosing the most effective treatment. However, making accurate assessments of cell types is time consuming and often hampered by human error and the limits of human sight. To overcome these challenges, scientists led by Professor Hideshi Ishii of Osaka University, Japan, have developed an AI system that can identify different types of cancer cells from microscopy images, achieving higher accuracy than human judgement. The system is based on a convolutional neural network, a form of AI modeled on the human visual system. “We first trained our system on 8,000 images of cells obtained from a phase-contrast microscope,” said corresponding author Ishii. “We then tested [the AI system’s] accuracy on another 2,000 images and showed that it had learned the features that distinguish mouse cancer cells from human ones, and radioresistant cancer cells from radiosensitive ones.” The researchers noted that the automation and high accuracy of their system could be very useful for determining exactly which cells are present in a tumor or circulating in the body. Knowing whether or not radioresistant cells are present is vital when deciding whether radiotherapy would be effective. Furthermore, the same procedure can be applied post-treatment to assess patient outcomes. In the future, the team hopes to train the system on more cancer cell types, with the eventual goal of establishing a universal system that can automatically identify and distinguish all variants of cancer cells. The article can be found at: Toratani et al. (2018) A Convolutional Neural Network Uses Microscopic Images to Differentiate between Mouse and Human Cell Lines and Their Radioresistant Clones. Read more from Asian Scientist Magazine at: https://www.asianscientist.com/2018/12/in-the-lab/artificial-intelligence-microscopy-cancer-cell-radiotherapy/
The Descartes Labs tree canopy layer around the Baltimore Beltway. Treeless main roads radiate from the dense pavement of the city to leafy suburbs.
All this fuss is not without good reason. Trees are great! They make oxygen for breathing, suck up CO₂, provide shade, reduce noise pollution, and just look at them — they’re beautiful!
[…]
So Descartes Labs built a machine learning model to identify tree canopy using a combination of lidar, aerial imagery and satellite imagery. Here’s the area surrounding the Boston Common, for example. We clearly see that the Public Garden, Common and Commonwealth Avenue all have lots of trees. But we also see some other fun artifacts. The trees in front of the CVS in Downtown Crossing, for instance, might seem inconsequential to a passer-by, but they’re one of the biggest concentrations of trees in the neighborhood.
[…]
The classifier can be run over any location in the world where we have approximately 1-meter resolution imagery. When using NAIP imagery, for instance, the resolution of the tree canopy map is as high as 60cm. Drone imagery would obviously yield an even higher resolution.
Washington, D.C. tree canopy created with NAIP source imagery shown at different scales—all the way down to individual “TREES!” on The Ellipse.
The ability to map tree canopy at a such a high resolution in areas that can’t be easily reached on foot would be helpful for utility companies to pinpoint encroachment issues—or for municipalities to find possible trouble spots beyond their official tree census (if they even have one). But by zooming out to a city level, patterns in the tree canopy show off urban greenspace quirks. For example, unexpected tree deserts can be identified and neighborhoods that would most benefit from a surge of saplings revealed.
The system, called Photo Wake-Up, creates a 3D animation from a single photo. In the paper, the researchers compare it to the moving portraits at Hogwarts, a fictitious part of the Harry Potter world that a number of tech companies have tried to recreate. Previous attempts have been mildly successful, but this system is impressive in its ability to isolate and create a pretty realistic 3D animation from a single image.
The researchers tested the system on 70 different photos they downloaded online, which included pictures of Stephen Curry, the anime character Goku, a Banksy artwork, and a Picasso painting. The team used a program called SMPL and deep learning, starting with a 2D cutout of the subject and then superimposing a 3D skeleton onto it. “Our key technical contribution, then, is a method for constructing an animatable 3D model that matches the silhouette in a single photo,” the team told MIT Technology Review.
The team reportedly used a warping algorithm to ensure the cutout and the skeleton were aligned. The team’s algorithm is also reportedly able to detect the direction a subject is looking and the way their head is angled. What’s more, in order to make sure the final animation is realistic and precise, the team used a proprietary user interface to correct for any errors and help with the animation’s texturing. An algorithm then isolates the subject from the 2D image, fills in the remaining space, and animates the subject.
n some states, solar energy accounts for upwards of 10 percent of total electricity generation. It’s definitely a source of power that’s on the rise, whether it be to lessen our dependence on fossil fuels, nuclear power, or the energy grid, or simply to take advantage of the low costs. This form of energy, however, is highly decentralized, so it’s tough to know how much solar energy is being extracted, where, and by whom.
[…]
The system developed by Rajagopal, along with his colleagues Jiafan Yu and Zhecheng Wang, is called DeepSolar, and it’s an automated process whereby hi-res satellite photos are analyzed by an algorithm driven by machine learning. DeepSolar can identify solar panels, register their locations, and calculate their size. The system identified 1.47 million individual solar installations across the United States, whether they be small rooftop configurations, solar farms, or utility-scale systems. This exceeds the previous estimate of 1.02 million installations. The researchers have made this data available at an open-source website.
By using this new approach, the researchers were able to accurately scan billions of tiles of high-resolution satellite imagery covering the continental U.S., allowing them to classify and measure the size of solar systems in a few weeks rather than years, as per previous methods. Importantly, DeepSolar requires minimal human supervision.
DeepSolar map of solar panel usage across the United States.
Image: Deep Solar/Stanford University
“The algorithm breaks satellite images into tiles. Each tile is processed by a deep neural net to produce a classification for each pixel in a tile. These classifications are combined together to detect if a system—or part of—is present in the tile,” Rajagopal told Gizmodo.
The neural net can then determine which tile is a solar panel, and which is not. The network architecture is such that after training, the layers of the network produce an activation map, also known as a heat map, that outlines the panels. This can be used to obtain the size of each solar panel system.
Machine-learning research published in two related papers today in Nature Geoscience reports the detection of seismic signals accurately predicting the Cascadia fault’s slow slippage, a type of failure observed to precede large earthquakes in other subduction zones.
Los Alamos National Laboratory researchers applied machine learning to analyze Cascadia data and discovered the megathrust broadcasts a constant tremor, a fingerprint of the fault’s displacement. More importantly, they found a direct parallel between the loudness of the fault’s acoustic signal and its physical changes. Cascadia’s groans, previously discounted as meaningless noise, foretold its fragility.
“Cascadia’s behavior was buried in the data. Until machine learning revealed precise patterns, we all discarded the continuous signal as noise, but it was full of rich information. We discovered a highly predictable sound pattern that indicates slippage and fault failure,” said Los Alamos scientist Paul Johnson. “We also found a precise link between the fragility of the fault and the signal’s strength, which can help us more accurately predict a megaquake.”
That means starting this holiday season, you should be able to ask the Google Assistant if your flight is on time and get a response showing the status of your flight, the length of a delay (if there is one), and even the cause (assuming that info is available)
“Over the next few weeks,” Google says its flight delay predictor will also start notifying you in cases where its system is 85 percent confident, which is deduced by looking at data from past flight records and combining that with a bit a machine learning smarts to determine if your flight might be late. That leaves some room for error, so it’s also important to note that even when Google predicts that your flight is delayed, it may still recommend for you to show up to the airport normally.
Still, in the space of a year, Google seems to have upped its confidence threshold for predicted delays from 80 to 85 percent
AI systems can now create images of humans that are so lifelike they look like photographs, except the people in them don’t really exist.
See for yourself. Each picture below is an output produced by a generative adversarial network (GAN), a system made up of two different networks including a generator and a discriminator. Developers have used GANs to create everything from artwork to dental crowns.
Some of the images created from Nvidia’s style transfer GAN. Image credit: Karras et al. and Nvidia
The performance of a GAN is often tied to how realistic its results are. What started out as tiny, blurry, greyscale images of human faces four years ago, has since morphed into full colour portraits.
Early results from when the idea of GANs were first introduced. Image credit: Goodfellow et al.
The new GAN built by Nvidia researchers rests on the idea of “style transfer”. First, the generator network learns a constant input taken from a photograph of a real person. This face is used as a reference, and encoded as a vector that is mapped to a latent space that describe all the features in the image.
These features correlate to the essential characteristics that make up a face: eyes, nose, mouth, hair, pose, face shape, etc. After the generator learns these features it can begin adjusting these details to create a new face.
The transformation that determines how the appearance of these features change is determined from another secondary photo. In other words, the original photo copies the style of another photo so the end result is a sort of mishmash between both images. Finally, an element of noise is also added to generate random details, such as the exact placement of hairs, stubble, freckles, or skin pores, to make the images
“Our generator thinks of an image as a collection of ‘styles,” where each style controls the effects at a particular scale,” the researchers explained. The different features can be broken down into various styles: Coarse styles include the pose, hair, face shape; Middle styles are made up of facial features; and Fine styles determines the overall colour.
How the different style types are learned and transferred by crossing a photo with a source photo. Image credit: Kerras et al. and Nvidia.
The different style types can, therefore, be crossed continuously with other photos to generate a range of completely new images to cover pictures of people of different ethnicities, genders and ages. You can watch a video demonstration of this happening below.
The discriminator network inspects the images coming from the generator and tries to work out if they’re real or fake. The generator improves over time so that its outputs consistently trick the discriminator.
De impact van een buitenreclame campagne wordt voor 40% bepaald door de creatie van de uiting, voor 30% door het merk en voor 30% door de mediadruk. De Outdoor Ad Impact Forecaster analyseert vooraf een campagne op basis van deze drie kenmerken en geeft een rapport dat binnen 24 tot 48 uur de impact van een Out-of-Home campagne voorspelt.
Deze voorspelling is gebaseerd op ruim 300 effectmetingen uitgevoerd door MeMo², waarvan de data op basis van machine learning in een voorspellingstool is verwerkt. De impact van de campagne wordt weergegeven in de vorm van een sterrenrating en daaropvolgend geeft de Forecaster een concreet advies over aanpassingen die de campagne impactvoller maken. Dit wordt aangevuld met professioneel advies van zowel de onderzoekers van MeMo² als de specialisten van Exterion Media. Dit tezamen vormt een compleet rapport voor een nog effectievere buitenreclame campagne.
The founders of Predictim want to be clear with me: Their product—an algorithm that scans the online footprint of a prospective babysitter to determine their “risk” levels for parents—is not racist. It is not biased.
“We take ethics and bias extremely seriously,” Sal Parsa, Predictim’s CEO, tells me warily over the phone. “In fact, in the last 18 months we trained our product, our machine, our algorithm to make sure it was ethical and not biased. We took sensitive attributes, protected classes, sex, gender, race, away from our training set. We continuously audit our model. And on top of that we added a human review process.”
At issue is the fact that I’ve used Predictim to scan a handful of people I very much trust with my own son. Our actual babysitter, Kianah Stover, returned a ranking of “Moderate Risk” (3 out 5) for “Disrespectfulness” for what appear to me to be innocuous Twitter jokes. She returned a worse ranking than a friend I also tested who routinely spews vulgarities, in fact. She’s black, and he’s white.
“I just want to clarify and say that Kianah was not flagged because she was African American,” says Joel Simonoff, Predictim’s CTO. “I can guarantee you 100 percent there was no bias that went into those posts being flagged. We don’t look at skin color, we don’t look at ethnicity, those aren’t even algorithmic inputs. There’s no way for us to enter that into the algorithm itself.”
So, the writer of this article tries to push for a racist angle, however unlikely this is. Oh well, it’s still a good article talking about how this system works.
[…]
When I entered the first person I aimed to scan into the system, Predictim returned a wealth of personal data—home addresses, names of relatives, phone numbers, alternate email addresses, the works. When I sent a screenshot to my son’s godfather of his scan, he replied, “Whoa.”
The goal was to allow parents to make sure they had found the right person before proceeding with the scan, but that’s an awful lot of data.
[…]
After you confirm the personal details and initiate the scan, the process can take up to 48 hours. You’ll get an email with a link to your personalized dashboard, which contains all the people you’ve scanned and their risk rankings, when it’s complete. That dashboard looks a bit like the backend to a content management system, or website analytics service Chartbeat, for those who have the misfortune of being familiar with that infernal service.
[…]
Potential babysitters are graded on a scale of 1-5 (5 being the riskiest) in four categories: “Bullying/Harassment,” “Disrespectful Attitude,” “Explicit Content,” and “Drug use.”
[…]
Neither Parsa nor Simonoff [Predictim’s founders – ed] have children, though Parsa is married, and both insist they are passionate about protecting families from bad babysitters. Joel, for example, once had a babysitter who would drive he and his brother around smoking cigarettes in the car. And Parsa points to Joel’s grandfather’s care provider. “Joel’s grandfather, he has an individual coming in and taking care of him—it’s kind of the elderly care—and all we know about that individual is that yes, he hasn’t done a—or he hasn’t been caught doing a crime.”
[…]
To be fair, I scanned another friend of mine who is black—someone whose posts are perhaps the most overwhelmingly positive and noncontroversial of anyone on my feed—and he was rated at the lowest risk level. (If he wasn’t, it’d be crystal that the thing was racist.) [Wait – what?!]
And Parsa, who is Afghan, says that he has experienced a lifetime of racism himself, and even changed his name from a more overtly Muslim name because he couldn’t get prospective employers to return his calls despite having top notch grades and a college degree. He is sensitive to racism, in other words, and says he made an effort to ensure Predictim is not. Parsa and Simonoff insist that their system, while not perfect, can detect nuances and avoid bias.
The predictors they use also seem to be a bit overly simplistic and unuanced. But I bet it’s something Americans will like – another way to easily devolve responsibility of childcare.
Nvidia announced that AI models can now draw new worlds without using traditional modeling techniques or graphics rendering engines. This new technology uses an AI deep neural network to analyze existing videos and then apply the visual elements to new 3D environments.
Nvidia claims this new technology could provide a revolutionary step forward in creating 3D worlds because the AI models are trained from video to automatically render buildings, trees, vehicles, and objects into new 3D worlds, instead of requiring the normal painstaking process of modeling the scene elements.
But the project is still a work in progress. As we can see from the image on the right, which was generated in real time on a Nvidia Titan V graphics card using its Tensor cores, the rendered scene isn’t as crisp as we would expect in real life, and it isn’t as clear as we would expect with a normal modeled scene in a 3D environment. However, the result is much more impressive when we see the real-time output in the YouTube video below. The key here is speed: The AI generates these scenes in real time.
Nvidia AI Rendering
Nvidia’s researchers have also used this technique to model other motions, such as dance moves, and then apply those same moves to other characters in real-time video. That does raise moral questions, especially given the proliferation of altered videos like deep fakes, but Nvidia feels that it is an enabler of technology and the issue should be treated as a security problem that requires a technological solution to prevent people from rendering things that aren’t real.
The big question is when this will come to the gaming realm, but Nvidia cautions that this isn’t a shipping product yet. The company did theorize that it would be useful for enhancing older games by analyzing the scenes and then applying trained models to improve the graphics, among many other potential uses. It could also be used to create new levels and content in older games. In time, the company expects the technology to spread and become another tool in the game developers’ toolbox. The company has open sourced the project, so anyone can download and begin using it today, though it is currently geared towards AI researchers.
Nvidia says this type of AI analysis and scene generation can occur with any type of processor, provided it can deliver enough AI throughput to manage the real-time feed. The company expects that performance and image quality will improve over time.
Nvidia sees this technique eventually taking hold in gaming, automotive, robotics, and virtual reality, but it isn’t committing to a timeline for an actual product. The work remains in the lab for now, but the company expects game developers to begin working with the technology in the future. Nvidia is also conducting a real-time demo of AI-generated worlds at the AI research-focused NeurIPS conference this week.
Dong Mingzhu, chairwoman of China’s biggest maker of air conditioners Gree Electric Appliances, who found her face splashed on a huge screen erected along a street in the port city of Ningbo that displays images of people caught jaywalking by surveillance cameras.
That artificial intelligence-backed surveillance system, however, erred in capturing Dong’s image on Wednesday from an advertisement on the side of a moving bus.
The traffic police in Ningbo, a city in the eastern coastal province of Zhejiang, were quick to recognise the mistake, writing in a post on microblog Sina Weibo on Wednesday that it had deleted the snapshot. It also said the surveillance system would be completely upgraded to cut incidents of false recognition in future.
[…]
Since last year, many cities across China have cracked down on jaywalking by investing in facial recognition systems and advanced AI-powered surveillance cameras. Jaywalkers are identified and shamed by displaying their photographs on large public screens.
First-tier cities like Beijing and Shanghai were among the first to employ those systems to help regulate traffic and identify drivers who violate road rules, while Shenzhen traffic police began displaying photos of jaywalkers on large screens at major intersections from April last year.
Machine learning is everywhere now, including recruiting. Take CV Compiler, a new product by Andrew Stetsenko and Alexandra Dosii. This web app uses machine learning to analyze and repair your technical resume, allowing you to shine to recruiters at Google, Yahoo and Facebook.
The founders are marketing and HR experts who have a combined 15 years of experience in making recruiting smarter. Stetsenko founded Relocate.me and GlossaryTech while Dosii worked at a number of marketing firms before settling on CV Compiler.
The app essentially checks your resume and tells you what to fix and where to submit it. It’s been completely bootstrapped thus far and they’re working on new and improved machine learning algorithms while maintaining a library of common CV fixes.
“There are lots of online resume analysis tools, but these services are too generic, meaning they can be used by multiple professionals and the results are poor and very general. After the feedback is received, users are often forced to buy some extra services,” said Stetsenko. “In contrast, the CV Compiler is designed exclusively for tech professionals. The online review technology scans for keywords from the world of programming and how they are used in the resume, relative to the best practices in the industry.”
just last month, AI-generated art arrived on the world auction stage under the auspices of Christie’s, proving that artificial intelligence can not only be creative but also produce world class works of art—another profound AI milestone blurring the line between human and machine.
Naturally, the news sparked debates about whether the work produced by Paris-based art collective Obvious could really be called art at all. Popular opinion among creatives is that art is a process by which human beings express some idea or emotion, filter it through personal experience and set it against a broader cultural context—suggesting then that what AI generates at the behest of computer scientists is definitely not art, or at all creative.
By artist #2 (see bottom of story for key). Credit: Artwork Commissioned by GumGum
The story raised additional questions about ownership. In this circumstance, who can really be named as author? The algorithm itself or the team behind it? Given that AI is taught and programmed by humans, has the human creative process really been identically replicated or are we still the ultimate masters?
AI VERSUS HUMAN
At GumGum, an AI company that focuses on computer vision, we wanted to explore the intersection of AI and art by devising a Turing Test of our own in association with Rutgers University’s Art and Artificial Intelligence Lab and Cloudpainter, an artificially intelligent painting robot. We were keen to see whether AI can, in fact, replicate the intent and imagination of traditional artists, and we wanted to explore the potential impact of AI on the creative sector.
By artist #3 (see bottom of story for key). Credit: Artwork Commissioned by GumGum
To do this, we enlisted a broad collection of diverse artists from “traditional” paint-on-canvas artists to 3-D rendering and modeling artists alongside Pindar Van Arman—a classically trained artist who has been coding art robots for 15 years. Van Arman was tasked with using his Cloudpainter machine to create pieces of art based on the same data set as the more traditional artists. This data set was a collection of art by 20th century American Abstract Expressionists. Then, we asked them to document the process, showing us their preferred tools and telling us how they came to their final work.
By artist #4 (see bottom of story for key). Credit: Artwork Commissioned by GumGum
Intriguingly, while at face value the AI artwork was indistinguishable from that of the more traditional artists, the test highlighted that the creative spark and ultimate agency behind creating a work of art is still very much human. Even though the Cloudpainter machine has evolved over time to become a highly intelligent system capable of making creative decisions of its own accord, the final piece of work could only be described as a collaboration between human and machine. Van Arman served as more of an “art director” for the painting. Although Cloudpainter made all of the aesthetic decisions independently, the machine was given parameters to meet and was programed to refine its results in order to deliver the desired outcome. This was not too dissimilar to the process used by Obvious and their GAN AI tool.
By artist #5 (see bottom of story for key). Credit: Artwork Commissioned by GumGum
Moreover, until AI can be programed to absorb inspiration, crave communication and want to express something in a creative way, the work it creates on its own simply cannot be considered art without the intention of its human masters. Creatives working with AI find the process to be more about negotiation than experimentation. It’s clear that even in the creative field, sophisticated technologies can be used to enhance our capabilities—but crucially they still require human intelligence to define the overarching rules and steer the way.
THERE’S AN ACTIVE ROLE BETWEEN ART AND VIEWER
How traditional art purveyors react to AI art on the world stage is yet to be seen, but in the words of Leandro Castelao—one of the artists we enlisted for the study—“there’s an active role between the piece of art and the viewer. In the end, the viewer is the co-creator, transforming, re-creating and changing.” This is a crucial point; when it’s difficult to tell AI art apart from human art, the old adage that beauty is in the eye of the beholder rings particularly true.
Earlier this year, researchers tried teaching an AI to play the original Sonic the Hedgehog as part of the The OpenAI Retro Contest. The AI was told to prioritize increasing its score, which in Sonic means doing stuff like defeating enemies and collecting rings while also trying to beat a level as fast as possible. This dogged pursuit of one particular definition of success led to strange results: In one case, the AI began glitching through walls in the game’s water zones in order to finish more quickly.
It was a creative solution to the problem laid out in front of the AI, which ended up discovering accidental shortcuts while trying to move right. But it wasn’t quite what the researchers had intended. One of researchers’ goals with machine-learning AIs in gaming is to try and emulate player behavior by feeding them large amounts of player generated data. In effect, the AI watches humans conduct an activity, like playing through a Sonic level, and then tries to do the same, while being able to incorporate its own attempts into its learning. In a lot of instances, machine learning AIs end up taking their directions literally. Instead of completing a variety of objectives, a machine-learning AI might try to take shortcuts that completely upend human beings’ understanding of how a game should be played.
Victoria Krakovna, a researcher on Google’s DeepMind AI project, has spent the last several months collecting examples like the Sonic one. Her growing collection has recently drawn new attention after being shared on Twitter by Jim Crawford, developer of the puzzle series Frog Fractions, among other developers and journalists. Each example includes what she calls “reinforcement learning agents hacking the reward function,” which results in part from unclear directions on the part of the programmers.
“While ‘specification gaming’ is a somewhat vague category, it is particularly referring to behaviors that are clearly hacks, not just suboptimal solutions,” she wrote in her initial blog post on the subject. “A classic example is OpenAI’s demo of a reinforcement learning agent in a boat racing game going in circles and repeatedly hitting the same reward targets instead of actually playing the game.”
