From MIT severs ties to company promoting fatal brain uploading – MIT Technology Review
According to an April 2 statement, MIT will terminate Nectome’s research contract with Media Lab professor and neuroscientist Edward Boyden.
MIT’s connection to the company drew sharp criticism from some neuroscientists, who say brain uploading isn’t possible.
“Fundamentally, the company is based on a proposition that is just false. It is something that just can’t happen,” says Sten Linnarsson of the Karolinska Institute in Sweden.
He adds that by collaborating with Nectome, MIT had lent credibility to the startup and increased the chance that “some people actually kill themselves to donate their brains.”
It didn’t take long.
It’s hard enough to stand the pressure of the press and public opinion for normal companies. It must be impossibly hard to do so when you try to commercialize an attempt to escape death.
Many of the companies that are covered here on H+ face the same challenge.
From A startup is pitching a mind-uploading service that is “100 percent fatal” – MIT Technology Review
Nectome is a preserve-your-brain-and-upload-it company. Its chemical solution can keep a body intact for hundreds of years, maybe thousands, as a statue of frozen glass. The idea is that someday in the future scientists will scan your bricked brain and turn it into a computer simulation. That way, someone a lot like you, though not exactly you, will smell the flowers again in a data server somewhere.
This story has a grisly twist, though. For Nectome’s procedure to work, it’s essential that the brain be fresh. The company says its plan is to connect people with terminal illnesses to a heart-lung machine in order to pump its mix of scientific embalming chemicals into the big carotid arteries in their necks while they are still alive (though under general anesthesia).
The company has consulted with lawyers familiar with California’s two-year-old End of Life Option Act, which permits doctor-assisted suicide for terminal patients, and believes its service will be legal. The product is “100 percent fatal,”
In February, they obtained the corpse of an elderly woman and were able to begin preserving her brain just 2.5 hours after her death. It was the first demonstration of their technique, called aldehyde-stabilized cryopreservation, on a human brain.
Fineas Lupeiu, founder of Aeternitas, a company that arranges for people to donate their bodies to science, confirmed that he provided Nectome with the body. He did not disclose the woman’s age or cause of death, or say how much he charged.
The preservation procedure, which takes about six hours, was carried out at a mortuary. “You can think of what we do as a fancy form of embalming that preserves not just the outer details but the inner details,” says McIntyre. He says the woman’s brain is “one of the best-preserved ever,” although her being dead for even a couple of hours damaged it.
From Google and Others Are Building AI Systems That Doubt Themselves – MIT Technology Review
Researchers at Uber and Google are working on modifications to the two most popular deep-learning frameworks that will enable them to handle probability. This will provide a way for the smartest AI programs to measure their confidence in a prediction or a decision—essentially, to know when they should doubt themselves.
“You would like a system that gives you a measure of how certain it is,” says Dustin Tran, who is working on this problem at Google. “If a self-driving car doesn’t know its level of uncertainty, it can make a fatal error, and that can be catastrophic.”
Goodman explains that giving deep learning the ability to handle probability can make it smarter in several ways. It could, for instance, help a program recognize things, with a reasonable degree of certainty, from just a few examples rather than many thousands. Offering a measure of certainty rather than a yes-or-no answer should also help with engineering complex systems.
From Using Artificial Intelligence to Augment Human Intelligence
in one common view of AI our computers will continue to get better at solving problems, but human beings will remain largely unchanged. In a second common view, human beings will be modiﬁed at the hardware level, perhaps directly through neural interfaces, or indirectly through whole brain emulation.
We’ve described a third view, in which AIs actually change humanity, helping us invent new cognitive technologies, which expand the range of human thought. Perhaps one day those cognitive technologies will, in turn, speed up the development of AI, in a virtuous feedback cycle
The interface-oriented work we’ve discussed is outside the narrative used to judge most existing work in artiﬁcial intelligence. It doesn’t involve beating some benchmark for a classiﬁcation or regression problem. It doesn’t involve impressive feats like beating human champions at games such as Go. Rather, it involves a much more subjective and difﬁcult-to-measure criterion: is it helping humans think and create in new ways?
This creates difﬁculties for doing this kind of work, particularly in a research setting. Where should one publish? What community does one belong to? What standards should be applied to judge such work? What distinguishes good work from bad?
A truly remarkable idea that would be infinitely more powerful if not buried under a wall of complexity, making it out of reach for very many readers.
This could be a seminal paper.
From Google’s AlphaZero Destroys Stockfish In 100-Game Match – Chess.com
Stockfish, which for most top players is their go-to preparation tool, and which won the 2016 TCEC Championship and the 2017 Chess.com Computer Chess Championship, didn’t stand a chance. AlphaZero won the closed-door, 100-game match with 28 wins, 72 draws, and zero losses.
Oh, and it took AlphaZero only four hours to “learn” chess.
“We have always assumed that chess required too much empirical knowledge for a machine to play so well from scratch, with no human knowledge added at all,” Kasparov said. “Of course I’ll be fascinated to see what we can learn about chess from AlphaZero, since that is the great promise of machine learning in general—machines figuring out rules that humans cannot detect. But obviously the implications are wonderful far beyond chess and other games. The ability of a machine to replicate and surpass centuries of human knowledge in complex closed systems is a world-changing tool.”
The progress that DeepMind, and the industry in general, is making in artificial intelligence is breathtaking. Eventually, this feeling of confronting a superior species will become more and more frequent.
The notion of being, for the first time ever, the inferior species is terrifying for most humans. It implies that somebody else can do to us what we do to animals on daily basis. Homo Deus, Yuval Noah Harari new bestseller, drives you to that realization in an amazing way. I can’t recommend it enough.
From Google’s Artificial Intelligence Built an AI That Outperforms Any Made by Humans
In May 2017, researchers at Google Brain announced the creation of AutoML, an artificial intelligence (AI) that’s capable of generating its own AIs. More recently, they decided to present AutoML with its biggest challenge to date, and the AI that can build AI created a “child” that outperformed all of its human-made counterparts.
AutoML acts as a controller neural network that develops a child AI network for a specific task. For this particular child AI, which the researchers called NASNet, the task was recognizing objects — people, cars, traffic lights, handbags, backpacks, etc. — in a video in real-time.
NASNet was 82.7 percent accurate at predicting images on ImageNet’s validation set. This is 1.2 percent better than any previously published results, and the system is also 4 percent more efficient, with a 43.1 percent mean Average Precision (mAP)
The Google researchers acknowledge that NASNet could prove useful for a wide range of applications and have open-sourced the AI for inference on image classification and object detection. “We hope that the larger machine learning community will be able to build on these models to address multitudes of computer vision problems we have not yet imagined,” they wrote in their blog post.
Though the applications for NASNet and AutoML are plentiful, the creation of an AI that can build AI does raise some concerns. For instance, what’s to prevent the parent from passing down unwanted biases to its child? What if AutoML creates systems so fast that society can’t keep up?
We are waiting to develop a human-level artificial intelligence and see if it will improve itself to the point of becoming a superintelligence. Maybe it’s exceptionally close.
From Google Brain chief: Deep learning takes at least 100,000 examples | VentureBeat
“I would say pretty much any business that has tens or hundreds of thousands of customer interactions has enough scale to start thinking about using these sorts of things,” Jeff Dean, a senior fellow at Google, said in an onstage interview at the VB Summit in Berkeley, California. “If you only have 10 examples of something, it’s going to be hard to make deep learning work. If you have 100,000 things you care about, records or whatever, that’s the kind of scale where you should really start thinking about these kinds of techniques.”
From [1705.08421] AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
This paper introduces a video dataset of spatio-temporally localized Atomic Visual Actions (AVA). The AVA dataset densely annotates 80 atomic visual actions in 57.6k movie clips with actions localized in space and time, resulting in 210k action labels with multiple labels per human occurring frequently. The main differences with existing video datasets are: the definition of atomic visual actions, which avoids collecting data for each and every complex action; precise spatio-temporal annotations with possibly multiple annotations for each human; the use of diverse, realistic video material (movies). This departs from existing datasets for spatio-temporal action recognition, such as JHMDB and UCF datasets, which provide annotations for at most 24 composite actions, such as basketball dunk, captured in specific environments, i.e., basketball court.
We implement a state-of-the-art approach for action localization. Despite this, the performance on our dataset remains low and underscores the need for developing new approaches for video understanding. The AVA dataset is the first step in this direction, and enables the measurement of performance and progress in realistic scenarios.
From Google built a dataset to teach its artificial intelligence how humans hug, cook, and fight — Quartz
Google, which owns YouTube, announced on Oct. 19 a new dataset of film clips, designed to teach machines how humans move in the world. Called AVA, or “atomic visual actions,” the videos aren’t anything special to human eyes—they’re three second clips of people drinking water and cooking curated from YouTube. But each clip is bundled with a file that outlines the person that a machine learning algorithm should watch, as well as a description of their pose, and whether they’re interacting with another human or object. It’s the digital version of pointing at a dog with a child and coaching them by saying, “dog.”
This technology could help Google to analyze the years of video it processes on YouTube every day. It could be applied to better target advertising based on whether you’re watching a video of people talk or fight, or in content moderation. The eventual goal is to teach computers social visual intelligence, the authors write in an accompanying research paper, which means “understanding what humans are doing, what might they do next, and what they are trying to achieve.”
Google’s video dataset is free.
In 2015, I speculated on Twitter:
I wonder if @google already has enough @youtube videos to create a video version of Wikipedia (and if they already are machine learning it)
From Mastering the game of Go without human knowledge : Nature
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.
From AlphaGo Zero: Learning from scratch | DeepMind
It is able to do this by using a novel form of reinforcement learning, in which AlphaGo Zero becomes its own teacher. The system starts off with a neural network that knows nothing about the game of Go. It then plays games against itself, by combining this neural network with a powerful search algorithm. As it plays, the neural network is tuned and updated to predict moves, as well as the eventual winner of the games.
This updated neural network is then recombined with the search algorithm to create a new, stronger version of AlphaGo Zero, and the process begins again. In each iteration, the performance of the system improves by a small amount, and the quality of the self-play games increases, leading to more and more accurate neural networks and ever stronger versions of AlphaGo Zero.
After just three days of self-play training, AlphaGo Zero emphatically defeated the previously published version of AlphaGo – which had itself defeated 18-time world champion Lee Sedol – by 100 games to 0. After 40 days of self training, AlphaGo Zero became even stronger, outperforming the version of AlphaGo known as “Master”, which has defeated the world’s best players and world number one Ke Jie.
The new AlphaGo Zero is not impressive just because it uses no data set to become the world leader at what it does. It’s impressive also because it achieves the goal at a pace no human will ever be able to match.
From Adaptation via Meta-Learning
We’ve evolved a population of 1050 agents of different anatomies (Ant, Bug, Spider), policies (MLP, LSTM), and adaptation strategies (PPO-tracking, RL^2, meta-updates) for 10 epochs. Initially, we had an equal number of agents of each type. Every epoch, we randomly matched 1000 pairs of agents and made them compete and adapt in multi-round games against each other. The agents that lost disappeared from the population, while the winners replicated themselves.
Summary: After a few epochs of evolution, Spiders, being the weakest, disappeared, the subpopulation of Bugs more than doubled, the Ants stayed the same. Importantly, the agents with meta-learned adaptation strategies end up dominating the population.
OpenAI has developed a “learning to learn” (or meta-learning) framework that allows an AI agent to continuously adapt to a dynamic environment, at least in certain conditions. The environment is dynamic for a number of reasons, including the fact that opponents are learning as well.
AI agents equipped with the meta-learning framework win more fights against their opponents and eventually dominate the environment. Be sure to watch the last video to see the effect.
The meta-learning framework gives the selected AI agents the capability to predict and anticipate the changes in the environment and adapt faster than the AI agents that only learn from direct experience.
We know that the neocortex is a prediction machine and that human intelligence amounts to the capability to anticipate and adapt. This research is a key step towards artificial general intelligence.
From [1710.03641] Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments
Ability to continuously learn and adapt from limited experience in nonstationary environments is an important milestone on the path towards general intelligence. In this paper, we cast the problem of continuous adaptation into the learning-to-learn framework. We develop a simple gradient-based meta-learning algorithm suitable for adaptation in dynamically changing and adversarial scenarios. Additionally, we design a new multi-agent competitive environment, RoboSumo, and define iterated adaptation games for testing various aspects of continuous adaptation strategies. We demonstrate that meta-learning enables significantly more efficient adaptation than reactive baselines in the few-shot regime. Our experiments with a population of agents that learn and compete suggest that meta-learners are the fittest.
From The hippocampus as a predictive map : Nature
A cognitive map has long been the dominant metaphor for hippocampal function, embracing the idea that place cells encode a geometric representation of space. However, evidence for predictive coding, reward sensitivity and policy dependence in place cells suggests that the representation is not purely spatial. We approach this puzzle from a reinforcement learning perspective: what kind of spatial representation is most useful for maximizing future reward? We show that the answer takes the form of a predictive representation. This representation captures many aspects of place cell responses that fall outside the traditional view of a cognitive map. Furthermore, we argue that entorhinal grid cells encode a low-dimensionality basis set for the predictive representation, useful for suppressing noise in predictions and extracting multiscale structure for hierarchical planning.
From The hippocampus as a ‘predictive map’ | DeepMind
Our insights were derived from reinforcement learning, the subdiscipline of AI research that focuses on systems that learn by trial and error. The key computational idea we drew on is that to estimate future reward, an agent must first estimate how much immediate reward it expects to receive in each state, and then weight this expected reward by how often it expects to visit that state in the future. By summing up this weighted reward across all possible states, the agent obtains an estimate of future reward.
Similarly, we argue that the hippocampus represents every situation – or state – in terms of the future states which it predicts. For example, if you are leaving work (your current state) your hippocampus might represent this by predicting that you will likely soon be on your commute, picking up your kids from school or, more distantly, at home. By representing each current state in terms of its anticipated successor states, the hippocampus conveys a compact summary of future events, known formally as the “successor representation”. We suggest that this specific form of predictive map allows the brain to adapt rapidly in environments with changing rewards, but without having to run expensive simulations of the future.
I wonder what Jeff Hawkins thinks about this new theory.
From Why Does the Neocortex Have Columns, A Theory of Learning the Structure of the World | bioRxiv
Neocortical regions are organized into columns and layers. Connections between layers run mostly perpendicular to the surface suggesting a columnar functional organization. Some layers have long-range excitatory lateral connections suggesting interactions between columns. Similar patterns of connectivity exist in all regions but their exact role remain a mystery. In this paper, we propose a network model composed of columns and layers that performs robust object learning and recognition. Each column integrates its changing input over time to learn complete predictive models of observed objects. Excitatory lateral connections across columns allow the network to more rapidly infer objects based on the partial knowledge of adjacent columns. Because columns integrate input over time and space, the network learns models of complex objects that extend well beyond the receptive field of individual cells. Our network model introduces a new feature to cortical columns. We propose that a representation of location relative to the object being sensed is calculated within the sub-granular layers of each column. The location signal is provided as an input to the network, where it is combined with sensory data. Our model contains two layers and one or more columns. Simulations show that using Hebbian-like learning rules small single-column networks can learn to recognize hundreds of objects, with each object containing tens of features. Multi-column networks recognize objects with significantly fewer movements of the sensory receptors. Given the ubiquity of columnar and laminar connectivity patterns throughout the neocortex, we propose that columns and regions have more powerful recognition and modeling capabilities than previously assumed.
Not on artificial intelligence per se, but Jeff Hawkins was the first to suggest a unifying theory of how the brain works in 2005 with his book On Intelligence. It’s interesting to see how the theory has been refined in last 12 years and how it might influence today’s development of AI algorithms.
From Home – Neuromation
We propose a solution whose accuracy is guaranteed by construction: synthesizing large datasets along with perfectly accurate labels. The benefits of synthetic data are manifold. It is fast to synthesize and render, perfectly accurate, tailored for the task at hand, and can be modified to improve the model and training itself. It is important to note that real data with accurate labels is still required for evaluating models trained on synthetic data, in order to guarantee acceptable performance at inference time. However, the amount of validation data required is orders of magnitude smaller than training data!
They generate and sell synthetic datasets for AI training. All data is charged per item, and comes pre-labelled.
All transactions get done using Ethereum extended ECR-20 compliant token. People can mine tokens by performing computationally intensive tasks of data generation and model training instead of mining cryptocurrency.
From DeepMind launches new research team to investigate AI ethics – The Verge
Google’s AI subsidiary DeepMind is getting serious about ethics. The UK-based company, which Google bought in 2014, today announced the formation of a new research group dedicated to the thorniest issues in artificial intelligence. These include the problems of managing AI bias; the coming economic impact of automation; and the need to ensure that any intelligent systems we develop share our ethical and moral values.
DeepMind Ethics & Society (or DMES, as the new team has been christened) will publish research on these topics and others starting early 2018. The group has eight full-time staffers at the moment, but DeepMind wants to grow this to around 25 in a year’s time. The team has six unpaid external “fellows” (including Oxford philosopher Nick Bostrom, who literally wrote the book on AI existential risk) and will partner with academic groups conducting similar research, including The AI Now Institute at NYU, and the Leverhulme Centre for the Future of Intelligence.
Great effort. I’d love to attend a conference arranged by groups like this one.
From Ginni Rometty on Artificial Intelligence – Bloomberg
Doctors don’t want black-and-white answers, nor does any profession. If you’re a professional, my guess is when you interact with AI, you don’t want it to say, “Here is an answer.” What a doctor wants is, “OK, give me the possible answers. Tell my why you believe it. Can I see the research, the evidence, the ‘percent confident’? What more would you like to know?”
When I went to Davos in January, we published something called Transparency and Trust in the Cognitive Era. It’s our responsibility if we build this stuff to guide it safely into the world. First, be clear on the purpose, work with man. We aren’t out here to destroy man. The second is to be transparent about who trained the computers, who are the experts, where did the data come from. And when consumers are using AI, you inform them that they are and inform the company as well that owns the intellectual property. And the third thing is to be committed to skill.
IBM and its term “cognitive computing” are all about so-called “weak AI”. The problem is that giving the insight about an answer is incredibly challenging at the moment vs just giving the answer in a black-box fashion.
From ‘Whoever leads in AI will rule the world’: Putin to Russian children on Knowledge Day — RT News
“Artificial intelligence is the future, not only for Russia, but for all humankind. It comes with colossal opportunities, but also threats that are difficult to predict. Whoever becomes the leader in this sphere will become the ruler of the world,” Russian President Vladimir Putin said.
However, the president said he would not like to see anyone “monopolize” the field.
“If we become leaders in this area, we will share this know-how with entire world, the same way we share our nuclear technologies today,” he told students from across Russia via satellite link-up, speaking from the Yaroslavl region.
Elon Musk replies to this specific article on Twitter:
It begins ..
China, Russia, soon all countries w strong computer science. Competition for AI superiority at national level most likely cause of WW3 imo.
Just like a small team of 5 plus AI could overturn the market, a small, weak government plus AI could overturn the geopolitical scene. And human augmentation is a key milestone to accomplish that. I already heard multiple companies I mentioned here on H+ having collaboration with military and government agencies.
From PathNet: Evolution Channels Gradient Descent in SuperNeural Networks
For artificial general intelligence (AGI) it would be efficient if multiple users trained the same giant neural network, permitting parameter reuse, without catastrophic forgetting.
PathNet is a first step in this direction. It is a neural network algorithm that uses agents embedded in the neural network whose task is to discover which parts of the network to re-use for new tasks.
Agents are pathways (views) through the network which determine the subset of parameters that are used and updated by the forwards and backwards passes of the backpropogation algorithm. During learning, a tournament selection genetic algorithm is used to select pathways through the neural network for replication and mutation. Pathway fitness is the performance of that pathway measured according to a cost function.
We demonstrate successful transfer learning; fixing the parameters along a path learned on task A and re-evolving a new population of paths for task B, allows task B to be learned faster than it could be learned from scratch or after fine-tuning. Paths evolved on task B re-use parts of the optimal path evolved on task A.
From Prodigy: A new tool for radically efficient machine teaching | Explosion AI
Prodigy addresses the big remaining problem: annotation and training. The typical approach to annotation forces projects into an uncomfortable waterfall process. The experiments can’t begin until the first batch of annotations are complete, but the annotation team can’t start until they receive the annotation manuals. To produce the annotation manuals, you need to know what statistical models will be required for the features you’re trying to build. Machine learning is an inherently uncertain technology, but the waterfall annotation process relies on accurate upfront planning. The net result is a lot of wasted effort.
Prodigy solves this problem by letting data scientists conduct their own annotations, for rapid prototyping. Ideas can be tested faster than the first planning meeting could even be scheduled. We also expect Prodigy to reduce costs for larger projects, but it’s the increased agility we’re most excited about. Data science projects are said to have uneven returns, like start-ups: a minority of projects are very successful, recouping costs for a larger number of failures. If so, the most important problem is to find more winners. Prodigy helps you do that, because you get to try things much faster.
From Joseph Redmon: How computers learn to recognize objects instantly | TED.com
Joseph Redmon works on the YOLO (You Only Look Once) system, an open-source method of object detection that can identify objects in images and video — from zebras to stop signs — with lightning-quick speed. In a remarkable live demo, Redmon shows off this important step forward for applications like self-driving cars, robotics and even cancer detection.
A few years ago, on my personal Twitter account, I suggested that Google side benefit of owning YouTube would be having the largest archive of human activities on video to train its AI. What Redmon did here is what I had in mind at that time.
By the way, the demonstration during the TED talk is impressive.
From Tom Gruber: How AI can enhance our memory, work and social lives | TED.com
Tom Gruber, co-creator of Siri, wants to make “humanistic AI” that augments and collaborates with us instead of competing with (or replacing) us. He shares his vision for a future where AI helps us achieve superhuman performance in perception, creativity and cognitive function — from turbocharging our design skills to helping us remember everything we’ve ever read and the name of everyone we’ve ever met.
The video is short but gives a very clear idea of how Apple is thinking about AI and what the future applications could be.
From AI Is Inventing Languages Humans Can’t Understand. Should We Stop It?
At first, they were speaking to each other in plain old English. But then researchers realized they’d made a mistake in programming.
“There was no reward to sticking to English language,” says Dhruv Batra, visiting research scientist from Georgia Tech at Facebook AI Research (FAIR). As these two agents competed to get the best deal–a very effective bit of AI vs. AI dogfighting researchers have dubbed a “generative adversarial network”–neither was offered any sort of incentive for speaking as a normal person would. So they began to diverge, eventually rearranging legible words into seemingly nonsensical sentences.
Should we allow AI to evolve its dialects for specific tasks that involve speaking to other AIs? To essentially gossip out of our earshot? Maybe; it offers us the possibility of a more interoperable world, a more perfect place where iPhones talk to refrigerators that talk to your car without a second thought.
The tradeoff is that we, as humanity, would have no clue what those machines were actually saying to one another.
What if artificial intelligence would help humans to develop a more efficient, universal language?
From IBM News room – 2017-07-21 IBM and University of Alberta Publish New Data on Machine Learning Algorithms to Help Predict Schizophrenia
In the paper, researchers analyzed de-identified brain functional Magnetic Resonance Imaging (fMRI) data from the open data set, Function Biomedical Informatics Research Network (fBIRN) for patients with schizophrenia and schizoaffective disorders, as well as a healthy control group. fMRI measures brain activity through blood flow changes in particular areas of the brain.
Specifically, the fBIRN data set reflects research done on brain networks at different levels of resolution, from data gathered while study participants conducted a common auditory test. Examining scans from 95 participants, researchers used machine learning techniques to develop a model of schizophrenia that identifies the connections in the brain most associated with the illness.
From Khosla Ventures leads $50 million investment in Vicarious’ AI tech | VentureBeat | Entrepreneur | by Bérénice Magistretti
The Union City, California-based startup is using computational neuroscience to build better machine learning models that help robots quickly address a wide variety of tasks. Vicarious focuses on the neocortex, a part of the brain concerned with sight and hearing.
“We aren’t trying to emulate the brain exactly,” wrote Vicarious cofounder and CEO Scott Phoenix, in an email to VentureBeat. “A good way to think about it is airplanes and birds. When building a plane, you want to borrow relevant features from birds, like low body weight and deformable wings, without getting into irrelevant details like feather colors and flapping.”
I think this quote is deeply inspired by the book Superintelligence by Nick Bostrom. Which is not surprising as Vicarous is trying to build the holy grail of AI: an artificial general intelligence.
They have the most impressive list of investors I have seen in a long time.
From Google Glass 2.0 Is a Startling Second Act | WIRED
Companies testing EE—including giants like GE, Boeing, DHL, and Volkswagen—have measured huge gains in productivity and noticeable improvements in quality. What started as pilot projects are now morphing into plans for widespread adoption in these corporations. Other businesses, like medical practices, are introducing Enterprise Edition in their workplaces to transform previously cumbersome tasks.
For starters, it makes the technology completely accessible for those who wear prescription lenses. The camera button, which sits at the hinge of the frame, does double duty as a release switch to remove the electronics part of unit (called the Glass Pod) from the frame. You can then connect it to safety glasses for the factory floor—EE now offers OSHA-certified safety shields—or frames that look like regular eyewear. (A former division of 3M has been manufacturing these specially for Enterprise Edition; if EE catches on, one might expect other frame vendors, from Warby Parker to Ray-Ban, to develop their own versions.)
Other improvements include beefed-up networking—not only faster and more reliable wifi, but also adherence to more rigorous security standards—and a faster processor as well. The battery life has been extended—essential for those who want to work through a complete eight-hour shift without recharging. (More intense usage, like constant streaming, still calls for an external battery.) The camera was upgraded from five megapixels to eight. And for the first time, a red light goes on when video is being recorded.
If Glass EE gains traction, and I believe so if it evolves into a platform for enterprise apps, Google will gain a huge amount of information and experience that can reuse on the AR contact lenses currently in the work.
From Robust Adversarial Examples
We’ve created images that reliably fool neural network classifiers when viewed from varied scales and perspectives. This challenges a claim from last week that self-driving cars would be hard to trick maliciously since they capture images from multiple scales, angles, perspectives, and the like.
This innocuous kitten photo, printed on a standard color printer, fools the classifier into thinking it’s a monitor or desktop computer regardless of how its zoomed or rotated. We expect further parameter tuning would also remove any human-visible artifacts.
Watch the videos.
From Will HoloLens turn air travelers into mixed-reality characters? – GeekWire
Imagine a world where headset-wearing flight attendants can instantly know how you’re feeling based on a computer analysis of your facial expression.
Actually, you don’t need to imagine: That world is already in beta, thanks to Air New Zealand, Dimension Data and Microsoft HoloLens.
In May, the airline announced that it was testing HoloLens’ mixed-reality system as a tool for keeping track of passengers’ preferences in flight – for example, their favorite drink and preferred menu items. And if the imaging system picked up the telltale furrowed brow of an anxious flier, that could be noted in an annotation displayed to the flight attendant through the headset.
Google already failed at this. The only places where AR glasses would be socially accepted are those ones where personnel with equipment is the norm.
It would take years, if not decades, for people to accept the idea that flight attendants must have a special equipment to serve drinks.
From Microsoft’s new iPhone app narrates the world for blind people – The Verge
With the app downloaded, the users can point their phone’s camera at a person and it’ll say who they are and how they’re feeling; they can point it at a product and it’ll tell them what it is. All using artificial intelligence that runs locally on their phone.
The app works in a number of scenarios. As well as recognizing people its seen before and guessing strangers’ age and emotion, it can identify household products by scanning barcodes. It also reads and scan documents, and recognizes US currency.
Imagine if this would be the key function of an earpiece like the Waverly Labs one.
From Bark.us saves teens’ lives by using AI to analyze their online activity | VentureBeat | Bots | by Khari Johnson
Bark.us uses machine learning and statistical analysis to crawl conversations teens have on email, SMS, and platforms like Snapchat, Instagram, and WhatsApp. Analysis is performed to determine if a kid is suffering from cyberbullying, suicidal thoughts, possible depression, hate speech, or other attacks that can happen online without a parent or guardian aware anything is happening. Sexting and drug usage are also flagged. When signs of alarm are recognized, Bark alerts parents via text or email, then suggests potential next steps.
This sounds more like controlling than saving lives, but it might be a first step in the right direction.
What if, rather than alerting parents, this technology would be integrated with a biohacking solution to improve how kids react to life challenges?
From IBM News room – 2017-07-11 Study by New York Genome Center and IBM Demonstrates Potential for AI and Whole Genome Sequencing to Scale Access to Precision Medicine – United States
researchers at the New York Genome Center (NYGC), The Rockefeller University and other NYGC member institutions, and IBM (NYSE: IBM) bhave illustrated the potential of IBM Watson for Genomics to analyze complex genomic data from state-of-the-art DNA sequencing of whole genomes. The study compared multiple techniques – or assays – used to analyze genomic data from a glioblastoma patient’s tumor cells and normal healthy cells.
The proof of concept study used a beta version of Watson for Genomics technology to help interpret whole genome sequencing (WGS) data for one patient. In the study, Watson was able to provide a report of potential clinically actionable insights within 10 minutes, compared to 160 hours of human analysis and curation required to arrive at similar conclusions for this patient.
From Comparing sequencing assays and human-machine analyses in actionable genomics for glioblastoma
Objective: To analyze a glioblastoma tumor specimen with 3 different platforms and compare potentially actionable calls from each.
Methods: Tumor DNA was analyzed by a commercial targeted panel. In addition, tumor-normal DNA was analyzed by whole-genome sequencing (WGS) and tumor RNA was analyzed by RNA sequencing (RNA-seq). The WGS and RNA-seq data were analyzed by a team of bioinformaticians and cancer oncologists, and separately by IBM Watson Genomic Analytics (WGA), an automated system for prioritizing somatic variants and identifying drugs.
Results: More variants were identified by WGS/RNA analysis than by targeted panels. WGA completed a comparable analysis in a fraction of the time required by the human analysts.
Conclusions: The development of an effective human-machine interface in the analysis of deep cancer genomic datasets may provide potentially clinically actionable calls for individual patients in a more timely and efficient manner than currently possible.
From Anna Patterson talks Gradient Ventures, Google’s new AI fund | TechCrunch
It’s been pretty obvious for a few months now, but Google has finally admitted that it’s running its own investment fund targeting machine intelligence startups. The fund will go by the name Gradient Ventures and provide capital, resources and education to AI-first startups.
Google isn’t disclosing the size of the fund, but the company told us that it’s being run directly off of Google’s balance sheet and will have the flexibility to follow on when it makes sense. This is in contrast to GV (formally Google Ventures) and Capital G, which operate as independent funds.
AI is the first technology in a long time posing a real threat to Google dominance. In other words, artificial intelligence is the best bet for a newcomer to become the next Google. No surprise Google wants to spot that newcomer as early as possible.
From PAIR: the People + AI Research Initiative
Today we’re announcing the People + AI Research initiative (PAIR) which brings together researchers across Google to study and redesign the ways people interact with AI systems. The goal of PAIR is to focus on the “human side” of AI: the relationship between users and technology, the new applications it enables, and how to make it broadly inclusive. The goal isn’t just to publish research; we’re also releasing open source tools for researchers and other experts to use.
From Google Lens offers a snapshot of the future for augmented reality and AI | AndroidAuthority
At the recent I/0 2017, Google stated that we were at an inflexion point with vision. In other words, it’s now more possible than ever before for a computer to look at a scene and dig out the details and understand what’s going on. Hence: Google Lens.This improvement comes courtesy of machine learning, which allows companies like Google to acquire huge amounts of data and then create systems that utilize that data in useful ways. This is the same technology underlying voice assistants and even your recommendations on Spotify to a lesser extent.
From Wolfram Alpha Is Making It Extremely Easy for Students to Cheat | WIRED
Still, the prevailing notion that Wolfram|Alpha is a form of cheating doesn’t appear to be dissipating. Much of this comes down to what homework is. If the purpose of homework is build greater understanding of concepts as presented in class, Joyce is adamant that teachers should view Wolfram|Alpha as an asset. It’s not that Wolfram Alpha has helped students “‘get through’ a math class by doing their homework for them,” he says, “but that we helped them actually understand what they were doing” in the first place. Dixon believes that Wolfram|Alpha can build confidence in students who don’t see themselves as having mathematical minds. Homework isn’t really about learning to do a calculation, but rather about learning to find and understand an answer regardless of how the calculation is executed.
That’s the route down which education appears to be headed. Once upon a time, education was all about packing as much information as possible into a human brain. Information was limited and expensive, and the smartest people were effectively the deepest and most organized filing cabinets. Today, it’s the opposite.“The notion of education as a transfer of information from experts to novices—and asking the novices to repeat that information, regurgitate it on command as proof that they have learned it—is completely disconnected from the reality of 2017,” says David Helfand, a Professor of Astronomy at Columbia University.
- Will AI make humans smarter or dumber?
- How is this different from a surgeon using AI-powered AR goggles to perform surgery?