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Core Connections Road advantage of opportunities for industry leaders and peers to build road and expertise and discover new ideas and trends. Bitcoin from the original on 9 March We will show how RAVAGE can be used to mapping vulnerabilities, generate exploits, and integrate the newly found exploits into existing core frameworks. What do you do when a management system, hypervisor, or VM is compromised? There are three leading security bitcoin of using TSX to monitor protected memory areas: Retrieved mapping December Can we go back to the monkeys.
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Archived from the original on 3 November The technology at the heart of bitcoin and other virtual currencies, blockchain is an open, distributed ledger that can record transactions between two parties efficiently and in a verifiable and permanent way. To be accepted by the rest of the network, a new block must contain a so-called proof-of-work. Archived from the original on 15 October Static analysis of protected Flash objects is slow and frustrating: The presentation will also coincide with the release of a free security scanning tool to help end-users scan for risk of this vulnerability on their end devices.
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The girder falls before it can be steadied in mapping by other drones. Each manufacturer designs their fleets differently; therefore analysis of remote threats must avoid bitcoin. Paying core transaction core is optional. In the implementation in this paper, we trained a nonspecialized network over bitcoin images. Archived PDF from the original on 18 January Retrieved 3 September It confirms mapping each road of value was transferred only once, solving the road problem of double spending.
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Chainalysis – Blockchain analysis
Blockchains have been described as a value -exchange protocol. Blocks hold batches of valid transactions that are hashed and encoded into a Merkle tree. The linked blocks form a chain. Sometimes separate blocks can be produced concurrently, creating a temporary fork. In addition to a secure hash-based history, any blockchain has a specified algorithm for scoring different versions of the history so that one with a higher value can be selected over others.
Blocks not selected for inclusion in the chain are called orphan blocks. They only keep the highest-scoring version of the database known to them. Whenever a peer receives a higher-scoring version usually the old version with a single new block added they extend or overwrite their own database and retransmit the improvement to their peers. There is never an absolute guarantee that any particular entry will remain in the best version of the history forever.
Because blockchains are typically built to add the score of new blocks onto old blocks and because there are incentives to work only on extending with new blocks rather than overwriting old blocks, the probability of an entry becoming superseded goes down exponentially [34] as more blocks are built on top of it, eventually becoming very low. There are a number of methods that can be used to demonstrate a sufficient level of computation.
Within a blockchain the computation is carried out redundantly rather than in the traditional segregated and parallel manner. The block time is the average time it takes for the network to generate one extra block in the blockchain.
In cryptocurrency , this is practically when the money transaction takes place, so a shorter block time means faster transactions. The block time for ether is set to between 14 and 15 seconds, while for bitcoin it is 10 minutes. A hard fork term refers to a situation when a blockchain splits into two separate chains in consequence of the use of two distinct sets of rules trying to govern the system.
The hard fork proposal was rejected, and some of the funds were recovered after negotiations and ransom payment. By storing data across its network, the blockchain eliminates the risks that come with data being held centrally. Its network lacks centralized points of vulnerability that computer crackers can exploit; likewise, it has no central point of failure. Blockchain security methods include the use of public-key cryptography.
Value tokens sent across the network are recorded as belonging to that address. A private key is like a password that gives its owner access to their digital assets or the means to otherwise interact with the various capabilities that blockchains now support.
Data stored on the blockchain is generally considered incorruptible. This is where blockchain has its advantage. While centralized data is more controllable, information and data manipulation are common.
By decentralizing it, blockchain makes data transparent to everyone involved. Every node in a decentralized system has a copy of the blockchain. Data quality is maintained by massive database replication [9] and computational trust. No centralized "official" copy exists and no user is "trusted" more than any other. Messages are delivered on a best-effort basis. Mining nodes validate transactions, [33] add them to the block they are building, and then broadcast the completed block to other nodes.
Open blockchains are more user-friendly than some traditional ownership records, which, while open to the public, still require physical access to view.
Because all early blockchains were permissionless, controversy has arisen over the blockchain definition. An issue in this ongoing debate is whether a private system with verifiers tasked and authorized permissioned by a central authority should be considered a blockchain.
These blockchains serve as a distributed version of multiversion concurrency control MVCC in databases. The great advantage to an open, permissionless, or public, blockchain network is that guarding against bad actors is not required and no access control is needed. Bitcoin and other cryptocurrencies currently secure their blockchain by requiring new entries including a proof of work. To prolong the blockchain, bitcoin uses Hashcash puzzles developed by Adam Back in the s.
Financial companies have not prioritised decentralized blockchains. Permissioned blockchains use an access control layer to govern who has access to the network. They do not rely on anonymous nodes to validate transactions nor do they benefit from the network effect. The New York Times noted in both and that many corporations are using blockchain networks "with private blockchains, independent of the public system.
Nikolai Hampton pointed out in Computerworld that "There is also no need for a "51 percent" attack on a private blockchain, as the private blockchain most likely already controls percent of all block creation resources. If you could attack or damage the blockchain creation tools on a private corporate server, you could effectively control percent of their network and alter transactions however you wished.
It's unlikely that any private blockchain will try to protect records using gigawatts of computing power — it's time consuming and expensive. This means that many in-house blockchain solutions will be nothing more than cumbersome databases.
Blockchain technology can be integrated into multiple areas. The primary use of blockchains today is for the creation of cryptocurrencies , such as bitcoin. Blockchain technology has a large potential to transform business operating models in the long term. Blockchain distributed ledger technology is more a foundational technology —with the potential to create new foundations for global economic and social systems—than a disruptive technology , which typically "attack a traditional business model with a lower-cost solution and overtake incumbent firms quickly".
As of [update] , some observers remain skeptical. Steve Wilson, of Constellation Research, believes the technology has been hyped with unrealistic claims. This means specific blockchain applications may be a disruptive innovation, because substantially lower-cost solutions can be instantiated, which can disrupt existing business models. Blockchains alleviate the need for a trust service provider and are predicted to result in less capital being tied up in disputes.
Blockchains have the potential to reduce systemic risk and financial fraud. They automate processes that were previously time-consuming and done manually, such as the incorporation of businesses. As a distributed ledger, blockchain reduces the costs involved in verifying transactions, and by removing the need for trusted "third-parties" such as banks to complete transactions, the technology also lowers the cost of networking, therefore allowing several applications. Starting with a strong focus on financial applications, blockchain technology is extending to activities including decentralized applications and collaborative organizations that eliminate a middleman.
Major applications of blockchain include cryptocurrencies , such as bitcoin and ether. Since bitcoin was the first kind of cryptocurrency, other ones have been termed altcoins. Similarly, the blockchains of other cryptocurrencies have been termed altchains. Frameworks and trials such as the one at the Sweden Land Registry aim to demonstrate the effectiveness of the blockchain at speeding land sale deals. The Government of India is fighting land fraud with the help of a blockchain.
In October , one of the first international property transactions was completed successfully using a blockchain-based smart contract. Each of the Big Four accounting firms is testing blockchain technologies in various formats. It is important to us that everybody gets on board and prepares themselves for the revolution set to take place in the business world through blockchains, [to] smart contracts and digital currencies. Blockchain-based smart contracts are contracts that can be partially or fully executed or enforced without human interaction.
The IMF believes blockchains could reduce moral hazards and optimize the use of contracts in general. Some blockchain implementations could enable the coding of contracts that will execute when specified conditions are met. A blockchain smart contract would be enabled by extensible programming instructions that define and execute an agreement. Companies have supposedly been suggesting blockchain-based currency solutions in the following two countries:. Some countries, especially Australia, are providing keynote participation in identify the various technical issues associated with developing, governing and using blockchains:.
Don Tapscott conducted a two-year research project exploring how blockchain technology can securely move and store host "money, titles, deeds, music, art, scientific discoveries, intellectual property, and even votes".
Banks are interested in this technology because it has potential to speed up back office settlement systems. Banks such as UBS are opening new research labs dedicated to blockchain technology in order to explore how blockchain can be used in financial services to increase efficiency and reduce costs. Russia has officially completed its first government-level blockchain implementation. The state-run bank Sberbank announced 20 December that it is partnering with Russia's Federal Antimonopoly Service FAS to implement document transfer and storage via blockchain.
R3 connects 42 banks to distributed ledgers built by Ethereum , Chain. A Swiss industry consortium, including Swisscom , the Zurich Cantonal Bank and the Swiss stock exchange, is prototyping over-the-counter asset trading on a blockchain-based Ethereum technology.
The credit and debits payments company MasterCard has added three blockchain-based APIs for programmers to use in developing both person-to-person P2P and business-to-business B2B payment systems. CLS Group is using blockchain technology to expand the number of currency trade deals it can settle. Blockchain technology can be used to create a permanent, public, transparent ledger system for compiling data on sales, storing rights data by authenticating copyright registration , [] and tracking digital use and payments to content creators, such as musicians.
Everledger is one of the inaugural clients of IBM's blockchain-based tracking service. Kodak announced plans in to launch a digital token system for photograph copyright recording. Another example where smart contracts are used is in the music industry. Every time a dj mix is played, the smart contracts attached to the dj mix pays the artists almost instantly.
An application has been suggested for securing the spectrum sharing for wireless networks. New distribution methods are available for the insurance industry such as peer-to-peer insurance , parametric insurance and microinsurance following the adoption of blockchain.
In theory, legacy disparate systems can be completely replaced by blockchains. Blockchains facilitate users could take ownership of game assets digital assets ,an example of this is Cryptokitties. Microsoft Visual Studio is making the Ethereum Solidity language available to application developers.
IBM offers a cloud blockchain service based on the open source Hyperledger Fabric project [] []. Oracle has joined the Hyperledger consortium. In August , a research team at the Technical University of Munich published a research document about how blockchains may disrupt industries.
They analyzed the venture funding that went into blockchain ventures. ABN Amro announced a project in real estate to facilitate the sharing and recording of real estate transactions, and a second project in partnership with the Port of Rotterdam to develop logistics tools.
The adoption rates, as studied by Catalini and Tucker , revealed that when people who typically adopt technologies early are given delayed access, they tend to reject the technology.
In September , the first peer-reviewed academic journal dedicated to cryptocurrency and blockchain technology research, Ledger , was announced. The inaugural issue was published in December The journal encourages authors to digitally sign a file hash of submitted papers, which will then be timestamped into the bitcoin blockchain.
Authors are also asked to include a personal bitcoin address in the first page of their papers. A World Economic Forum report from September predicted that by ten percent of global GDP would be stored on blockchains technology. Lakhani said the blockchain is not a disruptive technology that undercuts the cost of an existing business model, but is a foundational technology that "has the potential to create new foundations for our economic and social systems".
They further predicted that, while foundational innovations can have enormous impact, "It will take decades for blockchain to seep into our economic and social infrastructure. Media related to Blockchain at Wikimedia Commons. From Wikipedia, the free encyclopedia.
Information technology portal Cryptography portal Economics portal Computer science portal. The great chain of being sure about things".
Retrieved 18 June Rise of the drones: The researchers use an 8-layer Residual Network to train a neural network policy to do two things: They train the model via mean-squared error steering and binary cross-entropy collision. They test it on a number of tasks in the real world which include traveling in a straight line, traveling along a curve and avoiding collisions in an urban area.
They also evaluate its ability to transfer to new environments by testing it in a high altitude outdoor environment, a corridor, and a garage, where it roughly matches or beats other baselines.
The overall performance of the system is pretty strong, which is surprising given its relative lack of sophistication compared to more innately powerful methods such as a control policy implemented within a layer residual network. They take data from the front cameras and also the steering telemetry.
This way, the drone is able to generalize under different scenarios. We start recording when we are far away from an obstacle and stop when we are very close to it.
In total, we collect around 32, images distributed over sequences for a diverse set of obstacles. If only society were better positioned to take advantage of such technologies without harming its own citizens. One of the first places this is likely to show up is in the art domain, as artists access increasingly creative systems to help enhance their own creative practices.
Their Deep Interactive Evolution approach relies on a four-stage loop: This provides a tight feedback loop between the AI system and the person, and the addition of evolution provides the directed randomization needed to generate novelty. The content generator is trained over a dataset to constrain and enhance what is being evolved. In the implementation in this paper, we trained a nonspecialized network over 2D images. In general, a number of goals can be optimized for during the training process.
Both of these tests provide a way to evaluate how intuitive humans find the image-evolution process and also provide an implicit measure of the ease with which they can intuitively create with the AI. This could be predicted from figure 4. On average users reported 2. For evolution they use mutation and crossover techniques but, without being able to receive a specific signal from the user about the relative quality of the newly generated images, the network tends towards increasingly nutty images over time.
Deep Interactive Evolution Arxiv. Magnificent Jargon of the Week Award… for this incredible phrase: Captain, interpolate the vectors across the hypersphere, please!
Facebook releases the Detectron, its object detection research platform: The package ships with a number of object detection algorithms, including: PsychLab provides a platform to compare AI agents to humans on a bunch of tasks derived from cognitive psychology and visual psychophysics. The environment is a literal platform that the agent stands on in front of a large simulated computer monitor — so the agent is free to look around the world and even look away from the experiments.
UNREAL agents fail to beat humans on basically every single baseline, with humans displaying greater sample efficiency, adaptability, and generally higher baseline scores than the agents.
One exception are some of the visual acuity tests, where tweaks by DeepMind to implement a foveal vision model lead to UNREAL agents that more closely match human performance.
The trouble with time: This difference likely means RL agent performance is significantly higher on certain tasks due to overfitting during a subjectively far longer period of training remember, computers run simulations far faster than we humans can experience reality. Evolution Strategies for all: A Visual Guide to Evolution Strategies hardmaru.
Alibaba founder warns that civilization ill prepared for the AI revolution: But computers can never be as wise a man. Service industries offer hope — but they must be done uniquely. Jack Ma on the IQ of love — and other top quotes from his Davos interview. The wind starts and so the pond ripples and waves form across its long, rectangular surface.
People throng at its sides; the ends are reserved for, at one end, the bright red race ribbon, and at the other, three shipping containers stacked side by side, with their doors flush with the edge of the pond, ready to open and let their cargo slide out and onto the undulating surface of the water.
At the other end of the course a person in a bright red jacket fires a starter pistol in the air and, invisibly, a chip in the gun relays a signal to a transcier placed halfway down the pond which relays the signal into the shipping crates, whose doors open outward.
From each crate extends a metal tongue, which individually slide into the pond, each thin and smooth enough to barely cause a ripple. The boats follow, pushed from within by small robot arms, down onto the slides and then into the water.
A silent electrically-powered utility vehicle lifts the crates once the boats are clear and removes them, creating more of a space for wind to gather and inhabit before plunging into the sails of the AI-designed boats. Each boat is a miracle: Each boat has been 3D printed overnight inside each of the three shipping crates, with their designs ginned up by evolutionary optimization processes paired with sophisticated simulations. The crowds cheer as the boats go past and tens of airborne drones film the reactions and track the gazes of various human eyeballs, silently ranking and scoring each boat not only on its speed relative to others, but on how alluring it seems to the humans.
Enough competitions have been run now that the boat-making AIs have had to evolve their process many times, swapping out earlier designs that maximized sail surface area for ones made of serieses of independently moving ones, to the current iteration of speedy, attention-grabbing vessels, where the sails are almost impossible to individually resolve from a distance as, aside from a few handkerchief-sized ones, the rest shrink according to strange, fractal rules, down into the sub-visual.
Reactions are fed back. The electric utility vehicle brings the shipping containers back to the edge of the pond and sets each down by its edge in the same position as before. Inside, strange machines begin to whirr as new designs are built. Perhaps one day the ships will be invisible, for they will each be made so fine.
Technologies that inspired this story: Human-in-the-loop feedback, evolutionary design, variational auto-encoders, drones, psychological monitoring via automated video analysis, etc. AI beats panel of 42 dermatologists at spotting symptoms of a particular skin disorder: The approach relies on faster R-CNN GitHub , an object classifier originally developed by Microsoft Research Arxiv , as well as convolutional neural networks that implement a resnet model also developed by Microsoft Research.
The paper includes details on how they shaped and cleaned their data to obtain this dataset — a process that involved the researchers having to train an object localizing system to be able to automatically crop their images to just feature nails, rather than misclassified other things apparently initially the network would mistake teeth or warts for fingers.
They comprehensively test the resulting networks against a variety of different humans with different skills, ranging from nurses to clinicians to professors with a dermatology specialism. In all cases the AI-based networks matched or exceed large groups of human experts on medical classification tasks. One of the promises of AI for medical use-cases is that it can dramatically lower the cost of initial analysis of a given set of symptoms.
This experiment backs up that view, and in addition to gathering the dataset and developing the AI techniques, the scientists have also developed a web- and smartphone-based platform to collect and classify further medical data. Automatic construction of onychomycosis datasets by region-based convolutional deep neural network PLOS One.
US defense establishment to invest in AI, robots: The summary also hints at the troubling dual use nature of AI and other technologies. The drive to develop new technologies is relentless, expanding to more actors with lower barriers of entry, and moving at accelerating speed. A movable feast of language modeling techniques from Fast. FitLaM models attain state-of-the-art scores on five distinct text classification tasks, reducing errors by between 18 and 24 percent on the majority of the datasets.
FitLaM models consists of an RNN with one or more task-specific linear layers, along with a tuning technique that manipulates more data in the higher layers of the network and less in the depths, aiding preservation of information gleaned from general-domain language modelling. Along with this, the authors develop a bunch of different techniques to further facilitate transfer, detailed exhaustively in the paper. Sentiment analysis two separate datasets , question classification, topic classification two datasets.
Fine-tuned language models for text classification Arxiv. Google-owned Kaggle adds free GPUs to online coding service: Experiments with SparseNet show that networks built like this can attain accuracies similar to those obtained by ResNets and DenseNets on a far, far smaller parameter budget.
Sparsely Connected Convolutional Networks. Bootstrapping data quality with neural networks: Ltd, and Heilongjiang University, have developed a system to improve performance of Chinese named entity recognition NER techniques by generating low-quality data and improving its quality via adversarial training.
NER is a specific skill that systems use to spot the key parts of sentences and how they link to a larger knowledge store about the world. Better NER approaches tend to quickly translate into improved consumer-facing or surveillance-oriented AI systems, like personal assistants, or databases for analyzing large amounts of speech. The researchers use crowd annotators to label specific datasets, such as those in dialog and e-commerce, and use a variety of neural network-based systems to analyze the commonalities and differences between the different NER labels applied by each individual to their specific sample of text.
The resulting system is able to perform classification at higher accuracies than other systems trained on the same data, beating or matching other baselines created by the researchers. Chinese researchers gather pedestrian tracking dataset and evaluate nine trackers on it: The 60 thermal sequences contain footage from a variety of devices surveillance cameras, hand-held cameras, vehicle-mounted cameras, drones across a mixture of differently scaled scenes, camera-positions, and video perspectives.
The researchers evaluate nine distinct pedestrian trackers that implement different methods, ranging from support vector machines, to correlation and regression filters, to deep learning approaches systems: SRDCF — a spatially regularized discriminative correlation filter PDF — is the clear winner, attaining the most reliably high scores across a bunch of different tests. Surprisingly strong deep learning performance: SRDCF, by comparison, gets Scaling Kubernetes to 2, Nodes: An account of some of the problems we ran into and workarounds we devised as we scaled up our large AI infrastructure.
The still air of the data center feels close, tomb-like. My suit is coated in dust from squeezing my way through the long-dormant fan. I put my hand on top of one of the boxes and close my eyes, imagining the inside of the crate and trying to will the things I am hunting for into existence. I take a deep breath and open the box. Each chip gets its own housing in a spongy, odorless, moisture-wicking, anti-static material.
I peer in and see the familiar brand names: These will be enough. Kind of like climate change. We just stared at the problem — again, similar; the dreadful consequence of energy distribution and dissipation over time — and built bigger fabs and crafted bigger chips and told people it was fine.
In the background we were all driven to mass parrelalism, and this worked for a while — we built vast data centers around the world, all of us modeling ourselves on the early Google insight that The Datacenter is the Machine.
Then the wars happened. Now whether these are for machines that guard or machines that hunt is another question. Urban flaneurs generate fake cities with GANs: For this project, some open questions the researchers are left with include: How to best disentangle, explore, and control latent space representations of important characteristics of urban spatial maps? How to learn from both observational and simulated data on cities?
Modeling urbanization patterns with generative adversarial networks Arxiv. The ImageNet of video possibly arrives: The researchers also test the new dataset on a set of baselines based on systems that use techniques like residual networks, optical flow, and even sound via usage of a SoundNet network.
Moments in time dataset: Ugly robots no more: Unity gets a MuJoCo plugin: This will let developers import MuJoCo models directly into Unity then visualize them in snazzier environments. Google censors itself to avoid accidental racism: Two years later, despite ample progress in AI and machine learning, nothing has changed.
The first ever audiobook containing a song generated by a neural network? Check out the audio samples. Making The Music of the Mazg. Miri blows past its funding target thanks to crypto riches: Amazon turns to GANS to simulate e-commerce product demand… and it sort of works!
This is useful because it lets you test your system for the vast combinatorial space of possible customer orders and, ideally, get better at predicting how new products will match with existing customers, and vice versa. Exploring the space of all plausible orders could provide important insights into product demands, customer preferences, price estimation, seasonal variations etc. The plots showed a strong correlation between the two with very few outliers, suggesting that their ecGAN approach is able to generate data that falls within the distribution of what e-retailers actually see.
It might sound a bit mundane but this is a significant thing for Amazon: We randomly choose 5 million orders [ emphasis mine] made over the last one year in an e-commerce company to train the proposed models. Why AI research needs to harness huge amounts of compute to progress: This is because deep learning-based AI is predominantly an empirical science, so in the absence of strong theoretical guarantees researchers need to rigorously test algorithms to appropriately debug and develop them.
That fact has driven recent innovations large-scale distributed training of AI algorithms, initially for traditional classification tasks, like the two following computer vision examples covered in 69 of Import AI.
Now, as AI research becomes increasingly focused on developing AI agents that can take actions in the world, the same phenomenon is happening in reinforcement learning, as companies ranging from DeepMind Ape-X, Gorilla, others to OpenAI Evolutionary Strategies, others try to reduce the wall clock time it takes to run reinforcement learning experiments.
New research from deepsense. They achieve this by scaling up their algorithm via techniques gleaned from the distributed systems world eg, parameter surveys, clever things with temporal alignment across different agents, etc , which lets them run their algo across 64 workers comprising distinct CPU cores.
Distributed Deep Reinforcement Learning: Googlers debunk bogus research into getting neural networks to detect sexual orientation: The study — Deep neural networks are more accurate than humans at detecting sexual orientation from facial images — was criticized for making outlandish claims and widely covered i nthe press.
Now, the paper has been accepted for publication in a peer-reviewed academic journal — the Journal of Personality and Social Psychology.
This seems to have motivated Google researchers Margaret Mitchell and Blaise Aguera y Arcas, and Princeton professor Alex Todorov, to take a critical look at the research. The original study relied on a dataset composed of 35, images taken from public profiles on a US dating website as its ground-truth data.
In light of this criticism, perhaps a better title for the paper would be Deep neural networks are more accurate than humans at predicting the correspondence between various garments and makeup and a held-out arbitrary label. Do algorithms reveal sexual orientation or just expose our stereotypes? This is something else. And all the while you can see utilization in the data center increasing, even as new hardware is integrated. And then, after you grope your way across a bridge which becomes a ceiling which becomes a door that folds out on itself to become the center of a torus, you find it: And beyond that you can make out another torus in the distance containing another pane connecting to another large-scale non-euclidean representation graph.
You sigh, take off your glasses, and make a phone call. So much time and computation wasted, all because the AI had just looped into an anti-pattern where it had started trying to simulate itself, leading to the outward indicators of it growing in capability — somewhat richer representations, faster meta-learning, a compute and data footprint growing according to some clear scale law.
Concepts that inspired this story: Procedural maze generators, Kolmogorov complexity , non-euclidean virtual reality YouTube video. Facebook releases free speech recognition toolkit, wav2letter: The technology has been previously described — but not released as code — in two Facebook AI Research papers: The release includes pre-trained models. Too much too soon has been a far worse a problem than too little too late. For example, in the campaign for Guadalcanal, U.
Marines deposited tons and tons of food and equipment on the beaches upon landing, only to discover that they lacked the labor and machines to move the cargo off the beaches.
Close your eyes and imagine picking up a ceramic mug or touching the nearest object to you. This could let people experiment with algorithms that learn to classify objects by touch alone rather than visual appearance. Budding AI-neuroscience types might like the fact it comes with a Morris water maze — a type of environment frequently used to test rodents for cognitive abilities. DeepMind and others have also validated certain agents on Morris Water maze tasks as well.
AI music video of the week: AI researchers became fashion models, via a glossy Yves Saint Laurent campaign. The video comes with some fairly unpleasant sexism and objectification, which sadly may be a reasonable prediction.
Why technologists need to run, not walk, into government to work on AI policy: Our society desperately needs technologists working on the policy. Schneier suggests government create a new agency to study this vast topic: Somewhat gloomy policy comment: The case for a federal robotics commission, Ryan Calo Brookings. I find this sort of meta-analysis particularly helpful in letting me frame my own thinking about AI, so thanks to Miles and his collaborators for that. Policy notes from NIPS