What is the difference between data and big data? Where does the big part come in? Many authors who have authored books about this (Harkness, Mayer-Schoenberger and Cukier, 2013) describe it as the ability to deal with the whole population rather than a sample drawn from that population. To term this another way, to deal with a sample size N=all rather than a sample size = n.
Big data is all about analysing the population. the population need not be large. For example, in their book Freakonomics, Levitt and Dubner analyze the population of sumo wrestling matches, a not super large population for evidence of cheating. But it is the entire population, not a sample.
What are the characteristics for big data? Above all, it is messy data (it varies in quality, is collected at different times around the world, is kept in a wide variety of places). So at best, we can only hope for general directions vs. making precise causal inferences. in other words, we measure correlations not causations. But we may be able to infer things that we cannot do with standard causal inference analysis.
Some mobile phones support use of two SIM cards, described as dual SIM operation. When a second SIM card is installed, the phone either allows users to switch between two separate mobile network services manually, has hardware support for keeping both connections in a “standby” state for automatic switching, or has individual transceivers for maintaining both network connections at once.
Dual SIM phones are mainstream in many countries where phones are normally sold unlocked. Dual SIMs are popular for separating personal and business calls, in locations where lower prices apply to calls between clients of the same provider, where a single network may lack comprehensive coverage, and for travel across national and regional borders. In countries where dual SIM phones are the norm, people who require only one SIM simply leave the second SIM slot empty. Dual SIM phones will usually have two unique IMEI numbers, one for each SIM slot. Devices that use more than two SIM cards have also been developed and released.
An eSIM (embedded-SIM) is a form of programmable SIM card that is embedded directly into a device. Instead of an integrated circuit located on a removable universal integrated circuit card (UICC), typically made of PVC, an eSIM consists of software installed onto an eUICC chip permanently attached to a device.
Once an eSIM carrier profile has been installed on an eUICC, it operates the same as a physical SIM, complete with a unique ICCID and network authentication key generated by the carrier.
The eSIM standard was first released in 2016; since that point, eSIM has begun to replace physical SIM in domains including cellular telephony.
Generative AI (GenAI) is the part of Artificial Intelligence that can generate all kinds of data, including audio, code, images, text, simulations, 3D objects, videos, and so forth. It takes inspiration from existing data, but also generates new and unexpected outputs, breaking new ground in the world of product design, art, and many more. Much of it, thanks to recent breakthroughs in the field like (Chat)GPT and Midjourney.
A decentralized autonomous organization (DAO), sometimes called a decentralized autonomous corporation (DAC), is an organization constructed by rules encoded as a computer program that is often transparent, controlled by the organization’s members and not influenced by a central government. In general terms, DAOs are member-owned communities without centralized leadership. A DAO’s financial transaction records and program rules are maintained on a blockchain. The precise legal status of this type of business organization is unclear.
A well-known example, intended for venture capital funding, was The DAO, which amassed 3.6 million ether (ETH)—Ethereum’s mining reward—then worth more than US$70 million in May 2016, and was hacked and drained of US$50 million in cryptocurrency weeks later. The hack was reversed in the following weeks, and the money restored, via a hard fork of the Ethereum blockchain. Most Ethereum miners and clients switched to the new fork while the original chain became Ethereum Classic.
GitHub Copilot is an AI pair programmer that offers autocomplete-style suggestions as you code. You can receive suggestions from GitHub Copilot either by starting to write the code you want to use, or by writing a natural language comment describing what you want the code to do. GitHub Copilot analyzes the context in the file you are editing, as well as related files, and offers suggestions from within your text editor. GitHub Copilot is powered by OpenAI Codex, a new AI system created by OpenAI.
GitHub Copilot is available as an extension in Visual Studio Code, Visual Studio, Neovim and the JetBrains suite of IDEs.
Released two years ago, OpenAI’s remarkably capable, if flawed, GPT-3 was perhaps the first to demonstrate that AI can write convincingly — if not perfectly — like a human. The successor to GPT-3, most likely called GPT-4, is expected to be unveiled in the near future, perhaps as soon as 2023. But in the meantime, OpenAI has quietly rolled out a series of AI models based on “GPT-3.5,” a previously-unannounced, improved version of GPT-3.
GPT-3.5 broke cover on Wednesday with ChatGPT, a fine-tuned version of GPT-3.5 that’s essentially a general-purpose chatbot. Debuted in a public demo yesterday afternoon, ChatGPT can engage with a range of topics, including programming, TV scripts and scientific concepts.
Nice read on reverse engineering of GitHub Copilot 🪄. Copilot has dramatically accelerated my coding, it’s hard to imagine going back to “manual coding”. Still learning to use it but it already writes ~80% of my code, ~80% accuracy. I don’t even really code, I prompt. & edit.
systemd is a software suite that provides an array of system components for Linux operating systems. The main aim is to unify service configuration and behavior across Linux distributions. Its primary component is a “system and service manager” — an init system used to bootstrap user space and manage user processes. It also provides replacements for various daemons and utilities, including device management, login management, network connection management, and event logging. The name systemd adheres to the Unix convention of naming daemons by appending the letter d. It also plays on the term “System D”, which refers to a person’s ability to adapt quickly and improvise to solve problems.
In a shocking new report, Vice has revealed a next-gen version of the lethal O.MG cable which targeted Mac users has gone on sale, and iPhones are now in its sights. And it can hack you from up to a mile away.
Inspired by progress in large-scale language modelling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens. In this report we describe the model and the data, and document the current capabilities of Gato.
Whole brain emulation (WBE), is still decades, perhaps more than a century away. Outside of the pure science challenge, it could make us confront some of the most daunting questions about what it means to be human, and where man ends and machine begins.
The term “whole brain emulation” might sound new, but chances are you’ve seen it across popular fiction. In a 2008 whitepaper, futurists Nick Bostrom and Anders Sandberg of the University of Oxford’s Future of Humanity Institute published the first roadmap for WBE.
They identified three core components: 1) scanning a brain 2) interpreting the brain data and building a software model and 3) simulating this model “so faithful[ly] to the original that, when run on appropriate hardware, it will behave in essentially the same way as the original brain.” It’s closely tied to the concepts of “mind uploading” and “downloading”–but even that phrasing needs some unpacking.
The “brain” is that biological mash of neurons and synapses that makes you think, feel, and experience. The “mind” is more ambiguous: Some view it as separate from the brain, others as intrinsically woven together.
Distributed ledger technology (DLT)—and, specifically, blockchains—are used in a variety of contexts, such as digital currency, decentralized finance, and even electronic voting. While there are many different types of DLT, each built with fundamentally different design decisions, the overarching value proposition of DLT and blockchains is that they can operate securely without any centralized control. The cryptographic primitives that enable blockchains are, by this point, quite robust, and it is often taken for granted that these primitives enable blockchains to be immutable (not susceptible to change). This report gives examples of how that immutability can be broken not by exploiting cryptographic vulnerabilities but instead by subverting the properties of a blockchain’s implementations, networking, and consensus protocol. We show that a subset of participants can garner excessive, centralized control over the entire system.
Explainable artificial intelligence (XAI) is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact and potential biases. It helps characterize model accuracy, fairness, transparency and outcomes in AI-powered decision making. Explainable AI is crucial for an organization in building trust and confidence when putting AI models into production. AI explainability also helps an organization adopt a responsible approach to AI development. As AI becomes more advanced, humans are challenged to comprehend and retrace how the algorithm came to a result. The whole calculation process is turned into what is commonly referred to as a “black box” that is impossible to interpret. These black box models are created directly from the data. And, not even the engineers or data scientists who create the algorithm can understand or explain what exactly is happening inside them or how the AI algorithm arrived at a specific result.
There are many advantages to understanding how an AI-enabled system has led to a specific output. Explainability can help developers ensure that the system is working as expected, it might be necessary to meet regulatory standards, or it might be important in allowing those affected by a decision to challenge or change that outcome.
China has more or less matched the United States in terms of the two nations’ shares of world output in seven high-tech sectors: pharmaceuticals; medicinal, chemical and botanical products; electrical equipment; machinery and equipment (from engines to office gear); motor vehicle equipment; other transport equipment (mostly aerospace); computer, electronic and optical products; and information technology and information services.
Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect, which occurs when onlookers discount the behavior of an AI program by arguing that it is not real intelligence. As the British science fiction writer Arthur Clarke once said, “Any sufficiently advanced technology is indistinguishable from magic.” Yet when one understands the technology, the magic disappears.
Blockchain, at its most basic, allows humans to reach consensus on a shared digital history without a middleman. AI allows humans to find answers in vast amounts of data more efficiently than ever before. Together they can provide robust business and government processes that are both trustworthy and transparent, rather than trying to manage a hodgepodge of loosely connected entities and processes that were created through historical accident. The implications for society and business are huge.
Today, we see blockchain and AI poised to provide a solution for a set of very real 21st century challenges: the deluge of Big Data, the growth of the IoT, increasing automation and robotics deployment, use of the cloud, various threats to cybersecurity, mobile computing, and the growth of cryptoassets. We have now arrived at a seminal moment where blockchain and AI are the right tools to help humans address these challenges and in fact, take advantage of this next supercycle by providing both transparency and powerful analytics.
When MIT professor Regina Barzilay received her breast cancer diagnosis, she turned it into a science project. Learning that the disease could have been detected earlier if doctors had recognized the signs on previous mammograms, Barzilay, an expert in artificial intelligence, used a collection of 90,000 breast x-rays to create software for predicting a patient’s cancer risk.
Barzilay calculates the software could have flagged her own cancer two years before it was diagnosed by conventional means. “The AI was able to detect smaller details than the human eye could pick up,” she says.
“Do you want to see yourself acting in a movie or on TV?” said the description for one app on online stores, offering users the chance to create AI-generated synthetic media, also known as deepfakes.
“Do you want to see your best friend, colleague, or boss dancing?” it added. “Have you ever wondered how would you look if your face swapped with your friend’s or a celebrity’s?”
The same app was advertised differently on dozens of adult sites: “Make deepfake porn in a sec,” the ads said. “Deepfake anyone.”
How increasingly sophisticated technology is applied is one of the complexities facing synthetic media software, where machine learning is used to digitally model faces from images and then swap them into films as seamlessly as possible.
It’s now advanced enough that general viewers would struggle to distinguish many fake videos from reality, the experts said, and has proliferated to the extent that it’s available to almost anyone who has a smartphone, with no specialism needed.
たとえば、ゼロカーボンという『石油からの脱却』や、『移動』というものの概念を進化させていく MaaS (Mobility as a Service) や CASE (Connected, Autonomous, Shared, Electric) の社会実装が近づいてきているのも、人の活動が常に生み出し続けている『データ』という、いわば『人間の影』が、しっかりと社会を動かしていくエンジンになってきているからと言える。
Security: Today, the most common IoT sensor is the lowly RFID tag, found in everything from store merchandise to warehouse equipment to passports to that “security” (ahem) badge that gets you into your office at night. And what kind of security does that tag sport? Nada. Nothing. Zilch. And you don’t even have to touch the thing to hack it. Simply being in the general vicinity is good enough. Not like your passport is ever in the general vicinity of lowlife like you find in passport lines at airports.
Be afraid. Be very afraid. Privacy: Even if you can somehow secure that baby monitor and keep the perv down the street from spying on your little bubby boo, there’s still the problem that a lot of these IoT devices are supposed to spy on you. Why do you think there are so many buckets of cash pouring into the IoT hope-to-be-a-market? The Big Corporations don’t expect to make a big profit on the devices themselves, oh no. News flash: the Big Money in IoT is in Big Data. As in, Big Data about everything those sensors are learning about you and your nasty habits that you hide from your neighbors. Digital Fatigue: Now along comes the IoT, promising to connect the Internet to our eyeglasses and our wristwatches and our thermostats and our appliances and our streetlights and on and on. Can’t we just download a big-ass OFF switch so we can hear ourselves freakin’ THINK for once? Please?
The Internet of Things (IoT) is connecting people, places, and devices at a rapid pace. With the surge of connected devices comes the demand and necessity to implement security features for IoT devices. Qualcomm Technologies has a long heritage of providing mobile security solutions. Today, our security solutions are found in billions of commercial devices around the world, utilizing our proven mobile solutions for consumer and industrial IoT applications.
Digital communication is changing the very nature of how we engage with political ideas and how we understand ourselves as political actors.
Just as Netflix and YouTube replaced traditional mass-audience television with an increasingly personalised choice, so total connection and information overload offers up an infinite array of possible political options. The result is a fragmentation of singular, stable identities – like membership of a political party – and its replacement by ever-smaller units of like-minded people.
Online, anyone can find any type of community they wish (or invent their own), and with it, thousands of like-minded people with whom they can mobilise. Anyone who is upset can now automatically, sometimes algorithmically, find other people that are similarly upset. Sociologists call this ‘homophily’, political theorists call it ‘identity politics’ and common wisdom says ‘birds of a feather flock together’. I’m calling it re-tribalisation. There is a very natural and well-documented tendency for humans to flock together – but the key thing is that the more possible connections, the greater the opportunities to cluster with ever more refined and precise groups.
KSI is a blockchain technology designed in Estonia and used globally to ensure networks, systems, and data are free of compromise, all while retaining 100% data privacy.
A blockchain is a distributed public ledger – a database with a set of pre-defined rules for how the ledger is appended by the distributed consensus of the participants in the system. Due to its widely witnessed property, blockchain technology makes it also impossible to change the data already on the blockchain.
With KSI Blockchain deployed in Estonian government networks, history cannot be rewritten by anybody and the authenticity of the electronic data can be mathematically proven. It means that nobody – neither hackers, nor system administrators, nor even the government itself – can manipulate the data and get away with it.
Quorum is an open source blockchain protocol specially designed for use in a private blockchain network, where there is only a single member owning all the nodes, or, a consortium blockchain network, where multiple members each own a portion of the network. Quorum is derived from Ethereum by modifying the Geth client.
Some of the key features of Quorum include:
Privacy via private transactions: members of a Quorum network can send private transactions that are addressed to a subset of nodes, such that the contents of the transaction are not exposed to non-privy members.
Peer permissioning: a Quorum network can be configured to run in permissioned mode such that all nodes must be explicitly listed in an access control list enforced by all nodes. This prevents foreign nodes from tapping into the network and replicating blocks as is the case in permissionless networks.
Flexible consensus: described in greater detail later in this post, Quorum supports Raft and Istanbul BFT as valid consensus options. Both support transaction finality (i.e. lack of chain forking) and offer shorter block intervals than proof-of-work.
We live in a time of major technological changes.
The fear of artificial intelligence is perfectly legitimate. The acceptance of said technology by different demographic groups is also very thought-provoking. AI fears are most pronounced among the well-educated and women. Technology may once again prove to be a factor that deepens social divisions.
For now, the presentation of the new robot created by Tesla and Elon Musk was only a publicity stunt. It was a man in a costume. Almost no one fears revenge-seeking, Terminator-style AI robots invading their homes.
For a substantial part of society, the AI revolution could mean changing jobs or their lifestyles. The expected disruptions to the job market, education, or transportation should be our major concerns. Before things get out of hand.
Even the most conscientious companies cannot be expected to figure out the common good by themselves, without engaging in robust public discussion about the purposes of technology, about the ethical dilemmas that technology raises and about how we should address those ethical challenges.
The tech industry cannot answer those questions by itself. Only a sustained lively public debate about the ethical implications of technology can address those questions.
The most responsible technology companies will be those that welcome and encourage a broader public debate about how technology, rather than being disempowering, can be a force for the common good.
United Nations secretary-general António Guterres says the intergovernmental giant needs to embrace blockchain.
Coming at a time when the president of China has touted blockchain as a national priority, and the $6 billion United Nations Children’s Fund has started accepting bitcoin and ethereum donations for some of its projects, the statement from Guterres shows that cryptocurrency and the underlying blockchain technology is being seriously explored at the highest levels of the largest organizations in the world.
While China seems largely focused on using blockchain as a way to prevent money laundering and better track its citizens’ transactions, the United Nations work has been more focused on giving donors increased assurance their donations are being spent how they wish, while reducing waste in the organization’s giant supply chain.
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(China is positioning itself as a global leader in precision medicine, the use of a person’s genetic information to diagnose and treat diseases.)
When it comes to understanding precision medicine China has an historical advantage because Chinese herbal medicine was always tailored to the individual. Now China is trying to apply this principle to many cutting edge technologies.
Banking the unbanked of the world is one of the Holy Grails for blockchain technology and could help millions of people around the globe reach financial inclusion.
However, it may come as a surprise to many that this is not just a third-world problem or that it doesn’t only affect individuals. A whole industry in the United States is currently operating completely unbanked and cryptocurrencies could save the day.
Some 5 real-world usage of the blockchain that are already being tested:
A completely decentralized internet, where ISPs aren’t need anymore. This is what Skycoin does with Skywire. They will soon provide their custom built 1Gbps antennas for $100, which have a range of 10 miles and provide high speed internet to 7,000 people and with their mesh network on top probably 20,000 people. You only need 2,000 antennas per European country to cover the whole continent and the data is stored on Skyminers.
Decentralized Storage. This is directly competing with Google Drive and Dropbox.
An near infinitely scalable and near infinitely decentralized payment processor. This will replace all banks, ALL banks. IOTA, Skycoin and Nano are that, it only needs to be seen which one can get to adoption the quickest.
Security Identification, so that you don’t show your passport when traveling anymore, because security identification is now all handled by the block chain (Civic, THEKEY).
Voting, so that it doesn’t happy through intransparent and insecure voting machines, but through the blockchain (DistrictXo)
Superconducting uses an electrical current, flowing through special semiconductor chips cooled to near absolute zero, to produce computational “qubits.” Google, IBM, and Intel are pursuing this approach, which has so far been the front-runner.
Ion trap relies on charged atoms that are manipulated by lasers in a vacuum, which helps to reduce noisy interference that can contribute to errors. Industrial giant Honeywell is betting on this technique. So is IonQ, a startup with backing from Alphabet.
Neutral Atom Similar to the ion-trap method, except it uses, you guessed it, neutral atoms. Physicist Mikhail Lukin’s lab at Harvard is a pioneer.
Annealing designed to find the lowest-energy (and therefore speediest) solutions to math problems. Canadian firm D-Wave has sold multimillion-dollar machines based on the idea to Google and NASA. They’re fast, but skeptics question whether they qualify as “quantum.”
Silicon spin uses single electrons trapped in transistors. Intel is hedging its bets between the more mature superconducting qubits and this younger, equally semiconductor-friendly method.
Topological uses exotic, highly stable quasi-particles called “anyons.” Microsoft deems this unproven moonshot as the best candidate in the long run, though the company has yet to produce a single one.
Photonics uses light particles sent through special silicon chips. The particles interact with one another very little (good), but can scatter and disappear (bad). Three-year-old stealth startup Psi Quantum is tinkering away on this idea.
Among the most anticipated uses of quantum computers is the ability to create new chemicals, like catalysts for producing nitrogen-based fertilizers or for use in cells in higher-powered batteries. Quantum computing could also be used to crack most commonly used forms of digital encryption. It may one day also be used to streamline logistics and delivery operations, as well as speeding up machine learning applications.
IBM scientists have rejected Google researchers’ claim to have achieved “quantum supremacy,” a demonstration that a quantum computer can vastly outperform a traditional one on a particular task. They contend in a blog post published Monday evening that “the goal has not been met.”
The concept of “quantum supremacy” showcases the resources unique to quantum computers, such as direct access to entanglement and superposition. However, classical computers have resources of their own such as a hierarchy of memories and high-precision computations in hardware, various software assets, and a vast knowledge base of algorithms, and it is important to leverage all such capabilities when comparing quantum to classical.
Motivation-Driven (熱意を元に)： PFNの組織文化を表す上で欠かすことができないのは、メンバーのモチベーション主導である、ということです。モチベーションがある（つまり”熱中している”）ということは、真剣に成果と向き合っている、ということを意味します。プロジェクトの成果に意義を見出し、強いモチベーションがあれば、私たちはそれぞれのチームのメンバーの成果にも貢献しようとします。これはチームワークで成果を出す、ということとも同義です。このカルチャーがあるからこそ、私たちは非常にフラットで、フレキシブル、かつ高いパフォーマンスを誇る組織であり続けられるのです。 Learn or Die (死ぬ気で学べ)： PFNのメンバーは全員、学ぶことに非常に貪欲です。PFNが挑戦する分野は変化の大きな分野であり、その中で私たちが最先端であり続ける為には、学ぶことが唯一の方法です。私たちは一つのアイデアや、一つの技術、一つのドメインに固執しません。たとえば、PFNのソフトウェアエンジニアは、ハードウェアで新しいチャレンジをすることを望み、ハードウェアのリサーチャーはHCIデザインに自分の分野を切り替えることにわくわくします。その結果、PFNは多様なバックグラウンドを持つメンバーによって構成される、真の学習組織になるのです。 Proud, but Humble (誇りを持って、しかし謙虚に)： PFNはテクノロジーをコアとする会社です。私たちは、自分たちの成果に挑戦し続けます。そうすることで、最高の人材を魅了し、仲間を増やし続けます。同時に私たちは、何が自分たちでは実現できないか、ということについても正しい理解を持っています。世の中に知らない事象や技術があることを認識し、それだからこそ、多様な専門分野を持つメンバーのアイデアを最大限にリスペクトできるのです。 Boldly Do What No One Has Done Before (誰もしたことがないことを大胆に為せ)： より良い未来を描いて、技術で世界を変える ー 新しいソフトウェアやハードウェア、新しいサービスやビジネスの変革、これまでにない市場の創出に私たちは挑戦します。「PFNにしかできないこと」をやることが、社会における私たちの使命だと考えます。
Despite the dominance of companies in AI, universities and public research organizations play a leading role in inventions in selected AI fields such as distributed AI, some machine learning techniques and neuroscience/neurorobotics.
Chinese organizations make up 17 of the top 20 academic players in AI patenting as well as 10 of the top 20 in AI-related scientific publications. Chinese organizations are particularly strong in the emerging technique of deep learning. The leading public research organization applicant is the Chinese Academy of Sciences (CAS), with over 2,500 patent families and over 20,000 scientific papers published on AI. Moreover, CAS has the largest deep learning portfolio (235 patent families). Chinese organizations are consolidating their lead, with patent filings having grown on average by more than 20 percent per year from 2013 to 2016, matching or beating the growth rates of organizations from most other countries.
Этот комплекс – это шаг к абсолютно новому ведению боевых действий. Мы уверенно держимся в первых рядах среди стран-производителей такого рода вооружений. Высокоточный беспилотный снаряд летит до 30 минут со скоростью 130 км в час. Заряд на данном комплексе доставляется к цели вне зависимости от ее скрытности и рельефа местности – как малых, так и на больших высотах. Это очень точное и максимально эффективное оружие, с которым очень трудно бороться с помощью традиционных средств ПВО.
The planned Robot Science Museum in Seoul will have a humdinger of a first exhibition: its own robotic construction. It’s very much a publicity stunt, though a fun one — but who knows? Perhaps robots putting buildings together won’t be so uncommon in the next few years, in which case Korea will just be an early adopter.