The United Kingdom is hosting the AI Safety Summit, bringing politicians, computer scientists and tech executives to a site chosen for its symbolism: Bletchley Park, synonymous with codebreaking and the birth of computing.

The UK's AI summit is taking place at Bletchley Park, the wartime home of codebreaking and computing

Elon Musk and Sam Altman are among those who will go to the AI Safety Summit at Bletchley Park, where Alan Turing cracked Nazi Germany's Enigma code.

Elon Musk and Sam Altman are expected to attend the UK's AI Safety Summit. Here's why it's taking place at a former top-secret WWII facility.

Alan Turing was a brilliant mathematician and computer scientist who made history by cracking the German Enigma code during World War II, saving millions of lives and hastening the end of the war. He also laid the foundations for artificial intelligence,

How Alan Turing's codebreaking genius helped end World War II and shaped the future of computing

The summit is being convened at the famous Bletchley Park, where British mathematician-scientist Alan Turing and his team of mathematicians did early research to crack the messages sent by "Enigma", a device used by the German military to encode strategic messages during the second world war.

London AI summit throws spotlight on scientist Alan Turing's legacy

The local secondary School would be Twickenham Academy & Turing House. Other benefits are local bus routes including the H22. 111,110 and 481. Whitton also benefits from several parks and open spaces including Crane Park. We endeavour to make our ...

Waverley Avenue, Whitton, Twickenham, TW2

The adoption of quantum computers will fundamentally change cybersecurity. Because quantum computers are so much faster than the conventional computers of today, we don’t yet know how cyber security will look in a post quantum world.

The Quantum Bombe

Dozens of luminaries, including recipients of the Turing Award and Fields Medal, say 'While we individually have different views about the Israeli-Palestinian conflict, we all emphatically condemn the murder,

Renowned Scientists, Mathematicians Demand Release of Children Held by Hamas in Gaza

Mathematical proofs based on a technique called diagonalization can be relentlessly contrarian, but they help reveal the limits of algorithms.

Alan Turing and the Power of Negative Thinking

Dermot Turing is showing me a flower. “Take a look at this daisy,” he says, spinning the flower in his hands, the sole decoration on the table of the comfortably shabby Indian we’re meeting in. “If you look at the centre,

Dermot Turing: ‘My uncle Alan wasn’t a particularly good codebreaker’

Welcome, curious minds and lovers of technology, to a thrilling journey back in time! Today, we embark on a historical dive into the birth of Artificial Intelligence – an extraordinary milestone that forever transformed our world.

A Historical Dive into the Birth of Artificial Intelligence

Turing.com

turing

NB: This article was partly written by Qwhex who also made all the awesome images. Make sure to check him out! The time when we only put the label on efficient and productive programming languages has long gone and "esolang" as a genre has grown to be a common phenomenon. Esolangs are programming languages usually with the sole purpose of being useful. They are sophisticated in-jokes for programmers usually satirizing actual languages. Turing-complete not But what does mean anyway? It means that it can calculate anything computable. That's not a satisfactory answer so let's put it this way: something is Turing-complete if it can calculate anything that a can (it turns out there is no way to go beyond that currently). Turing-complete Universal Turing Machine So Turing-completeness is something that programming languages (real and esoteric) are aiming for but that doesn't mean they are some privileged creatures and the only candidates for the title. If you look carefully enough you'll find it everywhere you wouldn't have expected and in this article we want to list 6 of them that we found the most interesting. 1. Manufactoria is a game about putting robots in their place. More accurately it's a visual programming environment where you accept or reject robots based on the content of their tapes. You also need to transform their input to a valid output on some levels. Manufactoria The basic building blocks are branches to send robots to alternative directions based on the read bit, conveyors which send a robot to a pre-defined cell, and writers that add a new color-bit to the end of the tape. Starting with simple mazes the player learns how to create more and more intricate circuits. Beware! The learning curve is quite steep. It's not surprising that this game is Turing-complete. It is proven by which is itself Turing-complete. Nevertheless, it's a great place to start learning about Turing Machines and a good way to exercise the mind. this Rule 110 interpreter 2. Jonathan Blow's Platformer Classic: Braid First of all: if you haven't played the game, you seriously should. This is one of my all-time favorites and I was blown away when I learned that the game is not just beautifully designed with a fascinating storyline but also Turing-complete. To be fair, the game isn't Turing-complete as it is but rather the . Linus Hamilton in his 2014 whitepaper, walks us through the specific game elements we need to prove Turing-completeness. These elements are: game mechanics Braid is undecidable Monstars Bunnies Rewinding time Lever + Platform One-way surfaces Cannons He shows how to create a out of these elements which are: counter machine one of the simplest types of machines known to exhibit Turing-complete behavior. The machine is fairly complex but he provides images and explanation for every part so it's not so difficult to follow along. Later he also proves that Braid is undecidable but that's not the scope of this article. If you want to know all the details I highly recommend reading the whole paper. 3. Minesweeper Originally I was going to include Game of Life here. It's true that the final goal of Conway's game wasn't creating a Turing-complete system but something organic, chaotic and therefore life-like (hence the name). But it's still a product of computation research so not unintentional enough for this article. Then I came across a by Richard Kaye about Minesweeper. He compares its grid-like nature and the values contained by each cell to Game of Life and proposes that Minesweeper is Turing-complete as well. paper The paper goes into very fine details but the essence of the concept is handling an initial state (when some tiles are visible and some not) as an instruction set for a Turing Machine and the "extension" of that grid (when every tile is visible) as the result of the computation. The actual proof is a bit hard to grasp but if you are not afraid of maths then give the paper a try! 4. Music Notation Who thought that even music can represent arbitrary computations? Well, if you made it this far you probably don't get too excited anymore (I'm sorry, I guess...). But how crazy is that? The standard notation musicians use today is more than 300 years old which predates Turing by a fair bit. Utilizing it is not as straightforward though and requires some clever solutions. In 2002 Stephen Sykes released his esoteric programming language/interpreter Choon which needs standard music notation as an input and outputs real music in the form of a file. I think the most interesting thing about it is that it doesn't use alterable storage as opposed to common programming languages. Unfortunately, his original article is not available anymore and you need the Wayback Machine to access but it sure worth a read. .wav it 5. Power Point (PowerPoint™ Turing Machine™)™ is the realization of a joke that got too far. First at SIGBOVIK, the flagship conference of the Association for Computational Heresy, Tom Wildenhain manages to turn a PowerPoint slide into a classical Turing Machine with the usage of AutoShapes, Hyperlinks and On-Click Animations. presented The unique animation support made it possible to create this machine, but the user needs to click multiple times to advance the computation, but this limitation makes it even more amusing. Cookie Clicker skills translate to a faster CPU! Programs are created by manually editing the punch cards with the builtin PP tools. If you're interested in more technical details, check out the (typeset in PowerPoint) or download the itself. whitepaper machine 6. Rail Transportation Train transportation and the infrastructure around it comes at a humongous price so it's quite understandable why we've yet to witness a Turing-complete train system implementation in real life. But that doesn't mean such a thing couldn't be done and to prove it we just need a simulation platform that offers realistic building blocks and behavior that works the same as real trains. The best tool for this is and open source tycoon game where you can build and manage transportation systems but we obviously don't need the whole game just the trains in it. OpenTTD The first building block is a implemented in the game by Alienturnedhuman (the name seems accurate) who demonstrated his creation in a youtube video in early 2015. Not a full year after he released a 45 minute long in which he expands his full adder with a clock, memory and whole arsenal of "connection circuits" and basically creates a working computer. Well, not so much a working computer because the video shows a work-in-progress state and only outlines the path from there to create a fully working Universal Turing Machine. binary full adder update video Other interesting aspects of Alienturnedhuman's project is the motivation. He explains in the video how he had an argument with one of his friend who praised Minecraft for the fact that you can build functioning computers in it. He replied that you surely can build computers with older games and proved it with this mind-blowing project. Definitely watch the whole video for all the details! I hope you found these as interesting as I did. If yes, please don’t forget that the new clapping system on medium allows up to 50 claps. Evaluate the post accordingly :)

Beyond Esolangs: 6 Things That Are Unintentionally Turing-Complete

Human migration uses something called the gate test: if a state opens its gates, do people flee or enter? California has experienced increased outbound migration over the past few years; with the pandemic intensifying this trend, the Golden State for the very first time in 2020, costing it a Congressional seat. experienced a population decline For every new Bay Area resident, two are leaving The “techxodus” is taking place in the heart of California, its tech hubs. The Bay Area had been experiencing a net outflow of residents—more residents have left than moved in—long before the global pandemic. But as the pandemic accelerated the shift to remote and more companies allowed employees to work from anywhere, . It has averaged 49.8 percent since September 2020, which means that for every new Bay Area resident, two have left. Workers cite a better home environment and lower cost of living as the main reasons for leaving. the net outflow almost quadrupled Tech workers would leave the Bay Area if they could work remotely What’s more, a survey revealed that two out of every three tech workers if they had the option to work remotely. While 36 percent said they’d leave for other US regions, 16 percent said they’d consider leaving the US entirely. would leave the Bay Area To get—and keep—top talent, Silicon Valley will need to go remote Remote work experts expect many tech workers will demand the option to work remotely. At Turing’s recent , senior Cushman & Wakefield executive Gabe Burke said he expects that, if talent demands remote work, employers will likely provide the option. Boundaryless: #ScalingPostPandemic Conference

The “Techxodus” Is Real: Is Tech Done With The Bay Area?

This blog post explains all you need to know about React Native developers, their roles and responsibilities, and the skills required to become a proficient React Native Developer. What is React Native? React Native is a popular JavaScript-based mobile app framework that lets you create natively-rendered mobile applications for various operating systems like iOS and Android. The framework is based on ReactJS, which is a JavaScript library for building interactive user interfaces. Using JavaScript, developers can construct complex mobile applications with this popular framework. Some of the world's most popular mobile applications, such as Facebook, Instagram, Skype, among others, use React Native framework for faster and robust performance. Not only does React Native allow for faster mobile development, but it also allows for effective code exchange between iOS and Android. The framework currently supports the two popular operating systems, which are iOS and Android, and has the potential to expand to other platforms as well. Who is a React Native Developer? React Native developers have experienced app developers who can build native mobile applications on both the Android and iOS platforms. The React Native developers usually concentrate on the product's frontend part, but they are also knowledgeable about the API services as well as the infrastructure that the app must interface with. React Native developers can take your company to the next level by creating native applications without compromising the users' experiences. What Are The Job Responsibilities of a React Native Developer? Some of the crucial job responsibilities of React Native developers are as follows: Design, build and maintain React Native applications on multiple platforms Integrate mobile applications with native APIs Analyze and enhance the performance of the applications Write automated tests for quality assurance Diagnose and fix bugs in the application Build pixel-perfect user interfaces across multiple platforms Recommended React Native Developer Skills In order to become an efficient React Native developer, you must gain excellent proficiency in JavaScript language and its nuances. You must have experience with native build tools like Android Studio, Xcode, Gradle, etc. Besides, you should also have a keen understanding of various areas like CSS, HTML, web technologies, UI/UX standards, code versioning tools, automated testing suites, like Jest or Mocha, among others. Soft skills that can be considered to be a good React Native developer include good communication, problem-solving skills, adaptability, communication skills, and others. React Native Developer Tools If you want to become a React Native developer, here are some of the important tools that you need to know. These tools are being used thoroughly by React Native developers in their day-to-day app development projects: Android Studio: Android Studio provides a unified environment with the fastest tools for building apps on every type of Android device. Nuclide: Nuclide is built as a single package on top of Atom that provides a first-class development environment for React Native and more. Xcode: Xcode consists of a suite of tools that developers use to build apps for Apple platforms. Expo: Expo is an open-source platform to develop iOS, Android, and web applications. It comes with its own CLI and uses JavaScript and React to achieve the backend. Visual Studio Code: Visual Studio Code is a code editor redefined and optimized for building and debugging modern web and cloud applications. Redux: Redux is a pattern and library for managing and updating the application state. Redux assists developers to write predictable and testable code, giving the confidence that the application will perform as expected. React Native CLI: React Native CLI provides you with the react-native init command that helps you take control over the management of the project locally. React Native Certification Exam You can justify your React Native skills by taking some of the popular React Native developer certifications that are available online. These certifications help you learn the essentials of the framework as well as help in advancing your skills for the same. Besides the online available certifications, the official community of React developers also provides several where you can learn about the most important React topics. Below here are some of the React Native Certification Exams: free and paid courses React Native Certification Exam (Advanced) CS50's Mobile App Development with React Native by Harvard University React Native Developer Salaries Now that you have set your mind to become a React Native developer, you must also know the huge payout once you get certified. According to , the average salary of a React Native developer in the US is $120,417 per year or $61.75 per hour. Also, the entry-level positions of a React Native developer start at $102,500 per year, while most experienced workers make up to $155,000 per year. reports React Native Live Jobs To Apply Now Here we have listed down some of the latest React Native Jobs that you can apply for: Typescript/React Native Engineer A pioneering platform that allows its users to send and receive money to anybody around the globe is looking for a Typescript and React Native Engineer. The selected candidate will be responsible for building production consumer software using React Native and Typescript. Apply . here React Native Developer A fast-growing company providing the latest fintech services to make it easier and more secure for consumers to transfer value is looking for a React Native Developer. The selected candidate will be helping the existing team to build and maintain a scalable and innovative mobile app.  Apply . here React Native Engineer A fast-growing company creating a revolutionary, co-created digital banking experience for users is looking for a React Native Engineer. The selected candidate will be contributing towards building a community-first digital bank. Apply . here React Native Mobile Developer A US-based tech-driven company redefining hospitality and offering delightful accommodation spaces is looking for a React Native Mobile developer. The developer will be responsible for developing cross-platform mobile applications and implementing features. Apply . here React Native Developer A US-based company that is revolutionizing the industry with turnkey social selling software and technologies is looking for a React Native Developer. The selected candidate will be responsible for analyzing and enhancing the performance of the applications. Apply . here Written by Ambika Choudhury, Illustrator Header Image: Gaurang Sawant.

  How to be a React Native Developer

Increased job satisfaction among federal workers, better recruitment and employee retention, and ample successful federal telework models have caused the Biden administration to plan for a permanent transition to flexible work . Pre-pandemic, 23 percent of government employees for at least one day per week. During COVID-19, the number rose to . Now, senior government officials have revealed they plan to allow individual federal agencies to define flexible work options according to the department’s aims, employee needs, and manager preferences. worked remotely 74 percent Here’s a breakdown of why: Remote work increased job satisfaction among federal workers The Federal Employee Viewpoint Survey showed that government employees working remotely for at least three days a week (76) than those working in an office (62.5). reported a higher engagement score Eighty-two percent of federal employees eligible for remote work also reported they were either with their organization’s telework program. Only 5 percent said they were dissatisfied, and 12 percent were neutral. satisfied or very satisfied A similar survey conducted by the National Treasury Employees Union found that 92 percent of workers had a successful experience with telework, with 66 percent even claiming they were more productive while working remotely. Employers win, too: federal agencies can recruit and retain the best talent by going remote Recruiting the best talent is a top priority for the federal government. However, government officials have revealed that a lack of flexible work options has previously cost them the best candidates. “Candidates want to live in a location where you don’t pay half your salary in property taxes or lose most of your money trying to buy a house,” said Trent Fraizer, executive director for campaigns & academic engagement for Homeland Security and CISA. Frazier worries that a lack of competitiveness in hiring will burden federal agencies in the long run, given the tough competition they face from the private sector. Top firms like , Twitter, Lenovo, etc., are opting for all-remote or hybrid work options, and others are following suit. Google Economics and personnel experts also point to the benefits of having a larger talent pool while hiring remotely. They believe in hiring the best person for the role nationally, not locally. Agencies build on success with remote work Several federal agencies have implemented –and plan to continue following them post-pandemic. They include: successful remote work models Department of Defense (DOD) The Department of Defense made remote work functional despite ample digital security and privacy concerns—even launching a virtual work environment in 2020 to enable WFH. The agency is now scaling the project, dubbed DOD 365, to include a whole suite of collaboration tools with added security layers. US Department of Agriculture (USDA) The USDA has already declared that it will allow remote work for up to four days a week, along with expanded usage of virtual and remote duty stations and more flexible schedules. NASA Despite existing remote work arrangements, NASA had to scale to mass telework rapidly at the onset of the pandemic. However, the agency quickly managed to overcome the pitfalls of remote work, and at one point, had 90 percent of its workforce working remotely. General Services Administration (GSA) A majority of the GSA’s workforce has been working remotely for the past five years—which made transitioning to all-remote relatively easy for the team. Boundaryless work is popular with both federal employees and agency leadership. Past successes with federal telework programs have further shown that remote work is here to stay.

74% of US Government Employees Work Remotely and Are Permanently Transitioning to Flexible Work

Passwords are a bane of modern life. They never seem to end. If you use the same password for everything (not recommended), you run the risk of hacks and identity fraud. Too many passwords, you are liable to forget one at the wrong moment. As our digital life expands into more platforms, apps, and devices, things are almost at a breaking point. How many passwords do you have? How many forgotten? Enough is enough. Breaking passwords is an ancient art, dating back centuries. Spies and kings of centuries past relied on passwords and code-breaking to carry out their duties. Many historians credit the Allied effort to crack the Enigma device as a in the outcome of WWII. Mathematicians, “script-kiddies,” and criminals alike enjoy the rewards from breaking new passwords to peer into places they shouldn’t go. In the modern world, our entire global economy (and our intimate personal life) relies on a delicate balance between accessibility and security. key factor Recently, breaking passwords made an impact on everyday consumers of the world’s biggest brands. Sony, Apple, Yahoo, Adobe, Equifax — the list goes on and on. Even the popular password manager app LastPass suffered a hack — a virtual treasure trove for identity fraud artists. Hackers uncovered email addresses, passwords, authentication hashes and password hints for millions of users. If you want to feel sick to your stomach, read this . Can you find yourself on the list? list of major data breaches maintained by Wikipedia In a year of hacks with victims in the hundreds of millions, it’s time to talk tough about online security. As the pace of online commerce and communication increases, the world needs a better solution. A few young startups have embraced the blockchain as a potential cure to “password-itis” and a safer internet for everyone. Centralized architecture is a liability when one hack allows access to all of the data stored away safely behind a single wall. It’s not just personal photos and messages at risk. Critical infrastructure such as power plants, traffic lights, and financial markets rely on passwords to keep out nefarious elements looking to do harm. Online identity fraud costs society billions of dollars every year, and we all pick up the tab for the insured losses. In a connected world, criminals from around the globe are lurking at every turn. We all become all victims eventually. Phishing, server breaching, and password cracking all rely on a point of attack. Faking credentials is your ticket to entry. Online security is an elaborate game played by security professionals and hackers, one side trying to stay ahead of the other. Two-factor authentication was a beneficial recent development, but it relies on continuous uninterrupted access to a second device in most cases. A decentralized authentication protocol, similar to the blockchain behind cryptocurrencies, might just change the rules of the game for good. Companies like use the blockchain to protect everything from critical infrastructure, the Internet of Things, medical devices, financial information, and payment platforms. Decentralized ledgers are much harder to fake as opposed to a single resource. It’s much easier to forge a diary than a best-seller with millions of copies available for public reference. REMME In the case of REMME, a specific SSL certificate lies within the blockchain to authenticate each user. The data itself is encrypted and safe from prying eyes, but the fidelity of the record-keeping is impossible to disrupt using decentralized authentication. The days may be numbered for password servers, eight-character minimums, and “please use one of the following: capitals, lowercase, numbers, special characters.” Strong security protocols and user-friendliness are typically polar opposites. Given the freedom, most users choose “12345” or “password” for their online accounts. Developers solved the problem with draconian rules for passwords and behind-the-scenes tactics such as hashing and salting. Now, we live in a dystopian world, relying on human memory for our security and failing to stop major hacks on private information every year. The blockchain is already a major in the banking and financial world. Online security is an obvious next step for a better, safer internet for everyone. disrupting force As more and more of the world’s commercial activity moves onto the web, the demand for trusted communication and reliable payment architecture will grow. Passwords aren’t the only problem, but they are a major impediment to speed, convenience, and security. Even if everyone on Earth could memorize a thousand passwords with a hundred characters each with flawless ease, the problem won’t go away. We all need a better solution, and we may find it in the blockchain.

Will Blockchain Kill the Passwords?

How to Build a Turing Pi 2 Home Cluster

Як пабудаваць хатні кластар Turing Pi 2

Kako napraviti Turing Pi 2 kućni klaster

Cómo construir un clúster doméstico de Turing Pi 2

Kuinka rakentaa Turing Pi 2 -kotiklusteri

Come costruire un cluster domestico Turing Pi 2

Turing Pi 2 ホームクラスターの構築方法

របៀបបង្កើតចង្កោមផ្ទះ Turing Pi 2

Turing Pi 2 гэрийн кластерийг хэрхэн бүтээх вэ

څنګه د ټورینګ پای 2 کور کلستر جوړ کړئ

Maitiro ekugadzira Turing Pi 2 Kumba Cluster

Ndlela Yo Aka Xirhendzevutani Xa le Kaya xa Turing Pi 2

如何构建 Turing Pi 2 家庭集群

Table of Links Abstract and 1. Introduction Why ELIZA?
The Intelligence Engineers
Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs
From IPL to SLIP and Lisp
A Critical Tangent into Gomoku
Interpretation is the Core of Intelligence
The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion
Finally ELIZA: A Platform, Not a Chat Bot!
A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community
Another Wave: A BASIC ELIZA turns the PC Generation on to AI
Conclusion: A certain danger lurks there
Acknowledgements and References 3 The Intelligence Engineers The founding father of the effort to build intelligent machines was, of course, Alan Turing (although Ada Lovelace may have been the founding mother, as will be seen shortly). Turing is publicly most famous for having led the team that built the “bombe”, the machine that made it possible to decipher enemy messages in the second World War.[24] However, in academic circles, Turing is best known for the mathematical construct that now bears his name, the Universal Turing Machine. And in AI circles – and recently in public, as AI has come more into public focus – Turning is associated with the Imitation Game, which we now call the Turing Test.[44] Since we are discussing ELIZA, which was apparently a chatbot, the reader might expect me to go straight to the Turing Test. However, I am concerned here primarily with ELIZA as a computational artifact, not with whether ELIZA was or was not intelligent; We can all agree that it was not, and we shall see later that Weizenbaum himself had specific reasons for not thinking of ELIZA as intelligent. Thus the Turing Test plays little role with respect to ELIZA, at least as it that program was conceived by Weizenbaum, so we shall leave it aside in the present exploration, except for one important detail that arises, almost in passing, in Turing’s Mind paper. Turing notes that over a century before his effort, Ada Lovelace had described the potential of Babbage’s Analytical Engine to “act upon other things besides number[s]”: “The operating mechanism [...] might act upon other things besides number, were objects found whose mutual fundamental relations could be expressed by those of the abstract science of operations, and which should be also susceptible of adaptations to the action of the operating notation and mechanism of the engine. Supposing, for instance, that the fundamental relations of pitched sounds in the science of harmony and of musical composition were susceptible of such expression and adaptations, the engine might compose elaborate and scientific pieces of music of any degree of complexity or extent [...]”[23] We shall soon see that Ada’s insight that machines could act upon other things besides numbers was enormously prescient, foreshadowing the concept of symbolic computing, which was to become, and remains today, one of the foundations of artificial intelligence, and which is central to the pre-history of ELIZA. But first let us return to Turing’s mathematical contribution, Turing’s Universal Machine, the now so-called “Turing Machine”. The Turing Machine was Turing’s contribution to a line of inquiry in the early part of the 20th century which included Turning, Kurt Godel, and Alonso Church, all addressing a problem posed in 1928 by Hilbert and Ackerman called the “Entscheidungsproblem”.[3] Hilbert’s challenge was to find an algorithm to determine whether a mathematical proposition is provable. Godel addressed this problem through what we now call “Godel Numbering”, his method of turning mathematical expressions into numbers, and showing that there are expressions that cannot be proved in a complete system. Church reached the same conclusion by formalizing general recursive functions in a system called the “Lambda Calculus”. And Turing addressed the problem by describing a universal machine that can compute any function. Turing then showed, equivalently to Godel and Church, that there are programs for which it is impossible to prove that they will come to a halt on his machine, which we now call “the halting problem”.[37] The connection between Turing’s interests in universal computers, computable functions, and intelligence is obvious: If a machine can compute any function (leaving aside provability, which is not prima facie relevant as regards human intelligence), and intelligence is supposed to be some sort of function (which may be separately debated, but which was assumed by Turing), then if one’s goal is to understand intelligence and perhaps even to create an intelligent machine, it is important to be able to tell when you’ve succeeded – thus, Turing’s invention of The Imitation Game, now famously called the “Turing Test”. Turing’s Universal Machine was a theoretical construct. But in 1943, he designed and succeeded in building, with other engineers at Bletchley Park, a near-instantiation of his theoretical machine, called Colossus. Dyson observes that Colossus “was an electronic Turing machine, and if not yet universal, it had all the elements in place.”[24, p. 256] The Turing Machine, and its instantiation in Colossus, represented an extremely important advance in computing, even though computing was only in its infancy. Up until that time, all computers were “hardwired” – that is, they executed a program that was wired into their hardware. The bombe was of this type. The important conceptual advance due to Turing, and at about the same time John von Neumann in the EDVAC project, was to store the program on a medium that is not fixed, but can be changed by the program itself. In Turning’s machine (and Colossus) this was a tape. In von Neumann’s case it was an electronic memory[4]. This way of thinking about computers – as “stored program”, as opposed to “hardwired” – was the engineering revolution hidden in Turing’s theoretical construct – that computers could not only do complex calculations, but that they could manipulate their own programs, much like intelligent agents engage in reasoning, planning, and other meta-cognitive activities wherein we think about and modify our own thoughts. We now call such machines “von Neumann-style” machines, although they could just as reasonably, and perhaps even more so, be called “Turing-style”. Author:
(1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Why ELIZA? The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp A Critical Tangent into Gomoku Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Acknowledgements and References Why ELIZA? Why ELIZA? The Intelligence Engineers The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp From IPL to SLIP and Lisp A Critical Tangent into Gomoku A Critical Tangent into Gomoku Interpretation is the Core of Intelligence Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Conclusion: A certain danger lurks there Acknowledgements and References Acknowledgements and References 3 The Intelligence Engineers The founding father of the effort to build intelligent machines was, of course, Alan Turing (although Ada Lovelace may have been the founding mother, as will be seen shortly). Turing is publicly most famous for having led the team that built the “bombe”, the machine that made it possible to decipher enemy messages in the second World War.[24] However, in academic circles, Turing is best known for the mathematical construct that now bears his name, the Universal Turing Machine. And in AI circles – and recently in public, as AI has come more into public focus – Turning is associated with the Imitation Game, which we now call the Turing Test.[44] Since we are discussing ELIZA, which was apparently a chatbot, the reader might expect me to go straight to the Turing Test. However, I am concerned here primarily with ELIZA as a computational artifact, not with whether ELIZA was or was not intelligent; We can all agree that it was not, and we shall see later that Weizenbaum himself had specific reasons for not thinking of ELIZA as intelligent. Thus the Turing Test plays little role with respect to ELIZA, at least as it that program was conceived by Weizenbaum, so we shall leave it aside in the present exploration, except for one important detail that arises, almost in passing, in Turing’s Mind paper. not Turing notes that over a century before his effort, Ada Lovelace had described the potential of Babbage’s Analytical Engine to “act upon other things besides number[s]”: “The operating mechanism [...] might act upon other things besides number, were objects found whose mutual fundamental relations could be expressed by those of the abstract science of operations, and which should be also susceptible of adaptations to the action of the operating notation and mechanism of the engine. Supposing, for instance, that the fundamental relations of pitched sounds in the science of harmony and of musical composition were susceptible of such expression and adaptations, the engine might compose elaborate and scientific pieces of music of any degree of complexity or extent [...]”[23] We shall soon see that Ada’s insight that machines could act upon other things besides numbers was enormously prescient, foreshadowing the concept of symbolic computing, which was to become, and remains today, one of the foundations of artificial intelligence, and which is central to the pre-history of ELIZA. But first let us return to Turing’s mathematical contribution, Turing’s Universal Machine, the now so-called “Turing Machine”. The Turing Machine was Turing’s contribution to a line of inquiry in the early part of the 20th century which included Turning, Kurt Godel, and Alonso Church, all addressing a problem posed in 1928 by Hilbert and Ackerman called the “Entscheidungsproblem”.[3] Hilbert’s challenge was to find an algorithm to determine whether a mathematical proposition is provable. Godel addressed this problem through what we now call “Godel Numbering”, his method of turning mathematical expressions into numbers, and showing that there are expressions that cannot be proved in a complete system. Church reached the same conclusion by formalizing general recursive functions in a system called the “Lambda Calculus”. And Turing addressed the problem by describing a universal machine that can compute any function. Turing then showed, equivalently to Godel and Church, that there are programs for which it is impossible to prove that they will come to a halt on his machine, which we now call “the halting problem”.[37] The connection between Turing’s interests in universal computers, computable functions, and intelligence is obvious: If a machine can compute any function (leaving aside provability, which is not prima facie relevant as regards human intelligence), and intelligence is supposed to be some sort of function (which may be separately debated, but which was assumed by Turing), then if one’s goal is to understand intelligence and perhaps even to create an intelligent machine, it is important to be able to tell when you’ve succeeded – thus, Turing’s invention of The Imitation Game, now famously called the “Turing Test”. Turing’s Universal Machine was a theoretical construct. But in 1943, he designed and succeeded in building, with other engineers at Bletchley Park, a near-instantiation of his theoretical machine, called Colossus. Dyson observes that Colossus “was an electronic Turing machine, and if not yet universal, it had all the elements in place.”[24, p. 256] The Turing Machine, and its instantiation in Colossus, represented an extremely important advance in computing, even though computing was only in its infancy. Up until that time, all computers were “hardwired” – that is, they executed a program that was wired into their hardware. The bombe was of this type. The important conceptual advance due to Turing, and at about the same time John von Neumann in the EDVAC project, was to store the program on a medium that is not fixed, but can be changed by the program itself. In Turning’s machine (and Colossus) this was a tape. In von Neumann’s case it was an electronic memory[4]. This way of thinking about computers – as “stored program”, as opposed to “hardwired” – was the engineering revolution hidden in Turing’s theoretical construct – that computers could not only do complex calculations, but that they could manipulate their own programs, much like intelligent agents engage in reasoning, planning, and other meta-cognitive activities wherein we think about and modify our own thoughts. We now call such machines “von Neumann-style” machines, although they could just as reasonably, and perhaps even more so, be called “Turing-style”. manipulate their own programs, much like intelligent agents engage in reasoning, planning, and other meta-cognitive activities wherein we think about and modify our own thoughts. Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). Author: Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv

The Intelligence Engineers: How Turing and Lovelace Laid the Foundations for AI's Future

Addressing Kevin Mitchell’s Questions From the Perspective of Conscious Turing Machine Robots

How Conscious Turing Machine Robots Align with Other Theories of Consciousness

Authors:
(1) Lenore Blum (lblum@cs.cmu.edu);
(2) Manuel Blum (mblum@cs.cmu.edu). Table of Links Abstract and 1 Introduction 2 Brief Overview of CtmR, a Robot with a CTM Brain 2.1 Formal Definition of CtmR 2.2 Conscious Attention in CtmR 2.3 Conscious Awareness and the Feeling of Consciousness in CtmR 2.4 CtmR as a Framework for Artificial General Intelligence (AGI) 3 Alignment of CtmR with Other Theories of Consciousness 4 Addressing Kevin Mitchell’s questions from the perspective of CtmR 5 Summary and Conclusions 6 Acknowledgements 7 Appendix 7.1 A Brief History of the Theoretical Computer Science Approach to Computation 7.2 The Probabilistic Competition for Conscious Attention and the Influence of Disposition on it References 2.4 CtmR as a Framework for Artificial General Intelligence (AGI) Before we indicate CtmR’s alignment with a number of major theories of consciousness, we remark on CtmR’s potential to serve as a framework for constructing an Artificial General Intelligence (AGI). This is a result of CtmR’s global architecture (kindred to arguments made for global latent workspace by (VanRullen & Kanai, 2021)) and, at the same time, the result of an essential difference between CtmR and Baars’ global workspace. CtmR has no Central Executive. This is a feature, not a bug. It enables CtmR to become an AGI (Blum & Blum, 2023): The competition to get information on stage considers the |weight|ed information submitted by a huge collection of (N) processors. And it does this quickly (log2 N steps). This enables CtmR to engage processors to solve problems, even though CtmR does not know which of its processors might have the interest, expertise, or time to do so. [KM15] Specifically, if CtmR has a problem to solve, meaning that one (or several) of its LTM processors has a problem, the processor can submit the problem into the competition as a highly |weight|ed chunk. If no other chunks are highly |weight|ed, the chunk wins with high probability and becomes globally broadcast to all processors. Processors with the interest, expertise and time to work on the problem will respond with appropriately |weight|ed chunks. In this way, unexpected ideas (from unexpected sources) may turn out to have relevance to solving the problem, and useful collaborations can emerge. [KM15] A Central Executive would have to know which processors had the inclination, expertise, and resources to solve problems as they arise, and figure this out quickly. Baars’ does not say how a Central Executive could do this. [KM15] More generally, we predict that a Central Executive is not needed for consciousness or for general intelligence; indeed, it might prove to be an impediment. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Lenore Blum (lblum@cs.cmu.edu); (2) Manuel Blum (mblum@cs.cmu.edu). Authors: Authors: (1) Lenore Blum (lblum@cs.cmu.edu); (2) Manuel Blum (mblum@cs.cmu.edu). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Brief Overview of CtmR, a Robot with a CTM Brain 2.1 Formal Definition of CtmR 2.1 Formal Definition of CtmR 2.2 Conscious Attention in CtmR 2.2 Conscious Attention in CtmR 2.3 Conscious Awareness and the Feeling of Consciousness in CtmR 2.3 Conscious Awareness and the Feeling of Consciousness in CtmR 2.4 CtmR as a Framework for Artificial General Intelligence (AGI) 2.4 CtmR as a Framework for Artificial General Intelligence (AGI) 3 Alignment of CtmR with Other Theories of Consciousness 3 Alignment of CtmR with Other Theories of Consciousness 4 Addressing Kevin Mitchell’s questions from the perspective of CtmR 4 Addressing Kevin Mitchell’s questions from the perspective of CtmR 5 Summary and Conclusions 5 Summary and Conclusions 6 Acknowledgements 6 Acknowledgements 7 Appendix 7.1 A Brief History of the Theoretical Computer Science Approach to Computation 7.1 A Brief History of the Theoretical Computer Science Approach to Computation 7.2 The Probabilistic Competition for Conscious Attention and the Influence of Disposition on it 7.2 The Probabilistic Competition for Conscious Attention and the Influence of Disposition on it References References 2.4 CtmR as a Framework for Artificial General Intelligence (AGI) Before we indicate CtmR’s alignment with a number of major theories of consciousness, we remark on CtmR’s potential to serve as a framework for constructing an Artificial General Intelligence (AGI). This is a result of CtmR’s global architecture (kindred to arguments made for global latent workspace by (VanRullen & Kanai, 2021)) and, at the same time, the result of an essential difference between CtmR and Baars’ global workspace. CtmR has no Central Executive. This is a feature, not a bug. It enables CtmR to become an AGI (Blum & Blum, 2023): The competition to get information on stage considers the |weight|ed information submitted by a huge collection of (N) processors. And it does this quickly (log2 N steps). This enables CtmR to engage processors to solve problems, even though CtmR does not know which of its processors might have the interest, expertise, or time to do so. [KM15] Specifically, if CtmR has a problem to solve, meaning that one (or several) of its LTM processors has a problem, the processor can submit the problem into the competition as a highly |weight|ed chunk. If no other chunks are highly |weight|ed, the chunk wins with high probability and becomes globally broadcast to all processors. Processors with the interest, expertise and time to work on the problem will respond with appropriately |weight|ed chunks. In this way, unexpected ideas (from unexpected sources) may turn out to have relevance to solving the problem, and useful collaborations can emerge. [KM15] A Central Executive would have to know which processors had the inclination, expertise, and resources to solve problems as they arise, and figure this out quickly. Baars’ does not say how a Central Executive could do this. [KM15] More generally, we predict that a Central Executive is not needed for consciousness or for general intelligence; indeed, it might prove to be an impediment. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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