automatic OER processing

Boosting is an approach to machine learning based on the idea of creatinga highly accurate prediction rule by combining many relatively weak and inaccu-rate rules

Instead, current AI research on sustainability tends to emphasize the quantifiable effects of environmental pollution and climate change, and focus on solutions of continued measurement, monitoring, and optimizing for efficiency.

Guidance National AI Strategy

open source AI platform

open-source AI platform

Side note: When I flagged yours as a dupe during review, the review system slapped me in the face and seriously accused me of not paying attention, a ridiculous claim by itself since locating a (potential) dupe requires quite a lot of attention.

Normally, thousands of rabbits and guinea pigs are used andkilled, in scientific laboratories, for experiments which yieldgreat and tangible benefits to humanity. This war butcheredmillions of people and ruined the health and lives of tens ofmillions. Is this climax of the pre-war civilization to be passedunnoticed, except for the poetry and the manuring of the battlefields, that the“poppies blow”stronger and better fed? Or is thedeath of ten men on the battle field to be of as much worth inknowledge gained as is the life of one rabbit killed for experi-ment? Is the great sacrifice worth analysing? There can be onlyone answer—yes. But, if truth be desired, the analysis must bescientific.

An “attention map” of each prediction shows the important data points considered by the models as they make that prediction.

Recommendations DON'T use shifted PPMI with SVD. DON'T use SVD "correctly", i.e. without eigenvector weighting (performance drops 15 points compared to with eigenvalue weighting with (p = 0.5)). DO use PPMI and SVD with short contexts (window size of (2)). DO use many negative samples with SGNS. DO always use context distribution smoothing (raise unigram distribution to the power of (lpha = 0.75)) for all methods. DO use SGNS as a baseline (robust, fast and cheap to train). DO try adding context vectors in SGNS and GloVe.

many other systems that are already here or not far off will have to make all sorts of real ethical trade-offs

One thing that should be learned from the bitter lesson is the great power of general purpose methods, of methods that continue to scale with increased computation even as the available computation becomes very great. The two methods that seem to scale arbitrarily in this way are search and learning

Baudrillard

he intellectual equivalent of peacock feathers

Deep Reinforcement Learning and its Neuroscientific Implications In this paper, the authors provided a high-level introduction to deep RL, discussed some of its initial applications to neuroscience, and surveyed its wider implications for research on brain and behaviour and concluded with a list of opportunities for next-stage research. Although DeepRL seems to be promising, the authors wrote that it is still a work in progress and its implications in neuroscience should be looked at as a great opportunity. For instance, deep RL provides an agent-based framework for studying the way that reward shapes representation, and how representation, in turn, shapes learning and decision making — two issues which together span a large swath of what is most central to neuroscience.  Check the paper here.

Yes, autoexpect is a good tool, but it is used just to automatically create TCL-expect scripts, by watching for user. So it’s can be equal to writing expect-scripts by hand.

I can see what I was doing a handful of years ago or to see a forgotten picture of one of my children doing something cute

The digital universe could add some 175 zettabytes of data per year by 2025, according to the market-analysis firm IDC.

The process of DNA data storage combines DNA synthesis, DNA sequencing and an encoding and decoding algorithm to pack information into DNA more durably and at higher density than is possible in conventional media. That could be up to 17 exabytes per gram1.

Using chemicals to improve our economy of attention and become emotionally "fitter" is an option that penetrated public consciousness some time ago.

They have become more significant because social interaction is governed by social convention to a much lesser extent than it was fifty years ago.

Currently, the downsides of this merger are starting to become obvious, including the loss of privacy, political polarization, psycho‑logical manipulation, addictive use, social anxiety and distraction, misinformation, and mass narcissism.53

From a historical perspective of social change, the merger between biological and AI has already crossed beyond any point of return, at least from the social science perspective of society as a whole

Advancements in the field of AI have been dazzling. AI has not only superseded humans in many intellectual tasks, like several kinds of cancer diagnosis47 and speech recognition (reducing AI’s word-error rate from 26% to 4% just between 2012 and 2016)

move away from viewing AI systems as passive tools that can be assessed purely through their technical architecture, performance, and capabilities. They should instead be considered as active actors that change and influence their environments and the people and machines around them.

Koo's discovery makes it possible to peek inside the black box and identify some key features that lead to the computer's decision-making process.

A primary goal of AI design should be not just alignment, but legibility, to ensure that the humans interacting with the AI know its goals and failure modes, allowing critique, reuse, constraint etc.

- To biznes, eksperci i obywatele są prawdziwymi twórcami polskiego ekosystemu AI. Państwo powinno przede wszystkim ich wspierać. W najbliższym czasie planujemy serię otwartych spotkań z każdą z tych grup, na których będziemy wspólnie pracować nad uszczegółowieniem – zapowiedział Antoni Rytel, wicedyrektor GovTech Polska. - Oprócz tego, specjalne zespoły będą zapewniać ciągłe wsparcie wszystkim tym podmiotom. Uruchomimy też kanał bieżącego zgłaszania pomysłów technicznych i organizacyjnych wspierających rozwój AI w naszym kraju – dodał.

W okresie krótkoterminowym decydujące dla sukcesu polityki sztucznej inteligencji będzie ochrona talentów posiadających zdolności modelowania wiedzy i analityki danych w systemach AI oraz wsparcie dla rozwoju własności intelektualnej wytwarzanej w naszym kraju – dodaje Robert Kroplewski, pełnomocnik ministra cyfryzacji ds. społeczeństwa informacyjnego.

Dokument określa działania i cele dla Polski w perspektywie krótkoterminowej (do 2023 r.), średnioterminowej (do 2027 r.) i długoterminowej (po 2027 r.). Podzieliliśmy je na sześć obszarów: AI i społeczeństwo – działania, które mają uczynić z Polski jednego z większych beneficjentów gospodarki opartej na danych, a z Polaków - społeczeństwo świadome konieczności ciągłego podnoszenia kompetencji cyfrowych. AI i innowacyjne firmy – wsparcie polskich przedsiębiorstw AI, m.in. tworzenie mechanizmów finansowania ich rozwoju, współpracy start up-ów z rządem. AI i nauka – wsparcie polskiego środowiska naukowego i badawczego w projektowaniu interdyscyplinarnych wyzwań lub rozwiązań w obszarze AI, m.in. działania mające na celu przygotowanie kadry ekspertów AI. AI i edukacja – działania podejmowane od kształcenia podstawowego, aż do poziomu uczelni wyższych – programy kursów dla osób zagrożonych utratą pracy na skutek rozwoju nowych technologii, granty edukacyjne. AI i współpraca międzynarodowa – działania na rzecz wsparcia polskiego biznesu w zakresie AI oraz rozwój technologii na arenie międzynarodowej. AI i sektor publiczny – wsparcie sektora publicznego w realizacji zamówień na rzecz AI, lepszej koordynacji działań oraz dalszym rozwoju takich programów jak GovTech Polska.

Rozwój AI w Polsce zwiększy dynamikę PKB o nawet 2,65 pp w każdym roku. Do 2030 r. pozwoli zautomatyzować ok. 49% czasu pracy w Polsce, generując jednocześnie lepiej płatne miejsca pracy w kluczowych sektorach.

Artificial Intelligence Is Now Used to Predict Crime.

Help is coming in the form of specialized AI processors that can execute computations more efficiently and optimization techniques, such as model compression and cross-compilation, that reduce the number of computations needed. But it’s not clear what the shape of the efficiency curve will look like. In many problem domains, exponentially more processing and data are needed to get incrementally more accuracy. This means – as we’ve noted before – that model complexity is growing at an incredible rate, and it’s unlikely processors will be able to keep up. Moore’s Law is not enough. (For example, the compute resources required to train state-of-the-art AI models has grown over 300,000x since 2012, while the transistor count of NVIDIA GPUs has grown only ~4x!) Distributed computing is a compelling solution to this problem, but it primarily addresses speed – not cost.

At any rate, if CSHW can be used to build a good quantitative model of human-human interactions, it might also be possible to replicate these dynamics in human-computer interactions. This could take a weak form, such as building computer systems with a similar-enough interactional syntax to humans that some people could reach entrainment with it; affective computing done right.

The current public dialog about these issues too often uses “AI” as an intellectual wildcard, one that makes it difficult to reason about the scope and consequences of emerging technology. Let us begin by considering more carefully what “AI” has been used to refer to, both recently and historically.

Thus, just as humans built buildings and bridges before there was civil engineering, humans are proceeding with the building of societal-scale, inference-and-decision-making systems that involve machines, humans and the environment. Just as early buildings and bridges sometimes fell to the ground — in unforeseen ways and with tragic consequences — many of our early societal-scale inference-and-decision-making systems are already exposing serious conceptual flaws.

The Globe and Mail reports that Element AI sold for less than $500 million USD. This would place the purchase price well below the estimated valuation that the Montréal startup was said to have after its $200 million CAD Series B round in September 2019.

Despite being seen as a leader and a rising star in the Canadian AI sector, Element AI faced difficulties getting products to market.

Element AI had more than 500 employees, including 100 PhDs.

n 2017, the startup raised what was then a historic $137.5 million Series A funding round from a group of notable investors including Intel, Microsoft, National Bank of Canada, Development Bank of Canada (BDC), NVIDIA, and Real Ventures.

AI is not analogous to the big science projects of the previous century that brought us the atom bomb and the moon landing. AI is a science that can be conducted by many different groups with a variety of different resources, making it closer to computer design than the space race or nuclear competition. It doesn’t take a massive government-funded lab for AI research, nor the secrecy of the Manhattan Project. The research conducted in the open science literature will trump research done in secret because of the benefits of collaboration and the free exchange of ideas.

Facebook AI is introducing M2M-100, the first multilingual machine translation (MMT) model that can translate between any pair of 100 languages without relying on English data. It’s open sourced here. When translating, say, Chinese to French, most English-centric multilingual models train on Chinese to English and English to French, because English training data is the most widely available. Our model directly trains on Chinese to French data to better preserve meaning. It outperforms English-centric systems by 10 points on the widely used BLEU metric for evaluating machine translations. M2M-100 is trained on a total of 2,200 language directions — or 10x more than previous best, English-centric multilingual models. Deploying M2M-100 will improve the quality of translations for billions of people, especially those that speak low-resource languages. This milestone is a culmination of years of Facebook AI’s foundational work in machine translation. Today, we’re sharing details on how we built a more diverse MMT training data set and model for 100 languages. We’re also releasing the model, training, and evaluation setup to help other researchers reproduce and further advance multilingual models. 

Microsoft deletes 'teen girl' AI after it became a Hitler-loving sex robot within 24 hours

Google’s novel response has been to compare each app to its peers, identifying those that seem to be asking for more than they should, and alerting developers when that’s the case. In its update today, Google says “we aim to help developers boost the trust of their users—we surface a message to developers when we think their app is asking for a permission that is likely unnecessary.”

5A85F3

Machine learning has a limited scope

AI is a bigger concept to create intelligent machines that can simulate human thinking capability and behavior, whereas, machine learning is an application or subset of AI that allows machines to learn from data without being programmed explicitly

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed

machines tend to be designed for the lowest possible risk and the least casualties

machines must weigh the consequences of any action they take, as each action will impact the end result

goals of artificial intelligence include learning, reasoning, and perception

refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions

You can now distribute your add-on. Note, however, that your add-on may still be subject to further review, if it is you’ll receive notification of the outcome of the review later.

As the largest Voronoi regions belong to the states on the frontier of the search, this means that the tree preferentially expands towards large unsearched areas.

inherently biased to grow towards large unsearched areas of the problem

Natural Language Processing with Python – Analyzing Text with the Natural Language Toolkit Steven Bird, Ewan Klein, and Edward Loper

How to setup and use Stanford CoreNLP Server with Python Khalid Alnajjar August 20, 2017 Natural Language Processing (NLP) Leave a CommentStanford CoreNLP is a great Natural Language Processing (NLP) tool for analysing text. Given a paragraph, CoreNLP splits it into sentences then analyses it to return the base forms of words in the sentences, their dependencies, parts of speech, named entities and many more. Stanford CoreNLP not only supports English but also other 5 languages: Arabic, Chinese, French, German and Spanish. To try out Stanford CoreNLP, click here.Stanford CoreNLP is implemented in Java. In some cases (e.g. your main code-base is written in different language or you simply do not feel like coding in Java), you can setup a Stanford CoreNLP Server and, then, access it through an API. In this post, I will show how to setup a Stanford CoreNLP Server locally and access it using python.

CoreNLP includes a simple web API server for servicing your human language understanding needs (starting with version 3.6.0). This page describes how to set it up. CoreNLP server provides both a convenient graphical way to interface with your installation of CoreNLP and an API with which to call CoreNLP using any programming language. If you’re writing a new wrapper of CoreNLP for using it in another language, you’re advised to do it using the CoreNLP Server.

Programming languages and operating systems Stanford CoreNLP is written in Java; recent releases require Java 1.8+. You need to have Java installed to run CoreNLP. However, you can interact with CoreNLP via the command-line or its web service; many people use CoreNLP while writing their own code in Javascript, Python, or some other language. You can use Stanford CoreNLP from the command-line, via its original Java programmatic API, via the object-oriented simple API, via third party APIs for most major modern programming languages, or via a web service. It works on Linux, macOS, and Windows. License The full Stanford CoreNLP is licensed under the GNU General Public License v3 or later. More precisely, all the Stanford NLP code is GPL v2+, but CoreNLP uses some Apache-licensed libraries, and so our understanding is that the the composite is correctly licensed as v3+.

Stanford CoreNLP provides a set of human language technology tools. It can give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases and syntactic dependencies, indicate which noun phrases refer to the same entities, indicate sentiment, extract particular or open-class relations between entity mentions, get the quotes people said, etc. Choose Stanford CoreNLP if you need: An integrated NLP toolkit with a broad range of grammatical analysis tools A fast, robust annotator for arbitrary texts, widely used in production A modern, regularly updated package, with the overall highest quality text analytics Support for a number of major (human) languages Available APIs for most major modern programming languages Ability to run as a simple web service

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code. The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc. OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding 18 million. The library is used extensively in companies, research groups and by governmental bodies. Along with well-established companies like Google, Yahoo, Microsoft, Intel, IBM, Sony, Honda, Toyota that employ the library, there are many startups such as Applied Minds, VideoSurf, and Zeitera, that make extensive use of OpenCV. OpenCV’s deployed uses span the range from stitching streetview images together, detecting intrusions in surveillance video in Israel, monitoring mine equipment in China, helping robots navigate and pick up objects at Willow Garage, detection of swimming pool drowning accidents in Europe, running interactive art in Spain and New York, checking runways for debris in Turkey, inspecting labels on products in factories around the world on to rapid face detection in Japan. It has C++, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS. OpenCV leans mostly towards real-time vision applications and takes advantage of MMX and SSE instructions when available. A full-featured CUDAand OpenCL interfaces are being actively developed right now. There are over 500 algorithms and about 10 times as many functions that compose or support those algorithms. OpenCV is written natively in C++ and has a templated interface that works seamlessly with STL containers.