Artificial intelligence is rapidly changing many aspects of how we work and live. (How many stories did you read last week about self-driving cars and job-stealing robots? Perhaps your holiday shopping involved some AI algorithms, as well.) But despite the constant flow of news, many misconceptions about AI remain.
AI doesn’t think in our sense of the word at all, Scriffignano explains. “In many ways, it’s not really intelligence. It’s regressive.”
IT leaders should make deliberate choices about what AI can and can’t do on its own. “You have to pay attention to giving AI autonomy intentionally and not by accident,”
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Tomi Engdahl says:
AI for Social Good
https://www.blog.google/outreach-initiatives/google-org/ai-social-good/
In pop culture, artificial intelligence (AI) often shows up as a robot companion, like TARS in “Interstellar,” or some far-out superintelligence. But in reality, AI—computer programming tools that help us find patterns in complex data and make everyday products more useful—already powers a lot of technology around us, and is addressing some of society’s biggest unsolved challenges.
Tomi Engdahl says:
Welcome to the Elements of Artificial Intelligence free online course
https://www.elementsofai.com
Tomi Engdahl says:
Melanie Ehrenkranz / Gizmodo:
EU to launch a six-month trial of iBorderCtrl, an AI lie detector that looks for “biomarkers of deceit”, at its external borders in Hungary, Latvia, and Greece
An AI Lie Detector Is Going to Start Questioning Travelers in the EU
https://gizmodo.com/an-ai-lie-detector-is-going-to-start-questioning-travel-1830126881
A number of border control checkpoints in the European Union are about to get increasingly—and unsettlingly—futuristic.
In Hungary, Latvia, and Greece, travelers will be given an automated lie-detection test—by an animated AI border agent. The system, called iBorderCtrl, is part of a six-month pilot led by the Hungarian National Police at four different border crossing points.
“We’re employing existing and proven technologies—as well as novel ones—to empower border agents to increase the accuracy and efficiency of border checks,”
Tomi Engdahl says:
Could Reimbursement for AI Solutions Really Be That Tricky?
https://www.mddionline.com/could-reimbursement-ai-solutions-really-be-tricky
Eric Hargan, Deputy Secretary for Health and Human Services, spoke with MD+DI during Cleveland Clinic’s 2018 Medical Innovation Summit about the difficulties of reimbursement for AI solutions.
“The process by which software is integrated into medical care is a hard thing to quantify,” Hargan told MD+DI from the media room at the Innovation Summit. “Normally if you have software embedded in – say a pacemaker – it’s easy to know what the software is doing. But when you venture out where technology is starting to do things like possibly analyze a disease state or support clinical decisions, you end up with something where the program in itself is seeming to participate in medicine. It’s analyzing and diagnosing things.”
He added, “how do we deal with that? From a reimbursement point of view, how do we have an AI [solution] go through a set of medical records and have them to say this is highlighting potential medical issues and looking at past decisions that were made for a patient. How do we pay for that, what is the value that’s being provided by the AI?”
Hargan said the biggest issue was determining how to reimburse a technology that could move so rapidly. He gave the example of a hospital system with two million patients. He asked what happens when you reimburse a hospital system for using a solution that will give a second opinion on each patient. He gave a hypothetical example of where the AI solution could be run every two-to-three days on the two million patients, noting that in this scenario reimbursement costs could be astronomical.
“Because it’s capable of doing this quickly and in scale, you can very easily run into an issue of reimbursement,” Hargan said. “We have to be careful about how we use the AI or how we pay for it, because if we leave it entirely out of the equation, we’re not going to incentivize people to use a revolutionary technology. We don’t want to cut that off or chill that innovation, but on the other hand, if we step out in a way that’s not thoughtful we might end up breaking the bank for the taxpayer.”
Tomi Engdahl says:
http://www.etn.fi/index.php/13-news/8650-memristorein-energiatehokkuus-parani-selvasti
Tomi Engdahl says:
Will ‘Deepfakes’ Disrupt the Midterm Election?
https://www.wired.com/story/will-deepfakes-disrupt-the-midterm-election/
Plenty of people are following the final days of the midterm election campaigns. Yale law researcher Rebecca Crootof has a special interest—a small wager. If she wins, victory will be bitter sweet, like the Manhattan cocktail that will be her prize.
In June, Crootof bet that before 2018 is out an electoral campaign somewhere in the world will be roiled by a deepfake—a video generated by machine learning software that shows someone doing or saying something that in fact they did not do or say. Under the terms of the bet, the video must receive more than 2 million views before being debunked. If she loses, Crootof will owe a sporting tiki drink to Tim Hwang, director of a Harvard-MIT project on ethics and governance of artificial intelligence. If she wins, it will validate the fears of researchers and lawmakers that recent AI advances could be used to undermine democracy.
The US midterms are seen as a possible target that could prove the pessimists right. Facebook says the elections have already attracted other, more conventional disinformation campaigns.
Tomi Engdahl says:
https://www.tivi.fi/Kaikki_uutiset/tekoaly-ryhtyi-poliisiksi-tunnistaa-huijarit-hakellyttavalla-tarkkuudella-6747333
Tomi Engdahl says:
Can a Robot Learn a Language the Way a Child Does?
https://hardware.slashdot.org/story/18/10/31/234254/can-a-robot-learn-a-language-the-way-a-child-does?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+Slashdot%2Fslashdot%2Fto+%28%28Title%29Slashdot+%28rdf%29%29
MIT researchers have devised a way to train semantic parsers by mimicking the way a child learns language. “The system observes captioned videos and associates the words with recorded actions and objects,” ZDNet reports, citing the paper presented this week. “It could make it easier to train parsers, and it could potentially improve human interactions with robots.”
Can a robot learn a language the way a child does?
https://www.zdnet.com/article/can-a-robot-learn-a-language-the-way-a-child-does/
MIT researchers have devised a way to train semantic parsers by mimicking the way a child learns language.
Tomi Engdahl says:
Open Data Cam Combines Camera, GPU, and Neural Network in an Artisanal DIY Cereal Box
https://hackaday.com/2018/10/29/open-data-cam-combines-camera-gpu-and-neural-network-in-an-artisanal-diy-cereal-box/
The engineers and product designers at [moovel lab] have created the Open Data Cam – an AI camera platform that can identify and count objects as they move through its field of view – along with an open source guide for making your own.
https://opendatacam.moovellab.com/
Tomi Engdahl says:
‘Privacy is a human right’: Big cheese Sat-Nad lays out Microsoft’s stall at Future Decoded
It’s a triple A-rated keynote: Azure, AI and Accessibility
https://www.theregister.co.uk/2018/11/01/satya_nadella_microsoft_future_decoded_keynote/
Oggy oggy oggy, AI AI AI
AI was, however, the focus for Nadella. The CEO highlighted Microsoft’s firsts in the field to date, ending with human parity in translation in March 2018 and insisting that kind old Microsoft had “democratised” the technology thanks to Azure AI.
But Azure ain’t free so that “democratisation” comes at a fee. Just like the real thing (or so it seems these days).
Earlier in Future Decoded, Microsoft had announced the arrival of AccountGuard for UK customers “in the political space”. The service, which is already available for US users, ramps up protections on a politico’s Office 365 account as well as providing a direct line to Microsoft’s Defending Democracy team.
All part of protecting the democratic process. For Office 365 users at any rate.
The theme of AI continued throughout Nadella’s keynote as the CEO wrestled with the thorny issue of trust.
Taking a page from Apple’s playbook, Nadella was keen to highlight the efforts made by the Microsoft in privacy. Amazon, in its September gadgetfest, famously failed to utter the “P” word once. Nadella, on the other hand, was more forthright, seeing the recently introduced GDPR as a good first step before declaring: “Privacy is a human right.”
Tomi Engdahl says:
Now Europe wants a four-million-quid AI-powered lie detector at border checkpoints
Screening system would scan faces, flag ‘suspicious’ reactions for immigration cops
https://www.theregister.co.uk/2018/11/02/eu_ai_lie_detector/
The EU is readying an AI-based screening system designed to catch travelers who lie about their reasons for visiting the Continent.
The European Commission has thrown more than €4.5m (£4m, $5.1m) into iBorderCtrl, a self-described “intelligent control system” that analyzes answers given by travelers to a series of questions at border checkpoints, and their facial expressions, then highlights to agents those who it thinks are likely not telling the truth.
Tomi Engdahl says:
AI Begins To Reshape Chip Design
https://semiengineering.com/ai-begins-to-reshape-chip-design/
Technology adds more granularity, but starting point for design shifts as architectures cope with greater volumes of data.
Artificial intelligence is beginning to impact semiconductor design as architects begin leveraging its capabilities to improve performance and reduce power, setting the stage for a number of foundational shifts in how chips are developed, manufactured and updated in the future.
AI—and machine learning and deep learning subsets—can be used to greatly improve the functional control and power/performance of specific functions within chips. For those purposes it can be layered on top of existing devices, as well as incorporated into new designs, allowing it to be applied across a wide swath of functions or targeted at a very narrow one.
There are a number of benefits AI provides. Among them:
It adds increasing granularity for speeding up performance and reducing power by varying the accuracy of specific functions through sparser algorithms or data compression.
It provides the ability to process data as patterns rather than individual bits, effectively raising the abstraction level for computing and increasing the density of the software.
It allows processing and memory read/writes to be done as a matrix, greatly speeding up those operations.
But AI also requires a significant rethinking of how data moves—or doesn’t move—across a chip or between chips. Regardless of whether it is applied at the edge or in the data center, or whether it involves training or inferencing, the amount of data being processed and stored can be enormous.
Tomi Engdahl says:
Microprocessor Developed for Embedded AI Image Processing
https://www.eeweb.com/profile/eeweb/news/microprocessor-developed-for-embedded-ai-image-processing
The RZ/A2M microprocessor for embedded artificial intelligence (e-AI) image processing from Renesas Electronics Corp. uses the company’s proprietary Dynamically Reconfigurable Processor (DRP) to deliver real-time image processing while significantly reducing power consumption compared with its predecessors. Suitable for smart appliances, service robot and industrial machinery applications, the RZ/A2M eliminates the need for external DRAM by integrating a large-capacity (4 MB) on-chip RAM. It supports the MIPI camera interface commonly found in smartphones and other mobile devices and boasts increased network functionality using two-channel Ethernet support.
https://www.renesas.com/us/en/products/microcontrollers-microprocessors/rz.html
Tomi Engdahl says:
AI Training Chips
How to speed up algorithms and improve performance.
https://semiengineering.com/ai-training-chips/
Tomi Engdahl says:
The Multiple Faces And Phases Of AI
Confusion grows as AI finds its way into more applications.
https://semiengineering.com/the-multiple-faces-and-phases-of-ai/
AI is being used in more ways and more devices—and in more ways in those same devices—raising the level of confusion about exactly what people are talking about when they refer to AI and AI-enabled systems.
AI is both a tool and a process. It also is a thing, although not even remotely close to the singularity portrayed by Arthur C. Clarke in 2001. And as it proliferates, it’s becoming harder to distinguish one from the other. There also are subsets of AI, notably machine learning and deep learning, and literally dozens of neural network variants that enable AI to work.
On the tools side, AI is being added into everything from verification to manufacturing. It is a way of establishing a guide for what is considered an acceptable Gaussian distribution based upon huge amounts of data input.
Tomi Engdahl says:
Real-Time Object Recognition At Low Cost/Power/Latency
Benchmarks for a new neural inferencing architecture.
https://semiengineering.com/real-time-object-recognition-at-low-cost-power-latency/
Most neural network chips and IP talk about ResNet-50 benchmarks (image classification at 224×224 pixels). But we find that the number one neural network of interest for most customers is real-time object recognition, such as YOLOv3.
It’s not possible to do comparisons here because nobody shows a YOLOv3 benchmark for their inferencing. But it’s very possible to improve on the inferencing performance of other devices.
Tomi Engdahl says:
Machine learning spots natural selection at work in human genome
https://www.nature.com/articles/d41586-018-07225-z?utm_source=fbk_nnc&utm_medium=social&utm_campaign=naturenews&sf201323588=1
Scientists are using artificial intelligence to identify genetic sequences molded by evolutionary pressures.
Tomi Engdahl says:
How messaging, AI, and bots are rescuing customer service
https://venturebeat.com/2018/10/25/how-messaging-ai-and-bots-are-rescuing-customer-service/
Consumers want messaging-based customer service channels. Companies that transition from voice- and email-based customer support see a 25 percent greater annual growth in revenue — plus an 8.6 percent increase in average profit margin per customer.
http://stories.venturebeat.com/how-messaging-ai-bots-are-rescuing-customer-service/
Tomi Engdahl says:
Flex Logix Says It Has Solved Deep Learning’s DRAM Problem
https://spectrum.ieee.org/tech-talk/semiconductors/processors/flex-logix-says-its-solved-deep-learnings-dram-problem
Deep learning has a DRAM problem. Systems designed to do difficult things in real time, such as telling a cat from a kid in a car’s backup camera video stream, are continuously shuttling the data that makes up the neural network’s guts from memory to the processor.
The problem, according to startup Flex Logix, isn’t a lack of storage for that data; it’s a lack of bandwidth between the processor and memory.
Mountain View–based Flex Logix had started to commercialize a new architecture for embedded field-programmable gate arrays (eFPGAs). But after some exploration, one of the founders, Cheng C. Wang, realized the technology could speed neural networks.
Tomi Engdahl says:
Karen Hao / MIT Technology Review:
How US-based Truepic and UK-based Serelay discern true images from fakes by using proprietary algorithms to automatically verify photos the moment they’re made
Deepfake-busting apps can spot even a single pixel out of place
https://www.technologyreview.com/s/612357/deepfake-busting-apps-can-spot-even-a-single-pixel-out-of-place/
Two startups are using algorithms to track when images are edited—from the moment they’re taken.
Falsifying photos and videos used to take a lot of work. Either you used CGI to generate photorealistic images from scratch (both challenging and expensive) or you needed some mastery of Photoshop—and a lot of time—to convincingly modify existing pictures.
Now the advent of AI-generated imagery has made it easier for anyone to tweak an image or a video with confusingly realistic results. Earlier this year, MIT Technology Review senior AI editor Will Knight used off-the-shelf software to forge his own fake video of US senator Ted Cruz. The video is a little glitchy, but it won’t be for long.
That same technology is creating a growing class of footage and photos, called “deepfakes,” that have the potential to undermine truth, confuse viewers, and sow discord at a much larger scale than we’ve already seen with text-based fake news.
Tomi Engdahl says:
Tom Simonite / Wired:
A look at how deepfakes, with recent software advances that easily create AI-generated fake audio and video, could be misused to undermine elections
Will ‘Deepfakes’ Disrupt the Midterm Election?
https://www.wired.com/story/will-deepfakes-disrupt-the-midterm-election/
Tomi Engdahl says:
James Vincent / The Verge:
New research from OpenAI shows how an AI agent with a sense of curiosity outperformed its predecessors playing the classic 1984 Atari game Montezuma’s Revenge
How teaching AI to be curious helps machines learn for themselves
https://www.theverge.com/2018/11/1/18051196/ai-artificial-intelligence-curiosity-openai-montezumas-revenge-noisy-tv-problem
New research from OpenAI uses curious AI to beat video games
Tomi Engdahl says:
https://gizmodo.com/an-ai-lie-detector-is-going-to-start-questioning-travel-1830126881
Tomi Engdahl says:
Heikki Valkama: ”Naiset ovat tyhmiä ja pilanneet maailman” – ennakkoluulot siirtyvät koneille, siksi niitä on vahdittava
https://yle.fi/uutiset/3-10487218
Tomi Engdahl says:
Is AI the future of perfume? IBM is betting on it.
https://www.vox.com/the-goods/2018/10/24/18019918/ibm-artificial-intelligence-perfume-symrise-philyra
IBM has developed a scent algorithm, and it’s coming for the fragrance aisle
The creation of a perfume is often treated as a bespoke art. The French pride themselves on centuries in the olfactory business, and professional scent masters — often referred to as “noses” — spend decades learning the craft, apprenticing under masters.
A common theme here is that the skill of developing a fragrance is extremely valuable — and extremely human.
Now IBM is attempting to turn the traditional model on its head by harnessing the power of artificial intelligence to develop scents.
Tomi Engdahl says:
https://www.uusiteknologia.fi/2018/11/06/naista-kolmesta-teknologiajohtajat-ovat-kiinnostuneita/
Tomi Engdahl says:
https://www.uusiteknologia.fi/2018/11/06/tekoalytoteutuksissa-vaarana-on-jaada-lahtotelineisiin/
Pohjoismaiset yritykset suhtautuvat tekoälyyn konsulttiyritys Bostonin mukaan ulkomaisia kilpailijoitaan optimistisemmin. Silti vaikka into on kova, liian moni pohjoismainen yritys jää silti tekoälyn kanssa lähtötelineisiin, arvioi konsulttiyritys selvitykseen.
Tomi Engdahl says:
Microprocessor Developed for Embedded AI Image Processing
https://www.eeweb.com/profile/eeweb/news/microprocessor-developed-for-embedded-ai-image-processing
The RZ/A2M microprocessor for embedded artificial intelligence (e-AI) image processing from Renesas Electronics Corp. uses the company’s proprietary Dynamically Reconfigurable Processor (DRP) to deliver real-time image processing while significantly reducing power consumption compared with its predecessors. Suitable for smart appliances, service robot and industrial machinery applications, the RZ/A2M eliminates the need for external DRAM by integrating a large-capacity (4 MB) on-chip RAM. It supports the MIPI camera interface commonly found in smartphones and other mobile devices and boasts increased network functionality using two-channel Ethernet support.
Tomi Engdahl says:
Who’s Who in AI SoCs
https://www.eetimes.com/document.asp?doc_id=1333923
Beyond big guns like Google, Facebook, Amazon, and Baidu, who have been designing their own chips for deep learning (both for training and inference), we’re hearing — almost weekly — about “nouvelle” AI SoC architectures invented by startups that nobody’s ever heard of.
The flood of AI chip announcements prompted one veteran industry analyst, Kevin Krewell of Tirias Research, to remind us: “There’s a lot of claims and counter-claims in machine-learning processing, but it’s only working silicon and software that can tell us” their real capabilities.
Indeed, many of these products won’t reach market this year or even next. There is no way of knowing what’s real and what’s smoke and mirrors — until there’s an actual chip.
Tomi Engdahl says:
2November2018
‘Human brain’ supercomputer with 1 million processors switched on for first time
https://www.manchester.ac.uk/discover/news/human-brain-supercomputer-with-1million-processors-switched-on-for-first-time/
The world’s largest neuromorphic supercomputer designed and built to work in the same way a human brain does has been fitted with its landmark one-millionth processor core and is being switched on for the first time.
Tomi Engdahl says:
Engineer.ai raises $29.5M Series A for its AI+Humans software building platform
https://techcrunch.com/2018/11/06/engineer-ai-raises-29-5m-series-a-for-its-aihumans-software-building-platform/?sr_share=facebook&utm_source=tcfbpage
London and LA-based Engineer.ai launched in an invite-only manner two and a half years ago, bootstrapped by its founders. Its platform combines AI with crowdsourced teams of designers and developers to build bespoke digital products at – they say – twice the speed and less than a third of the cost of traditional software development.
Tomi Engdahl says:
Maailman ensimmäinen 7 nanometrin x86-prosessori – eikä se tule Inteliltä
http://www.etn.fi/index.php/13-news/8677-maailman-ensimmainen-7-nanometrin-x86-prosessori-eika-se-tule-intelilta
AMD
Sun esittelemässä demossa yksi 7 nanometrin Epyc-prosessori hakkasi – tosin niukasti – kaksi Intelin Skylake Xeon -prosessoria kuvankäsittelytestissä. Lisäksi uusi 7 nanometrin Vega-grafiikkaprosessori vastasi suorituskyvyltään Nvidian V100-prosessoria tekoälylaskennassa.
Tomi Engdahl says:
Dake Kang / Associated Press:
Chinese authorities have begun deploying “gait recognition” AI software in Beijing and Shanghai that identifies people via their body shapes and how they walk — BEIJING (AP) — Chinese authorities have begun deploying a new surveillance tool: “gait recognition” …
Chinese ‘gait recognition’ tech IDs people by how they walk
https://apnews.com/bf75dd1c26c947b7826d270a16e2658a
Chinese authorities have begun deploying a new surveillance tool: “gait recognition” software that uses people’s body shapes and how they walk to identify them, even when their faces are hidden from cameras.
Tomi Engdahl says:
Mary Ann Azevedo / Crunchbase News:
Engineer.ai, which has developed a human-assisted AI that aims to empower everyone to build and operate bespoke software, raises $29.5M Series A
Engineer.ai Raises $29.5M From Global VCs To Help Companies Build Their Own Software
https://news.crunchbase.com/news/engineer-ai-raises-29-5m-from-global-vcs-to-help-companies-build-their-own-software/
Tomi Engdahl says:
Opinion: Artificial Intelligence Hits the Barrier of Meaning
https://tech.slashdot.org/story/18/11/06/1813258/opinion-artificial-intelligence-hits-the-barrier-of-meaning?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+Slashdot%2Fslashdot%2Fto+%28%28Title%29Slashdot+%28rdf%29%29
Machine learning algorithms don’t yet understand things the way humans do — with sometimes disastrous consequences. Melanie Mitchell, a professor of Computer Science at Portland State University, writes:
As someone who has worked in A.I. for decades, I’ve witnessed the failure of similar predictions of imminent human-level A.I., and I’m certain these latest forecasts will fall short as well. The challenge of creating humanlike intelligence in machines remains greatly underestimated. Today’s A.I. systems sorely lack the essence of human intelligence: understanding the situations we experience, being able to grasp their meaning.
Artificial Intelligence Hits the Barrier of Meaning
https://www.nytimes.com/2018/11/05/opinion/artificial-intelligence-machine-learning.html
Machine learning algorithms don’t yet understand things the way humans do — with sometimes disastrous consequences.
Tomi Engdahl says:
Fight AI with AI! Code taught to finger naughty deepfake vids made by machine-learning algos
It works for now because the forgeries are quite easy to spot
https://www.theregister.co.uk/2018/11/06/fight_ai_deepfakes/
The rise of AI systems that can generate fake images and videos has spurred researchers in the US to develop a technique to sniff out these cyber-shams, also known as deepfakes.
Generative Adversarial Networks (GANs) are commonly used for creative purposes. These neural networks have helped researchers create made-up data to train artificially intelligent software when there is a lack of training material, and has also assisted artists in creating portraits.
However, like anything tech-related, there is also a sinister side. The technology has been abused by miscreants to paste the faces of actresses, ex-girlfriends, politicians, and other victims, onto the bodies of porn stars. The result is fairly realistic, computer-generated video of people seemingly performing X-rated acts.
Now, PhD student Yuezun Li and Siwei Lyu, an associate computer-science professor at the New York state university in Albany, have come up with a technique that attempts to identify deepfake videos, such as those crafted by the open-source DeepFake FaceSwap algorithm.
Deepfakes are, for now, not hard for humans to spot. The doctored videos are uncanny, the facial expressions aren’t very natural, and any motion is pretty laggy and glitchy. They also have a lower resolution than the source material. Thus, people should be able to realize they are being hoodwinked after more than a few seconds. However, as the technology improves, it would be nice if machines could be taught the tell-tale signs of these forgeries so as to alert unaware folks in future.
Tomi Engdahl says:
Three ways to avoid bias in machine learning
https://techcrunch.com/2018/11/06/3-ways-to-avoid-bias-in-machine-learning/?utm_source=tcfbpage&sr_share=facebook
At this moment in history it’s impossible not to see the problems that arise from human bias. Now magnify that by compute and you start to get a sense for just how dangerous human bias via machine learning can be.
Exposing human data to algorithms exposes bias, and if we are considering the outputs rationally, we can use machine learning’s aptitude for spotting anomalies.
But the machines can’t do it on their own.
Tomi Engdahl says:
Mything the point: The AI renaissance is simply expensive hardware and PR thrown at an old idea
There is no ghost in the machine
https://www.theregister.co.uk/2018/11/06/andrew_fentem_on_ai/
For the last few years the media has been awash with hyperbole about artificial intelligence (AI) and machine learning technologies. It could be said that never, in the field of computer science, have so many ridiculous things been said by so many people in possession of so little relevant expertise.
Discussions about computer technologies tend to be conducted via myths, metaphors, and human interpretations of what is presented to us via the computer screen. Metaphors such as “intuition”, “creativity”, and novel “strategies” are part of an emerging mythology. AI pundits identify patterns in its game moves and call them “strategies”, but the neural-network has no idea what a “strategy” is. If there really is any “creativity” here, it is the creativity of DeepMind researchers who devise and manage the processes that train the systems.
Tomi Engdahl says:
Tekoälyä käytetään jatkuvasti enemmän, siksi sitä opetetaan käyttämään vastuullisesti – “vahva eettinen ohjeisto”
https://www.tivi.fi/Kaikki_uutiset/tekoalya-kaytetaan-jatkuvasti-enemman-siksi-sita-opetetaan-kayttamaan-vastuullisesti-vahva-eettinen-ohjeisto-6748344
Tomi Engdahl says:
AI Momentum, Maturity and Models for Success
Based on findings from a global executive survey
https://www.sas.com/en/white-papers/ai-momentum-maturity-success-models-109926.html
Tomi Engdahl says:
https://www.uusiteknologia.fi/2018/11/08/suosittu-tekoalykurssi-myos-suomeksi/
Tomi Engdahl says:
We Need an FDA For Algorithms
http://m.nautil.us/issue/66/clockwork/we-need-an-fda-for-algorithms?utm_source=pocket&utm_medium=email&utm_campaign=pockethits
UK mathematician Hannah Fry on the promise and danger of an AI world.
Humans can’t tell you accurately how they’re arriving at decisions. Humans are sloppy and messy and irrational.
What is the most dangerous algorithm?
In terms of wide-reaching impact, the stuff that’s happened with Facebook’s Newsfeed is really, really concerning. Fifteen years ago, let’s say, all of us were watching all the same TV programs, were reading the same newspapers. The places we would get our news, and especially our politics, tended to be universal.
Tomi Engdahl says:
UN Special Rapporteur analyses AI’s impact on human rights
https://edri.org/un-special-rapporteur-report-artificial-intelligence-impact-human-rights/
Tomi Engdahl says:
Google Cloud wants to make it easier for data scientists to share models
https://techcrunch.com/2018/11/08/new-google-tools-aim-to-make-it-easier-for-data-scientists-to-share-models/?sr_share=facebook&utm_source=tcfbpage
Tomi Engdahl says:
This Security Camera for Hackers Has Built-In TensorFlow AI Recognition
https://blog.hackster.io/this-security-camera-for-hackers-has-built-in-tensorflow-ai-recognition-dec69d4c9edd
Tomi Engdahl says:
Walmart is building an AI lab inside one of its store
https://techcrunch.com/2018/11/08/walmart-is-building-an-ai-lab-inside-one-of-its-store/?sr_share=facebook&utm_source=tcfbpage
Tomi Engdahl says:
Can you tell the difference? First AI newsreader looks UNCANNILY like real deal
https://www.express.co.uk/news/world/1042682/China-AI-newsreader-video-Sogou-robotics-robots-Xinhua-News-Oxford-University
IN a world first, a Chinese state news agency has created a virtual newsreader that works 24/7 and says whatever it is told to.
Tomi Engdahl says:
Is this AI? We drew you a flowchart to work it out
https://www.technologyreview.com/s/612404/is-this-ai-we-drew-you-a-flowchart-to-work-it-out/
The definition of artificial intelligence is constantly evolving, and the term often gets mangled, so we are here to help.
Tomi Engdahl says:
Machine learning with Python: Essential hacks and tricks
https://opensource.com/article/18/10/machine-learning-python-essential-hacks-and-tricks?sc_cid=7016000000127ECAAY
Master machine learning, AI, and deep learning with Python.
Tomi Engdahl says:
Bonsai Algorithm Enables Machine Learning On Arduino With A 2KB RAM Footprint
https://www.cnx-software.com/2018/10/31/bonsai-algorithm-machine-learning-arduino-2kb-ram/
Machine learning used to be executed in the cloud, then the inference part moved to the edge, and we’ve even seen micro-controllers able to do image recognition with GAP8 RISC-V micro-controller.
But I’ve recently come across a white paper entitled “Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things” that shows how it’s possible to perform such tasks with very little resources
https://redirect.viglink.com/?format=go&jsonp=vglnk_154188066163815&key=6fb3a5065613e981f56f6ca8abc2f761&libId=jobvvrxo0102l9e0000MA1v7gln7qp9a3w&loc=https%3A%2F%2Fwww.cnx-software.com%2F2018%2F10%2F31%2Fbonsai-algorithm-machine-learning-arduino-2kb-ram%2F&v=1&out=http%3A%2F%2Fmanikvarma.org%2Fpubs%2Fkumar17.pdf&title=Bonsai%20Algorithm%20Enables%20Machine%20Learning%20on%20Arduino%20with%20a%202KB%20RAM%20Footprint&txt=Resource-efficient%20Machine%20Learning%20in%202%20KB%20RAM%20for%20the%20Internet%20of%20Things