3 AI misconceptions IT leaders must dispel


 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,”


  1. Tomi Engdahl says:

    Designs that take humans months can be matched or beaten by AI in six hours

    Google is using AI to design its next generation of AI chips more quickly than humans can
    Designs that take humans months can be matched or beaten by AI in six hours

  2. Tomi Engdahl says:

    Sage Lazzaro / VentureBeat:
    UNESCO: AI and robotics have “dominated scientific output” in recent years; ~150K research articles were published on those subjects in 2019, up 44% from 2015 — Elevate your enterprise data technology and strategy at Transform 2021. — The United Nations Educational …

    AI ‘dominated scientific output’ in recent years, UNESCO report shows

    The United Nations Educational, Scientific, and Cultural Organization (UNESCO) today unveiled its latest Science Report. The massive undertaking — this year’s report totals 762 pages, compiled by 70 authors from 52 countries over 18 months — is published every five years to examine current trends in science governance. This latest edition includes discussion of the rapid progress toward Industry 4.0 and, for the first time, a deep analysis of AI and robotics research around the globe. Going beyond just the global leaders, it offers an overview of almost two dozen countries and global regions, examining AI research, funding, strategies, and more. Overall, the report determines “it is the field of AI and robotics that dominated scientific output” in recent years.

    “We take a look at the broad field of cross-cutting strategic technologies and break it down comprehensively into the 10 subfields. Artificial intelligence and robotics is one of those subfields, and it’s the biggest based on the number of publications,” report team deputy editor Tiffany Straza told VentureBeat. “Globally, there was kind of an easing off of interest around 2015, and then it spiked right back up. To me, it represents that this is a priority topic around the world.”

  3. Tomi Engdahl says:

    Fields such as agriculture, forestry, fishing, and hunting in which you might not expect to see large number of AI and machine learning professionals are hiring like mad according to a recent analysis.

    Where Are the AI Jobs? Look to a Farm or a Forest

    Where are the AI jobs in the U.S.? The largest share, of course, is in the IT industry, with professional, scientific, and tech services coming in second place. But coming on strong are fields in which you might not expect to see large number of AI and machine learning professionals—agriculture, forestry, fishing, and hunting, according to an analysis of 2020 job postings.

    For AI is increasingly being applied to forest conservation and management. Meanwhile, farm equipment maker John Deere put big and early bets on machine learning, and other ag-related businesses large and small are using AI for soil analysis, monitoring crop health, planning planting cycles, and a host of other purposes.

  4. Tomi Engdahl says:

    Todd Feathers / VICE:
    A look at lip-reading AI, with development supported by Google, Sony, and Huawei, as startups begin deploying it in hospitals, public transport systems, more

    Tech Companies Are Training AI to Read Your Lips

    First came facial recognition. Now, an early form of lip-reading AI is being deployed in hospitals, power plants, public transportation, and more.

  5. Tomi Engdahl says:

    James Vincent / The Verge:
    Facebook partners with Michigan State University to create a method for reverse-engineering deepfakes by using AI to reveal the ML model that created it — The work could help future deepfake investigations — Deepfakes aren’t a big problem on Facebook right now, but the company continues …

    Facebook develops new method to reverse-engineer deepfakes and track their source
    The work could help future deepfake investigations

    Deepfakes aren’t a big problem on Facebook right now, but the company continues to fund research into the technology to guard against future threats. Its latest work is a collaboration with academics from Michigan State University (MSU), with the combined team creating a method to reverse-engineer deepfakes: analyzing AI-generated imagery to reveal identifying characteristics of the machine learning model that created it.

    The work is useful as it could help Facebook track down bad actors spreading deepfakes on its various social networks. This content might include misinformation but also non-consensual pornography — a depressingly common application of deepfake technology. Right now, the work is still in the research stage and isn’t ready to be deployed.

    Reverse engineering generative models from a single deepfake image

    Deepfakes have become more believable in recent years. In some cases, humans can no longer easily tell some of them apart from genuine images. Although detecting deepfakes remains a compelling challenge, their increasing sophistication opens up more potential lines of inquiry, such as: What happens when deepfakes are produced not just for amusement and awe, but for malicious intent on a grand scale? Today, we — in partnership with Michigan State University (MSU) — are presenting a research method of detecting and attributing deepfakes that relies on reverse engineering from a single AI-generated image to the generative model used to produce it. Our method will facilitate deepfake detection and tracing in real-world settings, where the deepfake image itself is often the only information detectors have to work with.

  6. Tomi Engdahl says:

    #GPT-3 AI text-generation algorithm has been known to reveal bias and racism. A new study used the “Two guys walked into a bar” joke template to test just how bad GPT-3 can get if prompted. The results weren’t funny.

  7. Tomi Engdahl says:

    Also a good read on how this team detects “deep fakes” video or images.

    Facebook’s latest AI doesn’t just detect deep fakes, it knows where they came from

    The social media platform worked with a team from Michigan State to develop the system.

  8. Tomi Engdahl says:

    Nicole Kobie / Wired UK:
    A look at the evolution of AI chips and where they are headed, as companies like Google, Amazon, Graphcore, and Cerebras look to challenge Nvidia’s dominance — NVIDIA’s GPUs dominate AI chips. But a raft of startups say new architecture is needed for the fast-evolving AI field


  9. Tomi Engdahl says:

    I just watched McDonald’s new AI drive-thru and I’ve lost my appetite

    You’d think new technology would work hard at making you feel welcome at the drive-thru. Not necessarily. And now, oh, a McDonald’s customer is suing the company for alleged breach of privacy laws. Yes, at the robot drive-thru.

    So when McDonald’s revealed it was testing the idea of replacing humans at the drive-thru with robots, I was filled with cautious optimism.

    Would customers be greeted with a surprisingly chirpy voice, redolent of a young person who really enjoys high school?

    Sadly, I haven’t been near Chicago lately and that’s where the burger chain is testing this as yet imperfect system — McDonald’s confesses the robot only grasps your order 85% of the time.

    McDonald’s is trying something new. It may drive customers mad

    Whenever a fast-food chain tries to inject more technology into its offering, there are always issues. In this case, 15% of the time, it seems.

  10. Tomi Engdahl says:

    Tekoäly nopeuttaa FPGA-piirien suunnittelua

    FPGA-yritys Xilinx on esitellyt ensimmäiset työkalut, joilla piirien suunnittelua voidaan tehoastaa koneoppimismalleilla. Yhtiön mukaan Vivado ML -työkaluilla koodin kääntämisestä tulee viisi kertaa nopeampaa ja koodin laatu paranee 10 prosenttia verrattuna täm10 prosenttia verrattuna tämän hetken Vivado HLx Editions -ohjelmaan.

    Suunnittelijoiden jatkuvasti kasvava monimutkaisuus on iso haaste tämän päivän suunnittelijoille. Koneoppiminen on seuraava iso edistysaskel suunnitteluprosessin nopeuttamisessa ja koodin laadun parantamisessa, kertoo Xilinxin markkinointi-, ohjelmisto- ja tekoälyratkaisujen johtaja Nick Ni.

    Vivado ML Editions mahdollistaa ML-pohjaiset algoritmit, jotka nopeuttavat suunnittelun valmiiksi saattamista. Teknologia sisältää ML-pohjaisen logiikan optimoinnin, viiveiden arvioinnin ja älykkäät suunnitteluajot. Tämän seurauksena suunnittelujen ajoitusongelmat voidaan ratkaista nopeammin.

  11. Tomi Engdahl says:

    Tesla shows off the AI supercomputer training https://www.theregister.com/2021/06/27/in_brief_ai/
    Tesla is using a 1.8-exaFLOP AI supercomputer packed with 5, 760 GPUs that train neural networks it hopes one day will power autonomous vehicles.

  12. Tomi Engdahl says:

    TOPS: The Truth Behind a Deep Learning Lie

    AI companies generally home in on one criterion: more tera operations per second (TOPS). Unfortunately, when silicon manufacturers promote their TOPS metrics, they are not really providing accurate guidance. In most cases, the numbers being hyped aren’t real TOPS, but peak TOPS. In other words, the TOPS number you think you’re getting in a card is actually the best-case scenario of how the chip would perform in a more than perfect world.

    I will discuss the problems the industry has created by mislabeling performance metrics and explain how users can independently evaluate real-world TOPS.

    Faux TOPS vs real TOPS

    AI application developers generally start performing due diligence by gauging whether a chip manufacturer’s published TOPS performance data is adequate for powering their project.

    Say you’re trying to remaster images in full HD on the U-Net neural network at 10 fps (frames per second). Since U-Net operations require 3 TOPS per image, simple math says you’ll need 30 TOPS to complete your project at the desired FPS. So, when shopping for a chip, you would assume that cards claiming to run 50, 40, or even 32 TOPS would be safe for the project. In a perfect world, yes, but you’ll soon find out that the card rarely hits the advertised number. And we’re not talking about drops of just a couple of TOPS; compute efficiency can be as low as 10 percent.

  13. Tomi Engdahl says:


    Tiede: Älykkäästi toimivat koneet eivät uhkaa ihmistä, mutta algoritmien etiikkaa on syytä pohtia – “Jokaisesta käynnistä somessa jää jälki”, sanoo tekoälyasiantuntija Anna-Mari Rusanen, jonka kissa koukuttui videopeliin
    Tietokoneohjelmistot ovat oman erikoisalansa superasiantuntijoita. Ne ratkovat vain sitä ongelmaa, mikä niille on annettu ratkottavaksi.

  14. Tomi Engdahl says:

    Jordan Novet / CNBC:
    Microsoft and OpenAI announce GitHub Copilot, an AI-based tool to recommend code to software developers in almost any programming language — – GitHub, GitHub’s parent company Microsoft and OpenAI have teamed up to deliver a tool that comes up with source code for programmers to use as they work.

    Microsoft and OpenAI have a new A.I. tool that will give coding suggestions to software developers

    GitHub, GitHub’s parent company Microsoft and OpenAI have teamed up to deliver a tool that comes up with source code for programmers to use as they work.
    The system can make recommendations in almost any programming language, although it works best with the popular JavaScript, Python and TypeScript languages.
    OpenAI will release the underlying online service for other companies to use this summer.

  15. Tomi Engdahl says:

    It’s Easy for Computers to Detect Sarcasm, Right?

    Sentiment analysis, which can already identify anger, joy, fear, sadness, and confidence, can now spot sarcasm as well

  16. Tomi Engdahl says:

    Fired by Bot at Amazon: ‘It’s You Against the Machine’
    Contract drivers say algorithms terminate them by email—even when they have done nothing wrong.

    Stephen Normandin spent almost four years racing around Phoenix delivering packages as a contract driver for Amazon.com Inc. Then one day, he received an automated email. The algorithms tracking him had decided he wasn’t doing his job properly.

    The 63-year-old Army veteran was stunned. He’d been fired by a machine.

    Normandin says Amazon punished him for things beyond his control that prevented him from completing his deliveries, such as locked apartment complexes.

    Normandin’s experience is a twist on the decades-old prediction that robots will replace workers. At Amazon, machines are often the boss—hiring, rating and firing millions of people with little or no human oversight.

    Amazon became the world’s largest online retailer in part by outsourcing its sprawling operations to algorithms

    For years, the company has used algorithms to manage the millions of third-party merchants on its online marketplace, drawing complaints that sellers have been booted off after being falsely accused of selling counterfeit goods and jacking up prices.

    Increasingly, the company is ceding its human-resources operation to machines as well, using software not only to manage workers in its warehouses but to oversee contract drivers, independent delivery companies and even the performance of its office workers. People familiar with the strategy say Chief Executive Officer Jeff Bezos believes machines make decisions more quickly and accurately than people, reducing costs and giving Amazon a competitive advantage.

    Rather than making the customer wait, Flex drivers ensure the packages are delivered the same day. They also handle a large number of same-day grocery deliveries from Amazon’s Whole Foods Market chain. Flex drivers helped keep Amazon humming during the pandemic and were only too happy to earn about $25 an hour shuttling packages after their Uber and Lyft gigs dried up.

    But the moment they sign on, Flex drivers discover algorithms are monitoring their every move. Did they get to the delivery station when they said they would? Did they complete their route in the prescribed window? Did they leave a package in full view of porch pirates instead of hidden behind a planter as requested? Amazon algorithms scan the gusher of incoming data for performance patterns and decide which drivers get more routes and which are deactivated. Human feedback is rare. Drivers occasionally receive automated emails, but mostly they’re left to obsess about their ratings, which include four categories: Fantastic, Great, Fair or At Risk.

    Bloomberg interviewed 15 Flex drivers, including four who say they were wrongly terminated, as well as former Amazon managers who say the largely automated system is insufficiently attuned to the real-world challenges drivers face every day. Amazon knew delegating work to machines would lead to mistakes and damaging headlines, these former managers said, but decided it was cheaper to trust the algorithms than pay people to investigate mistaken firings so long as the drivers could be replaced easily.

    So far, Amazon has had no trouble finding Flex contractors. Globally, some 4 million drivers have downloaded the app, including 2.9 million in the U.S., according to App Annie.

  17. Tomi Engdahl says:

    It’s thought this is the first time a swarm of AI drones has been used in combat.

    First “AI War”: Israel Used World’s First AI-Guided Swarm Of Combat Drones In Gaza Attacks

  18. Tomi Engdahl says:

    Here’s when machines will take your job, as predicted by A.I. gurus
    An MIT study predicts when artificial intelligence will take over for humans in different occupations.

  19. Tomi Engdahl says:

    Experts Doubt Ethical AI Design Will Be Broadly Adopted as the Norm Within the Next Decade

    A majority worries that the evolution of artificial intelligence by 2030 will continue to be primarily focused on optimizing profits and social control. They also cite the difficulty of achieving consensus about ethics. Many who expect progress say it is not likely within the next decade. Still, a portion celebrate coming AI breakthroughs that will improve life

  20. Tomi Engdahl says:

    Kyle Orland / Ars Technica:
    Makers of CVCheat claim their computer vision-based cheat tool can offer “undetectable” “full auto-aim and auto-shots” for any game on PC, Xbox, or PlayStation — Capture cards, input hardware, and machine learning get around system-level lockdowns.

    Cheat-maker brags of computer-vision auto-aim that works on “any game”
    Capture cards, input hardware, and machine learning get around system-level lockdowns.

    When it comes to the cat-and-mouse game of stopping cheaters in online games, anti-cheat efforts often rely in part on technology that ensures the wider system running the game itself isn’t compromised. On the PC, that can mean so-called “kernel-level drivers” which monitor system memory for modifications that could affect the game’s intended operation. On consoles, that can mean relying on system-level security that prevents unsigned code from being run at all (until and unless the system is effectively hacked, that is).

    How it works

    The basic toolchain used for these external emulated-input cheating methods is relatively simple. The first step is using an external video capture card to record a game’s live output and instantly send it to a separate computer. Those display frames are then run through a computer vision-based object detection algorithm like You Only Look Once (YOLO) that has been trained to find human-shaped enemies in the image (or at least in a small central portion of the image near the targeting reticle).

    Once the enemy is identified on the screen, these cheating engines can easily calculate precisely how far and in which direction the mouse needs to move to put that enemy (or even a specific body part, like the head) in the center of the crosshairs. That data is then sent to an input-passthrough device like the Titan Two or the Cronus Zen, which emulates the correct mouse input and fires a shot at superhuman speed.

    On their own, all of these external devices and tools have legitimate uses (though the automated macros enabled by input-passthrough devices are controversial in many competitive gaming circles). Put them all together, however, and you get an effective cheating engine that doesn’t require any modifications to the software or hardware that’s actually running the game. In a way, it’s kind of like printing a gun from basic 3D-printer resin, or building an explosive from chemicals derived from legal products.

  21. Tomi Engdahl says:

    AI Designs Quantum Physics Experiments Beyond What Any Human Has Conceived
    Originally built to speed up calculations, a machine-learning system is now making shocking progress at the frontiers of experimental quantum physics

  22. Tomi Engdahl says:

    Intruder-Blasting Sprinkler Is an AI-Powered Substitute for an Old Man Yelling at Kids to Get Off His Lawn
    You can finally be that grumpy old neighbor without actually being old or grumpy.

  23. Tomi Engdahl says:

    Cyber Valley researchers link AI and society

    Baden-Württemberg in Germany is home to Cyber Valley, where development of artificial intelligence (AI) reaches across society.

  24. Tomi Engdahl says:

    Researchers Hid Malware Inside an AI’s ‘Neurons’ And It Worked Scarily Well
    In a proof-of-concept, researchers reported they could embed malware in up to half of an AI model’s nodes and still obtain very high accuracy.

  25. Tomi Engdahl says:

    Soft, Wireless Brain-Computer Interface Turns Your Thoughts Into Actions
    Designed to transmit via Bluetooth, this prototype soft-circuit wireless EEG feeds a machine learning system to control things by thought

  26. Tomi Engdahl says:

    Man creates A.I. version of dead fiancée so he can still text her

    A man in the United States has created an Artificial intelligence (A.I.) version of his fiancée eight years after she passed away.

    Joshua Borbeau’s fiancée, Jessica Pereira, died from a rare form of liver disease and the grieving man has now created an electronic version of her so he can text her.

    The story was reported by the San Francisco Chronicle and details how Borbeau created the A.I. version of Pereira using machine-learning software online.


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