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:

    Graphcore Adds Financial Firepower with Another $200 Million

    Today, selling custom chips for artificial intelligence is still a small business. Intel, the largest manufacturer of computer processors, has appraised the current market at $2.5 billion, one half of one percent of the estimated value of the 2018 global semiconductor market. But startups are still raising billions of dollars of venture capital to plant a stake in the market, which research firm Morningstar says could be worth $20 billion in 2021.

  2. Tomi Engdahl says:

    Donie O’Sullivan / CNN:
    As “deepfake” videos become more common and convincing, the tech will give cover to those dismissing real events as fake; DARPA researches ways to detect fakes

    When seeing is no longer believing

    Inside the Pentagon’s race against deepfake videos

    Advances in artificial intelligence could soon make creating convincing fake audio and video – known as “deepfakes” – relatively easy. Making a person appear to say or do something they did not has the potential to take the war of disinformation to a whole new level. Scroll down for more on deepfakes and what the US government is doing to combat them.

  3. Tomi Engdahl says:

    Deep Learning in the Semiconductor Space

    Silicon system design with deep learning has the same challenges with other complex system-on-chip–based designs, only more so

  4. Tomi Engdahl says:

    What’s Next For AI, Quantum Chips

    Leaders of three R&D organizations, Imec, Leti and SRC, discuss the latest chip trends in AI, packaging and quantum computing.

  5. Tomi Engdahl says:

    AI + IoT = AIoT — What Lies Behind the Buzzwords?

    There is still a lot of hype around the AIoT, so it’s important to be able to separate what is currently feasible from what still lies in the future

    Buzzwords such as the internet of things (IoT), edge computing, and artificial intelligence (AI) have been circulating for quite some time. They tend to be thrown around quite liberally and can come across as somewhat nebulous to the average reader.

  6. Tomi Engdahl says:

    BD Zarley / The Verge:
    A look at computational psychiatry research at the Human Neuroimaging Lab at Virginia Tech, focused on developing AI algorithms for diagnosing mental illness

    Meet the scientists who are training AI to diagnose mental illness

  7. Tomi Engdahl says:

    Devin Coldewey / TechCrunch:
    IBM releases its Diversity in Faces (DiF) image set, comprised of 1M faces taken from a 100M image data set, to help reduce bias in AI

    IBM builds a more diverse million-face data set to help reduce bias in AI

  8. Tomi Engdahl says:

    AI Tradeoff: Accuracy or Robustness?

    Anyone poised to choose an AI model solely based on its accuracy might want to think again. A key issue, according to IBM Research, is how resistant the AI model is to adversarial attacks.

    IBM researchers, collaborating with other research institutes, are presenting two new papers on the vulnerability of AI. One study focuses on how to certify the robustness of AI against adversarial attacks. The other examines an efficient way to test resilience of AI models already deployed.

    Of course, accuracy is the Holy Grail of AI. If computers can’t beat humans, why bother with AI? Indeed, AI’s ability to recognize images and classify them has vastly improved over the last several years. As demonstrated in the results of ImageNet competitions between 2010 and 2017, computer vision can already outperform human abilities. AI’s accuracy in classifying objects in a dataset jumped from 71.8% to 97.3% in just seven years.

    Companies big and small have used ImageNet as a benchmark for their image classification algorithms against the dataset. Winning an ImageNet competition has bestowed bragging rights for AI algorithm superiority.

    Robustness gap
    However, the scientific community has begun paying attention to recent studies highlighting a robustness gap in well-trained deep neural networks versus adversarial examples.

  9. Tomi Engdahl says:

    A radical new neural network design could overcome big challenges in AI

    Researchers borrowed equations from calculus to redesign the core machinery of deep learning so it can model continuous processes like changes in health.

  10. Tomi Engdahl says:


    Aalto-yliopistossa kehitetty ARTIST-tekoäly (Artificial Intelligence for Spectroscopy) mullistaa sen, miten yksittäisten molekyylien spektri eli reaktio valoon voidaan selvittää.

  11. Tomi Engdahl says:

    Robots And AI: The Future Is Automated And Every Job Is At Risk [Automation, Pt. 1] | AJ+ Docs

    Robots are already changing jobs as an endless array of robots enter our everyday lives. From trucking to service work to high-end jobs like doctors and lawyers, this documentary explores how robotics and artificial intelligence are changing the workplace.

  12. Tomi Engdahl says:

    In 5 Years Robots Will Take Your Job! What Then?

    A Tale Of Two Cities: How Smart Robots And AI Will Transform America [Automation, Pt. 2] | AJ+ Docs

  13. Tomi Engdahl says:

    Pushing AI Into The Mainstream

    Why data scrubbing and social issues could limit the speed of adoption and the usefulness of this technology.

    But how deeply AI penetrates different market segments and technologies, and how quickly it pushes into the mainstream, depend on a variety of issues that still must be resolved. In addition to a plethora of technical issues, there needs to be progress in sanitizing data sets, resolving political, legal and ethical issues, and instilling trust in machines.

    None of these challenges is insurmountable, but a failure to deal with any of them will could delay the adoption of AI and slow or prevent it from reaching its full potential.

  14. Tomi Engdahl says:

    Using Memory Differently

    Optimizing complex chips requires decisions about overall system architecture, and memory is a key variable.

    What’s changed
    But with so much memory on the chip—in some cases it accounts for half the area of large SoCs—little changes can have a big impact. This is particularly true in AI applications, where small memories are often scattered around the chips with highly specific processing elements in order to process massive amounts of data more quickly. The challenge now is to minimize the movement of that data.

    “It has been through the emergence of the AI market and AI architectures that the idea of near-memory computing or in-memory computing has found its revival,” Macián said. “Once the idea was back on the table, people started looking at ways to use that same concept for things like RISC-V processors. Obviously, all processors have memories, and it has long been very important to select the right memories to obtain a good implementation of the processor in any die. We now have processor developers asking for memory that would perform some operation on the addresses before actually retrieving the data from the memory proper in order to simplify the circuitry around the memory. We are talking here about doing huge amounts of computation in the memory or near the memory, but with key elements, key operations that simplify greatly the logic around it.”

    What differentiates AI chips from other architectures is a relatively simple main computing element—the multiply accumulate (MAC) block. This block is parallelized and repeated thousands of times in an ASIC. Because of this replication, any small improvement made in the area or power, for example, has a huge overall effect in the chip, Macian explained.

  15. Tomi Engdahl says:

    Should AI Mimic Real Life?

    What the human body can teach us about the importance of communications.

    But what does the human body do? Does it have distributed intelligence or does everything go through the brain? Admittedly, communications within the body isn’t clogged with huge amounts of high-resolution video, meaning that it may not have the same bandwidth constraints, but here also is one departure between the two architectures. The human only transmits or processes the least amount of data possible. Of that 4K image on the screen, how much do the eyes actually take in and how much gets processed?

    It would appear that human vision works in an incremental fashion, first using the minimum amount of data necessary to make an initial determination. If there is uncertainty, then more data is brought in and this goes on until either a “match” is made, or we decide what it is closest to, and how it differs in an attempt to make an educated guess. How may that translate into ML – perhaps using the minimum accuracy first, say 4-bit, and then working its way up to longer data lengths only if necessary.

    That does not address distributed intelligence. The eyes and brain are too close together to effectively separate them. But movement and other body functions are different.

  16. Tomi Engdahl says:

    Brain-to-Speech System Bids Us “Hello”

    In the not-so-distant future, people denied the physical ability to speak will still be able to join in the conversation

    I don’t think that many of us have really grasped the implications of applying today’s emerging artificial intelligence (AI) and deep-learning technologies to tasks that are difficult to address any other way.

    For example, my chum Jay Dowling just sent me a link to an article describing how a group of engineers are working on a system that can translate brain signals directly into speech.


  17. Tomi Engdahl says:

    This Robot’s AI Can Develop Its Own Sense of Self-Awareness

    Humans and other animals possess a very useful awareness of their own bodies.


  18. Tomi Engdahl says:

    Let’s save the bees with machine learning

    Machine learning and all its related forms of “AI” are being used to work on just about every problem under the sun, but even so, stemming the alarming decline of the bee population still seems out of left field. In fact it’s a great application for the technology and may help both bees and beekeepers keep hives healthy

  19. Tomi Engdahl says:

    Foundations Built for a General Theory of Neural Networks
    January 31, 2019

    Neural networks can be as unpredictable as they are powerful. Now mathematicians are beginning to reveal how a neural network’s form will influence its function.

  20. Tomi Engdahl says:

    Sudo Find Me a Parking Space; Machine Learning Ends Circling the Block

    If you live in a bustling city and have anyone over who drives, it can be difficult for them to find parking. Maybe you have an assigned space, but they’re resigned to circling the block with an eagle eye. With those friends in mind, [Adam Geitgey] wrote a Python script that takes the video feed from a web cam and analyzes it frame by frame to figure out when a street parking space opens up. When the glorious moment arrives, he gets a text message via Twilio with a picture of the void.

    Snagging Parking Spaces with Mask R-CNN and Python
    Using Deep Learning to Solve Minor Annoyances

  21. Tomi Engdahl says:

    Ingrid Lunden / TechCrunch:
    Finland-based Relex, which helps retailers predict demand using AI, raises $200M from TCV

    Retail technology platform Relex raises $200M from TCV

    Relex — a company out of Finland that focuses on retail planning solutions by helping both brick-and-mortar as well as e-commerce companies make better forecasts of how products will sell using AI and machine learning, and in turn giving those retailers guidance on how and what should be stocked for purchasing — is today announcing that it has raised $200 million from TCV. The VC giant — which has backed iconic companies like Facebook, Airbnb, Netflix, Spotify and Splunk — last week announced a new $3 billion fund and this is the first investment out of it that is being made public.

    Relex is not disclosing its valuation but from what I understand it’s a minority stake, which would put it at between $400 million and $500 million.


  22. Tomi Engdahl says:

    Jaclyn Peiser / New York Times:
    Around 1/3 of the content published by Bloomberg News uses some form of automated tech, as more news outlets are employing AI for story recommendations and more

    The Rise of the Robot Reporter

    As reporters and editors find themselves the victims of layoffs at digital publishers and traditional newspaper chains alike, journalism generated by machine is on the rise.

    Roughly a third of the content published by Bloomberg News uses some form of automated technology. The system used by the company, Cyborg, is able to assist reporters in churning out thousands of articles on company earnings reports each quarter.

    The program can dissect a financial report the moment it appears and spit out an immediate news story that includes the most pertinent facts and figures. And unlike business reporters, who find working on that kind of thing a snooze, it does so without complaint.

    Untiring and accurate, Cyborg helps Bloomberg in its race against Reuters, its main rival in the field of quick-twitch business financial journalism

    “The financial markets are ahead of others in this,” said John Micklethwait, the editor in chief of Bloomberg.

    Last week, The Guardian’s Australia edition published its first machine-assisted article, an account of annual political donations to the country’s political parties. And Forbes recently announced that it was testing a tool called Bertie to provide reporters with rough drafts and story templates.

    A.I. journalism is not as simple as a shiny robot banging out copy. A lot of work goes into the front end, with editors and writers meticulously crafting several versions of a story, complete with text for different outcomes. Once the data is in — for a weather event, a baseball game or an earnings report — the system can create an article.

    A.I. in newsrooms may also go beyond the production of rote articles.

    “I hope we’ll see A.I. tools become a productivity tool in the practice of reporting and finding clues,”

  23. Tomi Engdahl says:

    Improve Airline Maintenance with AI & Analytics

    Historically, some of the worst aircraft disasters have been attributed to faulty or overlooked maintenance—and as recently as last year, maintenance was still factoring into the top five causes of domestic aircraft delays. It’s prompted aerospace companies to take a hard look at implementing analytics and artificial intelligence (AI) in order to predict potential maintenance failures on aircraft before the failures happen.

    As an aerospace manufacturer, Airbus is taking proactive steps to improve performance and reliability in the area of aircraft maintenance. It is doing this by migrating historical maintenance information from aircraft and fleets to a cloud-based data repository known as Skywise.

    “As an example, this data can record how the pressure of a certain type of hydraulic pump is gradually reducing over time,” said Norman Baker, senior vice president, Digital Solutions, at Airbus. “The Skywise analytics engine could then interpolate and ‘flag’ to the airline that the pump will likely be fine for the next five flights, but that failure is very likely to occur within ten more flights.”

  24. Tomi Engdahl says:

    James Vincent / The Verge:
    Google says Gmail is now blocking an extra 100M spam messages each day by using TensorFlow-trained machine learning models — Google is using its machine learning platform, TensorFlow, to eke out additional gains — Google has recruited its in-house machine learning framework, TensorFlow …

    Gmail is now blocking 100 million extra spam messages every day with AI

    Google is using its machine learning platform, TensorFlow, to eke out additional gains

  25. Tomi Engdahl says:

    Ruotsin Vinnova vauhdittaa tekoälykehitystä – suomalaiskoulutuksella

    Helsingin yliopiston ja teknologiayhtiö Reaktorin kehittämä Elements of AI -verkkokurssi alkaa tänä keväänä myös Ruotsissa. Kurssista kerrottiin eilen Vinnovan AI Innovation of Sweden -tekoälykeskuksen avajaisissa Göteborgissa.

  26. Tomi Engdahl says:

    In-Memory Computing Challenges Come Into Focus

    Researchers digging into ways around the von Neumann bottleneck.

  27. Tomi Engdahl says:

    Applied Materials’ Sanjay Natarajan argues that AI can be a vital disrupter of the semiconductor industry’s continual improvement mindset.

    AI is Changing the Way the Industry Thinks

    The emerging AI and Big Data era promises to be the biggest computing wave yet, becoming pervasive across industries, from healthcare to transportation. Enabling this future will require technology breakthroughs throughout the industry ecosystem, from materials to systems. At the center of making AI possible is the development of better neural processors. But to achieve this, we need a deeper understanding of how intelligence works in humans.

  28. Tomi Engdahl says:

    Jeff Buchanan / Xconomy:
    Seattle-based KenSci, which helps hospitals predict mortality risk using AI, raises $22M Series B led by Polaris Partners — Transitioning an elderly patient to palliative care can be a difficult decision for caregivers, but it’s one that families and clinicians across the globe make every day.

    KenSci Gets $22M for Tools to Help Hospitals Flag High-Risk Patients

  29. Tomi Engdahl says:

    James Vincent / The Verge:
    Google says Gmail is now blocking an extra 100M spam messages each day by using TensorFlow-trained machine learning models

    Gmail is now blocking 100 million extra spam messages every day with AI
    Google is using its machine learning platform, TensorFlow, to eke out additional gains

  30. Tomi Engdahl says:

    Benchmarks For The Edge
    What works, what doesn’t and why.

    February 7th, 2019 – By: Ed Sperling

    Geoff Tate, CEO of Flex Logix, talks about benchmarking in edge devices, particularly for convolutional neural networks.

  31. Tomi Engdahl says:

    Ruotsalaiset kehittivät oppivan transistorin

    Ruotsalaisen Linköpingin yliopiston tutkijat ovat kehittäneet orgaanisen transistorin, joka matkii aivojen toimintaa.

  32. Tomi Engdahl says:

    AI Could Make Quantum Computers a Reality

    New research is examining the use of artificial intelligence to handle the calculations necessary for quantum computers to function.

  33. Tomi Engdahl says:

    Mark Gurman / Bloomberg:
    Facebook acquires computer vision startup GrokStyle, whose tech is used in IKEA’s app to let users find furniture similar to that in their photos

    Facebook Acquires Visual Shopping Startup to Bolster AI Work

    Facebook Inc. said it acquired visual shopping and artificial intelligence startup GrokStyle Inc. in a move to bolster the social-media company’s own AI work.

  34. Tomi Engdahl says:


    Discover how AI has overtaken top lawyers for the first time in accurately spotting risks in everyday business contracts

  35. Tomi Engdahl says:

    Data: The Fuel Powering AI & Digital Transformation

    The currency of tomorrow isn’t what you think: It’s not cold hard cash, precious metals, land or even cryptocurrency – it’s data. In the very near future, every company in the world will either buy or sell data as this corporate asset continues to gain value.

    But, it’s not enough to have access to vast amounts of data, you need to understand it and use it.

    The promise of AI is that knowledge gained from applying analytics to the wealth of data that is available today will enhance any decision-making process with additional intelligence, helping us produce quicker, more effective outcomes

  36. Tomi Engdahl says:

    Is China’s corruption-busting AI system ‘Zero Trust’ being turned off for being too efficient?

    Despite being restricted to just 30 counties and cities, artificial intelligence system has already helped snare 8,721 officials
    System cross-references big data to evaluate work and personal lives of millions of government workers

  37. Tomi Engdahl says:

    Machine Learning’s ‘Amazing’ Ability to Predict Chaos
    April 18, 2018

    In new computer experiments, artificial-intelligence algorithms can tell the future of chaotic systems.

  38. Tomi Engdahl says:

    Trump’s planned AI initiative includes education, but lacks key details

    This weekend, the Trump administration shed more light on the “American A.I. Initiative,” a plan the President is set to sign today, in hopes of helping keep the U.S. at the forefront of innovation.

  39. Tomi Engdahl says:

    Is the artificial intelligence hype a bubble of misinformation? InsightsAtlas says yes.


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