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:

    Ambarella’s CV5 Chip Boasts 8K Image Processing, AI Acceleration, and Encodes in Just 2W

    Targeting AI cameras and supporting one 8K or four 4K video streams, the new part can encode 8K video in just 2W of power.

  2. Tomi Engdahl says:

    Facial recognition technology can expose political orientation from naturalistic facial images

  3. Tomi Engdahl says:

    Calculations Show It’ll Be Impossible to Control a Super-Intelligent AI

    The idea of artificial intelligence overthrowing humankind has been talked about for many decades, and scientists have just delivered their verdict on whether we’d be able to control a high-level computer super-intelligence. The answer? Almost definitely not.

  4. Tomi Engdahl says:

    AI capable of determining a person’s political affiliation based on their photo with finds liberals face the camera while conservatives have a look of disgust

  5. Tomi Engdahl says:

    Artificial Intelligence Has Discovered 1,210 New Gravitational Lenses

    Massive objects distort space-time, and some can warp it to such a degree where the light of distant background galaxies is turned into large magnified arcs and rings. Finding them, especially the biggest ones, is not an easy task, so researchers had to deploy a new approach: a machine learning algorithm designed specifically to find them. And it was greatly successful.

    The algorithm went through the exquisite map produced by the DESI (Dark Energy Spectroscopic Instrument) Legacy Imaging Surveys, the largest ever produced. It was able to discover 1,210 new lenses, almost doubling the number of gravitational lenses known to humanity. The discovery is reported in a paper accepted for publication in The Astrophysical Journal.

  6. Tomi Engdahl says:

    Google trained a trillion-parameter AI language model

    Parameters are the key to machine learning algorithms. They’re the part of the model that’s learned from historical training data. Generally speaking, in the language domain, the correlation between the number of parameters and sophistication has held up remarkably well. For example, OpenAI’s GPT-3 — one of the largest language models ever trained, at 175 billion parameters — can make primitive analogies, generate recipes, and even complete basic code.

    In what might be one of the most comprehensive tests of this correlation to date, Google researchers developed and benchmarked techniques they claim enabled them to train a language model containing more than a trillion parameters. They say their 1.6-trillion-parameter model, which appears to be the largest of its size to date, achieved an up to 4 times speedup over the previously largest Google-developed language model (T5-XXL).

  7. Tomi Engdahl says:

    How Artificial Neural Networks Paved the Way For A Dramatic New Theory of Dreams

    Machine learning experts struggle to deal with “overfitting” in neural networks. Evolution solved it with dreams, says new theory.

    the human brain sometimes links events that have little or no causal connection. Computer scientists have a different way of thinking about it. For them, this is an example of “overfitting” — using irrelevant detail to construct a model. There may be many factors that contribute to the success of a particular tennis shot or basketball throw or home run but the color of socks or underpants is probably not one of them.

    Exactly the same thing occurs with artificial neural networks. The networks learn relevant detail but also irrelevances. Indeed, overfitting is the bane of machine learning experts who have devised a wide range of techniques to get around it.

  8. Tomi Engdahl says:

    Will Knight / Wired:
    A Harvard medical student used OpenAI’s GPT-2 to submit comments on Idaho’s Medicaid draft proposal; volunteers couldn’t tell them apart from those by humans

    AI-Powered Text From This Program Could Fool the Government

    A Harvard medical student submitted auto-generated comments to Medicaid; volunteers couldn’t distinguish them from those penned by humans.

    In October 2019, Idaho proposed changing its Medicaid program. The state needed approval from the federal government, which solicited public feedback via Medicaid.gov.

    Roughly 1,000 comments arrived. But half came not from concerned citizens or even internet trolls. They were generated by artificial intelligence. And a study found that people could not distinguish the real comments from the fake ones.

    The project was the work of Max Weiss, a tech-savvy medical student at Harvard, but it received little attention at the time. Now, with AI language systems advancing rapidly, some say the government, and internet companies, need to rethink how they solicit and screen feedback to guard against deepfake text manipulation and other AI-powered interference.

  9. Tomi Engdahl says:

    Researchers Have Developed A Robot With A “Primitive Form Of Empathy”

    If robots are ever to interact socially with humans, they will first need to develop the capacity for Theory of Mind (ToM), which entails the ability to empathize with others. While the development of artificial intelligence (AI) systems with such advanced cognition remain some way off, researchers from Columbia University have succeeded in creating a robot with what they call “visual theory of behavior”. Describing their work in the journal Scientific Reports, the study authors explain that this trait may well have arisen in animals as an evolutionary precursor to ToM, and could represent a major step towards the creation of AI with complex social capabilities.

  10. Tomi Engdahl says:

    Computer scientists: We wouldn’t be able to control super intelligent machines
    New findings from theoretical computer science

  11. Tomi Engdahl says:

    Superintelligent AI May Be Impossible to Control; That’s the Good News

    It may be theoretically impossible for humans to control a superintelligent AI, a new study finds. Worse still, the research also quashes any hope for detecting such an unstoppable AI when it’s on the verge of being created.

    Slightly less grim is the timetable. By at least one estimate, many decades lie ahead before any such existential computational reckoning could be in the cards for humanity.

    Alongside news of AI besting humans at games such as chess, Go and Jeopardy have come fears that superintelligent machines smarter than the best human minds might one day run amok. “The question about whether superintelligence could be controlled if created is quite old,” says study lead author Manuel Alfonseca, a computer scientist at the Autonomous University of Madrid. “It goes back at least to Asimov’s First Law of Robotics, in the 1940s.”

  12. Tomi Engdahl says:

    Superintelligence Cannot be Contained: Lessons from Computability Theory

  13. Tomi Engdahl says:

    Eight “Illusion” Chips, Built with Resistive RAM, Prove Near-Ideal for Deep Neural Networks

    Based on a 3D layout, Illusion has a single-digit-percentage performance impact when slicing work up between multiple chips.

  14. Tomi Engdahl says:

    A Multi-Gigahertz “Photonic Chip” Driven by a Frequency Comb Could Blow Current AI Accelerators Away

    Processing multiple wavelengths of light in parallel, the new chip promises considerably boosted performance and efficiency.

  15. Tomi Engdahl says:

    Hungry for Knowledge? Get FedML

    Open source machine learning library FedML is an end-to-end toolkit for developing and benchmarking federated learning algorithms.

  16. Tomi Engdahl says:

    DALL·E: Creating Images from Text

    We’ve trained a neural network called DALL·E that creates images from text captions for a wide range of concepts expressible in natural language.

  17. Tomi Engdahl says:

    Forbes: How the EU is leading the way in AI powered social innovation – Estonia again among the frontrunners

    As the EU as a whole has made major strides in AI-powered social innovation, within Europe, there are a handful of key players that are impacting the ecosystem the most: Denmark, Slovenia and the country that, according to Forbes, is the world’s one and only e-Nation: Estonia.

  18. Tomi Engdahl says:

    Microsoft has patented a chatbot that could imitate a deceased loved one, celebrity, or fictional character

    Microsoft has been granted a patent for a chatbot that can take on the personality of real people.
    The chatbot would be created through content readily available on social media.
    Microsoft’s General Manager of AI Programs says he is unaware of any plans to build the chatbot.


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