3 AI misconceptions IT leaders must dispel

https://enterprisersproject.com/article/2017/12/3-ai-misconceptions-it-leaders-must-dispel?sc_cid=7016000000127ECAAY

 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,”

936 Comments

  1. Tomi Engdahl says:

    Cutting-Edge Face Recognition is Complicated. These Spreadsheets Make it Easier.
    https://towardsdatascience.com/cutting-edge-face-recognition-is-complicated-these-spreadsheets-make-it-easier-e7864dbf0e1a?fbclid=IwAR1R5VTzLSntR3VsiKLgXSSH1bGMjqfCk9L9qGqCHX8eJWHG1iG0QY5ohd4

    9 Steps to Building a Deep Convolutional Neural Net in Excel for Normal Humans.

    Reply
  2. Tomi Engdahl says:

    Space station robot goes rogue: International Space Station’s artificial intelligence has turned belligerent
    https://www.foxnews.com/tech/space-station-robot-goes-rogue-international-space-stations-artificial-intelligence-has-turned-belligerent

    CIMON isn’t much to look at. It’s just a floating ball with a cartoonish face on its touch screen. It’s built to be a personal assistant for astronauts working on the International Space Station (ISS).

    It’s also supposed to be something more.

    CIMON stands for Crew Interactive MObile compinioN.

    Yes, it’s a personality prototype.

    You can tell, can’t you?

    But, as numerous books and movies have clearly warned us — shortly after being switched on for the first time, CIMON has developed a mind of its own.

    And it appears CIMON wants to be the boss.

    This has CIMON’s ‘personality architects’ scratching their heads.

    Reply
  3. Tomi Engdahl says:

    A new brain-inspired architecture could improve how computers handle data and advance AI
    https://www.sciencedaily.com/releases/2018/10/181003162715.htm

    Reply
  4. Tomi Engdahl says:

    AI desperately needs regulation and public accountability, experts say
    https://techcrunch.com/2018/12/07/ai-desperately-needs-regulation-and-public-accountability-experts-say/?utm_source=tcfbpage&sr_share=facebook

    Artificial intelligence systems and creators are in dire need of direct intervention by governments and human rights watchdogs, according to a new report from researchers at Google, Microsoft and others at AI Now. Surprisingly, it looks like the tech industry just isn’t that good at regulating itself.

    In the 40-page report (PDF) published this week, the New York University-based organization

    https://ainowinstitute.org/AI_Now_2018_Report.pdf

    Reply
  5. Tomi Engdahl says:

    Neuromorphic computing gives AI a real-time boost
    https://www.edn.com/5G/4461349/Neuromorphic-computing-gives-AI-a-real-time-boost?utm_source=Aspencore&utm_medium=EDN&utm_campaign=social

    now there are real-time applications emerging at the network edge that require instantaneous learning from small data sets, using minimal computational resources, and these applications can’t afford the network latency inherent in relying on data centers. A type of machine learning called neuromorphic computing fits that bill. One type of neuromorphic computing, known as spiking neural networks (SNN), is particularly well-suited to those demands.

    Reply
  6. Tomi Engdahl says:

    DeepMind’s AlphaZero now showing human-like intuition in historical ‘turning point’ for AI
    https://www.telegraph.co.uk/science/2018/12/06/deepminds-alphazero-now-showing-human-like-intuition-creativity/

    DeepMind’s artificial intelligence programme AlphaZero is now showing signs of human-like intuition and creativity, in what developers have hailed as ‘turning point’ in history.

    Reply
  7. Tomi Engdahl says:

    AI in 2019: 8 trends to watch
    https://enterprisersproject.com/article/2018/12/ai-trends-2019?sc_cid=7016000000127eyAAA

    Forget the job-stealing robot predictions. Let’s focus on artificial intelligence trends – around talent, security, data analytics, and more – that will matter to IT leaders

    Reply
  8. Tomi Engdahl says:

    http://www.etn.fi/index.php/13-news/8815-koneoppiminen-tuli-sulautettavalle-fpga-piirille

    Piilaaksolainen Achronix tunnetaan sekä erittäin suorituskykyisistä Speedster-piireistä, että järjestelmäpiirille sulautettavista ohjelmoitavista eFPGA-piireistä. Nyt yhtiö on esitellyt neljännen polven sulautettavat Speedcore-ytimet, joilla suunnitteluihin saadaan tuotua erillinen koneoppimisprosessori.

    Reply
  9. Tomi Engdahl says:

    Inferencing In Hardware
    How to improve the efficiency of neural networks.
    https://semiengineering.com/inferencing-in-hardware/

    Cheng Wang, senior vice president of engineering at Flex Logix, examines shifting neural network models, how many multiply-accumulates are needed for different applications, and why programmable neural inferencing will be required for years to come.

    https://www.youtube.com/watch?v=jb7qYU2nhoo

    Reply
  10. Tomi Engdahl says:

    China’s Big AI Plan: Do Toys Count, Too?
    https://www.eetimes.com/author.asp?section_id=36&doc_id=1334061

    The most convincing indication of China’s rise in the AI market that I recently came across was the plethora of AI-driven toys displayed, demonstrated, and pitched at shop after shop in the Shenzhen airport.

    China’s big AI plan — to dominate the global market with its AI technology by 2030 — is widely known and deeply feared in the rest of the world. The lingering mystery, though, is how much of the best-laid plans of China will come to pass, if ever.

    China’s State Council proposed in July 2017 “A Next-Generation Artificial Intelligence Development Plan.” It says that China will ultimately become the world leader in artificial intelligence, with a domestic AI industry worth almost $150 billion by 2030. The first step is to catch up with the United States on AI technology and applications by 2020.

    It’s easy to call this mere bravado, but it’s probably not wise. As I see it, the Chinese genie is already out of the bottle.

    Beijing’s “national champion” technology leaders — Baidu, Alibaba, Tencent, and iFlyTek — are established, successful firms. They are leading the development of innovation platforms such as self-driving cars, smart cities, computer vision for medical diagnosis, and voice intelligence.

    Reply
  11. Tomi Engdahl says:

    Inferencing In Hardware
    How to improve the efficiency of neural networks.
    https://semiengineering.com/inferencing-in-hardware/

    Reply
  12. Tomi Engdahl says:

    Neuromorphic computing gives AI a real-time boost
    https://www.edn.com/5G/4461349/Neuromorphic-computing-gives-AI-a-real-time-boost?utm_source=newsletter&utm_campaign=link&utm_medium=EDNFunFriday-20181207

    There are a number of different approaches to machine learning, like decision tree learning, inductive logic programming, and association rule learning, but perhaps the most successful and widespread technique is the use of artificial neural networks, or ANNs.

    All neural networks might be considered “artificial” in that they all seek to imitate the neural activity in the brain.

    ANNs have proven to be very effective at a number of tasks, especially those involving pattern recognition. That includes such applications as computer vision, speech recognition, or medical diagnosis from symptoms or scans.

    Data centers versus the edge
    For the past few decades, neural networks have largely been implemented in software, operating as a model, executed on general-purpose processors. The software emulates the way that each individual neuron functions, as well as the interconnections between them that govern their collective behavior.

    Reply
  13. Tomi Engdahl says:

    Miten rakennat sillan tekoälyhypen ja todellisuuden välille?
    https://pulse.microsoft.com/fi-fi/business-leadership-fi-fi/na/fa2-miten-rakennat-sillan-tekoalyhypen-ja-todellisuuden-valille/?utm_campaign=AI&utm_medium=content_social_ad74_fifi&utm_content=none_FY19_Q2_BDM&utm_source=facebook

    Tekoälyhypeltä on mahdotonta välttyä, sillä tekoälystä kirjoitetaan nyt paljon. Ennen kuin otsikon kysymykseen voi järkevästi vastata, täytyy selventää pari perusasiaa.

    Reply
  14. Tomi Engdahl says:

    How to build deep learning inference through Knative serverless framework
    https://opensource.com/article/18/12/deep-learning-inference?sc_cid=7016000000127ECAAY

    Using deep learning to classify images when they arrive in object storage

    Reply
  15. Tomi Engdahl says:

    How to get started in AI
    https://opensource.com/article/18/12/how-get-started-ai?sc_cid=7016000000127ECAAY

    Before you can begin working in artificial intelligence, you need to acquire some human intelligence.

    Reply
  16. Tomi Engdahl says:

    Your Next SoC Will Probably Include AI Acceleration
    It may be possible to get an SoC without AI acceleration, but the trend is to provide mainstream machine-learning support.
    https://www.electronicdesign.com/industrial-automation/your-next-soc-will-probably-include-ai-acceleration?NL=ED-005&Issue=ED-005_20181212_ED-005_493&sfvc4enews=42&cl=article_1_b&utm_rid=CPG05000002750211&utm_campaign=22085&utm_medium=email&elq2=3e3e745262974c67a04d72240436fe77

    System-on-chip (SoC) solutions continue to get more complex as more specialized hardware is added to optimize the SoC for new applications. Qualcomm’s latest Snapdragon 855 (Fig. 1) highlights this change. The 855 includes a number of blocks including the Snapdragon X24 cellular modem and wireless Wi-Fi, Bluetooth, and GPS support from the Adreno 640 GPU, the Hexagon 690 DSP, the Kryo 485 processor cluster, and the Spectra 380 image signal processor (ISP).

    More Machine Learning

    ML support is showing up at all levels. MediaTek’s Helio P70 is built around an octal core big.LITTLE configuration with four 2.1-GHz Arm Cortex-A73s and four power-efficient, 2-GHz Cortex-A53s. There’s also a 900-MHz Arm Mali-G72 GPU and a dual-core AI processing unit (APU). The APU is designed to handle chores like human pose recognition in real-time as well as augmentation to still images and video. It can deliver 280 GMACs.

    Moving further down the scale is Renesas’ RZ/A2M with DRP. It’s designed to support human machine interfaces (HMIs) including systems with cameras. It has an Arm Cortex-A9 along with Renesas’ Dynamically Reconfigurable Processor (DRP) that provides ML support. The DRP is programmable in C and has optimized DMA support to minimize data movement

    Reply
  17. Tomi Engdahl says:

    Searching for the Perfect Artificial Synapse for AI
    https://spectrum.ieee.org/tech-talk/semiconductors/devices/searching-for-the-perfect-neuron-for-ai

    What’s the best type of device from which to build a neural network? Of course, it should be fast, small, consume little power, have the ability to reliably store many bits-worth of information. And if it’s going to be involved in learning new tricks as well as performing those tricks, it has to behave predictably during the learning process.

    Reply
  18. Tomi Engdahl says:

    A radical new neural network design could overcome big challenges in AI
    https://www.technologyreview.com/s/612561/a-radical-new-neural-network-design-could-overcome-big-challenges-in-ai/?utm_source=facebook&utm_campaign=site_visitor.unpaid.engagement&utm_medium=tr_social

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

    An AI researcher at the University of Toronto, he wanted to build a deep-learning model that would predict a patient’s health over time. But data from medical records is kind of messy: throughout your life, you might visit the doctor at different times for different reasons, generating a smattering of measurements at arbitrary intervals. A traditional neural network struggles to handle this. Its design requires it to learn from data with clear stages of observation. Thus it is a poor tool for modeling continuous processes, especially ones that are measured irregularly over time.

    Reply
  19. Tomi Engdahl says:

    Flex Logix’s Geoff Tate examines how to beat latency in neural networks while retaining high hardware utilization.

    High Neural Inferencing Throughput At Batch=1
    https://semiengineering.com/high-neural-inferencing-throughput-at-batch1/

    Beating latency in neural networks while retaining high hardware utilization.

    Reply
  20. Tomi Engdahl says:

    The Autonomous Car’s Big Challenge: Using the Hyperscale Server Fleet to Train AI Neural Networks
    Gloria Lau, head of hardware engineering for Uber Technologies, Inc, explains how hyperscale hardware technology accelerates the training of the neural networks behind self-driving vehicles.
    https://www.designnews.com/electronics-test/autonomous-car-s-big-challenge-using-hyperscale-server-fleet-train-ai-neural-networks/196274523759943?ADTRK=UBM&elq_mid=6800&elq_cid=876648

    Reply
  21. Tomi Engdahl says:

    Deep Learning Cars
    https://www.youtube.com/watch?v=Aut32pR5PQA

    A small 2D simulation in which cars learn to maneuver through a course by themselves, using a neural network and evolutionary algorithms.

    Explained In A Minute: Neural Networks
    https://www.youtube.com/watch?v=rEDzUT3ymw4

    Artificial Neural Networks explained in a minute.

    As you might have already guessed, there are a lot of things that didn’t fit into this one-minute explanation. Y

    Reply
  22. Tomi Engdahl says:

    But what *is* a Neural Network? | Deep learning, chapter 1
    https://www.youtube.com/watch?v=aircAruvnKk

    Reply
  23. Tomi Engdahl says:

    MarI/O – Machine Learning for Video Games
    https://www.youtube.com/watch?v=qv6UVOQ0F44

    MarI/O is a program made of neural networks and genetic algorithms that kicks butt at Super Mario World.
    Source Code: http://pastebin.com/ZZmSNaHX

    Reply
  24. Tomi Engdahl says:

    What are Deep Neural Networks Learning About Malware?
    https://www.fireeye.com/blog/threat-research/2018/12/what-are-deep-neural-networks-learning-about-malware.html

    Highlights

    FireEye’s deep learning classifier can successfully identify malware using only the unstructured bytes of the Windows PE file.
    Import-based features, like names and function call fingerprints, play a significant role in the features learned across all levels of the classifier.
    Unlike other deep learning application areas, where low-level features tend to generally capture properties across all classes, many of our low-level features focused on very specific sequences primarily found in malware.
    End-to-end analysis of the classifier identified important features that closely mirror those created through manual feature engineering, which demonstrates the importance of classifier depth in capturing meaningful features.

    Reply
  25. Tomi Engdahl says:

    Kent Walker / Google:
    Google says it won’t sell general-purpose facial recognition APIs before working through “important technology and policy questions” — More than 400 million people in the world have diabetes. A third of them have diabetic retinopathy, a complication that can cause permanent blindness.

    AI for Social Good in Asia Pacific
    https://www.blog.google/around-the-globe/google-asia/ai-social-good-asia-pacific/amp/

    More than 400 million people in the world have diabetes. A third of them have diabetic retinopathy, a complication that can cause permanent blindness. The good news is that this blindness can be prevented if diabetic retinopathy is detected early. The not-so-good news—the illness is often going undetected because people don’t always get screenings. In major part, this is due to limited access to eye care specialists and staff capable of screening for the disease. In Thailand, for example, there are only about 1,400 eye doctors for approximately five million diabetics.

    This is a problem that AI can help us solve. A few years ago, we worked with eye specialists in India and the U.S. on an AI system to help doctors analyze images of the back of the eye for signs of diabetic retinopathy. The results were promising. Our AI model now detects diabetic retinopathy with a level of accuracy on par with human retinal specialists.

    We should work to make the benefits of AI available to everyone. Besides rolling out this diabetic retinopathy initiative in clinics in India with our partner Verily, we’ve also been conducting research in Thailand over the past few months.

    To gather more of these ideas, we recently launched the Google AI Impact Challenge. Selected organizations who apply to the challenge will receive support from Google’s AI experts and Google.org grant funding from a $25 million pool.

    https://ai.google/social-good/impact-challenge

    Reply
  26. Tomi Engdahl says:

    AI Still Has Trust Issues
    https://www.eetimes.com/document.asp?doc_id=1334079

    A lot has been accomplished in the last year to improve comprehension, accuracy and scalability of artificial intelligence, but 2019 will see efforts focused on eliminating bias and making decision making more transparent.

    Jeff Welser, vice president at IBM Research, says the organization has hit several AI milestones in the past year and is predicting three key areas of focus for 2019. Bringing cognitive solutions powered by AI to a platform businesses can easily adopt is a strategic business imperative for the company, he said, while also increasing understanding of AI and addressing issues such as bias and trust.

    Reply
  27. Tomi Engdahl says:

    Searching for the Perfect Artificial Synapse for AI
    Researchers tried out several new devices to get closer to the ideal needed for deep learning and neuromorphic computing
    https://spectrum.ieee.org/tech-talk/semiconductors/devices/searching-for-the-perfect-neuron-for-ai

    Reply
  28. Tomi Engdahl says:

    This Canadian Genius Created Modern AI
    https://www.youtube.com/watch?v=l9RWTMNnvi4

    For nearly 40 years, Geoff Hinton has been trying to get computers to learn like people do, a quest almost everyone thought was crazy or at least hopeless – right up until the moment it revolutionized the field. In this Hello World video, Bloomberg Businessweek’s Ashlee Vance meets the Godfather of AI.

    Reply
  29. Tomi Engdahl says:

    What is backpropagation really doing? | Deep learning, chapter 3
    https://www.youtube.com/watch?v=Ilg3gGewQ5U

    What’s actually happening to a neural network as it learns?

    Backpropagation calculus | Deep learning, chapter 4
    https://www.youtube.com/watch?v=tIeHLnjs5U8

    Reply
  30. Tomi Engdahl says:

    This AI Can Select Materials for Design Engineers
    https://www.designnews.com/materials-assembly/ai-can-select-materials-design-engineers/55889105359909?ADTRK=UBM&elq_mid=6818&elq_cid=876648

    Researchers at the National Institute for Materials Science have developed an AI to assist engineers in materials design and selection.

    Reply
  31. Tomi Engdahl says:

    HOW IS AI GOING TO CHANGE THE WAY WE DESIGN?
    https://blog.taiste.fi/en/ai-going-change-way-design?utm_source=facebook&utm_medium=promotedpost&utm_campaign=website&utm_content=aidesign

    What if you could generate visual materials such as ads, illustrations and app layouts with a touch of a button? You already can. Kind of.

    Reply

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