The Internet of Things cannot be defined

#IoT cannot be defined... or can it? 📶 #AI #smartcities #IoTcouncil

  • As Internet of Things becomes a household term, it is logical and natural that people want to know what it is.
  • The Internet of Things can be the best possible feedback on my physical and mental health, the best possible deals based on real time monitoring for resource allocation, the best possible decision making based on real time data and information from open sources and the best possible alignments of my local providers with the global potential of wider communities.
  • Internet of Things is in its essence the seamless flow between the

    ¥ VWAN (very wide area network): the ‘wise’ city as e-gov services everywhere no longer tied to physical locations

    Whoever ensures traceability, sustainability and security linking up the gateways is de facto and de jure the new power.

  • In short; Internet of Things will define your future everyday.
  • Council hosts the Policy and Regulation Track at The 4th IEEE World Forum on The Internet of Things (WF-IoT 2018).

As Internet of Things becomes a household term, it is logical and natural that people want to know what it is. Definitions spring up.
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2 ex-Googlers are starting Humu, a machine learning company that wants to ‘make work better’

2 ex-Googlers are starting Humu, a #machinelearning company that wants to ‘make work better’

  • Less than a year after departing Google as its head of human resources, Laszlo Bock has become an entrepreneur.
  • Humu, likely named after the Hawaii state fish Humuhumunukunukuapua’a, could be an extension of Bock’s experience from shaping the culture at one of the largest tech companies in the world, and it may also leverage insights shared in his book Work Rules!
  • In a 2015 interview, Bock remarked: “We’ve learned a lot over the years about what makes work more meaningful and productive — we’ve studied this at Google, reviewed the academic research and listened to companies far away from Silicon Valley doing inspiring things on the people front.
  • Many services have tried to make work fun, but Humu wants to take a different approach, leveraging machine learning and science.
  • How that works exactly remains to be seen, but in a LinkedIn post, Bock wrote about how there should be a way to ensure that employees always have good days at work, and not one where people see it as a job: “We should be constantly learning and growing, and surrounded by people who are doing the same.

Less than a year after departing Google as its head of human resources, Laszlo Bock has become an entrepreneur. On Monday, he announced that he’s working on Humu, a startup designed to help us work better. He’s joined by Wayne Crosby, Google’s director of engineering who recently resigned. While details are sparse, the company plans on using science, machine learning, and “a little bit of love” to improve our jobs.
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Designing networks for IoT sensors can be a learning process

Designing networks for IoT sensors can be a learning process | #MachineLearning #IoT #RT

  • These are just a few examples of the various Internet of Things (IoT) sensors and other connected devices in Boulder, where electrical, solar and HVAC systems are also tied into IP networks.
  • Designing a wireless network to support these applications was a learning process for the city’s IT department, says Benjamin Edelen, a senior system administrator there.
  • Aimee Schumm, e-services manager at the Boulder Public Library, notes that staff members made sure to tuck access points in places where they couldn’t be reached easily — such as inside ceiling tiles or on the ceiling itself — so they won’t be tampered with.
  • Boulder built out its wireless network with more bandwidth than it needs currently, with the expectation that it will expand its use of IoT sensors and similar technologies in the future.
  • Once the IoT sensors were in place, the various city departments generally took ownership of the data, Edelen says.

As the city of Boulder optimized its wireless network to better support IoT sensors, the city’s IT pros found it had a “significant learning curve.”
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Hashtag Artificial Intelligence – Katherine Bailey – Medium

Good read

Hashtag #ArtificialIntelligence 

 #fintech @katherinebailey @Acquia #AI

  • Here’s one that was presented recently at a talk by one of Amazon’s top Machine Learning people:Artificial Intelligence: A system or service which can perform tasks that usually require human intelligenceThis is a fairly common way to define it.
  • Here’s a similar formulation from Nathan Benaich in his post 6 areas of AI and machine learning to watch closely:The ultimate goal of AI […] is to build machines capable of performing tasks and cognitive functions that are otherwise only within the scope of human intelligence.One problem with this definition is that it means the state of being an instance of Artificial Intelligence is temporary.
  • Just look at all the “What is the difference between Artificial Intelligence and Machine Learning?”
  • Really, why should Machine Learning be defined in relation to AI?The slippery definition issue above can be looked at as follows: it is the term “Artificial Intelligence” looking for things to refer to.
  • This is no more true now than it was 50 years ago but many smart people are utterly convinced of it.The term “Artificial Intelligence” has been around since the early days of computer science, when “thinking machines” were seen as the natural next step after programming basic logic.

The internet is awash with stories about something called Artificial Intelligence. Confusion around what it is is prompting many to proffer definitions of it, or corrections of wrong definitions…
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Google used your pictionary sketches to teach its AI to draw

Google used your pictionary sketches to teach its AI to draw

  • Google has been working on a wide range of AI-based projects lately – earlier this week, it showed off one that can identify what you’re trying to draw and surface clean clipart that resembles your doodle.
  • Its latest experiment is called Sketch-RNN, and it’s a neural network system that has learned to draw on its own by looking at roughly 5.5 million sketches from people who played Pictionary with Google’s AI-powered Quick, Draw!
  • By triaging sketches in 75 different categories like cats, pigs and trucks, the AI can now draw basic representations of these things when presented with hand-drawn sketches.
  • Sketch-RNN can also draw without the help of a starting sketch, and can even complete sketches that a human has started, but not finished.
  • Sure, the sketches aren’t exactly photorealistic, but the idea here is to ‘train a machine to draw and generalize abstract concepts in a manner similar to humans’, and Google has achieved that.

Google’s latest experiment is a neural network system that has learned to draw by looking at roughly 5.5 million sketches from people who played pictionary.
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Facebook Messenger adds payment prompts using artificial intelligence • NFC World

  • Facebook Messenger users across the US are now being prompted to send and request money transfers by an artificial intelligence-based feature that detects when a payment is being discussed in a conversation on the social media platform and responds with a suggestion designed to help the user complete that payment.
  • “M offers suggestions by popping into an open conversation to suggest relevant content and capabilities to enrich the way people communicate and get things done,” the social media giant says.
  • M may make a suggestion in a conversation relevant to one of the core actions, and then the M logo and suggestion will appear — it’s that simple.”
  • Facebook began testing payments through its Messenger service in July 2016.
  • The social media giant also updated its Messenger chatbot platform to enable bots to accept payments without having to send shoppers to external sites to complete the checkout process in September 2016.

Facebook Messenger users across the US are now being prompted to send and request money transfers by an artificial intelligence-based feature.
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Researchers have developed an evolution strategy that promises more powerful AI systems

  • They’ve developed an evolution strategy (no, it doesn’t relate much to biological evolution) that promises more powerful AI systems.
  • Rather than use standard reinforcement training, they create a “black box” where they forget that the environment and neural networks are even involved.
  • The technique eliminates a lot of the traditional cruft in training neural networks, making the code both easier to implement and roughly two to three times faster.
  • In tests, a large supercomputer with 1,440 cores could train a humanoid to walk in 10 minutes versus 10 hours for a typical setup, and even a “lowly” 720-core system could do in 1 hour what a 32-core system would take a full day to accomplish.
  • However, the practical implications are clear: neural network operators could spend more time actually using their systems instead of training them.

OpenAI researchers have developed an evolution strategy that promises more powerful AI systems. Rather than use standard reinforcement training, they create a
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