For the past two years, there’s been a lot of buzzing about the Internet of Things (IoT). This has to lead to the rapid selection of connected devices over industries and is determined to pass the 11 billion mark by the end of the year. Major Companies including IoT software development as their major services.

All these “things” are now creating their things, namely, lots and lots of data. This data will be at the core of commercial and industrial digital transformation (which is essentially the underlying force behind the fourth industrial revolution).

In other words, life as we know it is about to change forever! How is it going to change? Let’s take a look.

1. AI (Artifical Intelligence) can Effectively Manage Oceans of information

We can’t talk about IoT without AI as the latter has the power to make IoT a whole lot smarter and more efficient.

In fact, consultants believe that AI is the brains behind IoT systems that may facilitate build them run power tool.

For example, as more and more connected devices start communicating with each other, enterprises will need to leverage deep learning, image recognition, natural language processes, and neural-network driven decisions to help them understand each other (and us humans) better.

So far, we can say that IoT has felt like an isolated experience where it was just about simple data. Going forward, businesses will strive to achieve highly integrated experiences by using AI to better understand their employees, customers, and the general public living in smart cities.

Human pose estimation

A few months ago I came across one interesting open source project on the Internet — Openpose the aim of which is to estimate a human pose in real-time on a video stream. Due to my professional activities, I was interested to run it on the latest iOS device from Apple to check the performance and figure out if it is possible at all. It was also interesting to see how the performance of the neural network framework for iOS has been changed in the last years.

«It's a dangerous business, Frodo, going out your door. You step onto the road, and if you don't keep your feet, there's no knowing where you might be swept off to.»

― J.R.R. Tolkien, The Lord of the Rings

Europe’s roads are the safest in the world. Current figures show that there are 50 fatalities per one million inhabitants, compared to the global figure of 174 deaths per million. Despite this, each loss remains a tragedy. In 2017, 25,300 people lost their lives on European roads.

The cause of these accidents can vary from human error and weather conditions, to damaged structures and surfaces. While some things are beyond the realms of control, road and bridge conditions are a variable which can be governed.

As soon as a road is paved, a combination of traffic and weather conditions begin to degrade and erode the surface. Undetected cracks, abrasions or defects can quickly lead to bigger problems, such as costly repairs, major traffic delays, and in the worst cases, unsafe condition. These problems are also shared by bridges, particularly when concrete is critical in maintaining the integrity of the structure. The earlier faults are detected, the sooner they can be addressed, saving time and money, while minimising disruption. Ultimately, this helps ensure that the roads themselves are safer for those travelling on them.

The detection of these faults, however, can be very difficult to carry out manually, especially as early-forming cracks are hard to spot with the naked eye. Predicting where faults are likely to occur ahead of time so that appropriate measures can be taken in advance also possess a massive challenge. Thankfully, technology is here to help.

If you were asked to draw a picture of several people in ski gear, standing in the snow, chances are you’d start with an outline of three or four people reasonably positioned in the center of the canvas, then sketch in the skis under their feet. Though it was not specified, you might decide to add a backpack to each of the skiers to jibe with expectations of what skiers would be sporting. Finally, you’d carefully fill in the details, perhaps painting their clothes blue, scarves pink, all against a white background, rendering these people more realistic and ensuring that their surroundings match the description. Finally, to make the scene more vivid, you might even sketch in some brown stones protruding through the snow to suggest that these skiers are in the mountains.

Now there’s a bot that can do all that.

When I used to start a conversation about neural networks over a bottle of beer, people were casting glances at me of what seemed to be fear; they grew sad, sometimes with their eyelid twitching. In rare cases, they were even eager to take refuge under the table. Why? These networks are simple and instinctive, actually. Yes, believe me, they are! Just let me prove this is true!

Suppose there are two things I’m aware of about the girl: she looks pretty to my taste or not, and I have lots to talk about with her or I haven’t. True and false will be one and zero respectively. We’ll take similar principle for appearance. The question is: “What girl I’ll fall in love with, and why?”

We also can think it straight and uncompromisingly: “If she looks pretty and there’s plenty to talk about, then I will fall in love. If neither is true, then I quit”.

But what if I like the lady but there’s nothing to talk about with her? Or vice versa?

Hi guys,

Before you is an article guide to open data sets for machine learning. In it, I, for a start, will collect a selection of interesting and fresh (relatively) datasets. And as a bonus, at the end of the article, I will attach useful links on independent search of datasets.

Less words, more data.

A selection of datasets for machine learning:

• Data deaths and battles from the game of thrones — This data set combines three data sources, each based on information from a series of books.
• Global Terrorism Database — Over 180,000 terrorist attacks worldwide, 1970-2017.
• Bitcoin, historical data — Bitcoin data with an interval of 1 minute from selected exchanges, January 2012 — March 2019

Inspiration sometimes arrives in strange ways. Here is the story of how a dirty disposable diaper led to the development of an artificial intelligence (AI) solution to help rid the world’s coasts of massive amounts of waste and garbage.

With the recent progress in Neural Networks in general and image Recognition particularly, it might seem that creating an NN-based application for image recognition is a simple routine operation. Well, to some extent it is true: if you can imagine an application of image recognition, then most likely someone have already did something similar. All you need to do is to Google it up and to repeat.

However, there are still countless little details that… they are not insolvable, no. They simply take too much of your time, especially if you are a beginner. What would be of help is a step-by-step project, done right in front of you, start to end. A project that does not contain «this part is obvious so let's skip it» statements. Well, almost :)

In this tutorial we are going to walk through a Dog Breed Identifier: we will create and teach a Neural Network, then we will port it to Java for Android and publish on Google Play.

For those of you who want to see a end result, here is the link to NeuroDog App on Google Play.

Web site with my robotics: robotics.snowcron.com.
Web site with: NeuroDog User Guide.

Here is a screenshot of the program:

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The original post has been updated based on community input in order to remove confusion.

Final version of the whitepaper is available here:

https://github.com/cybercongress/cyber/releases

The biggest issue with AI is not that it is stupid but a lack of definition for intelligence and hence a lack of formal measure for it [1a] [1b].

Turing test is not a good measure because gorilla Koko [2a] and bonobo Kanzi [2b] wouldn't pass though they could solve more problems than many disabled human beings.

It is quite possible that people in the future might wonder why people back in 2019 thought that an agent trained to play a fixed game in a simulated environment such as Go had any intelligence [3a] [3b] [3c] [3d] [3e] [3f] [3g] [3h].

Intelligence is more about applying/transferring old knowledge to new tasks (playing Quake Arena good enough without any training after mastering Doom) than compressing agent's experience into heuristics to predict a game score and determining agent's action in a given game state to maximize final score (playing Quake Arena good enough after million games after mastering Doom) [4].

Human intelligence is about ability to adapt to the physical/social world, and playing Go is a particular adaptation performed by human intelligence, and developing an algorithm to learn to play Go is a more performant one, and developing a mathematical theory of Go might be even more performant.