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Black mirror robot dog

Black mirror robot dog


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Black mirror robot dog

by Dave on May 22, 2016

Robot dog by a British group of scientists.

There’s been a lot of discussion lately over the future of Artificial Intelligence ( ). As progresses, we think, we face more and more challenges as well as exciting opportunities to improve society, our jobs, and our lives. However, we’re not quite sure what the future holds for , and there’s lots of uncertainty about how it might affect us. This post will outline some of the major issues that are being talked about. One thing is for sure: we’ll see evolve from its current status to whatever version of self-aware intelligence we can imagine. But it’s also worth remembering that while is one of the most hotly debated topics today, our current understanding of it is based on very limited research. It’s unlikely we’ll get it right first time.

Let’s first discuss some important terms related to , as many of these terms are used interchangeably. For those of you with a scientific bent, you might know that is an abbreviation for Artificial Intelligence. For the less technical reader, you can think of as computer software that enables machines to learn and make decisions based on new or existing knowledge. For most of us, our only real experience with is the Siri voice assistant in our smart phones. As soon as it recognizes our voice, it’ll provide us with the best answer to our questions.

What if the dog’s got a question?

However, we’re not so reliant on Siri. It’s becoming increasingly common for to appear in other ways too. When you search for something on Google, you may be presented with a result set of similar pages ranked according to the strength of their match to your query. technology has enabled Google to use the same principles to detect when one of those pages should be included in your search results – even if you’ve never seen it before.

A related concept is known as Machine Learning, and it’s a key part of what is all about. Machine learning refers to a computer system that can learn from data, including patterns and relationships. In that sense, a Machine Learning system would allow it to predict or “learn” your next behaviour, based on what has happened in the past.

Google’s machine learning approach is now used to filter and display search results based on what you’ve recently looked at, your location, and a multitude of other data sources. It’s become so advanced that it’s able to rank websites on a scale of 0-10 and display those rankings in a bar graph, alongside your original search result. If you were to look at the chart next to a search result that ranks highly for a search term you’ve entered, you might well see a value of 8-9.9. If the same result is one of your top 3 results, it will have a rank in the region of 9-10. If it’s not among the top 3 results, it will still be a value of 0. In short, it’s a measure of how often a particular web page has come up in searches for the search term you entered.

Google’s machine learning software is also capable of creating new, useful concepts to help it learn. For example, it may detect certain links in web pages and use that to determine if the page should be included in search results. It may detect links that it thinks should point to a company website, and include it in a result set even if you’ve never seen it before.

This type of search is known as contextual search. The idea is that when you’re searching, Google already knows some of what you’re looking for. It’s not able to anticipate what you’re going to search for before you do so, but it has information about the concepts that you’re most likely to be interested in.

Google Trends is another tool that can give you some insight into your Google search habits. This shows you the relative popularity of certain search terms in different locations and over time. You can even see when searches spiked in popularity, for example, after a major event such as the death of a famous person.

Here’s a chart showing the results of the Google Trends queries “Google” and “web trends” since 2004.

I think this is interesting data, but I would have been less interested in knowing this had I come across it myself. It’s quite difficult to interpret without knowing what you’re looking at.

However, a very different example was recently unearthed from Google’s archive of historical search data, which shows how Google users behaved towards the Web during the dot-com bubble. You can find this by entering this search:

Search Query Volume and Growth over Time

This reveals a fascinating look at how users searched the Web for different topics.

A ‘burst’ of search traffic that peaked in January 1998 shows the impact of the news that Netscape was going to take over from Microsoft as the de facto standard Internet browser.

Looking at the spike in the graph below, you can see that many people were searching for “browser”, perhaps out of sheer confusion that they were suddenly unable to use a browser from their favourite operating system.

The search term “internet browser” was also used a lot in 1998 – I presume because we were all trying to figure out what to call it.

You can find similar graphs for different search queries here. The data has been processed and cleansed to remove spam, so if you search for something obscure like “I can’t live without Google Chrome” you’ll see no results, for example. The first graph in this blog post shows the traffic generated by Google queries containing “chrome”.

You can even find this chart in Google’s own Help documentation here:

I’ve never written a word about Chrome or Chrome dev tools. In fact I’ve written only one blog post that’s only about Chrome related: What if Chrome were Google? And that was to talk about its relationship to the rest of Google.

I’ve also spoken about Chrome Dev Tools in various places like this video and in the Web development sessions at Google I/O.

But it turns out that this traffic spike doesn’t just relate to a single web browser. Chrome developers were also busy creating code to debug web pages that used a similar feature, but used the browser’s JavaScript debugging features.

It’s clear that Google developers were busy all year long and we should all thank them!


Watch the video: Ο.. καυτός χορός του σκύλου - ρομπότ


Comments:

  1. Algar

    Shame and shame!

  2. Akizshura

    Congratulations)))

  3. Moogular

    Granted, a useful phrase



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