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Category Archives: Artificial Intelligence

A.I. Might Kill Us Through Incompetence, Not Malevolence

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Artificial intelligence has been looming large in the public conciousness recently, thanks to the likes of Elon Musk and Stephen Hawking telling us how we're going to die at the hands of robots (the upcoming Terminator reboot probably doesn't help, either). But amidst the techpocalypse talk, there's been limited discussion of what constitutes A.I, and how it might look completely different to Skynet.

As Benjamin H. Bratton explains in the New York Times, our idea of artificial intelligence has been engineered from the beginning to be anthropomorphic: a truly 'intelligent' computer is one that reflects humanity back at us. The Turing test, the flawed but oft-quoted determination of artificial intelligence, really just requires a computer to pose as a human for a few minutes something that Bratton finds bizarre:

That we would wish to define the very existence of A.I. in relation to its ability to mimic how humans think that humans think will be looked back upon as a weird sort of speciesism. The legacy of that conceit helped to steer some older A.I. research down disappointingly fruitless paths, hoping to recreate human minds from available parts. It just doesn't work that way.

He goes on to point out that planes don't fly like birds, so why should computers be hamstrung by human impressionism?

When it comes to the matter of the dangers of A.I, Bratton is concerned, but not about a robot coup. Rather, "what we really fear, even more than a Big Machine that wants to kill us, is one that sees us as irrelevant."

In a technology landscape a little overrun with faux-humanoid digital assistants and a decades-old public perception of A.I, Bratton's essay is an insightful take on an incredibly important topic. And, it might make you stop and think next time you swear at Siri. [New York Times]

Image: Shutterstock/Olga Nikonova

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A.I. Might Kill Us Through Incompetence, Not Malevolence

Myth Busting Artificial Intelligence | WIRED

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Weve all been seeing hype and excitement around artificial intelligence, big data, machine learning and deep learning. Theres also a lot of confusion about what they really mean and whats actually possible today. These terms are used arbitrarily and sometimes interchangeably, which further perpetuates confusion.

So, lets break down these terms and offer some perspective.

Artificial Intelligence is a branch of computer science that deals with algorithms inspired by various facets of natural intelligence. It includes performing tasks that normally require human intelligence, such as visual perception, speech recognition, problem solving and language translation. Artificial intelligence can be seen in many every day products, from intelligent personal assistants in your smartphone to the X-box 360 Kinect camera, allowing you to interact with games through body movement. There are also well-known examples of AI that are more experimental, from the self-aware Super Mario to the widely discussed driverless car. Other less commonly discussed examples include the ability to sift through millions of images to pull together notable insights.

Big Data is an important part of AI and is defined as extremely large data sets that are so large they cannot be analyzed, searched or interpreted using traditional data processing methods. As a result, they have to be analyzed computationally to reveal patterns, trends, and associations. This computational analysis, for instance, has helped businesses improve customer experience and their bottom line by better understand human behavior and interactions. There are many retailers that now rely heavily on Big Data to help adjust pricing in near-real time for millions of items, based on demand and inventory. However, processing of Big Data to make predictions or decisions like this often requires the use of Machine Learning techniques.

Machine Learningis a form of artificial intelligence which involves algorithms that can learn from data. Such algorithms operate by building a model based on inputsand using that information to make predictions or decisions, rather than following only explicitly programmed instructions. There are lots of basic decisions that can be performed leveraging machine learning, like Nest with its learning thermostats as one example. Machine Learning is widely used in spam detection, credit card fraud detection, and product recommendation systems, such as with Netflix or Amazon.

Deep Learningis a class of machine learning techniques that operate by constructing numerous layers of abstraction to help map inputs to classifications more accurately. The abstractions made by Deep Learning methods are often observed as being human like, and the big breakthrough in this field in recent years has been the scale of abstraction that can now be achieved. This, in recent years, has resulted in breakthroughs in computer vision and speech recognition accuracy. Deep Learning is inspired by a simplified model of the way Neural Networks are thought to operate in the brain.

No doubt AI is in a hype cycle these days. Recent breakthroughs in Distributed AI and Deep Learning, paired with the ever-increasing need for deriving value from huge stashes of data being collected in every industry, have helped renew interest in AI. Unfortunately, along with the hype, there has also been much concern about the risks of AI. In my opinion, much of this concern is misplaced and unhelpful most of the concerns raised apply equally to technology in general, and just because this specific branch of technology is inspired by natural intelligence should not make it particularly more or less of a concern.

[Recently on Insights:The Upside of Artificial Intelligence Development|Google and Elon Musk to Decide What Is Good for Humanity]

As mortal humans, we do not understand the functionality of many of the technologies we use, and in this age of information, many decisions are already being made for us automatically by computers. If not understanding how these technologies around us work is concerning, then there is plenty to be concerned about before we start worrying about AI. The fact of the matter is that AI technologies already enable many of the products and services we know and love, so better to start understanding more about what these technologies are and how they work, than to believe in the Hollywood-style hype about futuristic scenarios.

When it comes to the potential of the recent AI breakthroughs, there is, in spite of the hype, much to be excited about. While there is a vast and growing amount of data available related to critical problems, it remains mostly unmined, unrefined and un-monetized. There is an inability to analyze and utilize available data to make intelligent, bias-free, decisions. Companies should be using refined data to make the right decisions and solve the worlds most vexing challenges. The speed and computing scale required to make advances in mission critical problem solving has not existed until now.

Link:

Myth Busting Artificial Intelligence | WIRED

How artificial intelligence may change our lives

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Benedict Cumberbatch plays computer pioneer Alan Turing in the Oscar-nominated film "The Imitation Game." The "game" he speaks of has come to be known as the "Turing Test" for artificial intelligence, or A.I., which has long been a science-fiction staple. Now, it is no longer fiction.

Last summer, for the first time, a computer passed the Turing Test. Scientists are excited, but some people worry about where this all could lead.

Some are already putting these machines to practical use. When professor Manuela Veloso has a guest at her office, she doesn't greet them herself. She sends a robot.

"Hello, I'm here to take Anthony Mason to room 7002," the robot said to CBS News correspondent Anthony Mason. "Please press done when I can go."

More precisely, it's a CoBot, or collaborative robot.

Because the robot is without arms, it needs help pushing the elevator button. But inside the computer science building at Pittsburgh's Carnegie Mellon University, it knows exactly where it's going.

"Hello, I have brought Anthony Mason from room 4405. Please press done when I can leave," the robot said to Veloso.

Veloso and her students first began using the CoBots in 2010. In her building alone, the robot has gone more than 1,000 kilometers.

Four CoBots now roll through the halls. Each navigates on its own computing location and course by using onboard cameras and the detailed maps with which it is programmed.

To send the robot somewhere, you simply hit "schedule task," and the CoBot will ask how it can assist. If you stand in front of it, it will courteously say "please excuse me" until you move.

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How artificial intelligence may change our lives

Using Artificial Intelligence to Solve #SEO by @scott_stouffer

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I recently wrote about how to statistically model any given set of search results, which I hope gives marketing professionals a glimpse into how rapidly the SEO industry is currently changing in 2015. In that article, I had mentioned that the search engine model should be able to self-calibrate, or take its algorithms and weightings of those algorithms, and correlate the modeled data against real-world data from public search engines, to find a precise search engine modeling of any environment.

But taking thousands of parameters and trying to find the best combination of those that can curve fit search engine results is what we in computer science call a NP-Hard problem. Its astronomically expensive in terms of computational processing. Its really hard.

So how can we accomplish this task of self-calibrating a search engine model? Well, it turns out that we will turn to the birds yes, birds to solve this incredibly hard problem.

Full Disclosure: I am the CTO of MarketBrew, a company that uses artificial intelligence to develop and host a SaaS-based commercial search engine model.

I have always been a fan of huge problems. This one is no different, and it just so happens that huge problems comes with awesome solutions. I turn your attention to one such solution: Particle swarm optimization (PSO), which is an artificial intelligence (AI) technique that was first published in 1995 as a model of social behavior. The technique is actually modeled on the concept of bird flocking.

An Example Performance Landscape of Particle Swarm Optimization in Action

The optimization is really quite remarkable. In fact, all of our rules-based algorithms that we have invented to-date still cannot be used to find approximate solutions to extremely difficult or impossible numeric maximization and minimization problems. Yet, using a simple model of how birds flock can get you an answer within a fraction of time. We have heard the gloom and doom news about how AI might take over the world some day, but in this case, AI helps us solve a most amazing problem.

I actually have been involved with a number of Swarm Intelligence projects throughout my career. In February 1998, I worked as a communications engineer on the Millibot Project, formerly known as the Cyberscout Project, a project utilized by the United States Marines. The Cyperscout was basically a legion of tiny little robots that could be dispersed into a building and provide instant coverage throughout that building. The ability of the robots to communicate and relay information between one another, allowed the swarm of robots to act as one, effectively turning a very tedious task of searching an entire building into a leisurely stroll down one hallway (most of these tiny robots each had to travel a only few yards total).

The really cool thing about PSO is that it doesnt make any assumption about the problem you are solving. It is a cross between a rules-based algorithm that attempts to converge on a solution, and an AI-like neural network that attempts to explore the problem space. So, the algorithm is a tradeoff of exploratory behavior vs. exploitative behavior.

Without the exploratory nature of this optimization approach, the algorithm would certainly converge on what statisticians like to call a local maxima (a solution that appears to be optimal, but is not optimal).

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Using Artificial Intelligence to Solve #SEO by @scott_stouffer

Personal Robot NOT JUST A PRETTY ROBOT, SHE’S ARTIFICIAL INTELLIGENCE. – Video

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Personal Robot NOT JUST A PRETTY ROBOT, SHE #39;S ARTIFICIAL INTELLIGENCE.
https://www.youtube.com/channel/UCT_9amLoRyAOvovoQfxsf8w Personal robot will help you with your household chores. SHE #39;S THE WHOLE PACKAGE: a personal assista...

By: Popular Science

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Personal Robot NOT JUST A PRETTY ROBOT, SHE'S ARTIFICIAL INTELLIGENCE. - Video

Alex Jones Show: Commercial Free Video – Thursday (2-19-15) Dr. Hugo de Garis – Video

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Alex Jones Show: Commercial Free Video - Thursday (2-19-15) Dr. Hugo de Garis
COMPLETE SHOW HIGHER RESOLUTION HERE...https://www.youtube.com/watch?v=hvOmhqtjVhQ On this Thursday, February 19 edition of the Alex Jones Show, we cover the threat of Artificial ...

By: ConspiracyScope

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Alex Jones Show: Commercial Free Video - Thursday (2-19-15) Dr. Hugo de Garis - Video

Myth Busting Artificial Intelligence

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Weve all been seeing hype and excitement around artificial intelligence, big data, machine learning and deep learning. Theres also a lot of confusion about what they really mean and whats actually possible today. These terms are used arbitrarily and sometimes interchangeably, which further perpetuates confusion.

So, lets break down these terms and offer some perspective.

Artificial Intelligence is a branch of computer science that deals with algorithms inspired by various facets of natural intelligence. It includes performing tasks that normally require human intelligence, such as visual perception, speech recognition, problem solving and language translation. Artificial intelligence can be seen in many every day products, from intelligent personal assistants in your smartphone to the X-box 360 Kinect camera, allowing you to interact with games through body movement. There are also well-known examples of AI that are more experimental, from the self-aware Super Mario to the widely discussed driverless car. Other less commonly discussed examples include the ability to sift through millions of images to pull together notable insights.

Big Data is an important part of AI and is defined as extremely large data sets that are so large they cannot be analyzed, searched or interpreted using traditional data processing methods. As a result, they have to be analyzed computationally to reveal patterns, trends, and associations. This computational analysis, for instance, has helped businesses improve customer experience and their bottom line by better understand human behavior and interactions. There are many retailers that now rely heavily on Big Data to help adjust pricing in near-real time for millions of items, based on demand and inventory. However, processing of Big Data to make predictions or decisions like this often requires the use of Machine Learning techniques.

Machine Learningis a form of artificial intelligence which involves algorithms that can learn from data. Such algorithms operate by building a model based on inputsand using that information to make predictions or decisions, rather than following only explicitly programmed instructions. There are lots of basic decisions that can be performed leveraging machine learning, like Nest with its learning thermostats as one example. Machine Learning is widely used in spam detection, credit card fraud detection, and product recommendation systems, such as with Netflix or Amazon.

Deep Learningis a class of machine learning techniques that operate by constructing numerous layers of abstraction to help map inputs to classifications more accurately. The abstractions made by Deep Learning methods are often observed as being human like, and the big breakthrough in this field in recent years has been the scale of abstraction that can now be achieved. This, in recent years, has resulted in breakthroughs in computer vision and speech recognition accuracy. Deep Learning is inspired by a simplified model of the way Neural Networks are thought to operate in the brain.

No doubt AI is in a hype cycle these days. Recent breakthroughs in Distributed AI and Deep Learning, paired with the ever-increasing need for deriving value from huge stashes of data being collected in every industry, have helped renew interest in AI. Unfortunately, along with the hype, there has also been much concern about the risks of AI. In my opinion, much of this concern is misplaced and unhelpful most of the concerns raised apply equally to technology in general, and just because this specific branch of technology is inspired by natural intelligence should not make it particularly more or less of a concern.

[Recently on Insights:The Upside of Artificial Intelligence Development|Google and Elon Musk to Decide What Is Good for Humanity]

As mortal humans, we do not understand the functionality of many of the technologies we use, and in this age of information, many decisions are already being made for us automatically by computers. If not understanding how these technologies around us work is concerning, then there is plenty to be concerned about before we start worrying about AI. The fact of the matter is that AI technologies already enable many of the products and services we know and love, so better to start understanding more about what these technologies are and how they work, than to believe in the Hollywood-style hype about futuristic scenarios.

When it comes to the potential of the recent AI breakthroughs, there is, in spite of the hype, much to be excited about. While there is a vast and growing amount of data available related to critical problems, it remains mostly unmined, unrefined and un-monetized. There is an inability to analyze and utilize available data to make intelligent, bias-free, decisions. Companies should be using refined data to make the right decisions and solve the worlds most vexing challenges. The speed and computing scale required to make advances in mission critical problem solving has not existed until now.

Read the rest here:

Myth Busting Artificial Intelligence

Personal artificial intelligence named Cubic responds to Elon Musk’s claims – Video

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Personal artificial intelligence named Cubic responds to Elon Musk #39;s claims
Hello Elon, We are the Cubic Robotics team. We #39;re scientists, engineers, and communications experts who have dedicated our work to building an AI digital ass...

By: Cubic Robotics

Read this article:

Personal artificial intelligence named Cubic responds to Elon Musk's claims - Video

The ‘Plus Your Business’ Show. (with even NEWER format. – Video

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The #39;Plus Your Business #39; Show. (with even NEWER format.
This week, join myself and +David Amerland with the super tech support from +Travis Taylor as we discuss things like... How is Artificial Intelligence going to change business... ..and what...

By: Plus Your Business!

See the article here:

The 'Plus Your Business' Show. (with even NEWER format. - Video

Boffins join Musk, Hawking in saying AI is threat to humanity

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Technically Incorrect: Releasing a list of 12 threats to human civilization, academic researchers put artificial intelligence as an emerging and powerful risk.

Technically Incorrect offers a slightly twisted take on the tech that's taken over our lives.

And one day he'll have had enough of his creators. Documentary & Discovery HD Channel/YouTube screenshot by Chris Matyszczyk/CNET

If we're all going to die, there's something slightly exciting in the idea that the end will be unexpected.

For many, the most pulsating thought is that we'll build robots that will take a look at us one day and see us as mere detritus.

Stephen Hawking has warned of it. So has Elon Musk. Now artificial intelligence has appeared on the list of 12 Risks That Threaten Human Civilization.

Published by the Global Challenges Foundation and written by academics from Oxford University and elsewhere, the report seeks to identify risks to humanity that are, in its words, "infinite."

There, amid such well-known threats such as extreme climate change, nuclear war, major asteroid impact and nanotechnology is artificial intelligence. Yes, straight into the chart at No. 11.

The report's authors write of robots that we might create: "Such extreme intelligences could not easily be controlled (either by the groups creating them, or by some international regulatory regime), and would probably act to boost their own intelligence and acquire maximal resources for almost all initial AI motivations."

The use of "probably" is interesting. Is it logical that any artificial intelligence created would necessarily want to boost its own intelligence -- because that's what humans try to do (allegedly)?

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Boffins join Musk, Hawking in saying AI is threat to humanity


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