Artificial Brilliance

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Google’s autonomous vehicle program reaches major milestone

The self-driving car program that Google began seven and a half years ago just reached a major milestone on Wednesday: it logged its 2 millionth mile. Google’s two million miles is significantly more than any other company’s currently working on developing autonomous vehicles. Google reached the goal more quickly than anticipated by doubling the number of miles its driverless cars were logging each week from what it was doing last year.

A look at the numbers

Given the average number of miles a person will drive in a year, Google’s two-million-mile mark represents approximately 300 years of behind-the-wheel experience—talk about being over-prepared for a driver’s test.

This past year, Google’s self-driving car program has been averaging about 25,000 miles per week. In comparison, the average human travel travels only half that in an entire year.

Google’s self-driving fleet currently consists of 58 vehicles (24 Lexus RX450h SUVs converted into autonomous vehicles and 34 prototype cars).

Meaningful miles

Not only has Google lapped every other company in the autonomous vehicle industry in quantity of miles driven, but Google also claims each mile driven by Google’s cars are more meaningful then each mile driven by their competitors since they bring their considerable amount of technology to bear when outfitting their fleet. In addition to navigating freeways, Google’s cars have been driving around cities as well, learning how to use four way intersections, pull over for police officers and other emergency vehicles, and even how to recognize cyclists’ hand signals.

Autonomous vehicles and the future

At the current rate, autonomous vehicles will be street legal and available for purchase by consumers within the next few years.

Private car ownership in urban areas will be pretty much non-existent by 2025.

By 2035, the autonomous vehicle market will be a $77 billion-a-year industry.

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Source: wsj.com/articles/googles-self-driving-car-program-odometer-reaches-2-million-miles-1475683321

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World’s first pop song composed by AI released this week

Earlier this week, Sony CSL research released the first ever pop song to be composed entirely by an AI system which is called Flow Machines. A human musician, a French composer named Benoit Carre produced, mixed, and wrote the lyrics for the song but the melody and harmony of the track were composed entirely by Flow Machines.

In order to create an artificial intelligence system like Flow Machines capable of composing music, programmers had to feed the program thousands and thousands of examples of sheet music from a large database. The sheet music in this database consisted of songs of varying styles. After Flow Machines analyzed all of the sheet music, a human composer gave the AI system a style prompt as a basis for a composition. Flow Machines was able to take that initial prompt and turn it into a complete melody and harmony.

Flow Machines uses a type of artificial intelligence called machine learning that enables it to analyze vast amount of data, in this case sheet music, and recognize patterns which it can then draw from to recreate a new product, in this case, a composition.

The track is called “Daddy’s Car” and will soon be released as part of an album of songs composed entirely by artificial intelligence. The upcoming album is expected to be released some time in 2017.

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Source: factmag.com/2016/09/22/hear-first-complete-pop-song-composed-artificial-intelligence/

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Artificial intelligence vs. human intelligence: how do they measure up?

There’s no denying that artificial intelligence is lightyears ahead of what it was just a few years ago. The technology continues to advance at an ever-increasing rate. But the ultimate goal of artificial intelligence researchers is to replicate human intelligence. So how do artificial intelligence and human intelligence measure up? You be the judge

The so-called “deep learning” that artificial intelligence is capable of isn’t really the type of profound learning like humans are capable of. Rather deep learning refers to an interconnected neural network. That means that artificial intelligence has immediate access to a wider body of knowledge, but humans are still capable of more profound thought.

Artificial intelligence systems are able to beat the greatest chess masters in the world but they need millions of pictures (labeled by humans) to be able to learn to correctly identify a cat. Even a toddler can learn to differentiate between cats, dogs, and other animals after just a few instances of exposure to them.

Intel’s latest processor, the i7, is one of the best CPUs that the average person can go out and buy. With four cores, it can perform four separate tasks simultaneously. But that’s no match for human biology. Even super computers are no match for the human brain’s 80 billion cells.

The human brain can do what it does with just 10 watts of power. Artificial intelligence would need 10 terrawatts to imitate the human brain. That’s a trillion times more energy to do what the human brain is already capable of.

Artificial intelligence runs off of algorithms programmed by humans. The algorithms haven’t actually changed much. What’s changed is the ability for artificial intelligence to run algorithms much quicker than ever before. Certain tasks, like mastering chess, depends on algorithms which is why artificial intelligence has the upper hand when it comes to playing chess.

Why even try

If after all these years, human intelligence is still vastly superior to artificial intelligence, why do artificial intelligence researchers even bother? Because despite its weaknesses when it comes to certain cognitive tasks, it can still do some things exceptionally better than humans. Artificial intelligence can sort through vast amounts of data in seconds, a task that would take humans days, weeks, or even years. Artificial intelligence is much better at humans when it comes to recognizing patterns hidden amongst large amounts of data. Artificial intelligence is far superior to humans when it comes to mathematical reasoning and computing as well.

In the end, humans need artificial intelligence. They can automate some of the more simple cognitive tasks for us such as pulling up our favorite song or performing a quick mathematical calculation. They will make our lives easier. But artificial intelligence still needs us as well. If true artificial intelligence capable of rivaling human intelligence is ever a reality, it will only be because very intelligent humans created it.

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Source: bigthink.com/errors-we-live-by/should-we-fear-ai

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A brief history of artificial intelligence

The concept of artificial intelligence began as pure fiction, something to be imagined but never actually existing. Today, we know that that’s no longer the case. Artificial Intelligence is real and there are already real-world applications where artificial intelligence is helping us solve some of the biggest problems facing humanity. We’re still a ways off from creating true artificial intelligence but we’re getting closer every day. Here’s a look at how artificial intelligence has developed through the years.

Greek myths

The earliest known reference to something we could term artificial intelligence dates back to the ancient Greeks. According to their mythology, Hephaestus, the blacksmith of Olympus created life-like metal automatons that were created to carry out certain functions.

The birth of science fiction

The concept of artificial intelligence stays relatively quiet until the early 19th century when Mary Shelley wrote Frankenstein, considered by many to be the first true science fiction novel because of the emphasis on the use of scientific methods and equipment to create a semi-intelligent monster. This novel gave way to more science fiction, some of which deal with the theme of robots and robots taking over humanity.

The first computer that was never made

Charles Babbage, a Victorian era inventor designed the first computer (on paper anyways) in 1822. It was designed to carry out mathematical calculations. He died before he could build his device which he called the Difference Engine but based on his designs, the machine could have worked had it been built and would have been the first computer.

The Turing Machine and the Turing Test

Alan Turing was a brilliant mathematician who helped bring World War II to an end by using his Turing Machine to break the German’s code. He is considered the father of computer science and artificial intelligence. He is also famous for coming up with the Turing Test which is designed to differentiate between computers that can be said to have artificial consciousness and computers that can’t.

Dartmouth Conference

In the summer of 1956, the scientific field of artificial intelligence was born over the course of a month long conference held at Dartmouth College. The boundaries of the field were set and plans were made to recreate human intelligence in a machine.

Hot and cold seasons

Researchers left the Dartmouth Conference with a lot of research money and optimism. Early AI researchers quickly realized that creating artificial intelligence was going to be a lot harder than they previously thought. This led to discouragement, lack of funding and very little progress in the field of AI in the early 70s and 80s though there were also resurgences as well.

AI in Hollywood and the real world

This brings us to today. Artificial Intelligence is now a part of our pop culture thanks to dozens of Hollywood movies that deal with the concept of artificial intelligence. Many of these portrayals are negative and depict robots overthrowing humanity, but some are more nuanced and treat the subject in a very thoughtful way. Artificial intelligence is now a part of our daily lives thanks to personal assistants like Siri and Cortana.

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Source: historyextra.com/article/ancient-greece/7-phases-history-artificial-intelligence

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Just what is meant by “artificial intelligence”?

It has only been in recent years that we’ve come to see artificial intelligence as a reality and not something out of a science fiction story. Even now, we’re still struggling to come up with an adequate definition of the term “artificial intelligence” which isn’t surprising when you consider that after thousands of years, humans can’t even decide on a definition for “intelligence.”

Put one way, artificial intelligence is a term given to computer systems that attempt to simulate human intelligence and learning. But even that definition is too big to wrap your head around. To simplify it, you can break artificial intelligence into two categories: Strong (or broad) AI, and weak (or narrow) AI.

Strong vs. weak AI

Though you may not have heard the term “strong AI” before, you’re most likely familiar with the concept. Strong AI is what most people envision when they think of the term “artificial intelligence.” Strong AI hasn’t been invented yet; strong AI would be a system that can think and reason exactly as humans do: drawing from a large reservoir of knowledge and utility to handle whatever task it’s presented with.

Weak AI is what people are talking about when they talk about current AI applications. Weak AI doesn’t “think” in the same sense that humans do. They operate off of algorithms that can “learn” in a sense which means the AI systems that depend on them become more accurate over time. But weak AI is limited according to the narrow area of expertise for which they are programmed. These systems can be programmed to predict crime, beat the greatest chess masters, or detect illnesses but no single AI system can do it all and no single AI system can learn new areas of expertise as humans can.

Already, weak AI is accomplishing tremendous things and there are still untold possibilities and implications of the technology. If/when strong AI is developed, it will usher in a new era of artificial intelligence that we can scarcely imagine.

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Source: govtech.com/computing/What-Is-Artificial-Intelligence.html

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Say “hello” to the first artificially intelligent Barbie

After receiving widespread criticism for their Teen Talk Barbie that lamented, “Math class is tough,” Mattel is stepping up their game by releasing Hello Barbie, full name Barbara Millicent Roberts, the first Barbie with artificial intelligence. Their goal is to create a toy that seems more lifelike because of its ability to carry on a conversation with kids. Whereas Teen Talk Barbie, and other previous talking Barbies, simply selected a phrase at random from a small database of possible phrases, Hello Barbie knows 8,000 lines of dialogue. Even more impressive, she selects certain phrases based on what kids are saying to her or asking her.

How it works

The secret is in Barbie’s belt buckle which actually doubles as a button that can activate speech recognition software. When a child holds down the belt buckle button and speaks to Barbie, the doll records the audio and transmits it to a ToyTalk server (ToyTalk is a third party service not owned by Mattel that manages the databases of phrases for various toys). The ToyTalk server runs something called a decision engine to select an appropriate response to what the child said. Oren Jacob, the CEO of ToyTalk describes ToyTalk’s decision engine as a kind of map with forks in the road. It uses natural language processing to analyze what the child is saying or asking and arrives at an optimal response which is transmitted back to the Barbie Doll. This entire process takes only seconds.

It keeps getting better

One of the best things about Hello Barbie is that it has the ability to keep on improving when it comes to speech recognition and response selection. Because Hello Barbie’s 8,000 lines of dialogue are stored on ToyTalk’s servers and not on a chip within the doll itself, a team of ToyTalk employees have access to that database of dialogue and can continually improve it. As more children talk to Hello Barbie, ToyTalk can study patterns, tweak their decision engine to be more accurate, and add or remove lines of dialogue as needed.

Because the audio recordings are stored on ToyTalk servers, the child’s parents can go online and listen to or delete audio recordings. They also have the option to share recordings of their child interacting with Barbie.

According to Mattel, Hello Barbie will hit the shelves in November just in time for the holidays.

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Source: popsci.com/hello-barbie-learns-to-chat-using-artificial-intelligence

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Chatbots and deep learning will change the way businesses operate

You may have heard some talk about chatbots lately. But what are they? The word “chatbot” sounds fun and casual, but chatbots are actually very sophisticated software that have some very serious implications in the world of business. Think of chatbots as digital assistants not so different from iPhone’s Siri or Microsoft’s Cortana. A chatbot eliminates the need for many mobile apps because the chatbot can perform the same function as those apps. Want to know the forecast for tomorrow? You don’t have to open the weather app, just ask a chatbot. Is your flight still on time? Ask a chatbot. When will your package be delivered? Ask a—well, you get the picture.

Chatbots aren’t new, but they’re getting more advanced

Chatbots have been around for a while but now that the technology is advancing so quickly, technology firms are getting excited about their capabilities. For instance, the next generation of chatbots canstore, synthesize, and recall important information. They can make purchases for you using any stored credit cards in your device. They can sync your calendar with weather information to warn you when foul weather may be threatening your weekend plans.

Chatbots and deep learning

Chatbots rely on something called deep learning which is a type of machine learning in which a neural network recognizes human speech, data, and patterns and can transmit that data into its neural network. As a result, the AI becomes more and more accurate at performing tasks and answering questions the more it’s used by humans.

Businesses are hoping to integrate chatbots to revolutionize the way customers and businesses interact. Chatbots can free up a lot of personnel to focus on other things because they can handle customer questions. Imagine a machine that could access company wikis to find the information that customers are looking for and then relay that information to the customers.

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Source: forbes.com/sites/danielnewman/2016/06/28/how-chatbots-and-deep-learning-will-change-the-future-of-organizations/#5e2548406563

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The pros and cons of artificial intelligence

People have been wary about the concept of artificial intelligence since it was first invented. There’s something uncanny about the idea of machines that are human-like but not quite human and Hollywood has capitalized on these fears.

But artificial intelligence is basically a tool, and isn’t inherently good or bad. It can only be used in good and bad ways. Here are some of the pros and cons of artificial intelligence.

Pros

Humans get bored, machines don’t. That means that artificial intelligence is useful in that it can be programmed to do mundane tasks that humans don’t prefer.

AI is faster to act and react to incoming information. AI is already being used for fraud detection, planning, and scheduling, and cyber-security.

AI can process massive amounts of data quickly. Data that would take a human thousands of years to read through could be processed by AI in seconds.

AI can’t make mistakes so long as they’re programmed correctly.

AI can do risky jobs, like exploring space, without putting human lives in jeopardy.

Cons

Job loss. There’s no way around it, as artificial intelligence becomes more advanced, jobs, especially those in certain fields, will be filled by AI.

Though AI will create many new jobs and a lot of wealth, the disbursement of that wealth will be uneven. For instance, truck drivers, taxi drivers, and bus drivers will all be out of a job almost overnight when fully autonomous vehicles are legalized and become reasonably priced.

AI can result in a concentration of power. The wealthy and powerful will be the ones most likely to have control over AI making them even more wealthy and powerful.

Benign goals can be carried out perversely. Say artificial intelligence is tasked with solving a food shortage problem. AI would see nothing wrong with reducing the human population rather than increasing food supply if it was a simpler solution.

Artificial Intelligence News brought to you by artificialbrilliance.com Source: datamation.com/applications/pros-and-cons-of-artificial-intelligence.html

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There’s a major flaw in the Turing Test

In the 1950s when computer science was in its infancy and artificial intelligence was nothing but science fiction, Alan Turing, a pioneer in theoretical computer science felt that there needed to be a way to determine whether a machine could be said to be intelligent. So he created a test called the Turing Test, or Imitation Game. His test was very simple. A human and a machine would be separated so they could not see each other and an evaluator would listen as the human and the machine converse. If the evaluator, a third party that isn’t part of the conversation, can’t determine whether both parties are human or if one is a machine, then the machine is said to have passed the Turing Test.

So far, the Turing Test has been viewed as a fairly simple and accurate way to test a machine’s ability to pass itself off as a human. But Kevin Warwick and Huma Shah from Coventry University have just found a major flaw in the test which makes it unreliable. According to Warwick and Shah, all a machine has to do to pass the Turing Test is remain silent. If the machine gives no response to any questions or remarks made by a human, an evaluator cannot determine whether the other “participant” is choosing not to speak as the result of an intelligent choice, or whether it is a machine that is experiencing technical difficulties. Warwick and Shah did a study in which they looked at transcripts from past Turing Tests. They found that in tests where the machines malfunctioned, their silence could be interpreted as an intelligent choice and not a mistake. Though this flaw in the Turing Test may seem silly, it presents a major problem. It makes us question what it means to pass the Turing Test if a rock or some other inanimate object that obviously has no intelligence can pass it.

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Source: sciencedaily.com/releases/2016/07/160705092011.htm

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Ten artificial intelligence stats that will blow you away

Bill Gates recently declared artificial intelligence “the holy grail of computer science.” The industry has made massive strides in recent years and there are even more exciting things ahead. Here are ten incredible statistics about artificial intelligence:

The AI market will grow from $420 million in 2014 to over $5 billion by the year 2020.

By 2018, an estimated 6 billion things from appliances to cars to wearable tech will depend on AI technology.

There are currently more than 1,000 AI start-up companies and a total of $5.4 billion has been invested into them.

A study by an AI language company found that 80% of executives believed that AI solutions improved worker performance and created new jobs.

Apple has Siri, Microsoft has Cortana, and Amazon has Alexis, but until recently, the majority of people were never using the personal assistants available to them. Now, only 2% of iPhone users haven’t used Siri.

By the year 2020, 40% of all mobile interactions between users and personal assistants will be powered by data. That means that AI will enable personal assistants to make decisions for us and not just carry out requests.

By 2020, 85% of all customer interactions with companies won’t require a human customer service representative as chatbots will be able to use artificial intelligence to solve customers’ problems.

Over the next decade, artificial intelligence will take over 16% of all U.S. jobs, however, much of that will be offset by the fact that there will be many new jobs created to create and maintain new AI platforms and machines.

By 2018, the fastest-growing companies will “employ” more smart machines and virtual assistants than humans.

Artificial intelligence will be powered by GPUs rather than CPUs. Currently, Nvidia’s best GPU, the Tesla K80 is 2-5 times faster than Intel’s leading CPU, the Xeon Phi 7120.

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Source: fool.com/investing/2016/06/19/10-stats-about-artificial-intelligence-that-will-b.aspx