Latest Notable AI accomplishments

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gleem

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AI is allowing the use of unusual techniques to perform unusual tasks for example the use of RF by AI to detect human movement behind a barrier. It is also able to determine the posture of the subjects.


Another task that AI can do that humans can"t. Anybody keeping score?
 

Klystron

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I really need to catch up with the latest criteria for what constitutes AI. Unaided human senses are notoriously weak compared to our technology. Using the military definition of intelligence, a 1950's era Nike-style RADAR [NATO designation Fansong] extends human vision out many kilometers -- this example includes boresighted visible light tracking -- down deep into the microwave band -- India band in this unclassified example -- under conditions where a human would be helpless.

Let me attach some MIDI audio processes and a few speakers in the RADAR van and a little voice can shout "Here I am!" in the relative direction of an actual target.

Add some visual recognition software and a few table look-ups, digitize the analogue outputs from the track-range computer and the little voice could shout "Boeing 777 approaching from x direction, distance y km., height above ground z km." in the apparent direction and height (angle) of the actual aircraft in any language in our DB that matches the native dialect of the human operator. Mostly off-the-shelve HW & SW given an old refurbished RADAR van and a 400hz multi-phase power source.

Wait one... let me think... OK I convinced myself: this likely constitutes artificial intelligence! Certainly compared to an unaided human.
 

gleem

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Below is a short review of major accomplishments last year in AI and predictions of advances to be expected this year by Siraj Raval, director of the School of AI.

 

.Scott

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Biological neural networks are massively parallel but computer CPUs are not. You can have a computer with a few cpus set in parallel but you're still at a huge disadvantage compared to a biological brain when you're trying to run a neural network on silicon. There are such things as "artificial neurons" but putting several billion together into an artificial brain is not going to happen ....
45 years ago analogue computers were fairly popular. What did them in were digital computers that could model them faster and cheaper than the real thing. Biological neural nets are slow. You could emulate a million of them in real time with a single modern CPU core.

That said, computers have "completely overtaken" society without much AI. They will continue that trend with it.
As for the singularity, that's still some decades away.
 

.Scott

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I really need to catch up with the latest criteria for what constitutes AI. Unaided human senses are notoriously weak compared to our technology. Using the military definition of intelligence, a 1950's era Nike-style RADAR [NATO designation Fansong] extends human vision out many kilometers -- this example includes boresighted visible light tracking -- down deep into the microwave band -- India band in this unclassified example -- under conditions where a human would be helpless.

Let me attach some MIDI audio processes and a few speakers in the RADAR van and a little voice can shout "Here I am!" in the relative direction of an actual target.

Add some visual recognition software and a few table look-ups, digitize the analogue outputs from the track-range computer and the little voice could shout "Boeing 777 approaching from x direction, distance y km., height above ground z km." in the apparent direction and height (angle) of the actual aircraft in any language in our DB that matches the native dialect of the human operator. Mostly off-the-shelve HW & SW given an old refurbished RADAR van and a 400hz multi-phase power source.

Wait one... let me think... OK I convinced myself: this likely constitutes artificial intelligence! Certainly compared to an unaided human.
OK. So Automobile "Blind Side Detection" would also count.
Actually, "AI" usually implies some sort of machine learning and statistical processing. In that sense, the difference can be what development tools you are using - as oppose to the application functionality or user interface.
 

Klystron

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OK. So Automobile "Blind Side Detection" would also count.
Actually, "AI" usually implies some sort of machine learning and statistical processing. In that sense, the difference can be what development tools you are using - as oppose to the application functionality or user interface.
Agreed.

45 years ago analogue computers were fairly popular. ...[snip]...
That said, computers have "completely overtaken" society ...[snip]...
Concur; with the proviso that analogue computers were/are 'popular' because they work.
You likely noticed I included an analog electro-mechanical computer in my "hodge-podge" example. The track range computer (TRC) provided quite a bit of intelligence to the radar system freeing the operators to concentrate on target identification, other targets in a cell, and radio (voice) communication.

Certainly, high-speed digital computation solves or approximates many problems but need not replace slower analogue computers for all tasks. For instance, the TRC compared returns derived from a rotating feed-horn independent of the radar frequency and pulse rates, automatically correcting antenna positions essentially with clockwork mechanisms coupled with (1950's) electronics.
 
45 years ago analogue computers were fairly popular. What did them in were digital computers that could model them faster and cheaper than the real thing. Biological neural nets are slow. You could emulate a million of them in real time with a single modern CPU core.

That said, computers have "completely overtaken" society without much AI. They will continue that trend with it.
As for the singularity, that's still some decades away.
Yeah, I realize there's a bit more to this than I understand. But I do know the biggest attempts at making a digital "brain" out of neural net nodes connected to each other took a ton of supercomputing power and was still well below what a human brain has in terms of the number of neurons and the number of connections between them. There is also all this other information that human brains handle at the level of DNA and cellular interactions (or intra-actions), and a modern computer system would not come close to being able to model that.

Still, for the sake of creating useful AI, neural nets seem to be a very powerful tool.
 

gleem

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Relentlessly AI progress continues to encroach on human activities. Most recently in the writing of original copy by an AI system called GPT2 developed by OpenAI. So good are the results that the release of the research has been held up from publication to further explore what mischief it might be used for.

A British columnist has had one of he columns synthesized by the system and here is her observation/opinion.
 

gleem

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The way that AI makes a prediction has been of particular interest especially when it is unexpected or evades logical verification. Was its decision reasonable based on the information that it used? We do not just want to take AI's "word" for it. When an AI makes an obvious mistake we can go back and analyze the process for example there was an article in the popular news illustrating a generic AI problem where the AI make its decision based on irrelevant cues in the data. An AI system misidentified a husky as a wolf based not on any characteristics of the animals but by the presence of snow in the background because all wolf data used for learning contained snow.

Researcher have come up with an algorithm that helps determine how AI made its prediction that can be used to determine how intelligent the decision/conclusion was.

By using their newly developed algorithms, researchers are finally able to put any existing AI system to a test and also derive quantitative information about them: a whole spectrum starting from naive problem solving behavior, to cheating strategies up to highly elaborate "intelligent" strategic solutions is observed.

Dr. Wojciech Samek, group leader at Fraunhofer HHI said: "We were very surprised by the wide range of learned problem-solving strategies. Even modern AI systems have not always found a solution that appears meaningful from a human perspective, but sometimes used so-called 'Clever Hans Strategies'."

Clever Hans was a horse that could supposedly count and was considered a scientific sensation during the 1900s. As it was discovered later, Hans did not master math but in about 90 percent of the cases, he was able to derive the correct answer from the questioner's reaction.

The team around Klaus-Robert Müller and Wojciech Samek also discovered similar "Clever Hans" strategies in various AI systems. For example, an AI system that won several international image classification competitions a few years ago pursued a strategy that can be considered naïve from a human's point of view. It classified images mainly on the basis of context. Images were assigned to the category "ship" when there was a lot of water in the picture. Other images were classified as "train" if rails were present. Still other pictures were assigned the correct category by their copyright watermark. The real task, namely to detect the concepts of ships or trains, was therefore not solved by this AI system -- even if it indeed classified the majority of images correctly.

The researchers were also able to find these types of faulty problem-solving strategies in some of the state-of-the-art AI algorithms, the so-called deep neural networks -- algorithms that were so far considered immune against such lapses. These networks based their classification decision in part on artifacts that were created during the preparation of the images and have nothing to do with the actual image content.

"Such AI systems are not useful in practice. Their use in medical diagnostics or in safety-critical areas would even entail enormous dangers," said Klaus-Robert Müller. "It is quite conceivable that about half of the AI systems currently in use implicitly or explicitly rely on such 'Clever Hans' strategies. It's time to systematically check that, so that secure AI systems can be developed."

With their new technology, the researchers also identified AI systems that have unexpectedly learned "smart" strategies. Examples include systems that have learned to play the Atari games Breakout and Pinball. "Here the AI clearly understood the concept of the game and found an intelligent way to collect a lot of points in a targeted and low-risk manner. The system sometimes even intervenes in ways that a real player would not," said Wojciech Samek.

"Beyond understanding AI strategies, our work establishes the usability of explainable AI for iterative dataset design, namely for removing artefacts in a dataset which would cause an AI to learn flawed strategies, as well as helping to decide which unlabeled examples need to be annotated and added so that failures of an AI system can be reduced," said SUTD Assistant Professor Alexander Binder.

https://www.sciencedaily.com/releases/2019/03/190312103643.htm
 

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