Blind Spots Uncovered at the Intersection of AI and Neuroscience – Dozens of Scientific Papers Debunked

Findings debunk dozens of prominent published papers claiming to read minds with EEG.

By PURDUE UNIVERSITY 

Is it possible to read a person’s mind by analyzing the electric signals from the brain? The answer may be much more complex than most people think.

Purdue University researchers – working at the intersection of artificial intelligence and neuroscience – say a prominent dataset used to try to answer this question is confounded, and therefore many eye-popping findings that were based on this dataset and received high-profile recognition are false after all.

The Purdue team performed extensive tests over more than one year on the dataset, which looked at the brain activity of individuals taking part in a study where they looked at a series of images. Each individual wore a cap with dozens of electrodes while they viewed the images.

The Purdue team’s work is published in IEEE Transactions on Pattern Analysis and Machine Intelligence. The team received funding from the National Science Foundation.

Purdue University researchers are doing work at the intersection of artificial intelligence and neuroscience. In this photo, a research participant is wearing an EEG cap with electrodes.

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Artificial intelligence has advanced so much, it wrote this article

“Alter 3: Offloaded Agency,” part of the exhibition “AI: More than Human.”

By Jurica Dujmovic

Natural language processing rivals humans’ skills.

I did not write this article. 

In fact, it wasn’t written by any person. Every sentence you see after this introduction is the work of OpenAI’s GPT-3, a powerful language-prediction model capable of composing sequences of coherent text. The only thing I did was provide it with topics to write about. I did not even fix its grammar or spelling.

According to OpenAI, more than 300 applications are using GPT-3, which is part of a field called natural language processing. An average of 4.5 billion words are written per day. Some say the quality of GPT-3’s text is as good as that written by humans.

What follows is GPT-3’s response to topics in general investing.

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New AI Technique Can Generate 3D Holograms in Real-Time

Holographic display prototype used in the experiments

By  Derya Ozdemir

Not only can this technique run on a smartphone but it also needs less than 1 megabyte of memory.

Virtual reality has been around for decades, and every year, headlines all over the internet announce it to be the next big thing. However, those predictions are yet to become a reality, and VR technologies are far from being widespread. While there are many reasons for that, VR making users feel sick is definitely one of the culprits.

Better 3D visualization could help with that, and now, MIT researchers have developed a new way to produce holograms thanks to a deep learning-based method that works so efficiently that cuts down the computational power need in an instant, according to a press release by the university.

A hologram is an image that resembles a 2D window looking onto a 3D scene, and this 60-year-old technology remade for the digital world can deliver an outstanding image of the 3D world around us.


“People previously thought that with existing consumer-grade hardware, it was impossible to do real-time 3D holography computations,” explains Liang Shi, the study’s lead author and a Ph.D. student in MIT’s Department of Electrical Engineering and Computer Science. “It’s often been said that commercially available holographic displays will be around in 10 years, yet this statement has been around for decades.”

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Gartner: 75% of VCs will use AI to make investment decisions by 2025

By Kyle Wiggers

By 2025, more than 75% of venture capital and early-stage investor executive reviews will be informed by AI and data analytics. In other words, AI might determine whether a company makes it to a human evaluation at all, de-emphasizing the importance of pitch decks and financials. That’s according to a new whitepaper by Gartner, which predicts that in the next four years, the AI- and data-science-equipped investor will become commonplace.

Increased advanced analytics capabilities are shifting the early-stage venture investing strategy away from “gut feel” and qualitative decision-making to a “platform-based” quantitative process, according to Gartner senior research director Patrick Stakenas. Stakenas says data gathered from sources like LinkedIn, PitchBook, Crunchbase, and Owler, along with third-party data marketplaces, will be leveraged alongside diverse past and current investments.

“This data is increasingly being used to build sophisticated models that can better determine the viability, strategy, and potential outcome of an investment in a short amount of time. Questions such as when to invest, where to invest, and how much to invest are becoming almost automated,” Stakenas said. “The personality traits and work patterns required for success will be quantified in the same manner that the product and its use in the market, market size, and financial details are currently measured. AI tools will be used to determine how likely a leadership team is to succeed based on employment history, field expertise, and previous business success.”

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New AI Can Detect Emotion With Radio Waves

THERE ARE NATIONAL SECURITY AND PRIVACY IMPLICATIONS TO AN EXPERIMENTAL UK NEURAL NETWORK THAT DECIPHERS HOW PEOPLE RESPOND TO EMOTIONAL STIMULI.

By PATRICK TUCKER 

Picture: military interrogators are talking to a local man they suspect of helping to emplace roadside bombs. The man denies it, even as they show him photos of his purported accomplices. But an antenna in the interrogation room is detecting the man’s heartbeat as he looks at the pictures. The data is fed to an AI, which concludes that his emotions do not match his words…

A UK research team is using radio waves to pick up subtle changes in heart rhythm and then, using an advanced AI called a neural network, understand what those signals mean — in other words, what the subject is feeling. It’s a breakthrough that one day might help, say, human-intelligence analysts in Afghanistan figure out who represents an insider threat.

The paper from a team out of Queen Mary University of London and published in the online journal PLOS ONE, demonstrates how to apply a neural network to decipher emotions gathered with transmitting radio antenna. A neural network functions in a manner somewhat similar to a human brain, with cells creating links to other cells in patterns that create memory, as opposed to more conventional methods such as machine learning, which employ straightforward statistical methods on data sets. 

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Using AI to measure the demand for parking space

by Fraunhofer-Gesellschaft

The growth in the number of cars parked in urban areas has a major impact on public space. One key consequence of this is that parking availability is less predictable, both in downtown and in quieter, residential areas, where people are having to spend more and more time looking for a free space. One remedy is to create a residential parking zone. To justify this measure, however, a municipality must first commission reports and carry out a survey of parking availability, both of which take time and cost money. To reduce this effort, the Fraunhofer Institute for Industrial Engineering IAO is now exploring the use of AI to analyze the demand for parking space. This pilot project is running in partnership with the City of Karlsruhe and is being conducted by the Research and Innovation Center for Cognitive Service Systems (KODIS), a branch of Fraunhofer IAO.

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RESEARCHERS: IT WOULD BE IMPOSSIBLE TO CONTROL SUPER AI

But is superintelligent AI really possible? Some experts are skeptical   

From the media release for a recent paper:

The idea of artificial intelligence overthrowing humankind has been talked about for many decades, and scientists have just delivered their verdict on whether we’d be able to control a high-level computer super-intelligence. The answer? Almost definitely not.

The catch is that controlling a super-intelligence far beyond human comprehension would require a simulation of that super-intelligence which we can analyse. But if we’re unable to comprehend it, it’s impossible to create such a simulation. 

DAVID NIELD, “CALCULATIONS SHOW IT’LL BE IMPOSSIBLE TO CONTROL A SUPER-INTELLIGENT AI” AT SCIENCE ALERT THE OPEN ACCESS RESEARCH STUDY IS HERE.

First, the idea that machines can design smarter machines should be treated with skepticism: maybe we are looking at a Robogeddon that can’t happen and a crowd of experts who need it to.

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AI plastering robot developed for construction sites

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Amid the ever-increasing use of artificial intelligence (AI) robots at construction sites, a new robot that can perform concrete plastering work on its own has been developed.

 Hyundai Engineering Co., a plant engineering affiliate of Hyundai Motor Group, announced on Wednesday that it had developed the nation’s first AI plastering robot that can flatten concrete floors by itself, adding that it has applied for related patents.

The AI plastering robot, which was developed in collaboration with Robo Block Systems, is a device that rotates two motors with four micro blades to flatten a floor infilled with concrete.

Compared to existing floor plastering machines, the newly-developed AI robot features a lighter design and a greater usability. By making use of an electric motor, the AI robot generates less noise compared to existing machines that use gasoline motors.

The patented ‘AI plastering robot floor flattening technology’ precisely measures the concrete-infilled floor space with a 3D scanner.

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What can AI learn from Human intelligence?

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At HAI’s fall conference, scholars discussed novel ways AI can learn from human intelligence – and vice versa.

Can we teach robots to generalize their learning? How can algorithms become more commonsensical? Can a child’s learning style influence AI?

Stanford Institute for Human-Centered Artificial Intelligence’s fall conference considered those and other questions to understand how to mutually improve and better understand artificial and human intelligence. The event featured the theme of “triangulating intelligence” among the fields of AI, neuroscience, and psychology to develop research and applications for large-scale impact.

HAI faculty associate directors Christopher Manning, a Stanford professor of machine learning, linguistics, and computer science, and Surya Ganguli, a Stanford associate professor of neurobiology, served as hosts and panel moderators for the conference, which was co-sponsored by Stanford’s Wu-Tsai Neurosciences Institute, Department of Psychology, and Symbolic Systems program.

Speakers described cutting-edge approaches—some established, some new—to create a two-way flow of insights between research on human and machine-based intelligence, for powerful application. Here are some of their key takeaways.

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How artificial intelligence may be making you buy things

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If you are getting told off for spending too much on wine, maybe you can blame it on artificial intelligence

The shopping lists we used to scribble on the back of an envelope are increasingly already known by the supermarkets we frequent.

Firstly via the loyalty cards we scan at checkouts, and more and more so from our online baskets, our shopping habits are no longer a secret.

But now more retailers are using AI (artificial intelligence) – software systems that can learn for themselves – to try to automatically predict and encourage our very specific preferences and purchases like never before.

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Satellites are mapping out every tree on earth using artificial intelligence

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Scientists have mapped 1.8 billion individual tree canopies across millions of kilometres of the Sahel and Sahara regions of West Africa. It is the first time ever that trees have been mapped in detail over such a large area.

So how was it possible? Researchers analysed a huge database of satellite images using artificial intelligence. They employed neural networks which are able to recognise objects, like trees, based on their shapes and colours.

To train it, the AI system was shown satellite images where trees had been manually traced. This involved lead author Martin Brandt going through the arduous process of identifying and labelling nearly 90,000 trees himself, beforehand.

From these images, the computer learnt what a tree looked like and could pick out individual canopies from the thousands of images in the database. Brandt says it would have taken millions of people years to identify the trees without the AI system.

In a review of the research, commissioned by Nature, scientists at New Mexico State University wrote that “it will soon be possible, with certain limitations, to map the location and size of every tree worldwide”

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