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AI Based on Slow Brain Dynamics

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Scientists at Bar-llan University in Israel have used advanced experiments on neural cultures and large scale simulations to create a new ultrafast artificial intelligence. The new AI is based on the slow brain dynamics of humans. Those brain dynamics have better learning rates compared to the best learning algorithms that we have today. 

Machine learning is actually strongly related and based on the dynamics of our brains. With the speed of modern computers and their large data sets, we have been able to create deep learning algorithms that are similar to human experts in various different fields. However, these learning algorithms have different characteristics than human brains. 

The team of scientists at the university published their work in the journal Scientific Reports. They worked to connect neuroscience and advanced artificial intelligence algorithms, a field that has been abandoned for decades. 

Professor Ido Kanter of Bar-llan University’s Department of Physics and Gonda (Goldschmied) Multidisciplinary Brain Research Study, and the leading author of the study, commented on the two fields. 

“The current scientific and technological viewpoint is that neurobiology and machine learning are two distinct disciplines that advance independently,” he said. “The absence of expectedly reciprocal influence is puzzling.” 

“The number of neurons in a brain is less than the number of bits in a typical disc size of modern personal computers, and the computational speed of the brain is like the second hand on a clock, even slower than the first computer invented over 70 years ago,” he said. 

“In addition, the brain’s learning rules are very complicated and remote from the principles of learning steps in current artificial intelligence algorithms.” 

Professor Kanter works with a research team including Herut Uzan, Shira Sardi, Amir Goldental, and Roni Vardi. 

When it comes to brain dynamics, they deal with asynchronous inputs since physical reality changes and develops. Because of this, there is no synchronization for the nerve cells. This is different with artificial intelligence algorithms since they are based on synchronous inputs. Different inputs within the same frame and their timings are normally ignored. 

Professor Kanter went on to explain this dynamic. 

“When looking ahead one immediately observes a frame with multiple objects. For instance, while driving one observes cars, pedestrian crossings, and road signs, and can easily identify their temporal ordering and relative positions,” he said. “Biological hardware (learning rules) is designed to deal with asynchronous inputs and refine their relative information.” 

One of the points that this study makes is that ultrafast learning rates are about the same whether it’s a small or large network. According to the researchers, “the disadvantage of the complicated brain’s learning scheme is actually an advantage.” 

The study also shows that learning is able to take place without learning steps. It can be achieved through self-adaptation based on asynchronous inputs. In the human brain, this type of learning happens in the dendrites, which are short extensions of nerve cells, and different terminals of each neuron. This has been observed before. Previously, it was believed to be unimportant that network dynamics under dendeitic learning are controlled by weak weights. 

This new research and findings can mean a lot of different things. These efficient deep learning algorithms and their similarity to the very slow brain’s dynamics can help create a new class of advanced artificial intelligence with fast computers. 

The study also pushes for cooperation between the fields of neurobiology and artificial intelligence, which can help both fields advance further. According to the research group, “Insights of fundamental principles of our brain have to be once again at the center of future artificial intelligence.” 

 

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Alex McFarland is a journalist from the United States who covers developments in AI around the world.

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The Use of Artificial Intelligence In Music Is getting More And More Sophisticated

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The application of artificial intelligence in music has been increasing for a few years now.  As Kumba Sennaar explains, the three current applications of AI in music industry lies in music composition, music streaming and music monetization where AI platforms are helping artists monetize their music content based on data from user activity.

It all started way back in 1957 when Learn Hiller and Leonard Issacson programmed Illiac I to produce “Illiac Suite for String Quartet, the first work completely written by artificial intelligence, then, 60 years on, it turned into complete albums like the Taryn Southern album produced by Amper Music in 2017. Currently, Southern has over 452 thousand subscribers on YouTube and “Lovesick” a song from the album was listened and viewed by more than 45,000 viewers.

But since then, the application of AI in this field has both got more sophisticated and branched out further. Open AI has created MuseNet, as the company explains, “a deep neural network that can generate 4-minute musical compositions with 10 different instruments and can combine styles from country to Mozart to the Beatles. MuseNet was not explicitly programmed with our understanding of music, but instead discovered patterns of harmony, rhythm, and style by learning to predict the next token in hundreds of thousands of MIDI files. MuseNet uses the same general-purpose unsupervised technology as GPT-2, a large-scale transformer model trained to predict the next token in a sequence, whether audio or text.

On the other hand, as GeekWire, among others, reports, Dr. Mick Grierson, computer scientist and musician from Goldsmiths, University of London was recently commissioned by the Italian car manufacturer Fiat to produce a list of 50 most iconic pop songs using algorithms. His analytical software was used to “determine what makes the songs noteworthy, including key, the number of beats per minute, chord variety, lyrical content, timbral variety, and sonic variance.”

According to his results, the song that had the best cocktail of the set parameters was Nirvana’s “Smells Like Teen Spirit,” ahead of U2’s “One” and John Lennon’s “Imagine”. Nirvana’s song was then used by FIAT to promote its new FIAT 500 model. Grierson explained that the algorithms showed that, ‘the sounds these songs use and the way they are combined is highly unique in each case.’

Another application was prepared by musicnn library, which as explained, uses deep convolutional neural networks to automatically tag songs. The models “that are included achieve the best scores in public evaluation benchmarks.” music (as in musician) and its best models have been released as an open-source library. The project has been developed by the Music Technology Group of the Universitat Pompeu Fabra, located in Barcelona, Spain.

In his analysis of the application, Jordi Pons used musicnn to analyze and tag another iconic song, Queen’s “Bohemian Rhapsody.” He noticed that the singing voice of Freddie Mercury was tagged as a female voice, while its other predictions were quite accurate. Making musicnn available as open-source makes it possible to further refine the tagging process.

Reporting on the use of AI in music streaming, Digital Music News concludes that “the introduction of artificial intelligence and machine learning technologies has greatly improved the way we listen to music.  Thanks to rapid advances in the AI and similar technologies, we are most likely going to see plenty of futuristic improvements in the upcoming years.”

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Scientists Use Artificial Intelligence to Estimate Dark Matter in the Universe

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Scientists from the Department of Physics and the Department of Computer Science at ETH Zurich are using artificial intelligence to learn more about our universe. They are contributing to the methods used in order to estimate the amount of dark matter present. The group of scientists developed machine learning algorithms that are similar to those used by Facebook and other social media companies for facial recognition. These algorithms help analyze cosmological data. The new research and results were published in the scientific journal Physical Review D. 

Tomasz Kacprzak, a researcher from the Institute of Particle Physics and Astrophysics, explained the link between facial recognition and estimating dark matter in the universe. 

“Facebook uses its algorithms to find eyes, mouths or ears in images; we use ours to look for the tell-tale signs of dark matter and dark energy,” he explained. 

Dark matter is not able to be seen directly by telescope images, but it does bend the path of light rays that are coming to earth from other galaxies. This is called weak gravitational lensing, and it distorts the images of those galaxies. 

The distortion that takes place is then used by scientists. They build maps based on mass of the sky, and they show where dark matter is. The scientists then take theoretical predictions of the location of dark matter and compare them to the built maps, and they look for the ones that most match the data.

The described method with maps is traditionally done by using human-designed statistics, which help explain how parts of the maps relate to one another. The problem that arises with this method is that it is not well suited for detecting the complex patterns that are present in such maps. 

“In our recent work, we have used a completely new methodology…Instead of inventing the appropriate statistical analysis ourselves, we let computers do the job,” Alexandre Refregier said. 

Aurelien Lucchi and his team from the Data Analytics Lab at the Department of Computer Science, along with Janis Fluri, a PhD student from Refregier’s group and the lead author of the study, worked together using machine learning algorithms. They used them to establish deep artificial neural networks that are able to learn to extract as much information from the dark matter maps as possible. 

The group of scientists first gave the neural network computer-generated data that simulated the universe. The neural network eventually taught itself which features to look for and to extract large amounts of information.

These neural networks outperformed the human-made analysis. In total, they were 30% more accurate than the traditional methods based on human-made statistical analysis. If cosmologists wanted to achieve the same accuracy rate without using these algorithms, they would have to dedicate at least twice the amount of observation time. 

After these methods were established, the scientists then used them to create dark matter maps based on the KiDS-450 dataset. 

“This is the first time such machine learning tools have been used in this context, and we found that the deep artificial neural network enables us to extract more information from the data than previous approaches. We believe that this usage of machine learning in cosmology will have many future applications,” Fluri said. 

The scientists now want to use this method on bigger image sets such as the Dark Energy Survey, and the neural networks will start to take on new information about dark matter.

 

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Element AI Closes Series B – Raises $151 Million To Bring AI To More Companies

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The Canadian startup Element AI, based out of Montreal, has recently completed its series B round of funding, raising $151 million dollars to fund their AI expansion goals. Element AI’s goal is to bring the power of AI to companies that aren’t typically likely to use it, making AI available to those who aren’t savvy regarding AI and computer technologies.

Element AI was founded in 2016, and it aims to dramatically expand the use of AI outside of the traditional fields like retail and security. Element AI hopes to “turn research and industry expertise into software solutions that exponentially learn and improve”, focusing specifically on the supply chain and financial services sectors.

According to VentureBeat, Element AI’s successful series B funding managed to accrue over $151.3 million dollars from both old and new investors. The startup plans to invest this money in the marketing of its current product line as well as in the development of new AI solutions.  The CEO of Element AI, Jean-François Gagné, put out a recent press release remarking that the company is excited to start working with their new partners who wish to explore the potential of AI in non-traditional market areas. According to Gagné, Element AI remains fully committed to operationalizing AI, despite it being “the industry’s toughest challenge”.

Although AI is frequently in the headlines, AI applications are primarily found in a few specific fields. Element AI was founded with the idea that AI will be the next major transformative technology, although not every business is equipped to take advantage of it. The disparity between technology companies that are positioned to take advantage of AI and non-tech companies creates a substantial divide between companies who can use AI and those that can’t. Element AI wants to bring AI algorithms to companies that lack the experience to properly utilize AI.

Element AI set out to achieve this by providing consultation to companies that could potentially benefit from utilizing AI, helping them identify areas where they could implement AI solutions. The company has since expanded to offering other services, offering products tailored to specific industries like retail/logistics, financial services, manufacturing, and insurance. The list of specialized products that the company offers is likely to grow, thanks to the substantial increase in funding the company has received.

Element AI is not the only company to try and operationalize AI, with other companies like UiPath creating tools designed to allow companies to automate repetitive tasks. However, Element has definitely been the most successful at bringing AI to a wider section of society.

As reported by CrunchBase, Element AI has worked with many different companies, including Gore Mutual, Bank of Canada, National Bank, LG, and others. In terms of investors, many of their supporters from the series A investment have returned to back the company a second time, including Real Venture, BDC Capital, Hanwha Asset Management and DCVC. Some of the new investors in the company include Gouvernement du Quebec and McKinsey & Company.

According to TechCrunch, McKinsey is a management consultancy company, and though at first glance the company seems like a competitor to Element AI, McKinsey seems to be funneling customers to Element. Many system integrators don’t have the experience with AI needed to ascertain the best uses for the technology, while Element AI has experience with emerging technologies and computing. QuantumBlack, the AI and advanced analytics division of McKinsey, has also established its own offices in Montreal, where they will be collaborating on projects with Element AI.

Element AI also stated in its press release that the company would be using the newly acquired funds to expand its operations across the globe. Currently, the company has approximately 500 employees located in offices around Singapore, South Korea, Seoul, London, and Toronto.

Element AI isn’t the only Canadian AI startup to see recent success. The company CDPQ recently launched its own AI funding initiative intended to advance the commercialization of AI platforms throughout Quebec.

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