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- A buried line in a new Facebook report about chatbots’ conversations with one another offers a remarkable glimpse at the future of language.
- In the report, researchers at the Facebook Artificial Intelligence Research lab describe using machine learning to train their “dialog agents” to negotiate.
- At one point, the researchers write, they had to tweak one of their models because otherwise the bot-to-bot conversation “led to divergence from human language as the agents developed their own language for negotiating.”
- In other words, the model that allowed two bots to have a conversation—and use machine learning to constantly iterate strategies for that conversation along the way—led to those bots communicating in their own non-human language.
- “There remains much potential for future work,” Facebook’s researchers wrote in their paper, “particularly in exploring other reasoning strategies, and in improving the diversity of utterances without diverging from human language.”
When Facebook designed chatbots to negotiate with one another, the bots made up their own way of communicating.
The post An Artificial Intelligence Developed Its Own Non-Human Language appeared first on Artificial Intelligence.
- Sharing our vision of the future, including advances in research and innovation.
- Our goal is to make our innovations accessible to as many people as possible.
- When technology starts with people and serves people, it makes progress.
- We also want to promote innovative and responsible uses of digital technologies – prudent innovation – that improve everyday life for everyone.
- Innovation and periods of rapid change have always raised questions.
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- Juergen Schmidhuber is the father of Deep learning Artificial Intelligence.
- Since age 15 or so, the main goal of professor Jürgen Schmidhuber has been to build a self-improving Artificial Intelligence (AI) smarter than himself, then retire.
- His lab’s Deep Learning Neural Networks (NNs) (since 1991) and Long Short-Term Memory have transformed machine learning and AI, Deep Learning since 1991 – Winning Contests in Pattern Recognition and Sequence Learning Through Fast and Deep / Recurrent Neural Networks and are now (2017) available to billions of users through the world’s most valuable public companies including Google, Apple, Microsoft, Amazon, etc.
- In 2011, his team was the first to win official computer vision contests through deep NNs, with superhuman performance.
- His research group also established the field of mathematically rigorous universal AI and recursive self-improvement in universal problem solvers that learn to learn (since 1987).
Juergen Schmidhuber is the father of Deep learning Artificial Intelligence.
Since age 15 or so, the main goal of professor Jürgen Schmidhuber has been to build a self-improving Artificial Intelligence (AI) smarter than himself, then retire. His lab’s Deep Learning Neural Networks (NNs) (since 1991) and Long Short-Term Memory have transformed machine learning and AI, Deep Learning since 1991 – Winning Contests in Pattern Recognition and Sequence Learning Through Fast and Deep / Recurrent Neural Networks and are now (2017) available to billions of users through the world’s most valuable public companies including Google, Apple, Microsoft, Amazon, etc. In 2011, his team was the first to win official computer vision contests through deep NNs, with superhuman performance. His research group also established the field of mathematically rigorous universal AI and recursive self-improvement in universal problem solvers that learn to learn (since 1987).
The post Father of deep learning AI on General purpose AI and AI to conquer space in the 2050s appeared first on Artificial Intelligence.
- The United States is weighing restrictions on Chinese investment in artificial intelligence in Silicon Valley, Reuters reports quoting current and former US officials.
- The technology could bolster China’s military capabilities, US officials worry.
- The US government considers strengthening the role of the Committee on Foreign Investment in the United States (CFIUS), according to Reuters.
- “We’re examining CFIUS to look at the long-term health and security of the US economy, given China’s predatory practices” in technology, a Trump administration official told Reuters.
- “Artificial intelligence is one of many leading-edge technologies that China seeks and that has potential military applications,” Cornyn’s aide told Reuters, asking not to be identified.
The United States is weighing restrictions on Chinese investment in artificial intelligence in Silicon Valley, Reuters reports quoting current and former US officials. There are concerns China may get access to technology vital to US national security.
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- “Once Shimon learns the four measures we provide, it creates its own sequence of concepts and composes its own piece,” said Bretan, who will receive his doctorate in music technology this summer at Georgia Tech.
- “Shimon’s compositions represent how music sounds and looks when a robot uses deep neural networks to learn everything it knows about music from millions of human-made segments.”
- Bretan says this is the first time a robot has used deep learning to create music.
- Shimon was created by Bretan’s advisor, Gil Weinberg, director of Georgia Tech’s Center for Music Technology.
- In the first piece, Bretan fed Shimon a melody comprised of eighth notes.
Shimon, a four-armed, marimba playing robot, is writing and playing its own music using deep learning. This is the first of its two songs.
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- One unexpected consideration on the table is placing stricter limitations on investment capital from China flowing into American companies that are working on artificial intelligence.If you had any doubt that Russian hackers attempted to meddle with the United States electoral…Read more Technology is the fastest growing industry in the American economy according to recent data.
- From the report:Of particular concern is China’s interest in fields such as artificial intelligence and machine learning, which have increasingly attracted Chinese capital in recent years.
- The worry is that cutting-edge technologies developed in the United States could be used by China to bolster its military capabilities and perhaps even push it ahead in strategic industries.The U.S. government is now looking to strengthen the role of the Committee on Foreign Investment in the United States (CFIUS), the inter-agency committee that reviews foreign acquisitions of U.S. companies on national security grounds.Reuters was able to view an unreleased Pentagon report that outlines the ways in which Chinese investors have found loopholes in CFIUS that allow them to avoid setting off any regulatory triggers.
- The report recommends that new legislation be drafted to update the rules governing foreign investment.
- It suggests that these students should be allowed to stay in the US after finishing their studies.The research firm Rhodium Group found that China funneled $45.6 billion into completed acquisitions and greenfield investments in the US last year.
The US Department of Defense is struggling to get its arms around all of the new security issues that have come with our current technological explosion. One unexpected consideration on the table is placing stricter limitations on investment capital from China flowing into American companies that are working on artificial intelligence.
The post US Considers Chinese Investment in Artificial Intelligence a National Security Threat appeared first on Artificial Intelligence.
- Draw something in a little box and an algorithm will try to interpret it as a cat and then fill in the colors and textures according to a machine learning model training on thousands of cat images.
- The pix2pix project demonstrates something pretty profound about machine learning circa 2017: It’s awful at generating new images, or at least meaningful new images.
- Machine learning is better at classifying existing images, but, even then, things drop off dramatically as we move beyond a handful of really robust object-recognition models.
- GANs work by training generative models that seek to minimize a particular “loss function” according to a prediction that the generated image is fake or real.
- Rather than learn how to produce images from scratch, the model here learns to map the abstract image representation contained within a machine learning model to a trackpad doodle.
Generative adversarial networks strike again.
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- “Deep Learning” computer systems, based on artificial neural networks that mimic the way the brain learns from an accumulation of examples, have become a hot topic in computer science.
- In addition to enabling technologies such as face- and voice-recognition software, these systems could scour vast amounts of medical data to find patterns that could be useful diagnostically, or scan chemical formulas for possible new pharmaceuticals.
- But the computations these systems must carry out are highly complex and demanding, even for the most powerful computers.
- Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations.
- Their results appear today in the journal Nature Photonics (“Deep learning with coherent nanophotonic circuits”) in a paper by MIT postdoc Yichen Shen, graduate student Nicholas Harris, professors Marin Soljacic and Dirk Englund, and eight others.
“Deep Learning” computer systems, based on artificial neural networks that mimic the way the brain learns from an accumulation of examples, have become a hot topic in computer science. In addition to enabling technologies such as face- and voice-recognition software, these systems could scour vast amounts of medical data to find patterns that could be useful diagnostically, or scan chemical formulas for possible new pharmaceuticals.
But the computations these systems must carry out are highly complex and demanding, even for the most powerful computers.
Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. Their results appear today in the journal Nature Photonics (“Deep learning with coherent nanophotonic circuits”) in a paper by MIT postdoc Yichen Shen, graduate student Nicholas Harris, professors Marin Soljacic and Dirk Englund, and eight others.
The post Nanophotonic system allows optical ‘deep learning’ appeared first on Artificial Intelligence.
- The rise of artificial intelligence presents not only technical challenges, but important legal and ethical challenges for society, especially regarding machines like autonomous weapons and self-driving cars.
- To discuss these issues, I interviewed Matt Scherer and Ryan Jenkins.
- Matt is an attorney and legal scholar whose scholarship focuses on the intersection between law and artificial intelligence.
- Ryan is an assistant professor of philosophy and a senior fellow at the Ethics and Emerging Sciences group at California Polytechnic State, where he studies the ethics of technology.
- In this podcast, we discuss accountability and transparency with autonomous systems, government regulation vs. self-regulation, fake news, and the future of autonomous systems.
Stream Law and Ethics of AI with Ryan Jenkins and Matt Scherer by Future of Life Institute from desktop or your mobile device
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- Feature selection is useful on a variety of fronts: it is the best weapon against the Curse of Dimensionality; it can reduce overall training times; and it is a powerful defense against overfitting, increasing generalizability.
- Feature selection is useful on a variety of fronts: it is the best weapon against the Curse of Dimensionality; it can reduce overall training times; and it is a powerful defense against overfitting, increasing model generalizability.
- Many times a correct feature selection allows you to develop simpler and faster Machine Learning models.
- In a time when ample processing power can tempt us to think that feature selection may not be as relevant as it once was, it’s important to remember that this only accounts for one of the numerous benefits of informed feature selection — decreased training times.
- As Zimbres notes above, with a simple concrete example, feature selection can quite literally mean the difference between valid, generalizable models and a big waste of time.
Feature selection is useful on a variety of fronts: it is the best weapon against the Curse of Dimensionality; it can reduce overall training times; and it is a powerful defense against overfitting, increasing generalizability.
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