Artificial Psychology: The Quest for What It Means to Be Human
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Image-translation pioneer discusses the past, present, and future of generative adversarial networks, or GANs. Researchers submit deep learning models to a set of psychology tests to see which ones grasp key linguistic rules. Signals help neural network identify objects by touch; system could aid robotics and prosthetics design. In helping envision the MIT Schwarzman College of Computing, working group is focusing on ethical and societal questions. Machine learning reveals metabolic pathways disrupted by the drugs, offering new targets to combat resistance.
How Frightened Should We Be of A.I.?
In some cases, radio frequency signals may be more useful for caregivers than cameras or other data-collection methods. The DiCarlo lab finds that a recurrent architecture helps both artificial intelligence and our brains to better identify objects. Mouse study yields insights into the rare condition, may shed light on other neurological disorders.
EECS faculty member is recognized for technical innovation, educational excellence, and efforts to advance women and under-represented minorities in her field. Working groups of faculty, students, staff are meeting regularly to develop ideas for the MIT Schwarzman College of Computing. Researchers combine statistical and symbolic artificial intelligence techniques to speed learning and improve transparency.
Technique could improve machine-learning tasks in protein design, drug testing, and other applications. McGovern Institute researchers find that the brain starts to register gender and age before recognizing a face. Algorithm designs optimized machine-learning models up to times faster than traditional methods.
Study shows that a brain region called the inferotemporal cortex is key to differentiating bears from chairs. Schwarzman College of Computing. Stephen A.
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Popular expo highlights student creativity and ambition as celebration of the MIT Schwarzman College of Computing gets underway. Undergraduate research projects show how students are advancing research in human and artificial intelligence, and applying intelligence tools to other disciplines. MIT associate professor of aeronautics and astronautics describes the seamless flow of people, things, and materials.
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Study uncovers language patterns that AI models link to factual and false articles; underscores need for further testing. MIT designers, researchers, and students collaborate with The Metropolitan Museum of Art and Microsoft to improve the connection between people and art. A new database of images could pave a path for algorithmic models that ensure accurate diagnoses of conditions like pneumonia. Machine-learning approach could help robots assemble cellphones and other small parts in a manufacturing line. Algorithm could help autonomous underwater vehicles explore risky but scientifically-rewarding environments.
An algorithm that teaches robot agents how to exchange advice to complete a task helps them learn faster. Neural network assimilates multiple types of health data to help doctors make decisions with incomplete information. Model identifies instances when autonomous systems have learned from examples that may cause dangerous errors in the real world. Hackathons promote doctor-data scientist collaboration and expanded access to electronic medical-records to improve patient care.
New 3-D imaging technique can reveal, much more quickly than other methods, how neurons connect throughout the brain. Vinod Vaikuntanathan aims to improve encryption in a world with growing applications and evolving adversaries. Tool for nonstatisticians automatically generates models that glean insights from complex datasets.
Professor honored for work on the nature and origins of intelligence in the human mind and applying that knowledge to build human-like intelligence in machines.
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Technique for preserving tissue allows researchers to create maps of neural circuits with single-cell resolution. A recent MIT symposium explores methods for making artificial intelligence systems more reliable, secure, and transparent. Picower Institute researchers discover the brain mechanism that helps details come flooding back when you visit a scene again. MIT AI Ethics Reading Group was founded by students who saw firsthand how technology developed with good intentions could be problematic.
In a study that might enable earlier diagnosis, neuroscientists find abnormal brain connections that can predict onset of psychotic episodes. Computer model could improve human-machine interaction, provide insight into how children learn language. Neural network that securely finds potential drugs could encourage large-scale pooling of sensitive data.
This statistical technique, pioneered and perfected by several AI researchers including Geoff Hinton, Yann LeCun and Yoshua Bengio, involves multiple layers of processing that gradually refine results see this Nature article for an in depth explanation. It is an old technique that dates back to the s, s and s, but it suddenly showed its power when fed enough data and computing power.
Interestingly, however, as the rest of the world is starting to widely embrace deep learning across a number of consumer and enterprise applications, the AI research world is asking whether it is hitting diminishing returns. Geoff Hinton himself at a conference in September questioned back-propagation, the backbone of neural networks which he helped invent, and suggested starting over, which sent shockwaves in the AI research world. There are many variations of unsupervised learning, including autoencoders, deep belief networks and GANs. GANs work by creating a rivalry between two neural nets, trained on the same data.
One network the generator creates outputs like photos that are as realistic as possible; the other network the discriminator compares the photos against the data set it was trained on and tries to determine whether whether each photo is real or fake; the first network then adjusts its parameters for creating new images, and so and so forth. This last approach of progressively training GANs enabled Nvidia to generate high resolution facial photos of fake celebrities.
However, that all changed in late when DeepMind, then an independent startup, taught an AI to play 22 Atari games, including Space Invaders, at a superhuman level. Then just a few months ago in December , AlphaZero , a more generalized and powerful version of AlphaGo used the same approach to master not just Go, but also chess and shogi. Without any human guidance other than the game rules, AlphaZero taught itself how to play chess at a master level in only four hours.
Within 24 hours, AlphaZero was able to defeat all state of the art AI programs in those 3 games Stockfish, elmo and the 3-day version of AlphaGo. Seeing a computer program teach itself the most complex human games to a world-class level in a mere few hours is an unnerving experience that would appear close to a form of intelligence. In reinforcement learning, AI researchers point out that the AI has no idea what it is actually doing like playing a game and is limited to the specific constraints that it was given the rules of the game. Here is an interesting blog post disputing whether AlphaZero is a true scientific breakthrough.
When it comes to AGI, or even the success of machine learning in general, several researchers have high hopes for transfer learning. Transfer learning is a machine learning technique where a model trained on one task is re-purposed on a second related task. The idea is that with this precedent knowledge learned from the first task, the AI will perform better, train faster and require less labeled data than a new neural network trained from scratch on the second related task.
For transfer learning to lead to AGI, the AI would need to be able to do transfer learning across increasingly far apart tasks and domains, which would require increasing abstraction.
Review of Friedenberg, Jay: Artificial Psychology: The Quest for What It Means to Be Human
But this is a key area of focus for AI research. DeepMind made significant progress with its PathNet project see a good overview here , a network of neural networks. While considerable prowess has been displayed in creating and improving such algorithms, a common criticism against those methods is that machines are still not able to start from, or learn, principles.
A growing line of thinking in research is to rethink core principles of AI in light of how the human brain works, including in children. Teaching a machine of how to learn like a child is one of the oldest ideas of AI, going back to Turing and Minsky in the s, but progress is being made as both the field of artificial intelligence and the field of neuroscience are maturing.
While both fields are still getting to know each other, it was clear that some of the deepest AI thinkers are increasingly focused on neuroscience inspired research, including deep learning godfathers Yann LeCun video: What are the principles of learning in newborns?
His work has been propelled by progress in probabilistic languages part of the Bayesian world that incorporate a variety of methods such as symbolic languages for knowledge representation, probabilistic inference for reasoning under uncertainty and neural networks for pattern recognition. So, how far are we from AGI? This high level tour shows contradictory trends.
Regardless of whether we get to AGI in the near term or not, it is clear that AI is getting vastly more powerful, and will get even more so as it runs on ever more powerful computers, which raises legitimate concerns about what would happen if its power was left in the wrong hands whether human or artificial.
In its relentless quest to complete a task by all means, it could be harmful to humans just because they happened to be in the way, like a roadkill. Arxiv-sanity chart found on this blog post. Matt Turck mattturck. Tweet This. Continue the discussion. Matt Turck. Matt Turck Jun Scaling AI Startups.