How smart is artificial intelligence and how fast is it advancing? In a recent post I mentioned machine learning research that taught an algorithm to master 40-year-old video games. If Atari 1982 video games are the peak of 2020 research, how hard is it to train an algorithm to play Pong, which came out in 1972? Machine learning can master the game in about 250 lines of code. In fact, Pong is one of the most popular ways of teaching reinforcement learning theory and practice.

In years to come, robots could assist human users in a variety of ways, both when they are inside their homes and in other settings. To be more intuitive, robots should be able to follow natural language commands and instructions, as this allows users to communicate with them just as they would with other humans. With this in mind, researchers at MIT's Center for Brains, Minds & Machines have recently developed a sampling-based robotic planner that can be trained to understand sequences of natural language commands. The system they developed, presented in a paper pre-published on arXiv, combines a deep neural network with a sampling-based planner. "It's quite important to ensure that future robots in our homes understand us, both for safety reasons and because language is the most convenient interface to ask for what you want," Andrei Barbu, one of the researchers who conducted the study, told TechXplore.

"Alexa, what was the score of the Manchester United game yesterday?" If you've ever asked a question like that of your personal assistant device (such as Amazon Echo or Google Home), you are already enjoying the fruits of the deep learning revolution. As Ralf Herbrich, Director of Machine Learning & Managing Director of Amazon Development Germany explains, when you speak to your device, "features from a deep neural network are used to describe the audio stream in order to detect the wake-words… Once the wake-word is detected, neural networks are used to predict the sequence of phonetic states from the audio sequence of the whole microphone array." This same cutting-edge deep learning/neural network technology is already being used for a wide range of tasks, such as enabling the navigation of self-driving cars, colorizing old black-and-white films, assisting physicians in diagnosing illnesses, and beating the best human competitors at complex strategy games such as Go. But what, exactly, are deep learning and neural networks, and how do they work?

These results are remarkable in important respects but limited in others. For one thing, deep learning systems are notoriously opaque. Indeed, their logic cannot be easily interpreted using plain language. If we're going to rely on automated systems to assist in making critical decisions, such as deciding which patient requires critical care, the systems must be understandable by both physicians and patients. In practice, this requires including their perspectives in the planning, design and development of such an app so as to enable them to be part of the decision-making process.

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The blogosphere is full of descriptions about how data science and "AI' is changing the world. In financial services, applications include personalized financial offers, fraud detection, risk assessment (e.g. These applications outlined are largely not new, nor are "AI" algorithms like neural networks. However, increasingly commoditized, flexible and cheaper hardware with readily available algorithms and APIs have lowered barriers to data-compute intensive approaches common to data science, making the use of "AI" algorithms much more straightforward. For practitioners, definitions are well understood. For those less familiar and curious, here are some quick definitions and introductions to baseline everyone. At their heart, data science workflows transform data, from heterogenous sources of information, through models and learning, to derive information from which "useful" decisions can be expedited. Decisions may be automated (e.g. an online search or a retail credit fraud check) or ...

I'm very glad that the article includes a "Limitations" section, because while to most untrained listeners (and even trained listeners), these samples seem miraculous, in reality what is happening is that this is simply a more-impressive version of what has already been available. Specifically, Jukebox is able to provide locally-coherent sounds, which are recognizable as "music", but over long-term horizons it loses large-scale structure. They mention this themselves, and rightly so. While this is very impressive, it is primarily just an exercise in how nice they are able to make their short-term "sentences" sound (to borrow an analogy from speech synthesis). However, the broader challenge of long-term structure and musical form (here an analogy might be novel-length narrative structure) remains an open problem.

Hi, new to deep learning and know almost all the high-level concepts and implemented some with the help of tutorials. I am looking to advance my career and thinking to buy Deep Learning Book by Goodfellow. What should I expect from this book? Also, do you have any better recommendations?

As more artificial intelligence applications move to smartphones, deep learning models are getting smaller to allow apps to run faster and save battery power. Now, MIT researchers have a new and better way to compress models. It's so simple that they unveiled it in a tweet last month: Train the model, prune its weakest connections, retrain the model at its fast, early training rate, and repeat, until the model is as tiny as you want. "That's it," says Alex Renda, a PhD student at MIT. "The standard things people do to prune their models are crazy complicated." Renda discussed the technique when the International Conference of Learning Representations (ICLR) convened remotely this month.

As more artificial intelligence applications move to smartphones, deep learning models are getting smaller to allow apps to run faster and save battery power. Now, MIT researchers have a new and better way to compress models. It's so simple that they unveiled it in a tweet last month: Train the model, prune its weakest connections, retrain the model at its fast, early training rate, and repeat, until the model is as tiny as you want. "That's it," says Alex Renda, a PhD student at MIT. "The standard things people do to prune their models are crazy complicated." Renda discussed the technique when the International Conference of Learning Representations (ICLR) convened remotely this month.