On March 8, 2021, Stanford will host the inaugural 24-hour virtual Women in Data Science (WiDS) Worldwide conference. Find out speaker and registration information here.
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How Can New Deep Learning Initiatives Overcome Challenges in Robotics?
Deep Learning Problems in Robotics
When data scientists talk about Deep Learning, they’re usually speaking about image generation, detection, classification and regression tasks. Still, the thing that deep learning and artificial intelligence are getting vastly used for is in the field of robotics, and solving some its most significant challenges. It is deep learning for computer vision that is powering the pursuit of self driving autonomous cars. Reinforcement learning is also powering some of the initiatives like AlphaGo, where the agent tries to act in the world to maximize its rewards.
The advancements in deep learning have been many, but still, we want to reach the ultimate goal at some point in time — Artificial General Intelligence.
What is AGI and Can a Robot Perform a Task?
Artificial General Intelligence (AGI) is the hypothetical intelligence of a machine that can understand or learn any intellectual task that a human being can.
One of the tests to confirm human-level AGI is suggested by Steve Wozniak in which “A machine/robot is required to enter any average American home and figure out how to make coffee” — That is, the robot has to find the coffee machine and coffee, add water, find a mug, and brew the coffee.
Can a Robot Make coffee?
This task might look too easy for humans. Still, for a robot, it essentially means interacting with the environment in the right ways using various computer vision techniques to recognize and use the objects, and then taking all the right actions based on its understanding of the task.
Yet another test to confirm AGI is the Turing test itself, in which a human talks to a machine/human and the first human has to guess if he/she is talking to the machine/human. If the machine fools the human a significant number of times, the machine will pass the test.
This test involves a deep understanding of human language and its structure, a sense of how humans use language in continuity rather than single instances.
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How Can AI help?
A lot of public figures, chief among them Elon Musk, Bill Gates, and the late Stephen Hawking, endorse that AI might pose an existential risk (think Terminator’s Skynet); but we are nowhere near the point where we really must be afraid of it.
While advancements like AlphaGo beating a Go world champion do make us ask if the computers have really surpassed humans, the point we have to understand is that the machine is essentially trying to mimic logic based on the millions of games it has seen. These games all have a lot of training data present, and the degree of freedom when it comes to the actions taken by the AI is vastly limited compared to the real world. Another thing is that these AIs we have are not multi-functional. This means that while they could be great at doing a single thing, they don’t do anything else. Making something as powerful as a machine that could think on its own and have real-world consequences is virtually a thing of the future. In fact, it might be better to think along the lines of Andrew Ng, who states that AI existential risk is “like worrying about overpopulation on Mars when we have not even set foot on the planet yet.”
We are pretty far from a dystopian future.
That being said, we are making some progress towards realizing it. And, figuring out how to put filters or restrictions in place now to make sure an AI doesn’t get carried away and wipe out humanity should absolutely be considered. We are already struggling with AI bias based on how they are trained and by whom, so there are still some barriers in place before we realize a fully functioning, autonomous AI of the future.
Researchers are already working on deep learning for computer vision (CV) which can help a robot and robotics applications understand their environments using various sensors, and help them navigate through obstacles in their path. This is called the mapping challenge, and machine learning and CV technologies can really help with this. This is going to be an essential part of autonomous, self-driven cars which do have to learn how to see in new conditions even when no training data is present.
A lot of research has also been happening around speech recognition which can be used to convert spoken language into machine language, and Natural Language Processing (NLP) which can provide a machine with the ability to understand and talk to a human. A very inferior version of such technologies is already utilized by some of us through our Alexa, Portal and Google home devices. In fact, GPT-3, a new language-generation model that has been in the news for creating interesting write-ups, is again powered by using deep learning in natural language processing.
Some Parting Thoughts About Deep Learning in Robotics
The state of AI these days tends to solve problems in a very disconnected way. The vision problem is solved separately from the language/speech problem, for example.
The next thing we need to do is integrate all this into a working robot which can make its own decisions. The agent needs to be able to experience the real world first hand to create its own view of the world and acquire additional data from its environment that it could use to train itself.
While it is easier said than done, tiny step advancements are required to reach our destination, but we continue to move in the right direction. It’s only a matter of time until robots attain AGI and open a whole new world of possibilities.
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How Can New Deep Learning Initiatives Overcome Challenges in Robotics? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
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The Bridge Between Music and Machine Learning
I’ve always loved music, and I’ve always loved math. In the last 15 years, I’ve been fortunate enough to work in the music industry in a number of capacities; first, as a musician and performer, and later, as a freelance sound designer, producer, and audio engineer. But in that time, I largely ignored the side of my brain that loved reasoning out a good logic problem.
Last summer, as I struggled to work in an industry undergoing rapid, unpredictable restructuring, I finally decided I’d indulge that long-dormant love of math and problem solving, and now I find myself here, a week away from completing General Assembly’s three-month data science immersive course.
General Assembly pitches its immersive courses largely as a means to pivot into a new career. While I certainly entered the program with an intent to broaden my skillset, I must admit that I had no intention of turning my back on the music industry in favor of a career in data. I assumed my interests in math and technology would be stimulated, and that I’d exit the course with enough of a baseline to get a basic job in data until the arts recovered from the many trials of 2020.
But over the last three months, I’ve come to realize how harmonious these two industries are, and the immense possibilities that data, and the ability to process, model, and make predictions from that data afford in a musical context. And though it’s not a full pivot, my goal is now to build on the fundamentals I’ve learned and really incorporate data processing, engineering, and predictive modeling into my career moving forward.
I’ve always known that music and math have a deep and complex relationship, and for most of my career, I’ve used computers and software recording solutions as the centerpieces of my workflow. But audiophiles and engineers are a stubborn breed; walk into any modern recording studio, and you’ll see walls of analog equipment, reel-to-reel tape recorders, large-format analog mixing consoles with hundreds of thousands of discrete components, and in general, lots of mid-20th Century artifacts that rely on vacuum tubes, bulky iron transformers, germanium transistors, carbon-composition resistors, and about a million other components that haven’t been produced for decades.
Recordists and engineers, myself included, often have a penchant for these old, “outdated” pieces because of the character they impart on audio, often described in somewhat intangible terms like “warmth”, “fatness”, “airiness”, and other immeasurable qualities. What we’re really latching onto, however, are the inconsistencies and idiosyncrasies related to the varying plate voltage of a tube, harmonic excitement from overdriving a preamplifier circuit, the crosstalk between left and right channels on an analog console — in other words, the mechanical limitations and imperfections that the advent of digital recording sought to perfect.
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While I still prefer recording as much in the analog domain as possible, routinely choosing hardware over plugins, my time at General Assembly has taught me that data science, and more specifically, the intersection of digital signal processing (DSP) and machine learning can really help to quantify some of these mysterious metrics that have evaded tangible classification for so long.
My final project, which I’ll discuss in depth in my following posts, seeks to do just that. With data sourced from Spotify’s API and data I generated using the Python library Librosa, along with the help of many amazing friends and teachers at General Assembly, I’m beginning to see audio in a new, more mathematical way. For example, the advent of convolutional neural networks and their ability to recognize and classify images, along with high-resolution spectrograms representing something of a “sonic fingerprint” for audio files (extractable via Librosa) opens the door to some pretty amazing possibilities when it comes to quantifying and comparing audio. With more research, I hope to turn this into a means for young, budding artists to easily compare their songs against popular music worldwide, and see both who and what songs they most resemble, as well as where those artists and songs are popular.
So while my time at General Assembly still hasn’t changed my mind about fully pivoting away from music creation, it has certainly opened up a multitude of new doors and new ways of looking at the subjects I love more than anything. What’s more, the community I found myself a part of during this process became more like family than classmates and instructors. I’m excited to keep pushing the boundaries of my industry with these exceptional people by my side, and in the process, find a new and better way to connect artists with music lovers around the world.
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The Bridge Between Music and Machine Learning was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
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