MAKING AUTOMATED SYSTEMS WORK FOR THE AMERICAN PEOPLE
Among the great challenges posed to democracy today is the use of technology, data, and automated systems in ways that threaten the rights of the American public. Too often, these tools are used to limit our opportunities and prevent our access to critical resources or services. These problems are well documented. In America and around the world, systems supposed to help with patient care have proven unsafe, ineffective, or biased. Algorithms used in hiring and credit decisions have been found to reflect and reproduce existing unwanted inequities or embed new harmful bias and discrimination. Unchecked social media data collection has been used to threaten people’s opportunities, undermine their privacy, or pervasively track their activity—often without their knowledge or consent.
These outcomes are deeply harmful—but they are not inevitable. Automated systems have brought about extraordinary benefits, from technology that helps farmers grow food more efficiently and computers that predict storm paths, to algorithms that can identify diseases in patients. These tools now drive important decisions across sectors, while data is helping to revolutionize global industries. Fueled by the power of American innovation, these tools hold the potential to redefine every part of our society and make life better for everyone.
This important progress must not come at the price of civil rights or democratic values. Read More
Monthly Archives: October 2022
Modern Enterprise Data Architecture
Learn modern enterprise data architecture perspectives, including solution approaches and architectural models to develop new-age solutions.
Data plays a vital role in conceptualizing the preliminary design for an architecture. You may want to decide the requirements for security, performance, and infrastructure to handle workload, scalability, and agility in design. In this case, you need to understand data models and how to handle architectural decisions, including data privacy and security, compliance requirements, data size to handle, and user handling requirements.
This is the reason that data-driven architecture is the driving factor for an enterprise design development. The modern enterprise architectures that are referred to in this article include microservices, cloud-native applications, event-driven solutions, and data-intensive solutions. The article intends to share modern enterprise data architecture perspectives, including solution approaches and architectural models to develop new-age solutions catering to velocity, veracity, volume, and variety of data handling services. Read More
Will quantum computing kill Crypto?
Many claim quantum computing threats the existence of Crypto, but does it really?
“Blockchain is useless because it is going to be killed by quantum computers anyway.”
You’ve probably heard this many times.
If that statement was to be true, quantum computing can also have the potential to kill the entire Internet security infrastructure.
Indeed, certain aspects of blockchains, the Internet, and cryptography, are highly quantum unsafe, making quantum computers a very dangerous hazard for your portfolio if not taken into account at the right time.
But, to what extent are our Crypto investments in danger? Read More
#blockchain, #quantum
Who Are You (I Really Wanna Know)? Detecting Audio DeepFakes Through Vocal Tract Reconstruction
Generative machine learning models have made convincing voice synthesis a reality. While such tools can be extremely useful in applications where people consent to their voices being cloned (e.g., patients losing the ability to speak, actors not wanting to have to redo dialog, etc), they also allow for the creation of nonconsensual content known as deepfakes. This malicious audio is problematic not only because it can convincingly be used to impersonate arbitrary users, but because detecting deepfakes is challenging and generally requires knowledge of the specific deepfake generator. In this paper, we develop a new mechanism for detecting audio deepfakes using techniques from the field of articulatory phonetics. Specifically, we apply fluid dynamics to estimate the arrangement of the human vocal tract during speech generation and show that deepfakes often model impossible or highly-unlikely anatomical arrangements. When parameterized to achieve 99.9% precision, our detection mechanism achieves a recall of 99.5%, correctly identifying all but one deepfake sample in our dataset. We then discuss the limitations of this approach, and how deepfake models fail to reproduce all aspects of speech equally. In so doing, we demonstrate that subtle, but biologically constrained aspects of how humans generate speech are not captured by current models, and can therefore act as a powerful tool to detect audio deepfakes. Read More
Python is notoriously slow. Now its creators want to make it twice as fast
If they succeed, Python might have a bright long future after all
Python is slow. It’s one of the slowest programming languages that ever existed. If languages like C, C++, and Rust were hares, then Python wouldn’t even be a hedgehog. Python is a big old snail.
I’m not saying this to throw shame on Python. Most developers don’t care whether their code executes in 12 milliseconds or in 56. This time difference won’t make them late for lunch.
Testament to this carelessness about Python’s speed are the popularity statistics. In the TIOBE index, Python ranks at about the same popularity as C or C++. The PYPL index puts Python at four times the popularity that C or C++ enjoy. According to Google Trends, Python is about three times as popular as its competitors. And on StackOverflow, people ask five times more questions about Python than about C++, and eight times more than about C.
Most developers don’t care so much about runtime speeds as they do about ease of coding. Read More
Company Building in the Curiosity Phase of AI
If you’re on twitter these days you have likely seen a wave of videos that utilize things like Stable Diffusion for fun and novel product concepts/demos. This is happening now that designers have had the requisite time to mess around in DreamStudio or Dall-E, talk to engineers to understand what is possible, and spin up concept art.
This reminds me a lot of prior concept-heavy phases of AR and VR including the launch of Google Glass (we all remember this video), Magic Leap, Spectacles, and more. These concepts were fun the first time you watched them and lost luster over the subsequent viewings as we all litigated how much we really would use a given use-case (all while ignoring the UX of using the actual hardware).
Unfortunately, the steady flow of twitter retweets and likes led the AR/VR community to get too focused on fun use-cases versus high-value utility that people repeatedly wanted.1
Now that we have composability of AI models, there is a new wave of AI-first products/features making their way into the world. These effective demos will likely lead to a barrage of AI-first products that see high short-term usage followed by material churn, driven by the collective curiosity of a variety of industries. Read More
Bruce Willis Sells His Face
The actor is retiring following his aphasia diagnosis, but his likeness will live on via AI after he sold the rights to it to a deepfake company.
Bruce Willis is selling his face. The veteran actor has agreed to sell the rights to his likeness so a digital twin can be created using deepfake technology. Willis previously announced his retirement from acting following an aphasia diagnosis.
While movies have used deepfake technology to create digital versions of actors before, Bruce Willis has become one of the first actors to formally sell the rights to his own AI-generated recreation in perpetuity. The actor has partnered with Deepcake, a company specializing in artificial intelligence that will create Willis’ digital twin, who will appear in films after his retirement due to the cognitive disorder, which affects a person’s ability to communicate. Read More
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