Companies betting on data must value people as much as AI

AI scalability

The Pareto principle, also known as the 80-20 rule, asserts that 80% of consequences come from 20% of causes, rendering the remainder way less impactful. Those working with data may have heard a different rendition of the 80-20 rule: A data scientist spends 80% of their time at work cleaning up messy data as opposed to doing actual analysis or generating insights. Imagine a 30-minute drive expanded to two-and-a-half hours by traffic jams, and you’ll get the picture.

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Can AGI take the next step toward genuine intelligence?

Over the past decade, artificial intelligence (AI) has become part of our daily lives. Just ask anyone who has ever played a video game or asked Alexa a question. AIs have expert credentials when it comes to gameplay. They are able to find data correlations and patterns that no human mind could ever uncover. But despite these impressive achievements, nothing approaching human common sense has emerged so far from the AI we regularly encounter.

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MLOps: Bringing AI to the tactical edge – and making it work

Edge computing

Machine learning (ML)—the ability for machines to perceive, learn from, abstract, and act on data—has been a catalyst for innovation and advancement across sectors, with national security being no exception. In the last year alone, there have been several prime examples of the enormous opportunity ML offers regarding artificial intelligence (AI) for defense and the intelligence community. The U.S. Department of Defense (DoD) is continuing efforts to scale AI and celebrating new achievements, like using AI to help control a U-2 “Dragon Lady” reconnaissance aircraft – the first time AI has been put in command of a U.S. military system. 

The possibilities for advancement are endless: by helping with tasks related to data collection, processing, and analysis, ML can catch cyber breaches and hacks before humans can, speed up responses to electronic warfare attacks, and more closely target responses to kinetic fire through its continual updating and learning capabilities. Warfighters can also use ML to look across domains and resources, from ships to artillery, to match targets to resources.

As we settle into 2021, there’s one aspect of AI/ML that should not be overlooked: how to effectively get it into the hands of warfighters at the tactical edge, where fast decisions are at a premium and compute power and connectivity are often scarce. It is critical that these edge use cases characterize and shape planning for AI and ML-driven investment as digitization continues to accelerate the pace of war.

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Lying, corrupt, anti-American cops are running amok with AI


Hundreds of thousands of law enforcement agents in the US have the authority to use blackbox AI to conduct unethical surveillance, generate evidence, and circumvent our Fourth Amendment protections. And there’s little reason to believe anyone’s going to do anything about it. The problem is that blackbox AI systems are a goldmine for startups, big tech, and politicians. And, since the general public is ignorant about what they do or how they’re being used, law enforcement agencies have carte blanche do whatever they want.

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Deep reinforcement learning helps us master complexity

Deep reinforcement learning—where machines learn by testing the consequences of their actions—is one of the most promising and impactful areas of artificial intelligence. It combines deep neural networks with reinforcement learning, which together can be trained to achieve goals over many steps. It’s a crucial part of self-driving vehicles and industrial robots, which have to navigate complex environments safely and on time.

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[Video Highlights] A path into Data Science

Laptop in the dark

Are you interested in getting ahead in data science? On this TalkPython podcast episode, you’ll meet Sanyam Bhutani who studied computer science but found his education didn’t prepare him for getting a data science-focused job. That’s where he started his own path of self-education and advancement. Now he’s working at an AI startup and ranking high on Kaggle.

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The amazing applications of Graph Neural Networks

The predictive prowess of machine learning is widely hailed as the summit of statistical Artificial Intelligence. Vaunted for its ability to enhance everything from customer service to operations, its numerous neural networks, multiple models, and deep learning deployments are considered an enterprise surety for profiting from data. But according to Franz CEO Jans Aasman, there’s just one tiny problem with this lofty esteem that’s otherwise accurate.

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[Podcast] Joseph Turow author of Voice Catchers on Voice Tech, Marketing and Privacy

Voicebot podcast

Joseph Turow is a professor at the University of Pennsylvania’s Annenberg School for Communication — the same school where he earned his PhD. Turow is the author of over 150 articles and 10 books including the recently published “The Voice Catchers: How Marketers Listen In to Exploit Your Feelings, Your Privacy, and Your Wallet.”

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Quantum Machine Learning – An Introduction to QGANs

Quantum GANs which use a quantum generator or discriminator or both is an algorithm of similar architecture developed to run on Quantum systems. The quantum advantage of various algorithms is impeded by the assumption that data can be loaded to quantum states. However this can be achieved for specific but not generic data.

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These green tech companies are planning to capitalize on President Biden’s infrastructure plan – here’s how

Virgin hyperloop one

When Virgin Hyperloop cofounder Josh Giegel saw the Biden Administration’s infrastructure proposal, he couldn’t help but think some details sounded familiar. The American Jobs Plan calls for “the second great railroad revolution” — trains that are faster, cleaner, and more energy-efficient, which is the kind of technology Giegel’s California-based company has been working on since 2014. As such, Giegel has little doubt the company will be able to capitalize on America’s push toward greener infrastructure.

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Deployment should be a priority in any commercial data science project


In this article, I want to give some of the reasons why I became convinced that every data scientist should learn some data engineering skills (or become friends with some data engineers). I want to present my argument from two points of view: a more technical view and a user experience focused view.

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European AI needs strategic leadership, not overregulation

Arrows and shapes

The EU Commission recently proposed a new set of stringent rules to regulate AI, citing an urgent need. With the global race to regulate AI officially on, the EU published a detailed proposal on how AI should be regulated, explicitly banning some uses and defining those it considers “high-risk,” planning to ban the use of AI that threatens people’s rights and safety.

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Will Tesla survive Elon Musk’s bumbling leadership?

Tesla stock graph

On the one hand, Musk has a brilliant once-in-a-generation intellect. He’s the architect and driving force behind several incredibly successful tech companies ranging from PayPal to SpaceX. Musk is the epitome of a big-picture person with the vision and gumption to take far-fetched, pie-in-the-sky ideas and ground them in reality.

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To ensure inclusivity, the Biden administration must double down on AI development initiatives

US flag in code

The National Security Commission on Artificial Intelligence (NSCAI) issued a report last month delivering an uncomfortable public message: America is not prepared to defend or compete in the AI era. It leads to two key questions that demand our immediate response: Will the U.S. continue to be a global superpower if it falls behind in AI development and deployment? And what can we do to change this trajectory?

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The fate of Feature Engineering: No longer necessary, or much easier?

Feature engineering

Feature engineering occupies a unique place in the realm of data science. For most supervised and unsupervised learning deployments (which comprise the majority of enterprise cognitive computing efforts), this process of determining which characteristics in training data are influential for achieving predictive modeling accuracy is the gatekeeper for unlocking the wonders of statistical Artificial Intelligence.

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Facebook’s feckless ‘Fairness Flow’ won’t fix its broken AI

AI Fairness

Facebook today posted a blog post detailing a three-year-old solution to its modern AI problems: an algorithm inspector that only works on some of the company’s systems. Up front: Called Fairness Flow, the new diagnostic tool allows machine learning developers at Facebook to determine whether certain kinds of machine learning systems contain bias against or towards specific groups of people. It works by inspecting the data flow for a given model.

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How to choose the best MLOps platform for your organization

Man at workstation

MLOps (Machine Learning Operations) facilitates the collaboration between data scientists, ML engineers, and IT operations. The idea behind this practice is to have one place to deploy, manage, and govern machine learning models, in order to increase efficiency and lower failure rate.

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