The world of artificial intelligence and machine learning moves very fast. So fast, in fact, that it’s remarkable to think that it was only a decade ago when the AlexNet model dominated the ImageNet competition and kicked off the process that made deep learning a bona fide technology movement. Today, after years of headlines about game-playing, we see ever-increasing innovation that applies to the real world.
In the last couple of years alone, AI/ML models like GPT-3 and AlphaFold delivered capabilities that catalyzed new products and companies, and that stretched our understanding of what computers can do.
With that in mind, we thought we’d revisit our AI/ML coverage in Future over the first half of the year, as well as catch you up on some — but certainly not all — of the major industry developments during that time. As you’ll see, some combination of large language models, generative models, and foundation models are a major source of attention, and we’re just skimming the surface in terms of understanding what they can do and how the world outside of large research labs can utilize their power. Read More
Tag Archives: Artificial Intelligence
There’s no such thing as data
Data is the new oil, we are told. Every country needs a data strategy, and all of us should own our data, and be paid for it. But really, there is no such thing as data, it’s not yours, and it’s not worth anything.
Technology is full of narratives, but one of the loudest is around something called ‘data’. AI is the future, and it’s all about data, and data is the future, and we should own it and maybe be paid for it, and countries need data strategies and data sovereignty. Data is the new oil!
This is mostly nonsense. There is no such thing as ‘data’, it isn’t worth anything, and it doesn’t really belong to you anyway.
Most obviously, ‘data’ is not one thing, but innumerable different collections of information, each of them specific to a particular application, that aren’t interchangeable. Siemens has wind turbine telemetry and Transport for London has ticket swipes, and you can’t use the turbine telemetry to plan a new bus route. If you gave both sets of data to Google or Tencent, that wouldn’t help them build a better image recognition system. Read More
How Will Machine Learning Impact Economics?
#280 — The Future of Artificial Intelligence
In this episode of the podcast, Sam Harris speaks with Eric Schmidt about the ways artificial intelligence is shifting the foundations of human knowledge and posing questions of existential risk. Read More
#podcasts, #artificial-intelligenceArtificial intelligence is creating a new colonial world order
his story is the introduction to MIT Technology Review’s series on AI colonialism, which was supported by the MIT Knight Science Journalism Fellowship Program and the Pulitzer Center. YOU CAN READ PART ONE HERE.
…The AI industry does not seek to capture land as the conquistadors of the Caribbean and Latin America did, but the same desire for profit drives it to expand its reach. The more users a company can acquire for its products, the more subjects it can have for its algorithms, and the more resources—data—it can harvest from their activities, their movements, and even their bodies.
Neither does the industry still exploit labor through mass-scale slavery, which necessitated the propagation of racist beliefs that dehumanized entire populations. But it has developed new ways of exploiting cheap and precarious labor, often in the Global South, shaped by implicit ideas that such populations don’t need—or are less deserving of—livable wages and economic stability.
MIT Technology Review’s new AI Colonialism series, which will be publishing throughout this week, digs into these and other parallels between AI development and the colonial past by examining communities that have been profoundly changed by the technology. In part one, we head to South Africa, where AI surveillance tools, built on the extraction of people’s behaviors and faces, are re-entrenching racial hierarchies and fueling a digital apartheid. Read More
Trends in AI—March 2022
A monthly selection of ML papers by Zeta Alpha: Audio generation, Gradients without Backprop, Mixture of Experts, Multimodality, Information Retrieval, and more. Read More
HERE’S HOW AN ALGORITHM GUIDES A MEDICAL DECISION
Artificial intelligence algorithms are everywhere in healthcare. They sort through patients’ data to predict who will develop medical conditions like heart disease or diabetes, they help doctors figure out which people in an emergency room are the sickest, and they screen medical images to find evidence of diseases. But even as AI algorithms become more important to medicine, they’re often invisible to people receiving care.
To help demystify the AI tools used in medicine today, we’re going to break down the components of one specific algorithm and see how it works. We picked an algorithm that flags patients in the early stages of sepsis — a life-threatening complication from an infection that results in widespread inflammation through the body. It can be hard for doctors to identify sepsis because the signs are subtle, especially early on, so it’s a common target for artificial intelligence-based tools. This particular program also uses mathematical techniques, like neural networks, that are typical of medical algorithms. Read More
AI Researchers Portal
Connecting AI researchers to Federal resources that can support their AI work – from grant funding and datasets to computing and testbeds. The National AI Initiative Office’s official site for AI researchers to access datasets, computing resources, and federal grant information. Read More
#artificial-intelligence, #dod, #icAmerica Needs AI Literacy Now
Can artificial intelligence (AI) replace a doctor in the operating room? Are some AI algorithms inherently biased, or are they merely trained on biased data? If you’re not sure about the answers to these questions, you are not alone. We recently conducted a national survey with Echelon Insights of 1,547 US adults, including a twenty-question ‘True/False/Don’t Know’ quiz, and found that most Americans are remarkably ill-informed about AI. Only 16% of participants “passed” the test (scoring above 60%) indicating that the majority of Americans are AI illiterate. Read More
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