Machine Learning operations (let’s call it mlOps under the current buzzword pattern xxOps) are quite different from traditional software development operations (devOps). One of the reasons is that ML experiments demand large dataset and model artifact besides code (small plain file).
This post presents a solution to version control machine learning models with git and dvc (Data Version Control). Read More
Monthly Archives: September 2019
The Work of the Future: Shaping Technology and Institutions (MIT)
Technological change has been reshaping human life and work for centuries. The mechanization that began with the Industrial Revolution enabled dramatic improvements in human health, well-being, and quality of life—not only in the developed countries of the West, but increasingly throughout the world. At the same time, economic and social disruptions often accompanied those changes, with painful and lasting results for workers, their families, and communities. Along the way, valuable skills, industries, and ways of life were lost. Ultimately new and unforeseen occupations, industries, and amenities took their place. But the benefits of these upheavals often took decades to arrive. And the eventual beneficiaries were not necessarily those who bore the initial costs.
The world now stands on the cusp of a technological revolution in artificial intelligence and robotics that may prove as transformative for economic growth and human potential as were electrification, mass production, and electronic telecommunications in their eras. Read More
Identifying Artificial Intelligence ‘Blind Spots’
A novel model developed by MIT and Microsoft researchers identifies instances in which autonomous systems have “learned” from training examples that don’t match what’s actually happening in the real world. Engineers could use this model to improve the safety of artificial intelligence systems, such as driverless vehicles and autonomous robots. …
In a pair of papers — presented at last year’s Autonomous Agents and Multiagent Systems conference and the upcoming Association for the Advancement of Artificial Intelligence conference — the researchers describe a model that uses human input to uncover these training “blind spots.” Read More
The devil you know: trust in military applications of Artificial Intelligence
This article was submitted in response to the call for ideas issued by the co-chairs of the National Security Commission on Artificial Intelligence, Eric Schmidt and Robert Work. It is based on a chapter by the authors in the forthcoming book ‘AI at War’ and addresses the fifth question (part d.) which asks what measures the government should take to ensure AI systems for national security are trusted — by the public, end users, strategic decision-makers, and/or allies. Read More
Hacking the Brain: Dimensions of Cognitive Enhancement
In an increasingly complex information society, demands for cognitive functioning are growing steadily. In recent years, numerous strategies to augment brain function have been proposed. Evidence for their efficacy (or lack thereof) and side effects has prompted discussions about ethical, societal, and medical implications. In the public debate, cognitive enhancement is often seen as a monolithic phenomenon. On a closer look, however, cognitive enhancement turns out to be a multifaceted concept: There is not one cognitive enhancer that augments brain function per se, but a great variety of interventions that can be clustered into biochemical, physical, and behavioral enhancement strategies. These cognitive enhancers differ in their mode of action, the cognitive domain they target, the time scale they work on, their availability and side effects, and how they differentially affect different groups of subjects. Here we disentangle the dimensions of cognitive enhancement, review prominent examples of cognitive enhancers that differ across these dimensions, and thereby provide a framework for both theoretical discussions and empirical research. Read More
A Tentative Framework for Examining U.S. and Chinese Expenditures for Research and Development on Artificial Intelligence
China has serious ambitions to become a global leader in artificial intelligence (AI). The Chinese government, at the central and local levels, has announced large amounts of planned expenditures to support AI activities; however, it is not clear how much of the planned expenditures by the Chinese government is actually being expended, who is providing the money (e.g., central government, local government, enterprises, private investors), to whom the money is going, or on what the money will be spent. The absence of information on these issues makes it challenging for Western analysts, media, and policy makers to understand the extent of China’s activities in support of AI. This lack of information can lead to confusion and misleading comparisons between Chinese and U.S. expenditures on AI, which have caused alarm among some policy makers and observers. Read More
China’s Access to Foreign AI Technology — CSET Assessment
China’s technology transfer programs are broad, deeply rooted, and calculated to support the country’s development of artificial intelligence. These practices have been in use for decades and provide China early insight and access to foreign technical innovations.
While cyber theft and industrial espionage may or may not be employed, we judge that the main practices enabling AI-related transfers are not illegal. This inspires optimism on one level, but many—possibly most—of these transfers are unmonitored and unknown outside China. Read More
Deepfakes are a problem, what’s the solution?
Deepfakes are the latest moral panic, but the issues about consent, fake news, and political manipulation they raise are not new. They are also not issues that can be solved at a tech level.
A deepfake is essentially a video of something that didn’t happen, but made to look extremely realistic. That might sound like a basic case of ‘photoshopping’, but deepfakes go way beyond this. By training AI algorithms on vast libraries of photographs taken of famous people, the videos produced in this way are eerily real, and worryingly convincing.
As a result, plenty of analysts are worried that deepfakes might be used for political manipulation, or even to start World War 3. Read More
Artificial Swarm Intelligence
Swarm Intelligence (SI) is a natural phenomenon that enables social species to quickly converge on optimized group decisions by interacting as real-time closed-loop systems. This process, which has been shown to amplify the collective intelligence of biological groups, has been studied extensively in schools of fish, flocks of birds, and swarms of bees. This paper provides an overview of a new collaboration technology called Artificial Swarm Intelligence (ASI) that brings the same benefits to networked human groups. Sometimes referred to as “human swarming” or building “hive minds,” the process involves groups of networked users being connected in real-time by AI algorithms modeled after natural swarms. This paper presents the basic concepts of ASI and reviews recently published research that shows its effectiveness in amplifying the collective intelligence of human groups, increasing accuracy when groups make forecasts, generate assessments, reach decisions, and form predictions. Examples include significant performance increases when human teams generate financial predictions, business forecasts, subjective judgements, and medical diagnoses. Read More
AI-fueled organizations
FOR some organizations, harnessing artificial intelligence’s full potential begins tentatively with explorations of select enterprise opportunities and a few potential use cases. While testing the waters this way may deliver valuable insights, it likely won’t be enough to make your company a market maker (rather than a fast follower). To become a true AI-fueled organization, a company may need to fundamentally rethink the way humans and machines interact within working environments. Executives should also consider deploying machine learning and other cognitive tools systematically across every core business process and enterprise operation to support data-driven decision-making. Likewise, AI could drive new offerings and business models. These are not minor steps, but as AI technologies standardize rapidly across industries, becoming an AI-fueled organization will likely be more than a strategy for success—it could be table stakes for survival. Read More
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