The AI Index Report tracks, collates, distills, and visualizes data relating to artificial intelligence.
Its mission is to provide unbiased, rigorous, and comprehensive data for policymakers, researchers, journalists, executives, and the general public to develop a deeper understanding of the complex field of AI. Read More
Monthly Archives: December 2019
There’s No Such Thing As The Machine Learning Platform
In the past few years, you might have noticed the increasing pace at which vendors are rolling out “platforms” that serve the AI ecosystem, namely addressing data science and machine learning (ML) needs. The “Data Science Platform” and “Machine Learning Platform” are at the front lines of the battle for the mind share and wallets of data scientists, ML project managers, and others that manage AI projects and initiatives. If you’re a major technology vendor and you don’t have some sort of big play in the AI space, then you risk rapidly becoming irrelevant. But what exactly are these platforms and why is there such an intense market share grab going on? Read More
Stepping Stones — Google’s smart city project links its quality-of-life improvements to the elimination of human workers
Sidewalk Labs’ Toronto headquarters is located at 307 Lake Shore Boulevard, right on the city’s waterfront. The building’s exterior is brightly painted in the industrial-gentrification chic style. The interior is part of a community outreach effort, filled with a slew of engaging dioramas and exhibits about technology and cities. But in many ways, the floor beneath is the space’s centerpiece. As visitors move from exhibit to exhibit, they walk across a plywood surface of hexagonal tiles — a system that Sidewalk Labs and designer Carlo Ratti, the director of the Senseable City Lab at MIT, call the “Dynamic Street.”
The real tiles — which will be made of concrete and be capable of housing sensors, signage and heating coils to melt snow — will make up an urban surface system that Sidewalk hopes to deploy across its project area in Quayside, right outside 307’s door. Dynamic Street has been designed to enable the elimination of curbs, introducing one flat hardscape that can change from street to sidewalk to plaza to parking as needed, with tiles changing colors to designate the appropriate usage. Read More
Richard Feynman on Artificial General Intelligence
In a lecture held by Nobel Laureate Richard Feynman (1918–1988) on September 26th, 1985, the question of artificial general intelligence (also known as “strong-AI”) comes up.
Do you think there will ever be a machine that will think like human beings and be more intelligent than human beings? Read More
Artificial Intelligence Isn’t an Arms Race
At the last Democratic presidential debate, the technologist candidate Andrew Yang emphatically declared that “we’re in the process of potentially losing the AI arms race to China right now.” As evidence, he cited Beijing’s access to vast amounts of data and its substantial investment in research and development for artificial intelligence. Yang and others—most notably the National Security Commission on Artificial Intelligence, which released its interim report to Congress last month—are right about China’s current strengths in developing AI and the serious concerns this should raise in the United States. But framing advances in the field as an “arms race” is both wrong and counterproductive. Instead, while being clear-eyed about China’s aggressive pursuit of AI for military use and human rights-abusing technological surveillance, the United States and China must find their way to dialogue and cooperation on AI. A practical, nuanced mix of competition and cooperation would better serve U.S. interests than an arms race approach. Read More
Protocols, Not Platforms: A Technological Approach to Free Speech
After a decade or so of the general sentiment being in favor of the internet and social media as a way to enable more speech and improve the marketplace of ideas, in the last few years the view has shifted dramatically—now it seems that almost no one is happy. Some feel that these platforms have become cesspools of trolling, bigotry, and hatred. 1. Zachary Laub, Hate Speech on Social Media: Global Comparisons, Council on Foreign Rel. (Jun. 7, 2019), https://www.cfr.org/backgrounder/hate-speech-social-media-global-comparisons. Meanwhile, others feel that these platforms have become too aggressive in policing language and are systematically silencing or censoring certain viewpoints. 2. Tony Romm, Republicans Accused Facebook, Google and Twitter of Bias. Democrats Called the Hearing ‘Dumb.’, Wash. Post (Jul. 17, 2018), https://www.washingtonpost.com/technology/2018/07/17/republicans-accused-facebook-google-twitter-bias-democrats-called-hearing-dumb/?utm_term=.895b34499816. And that’s not even touching on the question of privacy and what these platforms are doing (or not doing) with all of the data they collect.
… This article proposes an entirely different approach—one that might seem counterintuitive but might actually provide for a workable plan that enables more free speech, while minimizing the impact of trolling, hateful speech, and large-scale disinformation efforts.
… That approach: build protocols, not platforms.
To be clear, this is an approach that would bring us back to the way the internet used to be. The early internet involved many different protocols—instructions and standards that anyone could then use to build a compatible interface. Email used SMTP (Simple Mail Transfer Protocol). Read More
Artificial Intelligence: the global landscape of ethics guidelines
In the last five years, private companies, research institutions as well as public sector organisations have issued principles and guidelines for ethical AI, yet there is debate about both what constitutes “ethical AI” and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analyzed the current corpus of principles and guidelines on ethical AI. Our results reveal a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted; why they are deemed important; what issue, domain or actors they pertain to; and how they should be implemented. Our findings highlight the importance of integrating guideline development efforts with substantive ethical analysis and adequate implementation strategies. Read More
This Year’s AI (Artificial Intelligence) Breakthroughs (2019)
When it comes to AI (Artificial Intelligence), VCs (venture capitalists) continue to be aggressive with their fundings. During the third quarter, 965 AI-related companies in the US raised a total of $13.5 billion. In fact, this year should see a record in total fundings (last year’s total came to $16.8 billion).
… So what has been the result of all this activity? What have been the breakthroughs for AI this year? Read More
Top Artificial Intelligence (AI) Predictions For 2020 From IDC and Forrester
Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
Many machine learning models operate on images, but ignore the fact that images are 2D projections formed by 3D geometry interacting with light, in a process called rendering. Enabling ML models to understand image formation might be key for generalization. However, due to an essential rasterization step involving discrete assignment operations, rendering pipelines are non-differentiable and thus largely inaccessible to gradient-based ML techniques. In this paper, we present DIB-R, a differentiable rendering framework which allows gradients to be analytically computed for all pixels in an image. Key to our approach is to view foreground rasterization as a weighted interpolation of local properties and background rasterization as an distance-based aggregation of global geometry. Our approach allows for accurate optimization over vertex positions, colors, normals, light directions and texture coordinates through a variety of lighting models. We showcase our approach in two ML applications: single-image 3D object prediction, and 3D textured object generation, both trained using exclusively using 2D supervision. Our project website is: https://nv-tlabs.github.io/DIB-R/ Read More
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