Nearly 1,300 people spent this past weekend racing to fill little boxes inside larger boxes, ever mindful of spelling, trivia, wordplay, and a ticking clock. They were competitors—newcomers, ardent hobbyists, and elite speed solvers—in the American Crossword Puzzle Tournament, the pastime’s most prestigious competition. And most of them got creamed by some software.
The annual event, normally set in a packed hotel ballroom with solvers separated by yellow dividers, was virtual this year, pencils swapped for keyboards. After millions of little boxes had been filled, a computer program topped the leaderboard for the first time. Read More
Daily Archives: April 27, 2021
Fingerspelling
Online game to learn sign language. Fingerspelling.xyz combines advanced hand recognition technology with machine learning to teach sign language. Read More
Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses
The rapid development of artificial intelligence,especially deep learning technology, has advanced autonomous driving systems (ADSs) by providing precise control decisions to counterpart almost any driving event, spanning from anti-fatigue safe driving to intelligent route planning. However, ADSs are still plagued by increasing threats from different attacks, which could be categorized into physical attacks, cyber attacks and learning-based adversarial attacks. Inevitably, the safety and security of deep learning-based autonomous driving are severely challenged by these attacks, from which the countermeasures should be analyzed and studied comprehensively to mitigate all potential risks. This survey provides a thorough analysis of different attacks that may jeopardize ADSs, as well as the corresponding state-of-the-art defense mechanisms. The analysis is unrolled by taking an in-depth overview of each step in the ADS workflow,covering adversarial attacks for various deep learning models and attacks in both physical and cyber context. Furthermore, some promising research directions are suggested in order to improve deep learning-based autonomous driving safety, including model robustness training, model testing and verification, and anomaly detection based on cloud/edge servers. Read More
#adversarial, #cyberGetting AI to Scale
Most companies are struggling to realize artificial intelligence’s potential to completely transform the way they do business. The problem is, they typically apply AI in a long list of discrete uses, an approach that doesn’t produce consequential change. Yet trying to overhaul the whole organization with AI all at once is simply too complicated to be practical.
What’s the solution? Using AI to reimagine one entire core business process, journey, or function end to end, say three McKinsey consultants. That allows each AI effort to build off the previous one by, say, reusing data or enhancing capabilities for a common set of stakeholders. An airline, for example, focused on its cargo function, and a telecom provider on its process for managing customer value.
Scaling up AI involves four steps: (1) Identify an area where AI will make a big difference reasonably quickly and there are multiple interconnected activities and opportunities to share technology. (2) Staff the team with the right people and remove the obstacles to their success. (3) Reimagine business as usual, working back from a key goal and then exploring in detail how to achieve it. (4) Support new AI-based processes with organizational changes, such as interdisciplinary collaboration and agile mindsets. Read More
Why ‘deepfake geography’ presents significant risks — and how researchers are detecting it
“Seeing is believing.” It’s an aphorism that used to be a lot more true than it is today, now that computers can easily produce all manner of fake images and altered recordings. Many of us have seen the photos of celebrities who don’t exist and videos of lip-synching politicians. These “deepfakes” have raised real concerns about what is and isn’t true in our newsfeeds and other media.
This problem even extends to the maps and satellite images that represent our world. Techniques such as “location spoofing” and deepfake geography present significant risks for our increasingly connected society.br>
Because of this, a team of researchers at University of Washington are working to identify ways to detect these fakes, as well as proposing the creation of a geographic fact-checking system. Read More
Optoelectronic intelligence
General intelligence involves the integration of many sources of information into a coherent, adaptive model of the world. To design and construct hardware for general intelligence, we must consider principles of both neuroscience and very-large-scale integration. For large neural systems capable of general intelligence, the attributes of photonics for communication and electronics for computation are complementary and interdependent. Using light for communication enables high fan-out as well as low-latency signaling across large systems with no traffic-dependent bottlenecks. For computation, the inherent nonlinearities, high speed, and low power consumption of Josephson circuits are conducive to complex neural functions. Operation at 4 K enables the use of single-photon detectors and silicon light sources, two features that lead to efficiency and economical scalability. Here, I sketch a concept for optoelectronic hardware, beginning with synaptic circuits, continuing through wafer-scale integration, and extending to systems interconnected with fiber-optic tracts, potentially at the scale of the human brain and beyond. Read More
Practical Privacy with Synthetic Data
In this post, we will implement a practical attack on synthetic data models that was described in the Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks by Nicholas Carlini et. al. We will use this attack to see how synthetic data models with various neural network and differential privacy parameter settings actually work at protecting sensitive data and secrets in datasets. And there are some pretty surprising results. Read More
Forbes AI 50: America’s most promising companies to watch 2021
The Covid-19 pandemic was devastating for many industries, but it only accelerated the use of artificial intelligence across the U.S. economy. Amid the crisis, companies scrambled to create new services for remote workers and students, beef up online shopping and dining options, make customer call centers more efficient and speed development of important new drugs.
Even as applications of machine learning and perception platforms become commonplace, a thick layer of hype and fuzzy jargon clings to AI-enabled software.That makes it tough to identify the most compelling companies in the space—especially those finding new ways to use AI that create value by making humans more efficient, not redundant.
With this in mind, Forbes has partnered with venture firms Sequoia Capital and Meritech Capital to create our third annual AI 50, a list of private, promising North American companies that are using artificial intelligence in ways that are fundamental to their operations. Read More
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