According to this April 2019 report, there are 41 AI unicorns globally, including 17 in China, 18 in the US, 3 in Japan, and 1 each in India, Germany and Israel.
China:
4paradigm
Bytedance
Cambricon
Horizon Robotics
CloudWalkTechnology
Megvii
iCarbon
TuSimple
UISEE
SenseTime
UniSound
Mobvoi
UBTECH
YITU
AIWAYS
Squirrel AI
Terminus
United States:
Afiniti
Automation Anywhere
Avant
Butterfly Network
C3
CrowdStrike
Dataminr
Indigo Agriculture
InsideSales.com
Pony.ai
SoundHound
Tanium
Tempus Labs
UiPath
Rubicon Global
Seismic
Uptake
UK
BenevolentAI
Darktrace
Graphcore
Japan
Preferred Networks
Germany
Celonis
Israel
OrCam Technologies
Read More
Daily Archives: August 19, 2019
China Now Has AI-Powered Judges
Beijing is bringing AI judges to court. The move, proclaimed by China as “the first of its kind in the world”, comes from the Beijing Internet Court, which has launched an online litigation service center featuring an artificially intelligent female judge, with a body, facial expressions, voice, and actions all modeled off a living, breathing human (one of the court’s actual female judges, to be exact). Read More
Measurable Counterfactual Local Explanations for Any Classifier
We propose a novel method for explaining the predictions of any classifier. In our approach, local explanations are expected to explain both the outcome of a prediction and how that prediction would change if ’things had been different’. Furthermore, we argue that satisfactory explanations cannot be dissociated from a notion and measure of fidelity, as advocated in the early days of neural networks’ knowledge extraction. We introduce a definition of fidelity to the underlying classifier for local explanation models which is based on distances to a target decision boundary. A system called CLEAR: Counterfactual Local Explanations via Regression, is introduced and evaluated. CLEAR generates w-counterfactual explanations that state minimum changes necessary to flip a prediction’s classification. CLEAR then builds local regression models, using the w-counterfactuals to measure and improve the fidelity of its regressions. By contrast, the popular LIME method [15],which also uses regression to generate local explanations, neither measures its own fidelity nor generates counterfactuals. CLEAR’s regressions are found to have significantly higher fidelity than LIME’s, averaging over 45% higher in this paper’s four case studies. Read More
Open-endedness: The last grand challenge you’ve never heard of
Artificial intelligence (AI) is a grand challenge for computer science. Lifetimes of effort and billions of dollars have powered its pursuit. Yet, today its most ambitious vision remains unmet: though progress continues, no human-competitive general digital intelligence is within our reach. However, such an elusive goal is exactly what we expect from a “grand challenge”—it’s something that will take astronomical effort over expansive time to achieve—and is likely worth the wait. Read More
#humanChina’s AI Talent Base Is Growing, and then Leaving
When China decides that it wants to establish leadership in a particular strategic area, its approach has tended to follow the mantra “the more, the better.” So, too, has the pursuit of strengthening artificial intelligence (AI) talent followed this principle.
AI talent is usually cultivated within universities and their extended research ecosystem. And in China, shifts in college majors are often perceived as an indicator of national priorities and resource allocation. So when Beijing deemed AI a “special discipline” as far back as 2012, dozens of universities rushed to set up their own AI specialization and degree programs, attracting thousands of students.
Credit where credit is due. China has been successful in producing AI talent, evidenced by the rapid growth of AI human capital over the last decade. But talent acquisition is only one part of the puzzle—equally important is retaining that talent so they contribute to China’s AI aspirations over the long term. On the retention front, however, China has not done nearly as well. Read More
Brain Development
Hierarchy of transcriptomic specialization across human cortex captured by myelin map topography
Hierarchy provides a unifying principle for the macroscale organization of anatomical and functional properties across primate cortex, yet microscale bases of specialization across human cortex are poorly understood. Anatomical hierarchy is conventionally informed by invasive tract-tracing measurements, creating a need for a principled proxy measure in humans. Moreover, cortex exhibits marked interareal variation in gene expression, yet organizing principles of cortical transcription remain unclear. We hypothesized that specialization of cortical microcircuitry involves hierarchical gradients of gene expression. We found that a noninvasive neuroimaging measure—MRI-derived T1-weighted/T2-weighted (T1w/T2w) mapping—reliably indexes anatomical hierarchy, and it captures the dominant pattern of transcriptional variation across human cortex. We found hierarchical gradients in expression profiles of genes related to microcircuit function, consistent with monkey microanatomy, and implicated in neuropsychiatric disorders. Our findings identify a hierarchical axis linking cortical transcription and anatomy, along which gradients of microscale properties may contribute to the macroscale specialization of cortical function. Read More
Efficient Video Generation on Complex Datasets
Generative models of natural images have progressed towards high fidelity samples by the strong leveraging of scale. We attempt to carry this success to the field of video modeling by showing that large Generative Adversarial Networks trained on the complex Kinetics-600 dataset are able to produce video samples of substantially higher complexity than previous work. Our proposed model, Dual Video Discriminator GAN (DVD-GAN), scales to longer and higher resolution videos by leveraging a computationally efficient decomposition of its discriminator. We evaluate on the related tasks of video synthesis and video prediction, and achieve new state of the art Fréchet Inception Distance on prediction for Kinetics-600,as well as state of the art Inception Score for synthesis on the UCF-101 dataset,alongside establishing a strong baseline for synthesis on Kinetics-600. Read More
Rick's Cafe AI 